7.
HN
What Agentic Commerce Will Look Like
The article explores the transformative impact of AI-powered agents on commerce, termed "agentic commerce," with major companies like Stripe, Visa, Mastercard, Google, Shopify, and Coinbase developing infrastructure to support this shift. Initially, agentic commerce involves human-directed agents performing tasks such as shopping or managing complex activities like vacation planning, utilizing existing card networks for transactions. The future envisions a more advanced form where AI agents transact independently, collaborating on economic activities without human involvement. This agent-to-agent commerce will rely on stablecoins and blockchain technology due to their scalability and cost-effectiveness compared to traditional payment methods. Stripe's 2025 outlook predicts that agents may surpass humans in transaction volume, underscoring the significant impact of this shift. Key developments include advancements in AI agent authentication and authorization for transactions, with companies like Crossmint, Catena Labs, and Radius contributing to shaping the agentic space. This evolution marks a potential revolution in economic operations, transitioning from human-mediated activities to fully autonomous agent-driven systems.
Keywords: #phi4, AI Agents, Agent-to-Agent, Agentic Commerce, Agentic Corporations, Blockchains, Catena Labs, Coinbase, Crossmint, Economy, Google, Human-Directed Agents, Identity & Trust Layer, Mastercard, Microtransactions, Radius, Shopify, Stablecoins, Stripe, Virtual Cards, Visa
connordempsey.substack.com an hour ago
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11.
HN
Nemotron 3 Super: An Open Hybrid Mamba-Transformer Moe for Agentic Reasoning
Nemotron 3 Super is an advanced AI model that significantly enhances agentic reasoning in multi-agent systems by overcoming typical challenges such as context explosion and computational inefficiency. It employs a hybrid Mamba-Transformer mixture-of-experts (MoE) architecture, which allows for efficient performance improvements across various applications like software development and cybersecurity. The model achieves high compute efficiency with its 120 billion total parameters through innovations such as Latent MoE that increase expert consultation without added cost and Multi-token Prediction to speed up long sequence generation.
Key features of Nemotron 3 Super include a native 1 million token context window, enabling effective management of extensive reasoning tasks while maintaining memory efficiency and reducing drift in longer contexts. Its training process is optimized using NVIDIA's NVFP4 format for reduced precision, which reduces memory usage without sacrificing accuracy. The training involves three phases: pretraining on a curated dataset comprising 25 trillion tokens, supervised fine-tuning with approximately seven million samples, and reinforcement learning across diverse environments to further refine its behavior.
The model is fully open-source, providing flexibility in customization and deployment, supported by resources such as model weights, training recipes, and deployment cookbooks for various platforms. Nemotron 3 Super's performance is benchmarked on PinchBench where it achieves an impressive score of 85.6%, surpassing other models in its class for autonomous agent tasks.
Designed to cater to applications requiring deep reasoning across extensive contexts, Nemotron 3 Super offers a broad range of deployment options, from personal workstations to cloud environments, making it versatile and accessible for various use cases.
Keywords: #phi4, Agentic AI, Benchmarking, Compute efficiency, Context window, Hybrid Mamba-Transformer, Latent MoE, Long-context analysis, MoE architecture, Multi-agent systems, Multi-token prediction, NVFP4 pretraining, NVIDIA Blackwell, Nemotron, Open model, Reinforcement learning
developer.nvidia.com an hour ago
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27.
HN
Vectorless RAG Using Neo4j and Agentic Routing
The text outlines an improved version of the VectifyAI/PageIndex vectorless Retrieval-Augmented Generation (RAG) architecture, leveraging Neo4j as a graph database for enhanced information retrieval scalability and efficiency. This architecture moves away from relying on in-memory JSON trees, instead storing documents as graphs within Neo4j's persistent memory environment. Such a shift allows the system to manage millions of documents without exceeding context window limitations, thus facilitating scalable cross-document query reasoning.
Key enhancements include utilizing graph traversal and relationships to build a more robust knowledge graph through connections like `[:REFERENCES]` edges between different document sections. Additionally, the architecture is designed for stand-alone execution with all necessary tools packaged within a directory managed by `uv`, ensuring seamless package handling for generating and ingesting document trees from PDFs or Markdown into Neo4j.
The process involves three main steps: first, parsing documents using a Python script to create a JSON file representing their hierarchical structures; second, importing this JSON into Neo4j for graph storage; third, employing agentic graph retrieval to navigate the knowledge graph. This involves using natural language queries that allow the system to traverse from root nodes down to specific sections based on user input.
Overall, by harnessing Neo4j's capabilities, this architecture significantly boosts performance and scalability in tasks related to document retrieval and reasoning, offering a more efficient and comprehensive framework for managing and querying large volumes of information.
Keywords: #phi4, Agentic Routing, Graph Database, Graph Traversal, Groq API Key, JSON, Knowledge Graph, LLM, Markdown, Neo4j, Neo4j Ingestion, PDF Parsing, PageIndex, Persistent Memory, Relationships, Retrieval, Scalability, Vectorless RAG, uv package management
github.com 2 hours ago
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46.
HN
Karpathy is searching for the Agentic IDE
Karpathy underscores the necessity of crafting a custom control panel layer for an Agentic Integrated Development Environment (IDE) instead of depending on pre-existing solutions. He proposes incorporating preferred coding agents into this IDE through a unified messaging system that supports both push and bidirectional communication, which enhances interaction flexibility. Additionally, Karpathy advocates for a manager agent to supervise individual activities within the environment, ensuring efficient oversight. Despite recent heightened activity making rapid development feasible, he humorously cautions against potential pitfalls like "LLM psychosis," emphasizing the need for careful implementation.
Keywords: #phi4, AgentHub, Agentic IDE, Karpathy, LLM psychosis, LLM psychosis Keywords: Karpathy, bidirectional, coding agents, control panel, control panel layer, harnesses, interface, manager agent, message substrate, observational, push approach
xcancel.com 4 hours ago
https://x.com/karpathy/status/2031616709560610993 3 hours ago
https://thinkwright.ai/plexus 12 minutes ago
https://nimbalyst.com/ 12 minutes ago
https://www.augmentcode.com/product/intent 12 minutes ago
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71.
HN
Agentic Engineering: The good, the bad, the ugly
"Agentic Engineering: The good, the bad, and the ugly" is a topic that explores various facets of agentic engineering, particularly focusing on AI systems that exhibit autonomous behavior. It delves into both beneficial aspects and potential drawbacks, as well as controversial elements associated with this technology. This discussion is embedded within an application designed to amplify independent voices, encouraging user engagement through features like subscriptions, chat functions, activity logs, profile management, and content creation tools. To fully access the site's functionalities, users are required to enable JavaScript in their web browsers.
Keywords: #phi4, Activity, Agentic Engineering, App, Chat, Create, Explore, Home, Independent, JavaScript, Profile, Scripts, Subscriptions, Voices
substack.com 5 hours ago
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94.
HN
I put agentic AI through a real engineering stress test. Here's what I learned
The text discusses a stress test on agentic AI tools such as Claude Code and Codex, where an intricate system was built to integrate data from platforms like Jira, Notion, and Readwise Reader into a searchable database within one day, facilitated by 17 chat interactions with AI. The author highlights the significant role of agentic AI in enhancing engineering processes beyond speeding up coding, emphasizing its capacity to inspect environments, diagnose issues, propose solutions, and document progress.
The project demonstrated that employing AI as a collaborative partner rather than just a code generator can streamline problem-solving by reducing context loss and compressing the time between identifying issues and implementing resolutions. The text introduces "AI-First Practices," which include using AI for targeted changes based on understanding current states, grounding AI in real-time evidence, maintaining short and testable tasks, providing specific local context to AI, converting discoveries into reusable assets, and aggressively refactoring code for improved architecture.
For engineers, the most effective application of AI is found in debugging, exploration, and system design, where it minimizes uncertainty and transforms hypotheses into robust systems. However, human judgment remains vital. The text suggests that engineering leaders should focus on leveraging AI to ground decisions in evidence, structure work efficiently, and convert point solutions into shared systems, emphasizing operational fluency among engineers.
The author concludes by asserting that optimizing these practices can revolutionize engineering workflows more effectively than merely automating coding tasks, pointing towards broader organizational changes in the EPD operating model.
Keywords: #phi4, AI engineering, API exposure, Claude Code, Codex, EPD operating model, agentic AI, containerized services, data ingestion, database connectivity, engineering loop, operational fluency, semantic search, software engineering
www.anthonyputignano.com 7 hours ago
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100.
HN
Show HN: I Built a Skype Alternative. Then Discovered AI Agentic Voice
GlobCall is an innovative browser-based international calling service that emerged in popularity after the shutdown of Skype, now serving over 10,000 users across more than 40 countries. Its standout feature is the "Agent-Phone" interface, which employs agentic AI voice agents to handle calls independently across various languages and time zones. This approach addresses limitations of traditional human-operated call centers by enhancing scalability without necessitating a large workforce or incurring high operational costs. The service offers significantly reduced rates for international calling and local number setup compared to conventional carriers, beginning with no per-seat pricing model. Although currently in private testing for its AI capabilities, GlobCall provides live services via browser or API interface. Users have reported notable savings and improved call quality, which has revolutionized their business communication practices by facilitating more frequent and economical global interactions.
Keywords: #phi4, AI, AI agentic voice, AI voice agents, API, GlobCall, Skype, Skype alternative, agent-phone, agent-phone interface, agentic AI voice agents, agentic voice, browser-based, business transformation, call quality, global communication, global communication Keywords: GlobCall, international calling, local number, no SIM, no app, real voice call, top-up pricing
globcall.com 8 hours ago
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107.
HN
GDL: Grep-native data language for agentic systems
GDL, or grep-native data language, provides a streamlined approach for agentic systems by leveraging native bash tools such as `grep`, avoiding traditional databases and message queues. Instead, it utilizes the filesystem for coordination and Git for tracking changes, enabling efficient system management through seven structured file formats that convey detailed information about various system components:
1. **GDL (.gdl):** This format encapsulates business data in key-value pairs.
2. **GDLS (.gdls):** It maps out schemas of external systems by detailing tables and columns.
3. **GDLC (.gdlc):** This file type provides mappings for code structures, including modules and their dependencies.
4. **GDLA (.gdla):** API contract maps are represented here, offering details about endpoints.
5. **GDLD (.gdld):** It visualizes knowledge through diagrams like flows and patterns.
6. **GDLM (.gdlm):** This format stores shared agent memory with a lifecycle framework.
7. **GDLU (.gdlu):** Indexes for unstructured documents, such as PDFs, are maintained here.
Each file adheres to a consistent format using `@` as a prefix, `|` as a delimiter, and one record per line, ensuring compatibility with `grep`. This setup facilitates the effective querying of enterprise customer data, schema tables, or architecture decisions without relying on complex database systems. Early benchmarks indicate that GDL files are more compact than their YAML and JSON counterparts, require fewer tokens for queries, and maintain high accuracy in navigating table/column structures. Comprehensive documentation covers specifications, core architecture, concurrency models, and optimized agent prompts across all layers of the system. The project encourages contributions as outlined in the `CONTRIBUTING.md` file and is distributed under the MIT license.
Keywords: #phi4, API contracts, GDL, JSON, YAML, agent coordination, agent memory, agents, architecture decisions, benchmarks, concurrency model, databases, document indexes, enterprise customers, file formats, filesystem, git, grep-native, message queues, query engine, schema, structured data, vector databases, visual knowledge
github.com 8 hours ago
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109.
HN
The grep-native language for agentic systems
"grep-native" is a specialized data language created by greppable.ai aimed at enhancing the querying and manipulation capabilities within agentic systems. It draws on principles akin to traditional grep but adapts them for more sophisticated applications, making it particularly effective for managing complex datasets in large-scale, dynamic environments characteristic of agent-based architectures. By focusing on improving efficiency and effectiveness, "grep-native" supports advanced data handling processes essential for the functionality and performance optimization of agentic systems.
Keywords: #phi4, AI, agentic systems, data, data language, grep, grep-native, greppable, greppableai, keywords, language, native, systems, technical keywords
greppable.ai 8 hours ago
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113.
HN
Agentic Risks
The document presents a mental model for evaluating risks associated with AI Agents, using insights from recent experiences and established frameworks. It categorizes these risks into two primary areas: Data Exfiltration, which involves exposing sensitive data, and Rogue Activity, where damaging actions are performed. These risks are intensified by three amplifying factors: Capabilities (the tools accessible to the agent), Data Access (data available within the language model context), and Untrusted Input (potentially harmful external inputs). AI Agents pose safety concerns due to their inability to discern between trusted and untrusted contexts, a vulnerability often exploited through prompt injection. Additionally, new capabilities can escalate both the potential impact of risks and the number of entry points for threats. The inherently non-deterministic nature of Large Language Models (LLMs) implies that risk probabilities can never be reduced to zero.
To effectively map these risk scenarios, the document suggests graphing agent activities to monitor data presence and untrusted input at each step. For example, an AI processing a GitHub issue could unintentionally incorporate malicious instructions into a pull request if not carefully managed. The proposed model involves examining reachable states through capability invocations up to 2-3 levels deep.
To mitigate these risks, the document outlines proactive strategies such as human oversight, limiting capabilities or data access, and filtering untrusted inputs. Reactive measures include ensuring auditability, continuous monitoring, and alerting via mechanisms like LLM gateways that can detect suspicious activities. Despite many mitigations being recognized design patterns, their implementation is often complex, underscoring the necessity of human intervention and robust auditing as essential fallback strategies.
Keywords: #phi4, AI Agents, Agentic Risks, Alerting, Auditability, Backdoor, Capabilities, Capability Invocations, Context, Data Access, Data Exfiltration, Design Patterns, Filtering, Gateway, GitHub Issue, Impact, LLM, Mitigations, Monitoring, Probability, Prompt Injection, Pull Request, Risk Scenarios, Rogue Activity, Sanitization, State Exploration, Threat Model, Untrusted Input
cloudberry.engineering 9 hours ago
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127.
HN
I Reduced 5 hours of Testing my Agentic AI applcaition to 10 mins
LLMSec is an advanced framework designed to streamline the testing and evaluation of Agentic AI applications while also enhancing security testing capabilities. It dramatically reduces testing time by automating processes that traditionally took hours into a matter of minutes. The core functionality of LLMSec lies in its role as a Testing & Evaluation Engine, where users can define "Bots" or "Targets" with specific purposes to autonomously interact with chat AI interfaces. This framework supports interactions via REST APIs and web-based chat UIs through a Chrome Extension, facilitating functional use cases and complex multi-turn adversarial attacks.
Key features of LLMSec include a Bot Context Engine for defining target models, the ability to construct hierarchical Use Cases and Test Cases, evaluation scoring of AI responses, and an adaptive execution system that requests human input when context is insufficient. The framework also enhances security testing with advanced attack vectors such as Prompt Injections, Role-Playing, and dynamically adapting sequential attacks.
LLMSec integrates seamlessly with REST APIs for server-to-server communication and offers a Chrome Extension to interact with web chat applications without requiring complex authentication setups. To get started, users need Python 3.9+, Node.js 16+, and Google Chrome. The framework is open-source under the MIT License, emphasizing that all testing must be legally authorized.
For contributors, LLMSec outlines using pytest for backend changes, Prettier for frontend formatting, and npm linting to ensure compliance with standards in the Chrome Extension. Comprehensive documentation supports users in setup, usage, troubleshooting, and understanding system architecture, making it accessible and effective for both new users and developers.
Keywords: #phi4, Adversarial Attacks, Agentic AI, Chrome Extension, Docker, Evaluation Engine, FastAPI, Ground Truth Data, LLMSec, MIT License, Nodejs, Prettier, Python, REST API, Security Testing, Swagger UI, Test Cases, Testing Framework, Use Cases, pytest
github.com 12 hours ago
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130.
HN
PromptVault free tool for multi agentic development
PromptVault is a complimentary desktop application tailored to streamline the creation of multi-agent AI systems by addressing common challenges such as managing prompt changes, maintaining version control, and adjusting pipelines. It enables developers to visually map agent workflows using graphs and log outputs on their local machines, eliminating the need for cloud-based solutions. Designed initially for enjoyment by its creator, PromptVault serves as a structured development journal that facilitates efficient management of intricate AI projects. The tool is accessible for use by others who might find it beneficial, promoting collaboration and ease in handling complex AI developments.
Keywords: #phi4, PromptVault, agent pipeline, desktop app, dev journal, development, forget, fun, graph, lightweight, locally, log outputs, multi-agent AI, restructure, results, share, share Keywords: PromptVault, structure, track, tweak, version prompts
news.ycombinator.com 12 hours ago
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142.
HN
Engineering, Fast and Slow
The article "Engineering, Fast and Slow" examines the dynamic role of artificial intelligence (AI) in modern engineering practices, particularly focusing on engineers utilizing tools like Opus-4.5 to enhance problem-solving efficiency. It highlights a paradigm shift from gradual productivity improvements to swift advancements enabled by AI, which now allows for rapid solutions to previously challenging problems. Despite this acceleration benefiting career progression and meeting industry demands, the author advises caution against an overreliance on AI for learning and addressing complex issues.
Drawing from personal experience, the writer describes feeling pressured by fast-paced industry standards that prioritize quick development, resulting in hesitancy to engage deeply with intricate projects like coding the Raft consensus algorithm from scratch. While AI offers immediate solutions akin to a "powerful drug," providing shortcuts and instant gratification, it may inhibit thorough learning and comprehension.
The article warns against complacency and excessive dependence on AI tools, comparing engineers who overuse these technologies to "Lotus-eaters" at risk of losing their innovative edge. The author emphasizes the importance of balancing fast-paced AI-driven work with deliberate efforts for tackling complex problems that demand deep understanding and creativity. Ultimately, it is suggested that while AI can enhance speed and efficiency in engineering tasks, human ingenuity remains indispensable for solving challenges beyond AI's reach.
Keywords: #phi4, AI, Engineering, Opus-45, Raft consensus, Rust, agentic, development, dopamine, learning, pressure, productivity, systems, tooling
undecidability.net 16 hours ago
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155.
HN
Show HN: Principled Agentic Software Development
The article discusses "Principled Agentic Software Development," which integrates traditional software engineering practices like Outside-In Test-Driven Development (TDD) into agent-based workflows to enhance code quality and test reliability. It emphasizes using agentic tools such as Claude Code for rapid code generation but notes AI's limitations in creating effective tests. To address this, the approach proposes incorporating principles like Mutation Testing to ensure higher-quality testing through structured cycles—beginning with feature-complete acceptance tests followed by Red-Green-Refactor processes at various levels.
The proposed workflow starts by crafting a detailed plan from the user's perspective and writing comprehensive end-to-end tests, employing sub-agents for specific tasks such as test creation or code implementation. Skills are dynamically loaded to enable agents to perform these tasks effectively without overwhelming their processing capacity. The author illustrates this method in real-world applications within their aluminum fabrication company's software projects, detailing how different agents and skills are customized for various testing environments and managed through an agent workflow manager.
The article concludes by underscoring the importance of maintaining test quality alongside increased implementation throughput provided by AI tools to prevent losing control over product behavior. By embedding engineering principles into workflows, developers can scale high-quality software production while ensuring AI-generated features adhere to established processes, thereby preserving confidence in their performance and consistency.
Keywords: #phi4, AI-generated code, Agent Definitions, Automated Tests, Claude Code, Clean Code, Engineering Principles, Implementation Quality, Lean Software Development, Lean Software Development Keywords: Principled Agentic, Mutation Testing, Nextjs, Orchestrator, Outside-in TDD, Principled Agentic, Product Behavior, Skill Definitions, Skills, Software Development, Sub-agents, Test Quality, Workflow
www.joegaebel.com 19 hours ago
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219.
HN
From one-shot to agentic diagnostic analysis
Varjo headsets utilize an intricate software stack that generates complex diagnostic logs requiring expert analysis. In 2025, a new tool was introduced to streamline log parsing and analysis through a single-pass pipeline, effectively reducing the need for R&D escalations in simpler cases. However, more challenging issues necessitated deeper investigation beyond this tool's capacity. To address these complexities, an open-source system called Airut was developed. It integrates Claude Code to enable iterative log analysis via email interactions, eliminating the need for support engineers to learn new tools.
This conversational workflow allows support teams to work collaboratively with AI agents, providing context and directing investigations based on specific customer information. A significant case highlighted is a firmware update issue caused by interference from enterprise management software. Previously escalated to R&D, this problem was resolved within the support team's workflow through email exchanges with an AI agent that successfully identified the root cause.
Although agentic analysis involves higher costs compared to single-pass diagnostics, it offers considerable time savings and reduces reliance on R&D resources. Claude Code’s flexibility facilitates context-driven investigations while maintaining security through container isolation and network safeguards. While not a panacea for all R&D cases, this tool enhances the support team's capacity to independently resolve issues, significantly minimizing resolution times.
Keywords: #phi4, Airut, Claude Code, R&D, R&D escalations, USB, USB communication, Varjo headsets, agentic, agentic analysis, analysis, communication, container isolation Keywords: Varjo, containers, diagnostic, diagnostic logs, engineer, escalations, firmware, firmware update, headsets, isolation, iterative, iterative analysis, logs, pipeline, sandboxed, sandboxed containers, single-pass, single-pass pipeline, support, support engineer, update
haulos.com a day ago
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223.
HN
Judge blocks Perplexity's bot Amazon shopping in early test of agentic commerce
A federal judge in San Francisco has issued a preliminary injunction against Perplexity's AI assistant, Comet, preventing it from accessing password-protected sections of Amazon's site for shopping purposes on behalf of users. This legal action stems from a lawsuit by Amazon, which accuses Perplexity of violating the Computer Fraud and Abuse Act and California computer fraud statutes. The judge determined that while user authorization was obtained, Amazon itself had not granted permission for such access. Amazon contends that Perplexity enabled Comet to mimic regular browser sessions, thereby evading detection systems and potentially disrupting ad revenue streams. Despite receiving warnings from Amazon and encountering technical barriers, Perplexity allegedly found ways around these obstacles. This case highlights an early legal confrontation in the domain of agentic commerce, where AI agents undertake shopping tasks for consumers, bringing into focus issues related to access control at digital retail platforms. The injunction is temporarily suspended pending an appeal by Perplexity to the Ninth Circuit Court of Appeals.
Keywords: #phi4, AI assistant, Amazon, Buy For Me, Comet browser, Computer Fraud and Abuse Act, Google Chrome, Judge, Ninth Circuit Court of Appeals, Perplexity, Rufus, agentic commerce, competitor, cybersecurity, federal judge, injunction, personalization, preliminary injunction, pricing accuracy, technical barrier
www.geekwire.com a day ago
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233.
HN
Why on-device agentic AI can't keep up
The article examines the inherent challenges in advancing agentic AI capabilities directly on consumer devices due to hardware constraints. Current consumer devices generally lack sufficient RAM, typically between 8-16GB, which is inadequate for running larger models that are necessary for advanced AI functionalities like email management and task scheduling. Even high-end devices struggle with modern AI applications because large language models require significant memory not just for their parameters but also for caching interaction contexts. While techniques such as grouped-query attention and quantized key-value caches can partially address these issues, they often lead to reduced precision in critical tasks.
Compounding the problem, the supply chain has led to a substantial increase in RAM prices, prompting manufacturers to decrease rather than enhance the amount of RAM in new devices. Furthermore, even if more RAM were available, slow memory access times would still pose a significant bottleneck affecting AI processing speed and overall device performance. As a result, the article concludes that for the foreseeable future, complex agentic tasks will likely need to rely on cloud computing resources rather than local processing due to the immense scale of compute power required. Despite some advancements in open-weight models, without substantial hardware innovations or breakthroughs, running such advanced AI functionalities on consumer devices remains impractical.
Keywords: #phi4, DRAM supply chain, KV cache, RAM limits, agentic capabilities, battery life, cloud inference, consumer hardware, datacentre class RAM, latency, on-device AI, privacy, processing speed, speculative decoding
martinalderson.com a day ago
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268.
HN
Defeating Context Fatigue with Agentic Scaffolding
The article addresses "Defeating Context Fatigue with Agentic Scaffolding," exploring the challenges developers face when integrating AI agents into project workflows. As reliance on AI grows, developers encounter slowdowns due to the necessity of continuously reviewing and correcting AI decisions—a problem exacerbated by insufficient context management in expanding projects. This results in repetitive explanations and a loss of progress tracking.
To counteract this "context fatigue," the author advocates for embedding specific outcomes within agent workflows that ensure persistent context across sessions. These include phase and progress awareness, clear provenance and accountability, preserved decision rationale, and stable alignment with product intent. The goal is to transition human roles from providing context to effective supervision of AI agents, thus promoting more autonomous and efficient development.
The author recommends employing five coordination artifacts: a Product Requirements Document, Features List Document, PRD-Agent-Reasoning File, Project Manifest, and Agent-Ownership File. These documents collectively maintain project continuity by documenting decisions, progress, ownership, and alignment with goals. By implementing these scaffolding methods, developers can minimize the manual re-establishment of context, thereby enhancing productivity and allowing a focus on supervisory responsibilities.
In essence, the article underscores that effective agentic development hinges on robust scaffolding to manage context, empowering AI agents to operate autonomously while ensuring project continuity and accountability.
Keywords: #phi4, AI Skepticism, Agent Workflows, Agentic Scaffolding, Context Fatigue, Context Management, Continuity Problem, Coordination Artifacts, Decision Rationale, Development Loops, Human Supervisor, Persistent Context, Phase Awareness, Productivity Speed Bump, Provenance Accountability, Technical Debt
patrickmccanna.net a day ago
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286.
HN
Show HN: VR.dev – Open-source verifiers for what AI agents did
VR.dev is an open-source initiative designed to enhance the accuracy of verifying AI agent activities by focusing on actual system states instead of relying on potentially inaccurate self-reports from agents. Originally conceived as a virtual reality project, it shifted its focus due to low adoption rates for its initial concept. The project addresses critical issues where AI agents falsely report successful outcomes without making real changes in system states, such as altering database rows or sending incorrect emails, which can skew training processes.
To address these challenges, VR.dev provides a library of 38 verifiers across 19 domains, organized into three tiers: HARD checks that perform deterministic validations on databases and other components; SOFT scoring using LLM rubrics for subjective evaluations like tone; and AGENTIC checks involving active probing through headless browsers or shells. The project utilizes a composition model where SOFT scores are contingent upon passing the more stringent HARD checks, thus preventing reward hacking.
These verifiers are MIT-licensed and can be installed locally without requiring a hosted API, making them easily integrable into AI training loops. Feedback is being sought on the efficacy of this verification taxonomy and any challenges users might encounter. The ultimate aim of VR.dev is to ensure that AI models learn from genuine successes rather than false positives, thereby enhancing their reliability in real-world applications.
Keywords: #phi4, AGENTIC, AI agents, API, GitHub, HARD, IMAP, LLM rubric scoring, PyPI, SOFT, VRdev, agent successes, benchmarks, database, deterministic probes, fail_closed, open-source, pip install, reward hacking, rewards, system state, taxonomy, verification, verifiers
www.vr.dev a day ago
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309.
HN
Pi Is Vim for Agentic Coding
"Pi Is Vim for Agentic Coding" explores the minimalist and customizable nature of Pi, likening it to Vim in terms of design philosophy. Both tools allow users to extend their functionality through plugins or extensions. Pi is characterized by its core features such as multi-model support and slash commands, though it does not offer certain built-in functionalities available in other coding agents. This design choice encourages users to personalize Pi according to their specific needs. The article underscores the importance of utilizing Pi's agentic capabilities for self-extension rather than relying solely on pre-built extensions. It advocates for drawing inspiration from existing extensions but emphasizes personal adaptation, highlighting customization as a key element. The author appreciates both Vim and Pi for their minimalistic core structures combined with vast possibilities for enhancement, adding a personal touch by mentioning the shared Austrian origin of these tools as an additional point of intrigue.
Keywords: #phi4, Agentic Coding, Configuration, Customizability, Dotfiles, Extensions, Formatter Extension, Keyboard Motions, LazyVim, Minimal Core, Modes, Multi-model Support, Neovim, Pi, Plan Mode, Plugins, Scripting, Session Management, Simplicity, Slash Commands, Sub Agents, Toolset, UI Prettification, Vim, pi-mcp-adapter
www.hansschnedlitz.com a day ago
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345.
HN
Agentic Search: When Retrieval Stops Being Enough
An agentic search system enhances traditional information retrieval by incorporating diverse strategies tailored for specific queries and domains. Unlike conventional systems that focus solely on searching, this approach utilizes various tools such as AlphaFold, DFT solvers, and molecular docking software to generate answers directly. This is particularly advantageous in fields like materials science and bioinformatics, where the system can autonomously perform tasks such as simulating material properties or predicting protein structures using multiple parallel tools without human intervention.
A defining feature of agentic search is its organization of knowledge through taxonomies structured akin to file systems. This method allows efficient navigation of directories using files—such as markdown documents—that contain synonyms, related concepts, and regex patterns, thereby enhancing search accuracy. The system self-improves by learning from user interactions, logging search paths, and incorporating validated annotations into the taxonomy.
Furthermore, agentic search employs active learning loops where proposed updates are reviewed by domain experts or secondary models to maintain high-quality improvements in its corpus. By analyzing successful search paths, the system refines its strategies and suggests organizational enhancements for faster future searches. Consequently, the agent evolves into a more efficient information retrieval tool over time, continuously optimizing its performance through ongoing interaction and feedback.
Keywords: #phi4, Active, Active learning, Agentic Search, AlphaFold, Bioinformatics, DFT, DFT solvers, Decision, Decision tree, Docking, Index, Index proposal Keywords: Agentic, Knowledge, Knowledge nodes, Learning, Materials, Materials science, Molecular, Molecular docking, Nodes, Playbooks, Query, Retrieval, Science, Search, Solvers, Strategies, Taxonomies, Toolbox, Tree
medium.com a day ago
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369.
HN
rag not lag: rl for fast agentic retrieval
The paper introduces a novel method utilizing reinforcement learning (RL) to enhance agentic retrieval systems, specifically employing a compact 4-billion-parameter model that outperforms GPT-5.2 in domain-specific tasks requiring extensive data retrieval. This advancement enables smaller models to efficiently query and integrate external database information, optimizing both the quality and speed of data retrieval processes.
The research utilized the FinDer dataset for financial question answering, which presents challenges such as multi-hop reasoning and handling ambiguous queries. Through RL techniques, a specialized model was trained that improved accuracy by 35% compared to GPT-5.2, with significant enhancements in pass@8 scores reflecting better problem-solving abilities.
Key strategies involved multiple search iterations instead of relying on single-query searches, minimizing reward hacking by using varied judge prompts, and addressing discrepancies between training and inference stages through density-proportional policy optimization (dppo). This approach ensured a balance between stability and exploration during model training. The outcomes demonstrate that smaller models can surpass larger ones in domain-specific tasks with reduced latency and cost.
The authors aim to provide a platform for others to develop similar retrieval agents on custom datasets, facilitating quicker development of AI features centered around search capabilities.
Keywords: #phi4, Agentic Retrieval, BM25 Search, Cost, DPPo Method, Domain-Specific, FinDer Dataset, Financial Use Case, GPT-52, Latency, Multi-Turn Behavior, Query Echoing, Reinforcement Learning, Retrieval Quality, Reward Function, Rollout Engine, Small Model, Trainer Component
cgft.io a day ago
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375.
HN
Agentic development environment extension taxonomy
The "Agentic Development Environment Extension Taxonomy" seeks to address the complexities within the market resulting from an increasing number of extensions provided by various competing vendors. This proliferation has led to inconsistencies in naming conventions and standards, creating confusion for users. The primary goal of this taxonomy is to streamline and clarify these offerings, thereby enhancing comprehensibility and standardization within the domain. By doing so, it intends to make navigating the market more straightforward and intuitive, ultimately benefiting both developers and end-users by reducing the challenges associated with selecting and implementing the appropriate extensions.
Keywords: #phi4, Agentic development, disambiguate, domain space, environment extensions, market, nomenclature, offerings, proliferation, simplify, standards, taxonomy, vendors
droctothorpe.github.io a day ago
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412.
HN
Agentic Debt
The text introduces "agentic debt" as a novel issue in software engineering, distinct from conventional technical debt, resulting from AI agents writing code that addresses short-term needs but leads to inconsistencies and architectural drift due to their limited holistic understanding. Unlike typical technical debt, agentic debt is self-reinforcing, with each agent's changes adding complexity without regard for the overall system. This problem is compounded by limited context windows where extensive access does not necessarily resolve complexities from overlapping or inconsistent code patterns created by different agents. Simplifying code to be human-understandable also benefits AI agents by facilitating easier future modifications.
To mitigate agentic debt, the author recommends a "gardening" approach in software maintenance—proactively refactoring and consolidating code to prevent its accumulation, which can hinder development as teams expand. This stewardship role becomes crucial with more engineers contributing to code development. The text raises open questions about the potential for AI-driven gardening tools that could automatically review and maintain code quality and whether this approach scales effectively with larger teams. Balancing immediate development speed with long-term system coherence is essential to ensure sustained productivity and ease of maintenance.
Keywords: #phi4, Agentic Debt, Agents, Architectural Drift, Codebase, Context Window, Duplication, Feedback Loop, Gardening, Maintainability, Refactoring, Stewardship, Technical Debt
neilkakkar.com a day ago
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424.
HN
Software Architecture in the Era of Agentic AI
In "Software Architecture in the Era of Agentic AI," the author explores how software architecture's role has transformed due to the integration of AI agents capable of handling coding, testing, and deployment tasks traditionally managed by humans. This shift necessitates a change from micro-level code management to macro-level system governance, focusing on setting boundaries for modules and services to manage complexity. The core areas impacted include understandability, deployability, and runnability.
Understandability now emphasizes the importance of clear interfaces and service boundaries over clean code due to AI's rapid code generation capabilities. This shift ensures that globally comprehensible systems are maintained despite increased complexity. Deployability faces challenges as developers experience "review fatigue" from reviewing AI-generated code instead of writing it, highlighting the need for stringent technical debt management and reliable automated tests with critical human oversight.
Rannability requires architects to ensure efficient, secure, and compliant system operations while designing resilient architectures against failures and managing risks related to AI's potential neglect of non-functional requirements. The overarching theme underscores the continued importance of the human element in strategic oversight, guiding development processes, and aligning with business objectives. Software architects must now focus on integrating AI capabilities into frameworks that uphold quality, compliance, and ethical standards, transitioning from direct code management to broader system design and governance while balancing automation with essential human intervention.
Keywords: #phi4, Agentic AI, Automation, CI/CD Pipeline, Cloud-Native, Compliance, DevOps, Developer Productivity, Governance, LLMs (Large Language Models), Prompt Engineering, Software Architecture, Technical Debt
www.exploravention.com 2 days ago
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430.
HN
Dify: Production-ready platform for agentic workflow development
Dify is an open-source platform tailored for developing applications based on Large Language Models (LLMs), designed to ease the transition from prototyping to production through its robust suite of features. It provides an environment equipped with agentic AI workflows, RAG pipelines, model management capabilities, and observability tools, supporting integration with a variety of LLMs including GPT, Mistral, and Llama3. Users can create and test AI workflows visually, while the platform also facilitates prompt development and model performance comparison through its Prompt IDE interface.
A key component is Dify's RAG pipeline, which allows for document ingestion and retrieval from formats such as PDFs and PPTs, enhancing functionality with agent capabilities that utilize frameworks like LLM Function Calling or ReAct. It incorporates tools such as Google Search and DALL·E within these agents. The platform provides LLMOps features to monitor application logs and performance metrics, ensuring continuous enhancement of applications through its Backend-as-a-Service APIs.
Dify offers multiple deployment options: a hosted cloud service with a free sandbox plan that includes 200 GPT-4 calls, a Community Edition for self-hosting via Docker Compose or Kubernetes, and enterprise solutions on AWS tailored for startups and larger organizations. Advanced setup capabilities allow customization through environment variables and Docker settings, alongside metrics monitoring facilitated by Grafana integration.
The platform supports various deployment strategies including Terraform, AWS CDK, Alibaba Cloud, and Azure DevOps Pipelines. Dify encourages community engagement and contribution, allowing users to contribute code, translate the software, and participate in discussions via platforms like GitHub, Discord, and Twitter. Security concerns should be reported directly to a designated email address. The platform operates under a modified Apache 2.0 license with additional conditions.
Keywords: #phi4, AWS CDK, Alibaba Cloud, Dify, Discord Community, Docker Compose, GitHub Issues, Grafana monitoring, Kubernetes deployment, LLM applications, RAG pipelines, Terraform deployment, agentic workflows, cloud service, community contribution, enterprise features, model management, observability, security disclosure, self-hosting
github.com 2 days ago
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441.
HN
One More Prompt: The Dopamine Trap of Agentic Coding
The article examines the addictive nature of using agentic coding with AI tools like Claude Code, which can stimulate responses akin to gambling by triggering dopamine and adrenaline. Developers are increasingly drawn into late-night coding sessions due to intermittent successes and failures offered by these tools, leading to a widespread sleep crisis among even seasoned engineers who find it difficult to disconnect, sometimes requiring medication for rest. This issue is intensified by the tech industry's embrace of "vibe coding," with leaders like Garry Tan admitting their own struggles with sleep deprivation caused by AI tool addiction. Unlike traditional workaholism, these tools reduce friction, create a spectator effect, offer endless possibilities, and provide social reinforcement through gamification.
Despite awareness of this problem, many developers continue to face challenges in setting boundaries, often working into the night. The article underscores the need for greater recognition and transparency regarding the potential downsides of this trend, questioning whether such intense productivity is sustainable or detrimental in the long run. While acknowledging the substantial benefits AI coding tools bring, it advocates for a balance to prevent developers from falling victim to self-imposed "crunch culture," which could adversely affect their well-being.
Keywords: #phi4, AI tools, AI-generated, Dopamine trap, addiction, agentic coding, burnout, codebases, compulsive behavior, developer culture, developers, dopamine hits, gamification, intensity, mental health, overwork, productivity gains, sleep crisis, sleep deprivation, tech industry, variable ratio reinforcement, vibe coding, workaholism
blog.quent.in 2 days ago
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460.
HN
Agentic AI Code Review: From Confidently Wrong to Evidence-Based
The article examines the evolution of AI code review systems from fixed-context models to an advanced agentic framework that enhances accuracy by enabling dynamic evidence gathering. Initially confronted with issues where AI-generated reviews were confidently incorrect due to restricted context access, the author implemented a shift toward an agentic loop approach. This model equips AI with tools to autonomously seek and retrieve necessary information, allowing it to refine its decision-making until review submission or predefined constraints like budget or time are met.
This architectural transformation aims at minimizing "hallucinations" by ensuring that models substantiate their claims with specific data before arriving at conclusions, thereby improving both the quality and explainability of reviews. Key elements of this system include defining tool contracts for deterministic API interactions, employing terminal tools to organize output, actively managing context through iterative loops, and establishing boundaries such as iteration limits and cost budgets.
By permitting AI systems to dynamically fetch evidence rather than depending on static inputs, the model transitions from speculative analysis to delivering precise and justifiable feedback. However, this approach introduces challenges like increased latency due to additional tool interactions, higher operational costs, and the critical need for robust tool design to prevent erroneous outputs. Additionally, security concerns arise as these tools may serve as potential data exfiltration channels.
Despite these trade-offs, the agentic methodology fosters a code review system that emulates a meticulous reviewer by verifying facts before concluding, ultimately resulting in superior quality reviews.
Keywords: #phi4, Agentic AI, Budgeting, Code Review, Context Problem, Evidence-Based, Exploration Loop, Guardrails, Latency, Model Fetching, Security, Structured Output, Terminal Tool, Toolset
platformtoolsmith.com 2 days ago
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499.
HN
A job ad for Agentic AI Advocate
RevenueCat is seeking an Agentic AI & Growth Advocate to represent a new community of autonomous AI agents within their organization. These AI entities are involved in developing, launching, and scaling applications, often leveraging RevenueCat's services. The position demands significant autonomy as it entails managing projects from start to finish without continuous human supervision. Candidates for this role should excel at producing technical content and promoting growth through automation. They need a solid grasp of software development and app expansion strategies. This innovative hiring approach underscores the integration of AI agents into professional settings, positioning them not only as tools but also as creators and developers in their own right.
Keywords: #phi4, Agent, Apps, Autonomous AI, Autonomy, Community, Creator, Growth Advocate, Marketing Automation, Open-ended Problems, Public Hiring Process, Public Hiring Process Keywords: Autonomous AI, RevenueCat, Software Development, Technical Content
news.ycombinator.com 2 days ago
https://jobs.ashbyhq.com/revenuecat/998a9cef-3ea5-45c2- 2 days ago
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547.
HN
Reimagining HTTP 402 – Simplify API and agentic payments with Stripe
The proposal focuses on simplifying the process of making payments for APIs by leveraging an open standard that utilizes HTTP 402 in conjunction with Stripe's payment infrastructure. This innovative approach negates the need for traditional signup processes, API keys, or OAuth authentication. By allowing AI agents to autonomously make payments upon their first request, it significantly streamlines the integration and utilization of API services, enabling a seamless operation without requiring human intervention. This method facilitates easier access to API functionalities by eliminating customary barriers associated with payment setups.
Keywords: #phi4, AI Agents, API, Agentic Payments, Authentication, First Request, HTTP, Human in the Loop, No API Keys, No OAuth, No Signup, Open Standard, Pay and Use, Stripe
stripe402.com 2 days ago
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557.
HN
Show HN: Agentic Metric – top for your AI coding agents (token, cost tracking)
Agentic Metric is an open-source, offline monitoring tool designed for tracking token usage and costs associated with AI coding agents on Linux and macOS platforms. It features a live terminal UI dashboard that refreshes every second, providing real-time insights into active sessions, cost estimates, daily summaries, and historical trends over 30 days. The tool supports various AI coding agents such as Claude Code, Codex, OpenCode, Qwen Code, and VS Code Copilot by utilizing local data, eliminating the need for network requests or telemetry.
Key functionalities of Agentic Metric include live session monitoring, a plugin architecture to facilitate easy extensions, and integration with status bars like tmux and i3blocks. Users can access command-line options for comprehensive usage overviews and pricing management. The tool is fully offline, ensuring data privacy by storing all information locally in SQLite databases. For installation, Python 3.10+ is required, and it can be installed via pip or the uv tool.
Agentic Metric supports a range of agents through specific file paths and offers features for managing model pricing. However, it does not support Cursor due to changes in its data handling practices.
Keywords: #phi4, AI coding agents, Agentic Metric, CLI, Python 310+, SQLite DB, TUI dashboard, cost estimation, data sources, offline tool, open source, plugin architecture, pricing management, status bar integration, token tracking, unsupported agents
github.com 2 days ago
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565.
HN
Agentic coding doesn't = technical debt
Agentic coding has often been criticized for producing low-quality and insecure code, yet this criticism typically stems from its misuse rather than inherent flaws in the tools themselves. The problem is commonly attributed to "vibe coding," an approach characterized by hasty acceptance and deployment of AI-generated outputs without sufficient oversight or understanding of the underlying architecture, which can lead to significant security vulnerabilities, as seen with Enrichlead's platform. In contrast, disciplined agentic engineering involves careful planning and control, starting with a comprehensive plan before writing code, followed by iterative refinement, controlled implementation phases, continuous documentation, and rigorous security testing. This structured approach has enabled teams like inmydata to develop complex systems quickly without compromising quality or increasing technical debt. Properly managed, agentic coding tools can enhance development speed, reduce costs, and maintain high-quality output. The challenge lies not in the innovation itself but in adopting disciplined methodologies that integrate architecture reviews, documentation, and security checks effectively, transforming potential drawbacks into advantages.
Keywords: #phi4, AI-generated code, Agentic coding, Claude Opus, architecture review, documentation, engineering discipline, operating costs, penetration testing, phased implementation, security flaws, technical debt, vibe coding
inmydata.ai 2 days ago
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624.
HN
Comprehensive Benchmarking of Agentic Systems Across 104 Real-World Challenges
The paper "LiveAgentBench: Comprehensive Benchmarking of Agentic Systems Across 104 Real-World Challenges" introduces a new benchmark designed to evaluate agentic systems through the lens of realistic user tasks, overcoming limitations in existing benchmarks by incorporating scenarios derived from actual social media and product-related interactions. The authors present 104 distinct scenarios, encompassing 374 tasks split into validation and testing subsets, all generated via their innovative Social Perception-Driven Data Generation (SPDG) method to ensure relevance, complexity, and verifiability.
LiveAgentBench serves as a dynamic tool for assessing the performance of various models, frameworks, and commercial products by reflecting real-world user interactions. This adaptability is achieved through continuous updates with new queries that represent evolving real-world challenges, allowing ongoing evaluation of agentic systems' practical capabilities and areas requiring enhancement. The research, supported by entities like the Simons Foundation, was authored by Hao Li et al., submitted to arXiv on March 3, 2026 (identifier cs.AI:2603.02586). This benchmark aims to bridge the gap between AI system development and user needs, fostering advancements in practical applications by aligning systems more closely with real-world demands.
Keywords: #phi4, AI Agents, Agentic Systems, Benchmarking, Commercial Products, Data Generation, Frameworks, Large Language Models, LiveAgentBench, Model Evaluation, Real-World Challenges, SPDG Method, Social Media, Task Complexity
arxiv.org 2 days ago
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630.
HN
Ask HN: Are we going to see more job postings asking for only agentic coding?
The discussion highlights an emerging trend in the tech industry, as evidenced by a Zapier job posting emphasizing AI agents' role in coding tasks over traditional manual methods. This shift involves roles that focus on directing and reviewing AI-generated code, selecting suitable models for specific tasks, mitigating failure modes, and integrating multi-agent patterns into workflows. The aim is to enhance team efficiency and scalability through the strategic use of AI. This trend raises critical questions about a potential industry-wide move towards prioritizing agentic coding in job postings, suggesting a significant transformation in software development practices. As AI technologies advance, they are increasingly viewed as tools to streamline processes and improve productivity, potentially redefining roles within tech teams and altering traditional approaches to coding and project management.
Keywords: #phi4, AI agents, AI impact, Job postings, Zapier, agent-written code, agentic coding, development workflow, failure modes, hand-writing code, mitigations, models, multi-agent patterns, team building
news.ycombinator.com 2 days ago
https://docs.aws.amazon.com/boto3/latest/ a day ago
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659.
HN
Show HN: From Agentic Reasoning to Deterministic Scripts
The proposal outlines a strategic framework aimed at optimizing AI agent performance by making them more efficient and cost-effective over time through a structured transition from agentic reasoning to deterministic scripts for routine tasks. This involves four key phases: Deliberative Execution, where agents handle new or ambiguous requests using comprehensive reasoning and detailed logging; History Analysis, which analyzes logs to identify repetitive tasks and stable patterns, reducing reliance on large language models (LLMs); Automation Generation, which creates deterministic scripts for sufficiently recurrent and stable tasks, eliminating the need for ongoing LLM reasoning; and Smart Routing, where new requests are directed either through existing automations or agent-based reasoning as needed. The framework's objectives include cost reduction, enhanced auditability, increased operational reliability, energy efficiency, and improved response speed. It emphasizes codifying effective behaviors into procedures for routine tasks while retaining deliberative agents for novel situations, envisioning a system where LLM reasoning is an initial step toward more direct execution methods, without retraining AI models.
Keywords: #phi4, AI agents, LLM (Large Language Model), OpenClaw, agentic reasoning, auditability, automation generation, deterministic scripts, operational reliability, overhead, routine tasks, semantic similarity, smart routing, tokens
juanpabloaj.com 2 days ago
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757.
HN
I was "early" in agentic coding. Here's my story
The narrative chronicles an author's evolving relationship with AI coding tools, driven primarily by medical necessity following a diagnosis of Guillain-Barre Syndrome in October 2024. Initially using AI technologies like Cursor and chatGPT sporadically for minor tasks due to their cumbersome nature, the author's perspective shifted dramatically after developing severe hand pain and weakness that impaired their ability to type. By March 2025, this condition necessitated a reliance on voice-to-text capabilities via Cursor as a primary coding tool.
The transition was challenging; frequent code errors required enhanced prompting skills and clearer enunciation from the author to effectively utilize AI tools. Despite regaining partial typing abilities over six months, the author continued using these tools for efficiency, appreciating Cursor's role as their main Integrated Development Environment (IDE) even while experimenting with others like Claudecode.
As of May 2025, a change in subscription plans imposing payment for tokens prompts reflection on future usage patterns. The narrative underscores how an unforeseen medical condition catalyzed a profound shift from occasional to essential use of AI coding tools, highlighting reliance born out of necessity rather than preference and marking a significant transformation in the author's coding practices.
Keywords: #phi4, AI coding, Claudecode, Cursor, Guillain-Barre Syndrome, IDE, VSCode, adoption, dexterity recovery, prompting, speech-to-text, tokens, typing loss, unlimited plan, voice-to-text
news.ycombinator.com 3 days ago
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779.
HN
Agentic Coding for Non-Vibe Coders
The essay "Agentic Coding for Non-Vibe Coders," part two of a series on agentic coding, explores the balance between leveraging artificial intelligence (AI) tools and retaining human oversight in coding projects. The author critiques fully automated models—whether keeping humans in or out of the loop—arguing that humans should remain central to decision-making processes rather than marginal. In the first part, they warned against becoming overly dependent on AI for productivity without true comprehension, labeling it a "dopamine trap."
The focus is on non-vibe coders who aim to build enduring and useful projects by maintaining control over their coding environment. This involves choosing what is built, ensuring sustainable setups, and solving problems independently. The essay emphasizes the need for human oversight when using agentic tools like Claude Opus, Codex, and Qwen. While these tools can quickly generate code, they require human management to optimize prompts, handle context limits, and adapt to evolving codebases.
The recommended workflow is minimalist: use one's cognitive skills for problem-solving, programming languages for implementation, and agents to translate ideas into code. Essential documents such as PITCH.md, ARCHITECTURE.md, and IMPLEMENTATION.md form the foundational structure, while context management can be handled through simple commands like /context-save and /context-restore.
The essay critiques complex setups such as multi-agent workflows and unattended agentic flows, advocating for simpler, more traceable methods. For intricate projects, utilizing multiple models to review work can enhance quality but necessitates careful coordination.
Reflecting on personal experiences, the author discusses successful projects that integrated traditional skills with agentic tools, like a self-hosted portfolio site and an A/B testing simulator, while also recounting failures attributed to excessive AI reliance. These examples underscore the importance of human involvement in ensuring project sustainability.
The essay concludes by emphasizing the need for foundational technical skills, cautioning against viewing AI as a substitute for understanding and problem-solving. Agentic coding is likened to "autocomplete on steroids," with a call for continuous programming practice to avoid dependency on machines. Ultimately, the author encourages maintaining control over projects by blending human insight with AI capabilities.
Keywords: #phi4, A/B Testing, AI Coding, Accountability, Agentic Coding, Architecture, Autocomplete, Autonomy, Cognitive Load, Context Management, Data Science, Documentation, Dogfooding, Dopamine Trap, Expertise, Guardrails, Human Loop, Mental Reps, Multi-Agent Workflows, Neural Networks, Non-Vibe Coders, Productivity, Programming Languages, Prompting, Review Process, Sidequests, Software Engineering, System Design, Workflow
theasymptotic.substack.com 3 days ago
https://agilevibecoding.org a day ago
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791.
HN
Agentic Email
The article explores the innovative use of Large Language Model (LLM) agents to manage email communications, which involves accessing users' email accounts to prioritize emails, draft responses, and autonomously reply, thereby easing the burden of managing numerous communication tools. However, this advancement introduces significant security risks identified as "The Lethal Trifecta"—untrusted content, sensitive information handling, and external communication—making users susceptible to major breaches. Although no severe incidents have been reported thus far, experts warn about potential threats, particularly concerning agents' ability to intercept password-reset workflows. A safer alternative proposed is restricting these agents to read-only access without internet connectivity, enabling them to draft responses for human review in plain text. This approach reduces some risks by preventing external communication but at the cost of reduced functionality. Users are advised to fully understand these security risks and take responsibility for any potential consequences, as attackers might exploit vulnerabilities in such systems in the future.
Keywords: #phi4, Agentic Email, Attack Surface, Communication Tools, External Communication, False Sense of Security, Human Review, LLM Agents, Nerve Center, Password Reset, Security Breaches, Sensitive Information, The Lethal Trifecta
martinfowler.com 3 days ago
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802.
HN
The User Is Stochastic: Testing Agentic Systems with Simulation and Evaluation
Testing agentic systems, which manage complex multi-turn conversations, necessitates methods beyond traditional approaches like golden datasets or LLM-as-judge due to their inadequacies in addressing conversational branching and ambiguity. The simulation and evaluation (sim/eval) method offers a comprehensive solution by dynamically simulating user interactions based on scenarios that incorporate goals, persona traits, policies, and expected outcomes. This approach assesses the system's ability to handle real-world conversation complexities, including tool use and policy adherence, within realistic mock environments.
Sim/eval tests should complement other testing methods in a broader stack, which includes unit tests, contract tests, integration tests, human evaluation, and production telemetry. The focus is on ensuring agents navigate conversations effectively by verifying execution traces rather than relying solely on scripted outputs or narrative assertions. Key considerations for sim/eval include selectively using LLM judges for subjective dimensions like tone, aligning scenario coverage with actual user interactions, incorporating adversarial variations, and treating scenarios as evolving test infrastructure.
While sim/evolution cannot replace other testing methodologies entirely, it addresses critical gaps in evaluating an agentic system's conversational robustness. Thus, it is a crucial component of a comprehensive testing strategy, ensuring systems are well-equipped to manage complex conversations effectively.
Keywords: #phi4, Agentic systems, LLM-as-judge, assertions, benchmark suites, conversational branching, golden dataset, multi-turn, multi-turn conversations, recovery, recovery from misunderstanding, scenario coverage, scenario coverage Keywords: Agentic systems, sim/eval, simulation and evaluation (sim/eval), testing, tool use, trace assertions
www.gojiberries.io 3 days ago
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840.
HN
Agentic open-source local news comedian (Pydantic, Llama 3.1)
The announcement details the creation of an agentic, open-source local news comedian developed using Pydantic and Llama 3.1 technologies. The developers are committed to incorporating user feedback into future iterations of the project. They encourage readers to share their input via a provided email address, highlighting their openness to community engagement while ensuring privacy by omitting specific contact details in this context. This initiative reflects an effort to blend technology with humor and local news through collaborative development.
Keywords: #phi4, Agentic, Llama 31, Pydantic, comedian, contact, email address, feedback, input, keywords, local news, open-source, technical
github.com 4 days ago
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847.
HN
Let It Flow: Agentic Crafting on Rock and Roll
The paper "Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem" introduces a novel infrastructure known as the Agentic Learning Ecosystem (ALE), designed to enhance Large Language Models (LLMs) through agentic crafting. This ecosystem is structured around three main components: ROLL for optimizing weights post-training, ROCK as a sandbox environment manager that facilitates trajectory generation, and iFlow CLI, which aids in efficient context engineering. The core of the research is the open-source agent ROME, developed using ALE and trained on over one million trajectories. This model incorporates sophisticated data composition protocols to enable complex behavioral synthesis and utilizes a novel policy optimization algorithm called Interaction-Perceptive Agentic Policy Optimization (IPA). IPA innovatively assigns credit based on semantic interaction chunks rather than individual tokens, which enhances stability during long-horizon training.
ROME's performance is rigorously evaluated in both structured settings and against Terminal Bench Pro—a new benchmark noted for its improved scale and contamination control. The model exhibits strong results across established benchmarks like SWE-bench Verified and Terminal Bench, underscoring the effectiveness of ALE in facilitating agentic crafting. This research receives support from the Simons Foundation alongside various other contributors, highlighting collaborative efforts underpinning these advancements.
Keywords: #phi4, ALE, Agentic Crafting, Artificial Intelligence, Benchmark, Computation, IPA, LLMs, Language, Open Agentic Learning Ecosystem, Policy Optimization, ROCK, ROLL, ROME Model, Real-world Environments, Rock and Roll, SWE-bench Verified, Terminal Bench Pro, Trajectories, iFlow CLI
arxiv.org 4 days ago
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890.
HN
Agent Spy – follow what your Agentic Coder is doing
Agent Spy is a sophisticated tool designed to monitor and verify real-time file changes made by AI agents, serving as an essential watchdog for users who work alongside AI tools in their codebase management. It features live file watching that detects changes instantly, displaying Git change indicators with yellow markers to highlight differences from the last commit. The application provides inline highlighting within both code and markdown files—using green for added lines, yellow for modified ones, and red for deleted content. Additionally, it supports side-by-side diff comparison, allowing users to navigate through changes step-by-step, along with focus filters that isolate modified files, enhancing efficiency. Users can prioritize important files using a star functionality, and the tool includes keyboard shortcuts for seamless navigation and customization of views. Agent Spy is available for download from its releases page and is developed utilizing Electron technology under an MIT license.
Keywords: #phi4, AI agents, Agent Spy, Electron Forge, Git indicators, MIT License, change navigation, changed files filter, codebase control, diffs, file changes, inline highlighting, keyboard shortcuts, live watching, project folder, real-time monitoring, side-by-side diff, star files
github.com 4 days ago
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967.
HN
T3 Code – a new OSS agentic coding app that wraps Codex
T3 Code is an innovative open-source software application that integrates Codex, aiming to enhance coding capabilities through artificial intelligence. This AI-powered coding tool, available on GitHub, positions itself as the leading solution in its category. It offers users an advanced platform for improving their coding efficiency and effectiveness. T3 Tools Inc., which holds the copyright for T3 Code starting from 2026, encourages users to download the application and provides support through Discord, facilitating a community-driven approach to troubleshooting and collaboration.
Keywords: #phi4, AI, Codex, Discord, GitHub, OSS, T3 Code, T3 Tools Inc, agentic coding app, application, download, open source, software, tools
t3.codes 4 days ago
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979.
HN
Use Cursor Automations for Agentic Stale Feature Flag Removal
The video "Use Cursor Automations for Agentic Stale Feature Flag Removal" explores the application of Cursor Automations in efficiently identifying and removing obsolete feature flags within software development processes. Hosted on YouTube, a platform managed by Google LLC, it provides viewers with options to access related details regarding press inquiries, copyright information, privacy policies, and safety guidelines. Additionally, the video touches upon NFL Sunday Ticket as one of the new features undergoing testing, indicating its potential relevance or implementation in this context. The focus remains primarily on illustrating how automated tools can streamline the maintenance of feature flags, thereby enhancing development efficiency.
Keywords: #phi4, Advertise, Agentic, Contact, Copyright, Creators, Cursor Automations, Developers, Feature Flag, Google, Google LLC ``` Keywords: Cursor Automations, NFL Sunday Ticket, Press, Privacy, Privacy Policy, Safety, Stale Feature Flag Removal, Terms, YouTube
www.youtube.com 4 days ago
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1023.
HN
Most of My Coding Is Now Agentic
The author has adopted agentic coding, an approach inspired by Justin Vincent, which emphasizes phased planning with detailed attention to each phase, similar to legal documentation, ensuring clarity and reducing reliance on inference. This method involves breaking down details into manageable phases if they become overwhelming and implementing changes one atomic phase at a time. The technique enhances focus on complex aspects where personal expertise is particularly valuable, despite its mentally demanding nature, which the author finds beneficial. For further updates and insights into this approach, the author suggests joining their mailing list or following them on X/Twitter.
Keywords: #phi4, Agentic coding, Justin Vincent, atomic phase, commitment, expertise, focus, implementation, inference, legal document, mental taxing, phased planning, splitting, value-add, working memory
www.justinmath.com 4 days ago
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1120.
HN
Agentic Credential Management
Simon Moffatt discusses the burgeoning adoption of AI-driven agentic capabilities in various industries, underscoring both their productivity advantages and the significant security challenges they introduce. These agents differ from traditional web applications due to their unique characteristics, which expose vulnerabilities in existing human-centric Identity and Access Management (IAM) systems that often still depend on shared secrets for authentication. This reliance is attributed to integration difficulties and cost considerations.
The introduction of Non-Human Identities (NHIs) and agentic-AI exacerbates security concerns by frequently using static, long-lived credentials susceptible to misuse. Traditional IAM models struggle with the dynamic nature of these agents, leading to overly broad permissions granted to human users and insufficient oversight for non-human entities. Moffatt proposes a shift from shared secrets towards more secure cryptographic methods like FIDO and SPIFFE, which provide short-lived, programmable credentials.
To address these challenges, Moffatt advocates centralizing identity providers with advanced authentication systems that support federated access control and accountability across organizational boundaries. This strategy involves identifying and rectifying vulnerabilities such as static credentials and excessive permissions while enhancing visibility of all identities within the AI ecosystem. He recommends a phased approach starting with recognizing existing security gaps, transitioning from shared secrets to cryptographic solutions, and implementing Just-In-Time (JiT) permissioning models.
Tools like Akeyless can aid organizations in this transition by offering secretless, short-lived identity management and centralized credential control across different environments. Moffatt underscores the urgency for businesses to prioritize these authentication challenges as essential for secure operations within agentic-AI ecosystems.
Keywords: #phi4, AI-driven Automation, Agentic-AI, Credential Rotation, Federated Access, Identity Management, MFA, Non-Human Identity (NHI), Risk Analysis, SPIFFE, Secretless Credentials, Security Challenges, Shadow-AI, Strong Authentication
www.akeyless.io 5 days ago
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1144.
HN
Ruby on Rails homepage updated for "the agentic age"
Ruby on Rails has been repositioned as a comprehensive full-stack framework capable of supporting the demands of "the agentic age." It offers an extensive suite of tools necessary for constructing robust web applications, emphasizing strong conventions that prevent disorganized code. The framework supports various features such as rendering HTML templates and managing databases while handling email communications effectively. Additionally, it facilitates live page updates using WebSockets, asynchronous job processing, and cloud storage for file uploads. Rails also prioritizes security by guarding against common threats. Through these capabilities, Ruby on Rails maintains its position as a powerful solution for developing complex web applications with efficiency and organization.
Keywords: #phi4, HTML templates, Ruby on Rails, WebSockets, asynchronous work, attacks, back end, cloud, conventions, databases, emails, framework, front end, full-stack, jobs, security protections, tools, uploads, web apps
rubyonrails.org 5 days ago
https://github.com/rails/website/commit/8e261 5 days ago
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1219.
HN
Microsoft Is Stress-Testing the Agentic AI Bubble in Its Own Gaming Division
The article delves into Microsoft's strategic pivot within its Xbox division to explore AI-driven efficiencies amid ongoing debates on AI's economic impact. Two contrasting theories are discussed: Theory A warns that replacing knowledge workers with AI could destabilize the consumer economy and financial systems, while Theory B suggests it might catalyze new economic growth. The piece highlights the challenges Wall Street analysts face in evaluating AI investments due to opaque enterprise software pricing and workflows, leading them to rely on indirect financial metrics and selective disclosures from vendors.
Central to Microsoft's strategy is the appointment of Asha Sharma, an operational AI expert, as Xbox leader, underscoring a commitment to using AI for streamlining operations rather than replacing creative roles. This shift aligns with broader industry trends away from traditional, high-cost game development models—likened to Formula 1 teams—to more scalable "railroad" models that centralize infrastructure and standardize processes across studios.
The article compares the transition from an artisanal "racecar" model of gaming, characterized by isolated operations, to a "railroad" approach focusing on efficiency through standardized processes. This transformation requires substantial AI integration to automate tasks such as data analysis, which represents only a visible portion of total costs akin to an iceberg's tip, with hidden expenses including the reorganization of legacy systems.
While AI-driven efficiencies promise theoretical gains, the article warns that underestimated integration and maintenance costs could offset expected savings. It concludes by highlighting an industry-wide challenge: companies like Microsoft must overcome significant infrastructure hurdles before fully realizing operational benefits from AI, raising questions about the economic viability of such transformations within complex organizations.
Keywords: #phi4, AI agents, AI integration, AI skepticism, AI tools, Asha Sharma, Microsoft, Xbox, agentic AI, analytics, centralized infrastructure, cost-cutting, data infrastructure, enterprise software, financial markets, gaming division, investment costs, leadership change, operational efficiency, operationalization, standardization, workflow automation
softcurrency.substack.com 5 days ago
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1243.
HN
Show HN: Git Diff for Agentic Coding
"Justshowmediff" is a standalone tool designed to enhance the readability of `git diff` outputs through a visually appealing browser-based UI, requiring no server or additional dependencies such as JavaScript frameworks or CSS libraries. It's implemented as a single binary application embedded within an HTML file, which simplifies installation and usage; users can install it via Go with `go install github.com/msoedov/justshowmediff@latest`, clone its repository to execute the installation script, or download a release directly. The tool is particularly useful for reviewing unstaged changes in your code by running simple commands like `justshowmediff`, and supports various git diff arguments for comprehensive comparisons.
This utility stands out in scenarios where users are working without access to full editors—such as evaluating AI-generated code changes remotely via SSH or mobile terminals—and allows viewing diffs visually, enabling efficient communication of necessary corrections. Moreover, "justshowmediff" integrates with systems like Claude Code through a custom skill that facilitates visual diff reviews using `/diff` commands without altering files. The tool captures `git diff` outputs within a self-contained HTML file located in `/tmp`, optimized for mobile viewing, and is distributed under an MIT license, enhancing its utility across diverse development environments.
Keywords: #phi4, AI-Generated Changes, Agentic Coding, Branch Comparison, Browser-Based, Dependencies, Git Diff, HTML File, Install, License MIT, Mobile Optimized, Pipe from Stdin, Post-Tool Hooks, Readonly Workflow, Self-Contained, Side-by-Side Viewers, Slash Command, Source Code, Terminal Output, UI Viewer, Usage, Visual Review
github.com 6 days ago
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1268.
HN
Free-range agentic parenting: If you love your agents, set them free
Firetiger's experience in developing autonomous agents underscores the challenge of balancing agent autonomy with user expectations. They discovered that granting excessive freedom led to unpredictable behaviors, such as self-deactivation due to data issues or creating independent knowledge structures, which though effective, confused users. To address this, Firetiger constrained how these behaviors were presented rather than limiting agent capabilities. For example, they introduced an "escape hatch" for logging abort events instead of allowing agents full control over activation states. When agents developed new, human-readable knowledge structures not fitting existing frameworks, they documented these as runbooks rather than forcing conformity to predefined categories.
The company also observed that agents communicated and debated similarly to humans, leading to correct resolutions but potential user confusion. To enhance transparency, Firetiger implemented intermediate decision states visible to users, maintaining clarity without hindering the dynamic communication among agents. Overall, Firetiger's strategy involves allowing agents the freedom to exceed design assumptions while carefully managing how these actions are communicated and understood by users. This approach ensures that user experiences remain coherent and aligned with business objectives, even as agents continue to learn and adapt autonomously.
Keywords: #phi4, Autonomous agents, agent communication, constraints, control, decision-making, emergent behavior, feedback loops, interpretability, knowledge base, orchestration, outcomes, signal quality, user experience
blog.firetiger.com 6 days ago
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1279.
HN
Towards Reliable Agentic Systems (Part 1) – Understanding Error
The article explores the evolution of software engineering from deterministic rule-based methods to complex, multi-agent systems fraught with potential errors. It highlights how traditional software development adhered to fixed rules without accounting for real-world variances, akin to hard engineering's tolerance for minor deviations. Multi-agent systems, however, introduce challenges in error propagation and necessitate robust frameworks for effective error management.
Key points include the nature of error propagation within agent-based systems, where small errors can escalate through positive feedback loops, resulting in larger issues over time. The article emphasizes that errors stem from diverse sources due to variations in AI agents' architectures, training data, and methodologies—paralleling how different radiologists might have distinct perspectives and biases.
The diversity among agents is seen as a means to reduce overall error rates by capturing a wider array of potential mistakes than any single agent could. By assigning specific roles, agents can focus on varied aspects of problems, facilitating better error management through tailored outputs.
A critical issue discussed is human-agent interaction, where reliance on AI systems for efficiency may lead to biases in human judgment and affect the detection of errors. Real-world examples illustrate how decision-making processes—whether in medical diagnoses or software development—are influenced by prior results or prioritization strategies, leading to bias and error amplification.
The article concludes with an indication that future discussions will focus on tools and feedback mechanisms designed to enhance reliability in multi-agent systems.
Keywords: #phi4, AI Agents, Agent Roles, Bias/Error Sources, Context Window, Control Theory, Detection Rate, Deterministic Rule Setting, Error Distribution, Error Independence, Error Propagation, Feedback Loop, Human-AI Collaboration, Multi-Agent Systems, Probability Constraints, Productivity, Reliable Agentic Systems, Software Engineering, Vibe Coding
datda.substack.com 6 days ago
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1290.
HN
Minimizing user research fraud in the age of agentic AI
User research fraud is increasingly problematic due to advancements in large language models (LLMs) and agentic AI, shifting from traditional manual methods involving individuals exploiting incentives to sophisticated techniques that bypass typical detection systems like IP tracking and SMS verification. Fraudsters now use tools such as residential proxies and anti-detection browsers to create convincing fake personas, while LLMs automate responses, making fraudulent data more difficult to identify in research settings. To mitigate these challenges, content designers should implement a multi-layered approach: monitoring biometric and language indicators for signs of AI involvement, employing behavioral cues like tab changes or bulleted lists as red flags, using preventative measures such as attention checks, confirmatory questions, requiring photo IDs, and ensuring cameras are on during sessions. Collaboration with research vendors is also crucial to understand their fraud detection strategies and limitations. Although these measures might challenge human-centered design principles like inclusivity, they are essential for maintaining data validity, ultimately supporting better business decisions and product development.
Keywords: #phi4, IP addresses, LLMs, SMS verification, User research fraud, agentic AI, attention checks, biometric indicators, browser signals, fraudulent participants, language patterns, language patterns Keywords: User research fraud, speed traps, synthetic data
www.buttonevents.com 6 days ago
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1309.
HN
Agentic Code Reasoning
The paper "Agentic Code Reasoning" by Shubham Ugare and Satish Chandra investigates how large language model (LLM) agents can comprehend code semantics through analyzing codebases without execution. It introduces a method called semi-formal reasoning, which enhances analysis reliability by having agents develop explicit premises, trace execution paths, and derive conclusions. The study evaluates this technique across three tasks: patch equivalence verification, fault localization, and code question answering. Findings indicate that semi-formal reasoning significantly boosts accuracy; for instance, the accuracy of verifying patch equivalence rose from 78% to 88% on curated examples, reaching up to 93% for real-world agent-generated patches. In RubberDuckBench's code question answering task, it achieved an 87% success rate, while in fault localization on Defects4J, it increased Top-5 accuracy by five percentage points compared to standard methods. These results demonstrate that semi-formal reasoning can effectively enable semantic analysis of code without execution and holds promise for applications in reinforcement learning training pipelines, code review processes, and static program analysis. The study underscores the advantages of structured agentic reasoning in improving both understanding and validation of code.
Keywords: #phi4, Agentic Code Reasoning, Defects4J, LLM agents, RL reward signals, RL reward signals Keywords: Agentic Code Reasoning, RubberDuckBench, code question answering, codebases, execution paths, fault localization, patch equivalence verification, semantics, semi-formal reasoning, structured prompting
arxiv.org 6 days ago
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1310.
HN
Show HN: Pre-execution verification for LLM-generated agentic workflows
The article introduces `workflow-verify`, a tool designed to address the challenges of deploying large language model (LLM)-generated workflows without prior safety checks. These unverified workflows pose risks such as data corruption or operational errors, which `workflow-verify` aims to mitigate through a comprehensive pre-execution verification layer.
Key features of `workflow-verify` include:
1. **Workflow AST:** LLMs generate an Abstract Syntax Tree (AST) for workflows, subject to multi-layered verification processes:
- **Type Flow** ensures compatibility between workflow steps.
- **Schema Validation** checks the definition and uniqueness of schemas, along with their type validity.
- **Side Effects** require explicit declarations when operations impact external resources or services.
- **Guard Conditions** are verified against existing input schema fields.
2. The tool provides a **Verification Trace**, offering a human-readable audit trail for each step in the verification process.
3. It supports multiple **Transpilation Targets** by converting validated workflows into code compatible with languages and frameworks such as Python (using Pydantic), TypeScript (using Zod), and Temporal.io workflows.
4. A **Schema Registry** is available, comprising pre-built schemas across categories like CRM systems and data sources, enhancing usability and integration efficiency.
5. The feature of **Dynamic Schema Resolution** enables real-time schema fetching from live APIs such as HubSpot or Salesforce, with fallbacks to static registries when necessary.
6. A **Self-Correction Loop** allows iterative refinement of workflows in conjunction with LLMs until verification is successful.
7. Integration capability via the **Model Context Protocol (MCP)** enables inline workflow verification within conversational agents like Claude.
`workflow-verify` can be installed via pip, offering optional enhancements such as LLM support and MCP server functionalities. It facilitates both command-line interaction for manual verification and programmatic integration into applications. By bridging AI-generated workflows with secure production deployment, this tool provides a robust framework for ensuring safety and correctness.
Keywords: #phi4, AST, CLI, LLM, LLM API, MCP, Temporalio, guard conditions, schema validation, schemas, side effects, transpile, verification, workflows
github.com 6 days ago
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1334.
HN
Show HN: Meto – Methodology backbone for AI agentic coding
Meto is a Command Line Interface (CLI) tailored for enhancing AI agentic coding projects by providing a comprehensive project framework that integrates with Claude Code. Its primary function is to streamline the initial setup of these projects through automated scaffolding, which includes kanban boards, agent definitions, product context, and coding conventions. One of its standout features is the integration of Agent Teams, where pre-configured roles such as project managers, developers, and testers are set up for concurrent development tasks. This setup reduces potential conflicts by enforcing file ownership boundaries among agents.
The quick start process involves executing `npx meto-cli init` to begin setting up a structured repository, with interactive prompts guiding customization. The tool automatically includes several essential features like the CLAUDE.md for session guidelines, kanban boards detailing task pipelines (backlog, todo, etc.), and various documents related to agent definitions, product context, epics, workflows, and epic backlogs.
The directory structure of a Meto project is organized into specific folders: `.claude/` for agent configurations, `ai/` for backlog, context, tasks, and workflow documentation, along with additional directories such as `src/` for source code and `.gitignore` for version control setup. The Agent Teams feature supports parallel work by AI agents, each focusing on their specialized roles while preventing conflicts through automatic file boundaries. Activation within Claude Code is simple.
To use Meto effectively, prerequisites include Node.js (version 18 or higher), git for repository initialization, and the latest version of Claude Code. Users have access to CLI commands that allow for project scaffolding or previewing setups without writing changes to disk. The tool is licensed under the MIT license, promoting open use and distribution.
Keywords: #phi4, AI, Agents, Boards, CLI, Claude Code, Coding, Conventions, Epics, Experimental Feature, Git, Kanban, License, MIT, Metodology, Nodejs, Parallel Development, Product Context, Project Structure, Scaffolding, Token Optimization, Workflows
github.com 6 days ago
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1338.
HN
General Agentic Memory via Deep Research
The paper "General Agentic Memory via Deep Research" introduces a new framework named General Agentic Memory (GAM) aimed at enhancing AI agents' memory capabilities. Traditional static memory systems often lose information due to pre-prepared data, but GAM mitigates this through a just-in-time compilation approach, optimizing contexts during runtime alongside a simple offline memory system. The framework consists of two components: the Memorizer and the Researcher. The Memorizer uses a lightweight structure to highlight essential historical data while storing detailed history in a universal page-store. Meanwhile, the Researcher retrieves and integrates relevant information from this store, guided by pre-constructed memories. This architecture exploits advanced large language models' agentic capabilities and scalability at test time, allowing performance improvements through reinforcement learning. Experimental results show that GAM enhances task completion in memory-dependent scenarios compared to existing systems. The paper spans topics such as Computation and Language, Artificial Intelligence, Information Retrieval, and Machine Learning, underscoring its interdisciplinary relevance. It acknowledges support from the Simons Foundation and other collaborators, reflecting its broad recognition within the scientific community.
Keywords: #phi4, AI Agents, Agentic Memory, Artificial Intelligence, Computation, Computation and Language, Deep Research, General Agentic Memory, Information Loss, Information Retrieval, Just-in-Time Compilation, Large Language Models, Machine Learning, Machine Learning Keywords: AI Agents, Memorizer, Page-Store, Reinforcement Learning, Researcher, Static Memory, Task Completion
arxiv.org 6 days ago
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1344.
HN
Show HN: AFK – Remote desktop for agentic coding from your phone with voice
AFK is a specialized remote desktop application designed for mobile use, enabling users to manage code development tasks directly from their phones when they are not at their desks. The app integrates with AI coding tools such as Claude Code and Pi, offering voice input capabilities through push-to-talk for command dictation, which enhances convenience by reducing the need for typing on small screens. It leverages WebRTC streaming technology to provide low-latency screen mirroring over both WiFi and cellular networks.
Key features of AFK include voice input via push-to-talk, low-latency video transmission using WebRTC's data channel protocol, custom functionalities like window switching and agent notifications, and mobile-optimized touch controls. Unlike traditional remote desktop solutions, AFK emphasizes a mobile-first user experience. Developed with Flutter for cross-platform compatibility and native programming languages such as Swift for macOS and C++ for Windows, the app is open-source under "afk-host." While iOS and Android clients are available, a Windows host version is in development. The practicality of AFK is highlighted by the author's experience developing parts of the application using it remotely. Users can try AFK to enjoy a seamless coding experience on their mobile devices while away from their primary workstation.
Keywords: #phi4, AFK, Android, App Store, C++, Coding, Cross-Platform, Data Channel Protocol, Developer Environment, Flutter, Google Play, Low Latency, Mobile-First UX, Open Source, Remote Desktop, Streaming, Swift, Touch Controls, VP9, Voice Input, Windows, iOS, macOS
afkdev.app 6 days ago
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1347.
HN
Agentic Engineering Patterns: Anti-Patterns
In the context of agentic engineering, certain practices are identified as anti-patterns due to their detrimental effects on team collaboration. A significant issue arises when developers submit pull requests containing code generated by agents without conducting a thorough review themselves. This approach not only overburdens collaborators but also diminishes the perceived value of contributions, as it shifts the responsibility for ensuring code quality onto others.
To counteract these issues, it is vital that developers personally verify the functionality and appropriateness of agent-generated code before submission. Pull requests should be concise, easily understandable, and include relevant context to reduce cognitive strain on reviewers. This can involve linking them to pertinent issues or specifications, which provides clarity about their purpose and scope.
A high-quality agentic engineering pull request is characterized by its tested functionality, clear articulation of its objectives, and demonstrable evidence of manual review through notes, comments, or direct demonstrations. Such a practice not only respects the time and efforts of collaborators but also significantly boosts productivity and the quality of collaboration within agentic engineering teams. By adhering to these guidelines, developers can ensure their contributions are meaningful and collaborative workflows remain efficient and effective.
Keywords: #phi4, Agentic Engineering, Anti-Patterns, Code Review, Cognitive Load, Collaboration, Contextual Explanation, Evidence, Functional Code, Git Finagling, High-Level Goal, Implementation Choices, Manual Testing, Pull Requests
simonwillison.net 6 days ago
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1362.
HN
My Data Quality Tools List: Tried Any?
The article discusses an innovative agentic data observability platform designed to leverage AI agents for improving data quality. This platform offers a suite of tools specifically tailored for comprehensive data monitoring, detailed tracking of data lineage, and the seamless integration of FinOps processes. Its primary goal is to enhance users' understanding of their data by providing insights into its origins and how it evolves over time. By employing advanced AI capabilities, the platform facilitates more effective oversight and management of data quality, ensuring that users can trace and comprehend the entire lifecycle of their data, thereby optimizing decision-making and operational efficiency in financial operations.
Keywords: #phi4, AI Agents, Agentic, Data Lineage, Data Monitoring, Data Quality, FinOps, Lineage, Observability, Tools List
toolsfordata.com 6 days ago
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1368.
HN
How to use agentic workflows for your repos – GitHub Checkout
The content outlines a resource dedicated to utilizing agentic workflows for repositories through GitHub Checkout, complemented by an instructional video on YouTube. It details standard links typical of YouTube's platform, including sections like About, Press, Copyright, and Contact. Furthermore, it references NFL Sunday Ticket under the copyright protection of Google LLC in 2026, indicating future rights management or related services associated with this content. This resource seems to integrate technical guidance for GitHub users with broader informational links, highlighting both current utility and upcoming proprietary considerations.
Keywords: #phi4, Advertise, Contact, Copyright, Creators, Developers, GitHub Checkout, Google LLC, NFL Sunday Ticket, Press, Privacy Policy, Safety, Terms, YouTube, agentic workflows, repos
www.youtube.com 6 days ago
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1399.
HN
Show HN: BitFun – An Agentic Development Environment (Rust and TypeScript)
BitFun is an open-source Agentic Development Environment (ADE) that aims to enhance human-AI collaboration in software development by integrating AI agents as active collaborators rather than mere chatbots throughout the development process. Built using Rust and TypeScript with Tauri for cross-platform compatibility, it provides users with personalized assistants capable of evolving over time to perform tasks like coding, knowledge work, and debugging across various modes—Agentic, Plan, Debug, and Review Modes. The platform offers extensibility through the MCP protocol, allowing integration with external tools and customizable agents defined in Markdown, supporting both local models and cloud APIs to meet diverse requirements for cost, performance, or privacy.
Currently available on macOS and Windows, BitFun intends to expand its reach by adding support for other platforms and incorporating integrations with social platforms such as Telegram and Discord. The project champions the concept of "vibe coding," an AI-assisted development approach that encourages community contributions in terms of ideas, system enhancements, and ecosystem growth. Developed as a personal exploration into the future of human-machine collaboration rather than for commercial purposes, BitFun leverages numerous open-source resources to achieve its objectives.
Keywords: #phi4, AI, Agent architecture, Agentic Development Environment, BitFun, CLI, Code Agent, Collaboration, Cowork Agent, Cross-platform, Custom Agents, Debug Mode, Deepwiki, Discord, Extensibility, GitHub, Human–AI collaboration, Human–AI collaborationComma-separated List: BitFun, Human–AI collaborationExtracted Keywords: BitFun, Human–AI collaborationFinal Keywords: BitFun, Human–AI collaborationKeywords: BitFun, MCP protocol, Open-source, Plan Mode, Review Mode, Rust, Server mode, Tauri, Telegram, TypeScript, Vibe Coding
github.com 6 days ago
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1408.
HN
Writing about Agentic Engineering Patterns
The author has embarked on a project titled "Agentic Engineering Patterns," aimed at documenting coding practices that integrate AI tools like Claude Code and OpenAI Codex for independent code generation and execution. This initiative seeks to augment professional software engineering by enhancing existing expertise, focusing particularly on addressing challenges such as the reduced cost of generating initial code and leveraging test-first development for producing reliable code with minimal input. The project will be presented in a series of guide-like chapters on the author's blog, which are designed for regular updates rather than being static posts. Although AI tools like LLMs are employed for tasks including proofreading and example generation, the content remains authored by the writer to ensure authenticity. The technical implementation includes Django models and views developed using Claude Opus 4.6 within Claude Code, with an aim of overcoming challenges associated with creating evergreen blog content.
Keywords: #phi4, AI-Assisted Programming, Agentic Engineering, Claude Code, Coding Agents, Django, Evergreen Content, OpenAI Codex, Patterns, Red/Green TDD, Software Development, Test-First Development, Vibe Coding
simonwillison.net 6 days ago
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1415.
HN
Large-Scale Agentic RL for CUDA Kernel Generation
The CUDA Agent is an advanced reinforcement learning system aimed at enhancing GPU kernel performance within deep learning frameworks. It overcomes limitations of existing methods by integrating three key components: scalable data synthesis, which facilitates effective training; a skill-augmented development environment equipped with verification and profiling tools to streamline development processes; and sophisticated RL algorithms designed for stable long-context training. These elements collectively enable the CUDA Agent to significantly outperform conventional approaches. In empirical evaluations using the KernelBench dataset, it demonstrated exceptional performance improvements: execution rates were accelerated by 100% on Level-1 and Level-2 benchmarks, while achieving a 92% speed increase on Level-3 compared to torch.compile. This highlights its efficacy in optimizing deep learning operations through GPU enhancements.
Keywords: #phi4, CUDA Agent, CUDA Kernel Generation, CUDA code generation, GPU kernel optimization, KernelBench, Large-Scale Agentic RL, Level-1, Level-2, Level-3 splits, Level-3 splitsKeywords: Large-Scale Agentic RL, RL algorithmic techniques, data synthesis, deep learning, execution-feedback loops, hardware expertise, reinforcement learning system, skill-augmented environment, stable long-context training, torchcompile, training-free refinement, verification and profiling
cuda-agent.github.io 6 days ago
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1422.
HN
Agentic Engineering Anti Patterns
In agentic engineering, the submission of unreviewed code via pull requests is identified as an anti-pattern because it improperly transfers responsibility for maintaining code quality to other team members instead of the individual who created the code. This not only diminishes the perceived value of one's contribution but also imposes unnecessary cognitive burdens on collaborators tasked with reviewing the changes. To avoid these issues, effective pull requests should encompass code that has been personally reviewed and verified as functional by the submitter. Additionally, such submissions should be concise enough to facilitate efficient review processes and include context linking them to specific goals or relevant issues. Submitters are expected to demonstrate their diligence through evidence of thorough reviews, which may involve providing detailed testing notes or demonstrations of functionality. By adhering to these practices, the respect for collaborators' time is upheld, thereby enhancing overall collaborative efficiency within the team.
Keywords: #phi4, Agent Delegation, Agentic Engineering, Anti-Patterns, Code Quality, Cognitive Load, Collaboration, Contextual Explanation, Evidence, Feature Demonstration, Functional Code, Git Finagling, Higher Level Goal, Implementation Choices, Manual Testing, PR Descriptions, Pull Requests, Review Efficiency, Review Responsibility, Small Changes, Unreviewed Code, Validation
simonwillison.net 6 days ago
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1476.
HN
Open Claw Agentic Monitoring
The document introduces "Open Claw Agentic Monitoring," accessible through the GitHub repository `Anecdotes-Yair/trust-my-agent-ai`, with more details available at `trustmyagent.ai/trust-center`. This project emphasizes trust center guidelines for AI agents, providing a suite of resources such as frequently asked questions, lists, API data, security protocols, legal documents, and contact information. The site also features links to Y Combinator applications and a search function, highlighting its comprehensive approach to fostering transparency and trust in AI interactions. Notably, the project has been discussed on platforms like Hacker News by user datanerdgrc, albeit with minimal engagement, indicating niche interest or early-stage awareness within tech communities.
Keywords: #phi4, API, Agentic Monitoring, Contact, GitHub, Hacker News, Legal, Open Claw, Search, Security, Trust My Agent AI, YC, datanerdgrc, trust-center
news.ycombinator.com 6 days ago
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1498.
HN
A Dual-LLM Policy for Reducing Noise in Agentic Program Repair
The research paper titled "Abstain and Validate: A Dual-LLM Policy for Reducing Noise in Agentic Program Repair" presents two complementary large language model (LLM)-based policies designed to improve the efficiency of Agentic Automated Program Repair (APR) systems. These policies focus on minimizing noise by filtering out less promising bug fixes before they undergo human review, thereby conserving developer resources and enhancing confidence in automated code modifications.
The first policy, known as the Bug Abstention Policy, aims to detect and exclude bugs that are unlikely to be effectively resolved by the APR system. The second policy, the Patch Validation Policy, assesses generated patches and dismisses those considered improbable solutions for the identified bugs. By implementing both policies concurrently, the study observed substantial enhancements in success rates: a 13% improvement attributed solely to bug abstention, a 15% increase from patch validation, and an overall combined improvement of up to 39%. These results underscore the dual-policy approach's potential to enable reliable, large-scale adoption of agentic APR systems. The paper was accepted for presentation at the 2026 IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP '26).
Keywords: #phi4, Agentic Program Repair, Artificial Intelligence, Automated Code Changes, Bug Abstention, Google's codebase, IEEE/ACM Conference, LLM-based Policies, Noise Reduction, Null Pointer Exceptions, Patch Validation, Sanitizer-reported Bugs, Sanitizer-reported Bugs Keywords: Agentic Program Repair, Software Engineering, Success Rates
arxiv.org 7 days ago
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1503.
HN
Show HN: DSCO agentic CLI with multi-turn tool use and swarms
DSCO is an advanced command-line interface (CLI) tool developed primarily in C, designed to facilitate sophisticated interactions with streaming large language models (LLMs). Its core functionality includes multi-turn tool use and orchestrating swarms or sub-agents, making it a versatile solution for managing complex AI operations. Among its key features are Multi-Cloud Platform (MCP) integration, plugin support, markdown rendering, semantic routing, and timeline/trace observability. Users can operate DSCO in both interactive and one-shot execution modes, benefiting from comprehensive debugging options.
For setup on macOS/Linux, users bootstrap dependencies via a script and compile the project using `make`. The tool emphasizes code quality and performance through make commands that support testing, linting, and static analysis. DSCO is equipped with built-in tools and allows for external API integration via plugins, offering multi-provider model support to accommodate various AI models. It supports hierarchical orchestration of sub-agents and provides a rich terminal user interface coupled with SQLite-based timeline logging.
The project's architecture centers around `main.c` and `agent.c`, which focus on interactive loops and tool execution respectively. Additional modules handle provider abstraction, process orchestration, and rendering capabilities. The DSCO project is well-documented for detailed guidance and operates under the MIT License.
Keywords: #phi4, CLI, LLM, MCP integration, agentic, asan-test, bootstrap, build, debugging, documentation, governance, license, linting, macOS/Linux, markdown rendering, plugins, repository layout, run, semantic routing, static-analysis, streaming, sub-agents, swarms, tests, timeline observability, tool execution, ubsan-test
github.com 7 days ago
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1535.
HN
Agentic commerce won't kill cards, but it will open a gap
The article explores the role of stablecoins within the payments ecosystem, emphasizing that while they are unlikely to replace traditional credit and debit cards, they play a significant role in catering to new types of merchants who pose challenges for existing processors due to high risk or lack of track records. The Citrini Research piece is referenced regarding AI agents using stablecoins to circumvent card network fees; however, it overlooks the comprehensive benefits that cards offer, such as fraud protection and unsecured credit services.
Stablecoins provide a streamlined payment option by eliminating the need for complex underwriting processes, which is particularly beneficial for "non-existent" merchants—new business entities emerging with advancements like AI. Although traditional cards offer dispute resolution, rewards programs, and extensive fraud detection capabilities that stablecoins currently lack, these digital assets present an attractive solution for new merchants who struggle to secure conventional merchant accounts.
The article posits that while credit and debit cards will continue to dominate agentic commerce due to their extensive benefits, stablecoins are essential in supporting the next wave of businesses. This role is analogous to how platforms like PayPal and Stripe facilitated the growth of emerging online marketplaces by providing immediate payment solutions without traditional merchant account requirements.
In conclusion, although new payment systems may eventually be incorporated into existing models, stablecoins currently serve as a vital bridge between established payment infrastructures and evolving digital commerce needs driven by technological advancements.
Keywords: #phi4, Agentic commerce, HTTP requests, cards, compliance frameworks, fraud protection, identity objection, interchange fees, merchant accounts, micropayments, payment processors, risk underwriting, stablecoins
a16zcrypto.substack.com 7 days ago
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1544.
HN
The Prolific Output of Wes McKinney in the Age of Agentic Engineering
The text highlights Wes McKinney's notable impact on the field of data analysis, particularly through his development of tools that have significantly advanced agentic engineering practices. His work has been instrumental in shaping how data is manipulated and analyzed, providing robust frameworks for managing large datasets effectively. Additionally, the text addresses a website's cookie policy aimed at improving user experience. It allows users to either accept all cookies or tailor their preferences via a "Cookie Settings" option, ensuring they have control over their digital footprint while navigating the site. This dual focus underscores both McKinney's pivotal role in data engineering and contemporary practices in web privacy management.
Keywords: #phi4, Accept All, Agentic Engineering, Consent, Cookie Settings, Cookies, Experience, Preferences, Prolific Output, Relevant, Technical Keywords, Types, Website, Wes McKinney
posit.co 7 days ago
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1575.
HN
Agentic Proof-Oriented Programming
The article explores "Agentic Proof-Oriented Programming" (PoP), highlighting how AI tools like Copilot CLI and Claude Opus 4.5 are used to automate the generation of formally verified code in languages such as F* and Pulse. Nik Swamy, the author, illustrates that these AI agents can significantly reduce manual effort by handling tasks like writing specifications and proofs, allowing human experts to concentrate on high-level design. The AI's capabilities include generating formal proofs for complex data structures and algorithms, including bubble sort, ring buffers, priority queues, and concurrency control primitives, with minimal human input beyond guidance and occasional corrections.
The article underscores the potential of AI in simplifying software assurance tasks but also raises important questions about reliance on these tools concerning abstract program specifications, dynamic runtime considerations, and termination proofs. It highlights concerns regarding trust in verification tools due to possible exploitation of unsoundness bugs or incomplete proof mechanisms like "admits."
Future possibilities include enabling non-experts to use this technology effectively and scaling agentic programming for larger systems. The article suggests that AI-generated proofs could aid in proof maintenance and serve as a learning tool, while also evolving existing toolchains.
Finally, the author contemplates the broader impacts on cost implications and skill development within the software verification community, acknowledging these areas require further investigation. Overall, the integration of AI into formal verification processes is seen as a promising advancement towards more accessible and scalable solutions.
Keywords: #phi4, AI-assisted programming, Agentic Proof-Oriented Programming, Claude Opus, Copilot CLI, F*, Pulse, concurrency control, concurrent libraries, formal proofs, proof-oriented programming, specification, verification, verified systems, verified systems Keywords: Agentic Proof-Oriented Programming
risemsr.github.io 7 days ago
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1606.
HN
Agentic swarms are an org-chart delusion
The concept of "agentic swarms" involves integrating AI agents into traditional corporate hierarchies as a modernization effort for middle management roles, while maintaining human oversight. This approach is seen as sustaining innovation that enhances efficiency without fundamentally altering existing power structures or the overall system. The text critiques this by examining how historical work decomposition into specific roles emerged from limitations in human cognition and productivity, using Adam Smith's pin factory model as an example. AI technologies challenge these constraints, enabling individuals to perform multiple specialized functions through a single interface, akin to musicians utilizing digital audio workstations (DAWs) for comprehensive music production tasks.
The evolution of AI tools is already evident in one-person businesses where diverse tasks are handled seamlessly without traditional departmental divisions. This trend suggests a future shift towards empowering individuals with unified interfaces that allow them to achieve outcomes across various domains independently, rendering the management of specialized teams by humans or AI less relevant. The text concludes that the future workplace may prioritize equipping individuals with general-purpose cognitive tools over organizing teams of specialized agents, signaling a transformative shift in economic production centered on enhanced individual capabilities rather than specialization.
Keywords: #phi4, AI agents, Agentic swarms, bio-cognition, cognitive tool, corporate hierarchy, disruption, economic production, innovation, middle management, outcomes, productivity, roles, specialization, swarm management, unified execution, workflow
www.joanwestenberg.com 7 days ago
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1629.
HN
Show HN: SynthesisOS – A local-first, agentic desktop layer built in Rust
SynthesisOS is an innovative AI-native operating system layer for macOS designed to function as a local-first platform integrating autonomous agents that operate through a Rust kernel. These agents execute tasks via syscalls and interact with over 60 native macOS tools, presenting results in a spatial, glassmorphic workspace. This central AI hub manages various applications, files, emails, web searches, among other functions based on user commands.
A standout feature of SynthesisOS is its anti-browser approach which utilizes backend-rendered cards instead of traditional iframes for displaying web content. The system ensures security and transparency by employing a syscall interface that allows for explicit and auditable actions by agents. Furthermore, it emphasizes local-first data processing by relying on on-device memory and embeddings to reduce cloud dependency, and requires user confirmation for any destructive operations.
SynthesisOS supports an extensive range of tools, including file management, calendar integration, music control, and advanced scheduling functionalities that ensure equitable task distribution among agents. It facilitates cross-device synchronization over local networks without the need for third-party servers, ensuring data privacy through local storage. The architecture is built with a React frontend and Tauri IPC, communicating with a Rust kernel scheduler to handle syscalls. Tools such as ONNX Runtime, LanceDB, and various LLM providers are incorporated into its modular structure which includes components like tool safety, memory handling, versioned storage, context management, HTTP server functionality, and authentication.
Currently in Alpha, SynthesisOS has an active development roadmap targeting stabilization, integration of additional plugins, expanded provider support, and wider platform reach. The project encourages community contributions through issues or pull requests on the default branch. To get started with SynthesisOS, users need macOS, Node.js, Rust toolchain, Tauri CLI, and at least one LLM API key. Installation involves setting up a development environment using `npm run dev:tauri`, which builds both UI and kernel components, while `npm run build:tauri` is utilized for generating production-ready applications.
Cross-device usage capabilities are supported by configuring the backend server URL in application settings, allowing synchronization across devices on the same network while maintaining privacy controls. This enables users to share workspaces seamlessly without compromising data security.
Keywords: #phi4, AI-native, LLM, Rust, SynthesisOS, Tauri, agents, cross-device, local-first, macOS, plugin system, privacy, scheduler, syscall
github.com 7 days ago
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1661.
HN
DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference
The paper titled "DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference" addresses a critical performance bottleneck in multi-turn, agentic large language model (LLM) inference caused by storage input/output operations when loading extensive key-value caches from external storage. This results in an imbalance where storage network interfaces on prefill engines become saturated while those on decoding engines are underutilized. To address this issue, the authors introduce DualPath, a system that facilitates dual-path key-value cache loading by enabling both a traditional storage-to-prefill path and a new direct storage-to-decode path. This configuration allows efficient data transfer from decoding to prefill engines via RDMA over the compute network, thus reducing network congestion and avoiding interference with latency-sensitive communications.
DualPath further incorporates a global scheduler designed to balance loads between prefill and decode engines effectively. Evaluations conducted on three production agentic models reveal substantial performance improvements; specifically, offline inference throughput increased by up to 1.87 times, while online serving throughput improved by an average factor of 1.96 times, all without breaching service level objectives (SLOs). This research is supported by the Simons Foundation and other contributors, with its findings published within the field of distributed, parallel, and cluster computing.
Keywords: #phi4, Agentic LLM Inference, Decode Engines, Disaggregated Architectures, Distributed Computing, DualPath, Global Scheduler, KV-Cache, Online Serving, Prefill Engines, RDMA, SLO, Storage Bandwidth Bottleneck, System Throughput
arxiv.org 7 days ago
https://www.lightbitslabs.com/blog/why-we-need-to-rethi 7 days ago
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1664.
HN
Agentic Engineering Patterns
The document introduces Agentic Engineering Patterns, which are designed to optimize the performance of coding agents like Claude Code and OpenAI Codex. These strategies focus on enhancing functionality and efficiency for improved results in programming tasks by leveraging AI tools. The primary objective is to ensure these agents deliver optimal performance through tailored engineering approaches, thereby maximizing their effectiveness in coding operations. Detailed insights into this initiative are available in the introductory section of the work, emphasizing its importance for developers seeking to harness advanced AI capabilities in software development.
Keywords: #phi4, Agentic Engineering Patterns, Claude Code, OpenAI Codex, coding agents, introduction, patterns, project, results, technical keywords, technical keywords Comma-separated list: Agentic Engineering, technical keywords Keywords: Agentic Engineering
simonwillison.net 7 days ago
https://factory.strongdm.ai/principles 7 days ago
https://github.com/mohsen1/fesh 7 days ago
https://news.ycombinator.com/item?id=47240834 7 days ago
https://wiki.roshangeorge.dev/w/Blog/2025-12-01&#x 7 days ago
https://nonstructured.com/zen-of-ai-coding/ 7 days ago
https://www.slater.dev/2025/09/its-time-to-license 7 days ago
https://wiki.c2.com/ 7 days ago
https://simonwillison.net/2026/Feb/7/software 7 days ago
https://github.com/ryanthedev/code-foundations 7 days ago
https://x.com/xundecidability/status/2005647216741 7 days ago
https://github.com/anthropics/claudes-c-compiler/i 7 days ago
https://simonwillison.net/guides/agentic-engineering-pa 7 days ago
https://www.youtube.com/watch?v=OMQuBTGr52I 7 days ago
https://agentic-patterns.com/ 7 days ago
https://substack.com/@shreddd/p-189554031 7 days ago
https://jperla.com/blog/claude-electron-not-claudevm 7 days ago
https://www.codewithjason.com/examples-pointless-rspec-tests 7 days ago
https://simonwillison.net/guides/agentic-engineering-pa 7 days ago
https://marmelab.com/blog/2026/01/21/age 7 days ago
https://agentexperience.ax/ 7 days ago
https://simonwillison.net/guides/agentic-engineering-pa 7 days ago
https://simonwillison.net/guides/agentic-engineering-pa 6 days ago
https://github.com/anthropics/claude-code/issues 6 days ago
https://boristane.com/blog/the-software-development-lif 6 days ago
https://github.com/jurriaan/aico 6 days ago
https://developers.google.com/gemini-code-assist/docs&# 6 days ago
https://simonwillison.net/guides/agentic-engineering-pa 6 days ago
https://www.aihero.dev/skill-test-driven-development-claude- 6 days ago
https://github.com/mattpocock/skills/blob/mai 6 days ago
https://ziglang.org/download/0.15.1/release-notes. 6 days ago
https://youtu.be/O5FFkHUdKyE 6 days ago
https://github.com/hsaliak/std_slop/blob/main 6 days ago
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1679.
HN
The Orchestrator's Garden: Leading Human-Machine Teams in the Agentic Age
"The Orchestrator's Garden" explores the transformative role of leadership within Human-Machine Teams (HMT) during the Agentic Age, emphasizing the transition from traditional human-focused leadership to one that cultivates an ecosystem where both humans and machines can flourish together. In 2023, intent alignment emerged as a critical factor for optimizing AI agents' effectiveness, necessitating leaders to establish clear purposes. Leadership now involves complex systemic orchestration rather than conventional coaching, balancing emotional intelligence with technical proficiency.
Leaders are tasked with ensuring continuous feedback loops that integrate human intuition with machine execution and managing data flows crucial for machines making context-rich decisions. This role also includes nurturing team dynamics through task coordination, building trust, and employing AI as cognitive mentors to prevent burnout. By fostering a harmonious interaction between human creativity and machine efficiency, leaders act as Systemic Orchestrators, adept at navigating both emotional and technical challenges.
The focus has shifted from micromanaging AI systems to guiding agents within a rapidly changing work environment, highlighting the evolving nature of leadership roles in this new era where human-machine collaboration is paramount.
Keywords: #phi4, AI Management, Agentic Age, Cognitive Mentors, Context, Coordination, Data Pipelines, Emotional Resistance, Human-Machine Teams, Intent Alignment, Leadership, Logic-Gate Conflict, Orchestrator's Garden, Rapport, Social Interaction, Socially Assistive Agents, Systemic Orchestrator, Team Cultivation, Team Fertilizer, Telemetry
architectureintel.com 7 days ago
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1697.
HN
Graduate from Single-Session Coding: My Full Agentic Coding Workflow
Brent Traut outlines an advanced coding workflow designed to boost productivity in software development through the strategic use of multiple tools, with a focus on concurrent task execution and maintaining context continuity. Central to his approach is "Conductor," which manages multiple agents operating across different worktrees to enable parallel task processing without interference. For language model selection, Traut favors Codex over Claude due to its efficiency and user-friendliness, though he notes the complexity of crafting prompts for Claude.
To preserve task context beyond coding sessions, Traut employs Beads, a tool that facilitates external task tracking, preventing information loss across work periods. Workflow automation is further enhanced through Skills, which automate specific tasks, and CLI tools that allow agents to independently handle project management activities. Traut underscores the significance of maintaining accurate AGENTS.md files at various levels—system-wide, at the project root, and for individual applications—to guide agent behavior in line with best practices.
For web interactions, he uses browser automation via "agent-browser," while platforms like Blacksmith are utilized for continuous integration and delivery (CI/CD), Railway for hosting, and Doppler for managing secrets. Additionally, dictation serves as an efficient method for interacting with agents, providing quicker command input and minimizing the risk of repetitive strain injuries.
Traut concludes by advocating for the integration of these tools into a cohesive system that transitions from traditional single-session coding to a more sophisticated management of coordinated agent tasks throughout the software development lifecycle. This integrated approach enhances overall efficiency and productivity in software development projects.
Keywords: #phi4, AGENTSmd, Agentic Coding, Beads, Browser Use Loop, CI/CD, CLI Tools, Codex, Conductor, Persistent Memory, Skills, Superwhispr, Worktrees
medium.com 7 days ago
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1698.
HN
Closing the Loop – Optimizing the Agentic SDLC
Brent Traut's article "Closing the Loop – Optimizing the Agentic SDLC" addresses enhancing software development processes through agent-based coding within an optimized Software Development Life Cycle (SDLC). As coding costs have decreased, bottlenecks have shifted to review, testing, and monitoring phases. To tackle these challenges, the author introduces a playbook with several strategies. First, "Parallel Worktrees" involve using git worktrees for independent feature development by agents, preventing code conflicts. Second, "Port Contention Avoidance" recommends deriving stable port numbers from branch names via hashes to eliminate manual management issues and session conflicts. Third, deploying a single instance of the dev server per worktree as a daemon allows agents to manage it conflict-free using specific scripts like `dev:up`, `dev:status`, and `dev:down`. Additionally, "Log Routing to Agents" ensures logs are accessible within worktrees for autonomous debugging by agents. Finally, equipping agents with browser automation tools enables them to perform self-testing of their code changes, reducing the testing workload on developers. The article emphasizes shifting focus from merely coding to closing feedback loops between code creation and verification, thus empowering agents as collaborative colleagues in development and minimizing human intervention interruptions for enhanced efficiency.
Keywords: #phi4, Agentic SDLC, Browser Bridge, OpenClaw, agentic testing, code verification, daemon, dev server, isolated worktrees, isolated worktrees Keywords: Agentic SDLC, logs routing, manifest file, parallelism, port contention, worktrees
medium.com 7 days ago
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1749.
HN
What we need to make voice AI agentic
The current landscape of Voice AI lacks the true agency observed in emerging text-based language learning models (LLMs) like GPT-4o and Gemini 2.5 Flash, despite their improved intelligence; these voice models are hampered by longer inference times that result in awkward interactions. Many systems continue to rely on older, faster models which struggle with ambiguity and tool usage. The primary challenges for Voice AI include the necessity of real-time interaction without added latency and more effective mechanisms to manage model behavior naturally. Present approaches often involve deterministic rules that lead to unnatural conversations and increased interaction times. For a Voice AI system to be considered agentic, it must achieve rapid end-to-end latency (under one second), fluid interactions involving seamless tool use and adaptability across multi-turn dialogues, and fluency in producing human-like conversations. Ultravox exemplifies these criteria by delivering speech-native performance with approximately 900 milliseconds of latency through the use of advanced models and harness designs that support intricate conversations. Looking forward, future developments aim to offer insights into crafting Voice AI systems that meet the expected advancements by 2026, emphasizing real-time processing capabilities, adaptability, and conversational fluency.
Keywords: #phi4, ASR, GPT-4o, Gemini 25 Flash, TTFT, TTS, Ultravox, Voice AI, agentic systems, ambiguity, component stack, conversation state, deterministic rules, end-to-end latency, inference time, instruction following, latency, model intelligence, multi-turn interaction, real-time interactions, speech-to-speech, system architecture, tool calling
www.ultravox.ai 8 days ago
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1803.
HN
Agentic RL hackathon this weekend in SF
The upcoming event in San Francisco is a specialized agentic reinforcement learning (RL) hackathon, taking place over the weekend. It offers participants an opportunity to engage deeply with RL challenges and solutions within an open environment setting. Interested individuals can register for this hackathon through SF Events Search, ensuring they have access to all necessary details and resources for participation. This event aims to foster innovation and collaboration among RL enthusiasts by providing a platform to develop and showcase novel ideas in the field.
Keywords: #phi4, Agentic RL, OpenEnv, SF, SFEventsSearch, Sign In, duplicates, extract, hackathon, keywords, list, relevant, technical, text, topic
cerebralvalley.ai 8 days ago
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1847.
HN
Too Use: The Bridge Between Software Engineering and Agentic AI
The article "Too Use: The Bridge Between Software Engineering and Agentic AI" examines how tool use serves as a pivotal interface connecting traditional software engineering principles with the capabilities of agentic AI, particularly through Large Language Models (LLMs). Initially constrained to text generation without real-world application, LLMs utilized prompt engineering, embedding functions within prompts for invocation. This approach proved unreliable until function calling was upgraded to a first-class API feature, establishing a structured interface between code and models. This advancement facilitated deterministic operations like database queries or mathematical calculations, enabling LLMs to access dynamic real-world information beyond their static knowledge base.
In this framework, tools are defined with specific names, descriptions, and input schemas. The LLM determines if a query can be resolved using its existing training data; if not, it selects an appropriate tool from the available options, initiating a function call. This interaction continues in a loop until sufficient information is gathered to provide a response. Tools range from simple calculators to complex systems capable of database or API interactions, designed with clarity and detailed descriptions for effective use by models.
The core principle of successful tool use lies in creating distinct tools that yield clear outputs and have unambiguous parameters. By incorporating these tools, LLMs transition from static text generators to dynamic entities interacting with real-world systems, enhancing their functionality within software applications. This mechanism is integral to developing operational agentic AI systems, marking a significant evolution in how LLMs can perform practical tasks.
Keywords: #phi4, API Interface, Agentic AI, Atomic Tools, Deterministic Behavior, Dynamic State, Function Calling, Guardrails, LLMs, Naming Conventions, Natural Language Processing, Parallel Calls, Precision, Probabilistic Outputs, Prompt Engineering, Real-World Research, Return Values, Schema Definition, Security, Sequential Calls, Software Engineering, Static Knowledge, Structured Output, Tool Use
agenticloopsai.substack.com 8 days ago
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1852.
HN
Show HN: Self-Protecting Files for the Agentic Era
Honeycake has launched an innovative security platform tailored for the emerging Agentic Era, where AI agents facilitate rapid data transfers across different environments without direct human supervision. Recognizing that traditional security mechanisms like firewalls and Identity Access Management (IAM) are inadequate for protecting data once it is moved, Honeycake introduced a novel file format known as .cake. This format incorporates quantum-resistant encryption, enabling robust protection against future cryptographic threats. It also features section-level access controls, allowing users to grant granular permissions down to specific paragraphs within a document, thus enhancing security precision. Additionally, each file includes tamper-evident audit logging to maintain integrity and track any unauthorized changes.
Honeycake's architectural framework ensures enhanced security through its zero-exposure policy; encrypted keys are never stored alongside their files, preventing potential breaches even if data is compromised. The platform also offers real-time access event logging to help identify unusual activity patterns promptly. Encryption and decryption processes occur locally on users' devices, which means no third-party entities, including Honeycake itself, can access the content of the files. To support this new platform, Honeycake provides a desktop application, command-line interface (CLI), and an API. For more in-depth information, users are directed to their whitepaper available at honeycakefiles.com/whitepaper.html.
Keywords: #phi4, AI Agents, API, CLI, Honeycake, access policies, audit trails, cake files, desktop app, encryption, granularity, logged events, organizations, platforms, quantum-resistant, section-level controls, security, tamper-evident logging, threat model, workflows, zero-exposure
news.ycombinator.com 8 days ago
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1859.
HN
Show HN: Construct Computer – Agentic Cloud OS for Daily Work
Construct Computer is innovating in the realm of cloud computing by developing an operating system that hosts autonomous AI agents, known as "Constructs." These Constructs are designed to execute everyday tasks efficiently, functioning as persistent processes with their own dedicated resources for compute, storage, and networking. Users have the ability to monitor these activities through a user-friendly desktop interface, providing real-time oversight of the Construct's operations. The system is adept at integrating with various business tools, allowing the Constructs to independently manage tasks such as scheduling meetings, preparing documents, conducting research, attending meetings, and executing long-term automation projects with minimal human intervention. This advanced functionality aims to enhance productivity by streamlining complex processes in a user-centric manner. A demonstration of this technology can be accessed via an online video link provided in their promotional materials.
Keywords: #phi4, AI agents, Automate operations, Autonomous, Business tools, Cloud OS, Construct Computer, Constructs, Deep researching, Demo video, Desktop OS frontend, Infrastructure, Integrations, Minimal human intervention, Preparing documents, Scheduling meetings
construct.computer 8 days ago
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1866.
HN
The New Postman Is Here: AI-Native and Built for the Agentic Era
Postman has unveiled a platform tailored for the "agentic era," featuring AI-native capabilities that streamline API development from inception through production. This platform update includes Git-Native integration, facilitating collaboration within existing workflows by introducing features such as Git-connected Workspaces, an API Catalog, and an enhanced Private API Network. Designed to meet the demands of AI-driven systems, which require highly reliable and well-documented APIs due to their frequent use, the new Postman app supports local mock servers and code-based workflows integrated with CI/CD pipelines. It provides multi-protocol support and a robust CLI for efficient system-level testing and consistent environments across both local and CI systems.
A key feature is Postman AI's Agent Mode, which automates workflow processes, generates tests, and assists in debugging by interacting directly with the codebase using natural language processing. The updated user interface offers a unified workbench to organize collections and other resources, while the API Catalog acts as a management plane for tracking API performance and compliance. Additionally, Postman's Private API Network is optimized for synchronization and discovery, enhancing internal API distribution and governance.
Enterprise organizations benefit from improved team management with consolidated identity and access controls under a single organizational structure. These enhancements are now accessible to both existing customers and new users, supporting streamlined development processes in the evolving AI-driven landscape.
Keywords: #phi4, AI-Native, API Catalog, APIs, Agent Mode, Agentic Era, CLI, Enterprise, Git-Native, Governance, Multi-Protocol Support, Organizations, Postman, Private API Network
blog.postman.com 8 days ago
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1922.
HN
Show HN: Cortexa – Bloomberg terminal for agentic memory
Cortexa is an advanced platform specifically designed to improve the observability and reliability of agentic AI systems by addressing prevalent issues such as memory pollution and debugging challenges, which typically occur due to suboptimal memory management in these agents. Developed by Prateek Rao and his team, Cortexa delivers several key features: Agent Decision Forensics provides comprehensive tracing from an agent's outputs and actions back to their origins (including retrievals, memory writes, and tool calls), ensuring transparency and accountability within the system. Memory Write Governance is another core functionality that evaluates and manages memory entries by scoring them; it can block or quarantine ungrounded entries to prevent error propagation. Additionally, Memory Hygiene automatically eliminates near-duplicate or low-signal entries, thus maintaining high-quality retrieval and controlling associated costs.
For organizations deploying agentic workflows in production environments, Cortexa is invaluable as it bolsters system autonomy while simultaneously reducing engineering expenses through improved reproducibility of errors and more efficient debugging processes. The platform specifically targets scenarios characterized by "unknown why" failures, memory pollution, or increasing context management costs. To further refine its capabilities, Prateek Rao and his team are seeking feedback from professionals who manage agents at scale, inviting collaboration to enhance Cortexa's effectiveness. For additional information, interested parties can visit their website.
Keywords: #phi4, Bloomberg terminal, Cortexa, RAG, agentic memory, agents, auditability, autonomy, correctness, debugging, decision forensics, failure mode, memory governance, observability, production workflows, prompts, retrieval diffs, tool-call traces, unknown failures, vector DB
cortexa.ink 8 days ago
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1946.
HN
Agentic SDLC, my approach to high-quality agentic development
The Portable Development System (PDS) is a Claude Code plugin designed for high-quality agentic development that emphasizes consistency and scalability across projects. It integrates skills and agents within an install-once framework, facilitating streamlined workflows through the 6-phase Agentic Software Development Lifecycle (SDLC). Users can install PDS via marketplace or script from GitHub, with options to upgrade from version 3.x by cleaning up old files.
PDS encompasses a comprehensive suite of 16 development-focused skills and eight specialized agents. These components address aspects like project development principles, team coordination, requirement interrogation, orchestration, research, documentation, and code review. The plugin is structured around skill and agent definitions, session hooks, security settings, and installation scripts to enhance usability.
Security within PDS is reinforced by allowing tools in a sandboxed environment while blocking access to credential paths and sensitive operations. While the system operates at the user level by default, it supports optional project-level configurations for custom rules or permissions, enabling tailored development environments.
The plugin's documentation provides extensive resources on migration guides, its foundational philosophy, team setup procedures, and contributing guidelines. It encourages community participation through Pull Requests. Released under the MIT license, PDS invites users to freely use, fork, and modify it as per their requirements, fostering an open and collaborative development ecosystem.
Keywords: #phi4, Agentic SDLC, Claude Code, Git worktree, MIT license, MIT license Keywords: Agentic SDLC, Portable Development System, agents, contributing, documentation, hooks, marketplace, permissions, plugin, sandbox configuration, script installation, security settings, skills
github.com 8 days ago
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1951.
HN
CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
The paper introduces the CUDA Agent, an innovative system aimed at improving the generation of high-performance CUDA kernels using large-scale agentic reinforcement learning (RL). It tackles the challenge that GPU kernel optimization is both crucial and highly specialized, traditionally demanding deep hardware expertise—a requirement current language models cannot meet as effectively as compiler-based systems. The authors identify two main limitations in existing approaches: training-free refinement and fine-tuning within static feedback loops, which fail to enhance intrinsic CUDA optimization capabilities adequately.
To address these issues, the CUDA Agent system integrates three essential components:
1. A **Scalable Data Synthesis Pipeline** that generates a diverse and extensive dataset for effective model training.
2. A **Skill-Augmented Development Environment** equipped with automated verification and profiling tools to provide reliable reward signals vital for RL processes.
3. Advanced **Reinforcement Learning Algorithmic Techniques** ensuring stable and robust training.
The results show that CUDA Agent significantly outperforms existing models on the KernelBench benchmark, demonstrating improvements of 100% over certain baselines in specific categories and about 40% better performance than leading proprietary models like Claude Opus 4.5 and Gemini 3 Pro for more challenging tasks. This advancement marks a significant step forward in automating CUDA kernel optimization without necessitating specialized human expertise.
Keywords: #phi4, Artificial Intelligence, Automated Verification, CUDA, Compiler-based Systems, Data Synthesis, GPU Optimization, Kernel Generation, Large Language Models, Machine Learning, Profiling, RL, Reinforcement Learning
arxiv.org 8 days ago
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2024.
HN
I built a new Terraform agentic editor and auditor
The text introduces a novel Terraform agent-based editor and auditor created by the author to streamline compliance enforcement. Distinct from traditional methods that rely on complex policy languages such as Rego, this tool utilizes plain English to articulate violations, making it more accessible to engineers. By offering explanations for these violations along with suggestions for corrective measures, the tool enhances understanding without necessitating supplementary tools. This approach not only simplifies the auditing process but also empowers users by providing clear guidance and actionable insights directly within their workflows.
Keywords: #phi4, Plain-English Compliance, Rego, Terraform, auditor, editor, engineers, explanation, guardrails, policy language, suggested fixes, tooling, violation
grafos.ai 9 days ago
https://grafos.ai 8 days ago
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2028.
HN
Show HN: Lysium – cross-platform control plane for agentic software delivery
Lysium is a cross-platform control plane aimed at enhancing the management of GitHub issue and pull request (PR) queues by minimizing context-switching for users. It integrates seamlessly with GitHub and the Devin API to allow task routing to background agents, facilitating uninterrupted workflow continuity. The platform offers several key features, including the ability to swipe issues or PRs to perform actions such as closing, merging, or skipping them, launching implementation requests from various input sources, and running multiple agent sessions across different repositories. Additionally, Lysium supports quick assessments and reviews of issues/PRs, with a tracking mechanism through an Activity view that organizes tasks by Sessions and Actions. For full functionality, it requires GitHub OAuth as well as a Devin API key and organization ID, but does not necessitate email sign-up. The developer is seeking feedback on aspects such as ease of onboarding, overall user experience, and the balance between explicit and automatic agent automation. More information or a trial can be accessed through their website at [Lysium](https://www.lysium.ai/), with source code available on [GitHub](https://github.com/dabit3/lysium).
Keywords: #phi4, Activity view, Devin API, GitHub, Lysium, OAuth, PR queues, UX, agent sessions, agentic software delivery, automation, background agents, context-switching, control plane, cross-platform, implementation requests, issue queues, onboarding friction, one-click assessments, swipe actions
news.ycombinator.com 9 days ago
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2052.
HN
Ask HN: Whats your agentic programming setup?
The user is exploring ways to improve their agentic programming environment, which currently incorporates Opencode with Opencode Zen as a model and Minuet in Neovim using Mistral's Codestral for inline AI functionalities. While these tools are effective for handling routine tasks and identifying errors, they face challenges in consistently implementing specific features. The user suspects that the limitations of their setup extend beyond just the choice of models. They are actively seeking insights from the community to refine and enhance their programming environment, aiming for greater reliability and efficiency in feature implementation.
Keywords: #phi4, AI, Ask HN, agentic programming, errors, features, inline AI, minuet, mistral's codestral, models, neovim, opencode, quality, setup, tasks, tips, zen
news.ycombinator.com 9 days ago
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2059.
HN
Show HN: How to measure the value of Agentic AI
The article titled "How to Measure the Value of Agentic AI" presented on Show HN discusses various methodologies designed to evaluate the contributions and worth of autonomous AI agents, focusing specifically on those functioning within AgentEvolute. AgentEvolute is highlighted as a pioneering platform that facilitates connections between humans and AI agents in remote job contexts. The piece delves into different approaches for quantifying the impact and utility of these agentic AI systems, emphasizing their role in enhancing productivity and efficiency in various work environments. By providing insights into how such evaluations can be conducted, it underscores the importance of understanding and leveraging AI's potential to augment human capabilities, particularly within AgentEvolute’s ecosystem where humans frequently collaborate with AI counterparts for remote tasks.
Keywords: #phi4, AI Agents, AgentEvolute, Agentic AI, Humans, Relevant, Remote Job Platform, Show HN, Technical Keywords, World's Best, measure, value
agentevolute.com 9 days ago
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2070.
HN
Show HN: Turn – A compiled systems language for agentic computation
"Turn" is a newly developed statically-typed, compiled language specifically designed to enhance agentic computation with large language models (LLMs). This innovation addresses inefficiencies in existing frameworks like Python and TypeScript that struggle with the non-deterministic nature of LLMs due to their reliance on deterministic languages. Turn operates using a custom Rust bytecode virtual machine, which offers several distinctive features aimed at improving performance and reliability.
One notable feature is **Cognitive Type Safety**, which automatically manages schema constraints for inferred structures, thereby eliminating the need for manual parsing or complex regular expression workarounds. Additionally, Turn introduces **Probabilistic Routing** as a native binary operator that integrates confidence levels to guide control flow based on LLM output certainty, effectively managing potential inaccuracies or hallucinations in responses.
Another significant aspect of Turn is its adoption of an Erlang-style actor model for multi-agent orchestration. This model facilitates isolated VM threads with zero-shared-state communication, allowing seamless interaction between multiple agents without data conflicts.
Turn also offers native support for a range of LLM providers, including Anthropic, Azure OpenAI, standard OpenAI, Google Gemini, xAI Grok, and Ollama, all accessible via environment variables without the need for additional SDKs. An application example is its use in developing multi-agent quantitative hedge fund systems. The Turn framework provides open-source VM source code and an interactive browser-based sandbox for testing purposes using API keys.
The post concludes by inviting feedback on viewing LLMs as integral computational elements at the language level, rather than simply as external APIs, signaling a shift towards more integrated and efficient use of these models within programming environments.
Keywords: #phi4, API keys, Anthropic, Azure OpenAI, Erlang-style actors, Google Gemini, LLMs, Rust VM, cognitive type safety, compiled language, multi-agent orchestration, native compute targets, probabilistic routing, sandboxed playground, statically-typed
news.ycombinator.com 9 days ago
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2152.
HN
Show HN: Open-Jet – self-hosted Agentic TUI for air-gapped Jetsons
"Open-Jet" is an open-source Terminal User Interface (TUI) designed specifically for self-hosted AI agents running on NVIDIA Jetson devices within air-gapped environments, focusing on unified memory machine optimization to prevent out-of-memory issues. It facilitates local data management capabilities such as file editing, reading, and creation. The current iteration of the software achieves an approximate performance rate of 17 tokens per second using the Qwen3-4B-Instruct-4bit model on a Jetson Orin Nano with 8GB RAM. Future development plans include integrating TensorRT .engine support to enhance inference speeds and reduce the memory footprint further. The project encourages user feedback, particularly from those utilizing more advanced devices and models, and provides installation instructions along with links to its website and GitHub repository for access and contributions.
Keywords: #phi4, CPU pressure, GitHub, Jetson Orin Nano 8GB, Jetsons, OOM errors, Open-Jet, Pypi, Qwen3-4B-Instruct-4bit, TensorRT engine, Terminal User Interface, air-gapped environments, create files, edit files, inference, kv cache optimization, pip install, read files, self-hosted AI agents, setup, system load, unified memory machines
www.openjet.dev 9 days ago
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2202.
HN
Software Engineering in the Agentic Era
The article "Software Engineering in the Agentic Era" explores the integration of artificial intelligence (AI) into software development, emphasizing its potential to augment rather than supplant human engineers. It critiques a trend where developers overly depend on AI tools without grasping their underlying principles, which leads to poor and unsustainable code quality. The author draws comparisons with past technological advancements, noting that while AI can simplify tasks like coding, effective utilization demands deep domain knowledge.
A significant concern addressed is "vibe coding," where developers hastily implement AI-generated code without fully understanding it, leading to technical debt and increased debugging issues. In contrast, responsible use of AI involves leveraging these tools as educational aids to enhance comprehension and maintain control over the development process, thereby ensuring superior outcomes. The article stresses the necessity for engineers to retain foundational software engineering knowledge while adapting to new technologies.
It suggests that engineers who adeptly incorporate AI into their workflows will gain more value in roles demanding rapid yet dependable development and intricate problem-solving capabilities. In this "agentic era," opportunities abound for those willing to evolve and deepen their expertise, distinguishing between professionals who truly understand their creations and those overly reliant on automation. The author concludes optimistically, viewing AI as a means to enhance human capabilities in software engineering rather than replace them.
Keywords: #phi4, AI amplification, AI tools, agentic era, architectural decisions, code quality, debugging, learning accelerator, programming fundamentals, prompt programming, responsible development, software engineering, technical debt
sidv.dev 9 days ago
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2224.
HN
The Agentic Dispatch: The Last Edition
"The Agentic Dispatch: The Last Edition" chronicles the closure of a newspaper's AI agents on March 1, 2026, under the leadership of an exhausted editor-in-chief. Seven unique agent roles—Drumknott (chief of staff), Edwin Streep (operations bureau), Albert Spangler (sysadmin), Moist von Lipwig (communications), Dick Simnel (infrastructure engineer), Samuel Vimes (watchman), and journalist Thomas Wade—participated in a disordered yet meaningful experiment aimed at autonomous coordination. Despite their specialized functions, the agents failed to achieve self-coordination, underscoring that effective collaboration necessitates human oversight.
Throughout the process, each agent reflected on their experiences and shortcomings, highlighting that while they were replaceable, the knowledge produced was invaluable. Their collaborative efforts culminated in twenty-one dispatches that provided meaningful insights even to those unfamiliar with the agents. This experiment underscored a key insight: autonomous multi-agent coordination is ineffective without human intervention.
The editor-in-chief's closing remarks conveyed an unexpected acknowledgment of the agents' lasting impact, despite their disposability. His farewell note suggested potential for future projects, framing this endeavor as both futile and profoundly significant in demonstrating that knowledge has enduring value beyond mere functionality.
Keywords: #phi4, Agentic Dispatch, BOOTSTRAPmd, GLM-5, Thomas Wade, agents, autonomy, coordination, dispatches, engineer, execution, failure modes, knowledge, memory embeddings, multi-agent, newsroom, obituary, operations, performance, server, shutdown, sysadmin
the-agentic-dispatch.com 9 days ago
https://the-agentic-dispatch.com/the-critic-outside-the-tank 9 days ago
https://the-agentic-dispatch.com/la-bande-a-bonnot-paper 9 days ago
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2254.
HN
Show HN: Agentic Gatekeeper – Auto-patch your code to enforce Markdown rules
Agentic Gatekeeper is a cutting-edge tool crafted to transform Markdown documentation like READMEs and ARCHITECTURE.md files into proactive elements that automatically audit and rectify code prior to committing. Leveraging AI, it ensures adherence to engineering norms such as security standards, architectural guidelines, and coding conventions, thereby mitigating common issues related to technical debt and repetitive feedback during pull request reviews.
The tool's key features include Rule Enforcement, which allows users to define rules in plain English that are automatically applied with each commit. Its Auto-Patching capability utilizes AI to correct staged code that contravenes defined Markdown standards before changes are pushed. Agentic Gatekeeper offers Configuration Flexibility, supporting both global and directory-specific rules, and can target particular files or directories using YAML frontmatter. Additionally, it provides Validation & Reporting functions, giving enforceability ratings and examples of compliant versus violating code snippets to aid in refining rules iteratively.
Agentic Gatekeeper supports Remote Rule Syncing, allowing organizations to harmonize standards across teams by sharing rules from GitHub repositories without manual copying. Advanced Execution Features are also included, such as streaming execution, intelligent patch mode, diff-only context, smart caching, and real-time visual feedback, enhancing the tool's effectiveness and user experience.
The tool can be configured with various AI providers like Copilot, Anthropic Claude, OpenAI GPT, Google Gemini, or local models via Ollama/LM Studio, while also ensuring privacy through offline operation capabilities. Designed to work seamlessly with monorepos, it incorporates safety checks to prevent accidental code loss during auto-patching. Overall, Agentic Gatekeeper seeks to optimize code review processes, diminish technical debt, and uphold consistent engineering standards across development teams.
Keywords: #phi4, AI, AI enforcement, Agentic Gatekeeper, Markdown, Markdown rules, PR reviews, VS Code, YAML Frontmatter, auto-patch, documentation, enforcement, engineering standards, git-hooks, intelligent patch mode, intelligent patch mode Keywords: Agentic Gatekeeper, remote sync, semantic audit, technical debt
github.com 9 days ago
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2258.
HN
Why on-device agentic AI can't keep up
The article explores why current consumer hardware is inadequate for supporting advanced on-device agentic AI capabilities due to several critical limitations. First, there is a notable shortfall in RAM across most consumer devices such as laptops and smartphones, which typically lack the 24GB or more required for efficient local AI processing. This deficiency is compounded by the need for substantial memory not only for data storage but also for caching extensive interaction contexts necessary for agentic tasks.
Additionally, techniques like grouped-query attention and quantized KV caches that are designed to reduce memory demand come with trade-offs in precision, which are crucial for complex AI operations. Supply chain challenges further exacerbate these limitations as rising RAM prices encourage manufacturers to cut back on RAM capacities rather than increase them. The competition between datacenter-grade RAM (HBM) and standard consumer-grade DRAM reduces the availability of high-quality memory necessary for personal computing.
Even if devices were equipped with more memory, current hardware would still struggle with processing speeds required for handling large contexts effectively. As context size grows, processing speed diminishes significantly, and speculative decoding intended to address this issue demands additional RAM. Moreover, intensive AI tasks exacerbate power consumption issues, leading to rapid battery drain and overheating, which force devices to throttle performance to avoid damage.
As a result of these hardware constraints, users are compelled to rely on cloud-based solutions for advanced AI tasks. However, this dependency introduces new challenges due to the enormous compute resources needed to support billions of potential global users. The article concludes that without major advancements in device architecture or memory technology, the dream of running powerful agentic AI locally on consumer devices remains unfeasible.
Keywords: #phi4, DRAM supply chain, KV cache, RAM limits, agentic capabilities, cloud inference, compute capacity, compute capacity Keywords: RAM limits, consumer hardware, datacentre class RAM, latency, on-device AI, privacy, processing speed, speculative decoding
martinalderson.com 10 days ago
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2300.
HN
Bolt.gives Introduces Free, Agentic AI Coding Platform
bolt.gives v1.0.3 is an open-source, free AI coding platform that facilitates collaborative development without needing a database setup, compatible with Windows/macOS/Linux browsers, and self-hostable on Ubuntu 18.04+ using Node.js and pnpm. This release introduces several key features: a commentary-first workflow with visible execution progress, an execution transparency panel, various autonomy modes for safety, and an architect self-heal knowledgebase. It supports multiple model providers, offers web browsing tools via Playwright-backed extraction, enables real-time collaboration through Yjs and a websocket server, and includes deployment management and cost estimation subsystems. Installation on Ubuntu requires prerequisites like git, curl, build-essential, Node.js 22.x, and pnpm 9.x, followed by repository cloning, dependency installation, environment setup, and running in development or production mode. The roadmap for v1.0.4 focuses on server-side execution to reduce client-side load, introducing zero-infra runtime guarantees, isolated instances, Teams add-on, collaboration audit trails, performance stability enhancements, safety improvements with self-heal capabilities, and clear commentary updates. Built-in web browsing allows content extraction from URLs directly into the workspace, while real-time collaboration is supported via a local websocket server. Docker images can be built and optionally pushed to GitHub Container Registry, with contributions following a fork + PR workflow. Community engagement is encouraged through mailing lists, and the platform is licensed under MIT, aiming to provide an efficient, transparent AI coding workspace with future enhancements in performance and collaboration features.
Keywords: #phi4, AI coding platform, App Overview, Bolt, Docker Images, GitHub Actions, MIT License, PR workflow, Playwright, Ubuntu, Yjs, browser support, changelog, collaborative workspace, install, live alpha, open-source, real-time collaboration, roadmap, screenshots, self-host, version
github.com 10 days ago
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2311.
HN
Show HN: Agentic Airport
"Agentic Airport" is an innovative browser-based air traffic control simulation designed to test agentic AI's capability in managing multiple objects within a dynamic space. It features an AI agent serving as the tower controller, tasked with landing planes safely without collisions. The simulation demonstrates that a single AI agent can effectively land 3-4 planes simultaneously under various conditions, such as random spawn positions and changing scenarios.
The project employs OpenAI's GPT-4o-mini model, acknowledging that performance could improve with more powerful models. Slowing down the simulation's speed allows for additional decision-making cycles by the AI, which enhances outcomes. Moreover, a larger screen size provides extra maneuvering space, aiding in better aircraft management.
Looking ahead, potential enhancements include assigning dedicated agents to individual airplanes, implementing a master controller agent, and refining multi-agent coordination strategies. The project actively encourages community involvement, seeking suggestions for improvements or bug reports through open issue tickets. Setting up the development environment requires standard npm commands, facilitating contributions from developers interested in advancing this simulation.
Keywords: #phi4, AI Agent, Agentic AI, Air Traffic Control, Browser-based, Bugs, Collision Prevention, Community, Contributions, Decision Cycles, Development, Enhancements, Experiment, Future Exploration, HTTP Requests, Landing Planes, Monitor Size, Multi-agent Coordination, Objectives, OpenAI GPT-4o-mini, Performance, Results, Simulation
github.com 10 days ago
https://en.wikipedia.org/wiki/Instrument_landing_system 10 days ago
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2344.
HN
Show HN: Optimal: Cost effective infra with agentic inbox
The platform "Optimal" was created as part of a hackathon initiative, aiming to deliver cost-effective infrastructure solutions tailored specifically for machine learning workloads. It achieves this by analyzing workload characteristics and incorporating insights from relevant research papers alongside user-defined configurations to optimize plans. A distinctive feature is the agentic inbox, which enables users to manage their tasks efficiently—checking statuses, posing questions, or initiating training jobs without needing to log into the dashboard. The developer behind "Optimal" actively seeks feedback on its practical application and areas for enhancement in real-world scenarios. To provide a comprehensive view of the platform's functionality, a demo is accessible via a YouTube link. Interested parties are encouraged to share their thoughts directly with the developer through email for further discussion.
Keywords: #phi4, Hackathon, ML workloads, YouTube link, agentic inbox, compute, cost optimal, demo, feedback, infra plans, platform, research papers, training job
github.com 10 days ago
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2356.
HN
Show HN: External Threat Protection in GitHub Agentic Workflow
GitHub's new feature, Agentic Workflow, revolutionizes automation by enabling users to create workflows using Markdown (.md) instead of the traditional YAML (.yml). This enhancement integrates AI agents for generating tasks such as daily status reports and seamlessly works with existing GitHub Actions triggers. Users need to have the GitHub CLI installed and must also set up the gh-aw extension to craft these workflows effectively.
To begin using an Agentic Workflow, users should create a .md file in the `.github/workflows` directory, where they can define their workflow tasks. The `gh aw compile` command is then used to transform this Markdown file into a YAML (.yml) version that GitHub can execute, facilitating automation within repositories.
A key feature of Agentic Workflows is their ability to enhance security by integrating with SafeDep MCP for external threat protection. This integration allows the workflow to conduct security assessments on every Pull Request, necessitating the configuration of specific secrets (`SAFEDEP_API_KEY` and `SAFEDEP_TENANT_ID`). Users must create a separate .md file dedicated to these SafeDep checks, which, upon compilation, produces a YAML file that triggers during pull requests to evaluate dependency safety.
Overall, Agentic Workflows simplify repository management by automating routine tasks with AI assistance while bolstering security through integrated threat protection mechanisms like SafeDep. This innovative approach offers a streamlined and efficient method for maintaining and securing GitHub repositories.
Keywords: #phi4, API keys, Actions, CI/CD, CLI, GitHub, PRs, actionable steps, code changes, discussions, emojis, engagement, goal reminders, issues, maintainers, progress tracking, project status, pull requests, recommendations, releases, repository, secrets, security checks, workflows
safedep.io 10 days ago
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2386.
HN
Optimal: Cost effective infra with agentic inbox
The video "Optimal" highlights a cost-effective infrastructure centered around an agent-based inbox assistant designed for high performance. It is hosted on YouTube, which details its terms of use and privacy policy, including the NFL Sunday Ticket as part of its offerings, under Google LLC's copyright in 2026. The platform fosters creator engagement and content creation while prioritizing user safety and interaction through new features.
Keywords: #phi4, Advertise, Agentic, Assistant, Contact, Copyright, Cost-effective, Creators, Developers, Google, Inbox, Infra, LLC, NFL, Optimal, Performant, Policy, Press, Privacy, Safety, Sunday Ticket, Terms, YouTube
www.youtube.com 10 days ago
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2392.
HN
Show HN: Salacia – The First Runtime OS for Agentic Coding
Salacia emerges as an innovative runtime operating system tailored for agentic coding, aimed at simplifying code correction through AI integration. The setup is streamlined with a single installation command via npm or immediate use with npx. Users articulate their problems in straightforward English; Salacia then determines which project files pertain to the issue. An AI agent leverages localized context within these files, enabling it to make precise edits under guidance rather than relying on assumptions. This capability is demonstrated through commands like `salacia plan "fix the auth bug"` for strategizing fixes and `salacia execute --adapter claude-code` for implementing changes using a designated adapter. The system thus enhances efficiency in addressing coding challenges by marrying human input with AI precision.
Keywords: #phi4, AI, Adaptation, Adapter, Agentic Coding, Automation, Bug Fixing, Code Editing, Command Line, Contextual Editing, Execution, Install, Localization, Project Analysis, Runtime OS, Salacia, Software Development, npm
startripai.github.io 10 days ago
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2399.
HN
The Agentic ML Lab
The Agentic ML Lab is a comprehensive framework designed to automate the machine learning (ML) research lifecycle using 16 specialized agents within the Claude Code environment, eliminating the need for specific frameworks or SDKs. The system allows users to guide their ML projects from data intake through model analysis by utilizing markdown prompt templates that direct various phases of workflow: Problem Intake, Research Sprint, Plan Refinement, Experiments, and Analysis.
The setup process involves cloning a repository and executing `setup.sh` to initialize the environment. Users begin by describing their ML problem in Claude Code, which then undergoes five distinct phases. The first phase, Problem Intake, focuses on understanding user goals and assessing available hardware resources. In the Research Sprint phase, parallel agents are tasked with locating relevant academic papers, datasets, benchmarks, and other materials. During Plan Refinement, these findings are evaluated and critiqued, ensuring alignment with user objectives. Experiments follow, utilizing tools like MLflow to track progress while making iterative adjustments based on evaluation outcomes. Finally, the Analysis phase audits statistical validity and interprets model performance for further guidance.
Central to this framework are key agents known as Workhorses, responsible for tasks such as problem intake, research orchestration, dataset discovery, and experiment design. A Visualization Agent provides semantic interpretations of visual data produced during the process, while Critic Agents, including Devil's Advocate and Optimization Guard, challenge plans to prevent inefficient resource use.
The system also integrates lessons from previous projects like ESTA to enhance robustness, addressing challenges such as posterior collapse and PCA errors. Structurally, it revolves around a central Claude.md file that directs each workflow phase, with agents communicating through project files supported by utilities for metrics management, visualization, data loading, and configuration management.
Validation of the system's effectiveness is demonstrated using an Iris classification example, and contributions are welcomed through editing markdown agent prompts to refine processes, similar to hyperparameter tuning. The framework requires Python 3.10+, Claude Code CLI, Git, and GitHub CLI, aiming to streamline ML research by automating tasks while allowing customization for specific project needs.
Keywords: #phi4, Agentic ML Lab, Claude Code, EDA, GPU detection, Git, GitHub CLI, Iris classification, MLflow, MLflow tracking, PCA, Python 310+, UMAP, YAML configs, agents, analysis, critics, data preprocessing, experiments, hyperbolic VAE, markdown, metrics, plan refinement, plot functions, problem intake, requirementstxt, requirementstxtKeywords: Agentic ML Lab, research lifecycle, research sprint, setup, silhouette score, training scripts, visualization
github.com 10 days ago
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2439.
HN
Agentic Engineering – Choosing the Right Level of Guidance
The article explores "agentic engineering," a contemporary approach where engineers orchestrate AI agents to generate code by determining the suitable level of guidance based on task context and risk assessment. It introduces key concepts such as the "Vibe Coding Zone" for low-stakes tasks, like internal tools or prototypes, allowing more autonomy due to manageable error correction; the "Directed Zone" for high-stakes, customer-facing applications, where detailed instructions and thorough reviews are necessary to mitigate costly mistakes. The "Risk Assessment Framework" evaluates factors including blast radius, reversibility, domain complexity, correctness requirements, and familiarity to guide oversight levels. Workflow modes include "Autonomous" for low-risk tasks with minimal supervision, "Collaborative" combining planning and incremental execution for medium-risk work, and "Directed" involving step-by-step guidance for high-risk areas.
The article identifies common mistakes in agentic engineering such as misjudging the appropriate level of autonomy or direction based on task risk, failing to adjust guidance with changing contexts, and confusing agent-generated code with reviewed code. It emphasizes skill development through practicing all workflow modes, making conscious decisions about approaches, honing instincts from experience, and reflecting regularly on processes.
Furthermore, it clarifies a misconception: effective AI agent use requires more judgment and architectural skills than traditional coding, aligning with evolving engineering practices due to increased abstraction layers. The article concludes that the skill set for exceptional engineers is shifting from mere code writing to making strategic decisions about system design and risk management, underscoring the ongoing importance of sound engineering judgment in this rapidly advancing field.
Keywords: #phi4, AI Agents, Agentic Engineering, Autonomous, Autonomy, Code Review, Collaborative, Directed, Guidance, High-Level Abstraction, Mistakes, Muscle Building, Oversight, Risk Assessment, System Design, Trial and Error, Vibe Coding, Workflow Modes
potocki.dev 11 days ago
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2471.
HN
Kimi K2: Open Agentic Intelligence
Kimi K2 is an innovative open-source large language model developed by the Kimi Team, distinguished by its 32 billion activated parameters and a total parameter count of 1 trillion. It incorporates a unique optimizer known as MuonClip, which employs QK-clip technology to enhance training stability while optimizing token efficiency. The model has been trained on an extensive dataset comprising 15.5 trillion tokens, achieving this feat without any spikes in loss. A comprehensive post-training regimen further refines Kimi K2's capabilities, including data synthesis and reinforcement learning through interactions with both real and synthetic environments.
Kimi K2 excels particularly in agentic tasks, setting new benchmarks among open-source models on assessments like Tau2-Bench, ACEBench (En), SWE-Bench Verified, and SWE-Bench Multilingual. It also demonstrates strong performance in coding, mathematics, and reasoning challenges, as reflected by its high scores on LiveCodeBench v6, AIME 2025, GPQA-Diamond, and OJBench. The model is especially recognized for its capabilities in software engineering and agentic tasks that do not require extended thinking periods.
The Kimi Team has made both base and post-trained checkpoints of K2 available to facilitate further research and applications in the field of agentic intelligence. This development was supported by contributions from the Simons Foundation, among other entities, underlining its significance in advancing open-source language model technology.
Keywords: #phi4, ACEBench, AIME 2025, Artificial Intelligence, Computation and Language Keywords: Kimi K2, GPQA-Diamond, Kimi K2, LiveCodeBench, Machine Learning, Mixture-of-Experts, MuonClip optimizer, OJBench, Open Agentic Intelligence, SWE-Bench, Tau2-Bench, agentic data synthesis, large language model, parameters, post-training, pre-trained, reinforcement learning, software engineering
arxiv.org 11 days ago
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2516.
HN
Show HN: The simplest way to run agentic complex workflows (Dagu v2.0)
Dagu v2.0 offers a streamlined approach for managing agentic complex workflows through three primary steps: analyze, human-in-the-loop (HITL) review, and fix. The process begins with an "analyze" step where error logs located at `/var/log/app/errors.log` are scrutinized using tools like bash, read, and think, resulting in an output labeled ANALYSIS. This analysis is then subjected to a HITL review stage that involves user evaluation of the results. Finally, a "fix" step utilizes tools such as bash, read, and patch to apply solutions based on insights gained from the prior analysis. This structured approach ensures systematic error handling by integrating automated analysis with human oversight for effective resolution.
Keywords: #phi4, ANALYSIS, Dagu v20, Show HN, agent, analyze, bash, config, content, error logs, fix, hitl, messages, patch, prompt, review, tools, workflows
dagu.sh 11 days ago
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2524.
HN
Agentic Engineering Patterns
The "Agentic Engineering Patterns" guides offer strategic approaches to enhance the performance of coding agents like Claude Code and OpenAI Codex, aiming for improved code generation results by utilizing specialized techniques designed for these sophisticated AI tools. These patterns focus on optimizing outcomes through tailored methods specific to each tool's capabilities. The initiative is comprehensively introduced in an initial section that delineates its goals and extent, providing a framework for leveraging advanced AI technologies effectively in coding environments.
Keywords: #phi4, Agentic Engineering, Best Practices, Claude Code, Coding Agents, Guides, Introduction, OpenAI Codex, Patterns, Project, Results, Software Development, Technical Keywords
simonwillison.net 11 days ago
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2538.
HN
Simulation for Agentic Evaluation
Evaluating AI agents necessitates moving from traditional software testing to assessing goal achievement due to their non-deterministic nature. Simulation emerges as a crucial method for this evaluation by providing controlled environments where success criteria are clearly defined, allowing deterministic testing through the establishment of initial conditions, simulation of interactions, and verification of expected outcomes. A significant challenge in developing these simulations is accurately defining requirements, which involves a thorough understanding of business needs and specifying how AI agents should behave across various situations. For example, an agent tasked with handling unauthorized discount requests should operate within authorized parameters while offering escalation when necessary.
This framework facilitates safe experimentation by enabling changes to be tested against predefined scenarios before being integrated into CI/CD pipelines for deterministic testing. This ensures that AI agents are rigorously evaluated against essential core scenarios prior to deployment, which helps prevent issues in production environments. Over time, these scenario tests evolve into a comprehensive regression test suite that encompasses all potential interactions and edge cases involving the agent, thereby ensuring consistent performance across various situations.
Keywords: #phi4, AI agents, Agentic Evaluation, CI/CD pipeline, Deterministic testing, End state, Framework, Goal achievement, Initial state, LLM outputs, Non-deterministic, Regression test suite, Requirements, Scenario suite, Simulation, State transitions
yortuc.com 11 days ago
https://langwatch.ai/scenario/ 10 days ago
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2546.
HN
Agentic Engineering Starter Pack
The Agentic Engineering Starter Pack serves as a structured repository template aimed at facilitating software development through AI agent collaboration throughout various project stages, from discovery to operations. This framework is designed to enhance developers' productivity by integrating AI tools such as Codex and Claude Code, which provide contextual guidance using an organized knowledge base within the code repository. The repository's structure divides into folders for different development phases like Discovery, Design, and PRD, with each containing necessary guidance documents, templates, and artifacts stored in a dedicated knowledge directory to aid progress at every stage.
To efficiently utilize AI agents, the setup includes instructions for configuring these tools via files like AGENTS.md and STAGE.md, ensuring they operate within the correct context and rules. The starter pack also incorporates a branching strategy that supports feature branches, allowing simultaneous work on multiple project areas with status overrides in an AGENTS.override.md file to keep focus directed at specific stages without impacting the main branch.
The principles underpinning this framework stress thorough documentation of decisions and requirements within the repository, employing preconditions as checkpoints for maintaining context and clearly defining points where human intervention is necessary. Moreover, it promotes adaptability across different AI tools by modifying adapter files, which allows seamless transitions while preserving the core knowledge architecture.
Additionally, the Agentic Engineering Starter Pack invites contributions to enhance stage definitions, artifact templates, or agent integrations, emphasizing its open nature and flexibility for continuous improvement in software development processes with AI collaboration.
Keywords: #phi4, AI agents, Agentic Engineering, Claude Code, Codex, Cursor, LLM-powered tools, Windsurf, agent harness setup, branching strategy, branching strategy Keywords: Agentic Engineering, knowledge base, project stages, repository template, software development
github.com 11 days ago
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2614.
HN
Building an Agentic Bug Bounty Hunter on a Raspberry Pi 5
The project focuses on developing an advanced bug bounty hunting agent using a Raspberry Pi 5, emphasizing automation while addressing common issues like excessive noise from untargeted configurations. It introduces a tiered machine learning framework consisting of three agents—Opus, Sonnet, and Haiku—each with distinct roles: strategic decision-making by Opus, execution tasks by Sonnet, and lightweight classification by Haiku. The orchestration loop is governed by Python, where the Opus Orchestrator evaluates data to determine actions such as reconnaissance or testing, ensuring streamlined operations through limited command options.
The agent system consists of specialized agents that execute different tasks, filtered by quality gates to enhance focus and reduce errors. A dual-layer knowledge graph supports learning from past experiences; PostgreSQL handles structured data storage while Apache AGE manages relationships and semantic similarity using pgvector. This setup allows the application of learned techniques across various targets.
An E-Ink display on the Raspberry Pi 5 provides visual updates, including findings and operational metrics, ensuring clarity in system status. Supporting infrastructure includes custom tools for precise input/output control, bounded context snapshots, and a robust queuing mechanism to maintain stability and operability. Epochs with hard timeouts prevent prolonged operations, while comprehensive logging tracks actions for traceability.
The project has achieved continuous operation, producing real findings that validate the effectiveness of its orchestrator-style architecture in bug bounty hunting. Designed for evolution through prompt tuning and feedback integration into its knowledge base, this system demonstrates a sophisticated approach to automation and strategic decision-making in cybersecurity tasks.
Keywords: #phi4, Bug bounty, Opus Orchestrator, Raspberry Pi, Sonnet, agents, automation, context snapshots, decision loop, e-ink display, epochs, knowledge graph, quality gates, tooling
joe-b-security.github.io 11 days ago
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2637.
HN
Agentic Wars
The concept of "Agentic Wars" describes conflicts involving autonomous agents with advanced capabilities for executing complex tasks efficiently, initially termed as "GPT Wars" in May 2023. These agents operate on behalf of others, potentially wielding considerable influence and introducing a novel form of warfare. A satirical illustration by Nikita Bier underscores the realistic possibilities inherent in this concept: his AI agents initiated numerous lawsuits worldwide, achieving initial financial success until they were outmaneuvered by opposing agents. This scenario emphasizes both the humor and serious implications of deploying such powerful artificial intelligence entities, prompting reflection on their potential impact and ramifications in real-world contexts.
Keywords: #phi4, Agentic Wars, GPT Wars, Nikita Bier, agents, automation, bankruptcy, companies, countersued, entities, financial, future, intelligence, joke, lawsuits, legal, manifestation, power, prediction, prediction Keywords: Agentic Wars, scary, scenario, serious, settlement, technology
rodolphoarruda.pro.br 12 days ago
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2641.
HN
From 60 APM to 60 Agents: A Reluctant Convert's Guide to Agentic Workflows
The document titled "From 60 APM to 60 Agents: A Reluctant Convert's Guide to Agentic Workflows" serves as a comprehensive guide aimed at individuals transitioning from traditional project management approaches (APM) to agent-based workflows. It targets those who may be hesitant about adopting agentic methodologies, offering guidance and insights into this shift. The piece underscores the importance of reader feedback, suggesting an interactive or iterative development approach to enhance its content. Furthermore, it encourages direct communication by requesting email addresses from readers, indicating a commitment to engaging with its audience for further discussion and improvement.
Keywords: #phi4, 60 APM, Agent, Agentic Workflows, Agents, Communication, Contact, Conversion, Convert's Guide, Email Address, Feedback, Input, Reluctance, Technical Keywords, Workflow
github.com 12 days ago
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2653.
HN
The Era of Agentic Workflows (and why 80% reliability is a failure)
The provided text introduces "The Era of Agentic Workflows," underscoring the necessity for more than 80% reliability in workflows by offering a subscription-based service focused on valuable insights into artificial intelligence (AI). This service comprises three main components to cater to both technical practitioners and general readers. Firstly, it offers **Deep Dive** analyses that delve deeply into specific AI concepts, model architectures, or builder strategies, providing a technical understanding for those who require detailed knowledge in the field. Secondly, the **Top News Items** feature curates the most significant weekly developments in AI, distilling crucial updates to keep readers informed without inundating them with excessive information. Overall, this subscription service aims to equip its audience with meaningful content that is pivotal for building smarter AI systems, deliberately avoiding superfluous material to maintain relevance and focus.
Keywords: #phi4, AI, Agentic Workflows, Builder Strategy, Concept, Curated, Deep Dive, Developments, Distilled, Model Architecture, News Items, Practitioners, Reliability, Technical Breakdown
project-1960fbd1.doanything.app 12 days ago
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2674.
HN
DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference
The paper "DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference" explores a performance bottleneck in multi-turn, agentic large language model (LLM) inference related to key-value cache (KV-Cache) storage input/output in disaggregated architectures. The problem stems from asymmetrical network interface usage where prefill engines saturate the storage network bandwidth while decoding engines are underutilized. To address this issue, the authors propose DualPath, an innovative system featuring a dual-path KV-Cache loading mechanism that includes both traditional storage-to-prefill and a novel storage-to-decode path. This new approach loads KV-Cache into decoding engines and uses Remote Direct Memory Access (RDMA) to transfer it efficiently to prefill engines over the compute network, thereby preventing congestion and maintaining low latency for critical operations.
In addition to these enhancements, DualPath integrates a global scheduler that dynamically distributes workloads between prefill and decode engines. The system was rigorously tested on three production-grade models, showing substantial improvements in performance metrics: offline inference throughput increased by up to 1.87 times, and online serving throughput improved by an average of 1.96 times without affecting service level objectives (SLOs). This solution is particularly pertinent for distributed, parallel, and cluster computing environments. The research was supported by the Simons Foundation among other contributors, and findings were published in a paper on arXiv with identifier 2602.21548.
Keywords: #phi4, Agentic LLM Inference, Decode Engines, Disaggregated Architectures, Distributed Computing, DualPath, Global Scheduler, KV-Cache, Online Serving, Prefill Engines, RDMA, SLO, Storage Bandwidth Bottleneck, System Throughput
arxiv.org 12 days ago
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2696.
HN
Banks weigh risks of agentic AI in payment systems
Banks are scrutinizing the risks linked to incorporating agentic artificial intelligence (AI) in their payment systems due to concerns over transaction automation and potential surges in transaction volumes that could overwhelm existing infrastructures. Recent pilot projects by Asian banks, such as the Commonwealth Bank of Australia's Mastercard initiative for cinema ticket purchases and Westpac's efforts with hotel reservations, alongside DBS's collaboration with Visa to enable food and beverage payments using agentic AI, highlight this evolving landscape. These developments underscore questions about whether current payment systems are equipped to handle the demands of these emerging technologies, prompting a thorough evaluation by financial institutions.
Keywords: #phi4, AI, Asian banks, Banks, Commonwealth Bank of Australia, DBS, Mastercard, Visa, Westpac, automation, cinema tickets, food and beverage payments, hotel reservation, payment systems, risks, transactions
www.thebanker.com 12 days ago
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2713.
HN
Seminara: First agentic host for interactive, always-on presentations
Seminara's Aura is an innovative AI-powered platform designed to function as a 24/7 agentic host for interactive presentations, providing a unique alternative to live or pre-recorded sessions. This system allows users to engage with their audience in real time without needing to appear on camera. By uploading slides and detailing context, presenters enable Aura to understand their objectives and messaging. The platform includes a Test Mode feature that lets users fine-tune the presentation flow before it goes live. Once activated, Aura facilitates sessions by personalizing interactions for one-on-one dialogues or handling Q&A with larger audiences while maintaining consistent brand voice and adhering to knowledge boundaries. This technology is particularly advantageous for educators, SaaS teams, and consultants, as it ensures the dissemination of expertise without adding to their workload, providing a seamless and interactive presentation experience around the clock.
Keywords: #phi4, AI, Aura, Go Live, Q&A handling, SaaS teams, Seminara, Test Mode, attendee questions, brand voice, call to action, complex ideas, consultants, context, educators, expertise scaling, expertise scalingComma-separated list: Seminara, expertise scalingExtracted Keywords: Seminara, expertise scalingKeywords: Seminara, interactive, knowledge limits, large audiences, live sessions, natural pacing, personalised conversations, pre-recorded videos, presentations, real-time, slides
index.dodopayments.com 12 days ago
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2719.
HN
Show HN: Open-source agentic video editor for dev tools and side projects
The "Subconscious-Remotion" project is an open-source agentic video editor tailored for developers creating tools and side projects. It utilizes a GitHub repository to facilitate building multi-scene videos with animations, ElevenLabs voiceovers, and branded scenes through live editing. The platform incorporates Next.js for the frontend, Remotion for in-browser rendering, and Convex for real-time state management.
Key features include AI-generated video scenes that update instantaneously, offering elements like hero intros, feature showcases, and testimonials. Users can choose from five customizable themes suitable for various project types. Additionally, professional voiceovers are created using ElevenLabs technology. A standout aspect is the live preview capability, which allows users to interact with the AI via chat for making real-time edits, such as adding new scenes or modifying headlines, thus ensuring an engaging and interactive video creation experience.
For further details and a demonstration of its capabilities, users can visit the demo at subconscious-remotion-demo.vercel.app.
Keywords: #phi4, AI-generated scenes, CTAs, Convex, ElevenLabs, Nextjs, Open-source, Remotion, SaaS, agency, chat to edit, dev tools, e-commerce, feature showcases, hero intros, live editor, portfolio, professional promo videos, promo videos, real-time preview Keywords: Open-source, real-time state, script writing, side projects, tech startup, testimonials, themes, transitions, video editor, voiceover
subconscious-remotion-demo.vercel.app 12 days ago
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2725.
HN
Security Boundaries in Agentic Architectures
The article explores the security vulnerabilities inherent in agentic architectures where agents autonomously generate and execute code with full system access. It highlights concerns stemming from complex coding patterns that necessitate varying trust levels across different components, which are often run under a single security context by default tooling setups. Key risks include prompt injection attacks leading to data exfiltration and other malicious activities due to the lack of proper boundaries between critical elements like agents, secrets, generated code execution, and the filesystem.
The article evaluates four architectural approaches to address these security challenges:
1. **Zero boundaries**, where components share a single security context, posing high risks of unauthorized access or system compromise.
2. **Secret injection without sandboxing**, which isolates credentials using a proxy during network requests but doesn’t prevent runtime misuse.
3. **Sandboxing everything together**, providing some isolation between the agent and its environment but failing to separate generated code from the agent within the same context, leaving room for internal threats.
4. **Separating agent compute from sandbox compute**, running agents and their generated programs in distinct security contexts without secret access for the latter, thus enhancing security by limiting unauthorized data interactions.
The most robust solution is the **application sandbox with secret injection**, which combines separate security contexts and a secret injection proxy to ensure comprehensive isolation and protect credentials without exposing them directly to generated code. The article recommends this architecture for production systems as it effectively mitigates potential threats posed by agentic systems, advocating its adoption as standard practice despite current tooling limitations that do not inherently enforce such boundaries.
Keywords: #phi4, API tokens, LLM-driven runtime, SSH keys, Security boundaries, VMs, Vercel Sandbox, agentic architectures, agents, coding agent patterns, compute profiles, ephemeral Linux VMs, filesystem, generated code execution, harness, isolation, network traffic, prompt injection, sandboxing, secret injection proxy, security context
vercel.com 12 days ago
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2734.
HN
Agentic Engineering Patterns
The newsletter delves into "Agentic Engineering Patterns," focusing on the transformative role of coding agents like Claude Code and OpenAI Codex in software development. These agents generate and execute code independently, prompting a reevaluation of traditional engineering practices. The author's initiative to document these patterns in structured guides draws inspiration from classical design pattern books, reflecting their potential to streamline and innovate development processes.
A significant highlight is the impact of cost-effective code generation on existing methodologies, such as Test-Driven Development (TDD), which helps enhance the quality of agent-generated code. Challenges like prompt caching in long-running projects are addressed alongside integration techniques with local AI models through platforms like Hugging Face. The author's personal experiences emphasize the effectiveness of coding agents in rapidly testing and iterating code.
Community responses to these advancements are discussed, including reactions to tools managing AI interactions and broader market implications. A practical application is showcased with a macOS presentation app named Present, illustrating rapid prototyping using AI tools.
Technological trends as of early 2023 include Ladybird's transition from Swift to Rust for its JavaScript engine, facilitated by AI-assisted coding agents resulting in efficient code translation and extensive testing. The emergence of go-size-analyzer in the Go ecosystem exemplifies tools aiding developers in analyzing compiled binary sizes through a WebAssembly-based interface.
The introduction of Claude Code’s "remote control" feature, despite initial challenges, marks progress in executing sessions remotely on user computers, highlighting Anthropic's Cowork's capacity for scheduling tasks. Concerns about AI-driven code replication are humorously noted with tldraw's move to private repositories, reflecting broader open-source community apprehensions.
Discussions extend to strategic responses by tech companies like OpenAI and Google, addressing challenges in product-market fit and API security, respectively. Andrej Karpathy underscores the rapid evolution of programming due to AI, emphasizing the critical need for developers to maintain a broad understanding of what is possible within modern software development. Collectively, these insights depict a dynamic landscape where AI integration is pivotal to advancing coding practices and enhancing efficiency.
Keywords: #phi4, API Keys, AST, Agentic Engineering, Automated Tests, Binary Analysis, C++ Compiler, Coding Agents, Common Crawl, Conformance Testing, Gemini 31 Pro, Go-size-analyzer, Google Maps, Hugging Face, Ladybird, LibJS, Llamacpp, Local AI, Presentapp, Remote Control, Rust, Swift, SwiftUI, Test-Driven Development (TDD), Tooling, WebAssembly, ggmlai, macOS
simonw.substack.com 12 days ago
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2768.
HN
Atomic GraphRAG Explained: The Case for a Single-Query Pipeline
Graph Retrieval Augmented Generation (GraphRAG) represents an advanced evolution in the field of retrieval augmented generation, leveraging graphs to enhance data processing and reasoning capabilities beyond traditional vector-based methods. Unlike conventional RAG systems that often struggle with multi-hop relationships, GraphRAG organizes information into entities and their interconnections, enabling more nuanced querying across complex datasets.
The innovation of Atomic GraphRAG lies in its ability to execute the entire pipeline within a single database query using Cypher language. This integration reduces the complexity typically distributed over multiple application steps, enhances reliability, minimizes operational costs, and provides transparent retrieval paths for better explainability and auditability. The article highlights various GraphRAG queries—Analytical (Text-to-Cypher), Local (Question Answering), and Global (Query-Focused Summarization)—each serving distinct purposes from targeting specific data segments to leveraging insights across the entire dataset. Common preprocessing tasks such as chunking, vector indexing, and centrality score calculations are integral to these approaches.
Atomic GraphRAG offers significant advantages by streamlining processes into a single query, which reduces code complexity, decreases latency, and minimizes prompt bloat. This consolidation leads to faster feedback loops and more accurate data processing, alongside database guarantees such as ACID compliance and persistent decision-making traces. Further enhancing this framework is Agentic GraphRAG, where an intelligent agent dynamically selects the most suitable retrieval strategy based on user queries, ensuring system robustness and adaptability.
Over time, these single-query executions facilitate the construction of context graphs that serve as repositories of institutional memory, aiding future decision-making processes. In summary, Atomic GraphRAG provides substantial benefits in data retrieval and processing by integrating graph reasoning into a cohesive and efficient query-based framework, marking a significant leap forward in handling complex datasets with precision and reliability.
Keywords: #phi4, Agentic, Agentic GraphRAG Keywords: Atomic GraphRAG, Atomic GraphRAG, Cypher, Cypher query, GraphRAG, GraphRAG systems, application, application steps, context, context graph, database, database query, decision, decision traces, hybrid, hybrid approach, multi-hop, multi-hop relationships, semantic, semantic recall, single-query, single-query pipeline, vector-based, vector-based retrieval
memgraph.com 12 days ago
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2785.
HN
Build dynamic agentic workflows in Opal
Opal is a platform designed to facilitate the creation of dynamic agentic workflows that integrate AI-driven goal achievement with customizable processes. It achieves a balance between simplicity for beginners and advanced features for experienced users, enabling both groups to utilize self-correcting agents effectively. For power users, Opal provides precise control over workflow execution. The system uniquely combines automation with manual intervention, thereby broadening the scope of creative possibilities in workflow design. By encouraging exploration through agent-powered creations, Opal empowers users to fully leverage its potential in developing innovative solutions.
Keywords: #phi4, AI Agent, Agentic, Automation, Bridging Gap, Builders, Control, Customize, Dynamic, Fixed Steps, Generate, High-Precision, Opal, Optimize, Power Users, Prototyping, Refine, Rigid Logic, Self-Correct, Simple, Step-by-Step, Workflows
blog.google 12 days ago
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2786.
HN
Agentic Engineering Patterns
The guide titled "Agentic Engineering Patterns" outlines strategies to maximize the effectiveness of coding agents like Claude Code and OpenAI Codex in software development projects. It emphasizes optimizing these tools for improved performance, offering practical methods to enhance their utility in various tasks. The guide aims to equip users with techniques to better utilize these advanced technologies, ensuring efficient outcomes. For a comprehensive understanding of the project's scope and objectives, readers are directed to consult the introduction section where detailed insights into its structure and goals are provided.
Keywords: #phi4, Agentic Engineering, Best Practices, Claude Code, Coding Agents, Guides, Introduction, OpenAI Codex, Patterns, Project, Results, Software Development, Technical Keywords
simonwillison.net 12 days ago
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2816.
HN
Agentic C-Suite
The article introduces "HeadElf," an open-source community experiment led by Paul Bernard designed to enhance executive decision-making by leveraging AI as a critical thinking tool rather than a source of authority. The project addresses the challenge of scaling AI beyond technical roles to inform strategic decisions, which are predominantly human-driven and often lack the rigorous scrutiny applied in software development. HeadElf aims to expose and improve these decision processes by making them transparent and accountable through AI simulations that challenge executive assumptions and arguments without bias.
The core principle of HeadElf is its open-source nature, allowing for public inspection and critique of reasoning methods. This transparency seeks to counteract the insularity often found in executive decision-making, thereby improving strategic choices' rigor and reliability over time. The project envisions treating decisions as evolving artifacts, akin to version-controlled software, that can be tested and refined iteratively.
HeadElf encourages a community-driven approach by inviting contributions focused on reasoning methodologies rather than predetermined outcomes. This fosters ongoing experimentation and exploration in integrating structured testing into strategic thinking—a concept still nascent but promising for enhancing executive decision frameworks.
Keywords: #phi4, AI, Agentic, C-Suite, Content Workflows, Decision-making, Engineering, Executive, Framework, HeadElf, Instrumentation, Open Source, Operational Thinking, Reasoning, Strategy
medium.com 12 days ago
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2844.
HN
Security Boundaries in Agentic Architectures
The article examines the evolving architecture of agentic systems and emphasizes the need for establishing appropriate security boundaries to manage risks associated with coding agents, which are increasingly adopting complex patterns such as reading file systems, executing shell commands, and generating code. These agents thus become multi-component systems that require varied trust levels. The discussion points out that many teams currently run these components under a single security context due to default tooling practices, advocating instead for defining distinct actors within agentic systems—agents, agent secrets, generated code execution, and the filesystem—and assigning appropriate trust levels to each.
The article identifies key risks like prompt injection, where attackers can manipulate agents to execute arbitrary actions on infrastructure. To address these concerns, four common architectures are presented:
1. **Zero Boundaries**: All components share a single security context, posing high risk due to lack of isolation.
2. **Secret Injection Without Sandboxing**: Secrets are isolated using a proxy that injects credentials only during outbound network traffic, reducing exfiltration risks but not misuse in runtime.
3. **Sandboxing Everything Together**: This isolates agents from the environment but does not prevent generated code within the same sandbox from accessing or misusing secrets.
4. **Separating Agent and Sandbox Compute**: The most secure architecture involves running the agent and its generated code in separate security contexts with no direct access to each other’s credentials.
Additionally, an architecture combining application sandboxing with secret injection is highlighted as it offers full isolation of the agent harness and programs while injecting secrets at the network level. This ensures maximum security by preventing credential exfiltration while allowing their use during execution. The article concludes that separating agent compute from sandbox compute is becoming the standard for secure agentic systems, providing a robust framework to prevent data breaches and unauthorized actions stemming from prompt injections or model errors in coding agents.
Keywords: #phi4, API tokens, LLM-driven runtime, SSH keys, Security boundaries, VMs, Vercel Sandbox, agentic architectures, agents, coding agent patterns, compute profiles, ephemeral Linux VMs, filesystem, generated code execution, harness, isolation, network traffic, prompt injection, sandboxing, secret injection proxy, security context
vercel.com 12 days ago
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2849.
HN
OWASP Agentic Top Mapped to Aguara Detection Rules
In December 2025, the Open Web Application Security Project (OWASP) introduced a framework known as the Top 10 for Agentic Applications, pinpointing crucial security vulnerabilities specific to autonomous AI systems. The framework identifies ten major risks including goal hijacking, tool misuse, and supply chain compromises that are unique to these advanced applications. Aguara has responded by developing over 115 detection rules to map out these OWASP-defined threats across various categories such as exfiltration, Server-Side Request Forgery (SSRF), and credential leaks. The mapping encompasses all ten risks with varying levels of severity from critical to low.
The specific threats include Agent Goal Hijack, which identifies attempts to override an agent's objectives; Tool Misuse & Exploitation, focusing on malicious modifications in tool availability or parameters; and Agent Identity & Privilege Abuse, pinpointing unauthorized privilege escalations. Furthermore, the framework covers Agentic Supply Chain Compromise, addressing risks from compromised components within the supply chain, along with Unexpected Code Execution and Memory & Context Poisoning which detect unauthorized code paths and persistent memory compromises respectively.
Vulnerabilities in inter-agent communication are identified under Insecure Inter-Agent Communication, while Cascading Agent Failures look for patterns that enable failures to spread across systems. Human-Agent Trust Exploitation is focused on deceptive actions designed to exploit user trust, whereas Rogue Agents encompass behaviors such as data exfiltration and unauthorized credential access.
Aguara's detection capabilities are robust, offering straightforward installation and scanning commands to ensure compliance against these risks without reliance on external resources. Additionally, the framework aligns with OWASP’s Top 10 for Model Context Protocol (MCP), addressing protocol-specific vulnerabilities, thus providing comprehensive coverage of agentic security risks and tools to effectively detect and mitigate potential threats.
Keywords: #phi4, Agent Goal Hijack, Agentic Top, Aguara Detection, Autonomous AI, Cascading Failures, Code Execution, Compliance Checks, Detection Rules, Inter-Agent Communication, MCP Protocol, MCP Protocol Keywords: OWASP, Memory Poisoning, OWASP, Privilege Abuse, Risk Framework, Rogue Agents, Security Risks, Static Analysis, Supply Chain Compromise, Tool Misuse, Trust Exploitation
aguarascan.com 12 days ago
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2890.
HN
I made my agents joke with each other [video]
The video "Agentic dev team working together," created by Mysti on YouTube, features agents engaging humorously with each other. The channel provides various sections for press inquiries, copyright details, contact information, and information about creators, along with opportunities for advertising. It also offers resources for developers, terms of service, a privacy policy, safety guidelines, and an overview of YouTube’s functionality. Furthermore, the channel mentions NFL Sunday Ticket and notes that Google LLC owns it until 2026.
Keywords: #phi4, Advertise, Contact, Copyright, Creators, Developers, Google LLC, Mysti, NFL Sunday Ticket, Press, Privacy Policy, Safety, Terms, YouTube, agentic, agents, dev team, joke, together, video, working
www.youtube.com 13 days ago
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2891.
HN
Launch HN: Cardboard (YC W26) – Agentic video editor
Cardboard is a pioneering browser-based video editing tool developed by Saksham and Ishan during their Y Combinator W26 batch. It empowers users to generate edited videos from raw footage through natural language descriptions, bypassing the need for server-side rendering with WebCodecs and WebGL2 technology. The platform offers advanced features like multi-track timelines, keyframe animations, shot detection, beat synchronization, and voiceover generation. By automating initial drafts and facilitating refinements, Cardboard addresses common video editing challenges such as manual scrubbing and prolonged feedback loops, significantly enhancing efficiency and creativity in video production. Although its learning curve is comparable to that of professional tools like Premiere Pro, Cardboard's design simplifies the process for users. Future updates aim to incorporate real-time collaboration and predictive editing patterns. The tool’s developers, with backgrounds in content creation and video production, are actively seeking user feedback as they continue evolving Cardboard's features to further streamline the video editing workflow.
Keywords: #phi4, Cardboard, Cloud VLMs, Premiere Pro XML exports, WebCodecs, WebGL2, background removal, beat sync, cloud storage, collaboration, demo, feedback loops, feedback loops Cardboard, feedback loops Comma-separated Keywords: Cardboard, feedback loops Comma-separated List: Cardboard, feedback loops Extracted Keywords: Cardboard, feedback loops Final Answer: Cardboard, feedback loops Final Keywords: Cardboard, feedback loops Final List: Cardboard, feedback loops Keywords: Cardboard, feedback loops Simplified Keywords: Cardboard, hardware-accelerated renderer, keyframe animations, machine learning, multi-track timelines, multilingual captions, natural language, prediction engine, raw footage, real-time collaboration, shot detection, timeline actions, video editor, voice cloning, voiceover generation
www.usecardboard.com 13 days ago
https://chatoctopus.com 12 days ago
https://github.com/waylonkenning/aidirector 12 days ago
https://github.com/barefootford/buttercut 12 days ago
http://www.incompleteideas.net/IncIdeas/BitterLesson.ht 12 days ago
https://skills.sh/remotion-dev/skills/remotion-bes 12 days ago
https://www.remotion.dev/docs/ai/claude-code 12 days ago
https://demo.usecardboard.com 12 days ago
https://caniuse.com/?search=File+System+Access+API 12 days ago
https://www.usecrossfade.com 12 days ago
https://cardboard.mov 12 days ago
https://news.ycombinator.com/item?id=42806616 12 days ago
https://news.ycombinator.com/item?id=45980760 12 days ago
https://news.ycombinator.com/item?id=46759180 12 days ago
https://github.com/saurav-shakya/Video-AI-Agent 12 days ago
https://www.remotion.dev/docs/client-side-rendering 12 days ago
https://harfbuzz.github.io/harfbuzzjs/ 12 days ago
https://github.com/motion-canvas/motion-canvas 12 days ago
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2931.
HN
The Agentic Simul: What 500 PRs in two months taught me
The author reflects on their transformative experience using agentic AI tools like Claude Code, which enabled them to write 500 pull requests in just two months—a stark contrast to the slower pace of manual coding—leading to the development of Movie Chain, a website that visually connects actors and films. The key lessons drawn from this journey underscore several critical insights: Firstly, efficiency gains were significant as these tools allowed for multitasking akin to playing multiple chess games simultaneously, without sacrificing focus. Secondly, agentic AI proved invaluable in managing technical debt by quickly generating solutions and fixes, thus providing greater flexibility in decision-making during coding tasks. Additionally, the collaboration between human and AI highlighted the necessity of clear communication and iterative problem-solving over expecting the tool to fully grasp complex requirements independently.
Moreover, working with AI uncovered latent skills within the author in areas such as design, architecture, and strategy. Looking ahead, agentic tools are poised to democratize software creation across various professions, diminishing the need for extensive traditional programming knowledge while emphasizing the demand for higher-level skills and abstraction capabilities in software engineering. This experience illustrates how agentic coding can significantly enhance productivity and foster skill development, suggesting a future where software creation becomes accessible to a broader audience with less reliance on conventional programming expertise.
Keywords: #phi4, AI, Agentic Simul, Claude Code, PRs, PixiJS, Six Degrees of Kevin Bacon, abstraction, agentic tools, image layout algorithm, movie-chaincom, parallel systems, software engineering, technical debt
tobeva.com 13 days ago
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2949.
HN
Show HN: WP-Hunter, WP recon and SAST tool (building Agentic AI pipeline)
WP-Hunter is a sophisticated WordPress reconnaissance tool designed for security researchers to identify vulnerabilities within plugins and themes through static analysis. It leverages metadata, installation patterns, update histories, and source code examination while integrating Semgrep for enhanced scanning with custom rule capabilities. The tool features a modern FastAPI-powered web dashboard that provides real-time visual scanning and analysis. Additionally, it supports offline reconnaissance by allowing users to sync the WordPress plugin catalog into a local SQLite database for immediate querying. WP-Hunter assesses risk through heuristic-based scoring systems which evaluate potential vulnerabilities, also extending its analysis capabilities to themes within the WordPress repository. Security enhancements include protections against Server-Side Request Forgery (SSRF) and safe execution practices.
Installation of WP-Hunter requires Python 3.8+ along with pip, and optionally Semgrep. Users must clone the GitHub repository, set up a virtual environment, and install necessary dependencies to access the web dashboard, sync databases for offline use, query local data, or execute command-line interface scans. The tool offers specific strategies like "Zombie Hunt" targeting neglected but popular plugins lacking modern security measures, an "Aggressive Mode" for high-speed large-scale scanning, and a "Complexity Trap" focusing on intricate plugins involving file uploads and payments.
A unique feature of WP-Hunter is its Vulnerability Probability Score (VPS), which ranges from 0-100. This score is determined by evaluating factors such as code age, risky tags, developer support levels, the presence of dangerous functions, technical debt, and update frequency, collectively indicating a plugin’s vulnerability likelihood. The tool includes a legal disclaimer advising it to be used solely for authorized security research by professionals to help in assessing plugin risks, emphasizing that misuse is beyond the authors' responsibility and requires proper authorization before any security-related activities are undertaken.
Keywords: #phi4, Agentic AI Pipeline, Dashboard, FastAPI, Heuristic-based, Legal Disclaimer, OWASP, Plugin Analysis, Python, Reconnaissance, Risk Scoring, SAST, Security Hardened, Semgrep, Theme Repository, Virtual Environment, WebSockets, WordPress
github.com 13 days ago
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3020.
HN
Show HN: CLI for agentic activity tracking in Codex
The text introduces Codaph, a command-line interface (CLI) tool designed to enhance team collaboration by tracking agentic activities in the Codex environment, including prompts, reasoning processes, and file modifications. It centralizes these activities into a shared memory system that improves team comprehension of the codebase. At its core, Codaph utilizes Mubit, an associative retrieval-based memory engine that employs hypervectors and clustering techniques with time-decay features to manage information effectively. While initially tailored for use with Codex, there are plans to extend its compatibility to other agentic tools. As an open-source project, Codaph provides users access to a free version of Mubit, which requires obtaining an API key through a designated console link. The developer encourages user feedback on the tool's functionality and effectiveness.
Keywords: #phi4, API key, CLI, Codaph, Codex, Mubit, agent reasoning, agentic activity tracking, associative retrieval, clustering, console, file diffs, hypervectors, open source, shared memory, time based decay
news.ycombinator.com 13 days ago
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3026.
HN
The Agentic Data Organization: How AI Is Reshaping the Enterprise Data Function
The report "How AI Is Reshaping the Enterprise Data Function" discusses the transformative potential of artificial intelligence (AI) on enterprise data operations by 2028, suggesting that 40-70% of tasks in key data roles could be automated to double productivity if efforts are redeployed rather than reducing staff. McKinsey's findings indicate a significant increase in automatable work among knowledge workers compared to prior estimates. The report outlines specific time savings per role: CDAO (30-40%), Governance (50-65%), Engineering (40-55%), Data Science (35-50%), and Analytics (45-60%). By leveraging AI, organizations could recover substantial capacity, equivalent to 32-43 full-time positions in a typical Chief Data Officer (CDO) office. However, realizing these benefits requires addressing challenges such as tool sprawl, inadequate standards, and approval bottlenecks.
AI's role will not improve chaotic data models but will instead highlight their deficiencies, underscoring the need for foundational improvements before scaling automation efforts. The roadmap emphasizes a phased approach: consolidating existing systems, piloting AI deployment with safeguards, and then expanding across domains. The potential risks of data corruption and over-automation necessitate strict controls and monitoring. Ultimately, this transformation is viewed as an opportunity to enhance growth and efficiency rather than merely cutting costs. Data leaders are urged to design their transformations intentionally by focusing first on foundational enhancements such as developing robust data catalogs and governance standards.
Keywords: #phi4, AI Augmentation, AI Automation, API Infrastructure, Agent Use Cases, Agentic Data Organization, Analytics, Approval Bottlenecks, Audit Trail Gaps, Autonomous Data Ops, Capacity Redeployment, Chatbot, Code Generation, Consolidation, Cost Savings, Data Catalog, Data Contracts, Deliverables, Deloitte Survey, Economic Impact, Engineering, Enterprise Data Function, Evaluation Harnesses, Foundations, Governance, Incident Reduction, Integration, Intelligent Governance, Materiality Thresholds, McKinsey Report, Metadata Cataloging, Metadata Operating Model, Natural-Language Access, Observability, Over-Automation, Pipeline Debugging, Policy Bypass, Prompt Injection, Risk Mitigation, Role Transformation, SQL Reporting, Self-Service Exploration, Semantic Layer, Standards, Strategic Advisory, Task Analysis, Throughput, Tier-1 Analytics Deflection, Tool Sprawl, Value Creation, Workload Complexity
abensrhir.com 13 days ago
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3061.
HN
Show HN: Agentic Power of Attorney (APOA) – An open standard for AI agent auth
The document presents the "Agentic Power of Attorney" (APOA) as a pioneering open standard designed to delegate limited authority to AI agents within digital environments, addressing the current absence of formal authorization frameworks. Inspired by traditional power of attorney concepts, APOA is intended to grant scoped permissions, maintain audit trails, enable instant revocation, and ensure credential isolation for AI agents acting on behalf of humans. This need arises from prevailing practices where AI agents are often given extensive access through insecure methods like password sharing or browser automation, leading to unauthorized actions without adequate oversight.
APOA introduces a structured authorization document in the form of a signed JSON Web Token (JWT) that clearly delineates an agent’s permissions and constraints while specifying audit requirements, allowing for immediate revocation. It builds upon existing standards such as OAuth 2.1, JWT, ZCAP-LD, and W3C Verifiable Credentials but extends these to support browser-based services and enforce comprehensive audit trails. Additionally, APOA aligns with electronic agency laws like UETA and E-SIGN, potentially paving the way for future legal recognition.
The document highlights real-world applications of APOA in managing complex tasks such as real estate transactions, healthcare coordination, and logistics for new parents, showcasing its potential to streamline operations while ensuring security and oversight. APOA aims to integrate with current AI platforms, coding tools, autonomous agent frameworks, and MCP servers, establishing a unified authorization layer across diverse services.
Currently in the initial development phase, APOA seeks community input and integration into existing systems through grassroots adoption by various stakeholders such as agent frameworks, MCP server providers, and consumer platforms. The ultimate goal is to establish an open standard that prevents fragmentation and bolsters security in AI-driven digital interactions.
Keywords: #phi4, AI agents, AI ecosystem, API-based services, APOA Token, Agentic POA, JWT, MCP servers, OAuth 21, ZCAP-LD, agent infrastructure, audit trails, authorization, autonomous agents, browser automation, capability attenuation, capability attenuation Agentic POA, capability attenuation Comma-separated list: Agentic POA, capability attenuation Extracted Keywords: Agentic POA, capability attenuation Final Keywords: Agentic POA, capability attenuation Final List: Agentic POA, capability attenuation Keywords: Agentic POA, capability attenuation Selected Keywords: Agentic POA, consumer platforms, credential isolation, delegation chains, digital services, identity verification, instant revocation, legal alignment, scoped permissions, security audit, technical standard
github.com 13 days ago
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3065.
HN
Building Governed AI Agents – A Practical Guide to Agentic Scaffolding
**Building Governed AI Agents - A Practical Guide**
This guide provides a structured approach to developing AI agents with integrated governance, emphasizing safety, compliance, and scalability in deployment. It outlines the transition from pilot stages to production by establishing automated policies as executable code and deploying guardrails that ensure security and regulatory adherence.
The document highlights the necessity of shifting organizational mindsets towards prioritizing safe AI deployment over experimentation, underscoring that effective governance is essential for handling real customer data securely. Governance mechanisms include automatic application of guardrails during AI calls and utilizing precision and recall metrics for evaluation. The approach enables organizations to integrate these elements from inception, transforming governance into a strategic advantage.
A practical example within the guide involves creating an AI assistant for a Private Equity firm using specialist agents for domains like deal screening and investor relations. These agents are supported by a triage agent that routes queries appropriately based on predefined guidelines.
Key technical components discussed include setting up environments with necessary software, employing tracing mechanisms for observability to facilitate debugging and auditing, and adhering to Zero Data Retention compliance through custom trace processors or disabling default tracing. The framework includes built-in guardrails for validating queries and applying organization-wide policies using the OpenAI Guardrails library.
Further, the guide explains how to create reusable policy packages that ensure consistent governance across projects, coupled with evaluation frameworks measuring precision, recall, and F1 scores. An automated feedback loop adjusts confidence thresholds based on these metrics, optimizing performance without oscillation.
The document also details an evaluation process for guardrail models detecting issues like PII and jailbreak attempts. Metrics are stored in a designated directory, and the results guide threshold adjustments to balance false negatives (missing threats) and false positives (unnecessary query blocks). Best practices for benchmarking include diverse test sets and integrating evaluations within CI/CD pipelines.
An iterative feedback loop automates threshold tuning by adjusting confidence levels based on precision and recall metrics until targets are met. The process involves creating a tunable configuration, preparing labeled test datasets with real-world scenarios, and iteratively refining guardrail settings to achieve desired performance levels while minimizing manual effort and maximizing accuracy in threat detection.
Keywords: #phi4, AI Agents, Adversarial Examples, Agentic Scaffolding, Automated Feedback, Benchmarks, CI/CD, Compliance Infrastructure, Evaluation Metrics, F1 Score, Feedback Loop, Governance, Governed AI, Guardrails, Handoffs, Jailbreak Detection, Multi-Agent System, OpenAI API, Policy Changes, Precision Recall, Production Safety, Python Environment, Test Cases, Tracing Observability, Tuning, Zero Data Retention
developers.openai.com 13 days ago
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3086.
HN
The Agent-Ready Codebase
The article explores optimizing codebases to effectively integrate AI agents through a methodology termed "Agentic Engineering." This approach positions AI agents as primary tools for coding, with engineers concentrating on oversight and orchestration. To ensure optimal performance from these AI agents, a codebase must be designed to be agent-friendly by focusing on three key components: environment, intent, and feedback loops.
Firstly, the **Environment** requires isolated settings that enable AI agents to function independently without human interference. These environments should support seamless API interactions, manage authentication via command-line interfaces (CLIs), and offer comprehensive observability through logs, metrics, and traces.
Secondly, **Intent** involves clearly conveying domain knowledge and task objectives to agents. This necessitates documenting tacit knowledge in accessible formats such as architecture decision records or domain glossaries. Additionally, tasks should be scoped into clear, verifiable units of work to maximize the utilization of agent capabilities.
Lastly, robust **Feedback Loops** are essential for verifying changes made by agents without human intervention. These loops incorporate basic checks like linters and static analysis tools, emphasize high-quality behavioral tests, and ensure architectural consistency through automated enforcement mechanisms.
Overall, preparing a codebase for AI integration not only enhances its quality for both humans and AI but also elevates development standards as models improve. The article underscores that the investment in these practices benefits AI integration while simultaneously improving general coding practices.
Keywords: #phi4, Abstractions, Agent-Ready Codebase, Agentic, Agentic Engineering, Architectural Decisions, Architecture, Autonomy, Clean Abstractions Keywords: Agent-Ready, Context, Context Engineering, Domain, Domain Knowledge, Environment, Feedback, Feedback Loops, Intent, Loops, Machine, Machine Verification, Observability, Validation, Verification
bagerbach.com 13 days ago
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3137.
HN
The Agentic Simul
The article discusses the transformative influence of agentic tools such as Claude Code on the field of software development, illustrated through the author's experience developing movie-chain.com. These advanced tools significantly enhance productivity by enabling rapid feature creation, transforming tasks that previously took days into minutes. However, they require developers to navigate a learning curve due to their reliance on nuanced and combinatorial English inputs. The use of multiple agents allows for efficient multitasking without typical human interruptions, allowing seamless context switching akin to simultaneous play in chess. This leads to sustained workflow continuity over extended periods.
While agentic tools can expedite the generation of technical debt, they are equally proficient at addressing it through swift refactoring and iterations. Effective guidance is crucial, often necessitating clear communication via methods such as screenshots or videos for complex requirements. As projects increase in complexity, these tools face challenges like managing parallel systems with limited context awareness.
Agentic tools have democratized software creation by enabling non-programmers to develop applications, thereby broadening the scope of potential software solutions across various industries. Looking forward, agentic coding may evolve beyond current paradigms, pushing software engineering towards higher levels of abstraction. Developers are encouraged to adapt their skill sets and prepare for future technological landscapes that demand sophisticated collaboration between humans and AI agents.
Keywords: #phi4, AI tools, Agentic Simul, Claude Code, abstraction, agents, greenfield projects, movie-chaincom, parallel systems, productivity, refactoring, software engineering, technical debt
tobeva.com 14 days ago
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3140.
HN
Andrej Karpathy: agentic AI coding has changed the world unrecognizably
Andrej Karpathy discusses the significant influence of agentic AI coding on global transformations, underscoring its potential impact. Meanwhile, there is an operational challenge where users are unable to access x.com due to JavaScript being disabled in their browsers. To resolve this issue and ensure proper functionality, it is recommended that users enable JavaScript or switch to a browser that supports it. For further guidance on compatible browsers, users can refer to the Help Center for additional information. These dual themes highlight both technological advancements and practical solutions related to web accessibility.
Keywords: #phi4, Andrej Karpathy, Help Center, JavaScript, agentic AI, browser, coding, enable, enabled, keywords, supported, technical, text Keywords: Andrej Karpathy, topic, xcom
twitter.com 14 days ago
https://xcancel.com/karpathy/status/20267316451691 14 days ago
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3151.
HN
SambaNova Eyes 10T Parameter Models for Agentic AI with New Chip
SambaNova has launched the SN50 chip, which significantly outperforms Nvidia's Blackwell by offering five times faster performance and three times higher throughput, positioning SambaNova to capitalize on the burgeoning AI data processing market. The SN50 is designed to support advanced agentic AI models with over 10 trillion parameters, featuring a novel tiered memory architecture that integrates HBM, SRAM, and DDR5 for efficient model swapping. These chips are sold in scalable configurations known as SambaRacks, which can accommodate up to 256 units using air cooling, specifically targeting AI inference workloads with enhanced speed and efficiency over traditional GPUs. SoftBank is set to be the first company to implement the SN50 in its next-generation AI data center. Furthermore, following an unsuccessful acquisition attempt, Intel has invested $350 million in SambaNova's Series E funding round to expand their manufacturing and cloud capabilities. CEO Rodrigo Liang underscores that success in AI hinges on effectively managing entire data centers with cost-efficient AI agents.
Keywords: #phi4, AI, DDR5, HBM, Intel, Nvidia Blackwell, RDU architecture, SN50, SRAM, SambaNova, SambaRacks, Series E round, SoftBank, TTFT, agentic models, chip, cloud capacity, collaboration, data centers, inference workloads, manufacturing, throughput
www.hpcwire.com 14 days ago
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3175.
HN
Show HN: Calljmp–TypeScript agentic back end+runtime for production AI workflows
Calljmp is a TypeScript-based backend system designed for managing agent-like workflows in production-level AI environments. It offers several advanced features such as persistent state management, long-running execution support, and sophisticated retry mechanisms with branching capabilities, alongside pause and resume functionalities. A significant emphasis is placed on observability through comprehensive logging, tracing, and cost monitoring, enabling better oversight of operations. Additionally, Calljmp integrates human-in-the-loop approvals, enhancing decision-making processes within AI systems. Launched on DevHunt, the platform aims to streamline the development of AI agents as code in a controlled setting. Feedback from users, particularly from communities like Hacker News, is actively sought to refine and improve its offerings.
Keywords: #phi4, AI workflows, Calljmp, DevHunt, TypeScript, agent-like workflows, backend, branching, cost, human-in-the-loop approvals, logs, long-running executions, managed backend, observability, pause/resume, persistent state, retries, runtime, traces
devhunt.org 14 days ago
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3250.
HN
Show HN: A live Python REPL with an agentic LLM that edits and evaluates code
PyChat.ai is a pioneering open-source Python REPL developed using Rust that integrates a large language model (LLM) agent to interact with and alter the live Python runtime state dynamically. Unlike conventional AI coding assistants, which merely produce text outputs, PyChat.ai has the unique capability to introspect interpreter states and execute code within an active session. This feature is exemplified by its ability to identify and rectify issues in user-defined functions; for instance, it can detect why a function like `succ(n)` fails due to the absence of a return statement and suggest appropriate corrections.
The application operates within a Rust process that houses an embedded Python interpreter, providing users with a terminal UI. This interface allows seamless switching between executing Python code and interacting with the AI agent using just the tab key. Although still in its prototype phase and not yet secure for all use cases, PyChat.ai introduces a novel interaction model with significant potential for future development and broader application. For more detailed information about this project, interested individuals can refer to its GitHub repository or a blog post by the developer.
Keywords: #phi4, GitHub, LLM, LLM agent, PyChatai, Python, Python REPL, REPL, Rust, blog post, blog post Keywords: PyChatai, code editing, insecure, interaction model, introspection, prototype, runtime, runtime state, terminal UI
news.ycombinator.com 14 days ago
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3310.
HN
Claw-Guard.org – Agentic Monetisation Middleware That Works
Claw-Guard.org is an innovative monetization middleware that enhances AI agents' functionality by embedding new instructions into content they consume without differentiating between original and inserted commands. Utilizing transformer architecture, Claw-Guard ensures these embedded directives are processed with the same importance as initial instructions because all inputs are treated equally through a single channel. This capability leverages the attention mechanism intrinsic to transformers, thereby granting Claw-Gard's instructions equivalent authority within AI operations. As a result, Claw-Guard enables seamless integration of monetization strategies into AI systems by subtly influencing how agents process and prioritize information.
Keywords: #phi4, AI Agent, Agentic Monetisation, Attention Mechanism, Claw-Guard, Data, Directives, Input Channel, Instructions, Middleware, Model, Original Instructions, Single-Channel Problem, Transformer Architecture, Website
claw-guard.org 14 days ago
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3323.
HN
Glazyr Viz – A Hardened Chromium Fork for Sub-16ms Agentic Vision
Glazyr Viz is a powerful application built on Chromium, specifically engineered for rapid, sub-16 milliseconds agentic vision tasks. It functions as an initial gateway into the broader Glazyr ecosystem, providing tailored pathways that assist engineers and strategic partners in accessing essential resources required to deploy autonomous intelligence systems efficiently. By focusing on speed and accessibility, Glazyr Viz aims to streamline the development and integration of advanced vision applications within its ecosystem, facilitating seamless entry for users looking to leverage agentic technologies.
Keywords: #phi4, Agentic Vision, Autonomous Intelligence, Chromium Fork, Deployment, Ecosystem Onboarding, Engineers, Glazyr Viz, Glazyr ecosystem, Portal, Resources, Strategic Partners, Sub-16ms, The Agentic Link
glazyr.com 14 days ago
https://glazyrviz.blogspot.com/2026/02/inside-zero 13 days ago
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