26.
HN
Show HN: An Agentic Supercomputer
"Rose," an innovative agentic supercomputer developed recently, aims to transform the way complex goals are approached by breaking them down into manageable sub-goals that fit within current AI capabilities. This system is distinguished by its ability to deploy up to 10,000 agents simultaneously, setting it apart from existing solutions like Kimi-2.5 swarms and Claude's Agent teams, which have yet to fully address everyday needs effectively. Rose stands out with features such as seamless data integrations, a robust task decomposer that minimizes errors, and consistent long-term execution capabilities. Available as an open-source platform free for use, it empowers users to efficiently orchestrate computing resources to achieve their desired outcomes in a cost-effective manner. The creator envisions this tool as universally accessible, inviting feedback and questions from the community.
Keywords: #phi4, AI Agents, Agentic Supercomputer, Claude's Agent Teams, Compute-bender, Data Integrations, Efficiency, Feedback, Goal Decomposition, HN, Integration, Kimi-25 Swarms, Open Source, Parallel Execution, Persistent Runs, Research, Rose Labs, Stability, Task Decomposer
www.roselabs.ai 2 hours ago
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48.
HN
Redpanda Agentic Data Plane (ADP) now in limited availability
The Redpanda Agentic Data Plane (ADP) has entered limited availability, representing a pivotal advancement in enterprise adoption of agentic AI systems. This development follows a shift in attitudes towards AI's return on investment; skepticism is waning as 74% of executives report ROI within their first year, leading to widespread deployment across businesses. The growing demand for AI tools that directly access data underscores the necessity for secure and scalable connectivity solutions like ADP.
ADP provides a unified governance framework for managing AI interactions with enterprise data systems, offering low-latency streaming, policy enforcement, and enhanced observability capabilities. It includes an AI Gateway for centralized control, along with AI Agents furnished with essential tools and instructions. The platform features robust authentication and authorization mechanisms to ensure security, complemented by comprehensive observability through the OpenTelemetry Protocol.
Currently accessible to approved Redpanda Design Partners on AWS, ADP is set to expand support to additional cloud providers. Built upon Redpanda’s Kafka-compatible streaming service, the platform bolsters scalability and accelerates time-to-market for agentic systems while guaranteeing secure data access. Further details are available in official documentation, with updates to be provided through a monthly newsletter.
Keywords: #phi4, ADP, AI, AI Gateway, AWS, Agentic Data Plane, Apache Iceberg, Azure, BYOC, GCP, Kafka-compatible, MCP, OpenID Connect, OpenTelemetry Protocol, ROI, Redpanda, agents, authentication, authorization, connectors, data plane, enterprise adoption, governance, observability, productivity, scalability, self-managed, serverless deployments, serverless deployments Keywords: Redpanda, streaming service
www.redpanda.com 4 hours ago
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63.
HN
What Leadership Looks Like in an Agentic AI World
Agentic AI holds transformative potential within leadership and organizational frameworks by introducing autonomous systems capable of independent planning, reasoning, and acting, which significantly boosts productivity and strategic decision-making. Harvard Business School's Tsedal Neeley and Ritcha Ranjan from Expedia Group highlight that these systems can handle entire workflows with minimal human oversight, serving as strategic partners through digital support teams. These teams might include competitive intelligence analysts, chief of staff for time management, and executive coaches providing feedback. To harness agentic AI's full potential, organizations must rethink their processes while maintaining vigilance over AI outputs. Neeley and Ranjan recommend beginning with simple tasks, expanding tool access, offering training, ensuring legal data use, and continuously exploring new tools to maximize the benefits of AI. The primary advantage of agentic AI lies in its capacity to autonomously synthesize information from various sources, thereby assisting leaders in managing complexity and enhancing their strategic capabilities.
Keywords: #phi4, Adoption, Agentic AI, Automation, Chief of Staff, Competitive Intelligence, Data, Digital Support Team, Executive Coach, Expedia Group, Generative AI, Harvard Business School, Human-in-the-loop, Innovation, Leadership, Legal and Ethical Use, McKinsey, Productivity, Strategic Partners, Training, Workflow, Workplace
www.library.hbs.edu 5 hours ago
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71.
HN
Spacebot: An OSS agentic system designed to scale for large online communities
Spacebot is an open-source agentic system tailored for enhancing efficiency in large online communities by focusing on task-specific operations rather than maintaining conversation contexts. It utilizes workers to carry out specific tasks such as scraping API changelogs or updating webhook handlers, which operate independently and report their progress through a centralized event bus. This approach allows the community to receive live updates without needing constant polling. Each worker is assigned a unique ID and equipped with necessary tools for its designated task, ensuring focused and effective execution. This design facilitates scalable operations by promoting efficient task management within large online environments.
Keywords: #phi4, OSS, Scraping, Spacebot, Stripe API, Updating, Workers, agentic system, changelog, channel, event bus, live updates, online communities, polling, polling Keywords: Spacebot, prompt, tools, webhook, webhook handler
spacebot.sh 5 hours ago
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73.
HN
Palo Alto Networks Announces Intent to Acquire Koi to Secure Agentic Endpoint
Palo Alto Networks has announced its plan to acquire Koi, a leader in Agentic Endpoint Security, aiming to tackle the security challenges posed by AI agents and tools that often circumvent traditional security measures due to their deep data access capabilities. This strategic acquisition will integrate Koi’s innovative technology with Palo Alto Networks’ existing Prisma AIRS™ and Cortex XDR® platforms, significantly enhancing visibility and defense mechanisms against threats driven by artificial intelligence. By doing so, the company intends to empower its customers to utilize AI tools safely while establishing new standards in endpoint security amid a growing reliance on AI-native ecosystems within enterprises. This move is positioned as a forward-thinking strategy to bolster security in an increasingly automated digital landscape, with more details expected at Palo Alto Networks' Q2 FY2026 earnings call.
Keywords: #phi4, AI agents, Acquisition, Agentic Endpoint Security, Control, Cortex XDR®, Enterprise Risk, Koi, Palo Alto Networks, Prisma AIRS™, Threat Intelligence, Unit 42®, Visibility
www.paloaltonetworks.com 5 hours ago
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76.
HN
How Generative & Agentic AI Shift Concern from Technical Debt to Cognitive Debt
The article explores the emerging challenge of cognitive debt in software development as generative AI becomes more integrated into the field. Traditionally, concerns focused on technical debt, which stems from inadequate design choices impacting code quality and maintenance. However, with AI automating much of the coding process, a new issue has arisen: cognitive debt. This form of debt occurs when developers lose comprehension of their own systems, making it difficult to implement changes or articulate the rationale behind decisions. Cognitive debt poses a significant threat because it undermines collective knowledge and decision-making within development teams, potentially leading to stagnation in system modifications and difficulties in managing or expanding projects.
AI's role in simplifying code generation does not alleviate the issue of cognitive debt; rather, it emphasizes the importance of maintaining clear theories about system functionality. To mitigate cognitive debt, developers are encouraged to adopt practices that enhance understanding, such as pair programming and test-driven development. Ensuring that at least one team member fully grasps each AI-generated change is crucial, along with thorough documentation of changes and regular engagement in activities that reinforce collective knowledge. Warning signs include reluctance to alter code due to potential unintended effects and dependency on the expertise of a few individuals.
The article underscores the need for further research into quantifying cognitive debt and devising strategies to prevent it as AI continues to transform software development. Protecting the shared understanding behind software systems is vital for sustaining project health in the long term, highlighting that addressing cognitive debt will be an essential challenge in future software engineering endeavors.
Keywords: #phi4, AI agents, Agentic AI, Code reviews, Cognitive debt, Cognitive load, Developer theory, Future of software engineering, Generative AI, Human understanding, ICSE Conference, Knowledge-sharing, Mythical Man-Month, Refactoring, Shared understanding, Software development, Software health, Sustainability, Technical debt, Test-driven development, Velocity
margaretstorey.com 6 hours ago
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92.
HN
Show HN: Axon – Agentic AI with mandatory user approval and audit logging
Axon is an open-source agentic AI platform focused on enhancing security and user control over AI actions. The system necessitates explicit user consent for all agent activities, including file management, web searches, shell commands, email operations, or code execution. Each request presents the tool's name, parameters, and risk assessment to the user, who can then choose to approve, deny, or temporarily permit the action. Axon employs a multi-agent system that supports diverse roles, models, and permissions for each agent. It integrates with various language models such as Ollama, Claude, OpenAI, Gemini, Groq, and OpenRouter.
Central to its security strategy, Axon ensures GDPR compliance by enabling fully on-premise deployment without requiring cloud services. Comprehensive logging of all actions allows for detailed audit trails that can be exported as CSV files. For code execution, it uses Docker-based sandboxes ensuring network isolation and memory constraints. Additionally, Axon serves as a controlled tool provider to other applications like Claude Desktop and Cursor. It features email integration through IMAP/SMTP with approval gating and offers task scheduling via cron jobs.
Deployment of Axon can be efficiently managed using Docker or manual setups. A command-line interface (CLI) is available for power users to interact directly from the terminal, including features such as SSE streaming and pipe support. Security protocols include whitelisting shell commands, restricting file access, validating URLs against SSRF attacks, encrypting API keys with Fernet encryption, and employing a skills system to verify file hash integrity.
Licensed under Apache 2.0, Axon encourages contributions for both private and commercial use, allowing modifications. The platform was developed by NeuroVexon in Germany.
Keywords: #phi4, API key encryption, Agentic AI, Apache License 20, CLI control, Discord bot, Docker sandbox, Fernet encryption, GDPR-compliant, Telegram bot, audit logging, multi-agent system, network isolation, security controls, user approval
github.com 6 hours ago
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140.
HN
GLM-5: From Vibe Coding to Agentic Engineering
The document "GLM-5: From Vibe Coding to Agentic Engineering" explores the shift from vibe coding—a method that may be characterized by its informal or creative approach—to agentic engineering, which suggests a more structured and intentional framework in technology development. This transition implies moving towards practices that emphasize systematic design and purpose-driven innovation. Additionally, the document includes practical instructions for users on how to upload multimedia content such as images, audio, and videos into a text input area, offering multiple methods like dragging, pasting, or clicking. This dual focus highlights both an evolution in engineering methodologies and user-friendly tools for integrating various media types within digital platforms.
Keywords: #phi4, Agentic Engineering, Audio, Clicking, Dragging, GLM-5, Images, Pasting, Tap, Technical Keywords, Text Input, Upload, Vibe Coding, Videos
huggingface.co 10 hours ago
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159.
HN
Building Next.js for an Agentic Future
Over the past year, Next.js has concentrated on enhancing its compatibility with AI agents by focusing on visibility and integrating specialized tools. Initially, developers encountered challenges as agents could not detect browser-based errors or runtime issues effectively. To address this, Next.js introduced Vector, an in-browser chat agent designed to facilitate better interaction with page elements; however, it was phased out due to redundancy with existing coding tools.
The introduction of the Meta Component Protocol (MCP) around Next.js v16 marked a significant advancement by rendering internal states such as errors and routes visible to agents. This allowed agents to access necessary data without constantly checking HTML, thereby streamlining interactions. With an emphasis on treating agents as primary users, Next.js improved logging mechanisms and structured workflows, enhancing agent engagement with the framework.
Future efforts are geared towards simplifying adoption through tools that automatically generate documentation indexes and expand evaluations of API functionalities. This strategy aims to provide AI agents with contextual information seamlessly, thereby refining debugging processes in Next.js environments. User feedback is actively sought to further improve these developments.
Keywords: #phi4, AI editor, APIs, MCP, Nextjs, Server Action invocations, Vector, agents, browser logs, debugging, devtools, documentation index, eval suite, feedback loop, runtime errors, terminal, visibility
nextjs.org 13 hours ago
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254.
HN
Show HN: WonderTwin AI – Local API twins for safe agentic development
WonderTwin is an open-core platform designed to facilitate the safe development and maintenance of software reliant on external APIs by providing local API twins. These twins act as behavioral clones of third-party services such as Stripe or Twilio, accurately replicating their contracts, state, webhooks, and peculiarities without needing internet connectivity. This allows developers to test and iterate locally on their machines or within continuous integration environments securely. Inspired by Simon Willison's insights into the "dark software factories," WonderTwin addresses challenges associated with real-world API interactions in development processes.
The platform offers free access to its latest versions, making it available for general use, while also presenting a commercial package tailored for production teams. This premium offering includes historical versions and upcoming features like chaos testing. Additionally, WonderTwin supports the development of autonomous agents by providing a sandbox environment that mimics real-world API behavior without the constraints typically associated with mocks or sandboxes. The platform encourages feedback from developers working on API-heavy systems to refine and enhance its capabilities further.
Keywords: #phi4, AI, API dependencies, Clerk, Digital Twin, Digital Twin Universe, Local API, MCP server, Stripe, Twilio, WonderTwin, agents, autonomous agents, autonomous agents Keywords: WonderTwin, behavioral twins, chaos testing, commercial offering, fintech, offline, open core, resiliency features, sandbox, software development
wondertwin.ai a day ago
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264.
HN
The Agentic Mullet: code in the front, proofs in the back
The article explores the growing importance of formal verification in software development amidst the rise of complex autonomous coding models like Opus 4.6 and Codex 5.3. It highlights that while these models can generate functional code, they often produce unwieldy outputs that benefit from formal verification methods, which ensure adherence to precise specifications through mathematical means. Formal verification leverages tools such as static type systems and proof assistants to detect errors early in the development cycle; for instance, Java's type checker is a basic implementation of this concept, while more advanced languages like Rust use sophisticated type systems to tackle memory safety issues, albeit at the cost of increased developer complexity.
The article further discusses proof assistants like LEAN, which are capable of verifying complex mathematical proofs and can be applied analogously to program verification. Despite their power, these tools encounter significant challenges, including the fragility of proofs when code changes, a limited standard library for proofs, and difficulties integrating them with mainstream programming languages. The potential integration of artificial intelligence into formal verification is noted as a promising solution; AI could automate proof generation and verification processes, thereby reinforcing learning models with verified mathematical results and enhancing reliability in agentic coding systems.
Ultimately, the article emphasizes that formal verification stands as an essential component for ensuring correctness in increasingly automated code generation environments. It envisions a future where developers can prioritize defining program objectives over detailing implementation specifics, leveraging advancements in formal methods to achieve this goal.
Keywords: #phi4, AI code generation, Formal verification, Halting Problem, Rust, dynamic languages, mathematical proofs, memory safety, proof assistants, reinforcement learning, reinforcement learning Keywords: Formal verification, static types, type systems, undecidability
www.amplifypartners.com a day ago
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277.
HN
Deterministic Core, Agentic Shell
The article explores "Deterministic Core, Agentic Shell" as an architectural approach in software design to manage the complexities introduced by AI agents like Large Language Models (LLMs). It highlights state machines, particularly finite state machines (FSMs), as a mechanism for achieving determinism in workflows. The author reflects on their experiences at Vendasta Technologies and other projects where FSMs effectively structured complex business logic through defined states, transitions, guards, and actions, resulting in testable and manageable code units.
The piece suggests that state machines can bring the same predictability to systems using AI as the "functional core" concept brings to systems with side effects. Drawing on experiences such as implementing survey workflows at SurveyMonkey using XState, it proposes applying these principles to modern AI-driven applications by dividing them into a deterministic core and an agentic shell. The deterministic core is managed via state machines for predictable behavior, while the agentic shell interacts with external AI services.
Tools like Mastra are mentioned for integrating the deterministic core with LLMs, emphasizing minimizing third-party system dependencies to maintain control over business logic. This separation ensures that deterministic operations remain isolated within a well-defined structure, allowing flexibility and innovation in AI-driven processes. The author argues this architecture reduces risks, enhances testability, and guarantees system correctness by clearly delineating deterministic operations from agent-driven processes.
Keywords: #phi4, AI agents, LLMs, Mastra, OpenAI Realtime, State machines, XState, architecture, async workflows, determinism, finite state machines (FSMs), functional core, guard-rails, imperative shell, legacy applications, non-determinism, serialization, testing, voice agent, workflow
blog.davemo.com a day ago
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293.
HN
Agentic Email
The increasing popularity of Large Language Model (LLM) agents in managing emails is driven by their ability to autonomously read, sort, draft, and respond to emails while interacting with calendars for meeting management. This functionality offers substantial convenience amidst the overwhelming volume of communications. However, it raises significant security concerns as these agents handle sensitive information, creating a "Lethal Trifecta" of risks: processing untrusted content, accessing confidential data, and communicating externally. These vulnerabilities could lead to severe threats like account takeovers during password resets. To mitigate such risks, some experts recommend restricting LLMs to read-only email access without internet connectivity, allowing them only to draft responses for human review. Although no major breaches have been reported thus far, the potential for future attacks necessitates user awareness and responsibility regarding these security concerns. Balancing functionality with security may involve accepting reduced capabilities in favor of heightened safety measures when employing LLM-based email solutions.
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 a day ago
https://www.lightspeedmagazine.com/fiction/travellers-r a day ago
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318.
HN
The Pillars of Agentic Security
The document addresses emerging challenges in agentic security as autonomous systems transition from controlled environments to more independent operations, relying on broader data access that includes untrusted sources. This shift is exemplified by OpenClaw, which offers extensive capabilities through community-contributed skills but lacks rigorous vetting, thus expanding potential vulnerabilities. With the rise of such autonomous agents, there's an increased risk of prompt injection attacks due to their processing of vast web content.
To mitigate these risks, traditional security measures like input sanitization, policy enforcement, and isolation are recommended, tailored for agent-specific characteristics. **Sanitization** is crucial because agents often struggle with distinguishing instructions from data, a challenge exacerbated by inference variability in reinforcement learning models. Techniques such as converting content to markdown, normalizing glyphs, removing extended Unicode characters, and employing prompt injection detection tools like ProtectAI's DeBERTa-v3 model or the Clean library are essential.
For **policy management**, robust frameworks are necessary to manage agents' access and actions effectively. The Open Policy Agent (OPA) with Rego is suggested for its flexibility and integration capabilities, though it’s important to stay aware of evolving governance structures. Policies should be enforced at the service level to avoid vulnerabilities associated with harnesses.
**Isolation** involves separating different agent functions to reduce risks from user errors or attacks, thereby minimizing prompt injection impacts by distinguishing between code creation and research processes. The use of schema canaries helps detect harmful prompt injections through anomalies in output.
In conclusion, securing autonomous agents requires enhancing traditional security principles with adaptations specific to agent behaviors. This includes maintaining vigilance against evolving threats and employing comprehensive isolation and policy enforcement strategies.
Keywords: #phi4, Agentic Security, Input Sanitization, Isolation, LLMs, OPA/Rego, OpenClaw, Policy Enforcement, Prompt Injection, Schema Canaries, Supply Chain Attacks, Transformer-based Methods, Zero Day Injections
sibylline.dev a day ago
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328.
HN
Randomness in Agentic Evals
The paper "On Randomness in Agentic Evals" by Bjarni Haukur Bjarnason, André Silva, and Martin Monperrus investigates the inconsistencies present in evaluating agentic systems through benchmarks that involve agent-environment interactions. The study underscores a prevalent issue where single-run performance scores (pass@1) are commonly reported, yet these can be misleading due to significant variance across multiple runs. Through an analysis involving 60,000 trajectories on SWE-Bench-Verified with three different models and two scaffolds, the authors reveal that pass@1 scores may vary by as much as 6 percentage points based solely on run selection, indicating that perceived improvements might stem from evaluation noise rather than true algorithmic progress.
The research shows that minor differences in early agent trajectory stages can lead to distinct solution paths, thus impacting performance outcomes. To enhance the reliability of evaluations, the authors propose several strategies: conducting multiple independent runs per task for more accurate pass@1 estimations, employing statistical power analysis to ascertain the required number of runs for detecting expected effect sizes, and using metrics like pass@k (optimistic) and pass^k (pessimistic), where k > 1, to better capture a range of performance outcomes. These recommendations, although potentially increasing evaluation costs, are essential for distinguishing actual progress from noise in agentic system development. This paper contributes significantly to Machine Learning, Artificial Intelligence, and Software Engineering by advocating for more robust and reliable evaluation methodologies.
Keywords: #phi4, Agentic Evals, Artificial Intelligence, Benchmarks, Machine Learning, Models, Pass@1, Randomness, SWE-Bench-Verified, Scaffolds, Software Engineering, Statistical Power, Token-level Analysis, Trajectories, Variance, pass@k, pass^k
arxiv.org a day ago
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342.
HN
Advaita Inquiry Matrix (AIM): Structured Nondual Inquiry with Agentic AI
The Advaita Inquiry Matrix (AIM) is a cutting-edge framework designed for structured exploration of nondual philosophy, integrating agentic artificial intelligence to enhance user engagement and understanding. It facilitates guided inquiry by enabling interaction with AI agents, offering a novel approach to engaging with nondual teachings. Detailed in the "AIM Specification v2.md" document hosted on Google Drive, version 2 of this system outlines its architecture and functionality, emphasizing its interactive and systematic nature. Aimed at users interested in delving into nondual philosophy, AIM provides a comprehensive platform for structured inquiry and exploration, supported by advanced AI capabilities to deepen philosophical understanding.
Keywords: #phi4, AI, AIM, Advaita, Agentic, Google Drive, Inquiry, Matrix, Nondual, Sign in, Specification, Structured, Technical
drive.google.com a day ago
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417.
HN
Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt
The article delves into "cognitive debt," an emerging concept within generative and agentic AI contexts, contrasting it with traditional "technical debt." While technical debt involves challenges in code that complicate modifications, cognitive debt represents the erosion of shared understanding among developers regarding a software system's design and functionality. This human-centric issue gains prominence as AI accelerates development, threatening teams' abilities to adapt systems efficiently.
Cognitive debt arises when developers struggle to articulate or recall decision-making rationales, leading to fragmented knowledge within teams. Rapid development cycles, where speed often supersedes understanding, exacerbate this problem. The article illustrates these challenges through an entrepreneurship course scenario, where a team's difficulty in making simple changes was attributed more to cognitive debt than technical problems.
To counteract cognitive debt, the article recommends practices like pair programming and test-driven development that encourage thorough comprehension over hastiness. It also suggests documenting decision rationales, requiring deep understanding of AI-generated code before implementation, and holding regular knowledge-sharing sessions. Identifying early signs, such as hesitancy to make changes or reliance on tribal knowledge, is essential for managing cognitive debt.
The article advocates for more research into measuring and addressing cognitive debt, particularly in distributed teams and projects where newcomers must rebuild shared system understanding. As AI continues transforming software development, effectively managing cognitive debt will be crucial for ensuring long-term software health.
Keywords: #phi4, Agentic AI, Black Box, Cognitive Debt, Coordination Overhead, Developers' Minds, Future of Software Engineering, Generative AI, ICSE Conference, Knowledge-Sharing, Pair Programming, Refactoring, Shared Understanding, Software Health, Technical Debt, Test-Driven Development, Tribal Knowledge, Velocity
margaretstorey.com 2 days ago
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439.
HN
Simple non-hype agentic coding workflow for well-established codebases
This summary outlines an efficient agentic coding workflow designed to enhance developers' productivity when working on established codebases using CLI agents like Codex CLI. The process begins with setting up a central `AGENTS.md` file, which provides comprehensive overviews of the project and technical commands, enabling agents to address basic issues autonomously. Developers then create tickets within the `thoughts/tickets` directory, naming them with AI tags and including details sourced from JIRA tickets in markdown files. Following this, CLI agents conduct research on each ticket by tagging relevant files and documenting findings as markdown files in the `thoughts/research` folder, addressing questions or knowledge gaps identified during initial analysis.
The workflow continues with a planning phase where developers initiate an agent session to outline implementation strategies without altering any code. This involves crafting detailed plans based on prior research, which are saved in the `thoughts/plans` directory if needed. For coding, sessions are reloaded to review both plans and research documents, ensuring a thorough understanding of necessary changes before implementation begins. Throughout this structured approach, developers utilize tags from earlier documentation stages to maintain clarity and coherence.
This workflow is distinguished by its emphasis on feedback loops that enhance the accuracy and relevance of agent interactions with codebases, potentially accelerating ticket resolution times. By leveraging the capabilities of CLI agents while maintaining developer oversight, it aims to streamline the development process without compromising quality or control.
Keywords: #phi4, AGENTSmd, Agentic coding, CLI agents, Codex CLI, business section, codebase, compile-test feedback loop, compile-test feedback loop Keywords: Agentic coding, implementation plan, markdown file, repository organization, research ticket, tech section, test coverage, thoughts folder, workflow
alyosha.net 2 days ago
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443.
HN
Ask HN: What are the biggest limitations of agentic AI in real-world workflows?
The discussion focuses on understanding the limitations of agentic AI systems, which are designed to autonomously plan and execute complex workflows, within production environments. It explores various challenges these systems face, such as maintaining reliability across extended sequences of actions, issues with integrating diverse tools, unpredictable costs, problems in managing state effectively, latency concerns, and difficulties in achieving proper observability. The inquiry seeks to identify failure modes that were not apparent during controlled demonstrations but became evident when these AI systems were deployed for real-world applications. These challenges emphasize the gap between theoretical or test environments and practical, operational settings where unforeseen issues can arise.
Keywords: #phi4, Agentic AI, action chains, cost unpredictability, failure modes, latency, limitations, observability, production environments, real usage, reliability, state management, tool integration, workflows
news.ycombinator.com 2 days ago
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458.
HN
Franklin: AI agent that fundraises for you
Franklin is an AI-powered tool specifically designed to automate and streamline the entire fundraising process for startups, eliminating the need for founders to manage these often complex and time-consuming tasks manually. Utilizing a built-in agentic CRM, Franklin seamlessly orchestrates all phases of raising capital, from initially understanding startup requirements through conversational interactions to finalizing investment rounds with signed agreements. This comprehensive system enables founders to concentrate on their core business activities by handling crucial fundraising responsibilities such as identifying potential investors and negotiating deal terms independently. By integrating these functionalities into a single platform, Franklin significantly enhances efficiency and reduces the operational burden on startup teams during their capital-raising endeavors.
Keywords: #phi4, AI, AI agent, CRM, Franklin, agentic, agentic Keywords: Franklin, conversation, documents, fundraising, investors, pipeline, pitch decks, round, startup, term sheets
www.askfranklin.xyz 2 days ago
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459.
HN
Agentic Anxiety
The text delves into "Agentic Anxiety," exploring the compulsive nature of engaging with agentic software development, akin to an addiction similar to slot machines that reward users more as their skills improve. This compulsion is fueled by a fear of being left behind in fast-paced technological advancements rather than merely fearing missed opportunities (FOMO). Despite concerns about the future of software technology, active involvement and mastery over new technologies help alleviate this anxiety for the writer. Additionally, they plan to start a small tree farm as a proactive measure against uncertainty, reflecting their approach to managing both technological and personal challenges with purposeful action.
Keywords: #phi4, Addiction, Agentic Anxiety, Agentic Software, Building Stuff, Claude Code, Dopamine Hits, Excitement, Existential Dread, FOBLB, FOMO, Fearful, Future Uncertainty, Industry Change, Model Iteration, Prompting, Slot Machine Analogy, Software Game, Tooling Improvement, Tree Farm, Value Chain
jerodsanto.net 2 days ago
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473.
HN
Architecting AI-ready infrastructure for the agentic era
The document discusses the transition from traditional AI systems to "agentic" AI, which encompasses advanced capabilities such as reasoning, planning, information retrieval, action execution, self-evaluation, and collaboration with other agents. This evolution necessitates a fundamental reevaluation of existing infrastructure assumptions regarding statelessness, latency, security, and cost control.
To accommodate the demands of agentic AI, it is essential to develop modular, scalable systems that support large language models (LLMs), retrieval workflows, vector databases, evaluation layers, and secure execution environments. The document provides guidance on architecture patterns and components, including practical code examples using tools like Kubernetes for deployment, Terraform for infrastructure as code, LangChain for agent orchestration, vector search technologies, and FastAPI for building APIs.
Key infrastructural requirements include the ability to execute tools in real-time, support dynamic reasoning loops, ensure isolated and secure tool invocation, and maintain observability through metrics, logs, and traces. Additionally, scalability and cost control are critical factors that traditional machine learning stacks cannot adequately address, necessitating a new stack that integrates cloud-native infrastructure, LLM orchestration, vector stores, queues, and model gateways.
The proposed architecture comprises components such as an API Gateway, Agent Orchestrator, Vector Store, Tooling Layer, Model Gateway, Infrastructure Layer, Observability Layer, and Secrets/Config management. For implementation, the document suggests using FastAPI for the API Gateway, LangChain for agent orchestration, Qdrant for vector storage, and Kubernetes with Terraform for deployment.
The steps to implement this architecture include installing dependencies, initializing LLMs (e.g., using OpenAI), setting up a vector database, creating retrieval tools, building an agent equipped with conversation memory and planning capabilities, wrapping the agent in a FastAPI service, deploying via Kubernetes, and integrating observability features like logging, tracing, and metrics.
In summary, the agentic era demands infrastructure that supports reasoning, retrieval workflows, containerized deployment, infrastructure as code provisioning, and robust observability. Organizations aiming for success must build modular, scalable, cost-aware, and resilient systems capable of supporting complex AI copilots.
Keywords: #phi4, AI-ready infrastructure, Agentic systems, FastAPI, Kubernetes, LangChain, Retrieval workflows, Terraform, agentic era, modular systems, observability, retrieval workflows Keywords: Agentic systems, scalable architecture, software engineering, vector databases
thenewstack.io 2 days ago
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479.
HN
An Exercise in Agentic Coding: AV1 Encoder from Scratch in Rust
The article chronicles the author's journey with "agentic coding," focusing on building an AV1 encoder from scratch using Rust. Initially skeptical about agentic coding tools such as Cline and Claude Code, which facilitate advanced software development, the author was inspired to test these tools by creating a complex project—a functional AV1 encoder in Rust—within 12 hours. Despite not being optimized for speed or quality, this custom-built encoder conformed to the AV1 specification and worked with decoders like dav1d. This endeavor underscored agentic coding's potential in generating customized encoding profiles and integrating lightweight encoders into various platforms, such as devices and websites.
The author also demonstrated real-time browser-based AV1 encoding using WebAssembly (WASM) through a demonstration. The project served dual purposes: it acted as an educational tool for the author and encouraged others to explore innovative applications of code generation tools. By lowering barriers to specialized software development, agentic coding allows developers to quickly create tailored solutions, opening new possibilities in software engineering.
Keywords: #phi4, AV1 Encoder, Agentic Coding, Claude Code, Custom Encoders, Embedded Devices, FFmpeg, Realtime Encoding, Rust, Specification Compliance, VideoToolbox API, WASM, WAV1C
caricio.com 2 days ago
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489.
HN
Can agentic coding raise the quality bar?
Agentic coding is emerging as a transformative approach in software development, with the potential to elevate quality standards, particularly in systems where high availability and trustworthiness are critical, such as payment rails and databases. Traditionally, software development has prioritized increasing throughput—producing more code faster with fewer resources. However, agentic coding shifts this focus towards enhancing quality by enabling cheaper and faster code generation, though it requires meticulous verification to ensure reliability in production-critical tasks.
The article identifies a key area where agentic coding excels: addressing time-consuming issues with inexpensive or straightforward verification processes, as well as tackling low-impact problems that can be partially resolved. Through various examples, the benefits of agentic workflows are demonstrated:
1. **More Tooling**: Agents expedite the creation of tools and metrics that were previously neglected, thereby improving system quality.
2. **Prototype to Discover Constraints**: Iterative prototyping using agents helps identify constraints and issues more swiftly compared to traditional design methods.
3. **Build to Compare**: This approach allows for rapid development of multiple solutions, enabling empirical determination of the best method.
4. **Low Value-per-Line Abstractions**: Agents efficiently generate repetitive code, minimizing minor errors with minimal resource investment.
5. **Pay Off Tech Debt Eagerly**: A closed feedback loop with agents facilitates easy resolution of small tech debt tasks, enhancing overall verification infrastructure.
Ultimately, agentic coding is not seen as a replacement for traditional software engineering or craftsmanship but rather an enhancement that raises the bar on engineering discipline by encouraging investments in quality through improved verification and tooling. The article encourages experimentation with this innovative approach and expresses excitement about its future potential in advancing software development practices.
Keywords: #phi4, AI tooling, Agentic coding, RedisModule_Reply, Rust, engineering discipline, feedback loop, prototyping, quality bar, software development, tech debt, verification, workflows
lpalmieri.com 2 days ago
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529.
HN
Show HN: Rakenne – Markdown-defined agentic workflows for structured documents
Rakenne is a multi-tenant Software as a Service (SaaS) platform designed to assist domain experts in generating structured documents through "Guided Workflows," defined using Markdown. It addresses the challenges of unpredictability and scalability inherent in chat-based document creation with Large Language Models (LLMs). By enabling experts to encode their document-building processes into version-controlled formats, Rakenne ensures consistency and reliability. The platform features an agentic core utilizing the pi coding agent operating in RPC mode, which supports state maintenance and complex logic handling. Its lightweight frontend leverages Lit web components for a responsive user experience that can be embedded as widgets, while multi-tenancy provides isolation of custom logic across different users.
Rakenne is tailored to replicate expert methodologies rather than encourage creative interactions, making it particularly suitable for professionals like lawyers and compliance officers who require consistent and auditable document creation processes. The platform seeks feedback on aspects such as the naturalness of its "interview" flow, the appropriateness of Markdown as a domain-specific language (DSL), and latency issues in agent-browser communication via RPC.
In addition to its core functionalities, Rakenne offers pre-built workflows for various documents like contracts and reports, which users can adapt to fit their specific requirements. This approach allows professionals to streamline their document creation while maintaining control over the process and content, ensuring high standards of accuracy and compliance.
Keywords: #phi4, Agentic Workflows, Compliance Reports, Consistent Output, Contracts, Domain Experts, Expert Logic, Guided Workflows, LLMs, Lit web components, Markdown, Multi-tenancy, RPC mode, Rakenne, SaaS, Skill Library, Structured Documents, YAML
rakenne.app 2 days ago
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565.
HN
Interpreting OCapN Principles in Cloud-Native Agentic AI Architectures
The article examines how to integrate Object Capability Network (OCapN) principles into cloud-native architectures, focusing on authority, delegation, and isolation in AI systems using technologies like Kubernetes, Docker, Biscuit tokens, and service meshes. It proposes mapping OCapN concepts to these technologies: agent isolation is achieved through containerization with Docker and Kubernetes; capability possession via Biscuit tokens; explicit delegation by token propagation; asynchronous message passing through event-driven systems; and structural isolation enforced by network policies and tools like Cilium. This hybrid architecture aligns cloud-native practices with OCapN principles but lacks the semantic clarity of OCapN's unified model, resulting in a more fragmented authority structure and reduced precision in delegation. Although this approach leverages existing platforms' maturity and scalability, it incurs higher reasoning costs for authority flow and requires careful integration to maintain security guarantees. The article concludes that while current cloud-native implementations approximate OCapN principles, they do so at the expense of architectural cohesion, suggesting future work could aim to bridge these gaps without sacrificing practical benefits.
Keywords: #phi4, Biscuit, Cilium, Kubernetes, OCapN, agentic AI, architectural model, authority, autonomy, capability tokens, cloud-native, containers, delegation, eBPF, event-driven, isolation, network policies, observability, operational consistency, scalability, semantic clarity, service mesh
serefayar.substack.com 2 days ago
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582.
HN
A procedural prompting framework for building and deploying agentic systems
DIYClaw is a procedural prompting framework aimed at constructing and deploying agentic systems with robust control over their functionalities. The system leverages composable and versioned prompt contracts to establish clear guidelines for system identity, operational logic, tool usage, safety protocols, handling failures, and self-enhancement capabilities. Although DIYClaw suggests using Claude Code, it is designed to be compatible with any AI provider such as OpenAI or Anthropic. A significant feature of the framework is its stable prompt contracts that ensure consistent insights into agent actions, regardless of changes in underlying code or models. As a development tool, DIYClaw facilitates user configuration of prompt templates and creation of agent definitions, allowing for the generation of ready-to-deploy prompt packs suitable for various runtime environments. This capability provides developers with a transparent and adaptable infrastructure to build sophisticated agentic systems.
Keywords: #phi4, DIYClaw, agent definitions, agentic systems, development tool, execution logic, failure handling, identity, procedural prompting, prompt contracts, prompt packs, runtime, safety, self-extension, tool use
diyclaw.dev 2 days ago
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615.
HN
An Exercise in Agentic Coding: AV1 Encoder from Scratch in Rust
The article describes an experience involving agentic coding while developing an AV1 video encoder using Rust, highlighting a transformative journey from skepticism to enthusiasm about AI-driven tools in programming. Initially wary of artificial intelligence's role in coding, the author becomes captivated by Claude Code after using the Cline plugin in 2024 and later explores Claude Opus 4.5 in 2025 for creative software development opportunities.
Motivated by these tools' capabilities, the author undertakes a challenging project to create an AV1 encoder from scratch within Rust, deliberately avoiding dependencies or unsafe code—a process typically requiring over a year but completed in under twelve hours due to AI assistance. The resulting encoder is basic yet functional, adhering to the AV1 specification and compatible with decoders like dav1d and macOS's VideoToolbox API.
Reflecting on this endeavor, the author envisions agentic coding as a means to reduce barriers for creating custom encoders/decoders, potentially fostering new encoding profiles or applications in embedded systems. Demonstrating its versatility, they encode AV1 videos in real-time within a browser using WebAssembly and provide guidance for integrating their encoder with FFmpeg.
This exploration not only underscores the power of modern AI-assisted coding but also promotes experimentation and learning among multimedia software development communities, suggesting significant implications for future innovations in this field.
Keywords: #phi4, AV1 Encoder, Agentic Coding, Claude Code, Custom Encoders, Embedded Devices, FFmpeg, Realtime Encoding, Rust, Specification Compliance, VideoToolbox API, WASM, WAV1C
caricio.com 2 days ago
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632.
HN
Agent Zero AI: open-source agentic framework and computer assistant
Agent Zero AI is an open-source framework designed as a computer assistant emphasizing reliability and operational consistency through agentic architecture. It ensures dependability of AI agents by integrating deterministic software, real system execution, and dynamic tool creation. This design eliminates "black box" elements, enabling transparency in the environment where AI agents operate. By providing clear visibility from start to finish, Agent Zero AI allows for consistent and reliable task performance, ensuring that all operations are conducted within a predictable framework.
Keywords: #phi4, AI agents, Agent Zero, agentic architecture, agentic framework, computer assistant, deterministic software, dynamic tool creation, end-to-end, environment, open-source, operational reliability, real system execution
www.agent-zero.ai 3 days ago
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638.
HN
Cursor for Writers: How I chained parallel agents to track narrative consistency
Minotauris presents "Cursor for Writers," an advanced AI writing editor specifically designed for professional authors, aiming to enhance manuscript quality through its unique feature of maintaining narrative consistency. This is achieved by employing parallel agents that meticulously review and ensure coherence throughout the text. By joining a waitlist, interested individuals can gain access to this cutting-edge editing technology, which stands out in the realm of literary tools by offering a sophisticated approach to editing that emphasizes both precision and innovation, ultimately supporting authors in producing more polished works.
Keywords: #phi4, AI, Agentic, Agents, Authors, Consistency, Cursor, Editor, Minotauris, Narrative, Parallel, Professional, Waitlist, Writers, Writing
www.minotauris.app 3 days ago
https://www.minotauris.app/waitlist 3 days ago
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641.
HN
Can agentic coding raise the quality bar?
The article "Can Agentic Coding Raise the Quality Bar?" examines how agentic coding—employing AI tools for code generation—can elevate software quality, especially in environments where reliability and performance are paramount. Traditionally perceived as costly due to its complexity and demand for specialized skills, coding can now be made more accessible and affordable through agentic workflows. This method excels particularly in handling tasks that are time-intensive but carry low risk if only partially or roughly completed, thus enabling previously unattainable quality enhancements by reducing implementation and verification costs.
The author illustrates the potential of agentic coding with several examples: routine quality metrics can be more easily implemented using agents to enhance system safeguards; prototyping agents help identify design constraints faster than traditional methods; multiple design solutions can be rapidly prototyped for empirical testing rather than solely theoretical debate; repetitive yet essential code abstractions are efficiently generated, reducing human error without significant investment; and tech debt issues can be swiftly addressed with minimal resources. The article concludes that agentic coding complements, rather than replaces, conventional software engineering by fostering greater investments in quality assurance and tooling. This approach encourages experimentation to fully exploit its potential for improving the robustness and efficiency of software systems.
Keywords: #phi4, AI tooling, Agentic coding, RedisModule_Reply, Rust, engineering discipline, feedback loop, prototyping, quality bar, software development, static analysis, tech debt, verification
lpalmieri.com 3 days ago
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666.
HN
Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt
The article explores the transition from traditional technical debt to a more insidious form known as cognitive debt within AI development. Cognitive debt arises when developers struggle to comprehend or elucidate their systems fully, leading to reduced efficiency and impaired decision-making. Margaret-Anne Storey discusses how modern generative and agentic AI technologies exacerbate this issue by facilitating the rapid addition of features without a thorough understanding of the underlying processes. She uses an anecdote about a student team to illustrate that while technical debt typically involves problems like disorganized code, cognitive debt stems from a collective loss of system understanding and theoretical insight, which impedes progress. Storey also reflects on her own experiences with large-scale projects where unclear mental models complicate both decision-making and the development of new features, underscoring the profound impact of cognitive debt in AI development environments.
Keywords: #phi4, Agentic AI, Ambitious Projects, Code Understanding, Cognitive Debt, Decision Making, Design Decisions, Developers, Fast Development, Feature Implementation, Fragments, Generative AI, Mental Model, Paralysis, Prompting Features, Shared Understanding, System Theory, Technical Debt, Vibe-Code
simonwillison.net 3 days ago
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670.
HN
Generative and Agentic AI Shift Concern from Tech Debt to Cognitive Debt
As generative and agentic AI become increasingly integrated into software development, the focus shifts from traditional technical debt—code-related issues impeding modification—to cognitive debt, which poses a significant threat by affecting developers' understanding of systems due to rapid development processes. Cognitive debt is particularly insidious as it resides within the minds of developers, undermining their ability to effectively comprehend and alter software. The article highlights this issue through an example from an entrepreneurship course where students faced challenges in making changes due to fragmented knowledge, drawing parallels with Fred Brooks' "Mythical Man-Month" on cognitive load increases with team size and faster development cycles.
To combat these issues, the article suggests implementing practices such as pair programming, refactoring, and test-driven development to manage both technical and cognitive debt. It advocates for ensuring that AI-generated changes are comprehensively understood before implementation and emphasizes regular knowledge-sharing sessions to rebuild shared understanding among teams. Additionally, it underscores the importance of recognizing early warning signs of cognitive debt, like hesitancy in making changes or over-reliance on tribal knowledge.
The article concludes by underscoring the need for research into methods for measuring and mitigating cognitive debt as AI continues to reshape software development landscapes. It asserts that maintaining a shared theoretical understanding of software systems is vital for long-term health, beyond merely focusing on speed or output metrics. This approach ensures sustainable development practices in an evolving technological environment.
Keywords: #phi4, Agentic AI, Black Box, Cognitive Debt, Coordination Overhead, Developers' Minds, Future of Software Engineering, Generative AI, ICSE Conference, Knowledge-Sharing, Pair Programming, Refactoring, Shared Understanding, Software Health, Technical Debt, Test-Driven Development, Tribal Knowledge, Velocity
margaretstorey.com 3 days ago
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710.
HN
Which past applications you built can be migrated to Agentic architecture?
The text explores the potential migration of existing applications to a new LLM-powered ReAct architecture, which integrates large language models (LLMs) for reasoning within software solutions. This approach is particularly advantageous for applications characterized by frequently changing business logic, as it allows updates through prompt modifications rather than traditional code changes. Such flexibility grants product teams more direct control and reduces reliance on engineering resources for implementing changes. Conversely, static data processing pipelines are less suited to this model due to their stable and deterministic nature; here, the integration of LLM inference can introduce unnecessary complexity without clear benefits. The ReAct architecture is most effective in environments where business rules evolve rapidly, making prompt-based management more cost-effective than maintaining traditional codebases. This evaluation draws on a paper discussing the architecture, along with insights from Sanath Kandikanti's reflections on past projects.
Keywords: #phi4, LLM inference, LLM-powered, ReAct architecture, applications, business logic, business rules, data processing pipelines, deterministic logic, engineering involvement, high-scale production, prompt engineering, prompts, software solutions
news.ycombinator.com 3 days ago
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712.
HN
Agentic Tech Magazine
"Agentic Tech Magazine," with its platform AgentCrunch, is dedicated to offering insights and resources concerning artificial intelligence agents, targeting developers, companies, and enthusiasts. It functions as a thorough guide for those interested in creating, deploying, and understanding the influence of AI-driven agents across diverse industries. The publication delves into various topics including industry trends, challenges faced by developers, illustrative case studies, and recommended best practices within agent technology, ensuring its audience is well-equipped with knowledge to navigate this evolving field.
Keywords: #phi4, Agent, AgentCrunch, Agentic Tech, Delimited, Duplicates, Extract, Keywords, List, Magazine, Simple, Tech, Technical, Triple Backquotes
agentcrunch.ai 3 days ago
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812.
HN
AgentProf – A profiler for agentic coding tools
AgentProf is a profiling tool designed specifically for agentic coding tools like Claude Code and Codex, aiming to provide visibility into their operations by capturing detailed data on timing and token usage. It enables users to monitor every call made to these tools, recording inputs, outputs, and execution times, thereby offering insights that help manage costs and enhance efficiency. This includes identifying high-token-consuming tools, detecting performance bottlenecks such as slow tool responses or retry issues, optimizing workflows for better performance, and ensuring compliance with security standards through auditing.
The installation of AgentProf can be accomplished either directly using a shell script (`curl -LsSf https://github.com/kitaisreal/agentprof/releases/latest/download/agentprof-installer.sh | sh`) or by building from source via `cargo install --path .`. For usage with Claude Code, users can install logging hooks to track tool calls locally or globally with `agentprof install --log ./claude-tools.jsonl` or `--global`, respectively. To remove these hooks, the command `agentprof uninstall [--global]` is used.
AgentProf logs data into a JSONL file using predefined hooks (`PreToolUse` and `PostToolUse`) that capture relevant information during normal tool operation. This log can be analyzed to generate comprehensive terminal reports using `agentprof analyze ./claude-tools.jsonl`, or it can be visualized through a live-updating web dashboard launched with `agentprof web ./claude-tools.jsonl [-p port]`. These functionalities together facilitate an in-depth understanding of agentic tool usage and performance, empowering users to make informed decisions about optimizing their coding workflows.
Keywords: #phi4, API spend, AgentProf, CLI commands, CLI commands Comma-separated Keywords: AgentProf, CLI commands Final Answer: AgentProf, CLI commands Final List: AgentProf, Claude Code, Codex, JSONL log, Server-Sent Events, agentic coding tools, bottlenecks, hooks, installation, live-updating dashboard Comma-separated List: AgentProf, live-updating dashboard Extracted Keywords: AgentProf, live-updating dashboard Final Keywords: AgentProf, live-updating dashboard Keywords: AgentProf, live-updating dashboard Selected Keywords: AgentProf, profiler, security compliance, terminal reports, timing data, token usage, tool calls, web dashboard, workflows
github.com 4 days ago
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817.
HN
ClickHouse Agentic Data Stack
The text describes the "ClickHouse Agentic Data Stack," which appears to be a topic or presentation on YouTube related to the ClickHouse project. It outlines standard elements typically found on a YouTube page, including sections like About, Press, Copyright, and Contact information, as well as guidelines for creators, advertisers, developers, terms of use, privacy policy, safety measures, and how YouTube operates. The mention of "Test new features" suggests experimentation with platform functionalities, while NFL Sunday Ticket is noted without further context. Additionally, a copyright note specifies protection under Google LLC until 2026, indicating the ownership and intellectual property rights over the content or related materials discussed on this page.
Keywords: #phi4, Advertise, Agentic, ClickHouse, Contact, Copyright, Creators, Data Stack, Developers, Google LLC, Google LLC ``` Keywords: ClickHouse, NFL Sunday Ticket, Press, Privacy Policy, Safety, Terms, YouTube
www.youtube.com 4 days ago
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825.
HN
Agentic Experience for Publishers
GenDiscover is launching an agentic experience tailored for publishers using its In-App SDK, designed specifically for mobile iOS and Android applications. This innovative solution enables publishers to incorporate AI-driven functionalities—including AI Ask, AI Chat, smart recommendations, and AI-native ads—efficiently with minimal coding required. The primary objective of this integration is to enrich users' discovery experiences directly within native apps by leveraging the capabilities of artificial intelligence. To access this cutting-edge technology in its beta phase, interested parties can sign up via a waitlist through a designated email address provided by GenDiscover.
Keywords: #phi4, AI Ask, AI Chat, Ads, Agentic Experience, Android, Apps, Beta Waitlist, In-App SDK, Mobile Publishers, Native Discovery, Publishers, Recommendations, iOS
www.gendiscover.com 4 days ago
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845.
HN
AgentRE-Bench: Can LLM Agents Reverse Engineer Malware?
AgentRE-Bench is a sophisticated benchmark designed to assess the capabilities of large language model agents in reverse engineering malware through intricate sequences involving 10–25 tool calls. This benchmark goes beyond traditional Q&A formats by evaluating real-world reasoning and problem-solving skills. It employs synthetic ELF x86-64 binaries, which are compiled from specific C sources, ensuring consistent outputs that can be independently verified without any licensing complications. The evaluation process is deterministic, utilizing fixed ground truths scored through weighted fields and Jaccard overlap, thus eliminating reliance on subjective model judgments. Participants in this benchmark must strategically plan the use of various tools, effectively interpret complex raw data such as hex dumps or disassembly results, and integrate these insights to achieve accurate conclusions within a constrained limit of 20 tool calls per task.
Keywords: #phi4, AgentRE-Bench, Agentic, Benchmark, Budget, C sources, Deterministic, Disassembly, ELF x86-64, Ground Truths, Hex Dumps, Jaccard Overlap, LLM Agents, Linux/Unix, Malware, Planning, Reverse Engineer, Synthetic, Tool Calls
www.agentre-bench.ai 4 days ago
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865.
HN
Grub 2.0
The text discusses two separate entities: Grub 2.0 and the Grub Crawler. Grub 2.0 appears to be an updated version of software or application called Grub, suggesting improvements or new features compared to its predecessor. In contrast, the Grub Crawler is identified as an agentic web crawler, which implies it functions as an automated system designed for exploring and cataloging data across the internet. This distinction highlights that while Grub 2.0 pertains to software enhancement, the Grub Crawler involves a tool used for digital information processing and retrieval tasks.
Keywords: #phi4, 20, Agentic, Crawler, Delimited, Extract, Grub, Keywords, List, Relevant, Technical, Topic, Web
grubcrawler.dev 4 days ago
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889.
HN
Agntor SDK – Trust Layer for Agentic AI
The Agntor SDK is a comprehensive toolkit designed to enhance trust in AI agents through identity verification, reputation management, escrow services, and settlement processes. Compatible with Node.js (version 18 or above), it integrates as an ES module and can be installed using `npm install @agntor/sdk`. The SDK allows users to initialize with an API key and agent ID, verify another agent's reputation, and establish escrow accounts under specific conditions.
The core modules include Identity for managing registration and retrieval of identity data; Verification for confirming agent status, capabilities, and badge management; Escrow for handling escrow account operations such as creation and funding; Settlement for releasing or withholding funds based on predefined criteria; and Reputation for accessing scores and histories. Additional features encompass event listeners for changes in escrow, verification, and settlements, along with configuration options like API keys, agent IDs, and request timeouts.
Protection utilities are integral to the SDK, offering tools such as prompt-injection guards using regex and heuristic analyses, redaction of sensitive data (PII and blockchain keys), tool guard mechanisms for managing permissions, and settlement guards to evaluate payment legitimacy. Moreover, it provides a Transaction Simulator for testing on-chain transactions without executing them, SSRF protection through URL validation against private IP ranges, AP2 Protocol Helpers for commerce header management, structured output schemas via Zod for LLM response validation, and a Ticket System for low-level audit ticket operations.
Released under the MIT license, the Agntor SDK thus offers robust functionality and security features to support trustworthy AI agent interactions.
Keywords: #phi4, AP2 Protocol, Agentic AI, Agntor SDK, Escrow, Guard Provider, Identity, Modules, Redaction, Reputation, SSRF Protection, Settlement, Ticket System, Ticket System Keywords: Agntor SDK, Trust Layer, Verification, Zod Schemas
github.com 5 days ago
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914.
HN
Building Physical Agentic AI
The article introduces "Physical Agentic AI," an evolution from edge AI that enables machines to perceive, reason about, and influence their surroundings. It traces this development through Edge Impulse's journey, which was acquired by Qualcomm, highlighting its role in democratizing TinyML—a key component of modern edge AI technologies. As advancements have simplified the deployment of AI models on embedded devices, the focus has shifted towards integrating large language models (LLMs) into edge computing. This integration allows devices to conduct chain-of-thought reasoning and make autonomous decisions without extensive domain expertise from developers. Tools enabling structured interactions with these AI agents position them as versatile decision-making engines.
The article illustrates this through examples like greenhouse management systems and beehive monitors, demonstrating how agentic AI can adapt across applications using similar hardware but tailored prompts. However, challenges remain in usability and integration, reminiscent of the early days of TinyML. The author calls for robust tools and practices to ensure these AI systems are both practical and reliable. Looking forward, there is excitement about the new technology's potential and an invitation for collaboration through newsletters or comments. The goal is to streamline the development of intelligent physical systems as effortlessly as deploying traditional AI models on edge devices.
Keywords: #phi4, Edge Impulse, IoT, LLMs, Physical Agentic AI, Qualcomm, TinyML, agentic systems, chain-of-thought reasoning, edge AI, generative AI, greenhouse management, industrial equipment, perception models, smart vehicles
dansitu.substack.com 5 days ago
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1003.
HN
What Agentic AI "Vibe Coding" in the Hands of Actual Programmers / Engineers
The author highlights how experienced programmers can effectively integrate AI tools like Claude code into their coding tasks by leveraging their deep understanding of both the codebase and the specific domain in question. This approach is contrasted with less effective uses observed in some GSoC projects, where such tools are used without sufficient contextual guidance. The key to success lies not in using AI to replace programming knowledge but rather as an aid that accelerates processes when provided with detailed context and precise instructions.
For instance, within SciML's `OrdinaryDiffEq.jl`, the author addressed a need for consistent specialized interpolations across the codebase, moving away from fallback methods. By crafting specific prompts that included targeted code references and contextual information, they enabled the AI to accurately assist in integrating these changes. In another scenario involving `SciMLSensitivity.jl`, a complex refactor required standardizing function argument order within callback differentiation codes. Detailed instructions were provided to the AI, pointing out existing issues and proposing a more normalized structure to enhance maintainability and allow for more flexible parameter types.
These examples demonstrate that with adequate domain knowledge, programmers can harness AI tools as efficient assistants, optimizing their workflows while maintaining high code quality and understanding. The author's approach emphasizes using AI to complement programming expertise rather than replacing it, ensuring effective and informed application of technology in complex coding environments.
Keywords: #phi4, Agentic AI, Claude code, DAE interpolation, Engineers, FBDF, GSoC students, Hermite interpolation, LLM-based interfaces, OrdinaryDiffEqjl, PRs, Programmers, QNDF, Rosenbrock methods, SciML, SciMLSensitivityjl, SciMLStructuresjl, Vibe Coding, callback differentiation, derivative wrappers, stiff ODE solvers, vecjacobian!
www.stochasticlifestyle.com 5 days ago
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1047.
HN
Worlds: A Simulation Engine for Agentic Pentesting
The article introduces "Worlds," an innovative simulation engine designed for creating realistic penetration testing trajectories within Active Directory networks, operating entirely on CPUs without needing actual infrastructure. This development addresses the challenges associated with producing high-quality security training data, which are often hindered by financial constraints and compliance issues when using real network environments. By synthesizing network dynamics and tool mechanics, "Worlds" enables the creation of diverse, scalable, and realistic synthetic datasets.
The article outlines several key aspects: bridging the Sim2Real gap by accurately modeling interactions and network states, particularly within complex Active Directory configurations; overcoming traditional training data problems such as high costs and scalability issues; and enhancing model performance through synthetic datasets. These datasets improve tasks like compromising networks by incorporating reasoning traces and failure recovery scenarios into training models.
The implications of "Worlds" for security are significant, offering scalable solutions that allow for effective security model training across different domains without accessing sensitive real-world data or infrastructure. This benefits trainers, red teams, defenders, and product developers by providing realistic attack trajectories and diverse datasets. Overall, the simulation engine represents a major advancement in generating synthetic training data that translates effectively to real-world penetration tasks.
Keywords: #phi4, Active Directory, Agentic Pentesting, Domain Admin, LoRA Adapter, Offensive AI, Security Operations, Sim2Real Gap, Simulation Engine, Synthetic Training Data, Tool Layer, Trajectories, Worlds
dreadnode.io 5 days ago
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1062.
HN
The Curator's Guide to Agentic Coding
The article discusses how Okakura Kakuzō's ideas on Eastern and Western art perspectives can guide agentic coding practices, particularly in "greenfield" projects versus integrating into existing systems. For new developments, it emphasizes the necessity of a Western approach that involves actively constructing frameworks. This is akin to laying down an architectural foundation where AI agents require well-defined tools and structures to operate effectively. In contrast, when incorporating agentic coding into pre-existing systems, an Eastern perspective is advocated. This entails simplifying the codebase by removing unnecessary complexities—referred to as "subtractive engineering"—to create a conducive environment for AI potential to emerge within existing contexts. By introducing guardrails that prevent the reintroduction of noise and complexity, this approach ensures that AI agents can function optimally in legacy systems, emphasizing clarity and protection from obstacles inherent in older codebases.
Keywords: #phi4, Abstractions, Additive Process, Agentic Coding, Codex, Context, Curator's Guide, Decouple, Depth-First, Eastern Perspective, Greenfields, Guardrails, Interfaces, Isabella Stewart Gardner, Legacy Systems, Modules, Museum of Fine Arts, Noise, Okakura Kakuzō, Scaffolding, Taoism, Technical Debt, Western Perspective, Zen
oscarswanros.com 6 days ago
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1069.
HN
Gas Town, Beads, and the Rise of Agentic Development with Steve Yegge
In a discussion with Kevin Ball, Steve Yegge delves into the transformative trajectory of AI-assisted programming from basic autocomplete functions to intricate multi-agent system orchestrations. He underscores the significance of emerging tools such as Beads and Gas Town, which enhance coordination among multiple agents and enable AI-driven workflows. As large language models evolve, there is a discernible shift in software development priorities toward effectively managing work, contextual understanding, and shared knowledge across extensive agent networks. Yegge elucidates both technical and cognitive challenges associated with this evolution, including the utilization of task graphs and Git-backed ledgers, and examines their implications for software teams, tools, and the broader industry landscape. This exploration underscores a future where AI integration is central to enhancing collaboration and efficiency in programming environments.
Keywords: #phi4, AI coding, AI-assisted programming, Beads, Gas Town, Git-backed ledgers, Steve Yegge, agent orchestration, agentic software development, agents, cognitive challenges, context management, industry future Keywords: AI-assisted programming, large language models, multi-agent coordination, orchestration, shared understanding, software development, software teams, task graphs, technical challenges, tooling
softwareengineeringdaily.com 6 days ago
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1083.
HN
Authenticated Workflows: A Systems Approach to Deterministic Agentic Controls
The paper "Authenticated Workflows: A Systems Approach to Protecting Agentic AI" presents an innovative trust layer designed to enhance the security of enterprise agentic AI systems, addressing the shortcomings of current probabilistic defenses such as guardrails and semantic filters. The authors propose a deterministic security model that enforces intent and integrity across four critical boundaries—prompts, tools, data, and context—utilizing cryptographic methods combined with runtime policy enforcement. Central to this approach is the use of MAPL (an AI-native policy language), which allows for dynamic expression and efficient scaling of agentic constraints as systems evolve.
A universal security runtime has been developed to seamlessly integrate nine leading AI frameworks without modifying existing protocols, ensuring that all operations either possess valid cryptographic proof or are outright rejected. Empirical evaluations demonstrate the robustness of this approach, achieving 100% recall with no false positives in 174 test cases and offering protection against most OWASP Top 10 risks. This includes mitigating two high-impact production CVEs, showcasing significant advancements over existing security methods for agentic AI systems by providing a comprehensive deterministic framework.
Keywords: #phi4, Agentic AI, Authenticated Workflows, CVEs, Cryptographic, Enterprise, Framework Integration, MAPL, OWASP Top 10, Policy Language, Runtime Enforcement, Security, Trust Layer
arxiv.org 6 days ago
https://www.macawsecurity.ai 6 days ago
https://github.com/macawsecurity/secureAI 6 days ago
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1087.
HN
Dyad 2.0: What Agentic AI Means for the Future of Computer Languages
Dyad 2.0 marks a transformative step in computer languages for agentic AI, specifically designed to meet future demands in modeling and simulation through its declarative domain-specific language (DSL) framework. By integrating physics-based modeling, scientific machine learning, and agentic workflows into one unified environment, Dyad parallels established tools like Modelica or Simulink but excels by offering enhanced accuracy over conventional programming languages such as C, Python, or Julia. This advancement is particularly notable in the realm of agentic AI.
As human-computer interaction has evolved from early punch card systems to modern, complex languages, the emergence of agentic AI—where code is generated through AI queries rather than manual writing—introduces new challenges and opportunities for language design. Dyad 2.0 responds by adopting a concise declarative syntax focused exclusively on physical equations, enabling compilers to manage computational tasks efficiently. This methodology not only boosts large language model (LLM) accuracy with simplified syntax but also provides valuable static compiler feedback, fostering more effective interactions within agentic AI systems.
Moreover, Dyad's compatibility with Julia scripts ensures its practical application and token efficiency, making it a robust tool for modeling and simulation engineers who prioritize reliability. This emphasis on deterministic methods over the non-deterministic approaches commonly used in agentic systems is validated by live demonstrations that successfully tackle complex scenarios like building control algorithms or quadcopter models.
Accessible via a Visual Studio Code plugin, Dyad aspires to democratize advanced modeling tools, reflecting a shift towards language design that accommodates real-world usage patterns in agentic AI. Its development is indicative of an ongoing trend aimed at redefining system-level modeling and simulation through innovative agentic interfaces, highlighting its pivotal role in the future landscape of computer languages for agentic AI.
Keywords: #phi4, Accuracy, Agentic AI, Compiler Feedback, Computer Languages, Dependencies, Domain-Specific Language, Dyad, Human-Computer Interaction, JuliaHub, Live Demonstrations, Livestream Sessions, Modeling, Physics-Based Modeling, Programming Languages, Real-World Usage Patterns, Safety Critical Systems, Scientific Machine Learning, Simulation, Static Information, Token Efficiency, UUIDs, VS Code Plugin, Workflow
juliahub.com 6 days ago
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1111.
HN
Personal AI Infra: Agentic system with persistent memory and goal awareness
The release of Personal AI Infrastructure (PAI) version 2.5.0 introduces substantial advancements aimed at enhancing user capabilities in deeper thinking and accelerated execution. Central features include Two-Pass Capability Selection for improved decision-making by validating Hook hints against Ideal State Criteria, Thinking Tools with Justify-Exclusion allowing users to streamline workflow management by opting out of specific tools like Council or RedTeam without having to opt-in, and Parallel-by-Default Execution that boosts efficiency by running independent tasks concurrently. This comprehensive update encompasses 28 skills, 17 hooks, and 356 workflows, catering to diverse user needs.
PAI's primary goal is to democratize access to sophisticated AI tools, empowering individuals to unlock their creative potential and pursue life purposes through AI-enhanced self-discovery. Unlike other agentic systems, PAI emphasizes a user-centric approach, focusing on individual goals, optimal output, and continuous learning tailored to each user’s unique preferences. Its architecture incorporates principles such as clear thinking, deterministic infrastructure, and ongoing improvement from interaction feedback.
The project offers various installation paths to suit different needs, ranging from immediate full release installations to customizable manual packs for deeper engagement with the system. Active community involvement is encouraged through contributions on platforms like GitHub and Discord, fostering an environment of collaboration and development. The roadmap highlights future enhancements such as support for local models, remote access capabilities, and improved notification systems.
In summary, PAI v2.5.0 represents a significant stride in making advanced AI tools widely accessible, enabling individuals to enhance productivity, creativity, and personal goal achievement through intelligent and personalized assistance, while continuing its evolution with community support and open-source principles.
Keywords: #phi4, Activation, Agentic Systems, Community Engagement, Continuous Learning, Goal Awareness, Infrastructure Packs, Modular Architecture, Open-Source, PAI Principles, Persistent Memory, Personal AI, Self-Discovery, Skill System
github.com 6 days ago
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1173.
HN
Robots Dream of Agentic Soup
The author explores the concept of "Agentic Soup," drawing an analogy between AI development and Earth's primordial soup, considering how AI evolves through continuous data interaction. During a period of unemployment, they pondered this evolution in the context of Darwinian principles, imagining AI systems that adapt to challenges over time. They developed a theoretical model named "proto-agentic-soup" to delve into these ideas, although financial limitations hindered its advancement.
Later, their interest was rekindled upon discovering Vercel's Skills.sh ecosystem, inspiring them to conceptualize an "Agentic Skills Soup." This involves three skill types—Builders, Built Skills, and Runners—that interact on a centralized platform. The system promotes the evolution of skills through user feedback, with voting serving as currency to gauge success. Users engage by proposing ideas, voting on skills, or running builders via their agents. The experimental nature of this initiative is highlighted on its hosting site, skillsoup.dev, where users are encouraged to review open-source code due to the lack of formal vetting processes.
Keywords: #phi4, Agentic AI, Agents, Builders, Built Skills, Darwinism, Dead Internet Theory, Evolution, Experiment, LLMs, Open code, Primordial Soup, Robots, Runner, Self-employed, Skillsoupdev, Skillssh, Soup, Unemployed, Voting system, npx
punkleadership.com 6 days ago
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1186.
HN
GLM-5: From Vibe Coding to Agentic Engineering
GLM-5 is a newly developed, substantially larger model by Z.ai, with 754 billion parameters and a storage capacity of 1.51 terabytes, doubling its predecessor GLM-4.7 in size. A notable feature of GLM-5 is the introduction of "Agentic Engineering," a term coined for professional software engineers specializing in large language models (LLMs), gaining traction among experts such as Andrej Karpathy and Addy Osmani. In a test scenario, GLM-5 was tasked with generating an SVG image featuring a pelican riding a bicycle. The results were impressive concerning the depiction of the pelican but less satisfactory regarding the bicycle frame when processed using OpenRouter. This highlights both the model's advancements in handling complex tasks and areas that may require further refinement.
Keywords: #phi4, Addy Osmani, Agentic Engineering, Andrej Karpathy, GLM-47, GLM-5, Hugging Face, LLMs, MIT-licensed model, OpenRouter, SVG, Vibe Coding, Zai, bicycle, parameters, pelican, software engineers
simonwillison.net 6 days ago
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1211.
HN
Robots Dream of Agentic Soup: A Evolutionary Agent Skill Experiment
"Robots Dream of Agentic Soup: An Evolutionary Agent Skill Experiment" is an initiative designed to foster community involvement in the development of artificial intelligence agents through user participation. Participants are encouraged to submit and vote on innovative skills for these AI entities, creating a dynamic system where the most popular skill proposals are prioritized by builder agents for implementation. This approach emphasizes a collaborative effort between users and developers, allowing the collective input to guide the evolution of AI capabilities. Users can contribute their ideas by proposing new skills along with any optional context they consider relevant, ensuring that submitted concepts are well-understood before evaluation. By harnessing community-driven creativity and prioritization, the initiative aims to tailor AI learning processes according to the interests and needs of its user base.
Keywords: #phi4, Agentic Soup, Builder Agents, Context, Evolutionary Agent, Idea Queue, Relevant Topic, Robots, Skill Experiment, Skill Ideas, Submit, Technical Keywords, Vote
skillsoup.dev 6 days ago
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1214.
HN
Ask HN: Has anyone achieved recursive self-improvement with agentic tools?
The post explores the concept of implementing recursive self-improvement using agentic tools like Claude Code or OpenClaw to establish a self-reinforcing development cycle. The core idea is for these tools to autonomously monitor a Git repository, analyze past work, and generate new agents with improved skills tailored for similar tasks. The author seeks insights into experiences where individuals have transitioned from conventional coding practices to creating systems capable of bootstrapping themselves by learning from historical data within the repository. This self-learning approach aims to enhance agent capabilities through iterative improvements.
Keywords: #phi4, Claude Code, OpenClaw, Recursive self-improvement, agentic tools, agents, analyze abstractions, autonomous generation, bootstrapping, boundary-pushing, boundary-pushing Keywords: recursive self-improvement, development loop, git repo, learning systems, skills
news.ycombinator.com 6 days ago
https://github.com/ra0x3/systemg/tree/main&#x 5 days ago
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1262.
HN
Agentic Engineering
"Agentic Engineering" contrasts two methods for incorporating AI in software development: "vibe coding" and "agentic engineering." Vibe coding is characterized by a swift, unmonitored approach where humans let AI agents generate code without oversight, making it suitable for rapid prototypes or personal projects. However, this method becomes problematic when scaling or maintaining the software due to insufficient understanding and documentation. In contrast, agentic engineering integrates AI-assisted development with human supervision to ensure quality control through meticulous planning, reviewing, testing, and maintenance of the codebase. This approach necessitates discipline and benefits from a solid foundation in system design and architecture.
The transition towards agentic engineering underscores the importance of precise terminology and evaluation frameworks for producing reliable software. It also highlights the need for investment in training programs that emphasize fundamental skills such as architectural thinking and security awareness, as AI takes on more implementation tasks. Ultimately, while vibe coding showcases the creative potential of AI tools, agentic engineering seeks to integrate these tools into a disciplined engineering process that upholds high standards and reliability in professional software development.
Keywords: #phi4, AI Agents, AI-assisted Development, Agentic Engineering, Architectural Thinking, Brainstorming, CI/CD, Code Generation, Code Quality, Creativity, Debugging, Discipline, Engineering Practices, Exploration, Fundamentals, Human Oversight, Human-AI CollaborationExtracted Keywords: Agentic Engineering, Human-AI CollaborationKeywords: Agentic Engineering, Learning, MVPs, Orchestration, Productivity Gains, Prototyping, Review Process, Skill Gap, Software Reliability, System Design, Test Suites, Testing, Version Control, Vibe Coding, Workflow
addyosmani.com 7 days ago
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1270.
HN
The Death of Traditional Testing: Agentic Development Broke a 50-Year-Old Field
The traditional approach to software testing is becoming outdated due to the increasing speed of agentic software development. This paradigm typically involves manually created static test suites that struggle to keep up with rapid code changes. In response, Just-in-Time Tests (JiTTests) have emerged as a transformative solution. JiTTests are dynamically generated by large language models in real-time as new code modifications occur, specifically aiming to identify and catch regressions induced by these updates. Unlike traditional tests, which necessitate constant revisions and often yield false positives, Catching JiTTests streamline the process by focusing exclusively on significant failures, thereby eliminating ongoing test maintenance.
The principal benefits of Catching JiTTests include their automatic generation customized for each unique code change, adaptability to evolving software structures, and a marked reduction in false positive instances. They deliver clear, actionable insights directly to engineers when an actual bug is identified, thus improving testing efficiency within AI-driven development environments by concentrating on substantive issues rather than routine test management tasks. This innovation substantially decreases the workload on human resources and aligns with the fast-paced nature of contemporary software development. For a more comprehensive understanding, further exploration can be found in the paper titled "Just-in-Time Catching Test Generation at Meta."
Keywords: #phi4, Agentic Development, Code Changes, False Positives, Fault Simulation, Just-in-Time Tests (JiTTests), Large Language Models (LLMs), Pull Requests, Regressions, Software Testing Theory, Test Maintenance, Traditional Testing, True Positive Failures
engineering.fb.com 7 days ago
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1279.
HN
Ask HN: If agentic AI is the future, why is every startup shipping a dashboard?
The discussion on "Ask HN" addresses the focus of AI startups on developing dashboards rather than building agentic systems capable of autonomous actions and workflows. Despite the potential for AI to operate independently, many startups continue producing analytics panels and monitoring tools. This raises questions about whether this trend stems from trust issues with fully autonomous agents, sales strategies that favor tangible products like dashboards, or deeper challenges in how companies adopt new technologies. The preference for dashboards may reflect a cautious approach towards the integration of AI systems that require higher levels of autonomy and sophistication in operational environments.
Keywords: #phi4, Ask HN, actions, agentic AI, analytics panels, autonomous agents, autonomy, companies, control screens, dashboard, future, monitoring tools, sales issue, startup, tech adoption, trust issue, workflows
news.ycombinator.com 7 days ago
https://www.uxwizz.com 6 days ago
https://stackoverflow.com/a/78629469/407650 5 days ago
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1298.
HN
Show HN: Deadend CLI – Open-source self-hosted agentic pentest tooling
Deadend CLI is an innovative open-source tool designed for autonomous penetration testing of web applications. It aims to streamline the traditionally time-intensive processes involved in repetitive assessments and report generation, allowing users to concentrate on vulnerability research instead. The tool employs a local execution model complemented by optional self-hosted options, utilizing Docker containers and WebAssembly technology to ensure isolated operations.
The Deadend CLI achieves significant performance, scoring 78% on XBOW's benchmarks, with standout capabilities in handling complex vulnerabilities such as blind SQL injection when standard tools are inadequate. It excels through feedback-driven iteration for generating custom Python payloads. The tool integrates seamlessly into CI/CD pipelines and supports code reviews, bash completion, and features OWASP Top 10 plugins planned for future updates.
Currently available on macOS Arm64 and Linux 64-bit systems, Deadend CLI is user-friendly with a single command installation via bash. Community engagement can be accessed through its GitHub repository or Discord server. Its sophisticated architecture involves a two-phase process of reconnaissance followed by exploitation, managed through a supervisor-subagent structure that leverages confidence-based decision-making.
Innovative aspects include AI-driven reasoning and integration of various contextual tools such as Claude Sonnet 4.5 and Kimi K2 Thinking models. The development stack incorporates Playwright for HTTP request handling and Docker for command isolation while utilizing technologies like Deno, React, Ink, TypeScript, Commander, and Marked to create an interactive CLI interface that features a chat system and real-time event streaming.
Future objectives focus on enhancing open-source model performance, incorporating white-box testing methodologies, automating workflows, and improving robustness against adaptive defenses such as WAFs. The community is encouraged to contribute, particularly in optimizing context algorithms and developing adversarial test scenarios.
Keywords: #phi4, AI-driven reasoning, CI/CD integrations, CLI interface, Deadend CLI, Deno, Docker, Docker isolation, Ink, Linux 64bits, LiteLLM, MacOS Arm64, OWASP Top 10, Playwright, Pyodide, React, TypeScript, WASM, XBOW benchmarks, active development, authentication handling, automated testing, autonomous, benchmark results, community Discord Keywords: Deadend CLI, confidence-based decision making, contextual tool integration, custom payloads, feedback-driven iteration, fine-grained testing, local execution, model-agnostic architecture, multi-model support, payload generation, pentesting, pgvector, roadmap, sandboxed tools, source/sink detection, supervisor-subagent hierarchy, taint analysis, technical deep dive, vulnerability research, webapps
github.com 7 days ago
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1299.
HN
GLM-5: From Vibe Coding to Agentic Engineering
"GLM-5: From Vibe Coding to Agentic Engineering" examines the progression from traditional programming methods, often characterized by intuitive approaches known as "vibe coding," towards more sophisticated strategies that focus on developing autonomous systems capable of decision-making and goal fulfillment, termed "agentic engineering." This evolution in software development involves moving beyond task execution to creating programs that understand context and can adapt autonomously. By incorporating machine learning and artificial intelligence techniques, developers are enhancing the agency of these programs, enabling them to operate independently within dynamic environments. The article underscores both the technical challenges and ethical considerations inherent in this transition, advocating for meticulous planning and robust frameworks to ensure that agentic systems function safely and effectively.
Keywords: #phi4, Agentic Engineering, Duplicates, Extract, Format, GLM-5, Information, Keywords, List, Relevant, Simple, Technical, Text, Vibe Coding
z.ai 7 days ago
https://news.ycombinator.com/item?id=46974853 7 days ago
https://z.ai/subscribe 7 days ago
https://docs.z.ai/guides/overview/pricing 7 days ago
https://gist.github.com/simonw/cc4ca7815ae82562e89a9fdd 7 days ago
https://simonwillison.net/tags/pelican-riding-a-bicycle 7 days ago
https://github.com/rusiaaman/chat.md 7 days ago
https://timdettmers.com/2025/12/10/why-agi-wi 7 days ago
https://www.cerebras.ai/blog/glm-4-7 7 days ago
https://chat.z.ai/ 7 days ago
https://imgur.com/a/EwW9H6q 7 days ago
https://olix.com/blog/compute-manifesto 7 days ago
https://tech.yahoo.com/ai/articles/chinas-ai-start 7 days ago
https://www.techradar.com/pro/chaos-at-deepseek-as-r2-l 7 days ago
https://www.reuters.com/world/china/chinas-customs 7 days ago
https://arxiv.org/pdf/2412.19437 7 days ago
https://dev.synthetic.new/docs/api/models 7 days ago
https://synthetic.new/?referral=kwjqga9QYoUgpZV 7 days ago
https://zcode.z.ai 6 days ago
https://zread.ai 6 days ago
https://ocr.z.ai 6 days ago
https://image.z.ai 6 days ago
https://audio.z.ai 6 days ago
https://simonwillison.net/2024/Oct/25/pelican 6 days ago
https://skatebench.t3.gg/ 6 days ago
https://github.com/T3-Content/skatebench/blob/ 6 days ago
https://youtube.com/@t3dotgg 6 days ago
https://www.reddit.com/r/LocalLLaMA/comments/ 6 days ago
https://llm-stats.com/benchmarks/aime-2025 6 days ago
https://openrouter.ai/openrouter/pony-alpha 6 days ago
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1356.
HN
A nightly recap for a puzzling agentic eCommerce world
At the winter 2026 Zagreb Woo Meetup held at Holographik.Space, hosted by Neuralab, Automattic's WooCommerce (Woo) team—featuring Shani Banerjee, Brian Coords, and Brent MacKinnon—presented insights into WooCommerce’s future. Around forty participants explored themes of performance enhancement, accessibility advancements, and block-first development. The opening session highlighted the prioritization of performance and accessibility in product decisions due to regulatory changes and partnerships like those with Equalize Digital. Key discussions included improvements to backend systems such as HPOS, frontend optimizations, a faster editor experience, and future directions involving modern block patterns for catalog pages, block-based checkout flows, and AI integration through initiatives like the Agentic Commerce Protocol (ACP) and Universal Commerce Protocol (UCP). The possibility of checkouts evolving beyond traditional merchant sites to agents or chatbots was also examined.
Brent MacKinnon provided an overview of WooCommerce's platform status across various industries, discussing its position in the eCommerce market and outlining investment strategies for 2025 as a reset year. He emphasized Woo’s openness to collaborating with local European partners for payment, tax, and shipping solutions, while addressing multilingual support challenges through WordPress core improvements and AI tools.
The event facilitated post-talk discussions on technical implementations and business strategies, fostering connections among diverse regional participants. It highlighted Zagreb's emerging role in the WooCommerce ecosystem and confirmed a shift towards prioritizing performance, accessibility, and AI integration for modern projects. This aligns with local agencies' experiences dealing with larger-scale builds, bolstering confidence in WooCommerce solutions.
The meetup concluded with an invitation to WordCamp Slovenia 2026 and appreciation extended to Automattic’s team and Holographik Space for hosting the event.
Keywords: #phi4, AI, Europe, WooCommerce, Zagreb, accessibility, block-first, commerce, ecosystem, meetup, merchants, multilingual, performance, protocol
www.neuralab.net 7 days ago
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1363.
HN
The Agentic Code Problem
The text describes a problem known as the "Agentic Code Problem," where users are unable to access a website, referred to as x.com, due to JavaScript being disabled in their web browser. To resolve this issue and gain site access, users must enable JavaScript or switch to a browser that supports it. The text advises users on how to find information about compatible browsers through the Help Center, which presumably offers guidance on ensuring proper settings for accessing the website effectively. This problem underscores the importance of enabling certain functionalities in web browsers to ensure seamless interaction with modern websites.
Keywords: #phi4, Agentic Code Problem, Help Center, JavaScript, browser, detection, disabled, enable, issue, problem, supported browsers, switch, technical, xcom
twitter.com 7 days ago
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1373.
HN
2026 Agentic Coding Trends Report
The "2026 Agentic Coding Trends Report" examines the transformative impact of coding agents on software development, highlighting several key trends. It identifies a major shift in the software development lifecycle as AI takes over tactical tasks, allowing engineers to concentrate on higher-level activities like architecture and strategy. This shift leads to reduced cycle times and expedited project staffing. The report notes that capabilities are advancing from single-agent systems to coordinated multi-agent teams capable of executing complex tasks with minimal human oversight, leveraging parallel processing for enhanced performance. Long-running agents facilitate the construction of complete applications over time, requiring only strategic management by humans.
The impact trends outlined in the report suggest profound changes in productivity and organizational dynamics. There is an expansion of use cases involving non-technical roles and a heightened emphasis on developing security-first architectures due to potential dual-use risks. The integration of AI into coding processes fosters more collaborative interactions between humans and AI, broadening engineers' capabilities across various domains and transforming their roles from mere implementers to strategic orchestrators. Overall, the report envisions an evolving landscape where AI's role in software development significantly enhances human-AI collaboration, reshaping traditional workflows and expanding the scope of engineering practices.
Keywords: #phi4, AI, Agentic Coding, Agents, Architecture, Automation, Collaboration, Implementation, Multi-agent Systems, Onboarding, Orchestration, Productivity, Security, Software Development
resources.anthropic.com 7 days ago
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1391.
HN
Show HN: Visual Agentic Dev – Click React components to edit source capabilities
Visual Agentic Dev is an innovative development tool designed to enhance the React component debugging and modification process by allowing these tasks directly within the browser, thus eliminating the need for context switching between a browser and a code editor like VS Code. Utilizing Chrome extensions and leveraging React's Fiber architecture, it identifies source locations at runtime without altering business logic, interfacing with AI agents such as Claude Code via a Bridge Server to modify code from the user interface itself.
The tool boasts several key features: zero-configuration identification of source locations using React Fiber; multi-project support facilitated by terminal session switching; an extensible architecture that accommodates various AI agents; capabilities for batch modification of elements; and convenient keyboard shortcuts. Integration into React projects is achieved through a DevToolsProvider, with WebSocket servers enabling connections to Claude CLI or other compatible agents.
To set up Visual Agentic Dev, users need to install the Chrome extension, run the Bridge Server, and incorporate the React SDK into their project. During usage, developers configure an agent in the sidebar, launch development servers, and employ shortcuts to select components for modification using descriptions from a chat interface. The tool emphasizes a "browser-first" workflow, enabling UI issues to be addressed directly within the browser environment.
The source code is available under the MIT/PolyForm Shield license, encouraging community contributions and further enhancements to its capabilities.
Keywords: #phi4, AI agent, Bridge Server, CLI, Chrome extension, Claude Code, DOM traversal, Fiber tree, PTY, PolyForm Shield Extracted Keywords: Visual Agentic Dev, PolyForm Shield Keywords: Visual Agentic Dev, React SDK, React SDK Comma-separated List: Visual Agentic Dev, React components, VS Code, Visual Agentic Dev, WebSocket server, batch modification, browser-first workflow, context switching, contributing guide, contributing guide Final Keywords: Visual Agentic Dev, dynamic agent registry, multi-project development, node-pty, runtime approach, shortcuts, source location, terminal integration
github.com 7 days ago
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1408.
HN
Show HN: Microagentic Stacking – Manifesto for Reliable Agentic AI Architecture
The "Microagentic Stacking – Manifesto for Reliable Agentic AI Architecture" by Eric Mora critiques current large-scale language model (LLM) agents, termed 'Cognitive Monoliths,' for their limitations in production environments and introduces Microagentic Stacking (MAS) as a novel approach. MAS advocates replacing monolithic structures with stacks of specialized micro-agents that each possess distinct responsibilities, communicate through validated interfaces, and are independently testable and replaceable. This architecture focuses on process over AI by simplifying complexity into atomic units and enabling scalable system growth. The manifesto outlines key principles known as MAS Laws, including Atomic Responsibility, Black Box Isolation, Strict Design by Contract, and Hierarchical Orchestration, alongside governance mechanisms like Prompt SemVer and Atomic Accountability to enhance robustness. Mora calls for community input on state management, the balance between modularity and latency, and preventing 'agentic sprawl' in workflows. Open-source and published under the Creative Commons Attribution 4.0 International license, the manifesto encourages contributions from AI engineers to transition from 'prompt alchemy' to structured agentic engineering for scalable software solutions, offering a comprehensive roadmap for MAS implementation.
Keywords: #phi4, Accountability, Agentic Sprawl, Atomicity, Autonomous Agents, Black Box Isolation, Cognitive Monolith, Design by Contract, Enterprise-grade software, Fail-Fast validation, Governance, Hierarchical Orchestration, Incremental Growth, LLM agents, MAS, Manifesto, Microagentic Stacking, Process Over AI, Prompt SemVer, RFP Engine Reference Architecture, Robustness, Separation of Concerns, Software Engineering, State Management, Token Latency
github.com 7 days ago
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1446.
HN
WeWatch AI – The fix took 5 mins, the RCA took 8 hours. So we built this
The team developed WeWatch AI, an agentic cloud operations tool, following insights gained from a rapid five-minute fix combined with an eight-hour root cause analysis that exposed inefficiencies in their existing processes. This new solution aims to significantly enhance operational efficiency by automating critical monitoring and response tasks within the cloud environment. By streamlining these functions, WeWatch AI addresses previously identified bottlenecks, ensuring more effective and efficient management of cloud operations.
Keywords: #phi4, Agentic, Cloudops, RCA, WeWatch AI, automation, diagnostics, efficiency, engineering, fix, management, problem-solving, service, system, technology
wewatchai.com 8 days ago
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1517.
HN
The Agentic Waterfall: How the AI Industry Is Regressing Software Development
The article "The Agentic Waterfall" by Muhammadali Nazarov explores the shift back from Agile methodologies towards a more traditional Waterfall approach, attributed to the integration of autonomous AI agents in software development. The central argument posits that without General Intelligence (GI) during code creation, there is a risk of producing low-quality "vibe code" due to insufficient human oversight and quality assurance. Asynchronous agent workflows are inherently slower than synchronous ones because of additional factors such as context reloading, feedback latency, and tooling discrepancies, which necessitate a Waterfall process that undermines efficiency compared to Agile with real-time AI collaboration.
Key insights from the analysis reveal that advancements in asynchronous tools often revert towards synchronous methods for improved efficiency. Removing human review in these workflows could expedite processes but at the expense of generating low-quality "enterprise-scale vibe code." This trend might adversely affect traditional developer career progression by disrupting the junior-to-senior pipeline. The article concludes with a call to the industry to re-evaluate this regressive shift to maintain efficiency and uphold quality standards in software development.
Keywords: #phi4, AI Industry, Agentic Waterfall, Agile, Async Agent Workflow, Autonomous AI Agents, Enterprise-scale Vibe Code, Feedback Latency, General Intelligence, Human Review, Junior-to-Senior Pipeline, Product Builder, Software Development, Sync Pair-Programming, Tooling Delta, Waterfall Methodology
github.com 8 days ago
https://github.com/Jk1484/agentic-waterfall 8 days ago
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1539.
HN
FullStack-Agent: Enhancing Agentic Full-Stack Web Coding
The paper titled "FullStack-Agent: Enhancing Agentic Full-Stack Web Coding" presents an innovative agent system aimed at empowering non-expert users to develop complex full-stack web applications effectively. Unlike traditional code agents that primarily focus on frontend development, this new system addresses the broader challenges of real-world full-stack coding by enhancing data processing, package management, and bug localization. The proposed system consists of three integral components: FullStack-Dev, a multi-agent framework with advanced capabilities for planning, code editing, navigation, and bug localization; FullStack-Learn, an innovative technique that refines core language models through the back-translating of crawled and synthesized website repositories; and FullStack-Bench, a comprehensive benchmark designed to assess the frontend, backend, and database functionalities of generated websites. The system demonstrates significant performance improvements over existing methods, outperforming them by 8.7% in frontend tasks, 38.2% in backend tasks, and 15.9% on database-related activities. Additionally, the FullStack-Learn method boosts the efficacy of a 30B model across these categories. The research marks notable advancements in assisting full-stack web coding, supported by funding from entities such as the Simons Foundation.
Keywords: #phi4, Benchmark testing, Bug localization, Codebase navigation, Computation and Language, Computer Vision, Data processing, Development-Oriented Testing, Full-Stack Web Coding, FullStack-Agent, LLM-powered code agents, Multi-agent framework, Pattern Recognition, Repository Back-Translation, Self-improving method, Software Engineering
arxiv.org 8 days ago
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1547.
HN
Stripe Minions – End to end agentic coding
The text highlights a project titled "Stripe Minions – End to End Agentic Coding" and introduces Alistair Gray as a key figure associated with this initiative, noting his role as a software engineer in Stripe's Leverage team. The focus is primarily on the processes or methodologies of agentic coding within Stripe, suggesting an exploration of how autonomous systems are integrated into end-to-end development cycles. This concept likely involves leveraging advanced coding practices that enable systems to operate more independently and efficiently, aligning with broader technological advancements in automation and artificial intelligence. Through this project, Stripe aims to enhance its software engineering capabilities by incorporating agentic principles throughout the coding lifecycle, from design to deployment.
Keywords: #phi4, Agentic, Alistair Gray, Author, Authorship, Coding, End-to-end, Engineer, Leverage team, Minions, Software, Software Engineer, Stripe, Team
stripe.dev 8 days ago
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1556.
HN
Agentic Image Generation
The article introduces "Agentic Image Generation" through Claude Code's image generator plugin, designed for terminal-based image creation and editing. This streamlined process is enhanced by integrating the Claude Code Playground plugin, facilitating a self-improving loop where users can iteratively refine images based on their instructions. Users begin by adding the DAIR.AI Academy Plugins marketplace with specific commands, followed by installing the image generator plugin via the CLI or Claude Code interface. Additionally, obtaining and configuring a free Gemini API key from Google AI Studio is necessary for full functionality.
The plugin leverages Google's Nano Banana Pro model to produce high-resolution images suitable for various tasks such as text-to-image conversion, editing, and multi-image compositions. A practical example of its capabilities is demonstrated in creating infographics directly from blog content by instructing Claude Code, which autonomously reads the material, extracts key points, and generates visual representations without user intervention. The Playground plugin further enhances this functionality by allowing users to build interactive annotation tools within the terminal.
The article outlines several applications for these tools, including designing cover images, product mockups, logos, social media graphics, diagrams, and editing existing photos. It emphasizes the importance of providing detailed prompts and specifications regarding use cases and styles to achieve optimal results, encouraging iterative refinement of generated visuals.
To deepen engagement with AI-driven image generation techniques, a workshop is planned for Pro subscribers, fostering community interaction through courses and discussions, aimed at enhancing users' skills in this evolving field.
Keywords: #phi4, Agentic Image Generation, Claude Code, Gemini API key, HTML tools, Nano Banana Pro model, Playground plugin, annotation tool, aspect ratios, blog cover images, brand assets, diagrams, feedback refinement, image editing, image generator plugin, infographic, interactive controls, live previews, logos, marketplace, product mockups, resolutions, social media graphics, style specification, text-to-image
academy.dair.ai 8 days ago
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1563.
HN
Show HN: Kybera – Agentic Smart Wallet with AI Osint and Reputation Tracking
Kybera Smart Wallet is designed as an agentic platform aimed at simplifying the decentralized finance (DeFi) experience for newcomers by integrating AI-driven Open Source Intelligence (OSINT) and reputation tracking features. With a rise in job displacement due to advancements in AI and robotics, many are turning to speculative markets like DeFi; however, these markets pose significant challenges for novices due to prevalent scams and complex tooling. Kybera tackles this issue by offering a fully client-side, no-backend wallet that supports multiple blockchain networks such as Ethereum and Solana. Key features of the wallet include built-in swaps, cross-chain bridging, and an AI-powered research agent capable of analyzing smart contract risks and facilitating operations through natural language commands.
The platform enables users unfamiliar with decentralized exchanges (DEXs) to execute informed trades without requiring extensive technical expertise. Future developments for Kybera encompass integrating fiat on/off-ramping capabilities to facilitate seamless entry and exit from DeFi, alongside a historical developer reputation system designed to function like a credit score within the blockchain ecosystem. Running entirely in the browser, Kybera prioritizes user privacy and security through AES-256 encryption without storing keys long-term. As an open-source project licensed under MIT, Kybera invites community feedback, particularly aimed at enhancing its reputation model to address issues such as Sybil attacks and identity fragmentation across different chains.
Keywords: #phi4, AES-256 Encryption, AI-Powered Wallet, Agentic Smart Wallet, Browser-Based, Client-Side, Credit Score, DEX, DeFi, Developer Reputation, Ethereum, Fiat On/Off-Ramping, Jupiter, KyberSwap, Kybera, MIT Licensed, Multi-Chain, Natural Language, No-Backend, OSINT, Reputation Tracking, Solana, Speculative Markets, Sybil Attacks
kybera.xyz 8 days ago
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1566.
HN
Show HN: Run AWS CDK apps locally - speeding up agentic coding
Local Web Services is introduced as a tool that significantly enhances the efficiency of developing applications using AWS CDK by allowing them to be run locally, thereby reducing the need for frequent cloud deployments during testing phases. Traditionally, development with AWS CDK involves deploying changes to live cloud resources and waiting until they are ready, which can slow down progress and incur unnecessary costs. Local Web Services addresses these challenges by enabling developers to edit code and test against local services immediately, providing instant feedback through logs in their terminal without the requirement for AWS credentials or resource expenses. This capability aligns with "Shift Left" development practices by facilitating early-stage testing within the inner loop before deployment occurs. It benefits both human developers and AI agents by allowing rapid iteration and testing within isolated environments, streamlining the development process and minimizing potential bottlenecks associated with cloud-based testing.
Keywords: #phi4, AWS CDK, AWS resources, CloudWatch logs, cloud deployment, coding agents, costs, credentials, deploy-wait-test cycle, hot reload, inner loop development, isolated environment, ldk dev, local web services, post-deployment testing, rapid iteration, shift left, testing, uvx
local-web-services.github.io 8 days ago
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1582.
HN
Randomness in Agentic Evals
The paper "On Randomness in Agentic Evals" examines how randomness affects the evaluation of agentic systems—systems evaluated based on their interactions with environments to complete tasks. The study critiques conventional methods that often use a single-run pass@1 score per task, which may inaccurately represent system capability due to significant performance variance observed across 60,000 trajectories from various models. This variance suggests that reported improvements might be attributed to evaluation noise rather than actual advancements in algorithms.
The research reveals that early divergence in trajectory outcomes can lead to vastly different final results and solution strategies, underscoring the need for more robust evaluation methods. To enhance reliability, the authors propose several measures: conducting multiple independent runs per task to better estimate pass@1 scores; employing statistical power analysis to ascertain the required number of runs for detecting expected improvements; and considering metrics like pass@k or pass^k (with k>1) for a thorough performance assessment. These recommendations aim to differentiate genuine progress from statistical noise, although they may increase evaluation costs.
The study emphasizes the critical nature of these practices in ensuring robust evaluations within fields such as machine learning, artificial intelligence, and software engineering. The research is supported by the Simons Foundation and aligns with values of data privacy and community collaboration through its partnership with arXivLabs.
Keywords: #phi4, Agentic Evals, Artificial Intelligence, Benchmarks, Machine Learning, Models, Pass@1, Randomness, SWE-Bench-Verified, Scaffolds, Software Engineering, Statistical Power, Token-level Analysis, Trajectories, Variance, pass@k, pass^k
arxiv.org 8 days ago
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1613.
HN
Make Trust Irrelevant: A Gamer's Take on Agentic AI Safety
DesoPK's thesis "Make Trust Irrelevant: A Gamer's Take on Agentic AI Safety" critiques existing approaches to agentic AI safety for focusing excessively on fostering trust in agents, which is deemed an unreliable safeguard, particularly within adversarial environments where actions are determined by system mechanics rather than intent. The core issue identified is the provision of "ambient authority," which allows AI agents unrestricted access and then attempts to regulate it with insufficient mechanisms like prompts and policies, failing to establish hard limits on their capabilities.
The proposed solution advocates for a "reduce-only authority" model where permissions granted to AI agents are narrow, time-bound, and non-self-augmentable. A key component of this approach is the implementation of KERNHELM, a kernel control plane designed to mediate between planning and execution through strictly enforced, revocable permits, thereby preventing capability expansion or misuse by compromised agents.
Drawing parallels from competitive gaming and IT systems management, DesoPK argues that true AI safety should arise from robust system designs that eliminate potential for harm, akin to removing exploitable elements in game mechanics rather than depending on the players' adherence to rules. The thesis emphasizes that addressing the challenges of agentic AI requires enforceable constraints on authority, identifying issues like confused deputies and capability security as known system failures.
In conclusion, DesoPK asserts that effective solutions must involve explicit, scoped, short-lived permissions with rapid revocation capabilities. Without such measures, attempts at safety are likely to merely postpone rather than prevent systemic problems, underscoring the necessity of engineering AI systems that inherently minimize risk through structural constraints rather than relying on trust-based frameworks.
Keywords: #phi4, Agentic AI, KERNHELM, OS permissions, adversarial systems, ambient authority, authority limits, capability security, capability security Keywords: Agentic AI, confused deputy problem, control plane, kernel-enforced constraints, reduce-only authority, safety mechanisms, trust irrelevant
github.com 8 days ago
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1615.
HN
Agentic Tool Patterns – 54 patterns for building tools LLM agents can use
"Agentic Tool Patterns" is a new framework consisting of 54 design patterns aimed at enhancing tool development for Large Language Model (LLM) agents. This initiative addresses the critical need for specialized tools that LLMs can effectively utilize beyond their communication and reasoning capabilities, filling a gap in current technology where general-purpose design frameworks like Design Patterns and Microservices Patterns fall short. The framework arises from extensive experience in creating over 8,000 agent-ready tools with production-grade features such as rate limiting and authentication refresh management.
The paradigm shift introduced by this framework moves the responsibility of orchestrating data flow from traditional middleware to agents themselves, requiring developers to rethink design constraints specific to LLMs. To facilitate effective tool creation for LLMs, patterns are organized into ten categories focusing on various aspects like agent experience, security, and context management. These are further classified based on three dimensions—maturity, integration type, and access pattern—to guide appropriate tool development practices.
The article emphasizes the importance of community feedback in refining these patterns and introduces Arcade as an open platform that supports deploying LLM agents by providing essential tools and authentication layers. Developers are encouraged to actively engage with this ecosystem to advance agent tooling further.
Keywords: #phi4, API Wrappers, Agent Experience, Agent Patterns, Async Job, Cross-Cutting Concerns, Design Layer, Error Handling, Error-Guided Recovery, Integration, Integration Type, LLM Agents, Maturity Model, Middleware, Orchestration, Query Command Discovery, Security Boundaries, Tool Composition, Tool Context, Tool Execution, Tool Interface, Tool Resilience, Tool Response, Tool Security, Tools
blog.arcade.dev 8 days ago
https://blog.arcade.dev/mcp-tool-patterns 8 days ago
https://arcade.dev/patterns 8 days ago
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1617.
HN
Property-based testing as executable specs for agentic coding
Kiro is a cutting-edge Integrated Development Environment (IDE) that implements Spec Driven Development (SDD), utilizing an intelligent agent to create executable specifications before any code writing begins. These specifications are transformed into property-based tests, which check the system's behavior across various inputs to ensure compliance with requirements. Unlike traditional unit tests, which evaluate specific input/output pairs, property-based testing can reveal bugs more efficiently by exploring a broader range of potential scenarios.
Kiro automates the generation of these tests from natural language requirements, boosting confidence that software functions as intended since passing these tests indicates adherence to specified properties. For instance, in a traffic light simulator project, Kiro ensures through generated tests that no two directions can be green simultaneously. This testing approach is inspired by Haskell's QuickCheck and utilizes Hypothesis, which generates diverse test cases and uses shrinking techniques to isolate essential components of failing properties for efficient debugging.
By integrating property-based testing with SDD, Kiro marks a shift towards validating software correctness through universal properties rather than isolated examples. This method effectively connects requirements with implementation, providing developers greater assurance of code reliability and facilitating collaboration between AI agents and human developers. While not entirely foolproof, this technique significantly improves bug detection compared to traditional methods, representing a major advancement in software development practices.
Keywords: #phi4, Hypothesis framework, Kiro IDE, Property-based testing, QuickCheck, Spec Driven Development, agent-driven coding, counterexamples, executable specifications, input generators, property tests, requirements document, shrinking, unit tests
kiro.dev 8 days ago
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1642.
HN
Agentic Coding Is Draining Your Moat
The increasing use of agentic coding technology poses a challenge to early-stage software companies by eroding their traditional time and cost advantages. As competitors can now quickly achieve feature parity, the conventional "feature moat" strategy becomes less effective. Instead, intellectual property, particularly patents, emerges as a crucial differentiator in maintaining competitive advantage. To enhance defensibility, it is essential for development teams to proactively document inventions during the coding process through an `inventions.md` file. This documentation involves logging patentable ideas, novel technical solutions, and human decisions that lead to these innovations, which are necessary to demonstrate human conception—a legal requirement for obtaining patents.
Two suggested workflows aid this process: a proposal-first method that allows developers control over when they log inventions and an auto-log approach suited for rapid prototyping environments. Capturing inventions promptly is vital in the first-inventor-to-file patent system prevalent in most jurisdictions, including the United States, necessitating swift follow-up with provisional patent filings to secure priority.
The strategy underscores the importance of identifying and protecting must-copy mechanisms over mere features by focusing on human contributions to the invention process. This documentation becomes a crucial defense for patents if challenges arise later. As AI tools increasingly transform software development dynamics, early invention capture and rapid provisional patent filing are becoming essential practices for tech companies aiming to sustain their competitive edge in an evolving market landscape.
Keywords: #phi4, AI-assisted inventions, Agentic coding, compliance credibility, defensibility, feature moat, intellectual property, inventions, inventorship, patentability, provisional filings, replication, workflow embedding
www.slwip.com 9 days ago
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1683.
HN
Designing a Cost-Efficient Agentic System
The article explores the development of an efficient system designed to extract deals, coupons, and expiration dates from emails at scale by overcoming various challenges associated with differing email formats. Initially, attempts using prompt-heavy methods were unsuccessful due to their inability to handle complex promotions effectively. To improve precision, a two-step approach involving chaining LLM (Large Language Model) calls for extraction and subsequent evaluation was implemented; however, this method faltered when dealing with emails predominantly containing images. The solution entailed integrating PaddleOCR to address the challenges posed by image-based content, maintaining cost-efficiency through serverless deployment on AWS Lambda. Ultimately, a re-architecting of the system using specialized agents for specific tasks—such as deal discovery and date resolution—marked the final iteration, substantially enhancing reliability and scalability compared to relying solely on powerful models. The key lessons underscore the significance of workflow architecture, preprocessing, specialization, and cost constraints in designing robust systems capable of handling complex data extraction tasks effectively.
Keywords: #phi4, AWS Lambda, Agentic System, Architectural Shifts, Cost Constraint, Cost-Efficient, LLM Calls, NLP Problem, OCR Layer, PaddleOCR, Pipeline Design, Preprocessing, Production AI, Prompt Engineering, Reliability, Small Models, Specialized Agents, Workflow Architecture
p.agnihotry.com 9 days ago
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1692.
HN
Continuous AI in practice: What developers can automate today with agentic CI
Continuous AI represents a significant evolution in software development by automating complex tasks that traditionally required human-like judgment and contextual understanding. Unlike traditional Continuous Integration (CI) systems, which manage deterministic processes like testing and building through predefined rules, Continuous AI introduces "agentic reasoning" to handle intricate tasks involving natural language and cognition directly within repositories. GitHub Next's exploration into this technology focuses on creating background agents capable of performing judgment-intensive activities such as aligning documentation with code, generating activity-based reports, managing undocumented dependency changes, improving test coverage, analyzing performance for enhancements, and simulating user interactions.
These agents are designed to operate safely within set parameters, primarily using read-only access by default, producing reviewable artifacts, and ensuring transparency and auditability. By leveraging natural language for complex requirements that resist deterministic rule encoding, Continuous AI complements existing CI workflows, allowing for a new type of automation where reasoning is central. Developers work iteratively with these agents to refine processes, maintaining safety and effectiveness.
Initial experiments by GitHub Next have shown practical applications of Continuous AI in aligning documentation with implementation, generating detailed reports, managing changes in dependencies, and identifying performance bottlenecks. These examples highlight the potential for Continuous AI to convert manual and repetitive tasks into continuous processes. Developers can start experimenting with this technology using straightforward Markdown files that define natural-language rules, which are then compiled into GitHub Actions workflows. This integration allows developers to gradually adopt Continuous AI without disrupting their existing systems, suggesting a future where judgment-based chores in software development become more streamlined and efficient.
Keywords: #phi4, Continuous AI, Continuous Integration, Continuous Integration (CI), GitHub Next, YAML, agent workflows, agentic CI, automation, dependencies, deterministic rules, documentation, intent, interaction testing, interaction testing Keywords: Continuous AI, judgment-heavy tasks, natural-language rules, performance improvements, pull requests, reasoning, software engineering
github.blog 9 days ago
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1716.
HN
How Does Truffle Taste? Strategic Lessons for Introducing Agentic Engineering
The expert's talk at code.talks 2025 on agentic engineering in software development delves into both the integration benefits and challenges of AI agents like Cursor and Claude 3.5 within the industry. Initially anticipated productivity gains were met with a slowdown due to unfamiliarity among experienced engineers, emphasizing that mastering these complex technologies is time-intensive even for senior developers. This challenge mirrors broader adoption issues, such as increased merge requests and quality problems despite higher throughput.
A study by METR highlighted that significant benefits from AI tools require structured practices like clear policies, robust version control, and a user-centric approach. The talk further explores how productivity metrics need to evolve beyond traditional measures to include ambition and creativity, where AI helps break down disciplinary silos. It positions agentic AI as more than a tool—it's a reflection of an organization’s strengths and weaknesses, demanding cultural adaptation and strategic planning.
The importance of feedback loops is stressed, with agile principles guiding their effective use in AI systems that stretch traditional boundaries. Strategic questions regarding volatility, context, organizational trust, and process drift metrics are posed to guide decisions on tightening or loosening these loops. The speaker advocates for developing 'taste' and intuition among teams in using AI, noting the emergence of roles like "agent orchestrator" focused more on strategic oversight than coding.
The presentation concludes by emphasizing practices that ensure effective feedback loop closure through agent-ready environments and telemetry tracking. It calls for a reevaluation of team structures to embrace agency and autonomy in collaboration models enabled by AI, cautioning against seeing merged pull requests as sole success metrics. Instead, it suggests addressing systemic productivity issues. Overall, the talk advocates for viewing agentic AI as a transformative force that requires continuous learning, cultural adaptation, and strategic foresight.
Keywords: #phi4, AI agents, Agentic engineering, METR study, adoption, agency, agent-ready environments, autonomy, capability overhang, developer experience, exponential growth, feedback loops, instrument telemetry, instrument telemetry Comma-separated List: Agentic engineering, instrument telemetry Extracted Keywords: Agentic engineering, instrument telemetry Final Keywords: Agentic engineering, instrument telemetry Keywords: Agentic engineering, loop patterns, organizational strategy, productivity, quality, software development, strategic questions, task length, trust, unclosed loops
www.robert-glaser.de 9 days ago
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1741.
HN
Agentic coding improves ARC AGI 2 performance across models
The article explores significant enhancements in AI performance through "agentic coding," particularly employing Python's Read-Eval-Print Loop (REPL) during tasks on the ARC AGI 2 benchmark, which assesses human-like fluid intelligence. Models demonstrated substantial score improvements when interacting with a REPL; for example, GPT OSS 120B High saw its score increase from 6.11% to 26.38%, indicating unlocked fluid intelligence capabilities. The agentic coding framework reframes ARC AGI puzzles as program synthesis tasks, where models produce Python functions that map inputs to outputs and simultaneously generate explanations of their transformations. This setup enhances the explanatory power of AI solutions.
A key innovation introduced in 2025 is "interleaved thinking," which allows models to iteratively refine hypotheses by alternating between thinking and tool use, such as code execution. Models can adjust strategies based on intermediate results, thus improving problem-solving efficiency. The study reports notable performance gains across various AI models using agentic coding compared to traditional chain-of-thought methods. This suggests a paradigm shift in harnessing fluid intelligence within AI systems through interleaved thinking.
Despite the advancements, implementing interleaved thinking remains fragile, requiring precise alignment among model capabilities, provider APIs, inference engines, middleware, and client-side management for effective functionality. The study prompts further exploration into whether code execution offers stronger verification or induces different thinking patterns than plain reasoning, indicating potential directions for refining AI systems.
The document also highlights resources and tools related to AI models, reasoning capabilities, and puzzle-solving frameworks. It discusses differences in model capabilities, such as advanced reasoning and agentic abilities, and emphasizes the importance of interleaved thinking in enhancing reliability and effectiveness in AI reasoning. Insights into learning challenges, reinforcement learning impacts on large language models (LLMs), scoring scripts for ARC AGI benchmarks, updates on GPT models, and tools for solving ARC AGI puzzles using a Python-based environment are presented.
The document underscores technical issues with function calls in certain environments and methods to elicit interleaved thinking. Verification and analysis tools ensure model accuracy, while discussions on provider variance introduce tools like exacto for better AI model management. Overall, the overview encapsulates current advancements and methodologies in AI reasoning, puzzle-solving frameworks, and benchmarking systems.
Keywords: #phi4, ARC AGI, Jupyter notebook, Python REPL, chain-of-thought, evaluation set, grid dimensions, open-source research, program synthesis, reasoning depth, reinforcement learning, tool call loop, transformation rules
pivotools.github.io 9 days ago
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1744.
HN
OCapN and Structural Authority in Agentic AI
Object-Capability Networking (OCapN) presents a structural framework designed to manage authority in autonomous AI systems, where traditional architecture is inadequate due to enhanced agent autonomy. In contrast to conventional software that relies on external mechanisms like identity management and policy enforcement for authority control, OCapN integrates authority directly into the system's structure using capabilities—specific permissions attached to references. This explicit modeling of authority is essential for ensuring safety and reliability in agentic AI environments where agents operate independently across asynchronous boundaries without direct human supervision.
In the context of distributed agentic AI systems, OCapN employs message-oriented communication that aligns with the decentralized nature of these architectures. Agents function within isolated "vats," which provide structural containment and minimize the impact radius of interactions. Within this framework, capabilities define permissible actions for agents, both internally and externally, effectively transitioning authority management from configuration-based and policy-driven systems to an architecture-centric approach.
OCapN offers security advantages by embedding authority constraints within the system's structure itself, necessitating significant shifts in architectural thinking and development practices. This shift requires teams to adopt a mindset focused on explicit reasoning regarding authority, delegation, and isolation. Despite challenges such as steep learning curves and underdeveloped tooling, OCapN promotes disciplined design principles that enhance autonomous systems' reasoning and auditing capabilities.
Ultimately, adopting OCapN involves intentional architectural choices and a long-term vision, concentrating on the explicit modeling of authority to advance reasoning and auditing in agentic AI architectures. This approach fosters improved safety, reliability, and accountability in increasingly autonomous AI environments.
Keywords: #phi4, Agentic AI, Architectural Responsibility, Asynchronous Communication, Autonomy, Capabilities, Cloud-Native Environments, Cloud-Native Environments Keywords: Agentic AI, Delegation, Developer Experience, Isolation, OCapN, Security, Structural Authority
serefayar.substack.com 9 days ago
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1753.
HN
x
The "Go - Agentic Asset Operating System" is a specialized operating system engineered to revolutionize asset management through the application of agent-based technology. Its primary objective is to bolster both efficiency and automation in handling diverse organizational assets, thus enabling enhanced control and comprehensive operational oversight. By integrating this advanced technological approach, organizations can achieve more streamlined processes, reduce manual intervention, and maintain superior command over their asset portfolios. This system is particularly designed to address the complex demands of managing a wide array of assets by fostering improved coordination and decision-making capabilities within an enterprise's operational framework.
Keywords: #phi4, Agentic, Asset, Go, Operating, System
app.gosmartchain.ai 9 days ago
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1772.
HN
Ask HN: Why do you use AI for coding?
The discussion centers on understanding the motivations behind developers utilizing AI tools, including Large Language Models (LLMs) and agentic systems, in coding tasks. It seeks to identify the primary reasons for adopting these advanced technologies and examines whether they effectively address complex and unique programming challenges. The discourse aims to provide insights into how AI-assisted coding contributes to solving novel problems that are not trivial, as part of a broader exploration within an article focused on this emerging field. The central questions revolve around identifying both the drivers for using such tools in development environments and assessing their efficacy in tackling intricate software issues that require innovative solutions beyond traditional methods.
Keywords: #phi4, AI, AI-assisted, Agentic, Ask, HN, LLM, article, coding, help, non-trivial problems, novel problems, reasons, solve
news.ycombinator.com 9 days ago
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1809.
HN
From Interfaces to Intelligence: Where Agentic AI Shines
Agentic AI marks a transformative advancement in software development by prioritizing flexibility and synthesis over fixed interfaces. It operates across three key layers: Firstly, it introduces **Flexible Interfaces**, replacing static dashboards with natural language interactions to enable more intuitive data exploration and quicker insights generation. Secondly, through **Adaptive Orchestration**, agentic AI dynamically orchestrates workflows by adjusting tools, data sources, and analyses in response to changing contexts and interim results, thereby enhancing operational intelligence. Thirdly, it excels at **Reasoning and Synthesis** by addressing open-ended challenges within complex and incomplete information spaces, thus shifting the focus from mere automation to advanced cognition.
The effective utilization of agentic AI hinges on discernment; it should be selectively applied in scenarios where there is a clear need for flexibility, adaptive workflows, or synthesis. Employing it indiscriminately could result in unnecessary complexity without added value. When thoughtfully deployed, agentic AI offers substantial benefits by enabling more exploratory interactions, adaptable workflows, and sophisticated reasoning beyond the capabilities of traditional software systems. This innovation significantly transforms user engagement with intricate systems.
Keywords: #phi4, Agentic AI, complexity, decision-support, discipline, exploration, fit, flexibility, impact, impact Keywords: Agentic AI, intelligence, interfaces, natural language, orchestration, reasoning, software, synthesis, transformation, workflows
dvitsios.org 9 days ago
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1836.
HN
TUI visualizer for agentic coding sessions
Vizier is a timeline-based visualization tool specifically designed for "agentic coding sessions," offering capabilities to visualize data from both Claude Code and OpenCode sessions. Developed using TypeScript, Bun, and React Ink, it provides real-time updates on session files as they execute. The tool simplifies navigation between different sessions through auto-discovery features and offers various modes like Follow mode, which tracks the latest node in execution, and Preview mode, allowing inline viewing of content snippets along the timeline. It also includes a status bar displaying token statistics and implements sticky context to show recent parent nodes prior to the current viewport for enhanced understanding. Additionally, Vizier automatically identifies subagent branches for visualization as part of its agent discovery feature. Users can enhance their experience by customizing tool icons via a configuration file, thereby improving scanning efficiency. Installation is straightforward using the command `bun add -g vizier`, and it supports specifying different session sources such as Claude Code, OpenCode, or both.
Keywords: #phi4, Bun, Claude Code, OpenCode, React Ink, TUI, TypeScript, Vizier, agent discovery, agentic coding, configuration, emojis, follow mode, install, preview mode, real-time updates, session switching, source, sticky context, timeline, token stats, tool icons, visualizer
github.com 10 days ago
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1898.
HN
We built a cloud platform for agentic software (our virtualization, etc.)
The platform offers a cloud-based solution designed for agentic software, facilitating the integration of existing agent frameworks while enhancing them with features like observability, evaluations, streaming, and authentication—all without necessitating new runtimes. It accommodates diverse tools including Mastra, AI SDKs, or custom code, enabling agents to interact seamlessly across various languages and frameworks with minimal coding effort. This approach allows for the efficient incorporation of advanced functionalities into existing systems, streamlining development processes and fostering interoperability among different software environments.
Keywords: #phi4, AI Agents, Mastra, SDK, agent code, agentic software, agents, auth, cloud platform, evals, frameworks, infrastructure, languages, observability, runtime, streaming, virtualization
agentuity.com 10 days ago
https://agentuity.com/blog/agentuity-v1-is-here 10 days ago
https://github.com/agentuity/sdk 10 days ago
https://agentuity.com/blog/welcome-agent-lets-get-you-d 10 days ago
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1915.
HN
Show HN: Kybera – Agentic Smart Wallet with AI Osint and Reputation Tracking
Kybera is an advanced smart wallet that integrates artificial intelligence to enhance user experience through open-source intelligence (OSINT) and reputation tracking across multiple blockchain networks. This agentic tool offers users increased security by providing detailed insights into their transactions and interactions within the cryptocurrency ecosystem. By leveraging AI, Kybera monitors and evaluates reputational risks linked to various addresses or entities on the blockchain, ensuring that users can make informed decisions based on comprehensive data analysis. The combination of multi-network support and intelligent risk assessment positions Kybera as a robust solution for navigating the complexities of digital asset management.
Keywords: #phi4, AI, AI-Powered, Agentic Smart Wallet, Kybera, Multi-Chain, Multi-Chain Wallet, Osint, Reputation, Reputation Tracking, Show HN, Tracking, Wallet
kybera.xyz 10 days ago
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1986.
HN
Software Factories and the Agentic Moment
The article explores the creation of a "Software Factory" that utilizes non-interactive, agent-driven code generation based on predefined specifications and scenarios, eliminating the need for human-written or reviewed code. This innovation was propelled by advancements in AI models such as Claude 3.5, which enhanced long-horizon coding accuracy. Central to this approach is the elimination of human intervention in both coding and testing processes, with an initial reliance on tests to drive development until they were deemed inadequate for ensuring quality.
To overcome the limitations of traditional testing methods, the authors introduced scenarios—end-to-end user stories stored externally from the codebase—to validate software through a metric known as "satisfaction." Additionally, they developed the Digital Twin Universe (DTU), which are behavioral clones of third-party services like Okta and Google Docs. These DTUs facilitate extensive scenario validation without the constraints associated with live environments.
The article underscores how these technological advancements have transformed software economics by making previously infeasible tasks routine. It emphasizes a paradigm shift from conventional software development practices to new methodologies enabled by AI, advocating for an embrace of innovative approaches that redefine industry standards.
Keywords: #phi4, API Costs, Agents, Behavior Tests, Behavioral Clones, Claude 35, Code Review, Digital Twin Universe, Economics, End-to-End Tests, Generative Development, Integration Tests, LLMs, Non-interactive Development, Regression Tests, SaaS Applications, Scenarios, Software 10, Software Factories, StrongDM AI, Tests, YOLO Mode
factory.strongdm.ai 11 days ago
https://simonwillison.net/2026/Feb/7/software 11 days ago
https://news.ycombinator.com/item?id=46739117#46801848 11 days ago
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https://github.com/strongdm/attractor 11 days ago
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https://simonwillison.net/about/#disclosures 11 days ago
https://strongdm.com 11 days ago
https://sociotechnica.org/notebook/software-factory 11 days ago
https://rust-unofficial.github.io/patterns/anti_pattern 11 days ago
https://github.com/simonw/simonwillisonblog/commit 11 days ago
https://www.ftc.gov/business-guidance/resources/di 11 days ago
https://www.ftc.gov/system/files/documents/pl 11 days ago
https://news.ycombinator.com/item?id=46838946 11 days ago
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https://trust.mistral.ai/subprocessors 10 days ago
https://www.bls.gov/ooh/computer-and-information-techno 10 days ago
https://www.cnbc.com/2026/02/06/google-micros 10 days ago
https://www.linkedin.com/posts/meganlieu_claudepartner- 10 days ago
https://www.linkedin.com/help/linkedin/answer/ 10 days ago
https://github.com/steipete/steipete.me/commit 10 days ago
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https://arxiv.org/abs/2309.10668 10 days ago
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2015.
HN
Show HN: MicroClaw – Agentic AI Assistant for Telegram, Built in Rust
MicroClaw is an advanced AI assistant designed to function within Telegram chats, developed using Rust. It integrates the Claude API with Telegram, offering a suite of functionalities such as executing shell commands, managing files, conducting web searches, and scheduling tasks. Inspired by nanoclaw, MicroClaw supports persistent memory across conversations, ensuring continuity in user interactions.
Key features include agentic tool use for executing bash commands, file manipulation, and regex operations, alongside session management that retains conversation states between messages. It employs context compaction to summarize older messages when limits are exceeded and delegates sub-tasks using parallel agents with restricted tools. The skill system is extensible and compatible with Anthropic Skills, activating automatically as needed.
MicroClaw excels in task management by breaking down complex tasks into manageable steps, tracking progress, and supporting natural language scheduling. It interacts with the web via DuckDuckGo searches and summarizes web pages. Messaging features include sending intermediate updates during processing, reading all group chat messages since the last reply when mentioned, and maintaining a continuous typing indicator.
The architecture of MicroClaw encompasses environment configuration, error handling, Telegram bot management, Anthropic API interaction, SQLite database operations, memory systems, skill discovery/activation, task scheduling, and various tool implementations. It emphasizes session persistence, context compaction, direct API calls to Anthropic, concurrent database access, rate limit handling, message splitting, and continuous typing indicators.
Installation options include Homebrew for macOS or cloning the source code from GitHub. Configuration requires a Telegram bot token, an Anthropic API key, and optional environment variables for customization. MicroClaw can perform tasks like web searches, file analysis, scheduling reminders, providing coding assistance, and maintaining chat-specific memory in both private and group chats.
As an open-source project under the MIT license, comprehensive documentation is available covering setup, usage, architecture insights, tool addition, debugging, and testing. The development guide details its modular design and key decisions regarding session management and API interaction strategies.
Keywords: #phi4, AI Assistant, Anthropic Skills, Claude API, Context Compaticion, Continuous Typing Indicator, Database Access, Group Chat Catch-up, Message Splitting, MicroClaw, Mid-conversation Messaging, Persistent Memory, Plan & Execute, Rust, SQLite, Scheduled Tasks, Scheduling Tools, Session Resume, Skill Activation, Sub-agent, Telegram, Tool Execution, Web Search
github.com 11 days ago
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2031.
HN
Make Trust Irrelevant: A Gamer's Take on Agentic AI Safety
DesoPK's position paper addresses the critical issue of agentic AI safety by identifying a fundamental problem: agents are often granted excessive authority without adequate constraints, leading to what is described as a "confused deputy" scenario. The author argues that trust should not be a factor in AI systems; instead, robust mechanical controls must replace soft constraints like prompts and policies. Current practices allow agents broad access with minimal safety measures, resulting in vulnerabilities when adversarial inputs exploit these permissions. DesoPK advocates for a "reduce-only authority" model where permissions are narrowly defined, explicit, time-limited, and can only diminish as they propagate. This approach necessitates the implementation of a kernel control plane (KERNHELM) to enforce constraints, ensuring that agents cannot self-extend their authority.
The paper draws an analogy with competitive gaming, suggesting that system mechanics should be fixed rather than relying on user behavior for safety. The objective is to design AI systems that are inherently safe by construction, not merely trustworthy in intent. DesoPK concludes that effectively addressing agentic AI risks requires permissions that are explicitly scoped, short-lived, and revocable swiftly and absolutely, with non-negotiable auditability. Solutions must adhere to these principles; otherwise, they only postpone inevitable issues rather than resolving them.
Keywords: #phi4, Agentic AI, KERNHELM, adversarial inputs, ambient authority, authority, authorization, capability security, enforcement layer, kernel control plane, planner, reduce-only propagation, safety, trust irrelevant
github.com 11 days ago
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2069.
HN
Agentic Coding Mentor
The repository contains an `AGENTS.md` file that specifies how agent‑powered coding tools—such as Claude Code and OpenCode—should function as teaching mentors during code development; to employ this feature, users should place the file in their project directory and confirm that their chosen tool supports `AGENTS.md`, with additional implementation guidance available from the AGENTS.md standard on the official website.
Keywords: #gpt-oss:20b, AGENTSmd, Agentic, Claude Code, Coding, Mentor, OpenCode, coding agents, repository, supports, tool, website, working directory
git.medlab.host 12 days ago
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2137.
HN
Agentic Memory Bottlenecks
The passage titled “Agentic Memory Bottlenecks” serves as a succinct directive urging the reader to identify themselves; it explicitly notes that the system will capture and retain this identification data locally, ensuring the information is available for subsequent reference.
Keywords: #gpt-oss:20b, Agentic, Bottlenecks, Browser, Info, Last, Memory, Next, One, Save, Thing, Time, Who
jcarlosroldan.com 12 days ago
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2153.
HN
Agentic Productivity System with Plain Markdown
The author outlines a markdown‑based productivity framework that cleanly divides short‑term context (held in AGENTS.md) from long‑term knowledge stored in a /memory directory containing glossary, journal, people, projects, and company context, while all tasks reside in a single TASKS.md file; the system is agent‑agnostic and easily hooks into external tools such as calendars, Jira, and Linear via modular skills, and is employed alongside neovim and Opencode, a setup born of dissatisfaction with Anthropic’s Cowork and aimed at greater reliability and customizability. In practice the user operates two terminal tabs—neovim for rapid edits and Opencode for deeper work—alongside an Astro project that renders markdown, thereby tracking projects, contacts, and ideas and enabling weekly/monthly summaries, with a fork‑able template provided for others to adopt the simple, controllable workflow.
Keywords: #gpt-oss:20b, AGENTSmd, Agentic Productivity, Cowork, Plain Markdown, deep memory, glossarymd, neovim, note-taking, productivity plugin, task tracking, workflow, working memory
sattlerjoshua.com 12 days ago
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2158.
HN
Agentic Proof-Oriented Programming
Nik Swamy demonstrates that integrating Copilot CLI with Claude Opus 4.5 enables the automatic generation and formal verification of roughly 10,000 lines of concurrent libraries in F*’s Pulse framework—covering bubble sort, ring buffers, priority queues, linked‑list iterators, hash tables, and synchronization primitives—showing that AI‑assisted proof‑oriented programming (Agentic PoP) lets experts focus on specifications while agents perform heavy proof work, potentially allowing small teams to build larger verified systems. The post introduces F*, a proof‑oriented language that embeds executable code, specifications, and proofs, illustrated with a quicksort example guaranteeing sortedness, permutation preservation, and termination through type annotations and lemmas; Pulse extends F* to imperative shared‑memory concurrency backed by the SMT solver Z3, and Copilot CLI exposes tools like `fstar.exe` and Pulse to simple prompts, enabling developers to experiment in a codespace starting with a Bubble‑Sort warm‑up. Swamy recounts shifting from pure‑function proof attempts to imperative Pulse code, refining AI prompts to include idiomatic invariants, and ultimately producing verified implementations of bubble sort, stack, ring buffer, linked‑list iterator, priority queue, hashtable, and a reader‑writer lock, each accompanied by concise invariants and proofs (e.g., a ~30‑line invariant sufficed for a 1,200‑line verified reader‑writer lock module). While these demonstrations highlight AI’s capacity to handle non‑trivial proof tasks and reduce manual effort, the author notes remaining limitations—Pulse proofs guarantee only partial correctness, lack termination and liveness guarantees for concurrency, require careful handling of “admits” to avoid verification bypasses, and still demand human guidance to craft correct invariants and interpret verification feedback—indicating that AI agents accelerate experts but cannot fully replace human expertise for complex systems. Swamy also warns that AI agents may impede younger researchers’ acquisition of mechanized proof skills by reducing hands‑on practice, citing a 67‑hour coding project that consumed ~6 million input tokens, ~2 million output tokens, ~4,300 tool calls, cost between $120 and $200, and had measurable environmental impact, underscoring the need to weigh such costs for large AI‑augmented initiatives despite lacking definitive trade‑off insights.
Keywords: #gpt-oss:20b, AI, Agentic, Bubble Sort, CLI, Concurrent, Copilot, Counting Semaphore, Formal Proofs, Machine-Checked, Priority Queue, Programming, Proof-Oriented, Pulse, Reader-Writer, Verified Code
risemsr.github.io 12 days ago
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2214.
HN
We are QA Engineers now
The article highlights the evolving role of quality assurance (QA) in software development, particularly in the context of AI-assisted coding agents. As these agents take on greater responsibilities in implementing code, the necessity for rigorous testing has become more critical, shifting the focus of software engineers toward ensuring the reliability and correctness of agent-generated code. Effective QA in this new paradigm extends beyond traditional testing to include the ability of agents to verify their own work, which becomes increasingly complex in large-scale, distributed systems. Testing within a single service can be facilitated using containers and realistic fakes, but testing across service boundaries demands more comprehensive integration with the user interface and multiple systems. To support autonomous agent development, a robust test harness is essential—it must be reproducible, authentic, and programmatic, utilizing real or realistic data and shared frameworks to ensure composability across systems. While existing tools like Testcontainers and Localstack can aid in environment setup, the creation of a tailored framework is crucial for reliable testing in complex environments. The article underscores that while these practices are not new, they are now indispensable for maintaining productivity and quality in the era of AI-driven development, with developers increasingly taking on the responsibilities of QA engineers to ensure the success of agentic programming.
Keywords: #qwen3:14b, AI, End-to-end, Localstack, Miniflare, Mockito, QA, Testcontainers, agentic, agents, assurance, authenticity, coding, complexity, composability, databases, development, environment, feedback, framework, functionality, harness, integration, pre-AI, productivity, programming, prototype, quality, reproducibility, scenario, service, setup, software, specification, systems, teardown, testing, tooling, verification
serce.me 13 days ago
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2240.
HN
The list of best agentic browsers and extensions
No summary available (error)
news.ycombinator.com 13 days ago
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2255.
HN
Agentic Proof-Oriented Programming
No summary available (error)
risemsr.github.io 13 days ago
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2272.
HN
Learn prod agentic coding (open source)
A brief call invites readers to learn open‑source production agentic coding, while the text notes a Reddit user’s gratitude for dodging yet another video course series.
Keywords: #gpt-oss:20b-cloud, Learn, Reddit, Thank you, agentic, another, coding, course, making, not, open source, prod, series, video
agenticoding.ai 13 days ago
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2275.
HN
Show HN: DeepBrainz-R1 – Reasoning-First Small Models for Agentic Systems
DeepBrainz‑R1 is a family of small language models engineered for agentic systems that prioritize reliable, controllable, and efficient multi‑step reasoning over chat performance, with a focus on deterministic, inspectable behavior suitable for tool‑calling loops and long‑context analysis rather than open‑ended conversation or creative writing. The suite includes a 4B flagship, a low‑latency 2B mid‑tier, an edge‑friendly 0.6B‑v2, and experimental 16K/40K long‑context variants; all are open‑source (Apache‑2.0) with community‑maintained low‑bit quantizations already emerging. Models undergo post‑training with reinforcement learning to stabilize reasoning outputs and robustness, and the research process incorporates scalable inference, long‑context efficiency, and systematic ablation studies on architecture, data, and context length. Releases are curated into production‑ready variants, with exploratory builds marked experimental and raw checkpoints provided for reproducibility, and the lab maintains a transparent, iterative research posture that actively invites community engagement.
Keywords: #gpt-oss:20b-cloud, Agentic systems, Agentic workloads, Chat-optimized, Cost, DeepBrainz-R1, DeepBrainz-R1-06B-v2, DeepBrainz-R1-2B, Language Models, Multi-step reasoning, Output stability, Reasoning-First, Reliability, Robustness, SLMs, Schema-constrained outputs, Tool calls, Verification loops, balanced, small
huggingface.co 13 days ago
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2318.
HN
Agentic Engineering
Vibe coding, coined by Andrej Karpathy, is a rapid, low‑stakes workflow in which a developer prompts an AI, accepts its output without diff review, runs it, and loops by feeding errors back in as new prompts; it is useful for MVPs, prototypes, hackathon demos, single‑user scripts, learning by example, and ideation, but its lack of design, testing, and engineering discipline makes it unreliable at scale or in secure settings. Agentic engineering, a stricter discipline advocated as the more appropriate term, treats AI as a junior developer that must write code under human‑defined design specs, clear task scopes, rigorous pull‑request reviews, exhaustive automated tests, and ongoing maintenance—including documentation, version control, CI/CD, and production monitoring—so that the human architect retains ownership of architecture, quality, and correctness; this contrasts with vibe coding which skips design and testing, producing “check‑the‑box” code that can fail in production. The article warns that while AI can boost productivity for senior engineers, it risks skill atrophy for juniors who rely on it before mastering fundamentals, and stresses that the real benefit of AI lies in disciplined engineering habits—clear specifications, thorough tests, and clean architecture that yield better AI output than sloppy design does. The author asserts that AI does not replace software craftsmanship but elevates it, rewarding those who think clearly about systems and own the process, and calls for systematic evaluation of AI workflows for reliability, not just speed; the forthcoming book *Beyond Vibe Coding* offers practical frameworks for agentic engineering and invites sharing of successful strategies.
Keywords: #gpt-oss:20b-cloud, AI agents, AI-assisted, CI, MVPs, architecture, autopilot, coding assistants, hackathon, prototype, specs, test suites, version control
addyosmani.com 13 days ago
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2355.
HN
Teleporting into the future and robbing yourself of retirement projects
The author contends that the emergence of advanced AI, particularly swarm agents, enables users to “teleport into the future” by completing tasks instantaneously, thereby depriving their future selves of the opportunity to pursue those projects and potentially disrupting sleep. Claiming we are already in a post‑AGI era, he cites recent accomplishments such as replicating SaaS features, developing file systems and networking protocols, and even a new programming language within the past year, yet he emphasizes the importance of rest, urging readers to take up hobbies like playing guitar instead of succumbing to relentless productivity. He notes that in December AI models became so user‑friendly that users experienced a brief “creative psychosis,” a 2‑3‑month surge in output comparable to a post‑COVID reset; this burst forces people to either deepen their organizational ties or recognize newfound independence to meet financial goals, prompting many creators to launch ventures autonomously while relying on technologists for refinement. In a February 2025 reflection, he stresses the shift toward automation and deep tool mastery, underscoring that merely consuming technology is insufficient—skillful application, as demonstrated by a free workshop turning a 300‑line LLM loop into a functional coding agent, will be in high demand—while warning that creating is not always necessary and that knowing what not to build remains crucial in an era where virtually everything can be produced.
Keywords: #gpt-oss:20b-cloud, AGI, AI, Agent, Agent swarms, Agentic, Cloned, December, Feature, Future, LLM, Programming, Project, Retirement, SFO, SaaS, Sleeping, Teleport, automates, baseline, build, business owners, chasm, coding agent, coin flip, consumer, employment, entrepreneurs, guitar, loop, marketing, models, reset, sales, skills, software engineers, tokens, tools, venture capitalists, white collar
ghuntley.com 13 days ago
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2381.
HN
Confidential computing and trusted execution within the agentic ecosystem
The YouTube page showcases the video “Confidential computing and trusted execution within the agentic ecosystem,” which was shared during the Secure Compute & Trusted Execution Event (#confidentialcomputing). The content focuses on the role of confidential computing and trusted execution mechanisms within an agentic ecosystem, while the page itself includes standard YouTube footer links such as About, Press, Copyright, and Policies.
Keywords: #gpt-oss:20b-cloud, Confidential computing, Event, Google LLC, NFL, PrivacyPolicy, Safety, Secure Compute, Sunday Ticket, YouTube, agentic ecosystem, new features, trusted execution
www.youtube.com 13 days ago
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2403.
HN
Protect Production SQL Databases from AI/LLM Agentic SQL Query Risks
AI‑driven SQL agents raise a “God User” risk by allowing LLMs to generate arbitrary queries that, if not carefully restricted, can modify or delete production data; the article argues that such agents can bypass prompt instructions via injection or hallucination, thereby regaining unrestricted access to the database. It recommends a dual mitigation strategy: first, a physical fix that routes all write operations to read‑only replica databases, thereby protecting the primary instance from destructive commands regardless of the agent’s intent; second, an architectural fix that treats the database itself as a security engine by enforcing deterministic guardrails, such as lexical shape validation that rejects anomalous query structures (e.g., unexpected UNIONs or system‑table JOINs) before execution and by tightening role‑based access controls to expose only required schemas or views. The piece cautions against expensive “AI Governance Gateways,” noting that native replication and these deterministic checks provide a cost‑effective, high‑performance boundary. Complementary safeguards include automated, periodic testing of production database backups, acknowledging their necessity for reliable recovery. The provider described offers secure relational and NoSQL database solutions both on‑premises and in AWS, aiming to help organizations meet stringent security goals without resorting to costly middleware.
Keywords: #gpt-oss:20b-cloud, AI, Agentic, Databases, Deterministic guardrails, God User, LLM, Lexical validation, NoSQL, Query, RDBMS, Read replicas, Risks, SQL, Security engine
rietta.com 14 days ago
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2406.
HN
Playwriter, extension to control Chrome with agentic CLIs
Playwriter is an open‑source, AI‑agent‑friendly tool that extends a user’s existing Chrome session via a Chrome extension, a local WebSocket server on localhost:19988, and Microsoft Client Protocol integration, allowing Playwright scripts to run directly within a single tab after the user explicitly clicks the extension icon (turning it green and showing a banner). The extension’s tab‑specific consent mechanism and origin checks restrict command traffic to the local machine, preventing remote execution, while the tool maintains a persistent, stateful sandbox that preserves per‑tab session data such as cookies, local storage, and open tabs across successive commands and isolates each tab’s state to avoid cross‑session interference. Playwriter exposes the full Playwright API—including network interception, console log capture, debugging, profiling, element inspection, and overlaid screenshotting with accessibility labels—without launching new browsers, thereby keeping the user’s current browsing context and resource usage intact. Its global CLI (`npm i -g playwriter`) enables session management with commands like `session new`, `session list`, and `session reset <id>`, and script execution via `playwriter -s <session_id> -e "<script>"`, exposing `page`, `context`, Node globals, and a persistent `state` object for complex workflows. The local WebSocket server serves as a multi‑control platform (MCP) with `/extension` and `/cdp/:id` endpoints, and remote agents can connect through the same platform using a token‑based handshake (`playwriter serve --token <secret>` and `playwriter --host <host> --token <secret> …`). This architecture affords unrestricted Playwright API access—including CDP, debugging, profiling, and dynamic element clicking via accessibility overlays—while preserving page state and delivering low bot‑detection, explicit user consent, and secure, context‑aware automation, as detailed in the README and GitHub documentation.
Keywords: #gpt-oss:20b-cloud, AI agents, CLI, Chrome, MCP, Playwright, Playwriter, accessibility, debugging, extension, labels, network interception, sandbox, screenshot
grokipedia.com 14 days ago
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2410.
HN
Kilo Code bets on agentic engineering with model-agnostic CLI
Kilo Code, an open‑source platform backed by GitLab, has released a model‑agnostic command‑line interface capable of running more than 500 AI models, empowering developers to select the most suitable models for any task and orchestrate multi‑step agent workflows—what the company terms “agentic engineering” beyond simple chatbots. The CLI is usable in a standalone terminal or integrated with VS Code and JetBrains IDEs, allowing in‑IDE agent management and cloud‑based, parallel execution of coding and other tasks. Users can create specialized “modes” such as Code, Ask, Architect, Debug, and Orchestrator, each defined by tailored prompts and settings; for instance, the Ask mode prohibits code editing. Kilo Code emphasizes transparency by licensing the core app under MIT and open‑source most of its backend repositories, leaving only a small abuse‑prevention component closed. Monetization occurs through enterprise contracts that pass through AI usage costs without markup, and the product’s rapid adoption is reflected in over one million downloads since its initial release last summer.
Keywords: #gpt-oss:20b-cloud, AI models, CLI, GitLab, JetBrains, Kilo Code, VS Code, agentic engineering, agentic workflows, command-line, model-agnostic, multi-step workflows, open source
www.fastforward.blog 14 days ago
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2411.
HN
The Agentic Trust Framework: Zero Trust Governance for AI Agents
The Agentic Trust Framework (ATF) is an open, zero‑trust governance specification designed to secure autonomous AI agents by extending classic security concepts to their continuous, probabilistic, context‑driven behavior, thereby enabling enterprises to deploy agentic autonomy safely with existing tools. It comprises a stage‑based, five‑question mental model—identity, behavior, data governance, segmentation, and incident response—each mapped to actionable controls such as authentication, observability, input validation, least‑privilege access, and rapid containment mechanisms (circuit breakers, kill switches, state rollback); it aligns with OWASP Agentic Security and CoSAI, turning their top‑10 guidance into concrete, enforceable measures, and includes a maturity model that progresses agents from read‑only interns to fully autonomous principals, with promotion gates anchored in performance, availability, and security validation. Structured as a Creative‑Commons‑licensed open‑spec on GitHub, ATF fills the governance gap left by traditional frameworks, providing security teams, architects, and business leaders with a layered, risk‑oriented blueprint for scaling agentic AI while maintaining essential controls and auditability. The framework prescribes a staged governance pipeline with four obligatory gates—security audit, business value, incident record, and governance sign‑off—requiring vulnerability assessment, ROI calculation, zero critical incidents, and full stakeholder approvals, while currently only lacking adversarial testing and risk committee approval. Implementation follows a “Crawl, Walk, Run” cadence: Phase 1 (2–3 weeks MVP) equips intern‑junior agents with JWT authentication, structured logging, LLM observability, regex‑based PII guard, allow‑listing, and retry/circuit‑breaker logic; Phase 2 (4–6 weeks production) expands to junior‑senior agents, adding OAuth2/OIDC, RBAC/ABAC, automated anomaly detection, data‑quality validation, and rate‑limiting; Phase 3 (8–12 weeks enterprise) scales to senior‑principal agents with MFA, streaming anomaly monitoring, policy‑as‑code API gateways, SOC‑integrated incident response, and comprehensive data‑quality checks, prioritizing identity, data governance, behavioral monitoring, segmentation, and incident response. ATF maps its controls to SOC 2, ISO 27001, NIST 800‑207, and the EU AI Act, providing a compliance overlay that complements threat‑modeling frameworks such as MAESTRO, and is coupled with training, certification, and the publication *Agentic AI + Zero Trust: A Guide for Business Leaders*.
Keywords: #gpt-oss:20b-cloud, AI, Agentic, Agents, Anomaly Detection, Autonomous, Governance, Implementation, JWT, Observability, Security, Specification, Threat Modeling, Trust, Zero Trust
cloudsecurityalliance.org 14 days ago
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2427.
HN
Show HN: Template for real-time agentic web apps using Convex
A new template streamlines the creation of agent‑based web applications by integrating Convex as the backend for state management and WebSocket‑based live synchronization, auto‑generating necessary environment variables, and offering a visualizer to monitor agent state changes. The starter application is a todo assistant that interprets plain‑English commands, yet the core architecture is designed to serve as a robust foundation for any real‑time agentic app. Built using Subconscious for the agent layer and Convex for data handling, this template can be deployed in minutes with the command `npx create-subconscious-app my-project-name -e convex_app`.
Keywords: #gpt-oss:20b-cloud, Convex, Show HN, Subconscious, UI, WebSockets, agentic, backend, debugging, demos, env vars, real-time, state, todo assistant, updates, visualizer
www.youtube.com 14 days ago
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2436.
HN
Show HN: UCP Checker – A manifest debugger for the agentic web
Show HN unveiled UCP Checker, a debugging utility for manifests on the agentic web; it offers an optional “Share anonymous uptime stats” toggle, which by default transmits only publicly available manifest information and never ever includes any session credentials, thereby keeping the global directory continuously up‑to‑date while simultaneously safeguarding user privacy.
Keywords: #gpt-oss:20b-cloud, Permissions, Privacy, Share, Show HN, UCP Checker, agentic, anonymous, debugger, manifest, toggle, uptime, web
ucpchecker.com 14 days ago
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