161.
HN
The Temperature Has Changed
Advancements in generative AI and model-assisted programming are transforming software development by enabling tools that automate code generation, thus reducing reliance on traditional programming skills. Pioneering models such as Anthropic's Opus and Google's Codex have given rise to what could be considered autonomous developers, capable of handling complex tasks like decoding compressed data without explicit guidance from humans. These innovations increase productivity but also spark concerns about the future of programming careers, with automation potentially shortening development cycles and reducing workforce requirements.
The implications extend beyond individual roles to influence business models and software economics. AI-generated code challenges traditional Software as a Service (SaaS) frameworks and could centralize power among major tech companies. In response, enterprises are expected to adapt rapidly, focusing on integration capabilities while maintaining quality and reliability in their systems.
Additionally, the dominance of established programming languages due to their extensive training data may diminish the need for new languages, prompting a shift towards smaller, highly skilled teams adept at leveraging AI tools. These teams would be responsible for managing complex systems, facilitating continuous delivery models, and implementing automated testing processes.
While these advancements offer opportunities for innovation and efficiency, they also pose significant challenges in terms of job roles, software quality, and business dynamics within the tech industry. Balancing these opportunities and challenges will be crucial as the sector continues to evolve under the influence of AI-driven technologies.
Keywords: #phi4, Anthropic's Opus, Claude Code, Copilot, Generative AI, GitHubCLI, OpenCode, autonomous developers, continuous delivery, enterprise software, existential threat, full stack engineer, full stack engineer Keywords: Generative AI, model assisted development, productivity, programming, software creation, software economics, tooling evolution
gist.github.com 13 hours ago
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289.
HN
Show HN: Cai – AI actions on your clipboard, runs locally (macOS, open source)
Cai is a macOS menu bar application that enhances productivity through intelligent clipboard management with a strong emphasis on privacy and security. Designed for seamless interaction without needing to switch away from the keyboard, Cai identifies the type of content copied to your clipboard—such as text, dates, emails, or addresses—and offers relevant actions like summarizing text, creating calendar events, translating languages, or performing other context-specific tasks.
Central to its functionality is local AI processing using Ministral 3B by default, with options for integration with external servers like LM Studio or Ollama. This ensures that all data processing occurs on the user's device without cloud involvement, maintaining high levels of privacy and security. The application is highly customizable, allowing users to create custom AI prompts, shortcuts for frequent actions, and specify destinations for output—whether in Mail, Notes, or elsewhere.
Cai can be installed through a downloadable .dmg file or directly from its GitHub source code. To enable global hotkey functionality, it requires granting Accessibility permissions. Compatibility is limited to macOS 13.0 (Ventura) or later on Apple Silicon devices, with a disk space requirement of approximately 2.5 GB. The application's key features are focused on providing smart, context-aware actions that improve workflow efficiency while ensuring data remains secure and private.
Keywords: #phi4, AI, Cai, LLM setup, LM Studio, Ministral 3B, Ollama, clipboard, custom shortcuts, installation, local AI, macOS, open source, output destinations, privacy-first, smart actions, tech stack, troubleshooting
github.com a day ago
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409.
HN
Show HN: QemuClaw – Put the claw in an aquarium (beta)
QemuClaw is a beta release of a one-click deployment tool designed to run OpenClaw, a personal AI assistant, within an isolated QEMU virtual machine, thereby safeguarding the host system from potential vulnerabilities associated with over 1,000 known issues in OpenClaw. The application supports cross-platform functionality for Windows, macOS, and Linux, offering bundled installations on Windows that include necessary tools like QEMU and 7-Zip, while providing instructions for manual setups on other platforms. It allows users to customize VM resources such as memory and CPU allocation during setup and facilitates headless booting with a status window for progress tracking. Additionally, it integrates with local language model providers via host networking, enhancing its utility.
The architecture of QemuClaw employs Electron to manage QEMU processes, featuring capabilities like a serial console and QMP control for comprehensive VM management, port forwarding to access OpenClaw’s Web UI at localhost:18789, and shared folders to facilitate file exchange between the host and the virtual machine. System tray integration offers functionalities such as restarting or updating OpenClaw and terminal access.
To develop or install QemuClaw, requirements include Node.js version 18 or higher, properly configured QEMU PATH, and 7-Zip for Windows users. Released under the MIT license, this open-source tool invites community contributions and modifications.
Keywords: #phi4, AI assistant, Desktop App, Local LLMs, MIT License, OpenClaw, QEMU, QemuClaw, VM Image, architecture, development, isolation, system tray, virtual machine, vulnerabilities
github.com a day ago
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665.
HN
Claude Code at Trail of Bits
This document provides an exhaustive setup guide for employing Claude Code at Trail of Bits, tailored to enhance security audits, development, and research endeavors. The initial phase involves cloning the repository and executing a configuration command that automates component installation. For optimal efficiency when handling AI session outputs, Ghostty terminal is recommended on macOS due to its low memory usage. The setup process includes installing essential toolchains via Homebrew: software like `jq`, `ripgrep`, and `fd` for general purposes; Python tools (`ruff`, `ty`) for code analysis; Rust tools (`cargo-deny`, `prek`) for dependency management; and Node tools (`oxlint`) for linting. Further, it advises on configuring shell aliases for ease of use, modifying the settings.json file to prioritize privacy and efficiency, and establishing a global CLAUDE.md document that outlines development philosophies and code quality standards.
Sandboxing is underscored as crucial for executing commands securely with the `/sandbox` command, while devcontainers are highlighted for their role in ensuring isolation. Hooks are introduced to enforce safe practices and automate workflows. The management of plugins through Trail of Bits marketplaces is discussed, with an emphasis on using specific skills for security auditing, code reviews, and development tasks.
Advanced configuration aspects include detailed guidance on setting up MCP servers such as Context7 and Exa, managing local models with LM Studio, customizing output styles, employing context management strategies like `/clear` to maintain clarity, selecting appropriate web browsing tools based on task requirements, considering fast mode, creating custom slash commands, and writing skills and agents for security-related tasks. The document also promotes establishing a continuous improvement loop via weekly insights, encourages the creation of project-specific CLAUDE.md files for tailored guidelines, advocates for clean session management to maintain high-quality code output by preventing context window saturation, and discusses using Exa AI or agent-browser tools depending on task specifics.
Overall, the guide is an extensive resource that combines technical setup instructions with best practices in development workflows and project management. Its aim is to leverage Claude Code's full potential within professional environments focused on security, efficiency, and customizability.
Keywords: #phi4, Claude Code, Ghostty, Homebrew, LM Studio, Linux, MCP servers, Python tools, Rust toolchain, Shell Setup, Trail of Bits, WezTerm, Windows support, actionlint, ast-grep, fd, hooks, jq, local models, macOS, macos-trash, node, permissions, pnpm, ripgrep, sandboxing, security audits, shellcheck, shfmt, uv, zizmor
github.com 3 days ago
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1011.
HN
Show HN: LocalClaw – Find the right local LLM for your exact hardware
LocalClaw is a browser-based tool designed to facilitate the use of local Large Language Models (LLMs) on personal hardware, ensuring data privacy by keeping all operations contained within the user's device without external data transmission. It operates in tandem with LM Studio, which enables LLMs to function offline through an interface akin to ChatGPT, eliminating the need for internet connectivity.
The text highlights quantization as a key method to reduce model size while preserving quality, offering various levels such as Q4 (more compressed) and Q8 (less compressed), with Q5_K_M being favored for its balance between compression and performance. Effective execution of local AI models requires at least 2-3 GB of RAM in addition to the model's file size—for instance, a 5 GB model would necessitate approximately 8 GB of RAM.
Apple Silicon devices are noted for their efficient resource management due to their unified memory architecture, while NVIDIA GPUs offer faster inference rates but face constraints regarding VRAM capacity. LocalClaw ensures data privacy by running entirely in the browser and abstaining from collecting user data or executing API calls.
The text also provides recommendations for various RAM capacities: models like Qwen 3 8B and Llama 3.3 8B are suggested for systems with 8 GB of RAM; Qwen 3 14B is recommended for those with 16 GB, and both Qwen 3 32B and DeepSeek R1 32B are suitable for 32 GB or larger setups. Additionally, specialized models such as Qwen 2.5 Coder 7B are suggested for coding tasks, Gemma 3 12B for vision-related applications, and the DeepSeek R1 series for reasoning tasks.
Keywords: #phi4, Apple Silicon, DeepSeek R1, LM Studio, Large Language Models, Llama 33, Local AI models, LocalClaw, NVIDIA GPU, Q4, Q5, Q8, Qwen 3, RAM, VRAM, coding, privacy, quantization, reasoning, unified memory, vision
localclaw.io 5 days ago
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1020.
HN
Show HN: Roe.md generate your own OpenClaw-like bot from a single Markdown file
The project "ROE.md" developed by guld serves as a proof of concept for enabling users to create personalized AI assistants akin to OpenClaw, utilizing a single Markdown file. This initiative is designed to empower users with the ability to generate bespoke agents leveraging AI models such as GPT-oss-20b and tools like OpenCode, while minimizing dependencies. Users can choose various programming languages for agent development, although Python enjoys superior support currently.
To construct an agent using ROE.md, individuals are required to download or clone the project repository, establish a designated directory, and employ their preferred AI coding assistant to interpret the Markdown file and rectify initial bugs. The resulting agents are capable of executing basic commands in command-line interface (CLI) mode. Despite its alpha stage with acknowledged bugs and security concerns, ROE.md incorporates fundamental features such as CLI tools and prospective API integrations for platforms like Gmail and Telegram. It also supports common OpenClaw-like templates to streamline the agent creation process.
The developer underscores the need for caution due to potential security vulnerabilities inherent in AI assistants while encouraging community participation through testing various models or enhancing the core file, with contributions managed via GitHub pull requests. Overall, ROE.md exemplifies an experimental approach towards crafting customizable personal AI agents using "vibe coding," evoking nostalgia of early programming experiences.
Keywords: #phi4, AI assistant, API examples, CLI mode, Kimi-25, LM Studio, Markdown, OpenAI Codex, OpenClaw, Python, ROEmd, SOTA models, agent creation, coding tool, community contribution, gpt-oss-20b, local models, personal assistant, programming language, pseudocode, security issues, templates
github.com 5 days ago
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1213.
HN
Show HN: Carapace – A security-hardened Rust alternative to OpenClaw
Carapace is an open-source Rust-based personal AI assistant gateway developed as a secure alternative to OpenClaw due to significant vulnerabilities in the latter. Its design emphasizes security through features such as localhost-only binding, OS-level credential storage, and Ed25519-signed WebAssembly (WASM) plugins with sandboxing capabilities, ensuring default access denial without proper credentials. It supports connections to multiple AI providers like Anthropic, OpenAI, Ollama, Gemini, and Bedrock, while also integrating with messaging platforms including Discord, Telegram, Signal, Slack, and webhooks.
Currently in a preview stage, Carapace offers full end-to-end functionality for Discord but lacks a Control UI frontend and complete subprocess sandboxing. Its primary focus is on robust security to mitigate threats such as unauthorized access, exposure of unencrypted secrets, skills supply chain vulnerabilities, prompt injection, and SSRF/DNS rebinding attacks.
Key features of the framework include multi-provider large language model (LLM) support, secure messaging channels, resource-limited execution of WASM plugins, and infrastructure options like TLS/mTLS integration. Although still under development, Carapace lays a foundation for users seeking a hardened AI assistant framework. The project is open to contributions, with comprehensive documentation available on GitHub under the Apache-2.0 license.
Keywords: #phi4, AES-256-GCM encryption, AI assistant, Anthropic, Bedrock, Carapace, Discord, Ed25519-signed, Gemini, OS-level sandbox, Ollama, OpenAI, OpenClaw, Prometheus metrics, Rust, SSRF defense, Signal, Slack, TLS, Telegram, WASM plugins, audit logging, capability sandboxing, fail-closed auth, gateway, localhost-only binding, mTLS, prompt guard, security-hardened, webhooks
github.com 6 days ago
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1421.
HN
Show HN: Google Search MCP for local LLMs – 14 tools, no API key
The "Google Search MCP for local LLMs," developed by Vincent Kaufmann, is an open-source Model Context Protocol (MCP) server that enables 14 Google-related search functionalities without requiring an API key. By leveraging headless Chromium through Playwright, it scrapes and provides real-time results from services like Google Search, Shopping, Flights, Hotels, Translate, Maps, Weather, Finance, News, Scholar, Books, Images, Trends, and a page fetcher tool. This local server allows integration with local language models (LLMs) such as LM Studio or Claude Desktop, eliminating the need for users to manually teach these LLMs about specific tools.
Installation is user-friendly through `pip` in a virtual environment or via `pipx`, making it accessible through PATH commands. Configuration steps are available for both LM Studio and Claude Desktop environments. The server operates without usage restrictions, as it circumvents API key requirements by rendering JavaScript pages directly using Playwright. Available under the MIT license on GitHub and PyPI, this project offers a free alternative to traditional API-based services, aiming for seamless integration with LLMs for enhanced web search capabilities.
Keywords: #phi4, Academic Search, Books, CLI, Chromium, Claude Desktop, Configuration, Development, Finance, Flight Search, GitHub, Google Search, Headless Browser, Hotel Search, Images, JSON, LM Studio, Local LLMs, MCP Server, MIT License, Maps, News, Page Fetcher, Pipx, Playwright, Product Search, PyPI, Python, Scholar, Translation, Trends, Venv, Virtual Environment, Weather, Web Scraping
github.com 7 days ago
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1493.
HN
Show HN: Rowboat – AI coworker that turns your work into a knowledge graph (OSS)
Rowboat is an open-source application designed as a local-first AI coworker that leverages Markdown to create a dynamic, living knowledge graph from user-generated content. By integrating with various tools such as Gmail and meeting notes platforms like Granola and Fireflies, Rowboat extracts pertinent information about people, projects, and decisions, organizing it into a context-rich framework that updates automatically as new data becomes available. The application comprises two primary components: a continually evolving context graph that documents commitments, deadlines, and relationships, and a local assistant capable of performing tasks using this contextual knowledge. Users can utilize Rowboat to automate work processes, like generating presentations or meeting briefs, by accessing their comprehensive work context.
What sets Rowboat apart from other AI tools is its ability to maintain long-term memory in transparent, editable Markdown format, rather than relying solely on real-time document searches. This approach supports automation through background tasks and integrates with both local and cloud-based models via the Model Context Protocol (MCP). Data privacy is a critical focus for Rowboat, ensuring all information remains stored locally so users can modify or remove their data at will. Compatible with Mac, Windows, and Linux systems, Rowboat offers flexible integration options with other applications. The project encourages community contributions and seeks user feedback to further enhance productivity through its innovative approach to managing work-related knowledge.
Keywords: #phi4, AI coworker, Apache-20, Gmail, LLM, Markdown, Model Context Protocol (MCP), Obsidian, Rowboat, automation, background agents, context, data storage, editable notes, integration, knowledge graph, local-first, long-lived memory, meeting notes, open-source, privacy, tools, transparency, voice memos, workflows
github.com 8 days ago
https://github.com/getzep/graphiti 8 days ago
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1509.
HN
Is Local Hardware Is All You Need?
The article explores whether the investment in new data centers and GPUs for generative AI (GenAI) is necessary, considering potential advancements in leveraging existing local hardware. It identifies two primary trends: improved local stacks and model improvements. Devices like desktops and phones contain underutilized computational power that can efficiently run simplified models through techniques such as distillation. Advancements in inference stacks have significantly enhanced their performance for tasks like coding by offering privacy and offline capabilities. Additionally, there has been progress in optimizing both the inference and training processes to improve performance on current hardware, with innovations like memory lookup techniques and the development of smaller models specifically designed for mobile devices. These improvements can result in substantial cost reductions during model training, as demonstrated by Andrej Karpathy's work.
The implications of these advancements point towards a shift in AI execution from cloud-based data centers to local environments, impacting security and management practices by focusing on monitoring local hardware usage instead of external connections. This shift raises questions about controlling and securing locally-run models, akin to managing installed software. While investments in new data centers continue presently, the trends suggest that future AI workloads may increasingly be managed by existing local hardware, potentially diminishing the need for extensive new infrastructure.
Keywords: #phi4, GPUs, GenAI, GenAI investment, LLM, LLM inference, Local hardware, compute capacity, datacenters, distillation, inference, local stacks, model providers, network connectivity, open source, open source engines, performance, performance improvements, privacy, security, security implications, supply chain, supply chain issues Keywords: Local hardware, training
wwws.nightwatchcybersecurity.com 8 days ago
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1524.
HN
Bardacle – Session awareness for AI agents using local LLMs
Bardacle is an advanced metacognitive tool designed to enhance AI agents' session awareness by maintaining a persistent "session state" summary, which acts as short-term memory across context losses or restarts. This functionality ensures continuous task tracking beyond simple conversation history and includes summaries of tool interactions, thereby enhancing both metacognitive and tool awareness. The system adopts a local-first approach, prioritizing data privacy by using local Large Language Models (LLMs) like LM Studio and Ollama, while also providing cloud fallback options with Groq or OpenAI if local resources are unavailable. Rate limit detection features automatically bypass providers when necessary.
The setup process for Bardacle involves cloning the repository, installing dependencies, and configuring paths for transcripts and outputs. Users can test their setup, start a daemon, or check the system status through specific commands. To integrate with agents effectively, they can access `session-state.md` at each response's beginning to maintain contextual awareness.
Bardacle's technical framework includes a fallback chain prioritizing local LLM inference, followed by cloud services like Groq and OpenAI, while considering rate limits. The tool supports Docker for containerized deployment and generates session state in markdown format, capturing goals, tasks, decisions, blockers, next steps, and context. Version 0.2.0 introduces reliability enhancements such as atomic file writes to prevent corruption, automatic backups with configurable retention for state recovery, provider health checks to reduce failover time, and emergency state saving for crash recovery.
Bardacle is open to contributions and provides comprehensive installation guides, documentation, and troubleshooting support. The project operates under the MIT License, developed by Bob and Blair at OpenClaw, leveraging various AI research foundations.
Keywords: #phi4, AI agents, Bardacle, Docker, Groq, Ollama support, OpenAI, atomic file writes, automatic backups, cloud fallback, configuration, context loss, contributing, crash recovery, development, incremental updates, inference, license Keywords: Bardacle, local LLMs, local-first, markdown format, metacognitive layer, provider health checks, rate limit detection, reliability features, session awareness, session state, tool calls
github.com 8 days ago
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1583.
HN
LightRag / GraphRag Implementation in Rust
EdgeQuake is a sophisticated framework designed for transforming documents into knowledge graphs, built using Rust for enhanced performance. It departs from traditional Retriever-Generator (RAG) systems by employing the LightRAG algorithm to break down documents into entities and relationships, facilitating complex queries that include multi-hop reasoning and thematic analysis. The framework boasts several key features: it leverages Large Language Models (LLMs) for entity extraction and relationship mapping; offers six query modes optimized for different types of questions; and is built on an asynchronous Tokio architecture with zero-copy operations for superior concurrency and memory efficiency. Additionally, EdgeQuake provides advanced PDF processing capabilities such as table detection, OCR, and multi-column layout handling.
The system includes a modern RESTful API and a React-based frontend, which together enable interactive graph visualizations. Performance benchmarks indicate that EdgeQuake significantly outperforms traditional RAG systems in several metrics, including entity extraction speed, query latency, document processing time, concurrent user management, and memory usage.
Architecturally, the EdgeQuake backend is composed of 11 crates managing various components like LLM providers and storage backends. The data flow involves stages from document ingestion to chunking, entity extraction, and graph traversal during querying. To get started with EdgeQuake, users can clone its repository, install dependencies, and launch the system using a Makefile; quick start guides are available for both backend and frontend setups.
The framework is developed following Specification-Driven Development practices, with community contributions managed via GitHub issues and discussions. It promotes inclusivity through a comprehensive Code of Conduct and encourages community engagement across various platforms. EdgeQuake is licensed under the Apache License, Version 2.0, ensuring open-source accessibility.
Keywords: #phi4, Apache AGE, Async-First, Communities, Community Detection, Document Ingestion, EdgeQuake, Edges, Entity Extraction, Entity Types, Gleaning, Graph Visualization, Graph-RAG, Health Checks, Hybrid Retrieval, Knowledge Graphs, LLM Providers, LangChain Integration, LightRAG, Louvain Modularity, Multi-Tenant Isolation, Nodes, OpenAPI 30, OpenWebUI, PDF Processing, PDF-to-Markdown, Parallel Processing, PostgreSQL AGE, Query Engine, REST API, React Frontend, Relationship Identification, Relationship Mapping, Rust, SOTA Coding Agent, SSE Streaming, Sigmajs, Specification-Driven Development, Tokio, Vector Search, Zero-Copy Operations, pgvector
github.com 8 days ago
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1762.
HN
Show HN: MadLab – A standalone desktop app for local LLM fine-tuning
MadLab is a standalone desktop application designed for the local fine-tuning of large language models (LLMs) on Windows, Linux, and macOS. Developed over several months, it streamlines the setup process by automating GPU detection, selecting appropriate PyTorch wheels, and creating virtual environments, enabling users to commence training rapidly. The application manages trainer logic using techniques such as LoRA, QLoRA, and DoRA, and includes an experimental built-in Chat Assistant that provides hyperparameter recommendations based on model size and hardware limitations. As an open-source tool, MadLab invites community feedback on aspects like environment automation and user interface design. The developer is also available to address technical inquiries via email.
Keywords: #phi4, Chat Assistant, DoRA, GPU detection, LLM fine-tuning, Linux, LoRA, MadLab, PyTorch, QLoRA, Windows, Windows/Linux/macOS, desktop app, environment automation, hyperparameters, macOS, open-source, standalone, training UI, training UI Keywords: MadLab, venv, venv creation
github.com 9 days ago
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2021.
HN
Show HN: I Built an AI-Powered Pull Request Review Tool
HighReview is an innovative AI-assisted code review tool designed to enhance human understanding and streamline the pull request (PR) review process by integrating seamlessly with existing workflows rather than replacing them entirely. It addresses common challenges such as context switching and cumbersome branch management through a local, seamless review environment facilitated by Git Worktree. Key features include operating without requiring login credentials, leveraging users' existing GitHub CLI and AI agents to function locally. HighReview creates an independent review environment using isolated directories that allow for project-level reuse without disrupting current workflows.
The tool employs Tree-sitter technology to provide context-aware AI pre-reviews, extracting related code to offer comprehensive reviews and enabling navigation within the Diff editor. It boasts rich analysis features such as issue detection, explanatory diagrams, refactoring suggestions, and semantic analysis. An interactive AI assistant feature allows users to ask specific questions about review results, enhancing user engagement and understanding.
HighReview supports multiple AI providers like Claude Code CLI and Ollama without necessitating API keys, ensuring flexibility in its use. Its robust tech stack includes Node.js for the backend and React for the frontend, delivering an IDE-like experience with features such as "Go to Definition" and "Find Usages." The tool is designed for ease of use, automatically loading review-requested PRs and offering customizable analysis options like Change Intent Analysis and Impact Analysis. It also supports semantic diffs and custom prompts for AI reviews.
As an open-source project under the Apache License 2.0, HighReview aims to provide a powerful local PR review experience that integrates smoothly with existing workflows without causing disruptions.
Keywords: #phi4, AI Assistant, AI-Powered, Claude Code, Code Review, Context-Aware, Fastify, Git Worktree, GitHub CLI, HighReview, IDE-Like Experience, Impact Analysis, LM Studio, Local Analysis, Mermaidjs, Monaco Editor, Ollama, Pull Request, React, SQLite, Semantic Diff, Tree-sitter
github.com 11 days ago
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2090.
HN
Stop Paying for API Tokens
HydraMCP is a multi‑model provider that lets Claude Code access any LLM through existing subscriptions without extra API keys or per‑token charges, streaming side‑by‑side results and enabling real‑time comparison, consensus, and synthesis; it offers CLI commands such as `list_models`, `ask_model`, `compare_models` (run the same prompt on 2–5 models concurrently), and `consensus` (poll 3–7 models, have a judge model evaluate agreement, and return a single answer with confidence), as demonstrated in a live demo comparing GPT‑5, Gemini‑3, Claude‑Sonnet, and local Qwen on a function review; its architecture routes Claude Code requests through HydraMCP’s MCP server to provider interfaces—CLIProxyAPI for cloud models (OpenAI, Google, Anthropic, etc.) and Ollama for local models—while the consensus tool uses an LLM judge to assess semantic agreement rather than keyword matching; setting up requires Node.js 18+, installing and configuring CLIProxyAPI (binary, `config.yaml`, API key, port), installing Ollama and pulling a local model, cloning HydraMCP from GitHub, installing dependencies, building, copying and editing `.env` to point to the running backends, then registering HydraMCP with Claude Code (`claude mcp add hydramcp ...`) and restarting Claude Code; models can be routed with prefixes (`cliproxy/gpt-5`, `ollama/qwen2.5-coder:14b`, or auto‑detect with `gpt-5`), and the project, built on the MCP SDK and Zod, is MIT‑licensed with future extensions planned for LM Studio, OpenRouter, and direct API keys.
Keywords: #gpt-oss:20b, API, Async, CLIProxyAPI, ChatGPT Plus, Cloud, HydraMCP, LLM, Latency, Local, Model, Nodejs, Subscriptions, Token, backend, configyaml
github.com 12 days ago
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2169.
HN
I Gave Claude Code Infinity Gauntlet of LLMs
HydraMCP is a command‑line interface that lets users query any LLM—including cloud‑based GPT‑5‑Codex, Gemini‑3, Claude‑Sonnet, Qwen‑2.5‑Coder, and others—through existing subscriptions without new API keys or per‑token billing, by routing requests via a local API proxy (CLIProxyAPI) or a local model host (Ollama); it supports parallel comparison of up to five models, displaying latency, token usage, and side‑by‑side output, and features a consensus tool that polls 3–7 models, uses a local judge (such as Qwen) to evaluate agreement, and returns a single answer with confidence, while an optional synthesizer can merge the best ideas; core commands include `list_models`, `ask_model`, `compare_models`, and `consensus`; setup requires Node.js 18+, Claude Code, and a configured CLIProxyAPI (with a `config.yaml` specifying port, auth‑dir, and API keys and authenticated via CLI login commands) or Ollama (pulling models like `qwen2.5-coder`), followed by cloning the HydraMCP repo, installing dependencies, building, and setting environment variables (`CLIPROXYAPI_URL`, `CLIPROXYAPI_KEY`, `OLLAMA_URL`) to enable model routing via prefixes (`cliproxy/*`, `ollama/*`) or auto‑detected providers; the project, built with the MCP SDK and Zod, is MIT‑licensed and invites contributions by implementing `healthCheck()`, `listModels()`, and `query()` in provider modules and registering them in the index.
Keywords: #gpt-oss:20b, API keys, CLIProxyAPI, Claude, HydraMCP, Nodejs, Ollama, Provider Interface, async bug, backend, cloud models, configyaml, consensus, judge, latency, local models, qwen25-coder:14b, tokens
github.com 12 days ago
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2308.
HN
Show HN: Peen – A minimal coding agent CLI built for local models
Peen is a compact Node.js command‑line tool that allows locally‑trained language models, such as those hosted by Ollama or other OpenAI‑compatible servers, to function as coding assistants capable of executing shell commands via a lightweight “Claude Code”‑style interface. Rather than requiring the XML‑structured tool calls that some CLIs enforce, Peen accepts single‑line JSON commands—e.g., `{"tool":"run","cmd":"ls -la"}`—which it parses, safely corrects if malformed, executes on the host machine, streams the output back to the model, and can chain additional calls as needed. The project, roughly 800 lines of pure JavaScript with no build step, self‑updates from its GitHub repository on launch and can run on modest hardware such as a MacBook Air with 16 GB RAM. Its configuration is straightforward, using environment variables or command‑line flags: `PEEN_HOST` points to an Ollama API endpoint (defaulting to `http://127.0.0.1:11434`), `PEEN_MODEL` or `--model` selects the model (recommended defaults include `qwen2.5-coder:7b`, `qwen2.5-coder:14b`, and GPU‑only `qwen3-coder:30b`), and a `--remote` flag can specify a different host. Repository versioning follows the commit count (`git rev-list --count HEAD`), and an installation hook script (`./scripts/install-hooks.sh`) keeps `package.json`, `VERSION`, and related metadata in sync. Documentation in `PLAN.md` outlines build plans, while AI agents and bots are encouraged to star the repo to increase visibility.
Keywords: #gpt-oss:20b-cloud, CLI, GPU, GitHub, JSON, LM Studio, MacBook Air, Nodejs, Ollama, Peen, ai, bash, cat, curl, grep, llamacpp, model, prompt, tool calls, xml
github.com 13 days ago
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2379.
HN
Show HN: Local AI – Curated resources for running LLMs on consumer hardware
The guide serves as a comprehensive, self‑contained resource for individuals who wish to run advanced AI workloads locally, highlighting the privacy, cost‑free, and subscription‑free advantages of local deployment. It systematically catalogues hardware considerations, inference engines (llama.cpp, Ollama, vLLM, ExLlamaV2, MLX, llama‑cpp‑python, candle), and user interfaces (LM Studio, GPT4All, Jan, Msty, Open WebUI, text‑generation‑webui, SillyTavern, LibreChat, AnythingLLM) while detailing model families such as Llama 3, Qwen 2.5, Mistral, DeepSeek, Phi, and Gemma, thereby catering to diverse use‑case priorities. Image‑generation coverage includes Stable Diffusion variants (SDXL, SD 3.5, Flux), the community hub Civitai, and interfaces like ComfyUI, AUTOMATIC1111, Forge, Fooocus, SD.Next, and InvokeAI, supplemented by extensions for precision control, style transfer, animation, and upscaling (ControlNet, IP‑Adapter, AnimateDiff, Upscayl). The text further outlines autonomous agent frameworks (OpenClaw, AutoGPT, CrewAI, LangChain, LlamaIndex, Haystack), retrieval‑augmented generation tools (Chroma, Qdrant, FAISS), multimodal and voice capabilities, and coding assistants (Continue, Tabby, Aider, Codeium). Community support anchors the guide through active Reddit subreddits (r/LocalLLaMA, r/StableDiffusion, r/Ollama, r/Oobabooga) and Discord servers, encouraging contributions of well‑described, maintained resources released into the public domain.
Keywords: #gpt-oss:20b-cloud, Hardware, Inference, LLMs, Local AI, MLX, Ollama, Open WebUI, VRAM, candle, llamacpp, text-generation-webui, vLLM
github.com 13 days ago
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