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Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace
GitHub - artem-mangilev/ctxbrew: 📦 Ship & Use AI-friendly...
mangilev · 2026-04-26 · via Hacker News - Newest: "LLM"

ctxbrew logo

Ship & Use AI-friendly package context.

ctxbrew is a CLI and protocol for shipping and consuming AI-friendly library context.

It helps:

  • library authors ship context with minimal effort and configuration
  • library users get better AI responses to prompts related to their dependencies.

✨ Features

  • 🧑‍💻 Simple authoring: define a ctxbrew.yaml, build context artifacts, and publish them with your package.
  • 🤖 Simple consumption: install ctxbrew, generate an agent skill, and let the LLM discover context from installed libraries.
  • 📦 No extra hosting: ship context as part of your library.
  • 🏷️ Version correctness: read context from the installed package version.
  • Fast local access: extract context from local package files with no network calls.
  • 🪄 Token efficiency: split context into focused slices and compress supported sources into top-level signatures.

🗺️ Roadmap

  • JavaScript ecosystem support
  • Python and pip support
  • Go modules support
  • Rust and Cargo support
  • Java and Gradle support
  • C# and NuGet support
  • PHP and Composer support
  • Ruby and RubyGems support
  • Dart and pub.dev support
  • Homebrew support
  • Community context registry for packages without first-party support
  • Website

🚀 Quick Start

🧑‍🍳 Library Author Workflow

  1. Install ctxbrew and create a config:
npm install ctxbrew --save-dev
npx ctxbrew init

This creates ctxbrew.yaml.

  1. Edit ctxbrew.yaml and describe context slices.

Each slice may cover one focused feature, workflow, or concept. Smaller slices help agents request only the context they need and keep token usage lower.

  1. Validate and build the context:
# Validate config and input files without writing artifacts.
npx ctxbrew build --check

# Generate ctxbrew/index.yaml and ctxbrew/<slice-id>.md files.
npx ctxbrew build
  1. Publish the generated ctxbrew/ folder with your package.

The exact setup depends on your release pipeline. You can see an example integration.

🧭 Library Consumer Workflow

  1. Install ctxbrew globally to use it across repositories:
npm install -g ctxbrew
  1. Set up agent skills:
# Generate ctxbrew skills in supported agent locations.
ctxbrew setup

# OR print the skill markdown to stdout.
ctxbrew skill
  1. Let the generated skill guide your agent:
# List packages with ctxbrew context in node_modules.
ctxbrew list

# List slices for one package.
ctxbrew list @org/library

# Read one slice.
ctxbrew get @org/library components

# Search slices by id and description.
ctxbrew search "dialog focus trap"

🧩 Config Format

ctxbrew.yaml describes the context artifacts that will be generated into ctxbrew/.

version: 1
slices:
    - id: overview
      description: High-level architecture
      include:
          - README.md

    - id: components
      title: Components
      description: UI components and usage
      compress: true
      include:
          - src/components/**
          - docs/components/**

✅ Rules

  • version is required and currently must be 1.
  • slices must contain at least one slice.
  • id must be unique kebab-case.
  • description is required and is used by ctxbrew search.
  • include is required and must match at least one file during build.
  • title is optional; when omitted, it is generated from id.
  • compress is optional and defaults to false. When enabled, supported files are reduced to top-level signatures.

🛠️ CLI Reference

ctxbrew init [--cwd <dir>] [--force]
ctxbrew build [--check] [--cwd <dir>]
ctxbrew list [package]
ctxbrew get <package> <slice>
ctxbrew search <query> [--limit <n>]
ctxbrew setup [--cwd <dir>]
ctxbrew skill

🧪 Development

bun install
bun run dev -- --help
bun test
bun run typecheck
bun run build

📄 License

This project is licensed under the MIT License.