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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 - AronDaron/dataset-generator: No-code desktop app for generating high-quality synthetic datasets to fine-tune LLMs — plan-then-execute pipeline, LLM-as-judge, HuggingFace upload. 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 - Nyquest-ai/nyquest-rust-fullstack-pub: Nyquest — Semantic Compression Proxy for LLMs. 350+ rules, local LLM stage, 15-75% token savings. Full Rust stack. GitHub - TheoV823/mneme: Enforce architectural decisions in AI-assisted development. GitHub - klemenvod/TokenBrawl: A 1v1 Bomberman-style game where two LLM agents play autonomously against each other. No human plays — you watch the AIs fight. Each agent receives a text description of the board state, reasons about it, and outputs a move as JSON. The game engine executes it. Introducing the Common AI Provider: LLM and AI Agent Support for Apache Airflow Power Circuit AI: Designing Power Electronic Circuits for Motor Drives with Generative Artificial Intelligence Ask HN: How to program with IDE and LLM on CPU locally? Show HN: Agent-cache – Multi-tier LLM/tool/session caching for Valkey and Redis Bonsai 1-bit WebGPU - a Hugging Face Space by webml-community The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows Ask HN: Simple tooling for local LLM code critique without IDE integration? Can a General LLM Diagnose a DICOM Slice? A 10-Case Public Benchmark Charts-of-Thought: Enhancing LLM Visualization Literacy (PDF, 2026) GitHub - Mesh-LLM/mesh-llm: Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat. GitHub - seamus-brady/springdrift: A persistent runtime for long-lived LLM agents Writing an LLM from scratch, part 32k -- Interventions: training a better model locally with gradient accumulation Ask HN: Which LLM model and agentic CLI are you using for local development? GitHub - wayneColt/modelcascade: Route local. Escalate smart. Never overspend. Open-source multi-model cascade routing for autonomous agents. LLM pricing is 100x harder than you think GitHub - asakin/llm-primer: Pre-warmed Claude Code sessions in tmux. No startup wait. GitHub - EggerMarc/chat-rs: A multi-provider LLM framework for Rust. GitHub - SynapseKit/SynapseKit: Minimal, async-first Python framework for production LLM apps- 2 hard deps, no magic, no SaaS. A Claude Skill that Makes LLM Paragraphs More Bearable Does Gas Town 'steal' usage from users' LLM credits & paid services to improve itself? What's Claude Code Actually Doing? Open the Black Box with the Arthur Engine Milla Jovovich's New Open Source LLM Memory App and the Dark Code Problem Your intuition of LLM token usage might be wrong Show HN: Bloomberg Terminal for LLM ops – free and open source GitHub - 0xchamin/mcptube: Transform YouTube videos into a compounding knowledge base with transcripts, vision analysis, and agentic search. Works as an MCP server for Claude, Copilot & more. Show HN: Open KB: Open LLM Knowledge Base Your LLM is a compiler, not a runtime GitHub - sapountzis/Unslop: A Web Feed That Deserves You crates.io: Rust Package Registry Beyond Karpathy's LLM-Wiki: The Necessity of Cognitive Governance GitHub - amitshekhariitbhu/llm-internals: Learn LLM internals step by step - from tokenization to attention to inference optimization. GitHub - parallem-ai/parallem: An expressive library for running agents with the Batch API. GitHub - stfurkan/pi-llm LLM-Wiki Show HN: Formal – Formal verification for AI-generated code using Lean 4 LRTS – Regression testing for LLM prompts (open source, local-first) LLM Wiki Skill: Build a Second Brain with Claude Code and Obsidian I built an LLM Wiki and RAG solution: here's a demo for a security KB The biggest advance in AI since the LLM Predict-Rlm: The LLM Runtime That Lets Models Write Their Own Control Flow the-synthetic-library/the-synthetic-mind at main · joshferrer1/the-synthetic-library GitHub - yisding/reviewwiggum GitHub - Donnyb369/mcp-spine: Context Minifier & State Guard — Local-first MCP middleware proxy GitHub - Beledarian/wgpu-llm: A from-scratch LLM inference engine that uses wgpu (the cross-platform WebGPU implementation) to dispatch WGSL compute shaders for every math operation a Transformer needs. No CUDA. No Python. No massive framework dependencies. Just Rust, raw shaders, and your GPU. GitHub - anitiue/Hindsight: An experience-driven self-improvement framework for LLM agents — 基于经验的 LLM Agent 自我改进框架 GitHub - stef41/lmscan: 🔍 Detect AI-generated text and fingerprint which LLM wrote it. Open-source GPTZero alternative. Zero dependencies, works offline. GitHub - alainnothere/AmdPerformanceTesting: Amd Performance Testing Ask HN: Is a purely Markdown-based CRM a terrible idea? Optimized for LLM agents Context Engineering - LLM Memory and Retrieval for AI Agents | Weaviate little_helper_tui/letter.md at main · sleepyeldrazi/little_helper_tui GitHub - EvanZhouDev/umr: The Unified Model Registry for all your local AI apps. GitHub - JordanCT/VigIA-Orchestrator Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain A Taxonomy of RL Environments for LLM Agents Llama LLM Network Feture GitHub - genedeng-ca/ai-mac-migration: AI-powered Mac-to-Mac migration tool - replace Apple Migration Assistant with intelligent, selective transfer using local LLMs GitHub - lunargate-ai/gateway: High-performance self-hosted AI gateway (OpenAI-compatible) with routing, retries, and streaming GitHub - AuthBits/webmcp: A lightweight, prompt-driven MCP web research server for high-quality LLM powered information extraction. Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception High-Stakes Personalization: Rethinking LLM Customization for Individual Investor Decision-Making From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents HUOZIIME: An On-Device LLM-enhanced Input Method for Deep Personalization TIDE: Token-Informed Depth Execution for Per-Token Early Exit in LLM Inference Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users
GitHub - Anhydrite/doc-torn: Project that provides structured documentation skills for AI coding agents.
anhydrite · 2026-05-21 · via Hacker News - Newest: "LLM"

Project that provides structured documentation skills for AI coding agents.

This repository contains skills that maintain structured documentation always in sync with the code, following a hierarchy (L0 → L1 → L2 → L3) with an explicit dependency matrix between features.

Skills:

Tool:

  • doc-torn-scan — Go binary for iterative feature-by-feature documentation (tree scan, scaffold generation, meta-doc generation)

Installation

Prerequisites

  • Go 1.23+ — to build doc-torn-scan
  • Git — to clone hooks and track doc changes

One-shot Install (all harnesses)

Copy this prompt to any AI coding agent — it will adapt to its own platform:

Install doc-torn for your platform: clone https://github.com/Anhydrite/doc-torn in /opt/doc-torn, register the 3 skills (structured-documentation, doc-driven-exploration, documentation-consistency), and build the doc-torn-scan binary.

Per-Harness Guides

doc-torn-scan Binary

Optional — the structured-documentation skill auto-installs it when missing (which doc-torn-scan || go build ...). To build manually:

cd tools/doc-torn-scan && go build -o ~/.local/bin/doc-torn-scan .

Usage

The agent selects and loads the relevant skill based on the task:

Task Load Then
Document a codebase for the first time structured-documentation (init mode) Follows the iterative doc-torn-scan workflow
Before implementing a new feature doc-driven-exploration Reads skeleton + feature docs before opening code
After completing a feature structured-documentation (update mode) Syncs docs, recalculates dependencies, updates AGENTS.md
Periodically or before release documentation-consistency Full audit of all docs against code with auto-fix

Companion: doc-driven-exploration

Every feature request should start with doc-driven-exploration:

  1. Load the documentation skeleton (architecture, glossary, dependency matrix, dev-process)
  2. Navigate to relevant feature docs by filename
  3. Read feature docs thoroughly (L1 → L2 → L3)
  4. Only then reach for source code — and only if docs are insufficient
  5. Update docs and definitions.md with findings

Companion: documentation-consistency

Run documentation-consistency for periodic or pre-release audits:

  1. Scans all docs against real code line by line
  2. Detects drift: missing files, outdated descriptions, wrong dependency info
  3. Auto-fixes discrepancies found — real code always wins
  4. Suggested cadence: after any completed feature, before any commit, or whenever docs feel stale

Quick start in a project

Place examples/AGENTS.md at your project root and copy the git hooks:

cp examples/AGENTS.md /path/to/your/project/AGENTS.md
cp examples/hooks/* /path/to/your/project/.git/hooks/
chmod +x /path/to/your/project/.git/hooks/pre-commit
chmod +x /path/to/your/project/.git/hooks/post-commit

Documentation Hierarchy (L0 → L3)

Documentation follows a 4-level hierarchy that separates concerns by abstraction level. Each level answers a different question:

Level File Reader Answers
L0 docs/README.md Anyone "What is this project?" — one-line summary, architecture diagram, feature list. 5-minute read.
L1 docs/features/<name>/README.md Feature developer "What does this feature do?" — objective, logic, dependencies, API, key files.
L2 docs/features/<name>/sub-features/*.md Implementer "What are the details?" — edge cases, business rules, sub-flows.
L3 docs/features/<name>/implementation/*.md Maintainer "Why was it done this way?" — technical decisions, rationale, tradeoffs.

The hierarchy lets readers skip what they don't need: a new joiner reads L0, a developer reads L1+L2, a maintainer reads L3. The same hierarchy applies across all features — each feature under docs/features/ has its own L1, L2, and L3 files.

Repository Structure

skills/
  structured-documentation/       # Skill: init + update lifecycle
  doc-driven-exploration/         # Skill: read docs before touching code
  documentation-consistency/      # Skill: full doc audit against code
tools/
  doc-torn-scan/                  # Go binary: iterative doc scanner
    main.go                        # Entrypoint
    state/                         # State file persistence
    scan/                          # Filesystem traversal
    generate/                      # Markdown + meta-doc generation
    cmd/                           # CLI handlers
examples/
  AGENTS.md                        # Template for projects using doc-torn
  hooks/                           # Git hooks (pre-commit, post-commit)

Project files

AGENTS.md                         # Agent cheat sheet: stakes, features index, rules
tools/doc-torn-scan/               # Go binary for iterative doc scanning
skills/
  structured-documentation/        # init + update lifecycle
  doc-driven-exploration/          # doc-first exploration (read before code)
  documentation-consistency/       # full doc vs code audit + auto-fix
docs/
  README.md                        # L0: Lightning overview (5 min)
  architecture/
    architecture.md                 # Functional blocks, flows, boundaries
    dependency-matrix.md            # Dependencies between features
  features/
    <feature-name>/
      README.md                    # L1: Main feature
      sub-features/
        <detail>.md                # L2: Sub-feature
      implementation/
        <detail>.md                # L3: Implementation detail
  user/
    definitions.md                 # Evolving business glossary
    dev-process.md                 # Dev conventions, validation practices
examples/
  AGENTS.md                        # Template for projects
  hooks/                           # Git hooks templates

Lifecycle

graph LR
    A[doc-torn-scan tree] --> B[Agent identifies features]
    B --> C[doc-torn-scan scaffold]
    C --> D[Agent writes 'why']
    D --> E[doc-torn-scan complete]
    E -->|more features| B
    E -->|all done| F[doc-torn-scan meta]
    F --> G[Review + adjust]
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License

MIT