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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 - MTimma/knowerage: Local MCP server that tracks AI analysis coverage against your codebase
mtimma · 2026-04-28 · via Hacker News - Newest: "LLM"

Knowerage project icon

Links: GitHub · Glama MCP listing · npm @mtimma/knowerage

Quick Start

Requirements: Node.js 18 or newernpx must be on your PATH (it comes with npm, which is included with Node).

MCP server configuration

Register Knowerage wherever your MCP host expects server definitions (for example some clients use .cursor/mcp.json or .vscode/mcp.json; others use environment variables or a UI—follow your host’s documentation). Use the same server entry shape:

{
  "mcpServers": {
    "knowerage": {
      "command": "npx",
      "args": ["@mtimma/knowerage"],
      "env": {
        "KNOWERAGE_WORKSPACE_ROOT": "${workspaceFolder}",
        "KNOWERAGE_AUTO_FULL_RECONCILE": "true"
      }
    }
  }
}

Replace ${workspaceFolder} with your project root if your host does not expand that variable.

KNOWERAGE_AUTO_FULL_RECONCILE is optional: when unset, empty, or not a truthy value, the file watcher defaults to off. Set to 1, true, yes, or on (trimmed, case-insensitive) to enable. When on, the server watches knowerage/ and, after a short debounce, runs knowerage_reconcile_all on filesystem changes. That is not the same as running a full reconcile after every MCP tool call—it only reacts to file changes under knowerage/. Registry writes to registry.json are ignored by the watcher so saves do not loop.

How to use Knowerage

After the MCP server is configured, you talk to your assistant in normal sentences. You do not need to memorize tool names.

Analyse or document code

Point at files, classes, or behaviour you care about. For example:

  • Using Knowerage, analyse the logical algorithm workflow in main.java.
  • Analyze the data entity reconciliation and versioning logic in the ETL service.

The assistant creates or updates markdown under knowerage/analysis/ and records coverage in knowerage/registry.json (see How It Works below).

Coverage and gaps (same project, later chat or another agent)

When you already have analyses in the tree, you can ask:

  • In percentage, how much of the code has our analysis covered?
  • What part of this codebase is not yet analysed?

Knowerage answers these from the registry and coverage helpers (for example overview, per-file status, and stale lists)—not from hand-waving over the repo.

Alternative approaches

Install via npm

npx @mtimma/knowerage

Or build from source

cargo build --release
./target/release/knowerage-mcp

How It Works

  1. AI agent creates analysis .md files with YAML frontmatter declaring source file and covered line ranges
  2. Registry (knowerage/registry.json) tracks analysis records with SHA-256 hashes for freshness
  3. MCP tools expose create, reconcile, query, and export operations
  4. Agent says "analyze X" → full workflow runs automatically (create → reconcile → record)

Registry file shape (knowerage/registry.json)

The on-disk format is a JSON object whose keys are analysis paths (strings). Each value is one record (see contracts/contracts.md). A full sample with two records lives at examples/registry.sample.json.

flowchart TB
  subgraph file["knowerage/registry.json"]
    O["Top-level JSON object"]
    O --> K["Each key: analysis markdown path, e.g. knowerage/analysis/.../topic.md"]
    K --> V["Value: one RegistryRecord"]
  end

  subgraph rec["RegistryRecord fields"]
    ap["analysis_path · source_path"]
    cr["covered_ranges: [[start,end], ...]"]
    h["analysis_hash · source_hash (sha256:… )"]
    t["record_created_at · record_updated_at (ISO 8601)"]
    st["status: fresh | stale_doc | stale_src | missing_src | dangling_doc"]
  end

  V --> rec
Loading

Frontmatter for analysis .md files is specified separately in the contracts doc (metadata schema), not inside registry.json.

MCP Tools

Tool Purpose
knowerage_create_or_update_doc Create/update analysis document
knowerage_parse_doc_metadata Parse and validate frontmatter
knowerage_reconcile_record Reconcile one analysis record
knowerage_reconcile_all Full rescan/rebuild
knowerage_get_file_status Analyzed vs missing ranges
knowerage_list_stale List stale/problematic records
knowerage_list_registry Full registry snapshot (same shape as registry.json, sorted keys)
knowerage_get_tree Tree/grouped coverage
registry_export_report Export snapshot (JSON/YAML/TXT/HTML)
knowerage_generate_bundle Chunked export of selected analyses (toc*.md, combined*.md, manifest.json)

Project Structure

knowerage/                  # Created per-project
├── analysis/              # Analysis markdown files
│   └── **/*.md
└── registry.json          # Coverage registry

src/                       # Rust MCP server
├── main.rs
├── lib.rs
├── types.rs
├── parser.rs
├── registry.rs
├── mcp.rs
├── security.rs
└── export.rs

Documentation

Security

  • All paths validated against workspace root
  • Path traversal (..) rejected
  • Atomic writes for registry (crash-safe)
  • No secrets in analysis files or reports
  • SHA-256 hash-based freshness (survives git pull)

License

MIT — copyright Martins Timma.

Parts of this project were written or refined with generative AI coding assistants. Human review applies to design, security-sensitive behavior, and releases.