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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 - l-zhi/pith-wiki: Karpathy-style LLM Wiki Desktop...
a569171010 · 2026-06-25 · via Hacker News - Newest: "LLM"

中文版 → README.zh-CN.md

A local-first LLM knowledge base. Drop in your notes, PDFs, and emails — pith hydrates each into a dense Markdown entry an LLM can read, then lets you chat with your whole library, retrieved by keyword + link traversal. No embeddings, no vector DB. Everything stays in plain files on your disk.

Obsidian vault + pith side by side

Drop a note into your Obsidian vault — pith auto-ingests it into an entry the LLM can pull from mid-conversation.

  • 🗂️ Plain files, not a black box — every entry is Markdown + YAML frontmatter; Obsidian, VS Code, and Git open them natively.
  • 🔍 Retrieval you can reason about — weighted keyword search + link-graph traversal + exact grep. No embedding drift, no vendor lock-in.
  • 💬 Chat that writes back — the agent reads your library through file/wiki tools; /digest distills a conversation into a new entry.
  • 🤖 Auto-ingest + schedule — watch a folder for new files; run agent tasks on a cron (e.g. a daily digest of yesterday's additions).
  • 🔒 Local-first — all data under ~/.pith-wiki/; outbound PII filtering on by default. No cloud, no lock-in.

Design philosophy: data engineering > retrieval algorithms. Don't dump raw docs into a store and hope embeddings pull them back. Use an LLM to hydrate each source into a high-density Markdown entry, then retrieve by keyword + link traversal. Simple, file-based, human-readable.

Input formats: .docx .eml .htm .html .md .pdf .txt. The product is pith; the npm package and repo are historically named pith-wiki.

Install

Desktop app (recommended) — the full experience: chat, inbox, dashboard, link graph, skills, and a scheduled-tasks calendar, all over the same engine and on-disk library. No packaged installer yet, so run it from source:

git clone https://github.com/l-zhi/pith-wiki.git
cd pith-wiki/desktop
npm install
npm run dev      # electron-vite dev (HMR)

CLI (optional — for automation / headless use):

npm install -g pith-wiki
pith-wiki        # launch the REPL

On first launch, onboarding walks you through setup — pick a provider, paste an API key, and point it at a notes folder to watch. Everything lives under ~/.pith-wiki/ (config + wiki data); set PITH_WIKI_HOME for an isolated profile.

Platforms: macOS + Linux are CI-tested (Node 20 / 22); Windows is usable but not yet CI-covered. Dev scripts: npm test / npm run typecheck / npm run build (inside desktop/, or repo root for engine/core). See CONTRIBUTING.md.

What it does

1. Hydrate — compress raw documents (markdown / PDF / DOCX / HTML / email) into Markdown entries roughly 30% of the original size. Strip filler, keep signal. LLMs read these directly.

2. Retrieve — no embeddings, no vector DB. Weighted keyword search (title × 2, tags × 2, summary × 1, content × 0.5) + BFS link traversal, plus exact substring/regex search (wiki_grep) and date-range filters (when an entry was added to the library, or the content's own date). Boring on purpose. Entries are plain Markdown; Obsidian, VS Code, and Git all open them natively.

3. Chat — the agent talks to your library through file + wiki tools (wiki_query fuzzy search, wiki_grep exact search, wiki_get, wiki_read_source, wiki_ingest, read_file / write_file / list_dir, …). Every turn writes a transcript; /digest distills the conversation back into a wiki entry, closing the loop chat → store → retrieve.

4. Auto-ingest — point a watch folder (Obsidian vault, inbox, etc.) at pith in Settings, and changes are auto-enqueued for a background worker to hydrate. Built-in health checks flag orphan links, broken frontmatter, and ID collisions.

5. Schedule (desktop) — set tasks that run an agent prompt on a schedule (once, or cron) — e.g. a daily digest of everything added yesterday. Each fire opens a fresh session you can reopen; ${yyyy-mm-dd -1}-style date placeholders are resolved at run time so "yesterday" is always correct.

Full documentation

Document When to read it
docs/config.md Configuration field reference, additionalReadPaths, on-disk layout
docs/config.example.json Full ~/.pith-wiki/config.json example (multi-provider + watchDirs + queue)
docs/entry-format.md YAML frontmatter spec for entries
docs/architecture.md Three core services + data-flow diagram
docs/security-model.md Sandbox invariants (required reading for contributors)
docs/usage.md CLI reference (advanced / automation — the app is the primary way to run)
SECURITY.md Vulnerability reporting
CONTRIBUTING.md Contribution flow
CHANGELOG.md Version history

License

Apache 2.0 · Copyright (c) 2026 lizhi