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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 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 - wastedcode/memex: File system based wiki, mainta...
zeppelin_7 · 2026-04-18 · via Hacker News - Newest: "LLM"

Stop re-discovering what you already know. Memex is an LLM runtime that builds and maintains a personal wiki from your raw sources — so the knowledge compounds instead of disappearing into chat history.

The problem

Most people's experience with LLMs and documents looks like RAG: upload files, retrieve chunks, generate an answer, forget everything. Ask the same subtle question tomorrow and the LLM starts from scratch. Nothing accumulates. There's no memory between sessions, no synthesis across sources, no evolving understanding. Your tenth conversation is no smarter than your first.

The idea

Instead of retrieving from raw documents at query time, Memex has the LLM incrementally build and maintain a persistent wiki — a structured, interlinked collection of markdown files that sits between you and your sources.

When you add a new source, the LLM doesn't just index it for later retrieval. It reads it, extracts the key information, and integrates it into the existing wiki — updating entity pages, revising topic summaries, noting where new data contradicts old claims, strengthening the evolving synthesis. The knowledge is compiled once and kept current.

The wiki is a persistent, compounding artifact. The cross-references are already there. The contradictions have been flagged. The synthesis reflects everything you've ingested. Every source makes the whole richer. Every question you ask can be filed back as a new page, so your explorations compound too.

You never write the wiki yourself. You source, explore, and ask questions. The LLM does the summarizing, cross-referencing, filing, and bookkeeping that makes a knowledge base actually useful over time. The tedious part of maintaining a knowledge base isn't the reading or the thinking — it's the bookkeeping. LLMs don't get bored, don't forget to update a cross-reference, and can touch 15 files in one pass.

This can apply to anything where you accumulate knowledge over time:

  • Research — reading papers over weeks, building up an evolving synthesis with citations and cross-references
  • Reading a book — filing each chapter, building out pages for characters, themes, and plot threads as you go
  • Business intelligence — competitive analysis, customer signals, market trends fed from Slack, calls, and reports
  • Personal — health, goals, self-improvement, journal entries, podcast notes, building a structured picture of yourself
  • Learning — course notes, hobby deep-dives, trip planning, anything you want organized rather than scattered

The idea is related in spirit to Vannevar Bush's Memex (1945) — a personal, curated knowledge store with associative trails between documents. Bush's vision was closer to this than to what the web became: private, actively curated, with the connections between documents as valuable as the documents themselves. The part he couldn't solve was who does the maintenance. The LLM handles that.

Why Memex

Just files, no RAG. The wiki is a directory of markdown files. No vector databases, no embeddings, no retrieval pipelines. Claude is already smart enough to grep through files, read what's relevant, and figure out what to update. The simplest architecture that works.

Safe by default. Each wiki runs in its own Linux mount namespace — Claude can only see /workspace, not your home directory, not other wikis, not the host filesystem. No Docker required. Tool access is restricted to file operations (Read, Write, Edit, Glob, Grep) unless you explicitly opt in to more.

CLI-first. Everything is a single command: memex ingest, memex query, memex lint. Runs from your existing Claude machine. No web UI to host, no API keys to juggle beyond what you already have for Claude.

Feed it from anywhere. Ingest markdown, PDFs, HTML, images, plain text — anything Claude can read. Clip web articles with Obsidian Web Clipper, pipe in Slack exports, drop in meeting transcripts. Add MCP servers to pull directly from Notion, Google Drive, or any app with an API. Local files or remote sources, it all compiles into the same wiki.

Fully yours to shape. Edit .claude.md to change how the LLM thinks about your domain. Whitelist extra tools. Swap models. The prompts, conventions, and filing structure are all configurable — the wiki evolves with you, not against a fixed template.

What Memex does

Memex wraps claude -p (Claude Code's programmatic mode) into a daemon that gives each wiki its own isolated filesystem, job queue, and configuration. Claude operates on the wiki with the same file tools a developer would use — Read, Write, Edit, Glob, Grep — against a real filesystem of markdown files.

Three layers:

  1. Raw sources — your curated collection of articles, papers, images, data files. Immutable. The LLM reads them but never modifies them.
  2. The wiki — LLM-generated markdown. Summaries, entity pages, concept pages, cross-references, an index. The LLM owns this layer entirely.
  3. The schema — conventions for how the wiki is structured. Co-evolved by you and the LLM over time.

Three operations:

  • Ingest — drop a source in, the LLM reads it, writes a summary, updates the index, and touches every related page across the wiki.
  • Query — ask a question, the LLM searches the wiki and synthesizes an answer grounded in your accumulated knowledge. Good answers get filed back as new pages.
  • Lint — health-check the wiki. Find contradictions, orphan pages, missing cross-references, stale claims. The LLM fixes what it can and flags what needs your judgment.

Quick start

# Install
npm install -g @wastedcode/memex

# Start the daemon (requires CAP_SYS_ADMIN for namespace isolation)
sudo memex serve

# Create a wiki
memex create my-wiki --name "My Knowledge Base"

# Authenticate Claude for this wiki
memex login my-wiki

# Ingest some sources
memex ingest my-wiki paper.pdf notes.md article.html

# Ask a question
memex query my-wiki "What are the key themes across these documents?"

# Run a health check
memex lint my-wiki

Commands

Command Description
memex serve Start the daemon
memex create <wiki> Create a new wiki
memex destroy <wiki> Destroy a wiki
memex config <wiki> --edit Edit wiki conventions in $EDITOR
memex config <wiki> --set-key Set API key
memex config <wiki> --model opus Set default model
memex login <wiki> Authenticate Claude (OAuth)
memex ingest <wiki> <files...> Ingest source documents
memex query <wiki> "question" Ask a question against the wiki
memex lint <wiki> Run wiki health check
memex logs <wiki> View audit log
memex list List all wikis
memex status <wiki> Check job status

Most commands block until complete. Pass --async to get a job ID and check status later.

How it works

┌──────────────────────────────────────────────────────────────┐
│  CLI (unprivileged)                                          │
│                                                              │
│  memex ingest acme notes.md                                  │
│       │                                                      │
│       ▼                                                      │
│  /run/memex/memex.sock  ─────────────────────────────────    │
└──────────────────────────┼───────────────────────────────────┘
                           │
                           ▼
┌──────────────────────────────────────────────────────────────┐
│  DAEMON (memex serve)                            privileged  │
│                                                              │
│  Per-Wiki Job Queues          Wiki Registry (SQLite)         │
│  ┌─────────────────┐        ┌─────────────────────┐         │
│  │ acme: [▶ingest]  │        │ wikis, jobs, audit  │         │
│  │ beta: (idle)     │        └─────────────────────┘         │
│  └────────┬────────┘                                         │
│           │                                                  │
│           ▼                                                  │
│  unshare -m -- mount --bind .../wikis/acme /workspace \      │
│    claude -p "..." --tools Read,Write,Edit,Glob,Grep         │
│                                                              │
│  ┌────────────────────────────────────────────────────────┐  │
│  │  MOUNT NAMESPACE — what Claude sees                    │  │
│  │  /workspace/                                           │  │
│  │    .claude.md        wiki/_schema.md                   │  │
│  │    .claude/          wiki/_index.md                    │  │
│  │    .tools/           wiki/_log.md                      │  │
│  │    wiki/             wiki/raw/                         │  │
│  │                                                        │  │
│  │  No /home. No /etc. No other wikis.                    │  │
│  └────────────────────────────────────────────────────────┘  │
└──────────────────────────────────────────────────────────────┘

Each wiki's queue processes jobs serially — no concurrent writes. Independent wikis run in parallel. Mount namespaces provide filesystem isolation without Docker overhead.

Customization

Wiki conventions (.claude.md)

The most important customization point. This file extends the base system prompt with domain-specific behavior:

# Wiki Conventions

## Domain
This knowledge base tracks competitive intelligence in the SaaS analytics space.

## Filing conventions
- Organize competitors under competitors/{name}.md
- Track pricing changes with dates
- Flag acquisitions and funding rounds prominently

## Things to ignore
- Job postings
- Social media noise without substance

Edit with memex config my-wiki --edit.

MCP servers (.tools/mcp.json)

Connect external data sources:

{
  "mcpServers": {
    "notion": {
      "command": "npx",
      "args": ["-y", "@notionhq/mcp-server"],
      "env": { "NOTION_API_KEY": "secret_..." }
    }
  }
}

Tool whitelist (.tools/allowed-tools.txt)

By default, Claude only gets safe file tools. Opt into more:

Bash
WebSearch

Requirements

  • Node.js >= 20
  • Linux with mount namespace support
  • CAP_SYS_ADMIN capability (or root)
  • claude CLI installed and in $PATH

Documentation

  • Architecture — design principles, components, data flow, filesystem layout
  • Operations — installation, deployment, wiki lifecycle, debugging
  • Prompt design — how the system prompt, job prompts, and conventions work together

License

MIT