Cabinet – Kb+LLM (Like Paperclip+Obsidian)
Hi HN, for quite some time I've been thinking how LLMs are missing the knowledge base, where I can dump CSVs, PDFs, and most important, inline web app. running on Claude Code (bring your own agent) with agents with heartbeats and jobs https://runcabinet.com It runs locally and is installable via npm. GitHub (open source): https://github.com/hilash/cabinet This is still very early. I put the first version together quickly after seeing a post by Andrej Karpathy about LLM knowledge bases, which matched closely with what I’d been building. Some people have already…
Cabinet – Kb+LLM (Like Paperclip+Obsidian) launched on April 5, 2026 with 16 votes, #1,052 of 21,248 launches that month and more than 90% of that year's launches.
In plain words
Cabinet is a knowledge base tool that lets users upload and organize CSVs, PDFs, and web applications to work alongside Claude AI agents. It runs locally and installs via npm, with agents that can execute jobs and maintain persistent connections. Built with Claude Code integration, it allows users to bring their own AI agents to query and interact with stored information. The open-source project is in early development and can be self-hosted or accessed through a planned hosted version.
written from the facts on this page · September 2026
From the sources
Cabinet shows your entire knowledge base and files, puts a team of AI specialists to work on it, and renders results as live apps and dashboards. Self-hosted, and connected to the AI providers you already trust.
Cabinet Cloud is your AI team, running on your own machine in the cloud, 24/7. A dedicated container, not a shared slice of someone else's server. A dedicated container, not a shared slice of someone else's server. A dedicated container, not a shared slice of someone else's server. A dedicated container, not a shared slice of someone else's server. A dedicated container, not a shared slice of someone else's server. A dedicated container, not a shared slice of someone else's server. A dedicated container, not a shared slice of someone else's server. A dedicated container, not a shared slice of someone else's server. A dedicated container, not a shared slice of someone else's server. A…from runcabinet.com
In the maker’s words, at launch
Hi HN, for quite some time I've been thinking how LLMs are missing the knowledge base, where I can dump CSVs, PDFs, and most important, inline web app. running on Claude Code (bring your own agent) with agents with heartbeats and jobs https://runcabinet.com It runs locally and is installable via npm. GitHub…
Does a similar job
all alternatives →
I used Claude Code to discover connections between 100 booksJan 2026 · trails.pieterma.es · ▲524I think LLMs are overused to summarise and underused to help us read deeper. I built a system for Claude Code to browse 100 non-fiction books and find interesting connections between them. I started out with a pipeline in stages, chaining together LLM calls to build up a context of the library. I was mainly getting back the insight that I was baking into the prompts, and the results weren't particularly surprising. On a whim, I gave CC access to my debug CLI tools and found that it wiped the floor with that approach. It gave actually interesting results and required very little orchestration…
ReplicasJun 2026 · replicas.dev · ▲239Hey HN! I'm Connor, creator of Replicas. I'm a heavy Codex/Claude Code user, and I wanted to hand off tasks to agents like I would to an engineer - kick something off before work, let it scaffold while I'm at lunch, even come back to a ready PR. The problem: existing alternatives use workspace VMs that are black boxes. I have no access to any of them - they can't run tests, read logs, or use tools like the Playwright MCP (which I use to verify agents actually fixed what they claim to fix). Replicas gives agents real cloud workspaces where they can: - Run your test suite and read the…
A Karpathy-style LLM wiki your agents maintain (Markdown and Git)Apr 2026 · github.com · ▲260I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet. It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you. The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard. I…
20+ Claude Code agents coordinating on real work (open source)Feb 2026 · github.com · ▲53Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
10x better performance from the Coding Harnesses with LLM-wikiJun 2026 · llm-wiki.net · ▲16Build source-backed knowledge bases with Claude Code, Codex, OpenCode, or any AI agent. Export Project Knowledge Checkpoints, apply personal specialist review methods, shape Ideas, and promote approved work into Projects.
DocMason – Agent Knowledge Base for local complex office filesApr 2026 · github.com · ▲11I think everyone has already read Karpathy's Post about LLM Knowledge Bases. Actually for recent weeks I am already working on agent-native knowledge base for complex research (DocMason). And it is purely running in Codex/Claude Code. I call this paradigm is: The repo is the app. Codex is the runtime. During my daily working life, I have tons of office documents with knowledge from all teams, and as an IT Architect, I need to combine them altogether to handle complex deep research (which normal LLM definitely could not help). That is the originally reason I built DocMason, and I am…
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