DocMason, an agent-native knowledge base for complex research using local office files.
ROIPAD RESEARCH · PUBLIC SOURCES
Discussion evidence and market context
Snapshot 2026-11-02 04:15:30 UTC · Sources and methodology
LLM Applications
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What the discussion sample says
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Related public records from semantic retrieval
Connections for further investigation, not endorsements or verified substitutes.
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Shared subject terms: show, local, agents. Retrieved by text similarity and checked for topic overlap; practical equivalence has not been established.
Raw Developer Origin & Technical Request
Hacker News
Apr 5, 2026
I 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 using it in everyday which support me on lots of complex topics.I have already open-sourced this repo. And I think it takes Karpathy's concept a step further for real-world usage in three ways:
1. It could handle most kinds of office docs (pptx, docx, excels, even .eml). And really extract multimodal information from all IT architecture diagram or excel sheets.
2. It is running as a Real APP but not a naive RAG tool. DocMason could run smoothly and intelligently to prepare environment, auto update, and auto incrementally sync Knowledge base.
3. Most importantly it is running in Native AI Agents, which could leverage powerful AI Agents engine (e.g. Codex or Claude Code)View detail architecture diagram in DocMason Readme, and then download have a try :) You will find it could help a lot during daily work. Would love to hear your feedback and issues in Github!
Developer Debate & Comments
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Frequently Asked Questions
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Engagement Signals
Cross-Market Term Frequency
Counts stored text matches for foundational terms like Karpathy's Post and LLM Knowledge Bases in analysed public discussions. These counts do not measure adoption.
Stored text matches, not adoption or market-share measurements.
- Karpathy's Post: 1 records
- LLM Knowledge Bases: 1 records
- agent-native knowledge base: 1 records
- complex research: 1 records
- local office files: 1 records
- Codex/Claude Code: 1 records
- office docs (pptx, docx, excels, .eml): 1 records
- multimodal information: 1 records
- IT architecture diagram: 1 records
- excel sheets: 1 records
- Real APP: 1 records
- naive RAG tool: 1 records
- prepare environment: 1 records
- auto update: 1 records
- auto incrementally sync Knowledge base: 1 records
- Native AI Agents: 1 records
- AI Agents engine: 1 records
- "The repo is the app. Codex is the runtime.": 0 records
SaaS Metrics