kothari-nikunj

kothari-nikunj / llm-wiki

Public

Personal Wiki

39
5
100% credibility
Found Apr 09, 2026 at 39 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

A personal knowledge base builder that uses AI to transform user-provided writings, social media archives, messages, and bookmarks into an interconnected, browsable wiki website.

How It Works

1
🔍 Discover your personal wiki maker

You find this tool that turns your notes, messages, chats, and posts into your own Wikipedia about your life.

2
📥 Set it up on your computer

Download everything to your machine, pick a private password, and get ready to add your stuff.

3
📁 Gather your personal data

Drop your writings, tweet histories, chat logs, and bookmarks into a simple folder.

4
⚙️ Organize your data

Run a quick tidy-up so everything is sorted into daily entries ready for magic.

5
🧠 AI compiles your knowledge

Let the smart helper read it all and weave interconnected stories, people, ideas, and patterns into wiki pages.

6
🌐 Launch the viewer

Open your web browser, enter your password, and step into your wiki world.

✨ Explore your life wiki

Browse categories, click links between ideas and people, search deeply, and uncover patterns in your own story.

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Star Growth

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AI-Generated Review

What is llm-wiki?

LLM Wiki is a personal wiki app that ingests your raw data—writing, tweets, X bookmarks, iMessages, WhatsApp chats—and uses Claude to compile it into interconnected markdown articles like a Wikipedia dataset tailored to your life. Drop files into a data folder, run a Python ingest script, then trigger Claude Code commands like `/wiki absorb all` to generate themed pages with wikilinks. A TypeScript Next.js viewer renders it as a searchable, password-protected site you can run locally or deploy anywhere.

Why is it gaining traction?

It stands out by turning noisy personal data into thematic, linked knowledge—people, concepts, patterns—without manual editing, using a Claude skill for smart synthesis over raw dumps. macOS users get seamless iMessage and WhatsApp pulls, while the file-based output works with any LLM or editor, unlike locked-in apps like Notion. Developers dig the quick deploy to Railway with just a personal GitHub token and env vars.

Who should use this?

Solo founders or indie devs building personal knowledge bases from scattered tweets and notes. Writers exporting Substack to markdown who want an LLM Wikipedia RAG layer for queries like career patterns. macOS power users tired of siloed messages, seeking a personal wiki Linux-compatible via Docker.

Verdict

Try it if you have Claude Code and data ready—39 stars and 1.0% credibility score signal early maturity, but thorough docs and idempotent ingest make setup reliable. Solid for personal use, less so for teams without custom tweaks.

(198 words)

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