Self-updating wiki from uploaded files
Almanac is a tool that automatically creates and updates a wiki from user-uploaded source files. It uses LLMs to organize and summarize the content, making it easy for individuals or teams to maintain a shared knowledge base. The product is designed to be agent-native, allowing interaction through AI assistants like CC or Codex.
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Upload project folder to get optimized markdown for LLM context
A web tool that lets users upload an entire project folder and receive a single, clean, optimized markdown file ready to paste into Claude or Codex. It solves the problem of manually preparing context for LLMs by automatically consolidating and formatting code files. All processing is done client-side for privacy.
Linkwise: AI read-later and knowledge app with curated Discover feed
Linkwise is an AI-powered read-later and knowledge app that helps users save and organize articles, essays, videos, and highlights. It features a curated public feed called Discover, generated using Fable 5, which selects content worth reading. The app solves information overload for knowledge workers and avid readers.
LLM assistant that files Telegram notes into precise Notion pages with matching style
An LLM-powered assistant that takes quick notes from Telegram and automatically places them on the correct Notion page, formatted to match that page's style. It helps users with large Notion workspaces keep organized without manually navigating to the right page. Currently seeking beta testers.
AI-powered tool that auto-generates and syncs code documentation from code changes
A tool that automatically generates and updates documentation by analyzing code changes, ensuring docs always match the actual codebase. It uses LLMs to detect discrepancies and rewrite documentation, solving the problem of outdated docs for developers.
abap_wiki: Agent-driven engine turns SAP S/4HANA custom objects into citable Markdown/Obsidian knowledge base
A new open-source tool, abap_wiki, uses AI agents to extract SAP/ABAP custom objects from S/4HANA systems and convert them into citable Markdown/Obsidian pages. The engine aims to create a verifiable, AI-native knowledge base for both humans and AI agents, differentiating itself from simple RAG by providing structured, citable context. The project includes a measured benchmark for model selection.