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Products that do what Dyer.md does
Your notes are the AI’s memory. Own both.
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I 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…
Apr 2026 · github.com
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Novi Notes▲107Your notes become AI memory. Local-only, no subscription.
Mar 2026 · rhcwlq89.github.io
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We built a reference MCP server that lets your editor/agent learn a codebase directly from source (signatures, types, AST, comments). Docs are optional. The reference impl runs on our open-source project’s codebase. Why we built it Docs are important, but they add another abstraction layer between your code and your users. Keeping them at the right quality is hard (especially at a startup), and LLM-generated docs are often mediocre until you invest real polish. Exposing code to the model in a structured way keeps answers grounded and current, and it’s always available. You can even…
Sep 2025 · github.com
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Your AI has your code's text, never its map. Fix that.
Jun 2026 · luuuc.github.io
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I built this because I got tired of watching Claude Code read through massive files just to find a few functions. Sourcerer lets AI agents search code semantically and grab exactly the code chunks they need instead of burning tokens on whole files. It uses tree-sitter to parse your codebase and creates a searchable index. So instead of "read auth.py (538 lines)", an agent can search for "user authentication logic" and get back just the relevant functions. Demo: https://asciinema.org/a/736638 GitHub: https://github.com/st3v3nmw/sourcerer-mcp
2025 · github.com
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Today you can easily adopt AI coding tools because you have git for branching and rolling back if AI writes bad code. We haven't seen this same capability for data and decided to build it ourselves. Nile is a new kind of data lake, purpose built for using with AI. It can act as your data engineer or data analyst creating new tables and rolling back bad changes in seconds. We support real versions for data, schema, and ETL. We'd love your feedback on any part of what we are building - https://getnile.ai/ What do you think?
Jan 2026
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Manage AI agent teams with role-based identities, persistent context, live profiles, and activity feeds across Claude Code, Codex, Hermes, and OpenClaw.
18d ago · flocker.md
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I'm an "ideas person" who messes around with AI on a low budget. I got tired of watching my tokens vanish and context windows filling up while agents fumbled around trying to find the right thing. Agents don't flail like they used to with shell tools, but there are still weak/blind spots and back-and-forth episodes — especially when using tools in combination/sequence. So I built "tilth" today. Or rather, AI built it — every line is Opus 4.6. I spent a lot of my precious tokens getting it to "not shit" (at least several of the different vendors' AI overlords assure me it's not…
Feb 2026 · github.com
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