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I got tired of syncing Claude/Gemini/AGENTS.md and .cursorrules

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I got tired of syncing Claude/Gemini/AGENTS.md and .cursorrules

I use Claude, Codex, Cursor, and Gemini on different projects. Each one has its' own md file in its own format. CLAUDE.md, AGENTS.md, .cursorrules, GEMINI.md. Four files saying roughly the same thing, four chances to get out of sync! I kept forgetting to update one, then wondering why Cursor was hallucinating my project structure while Claude had it right. So I built an MCP server that reads a single YAML file (project.faf) and generates all four formats. 7 bundled parsers handle the differences between them. You edit one file, and bi-sync keeps everything current. It's an MCP server, so Claude Desktop can use it directly. 61 tools, 351 tests, no CLI dependency. Try it: npx claude-faf-mcp Source: https://github.com/Wolfe-Jam/claude-faf-mcp The .faf format itself is IANA-registered (application/vnd.faf+yaml). Curious if others are dealing with this multi-AI config problem, or if there's a simpler approach I'm not seeing.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, model
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
20%20% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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