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Think Fu – Metacognition as a service

Hacker News

Think Fu – Metacognition as a service

Hi HN! I've been very unimpressed with how LLMs think when it comes to anything creative. It's no surprise - they've been lobotomized by RLHF to be helpful, predictable and consistent - all of these things are counterproductive in a more creative setting. So I've built a little something to help LLMs be less creatively bland and dumb. It's called ThinkFu (because I believe creative thinking, when practiced well, is a martial art). Anyway, you can watch a 3-min video demo here: https://www.youtube.com/watch?v=x-LQvX105SI Or you can just add it to your claude code like this: /plugin marketplace add move38studios/thinkfu /plugin install thinkfu@move38studios-thinkfu You can also explore what's happening in the code here: https://github.com/move38studios/thinkfu The TL;DR is: 1. I've collected 200+ "thinking moves" from things like oblique strategies, triz, systems thinking, design thinking etc. and adapted them for LLMs 2. I've added a little randomness with variables, so that the total number of moves that you can get is somewhere around 500 billion. Because in creative thinking randomness is a feature not a bug. 3. There is a vector search and a tiny LLM router (behind an MCP) that tries to get you the right "thinking move" for whatever your problem may be. Everything runs on cloudflare (still love you guys, despite all the recent issues). Oh, also on first use you can opt in or out of sending the anonymized ratings back to my endpoint. The idea is that the LLM (or user) can rate whether a certain move was productive or not in a certain context. If I collect enough ratings, I'll finetune a custom LLM for the router and I think routing will get better as a result. Hope y'all find it interesting! I'd be happy to answer questions and get bug reports and PR requests and what not. It's a tiny experiment - but if it works it could be fun to scale with the community.

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, mcp, user · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, video, way · Missing: mobile apps, personal, entrepreneurs
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
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Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
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