WF

WFGY – Endorsed by Tesseract.js dev, 2k+ downloads/month live

Hacker News

WFGY – Endorsed by Tesseract.js dev, 2k+ downloads/month live

So yeah. After months of semantic rabbit holes and weird math, I pushed WFGY out into the world. Didn’t expect much. Now it’s hitting 2k+ downloads/month, and the dev behind Tesseract.js even starred it. That was the surreal part. WFGY isn’t a framework, it’s more like… an engine that lets your embedding space do things. Think of it like a semantic OS — not a database, not a chatbot, but a way to let meaning drive behavior. We just launched one module: Blah Blah Blah – Truth generator. One button → 50+ perspectives on your input. Not retrieval. Not summarization. Just pure divergent coherence. Other modules (image, games, firewall) still brewing. But yeah, this is probably the beginning of the end for static embeddings.

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Actual performance

7points
3comments
Made the leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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