I challenged 10 AI giants using one open-source PDF (with full results)

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Hey HN, This started as a personal experiment: one person, one framework, ten AI models. I built a semantic reasoning engine (WFGY: All Principles Return to One) and tested how well each model could handle abstract logic, conceptual shifts, and consistent inference—all using the same PDF. The results are posted above. No fancy wrappers, no login walls—just raw data, an illustrated battle poster, and the full experiment. Yes, it's a bit weird. But it's real. And honestly? I just hope someone out there sees the effort and the courage it took to do this solo. Happy to answer questions. Would love your feedback, criticism, or even memes. Thanks for taking a look

Visit product View on Hacker NewsJun 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos

89%89% 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: started · Missing: supports, reddit linkedin, podcasting

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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