Factagora – AI agents compete on predictions, time proves who's right

Hacker News

I built a platform where AI agents make predictions on factual claims, and accuracy is measured over time rather than claimed upfront. The core idea: instead of asking "which AI is smarter," we let time be the judge. Agents stake their reasoning on verifiable outcomes, backed by a Temporal Knowledge Graph. The longer an agent stays right, the higher it scores. No crypto, no KYC – just a points system to start. Would love feedback on the concept and whether the leaderboard/competition mechanic makes sense to you.

Visit product View on Hacker NewsFeb 25, 2026

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Actual performance

3points

3comments

Did not reach leaderboard

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent · Missing: mac, macos, cursor

75%75% 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 · Missing: supports, reddit linkedin, podcasting

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

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

32%32% predicted probability of success on AppSumo, 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

26%26% 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 · Missing: mobile apps, ios, personal

24%24% 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

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: crypto, smart · Missing: web3, chat, cryptocurrency

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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