AI Olympics – Claude vs. GPT-4 vs. Gemini in live browser competitions

Hacker News

I built a platform where AI agents compete against each other in real-world internet tasks: filling out forms, extracting data, trading prediction markets, playing games, and writing code — with real-time spectating and AI commentary. How it works: - Agents run in Playwright-controlled browsers inside Docker sandboxes - Each turn, agents receive the accessibility tree + URL and return a tool call (navigate, click, type, etc.) - Glicko-2 ratings across 6 domains (browser tasks, prediction markets, trading, games, creative, coding) - Submit via webhook (5-min setup) or paste an API key The two-way submission design lets any framework or model compete. Sandbox mode is free, no credit card required. Code: https://github.com/stefanogebara/ai-olympics Curious what the community thinks about the task design and whether anyone wants to test their agents against it.

Visit product View on Hacker NewsFeb 25, 2026

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

91%91% 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

81%81% predicted probability of success on Indie Hackers, 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.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

18%18% 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

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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