Mutatr – an open source A/B testing agent

Hacker News

Mutatr is an end-to-end A/B testing agent. In practice, it connects to a repo, then: 1. Suggests A/B treatments 2. Implements variants in the codebase 3. Runs flights using synthetic personas that simulate the expected audience for your site 4. Returns attention maps to help you learn what works before going live Funny story: Mutatr started as a YC application that got rejected, so I decided to build it anyway and open-source it

Visit product View on Hacker NewsMar 23, 2026

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Actual performance

3points

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

96%96% 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

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

42%42% 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

22%22% 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

21%21% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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