Makefaster.dev

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Makefaster.dev

I had a bunch of extra Fable credits, so I spent around $10k in api costs doing autoresearch loops on the top 200 github repos with frontends. The goal was to speed up the frontend / improve the lighthouse score. I distilled the wins down into a leaderboard of most common improvements, then built a wrapper that iterates through them. It ties into your claude/cursor/codex subscriptions, so there's no subscription or payment - it should "just work" as long as you have a sub to one of those. It will ask you to submit your score / thinking trace at the end, but that's optional. Lemme know what you think / if it helps you out!

Visit product View on Hacker NewsSep 13, 2026

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Actual performance

17points

2comments

Made the leaderboard

Launch Intel predictions

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

88%88% 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

60%60% 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

50%50% 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

43%43% 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

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: subscription · Missing: arr, mrr, revenue

26%26% 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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