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A Curated, Vetted Directory of AI Tools for Engineers

Hacker News

A Curated, Vetted Directory of AI Tools for Engineers

he AI landscape is moving at a terrifying pace. Every day brings a new tool, framework, or paper. As engineers who actually use this stuff, we were spending more time sifting through endless "Top 100" lists, Product Hunt launches, and Twitter threads than we were building. The signal-to-noise ratio has hit an all-time low. We were tired of wasting hours on "breakthrough" tools that turned out to be thin wrappers around an API or abandoned after three weeks. So we built AI Things to give you something that keeps up with AI so you don't have to

Visit product View on Hacker NewsNov 24, 2025

Actual performance

1points

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

49%49% 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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

32%32% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

31%31% 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

22%22% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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