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Deff – Review AI-generated code changes

Hacker News

Deff – Review AI-generated code changes

I built Deff because AI coding tools sped up writing code, but reviewing their changes became the new bottleneck. Most of my time was going into checking noisy diffs, understanding intent, and making sure nothing subtle broke. Deff is focused on one thing: helping you review AI-generated code changes faster and with more confidence. It helps surface what changed and is allowing me to use it with some vim motions so I can easily jump from nvim to opencode to deff. Would love feedback from people using AI coding assistants heavily — especially on review workflows, false positives, and what still feels slow.

Visit product View on Hacker NewsFeb 25, 2026

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87%87% 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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% predicted probability of success on TrustMRR, 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

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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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