Sensus – Constructive PR Comments

Hacker News

Being rude and dismissive in code reviews is as destructive as shouting at your kid — you both will suffer. We all have good intentions, but even the best have bad days. We all can be grumpy, impatient, or insecure. “Am I helping to resolve this PR?” is a valuable question to ask yourself before posting a comment. My friend and I came up with Sensus - a tiny AI helper that asks this question and provides an answer as a score between 1 and 5. It incorporates constructiveness, politeness, and agreeableness into the calculations. Sensus is free. It is available as a Chrome extension that works in GitHub comments. At this stage, we are figuring out how many people may find this extension worth using. If we see enough interest in this project, we will add it to other platforms. Feel free to leave us any feedback in the comments.

Visit product View on Hacker NewsNov 24, 2023

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7points

5comments

Made the leaderboard

Launch Intel predictions

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

65%65% 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.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, code review, ide · 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

15%15% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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