Hypertest - A test runner for developers with ADHD

Hacker News

TL;DR: a test runner focused on making devs w/ ADHD happy. Hello HN! My name is [redacted], a dev with ADHD. I believe there's different design constraints on tooling for devs with ADHD compared to their "normal"counterparts. That's what I'm here to solve. I gathered experiences of other ADHD devs, to summarize: ( https://news.ycombinator.com/item?id=33280461 ) - Memory: People w/ ADHD have horrendous short term memory (hard to keep grasp of the current *thing/task*) - Distraction: Are easily distracted, tend to fall off the focus wagon easily. - Boring Maintenance: They need and use lists, but are bad at maintaining them. A star would feel awesome (MIT licensed): https://github.com/nabaz-io/nabaz

Visit product View on Hacker NewsNov 9, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: adhd, io · Missing: https docs, excited, just released

81%81% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% 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

36%36% 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

19%19% 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

12%12% 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.

Correct prediction on native model

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