AI-generated to-do lists for design / website feedback

Hacker News

Hey hacker news I'm the founder and lead developer at Opine.cm, and we've just rolled out a new feature that hopefully will reduce the number iterations in your design or web-dev projects. I've always been a big fan of checklists. When checklists were introduced to hospitals prior to surgeries, the number of complications and deaths dropped by 35%. I use them a lot personally - but I know many designers and devs find it too much effort. The reality is, we easily forget verbal feedback; the humble to-do list solves this. We've just taken the added step of using AI to turn any video feedback that you get into a to-do list. To try it for yourself - Create an account at https://Opine.cm - Record some feedback on the design examples - Watch as the AI creates your to-do list. Or here's a demo https://www.loom.com/share/e7a6ccd0df0b4dfb8552bd08c7c8ab56 (Quick note - The demo shows design work - but you can add also add websites in Opine.)

Visit product View on Hacker NewsJun 16, 2023

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

82%82% 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.

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

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

64%64% 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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs

46%46% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

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

Incorrect prediction on native model

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