Hone: A deadline-focused problem-solving tool for developers

Hacker News

Hey, HN! I'm Tuhin. I craved a tool that allowed me to express my experience of software-development: Being delayed a lot and being frazzled a lot. Hone is a problem solving tool for the working software developer. It allows expressing uncertainty and helps make smaller more frequent changes to a design. The hope is to have less panic and gain more precision on how to change an implementation plan. Here's a 12 minute in-depth video on what Hone is trying to do: https://www.idiotlamborghini.com/strategies/hone#concept-wal... And here's an offline demo for you to see how it feels to use: https://www.idiotlamborghini.com/strategies/hone#interactive... It's in early-access right now, and I still haven't hit the experience I'm looking for, where being wrong or going down the wrong rabbit-hole doesn't feel punishing when against a deadline. But I think it's ready for people to try. So, let me know what you think!

Visit product View on Hacker NewsSep 18, 2024

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Actual performance

1points

Did not reach leaderboard

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

74%74% 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 NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

37%37% 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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

8%8% 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.

Incorrect prediction on native model

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