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Precisionconvert.io – Professional unit converter for engineers

Hacker News

Precisionconvert.io – Professional unit converter for engineers

I built this after getting frustrated with existing converters that are either ad-heavy or lack precision controls. Designed specifically for professionals who need accuracy in their calculations. Key technical decisions: - Single HTML file for fast loading and offline capability - Vanilla JavaScript (no frameworks) for maximum performance - PWA implementation for mobile/desktop use - Precision controls up to 15 decimal places - Specialized units for cooking, scientific, and historical conversions Built it in 24 hours using Claude Code and deployed on Netlify. The challenge was balancing simplicity with professional features that justify premium pricing. Tech stack: HTML5/CSS3/JS, Stripe for payments, Netlify Functions for backend processing. Would love feedback on the technical approach and the specialized unit categories. What units would be most valuable for your work? Happy to answer questions about the rapid development process or specific implementation details.

Visit product View on Hacker NewsAug 21, 2025

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

77%77% 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: claude, stripe, single · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, lua, existing · Missing: https docs, excited, just released

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

23%23% 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 · 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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