JW

JW Tool Box – Free, privacy-first web tools (PDF, Image, Converters)

Hacker News

JW Tool Box – Free, privacy-first web tools (PDF, Image, Converters)

I got tired of "free" online tools that may steal data or force you to create an account. So I built a suite of 40+ utilities that run entirely in your browser (client-side only). - No server uploads (Privacy first) - No login required - No paywalls It includes PDF tools, image converters (HEIC/WebP), and developer utilities. Built with React, Vite, and WASM. Feedback welcome!

Visit product View on Hacker NewsNov 27, 2025

Actual performance

2points

1comments

Did not reach leaderboard

Launch Intel predictions

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

44%44% 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.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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

36%36% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

6%6% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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