LocallyTools – A privacy-first free local toolbox, supports offline use

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Hi HN, I'm an indie developer, and this is my project: LocallyTools( https://locallytools.com ). I built this out of a personal need. I grew tired of uploading sensitive files to random online services for simple tasks like image compression or PDF merging. Especially in the age of AI, the lack of transparency about data usage is a serious concern for me. LocallyTools is my solution: a suite of tools that runs 100% in your browser. All processing is done client-side using JavaScript and WebAssembly. Your files never leave your machine. Here are the core principles: - 100% Local Processing: Your data's security is guaranteed by design. You can prove it yourself by watching the network tab in dev tools. Nothing is sent out. - True Offline Support (PWA): Once you visit a tool's page and the cache indicator at the top turns green, the tool is fully cached. You can disconnect from the internet and it will continue to work flawlessly. - Sustainably Free: People often ask "what's the catch?". There is none. Because all the heavy lifting is done on your device, my server costs are incredibly low—just basic static hosting. This lightweight model makes it sustainable to offer the service for free, long-term. - No Registration, No Friction: Just a clean interface that gets the job done. I've just launched and I'm committed to continuously adding more tools based on user feedback. The goal is to build a complete, trusted, local-first tool ecosystem. I'd be grateful for any feedback from the HN community. What do you think? Any features you'd like to see next? Thanks for your time.

Visit product View on Hacker NewsJul 29, 2025

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

93%93% 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: mac, model, user · Missing: agents, macos, agent

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

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

54%54% 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 · Strong signals: host, interface · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% 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

11%11% 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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