Grammit – Local-only AI grammar checker (Chrome extension)

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Hey HN, I wanted a grammar checker that didn’t send my writing to someone's servers, so we built Grammit, a Chrome extension that runs grammar checks locally using an LLM. Your text never leaves your computer during checking. Because it uses an LLM, it catches more than spelling and grammar. For example, it can correct some wrong statements like “The first US president was Benjamin Franklin.” We used many new web features to build this, such as: - Chrome’s new Prompt API to talk to the local model. - Anchor Positioning API to place the UI with minimal impact on the DOM. - CSS Custom Highlights API for inline error marking. - The new CSS sign() function to create CSS-driven layout with discontinuities. Part of the fun of being early adopters of bleeding edge tech is we’re discovering new Chrome bugs (e.g., https://issues.chromium.org/issues/428354426 , https://issues.chromium.org/issues/428039224 ). I’d love your feedback on: - Where the UX feels rough - What you think of the corrections and suggestions - Other local AI features you'd find useful Happy to answer questions about the tech or the Prompt API. Thanks for trying it out! Chrome Web Store extension link: https://chromewebstore.google.com/detail/grammit-the-ai-gram...

Visit product View on Hacker NewsJul 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, computer · Missing: mac, agents, macos

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal

53%53% 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

38%38% 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, io · Missing: https docs, excited, just released

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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