Cursor Stats – See your Cursor usage like GitHub contributions

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I use Cursor every day. After their recent pricing change, I kept blowing past the new token caps without really knowing why. The built-in dashboard only shows basic totals, with no trends or breakdowns. I built a browser extension that injects proper usage charts directly into the Cursor dashboard to make this data more useful. The extension adds a GitHub-style calendar heatmap for token usage and accepted code suggestions. It also displays your day-of-week activity patterns and a breakdown of daily cost and token spend. Everything runs locally in your browser and only uses the analytics endpoints Cursor already loads. No extra data leaves your machine. The extension is available on the Chrome Web Store: https://chromewebstore.google.com/detail/cursor-stats/fdlfea... Source code, Firefox version, and MIT license are here: https://github.com/alexerm/cursor-stats-extension Once you have it installed, go to: https://cursor.com/dashboard?tab=usage The project is built on WXT. I use React components to render the charts directly in the Cursor dashboard. Charts are powered by Nivo, and the extension caches results locally so it does not overload Cursor’s API. There is no reverse engineering. The extension just repurposes data Cursor already provides. Feedback on other useful metrics or suggestions to improve the install process are welcome. Pull requests are open. I am not affiliated with Cursor.

Visit product View on Hacker NewsJul 22, 2025

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Actual performance

2points

Did not reach leaderboard

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

88%88% 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

68%68% 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

51%51% predicted probability of success on TrustMRR, 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

39%39% 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 · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% 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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