Personal AI Metrics Dashboard

Hacker News

Hi HN, I built WakaTime 13 years ago before AI. Things have changed a lot since then, and the time you spend typing in your IDE isn't as valuable as it used to be... That's why I built a new WakaTime dashboard specifically for AI metrics. It tracks things like: * Lines of code - AI vs. Human * Average Prompt Length over time - How much context do you give AI * Follow-up Edit Rate - How many times do you have to edit/fix the code AI generated * Token usage over time - Input and Output tokens It does this for most popular agents (Cursor, Claude, Codex, Copilot, Gemini, etc.) If you already use WakaTime plugins, you should have AI data since last week. If not, install one of the WakaTime official plugins ( https://wakatime.com/plugins ) then edit your ~/.wakatime/wakatime-internal.cfg to change the ai_heartbeats_last_parsed_at to some date in the past and it will backfill your AI stats since that date. By default it only backfills a few minutes from plugin install. Let me know what u think!

Visit product View on Hacker NewsApr 22, 2026

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

92%92% 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 · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% 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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

21%21% predicted probability of success on AppSumo, 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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