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Screenshock.me – Zap yourself into being productive with browser AI

Hacker News

Screenshock.me – Zap yourself into being productive with browser AI

I recently got the Pavlok, a wrist worn device that is capable of delivering a shock (intended for negative reinforcement of behaviors). But, you have to manually trigger it yourself. So, I decided to continuously record my screen and use vision AI (via gemini-flash) to detect lapses in focus and trigger the negative stimulus via the Pavlok API. It's 100% browser based -- super simple to try. For those who don't have the device, you can still try it in your browser -- just select "loud beep (through computer)". It's also open-source: https://github.com/gr-b/screenshockme

Visit product View on Hacker NewsJun 23, 2025

Actual performance

3points

1comments

Did not reach leaderboard

Launch Intel predictions

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

89%89% 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

61%61% predicted probability of success on Indie Hackers, 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

59%59% 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

41%41% 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

27%27% 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

18%18% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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