My

My focus had a pattern. I built a macOS app to make it visible

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My focus had a pattern. I built a macOS app to make it visible

I work long hours with headphones on, and I kept running into the same pattern: I’d start a session feeling fine, then a couple hours later I’d realize I’d been leaning forward or holding my head in one position without noticing. Not because I was trying to. Just because focus has a way of quietly rearranging you. I tried a few “reminder” style approaches and apps, but they didn’t stick. The problem wasn’t motivation. It was awareness. I didn’t want an app telling me what to do. I wanted something that simply showed me what was happening, in the moment, with zero friction. So I built Headjust: a small native macOS app that lives in the notch area and visualizes head movement from AirPods (or compatible Beats). You can start and stop sessions while you work, then look back at simple session insights and trends with head movement visuals for better readability. What surprised me after using it myself is how quickly it changed my behavior without any prompts. Just seeing the pattern made it easier to reset between tasks, notice when I’d gone “still” for too long, and make small adjustments before hours passed. What it does: - Live head-movement visualization in the notch area - Start/stop sessions anytime during work - Session analytics and history to spot patterns over time - Lightweight and designed to stay out of the way Public beta (TestFlight): https://testflight.apple.com/join/55JfhrPA Website: https://headjust.app/ I’d love feedback on: - whether the session flow fits naturally into real work - whether the notch UI is unobtrusive (especially on external monitors) - which insights feel meaningful, and what’s missing Thanks for taking a look!

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apple · Missing: agents, agent, cursor
93%93% 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: compatible · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, visualize, way · Missing: mobile apps, ios, personal
50%50% 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
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
38%38% 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 · Strong signals: arr · Missing: mrr, revenue, profit
21%21% 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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