FlouState – See if you're debugging, creating, or refactoring (free)

Hacker News

I built FlouState because "4 hours of coding" tells you nothing. Was it 4 hours debugging or 4 hours building features? Very different. It's a VS Code extension that automatically categorizes your coding into: Creating (new features), Debugging (fixing issues), Refactoring (improving code), or Exploring (learning codebase). Example: Yesterday I "coded" 6 hours but it was actually 3.5h debugging a race condition, 1.5h refactoring, 0.5h tests, 0.5h exploring. Traditional time trackers just show "6 hours in TypeScript." Technical: Tracks file changes, debug sessions, and edit patterns locally. No code content sent. Updates web dashboard every 30 seconds via Supabase. Free with 7 days history. Considering open sourcing the detection algorithms. https://floustate.com or search "FlouState" in VS Code marketplace. Curious what your debugging % is - mine was way higher than expected.

Visit product View on Hacker NewsJul 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, coding, code · Missing: mac, agents, macos

81%81% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

19%19% 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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