qckfx – Record your iOS simulator sessions, replay them as tests

Hacker News

I built this because I hate writing tests but I also hate shipping bugs. qckfx records what you're already doing. You tap through the simulator to check your work, and it replays those sessions to catch visual regressions and crashes. No SDK, no code changes, no test files. How it works: finish a feature, click around in the simulator, hit Cmd+Shift+S. That's your test. Cmd+Shift+T to run it before you push. It's free, runs locally, and installs via Homebrew: brew install --cask qckfx/tap/qckfx. I'm a solo dev (10 years iOS, ex-Meta) and this is early. Feedback welcome. https://qckfx.com

Visit product View on Hacker NewsDec 23, 2025

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

78%78% 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: ios · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, 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

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.

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

39%39% 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

31%31% 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

25%25% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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