Smart Switcher – A data driven window switcher

Hacker News

Hello, my name is Andrew. I'm an indie developer and I'm excited to release Smart Switcher. Smart Switcher is a data driven window switcher aimed at improving the overall window switching experience. It logs data on your windows switching, then a prediction algorithm analyzes this data and uses it to predict which window you would want to switch to next. When you need to switch windows, you press the switch shortcut to switch to the next predicted window. If this isn't the window you wanted, press the override shortcut to switch to the next most likely window. You can press the override shortcut as many times as needed until you arrive at your desired window. This is an early release but should be mostly stable by now. Any feedback is appreciated! Check it out here. https://aboveaverageuser.com/smartswitcher

Visit product View on Hacker NewsJun 24, 2025

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

55%55% 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 · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% 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

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

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

33%33% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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