SwipeSort – Tinder for Your Text Lists (PowerShell/Batch/EXE)

Hacker News

I've created SwipeSort, a tool that lets you rapidly categorize lines in a text file using a Tinder-like swipe interface. It's available as a PowerShell script, batch file, and Windows executable. Key features: - Left/right arrow keys to categorize - Up arrow to rewind multiple steps - Saves progress between sessions - Configurable UI (verbose/minimal modes) I developed SwipeSort mostly using ChatGPT and Claude, refining the concept and debugging issues as they arose. The AI assistants helped with the initial script structure, feature implementation, and documentation, while I provided the core concept, guided the development, and handled real-world testing and debugging. It's particularly useful for quickly sorting through any text-based data that requires human judgment or subjective decision-making, such as book or video titles, things to keep/toss, etc. GitHub: https://github.com/mollyrealized/swipesort Feedback and contributions are welcome!

Visit product View on Hacker NewsJul 27, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, chatgpt, using · 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: created · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

27%27% 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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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