Picker Wheel Pro – Custom spinner wheel with weights and AI

Hacker News

Hey HN, I built Picker Wheel Pro because I wanted a flexible, web-based spinner wheel that went beyond the basics. It's useful for random decisions, giveaways, classroom activities, or just breaking choice paralysis. Key features beyond standard wheels: * *Weighted Options:* You can assign a weight (1-10) to each option to control its probability of being selected. Useful for tiered prizes or making some outcomes more likely. (Calculation is `Weight / Total_Visible_Weight`). * *AI List Generation:* If you're stuck for ideas, you can provide a prompt (e.g., "5 European capitals", "types of birds") and it uses an API (currently GPT-4o-mini via OpenRouter) to generate the list for you. There's a 60s cooldown between AI requests. * *Customization:* Change colors, sounds, add images to options, customize title/description, apply color patterns. * *Results Tracking:* Logs spin results with timestamps and optional win counts. * *CSV Import/Export:* For managing larger option lists easily. * *No Sign-up:* It's free and saves settings/options directly in your browser's local storage. Uses cryptographic RNG for selection. It's built with vanilla JS, HTML, and CSS. Would love to get your feedback! Any interesting use cases you can think of, or features you'd find useful? Thanks!

Visit product View on Hacker NewsApr 22, 2025

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best fitHighest predicted score across all platforms for this description.

Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, 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

21%21% 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 · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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