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Offline batch icon pack generator (Windows desktop)

Hacker News

Offline batch icon pack generator (Windows desktop)

I built a Windows desktop tool called Icon Pack Builder. It generates complete multi-platform icon packs from one or multiple source images. Windows .ico files, Android density folders, Apple and macOS sizes, web favicons. The idea was simple. When you ship across platforms, packaging icons gets repetitive fast. Resize, export, rename, rebuild folder structure, repeat. I wanted something deterministic that just does it cleanly every time, especially if you’re processing multiple assets. It runs fully offline. No uploads. No subscriptions. I wrote a short breakdown here: https://dev.to/bassthermal/building-a-deterministic-batch-ic... Microsoft Store page: https://apps.microsoft.com/detail/9mxvt3dfq295 Curious what other devs think. Is this actually useful? What would make it better?

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apple · Missing: agents, agent, cursor
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, builder · Missing: plus, intuitive, reviews
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
36%36% 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
28%28% 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: subscription · Missing: arr, mrr, revenue
21%21% 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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