Browser-only 64-color Wplace image quantizer (no server, free)

Hacker News

Hi HN, I play a lot of *[Wplace]( https://wplace.live )*, the pixel-art world map that limits every drawing to the official 64-colour palette (32 free, 32 paid). Re-colouring images by hand was painful, so I built a *client-side tool* that: * takes any PNG/JPG * maps each pixel to the nearest of the 64 official colours * lets you *toggle paid colours off* → no wasted droplets * adds optional Floyd–Steinberg dithering * shows live pixel / colour stats for cost estimation * exports a ready-to-place PNG or copies it to the clipboard (all computation in the browser, nothing uploaded) *Link* < https://wplacecolorconverter.online > *Tech notes* * TypeScript + Vite + React + Tailwind * Pixel work happens in a Web Worker on an `OffscreenCanvas` * Quantisation is ~300 LOC (RGB Euclidean distance; optional error diffusion) * No back-end, so it runs offline and keeps images local I’d love feedback on: * performance for big images (>512 px) * better distance metrics (ΔE?) or a faster lookup table * whether a WebGPU / wasm port is worth it * other pixel-art communities that might need custom palettes Thanks for reading!

Visit product View on Hacker NewsAug 20, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

74%74% 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.

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

49%49% 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

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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: paid · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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