Wplace Pixel Art – Convert Images to Wplace-Compatible Pixel Art

Hacker News

We built a free browser tool for the wplace.live community that converts any image into pixel-perfect art using Wplace’s official 64-color palette. Features Exact color matching with Wplace’s palette (from lospec) Highlights free vs paid pixels (Wplace has tiered pricing) Real-time preview with grid overlays Floyd–Steinberg dithering for smoother gradients All processing is local – no uploads, no tracking Why we made it Wplace is a collaborative pixel canvas, but placing art manually is slow and error-prone. This tool helps creators preview, plan, and optimize their art before placing it on the canvas. Built with vanilla JS + canvas. No backend. Try it: https://wplacepixelart.com Feedback welcome!

Visit product View on Hacker NewsAug 26, 2025

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3points

Did not reach leaderboard

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AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

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

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting

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

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

46%46% 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

38%38% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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