Sticky Icky – a ditherpunk sticker designer for the Zebra LP2844

Hacker News

Browser-based sticker designer for a Zebra LP2844 thermal label printer. I bought an $40 UPS branded LP2844 to print stickers and ended up building this after I couldn't find existing tools that did what I wanted. GW raster appears to be broken over USB on firmware V4.29, so it prints over serial from a FastAPI backend. More notes on what I ran into are in the README. Image pipeline does XOR compositing, EPX upscale, Otsu/manual threshold, and five dithering algorithms (Bayer 4x4/8x8, Floyd-Steinberg, Atkinson, Riemersma). Part of this was just an experiment to see if I could use Claude to diagnose the print issues I was running into. Some of the conclusions in the project may be entirely wrong, or entirely specific to my setup. I've arrived at a workflow that works for me but may be based on wrong assumptions. Would love for someone else with an LP2844 to test it out.

Visit product View on Hacker NewsMay 20, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, notes · Missing: mac, agents, macos

77%77% 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 · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, pipe · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

19%19% 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.

Incorrect prediction on native model

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