Mi

Michelangelo.best – free AI image generator with glitch mode

Hacker News

Michelangelo.best – free AI image generator with glitch mode

Hi HN, *I built michelangelo.best — a 100% free, no-login text-to-image playground.* ### Why another T2I site? - I kept getting locked behind paywalls or signup walls when showing AI art to friends. - I wanted something dead-simple: open the page, type a prompt, get a PNG. ### What’s different? 1. *Fast & lightweight*: running a quantized model on a bare-metal 4060 (8 GB) with queueing + auto-cleanup. 2. *Glitch bonus*: the model is not SOTA. Rough edges produce occasional “cursed” or surreal results — the community started calling it free glitch-art mode. I embraced it: there’s a toggle to “allow wild mutations”. 3. *Public REST endpoint*: `POST /generate` with `prompt`, `steps`, etc. (docs in footer). ### Known issues - Faces can melt; hands can go Lovecraftian. - Only English/CN prompts are tested. - Heavy traffic will queue (I show ETA). ### Roadmap - Switch to SDXL Turbo when VRAM budget allows. - Add seed & negative-prompt fields. - Open-source the FastAPI backend if there’s interest. Would love your feedback! Demo images (normal + glitchy): https://michelangelo.best/demo/normal.png | [link2] | [link3] Thanks for checking it out — happy to answer anything.

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Actual performance

1points
2comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
86%86% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: model, open · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: occasional · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
35%35% 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
20%20% 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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