I made a collaborative ASCII editor

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I made a website for drawing ascii art with other people https://ascii-collab.app/ It's been online for a little over a year so there's a fair bit of stuff to browse if you want to look around (so much that I even made a poster https://ascii-collab.app/poster.png) There are other websites like yourworldoftext that do this but ascii-collab has some extra features like per-user undo/redo, box selection, a color highlight mode to see who made particular changes, and there's admin tools so I can remove spam. The code is open sourced here https://github.com/MartinSStewart/ascii-collab if anyone is interested. Enjoy!

Visit product View on Hacker NewsDec 15, 2021

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, code, open · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

36%36% predicted probability of success on AppSumo, 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 · 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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