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Neko.js, a recreation of the first virtual pet

Hacker News

Neko.js, a recreation of the first virtual pet

Hi HN, Here is a late Christmas present: I rebuilt Neko [1], the classic desktop cat that chases your mouse, as a tiny, dependency-free JavaScript library that runs directly on web pages. Live demo: https://louisabraham.github.io/nekojs/ GitHub: https://github.com/louisabraham/nekojs Drop-in usage is a single script tag: <script src="https://louisabraham.github.io/nekojs/neko.js" data-autostart></script> This is a fairly faithful recreation of Neko98: same state machine, same behaviors, same original 32×32 pixel sprites. It follows your cursor, falls asleep when idle, claws walls, and you can click it to cycle behavior modes. What made this project interesting to me is how I built it. I started by feeding the original C++ source (from the Wayback Machine) to Claude and let it "vibe code" a first JS implementation. That worked surprisingly well as a starting point, but getting it truly accurate required a lot of manual fixes: rewriting movement logic, fixing animation timing, handling edge cases the AI missed, etc. My takeaway: coding agents are very useful at resurrecting old codebases, and this is probably the best non-soulless use of AI for coding. It gets you 60–70% of the way there very fast, especially for legacy code that would otherwise rot unread. The last 30% still needs a human who cares about details. The final result is ~38KB uncompressed (~14KB brotli), zero dependencies, and can be dropped into a page with a single <script> tag. Happy to hear thoughts from desktop pets nostalgics! [1]: https://en.wikipedia.org/wiki/Neko_(software)

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
89%89% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, model, apple
83%83% predicted probability of success on Product Hunt, 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
41%41% 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
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
24%24% 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
18%18% 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.

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