Lemmings in HTML in Canvas

Hacker News

For fun I decided I wanted to reimplement Lemmings using HTML-in-cavas and a DOM-based Entity-Component-System. I made heavy use of Claude Code (using only Sonnet 5 High) and I used everything already published on this topic. I used already existing HTML ports (like DHTML Lemmings, LemmingsJS and Lemmings.ts) to kickstart development. Even though I was able to add some new stuff like EGA, CGA and Tandy as well as Adlib, Tandy and PC Speaker that wasn't documented (or I didn't find it). I, or rather Sonnet, was able to do so because I hooked it up to a Ghidra MCP and a DOSBox MCP. At some point feature creep set in so I am guessing there are still a few things not working as intended. I'd love to hear all about it.

Visit product View on Hacker NewsJul 26, 2026

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

95%95% 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

50%50% 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

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

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

37%37% 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

34%34% 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

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.

Correct prediction on native model

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