LunarBasic, a BASIC that compiles 2D games to native executables

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I built LunarBasic because I wanted a modern recreation of BlitzBasic from back in the day. I wanted it to have good step debugging features, and I wanted it to compile down to a native binary with no external dependencies. I do know there's an open source BlitzBasic out there but I wanted something simpler and more concise. It does have a "pro" feature set, but for the foreseeable future, anyone joining up will have the pro version in perpetuity (eg I'm not trying to sell anything here). It's early (v0.8.0). Would love feedback on the language design and compiler output, especially from anyone who's used QB64, Blitz, or FreeBASIC. Currently runs on x64/arm64 Windows, arm64 macOS, and arm64 Linux (x64 linux coming soon once I get the toolchain together).

Visit product View on Hacker NewsAug 24, 2026

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

80%80% 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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, 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 · Strong signals: soon · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% 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

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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