Production-ready Zig template for Flipper Zero apps

Hacker News

If you've debugged a null pointer exception on a Flipper Zero, you know the pain - the device crashes, you reflash, add print statements, repeat. Memory bugs in embedded C are brutal because they only surface at runtime on the actual hardware. This template lets you write Flipper Zero applications in Zig, giving you: - Memory safety and compile-time error checking - Bounds-checked arrays and explicit error handling - Clean build system that works cross-platform - Integration with UFBT for packaging and deployment - No special IDE required - just Zig, UFBT, and your editor The interesting technical challenge was bridging Zig's build system with the Flipper SDK's ARM Cortex-M4 target. The two-stage compilation process (Zig -> object file -> UFBT packaging) required careful handling of calling conventions and ABI compatibility. The compile-time safety catches issues before they become device crashes. Optional pointers and explicit allocator tracking eliminate entire classes of bugs that C would miss entirely. Feedback welcome, especially on: - SDK coverage (I manually declare some functions Zig's C translator can't handle) - Optimization strategies for binary size - Debugging workflow improvements

Visit product View on Hacker NewsOct 28, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

90%90% 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: apps · Missing: mac, agents, macos

79%79% 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: ide, io · Missing: https docs, excited, just released

45%45% 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: platform · Missing: plus, intuitive, reviews

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · 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 · Strong signals: arr · Missing: mrr, revenue, profit

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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