I built Sapphire, a C-style scripting language and VM for desktop apps

Hacker News

Hi HN, I’ve always wanted a way to build native desktop applications that feel 'modern' without having to deal with the verbosity of C++ or the memory bloat of Electron. So, for the past few months, I’ve been building Sapphire. It’s a general-purpose scripting language with a familiar C-style syntax, running on its own stack-based VM. My goal is to make it a go-to tool for creating fast, hardware-accelerated desktop apps. Link: github.com/foxzyt/Sapphire Why Sapphire? Native Desktop Apps: It’s designed from the ground up for desktop environments, using SFML for hardware-accelerated rendering. Custom VM: I implemented a dispatch table with computed gotos to keep instruction execution fast. Immediate Mode UI: The language includes SapphireUI, a procedural UI system that renders everything in real-time. Batteries Included: It already handles HTTP (GET/POST/Download), JSON parsing, and filesystem I/O natively. Current Status: The core VM, the garbage collector, and the networking/JSON modules are quite solid. I’m currently refining the layout engine to make it even easier to build complex, responsive interfaces.

Visit product View on Hacker NewsDec 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using · Missing: mac, agents, macos

91%91% 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.

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

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

nativeThis product was originally launched on this platform.

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, month, way · Missing: mobile apps, ios, personal

39%39% 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.

Incorrect prediction on native model

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