Sirius – A type system for array programming

Hacker News

Hi HN, this page is a pitch & demo for my WIP array programming language. Nothing in the type system is novel -- shape-dependent types with constraint solving, loop nest lattice counting, existentials -- but I think the combination is unique (hard to say considering the activity in this space). I find it surprisingly easy to read and debug. I'd love to hear your thoughts if you're a PL person or just work day-to-day with array langs. Sorry if the web demo doesn't work for you; it depends on a Z3 Wasm build which seems flaky on some browsers. You'll have to try the CLI to get a hands-on experience.

Visit product View on Hacker NewsSep 10, 2026

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

82%82% 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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

61%61% 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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

23%23% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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