Styleframe – Type-Safe, Composable CSS in TypeScript

Hacker News

Hi HN, Over the past 10+ years I’ve been building design systems at various companies, tooling, and UI libraries, including an open-source UI library called Inkline. Through that work I kept running into the same recurring issues: CSS styles breaking due to typos or wrong values, design tokens getting duplicated or drifting, themes becoming unmanageable, and tooling that didn't scale in TypeScript-heavy front-end stacks. I built Styleframe to address these problems. It’s a TypeScript-native way to build type-safe, composable, and scalable design systems. Key features: - Type-safe CSS: Write selectors, variables, utilities, themes and tokens in TS and have compile-time checking (no silent runtime CSS bugs). - Composable design systems: Build tokens, themes, selectors and utilities as reusable pieces, combine modules cleanly, share across projects. - Rich design tokens and theming: Variables for color, spacing, typography, breakpoints, border-radius and shadows; theme overrides for light/dark/brand variants; output optimized CSS. - Framework-agnostic usage: Works with React, Vue, Svelte, Solid, Astro, any bundler. In large teams or systems, styling becomes a fragile surface area: styles leak, tokens diverge, themes multiply, build complexity increases, and developer experience suffers. By writing styling in a typed API, Styleframe aims to make CSS first-class and manageable in modern TS-driven design systems. What I’m looking for I’d appreciate feedback from the HN community — especially if you: - Are maintaining or have built design systems in TypeScript-heavy stacks - Have experienced pain with CSS scalability, theming or token drift - Are interested in typed styling APIs or trying out Styleframe in your stack Happy to answer questions, walk through how the core engine works, or compare to existing solutions. Thanks for reading — looking forward to your thoughts. — Alex

Visit product View on Hacker NewsNov 22, 2025

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

94%94% 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: apis, open · Missing: mac, agents, macos

83%83% 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: exist, existing, ide · Missing: https docs, excited, just released

49%49% 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 · Strong signals: way · Missing: mobile apps, ios, personal

43%43% 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

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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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