Figr Identity – Generate Design Systems in Figma Instantly

Hacker News

Hi everyone, I’d love to introduce Figr Identity, which helps you generate design systems with variables, styles, Styles sheets and custom components in your design language. We built this plugin to take away the non-creative work from the design system setup process. 1. Setup the design tokens: Colours, typography, corner radius, spacing system, grids and elevation. 2. Setup the component Library: All the design tokens are automatically reflected in custom component library. 3. Export to Figma: Get the style guideline, variables, and component library to your file in a single click. Would love to hear your feedback as we continue to improve the plugin :)

Visit product View on Hacker NewsMar 27, 2024

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

65%65% 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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, 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

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

nativeThis product was originally launched on this platform.

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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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