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Researched and Developed Embedded System for AI Powered Window Cleaning Drone 🧠
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Creatives on Contra have earned over $150M and we are just getting started
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I built a design system generator inside Figma Make. Here's the workflow.
The problem I kept running into: every new client project meant re-explaining the brand to every AI tool from scratch. Figma Make didn't know the colors. Cursor didn't know the tokens. Claude didn't know the rules. I was the shared memory — and I kept losing things.
So I built Design System Gen. Entirely inside Figma Make. Powered by Claude API.
The workflow:
Upload brand references → Claude API analyzes them across 8 dimensions (palette, mood, typography, composition, lighting, texture, motion, UI DNA) → structured data, not vibes → generates 11 files, each formatted for a specific tool in your stack.
figma-make.md → paste once, Figma Make knows your entire design system
design-system.css → all tokens as CSS custom properties
tokens.json → W3C format, imports directly into Figma Variables
starter.tsx → React starter with tokens already bound
.cursorrules → Cursor knows your system from root
Claude.md → Claude reads it automatically every session
+ 5 more for docs, LLMs, prompts, components
Every tool gets its own context file. All consistent with each other.
The part I'm most proud of: I'm a designer. I don't write code. The entire tool — UI, API integration, file generation logic — was built and shipped inside Figma Make. No external hosting. No separate codebase.
Live at: vibecodingdsgen.figma.site
Add your Claude API key and try it with your own moodboard.
#FigmaMake #DesignSystem #VibeCoding #AI #Claude #UXUI #DesignSystems #ConfigMakeathon
As someone who constantly jumps between Figma, Claude, ChatGPT, Cursor, Lovable, and other AI tools, this problem feels very familiar 😅
The tools are getting smarter, but keeping context consistent is still surprisingly manual. Love seeing people tackle that gap.
Hey community! Dropping a quick style frame and motion pipeline test I wrapped up recently.
The Goal: Achieve a gritty, found-footage aesthetic with an FPV drone chase that feels organic rather than a clean, digital AI generation.
The Process & Workarounds:
Base generations focused heavily on camera physics (banking, velocity, and high-speed tracking).
Post-production work to heavily crush the color grade into a moody, midnight teal palette.
Implemented heavy digital grain overlays and timed lightning flashes to mask geometry clipping and maintain the dreamlike atmosphere.
I’m currently booking for late Q2/Q3 for AI video pipelines, motion design, and digital brand storytelling. If you have a project that needs a raw, cinematic edge, let’s connect and collaborate. 🚀
nice watching!
I've been watching Formula 1 since 2014. Through the Mercedes era, the 2021 title fight that broke my heart and rebuilt it, and the Sunday-afternoon alarms for flyaway races, F1 has been a constant for me for over a decade.
So I finally built the thing I always wanted to exist: a real-time F1 race dashboard that doesn't just show what's happening but predicts what's about to.
🏎️ What it does:
A live track map with every car moving in real time, built from real GPS data.
Live timing, tires, gaps, and pit stops.
And the part I'm most proud of: AI predictions for tire life, pit windows, safety-car risk, and where each car will actually finish
The honest results (because numbers should mean something):
Its finishing-position guess is on average within ~2 places of where a car really ends up. So if it says "P5," reality is usually P3–P7.
It gets about 3 in 4 of its podium calls right while the race is still running.
For safety cars: when it says there's roughly a 1-in-8 chance of one soon, that's about how often one actually appears, i.e., the percentages are honest, not vibes.
What I learned the hard way: in a sport this strategic, the live running order is already a brutally good predictor. Beating it isn't about more data; it's about modeling the chaos (safety cars, rain, and DNFs). That reframing was the whole project.
Built with Python, FastAPI, React, and a Monte Carlo simulation engine. Every predicted number is labelled so it's never confused with real telemetry.
From fan to builder, easily the most fun I've had with a side project. 🏁
Repo + write-up in the comments. Would love feedback from other F1 + data folks.
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