@21st_dev (YC W26)

Joined January 2014
430 Photos and videos
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I had 14 tabs open just to keep up with AI. arXiv, Papers With Code, every leaderboard, HuggingFace, half a dozen RL-env hubs... So I built one screen for all of it. The Bloomberg terminal for AI research. It's called Sophon ๐Ÿงต
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felt cute. made the whole layout recede on โŒ˜K
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Is it ๐Ÿถ๐Ÿ• or ๐Ÿฎ๐Ÿ„?
wwdc 2026 apple swag #wwdc
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๐ŸŒฌ๏ธ๐Ÿ”–
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What do you think?
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You can now publish @expo components to @21st_dev
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Subscribe to any Sophon feed with custom filters, via email or RSS
I had 14 tabs open just to keep up with AI. arXiv, Papers With Code, every leaderboard, HuggingFace, half a dozen RL-env hubs... So I built one screen for all of it. The Bloomberg terminal for AI research. It's called Sophon ๐Ÿงต
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A closer look at some details ๐Ÿ’…
I had 14 tabs open just to keep up with AI. arXiv, Papers With Code, every leaderboard, HuggingFace, half a dozen RL-env hubs... So I built one screen for all of it. The Bloomberg terminal for AI research. It's called Sophon ๐Ÿงต
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I had 14 tabs open just to keep up with AI. arXiv, Papers With Code, every leaderboard, HuggingFace, half a dozen RL-env hubs... So I built one screen for all of it. The Bloomberg terminal for AI research. It's called Sophon ๐Ÿงต
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AI moves too fast for 14 tabs. Sophon is one screen for the whole field. Free, no signup. Open the terminal โ†’ sophon.at Tell me what's missing ๐Ÿ™
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ps - a "sophon" is the proton-sized supercomputer the Trisolarans use to watch all of human science in The Three-Body Problem. Felt right.
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Claude Code with Opus 8 one-shotted it. Design cooked?
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Prompt - "We need to create visualizations for each feature - like components with mocks and overlays blurred in the background color - just like they usually do with Bento cards for features."
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serafim retweeted
May 23
What started as building a personal taste.md skill for myself, turned into building a pipeline to create any taste as a skill. The most important piece is references. This is where you should spend time. If the references suck, so does the skill. I find that references cropped tightly on details in high resolution work the best. Each image gets analyzed by both Opus 4.7 and GPT 5.5. The analysis is based on why the reference is successful as a piece of design - not what it does functionally. Using two models helps rule out biases and gaps from each. The models focus on layout, spacing, typography, rhythm, composition, hierarchy, etc. At the end, each image has: reference-01/ - opus-4-7-analysis.md - gpt-5-5-analysis.md Then we fuse them together using GPT 5.5 - but the md files are anonymized so 5.5 doesn't prefer itself. reference-01/ - fused-analysis.md reference-02/ - fused-analysis.md etc. After fusion, we have one synthesized analysis per reference. Now the goal is to combine all of those into a single rule set. This is where chunking matters. If you ask one model to combine 100 image analyses at once, the result becomes too broad. It summarizes instead of preserving the granular design rules we want. Instead we chunk the fused analyses into smaller groups. Each group gets merged into a chunk-level synthesis, usually from around 6 to 8 image notes at a time. Then one final model pass fuses those chunks into a single md rule set. Finally, using the rule set, we write a skill of concrete instructions. It enforces constraints, uses imperative wording, and avoids vague taste words.
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