
Sebs
130 posts



excited to share a new toy called bash. it’s a multiplayer coding agent with social built around it starting with invites and a waitlist, and we’ll open it up as quickly as possible bash.tv


We raised $1M dollars to reinvent how people read. Introducing Mark II - a $159 AI bookmark. Thread below

I reopened my wip portfolio a while back because I wanted to redo the type. Simple, normal designer spiral. But then something funny happened — I realized I actually loved the space the old type was taking up. The rhythm was right. The air was right. The letters were holding the page in this really nice way. I just… did not like the actual typeface anymore lol. So I went looking for a replacement, and that’s where I got annoyed. I didn’t want a font that was “better” in some vague taste-board way. I wanted a font that could drop into the same exact footprint as the old one. Same space. Same presence. Different voice. And basically every tool made that weirdly hard. You eyeball it. You resize it. You guess. You pretend it’s close enough. So I built Cotejo. Cotejo means a careful side-by-side comparison in Spanish, which felt right because that’s the whole point. It cap-matches every candidate to the typeface you already have, so everything shares the same cap height and fills the same space. You’re not judging size anymore. You’re judging the actual letterforms. A type swap should feel like it slots in, not like you’re wrestling the page back into place. It also recommends font partners across your own library and Google Fonts, lets you fine-tune pairings on a tracing-paper-style overlay, and exports real CSS / tokens when you’re done. @joshpuckett — Pica and Locale were a huge north star for this. That quiet, instrument-grade kind of craft where the tool gets out of the way and the type becomes the whole thing. I kept thinking: this is such a small, specific problem, but if you’ve been stuck in it, it is painfully real. Would genuinely love your eyes on it. cotejotype.vercel.app github.com/SXM4434/cotejo









Thinking about building a curated archive for designers building with vibe coding. Tools, visual experiments, data visualizations, prototypes, tiny products, and interfaces that feel human-designed, amplified by AI. Would this be useful?







Ok happy Monday! At risk of giving a slightly trope-y response, I think of design as the ability to identify and solve leveraged problems. I think of engineering almost exactly the same as design, just with more emphasis on the solving part. I see many people who are adopting the persona of “design engineer” who seem mostly into the veneer layer of software, like making interfaces a little more animated or expressive. Those aren’t in and of themselves bad things necessarily, but if all you are doing is taking someone else’s design (their solution, their intent, maybe exemplified in a facsimile like a mockup) and implementing it into an existing system (following established principles and frameworks, never really concerning yourself with anything beyond the DOM), then that’s just like being an assembly line decorator. Which doesn’t have zero value! But it has far less value than someone I could task with figuring out how we should increase a specific KPI or take better advantage of caching and bundling to reduce costs and load times for our mobile users if we are blowing up in parts of Asia or Africa and understands the tradeoffs of multiple competing solutions. So instead of chasing trendy titles I think it’s more valuable to just excel at design or engineering, first, before trying to do both and, like most fusion restaurants, just being bad at either.





Stanford professor Judy Fan went on stage at MIT and broke down why humans are so good at making the invisible visible... And why AI hasn't actually learned to "see" the way we do. It completely changes how you think about Human Intelligence v/s Artificial Intelligence: 1. Nature never gave us straight lines or sharp corners. The number line, the coordinate plane, even basic geometry are all human inventions. We created tools that do not exist in nature simply because we needed a way to think more clearly. 2. The coordinate system Descartes invented solved a problem that had stumped mathematicians for centuries, doubling the volume of a cube. Once invented, this tool became so indispensable that virtually every math curriculum on Earth still depends on it. 3. Humans have been doing this for at least 30,000 to 80,000 years. The story of human progress is inseparable from the story of marking up our environment, from cave walls to Galileo's telescope to Feynman diagrams of particles we will never see with our own eyes. 4. Every major scientific breakthrough relied on a visual tool that made something invisible visible. Darwin needed side-by-side illustrations of finches to see variation that was otherwise too subtle to notice. Cajal needed detailed drawings of neurons under a microscope to map how the nervous system was wired. 5. Fan's research group studies something deceptively simple: how people decide what to put into a drawing and what to leave out. When two people played a drawing game, sketchers used far more detail when the target object had close competitors than when it stood alone, all the way down to using fewer strokes and less time when more detail was not necessary. 6. People are not just copying what they see. They are making constant judgment calls about what level of detail actually serves the goal of communication, and they do this naturally without ever being taught the theory behind it. 7. There is a real difference between drawing something so someone can identify it and drawing something so someone can understand how it works. In one study, participants drew explanatory diagrams that emphasized moving, causal parts of a machine while depictive drawings emphasized background and overall appearance, even though both were drawing the exact same object. 8. Explanatory drawings were genuinely better at helping someone figure out how to operate a machine, but worse at helping someone identify which machine it actually was. You cannot optimize a single drawing for both goals at once. Communication always involves tradeoffs. 9. AI vision models trained on photographs generalize surprisingly well to simple, sparse sketches, suggesting that resemblance based recognition is not just a story we tell ourselves. It is something modern neural networks can replicate with real accuracy. 10. But there remains a large, measurable gap between how confidently AI models recognize sketches and how confidently humans do, even when both groups answer the same questions about the same images. Humans are simply far more reliable and far more consistent in their judgments. 11. When researchers compared human-made sketches to AI-generated sketches under tight stroke budgets, both were similarly recognizable at higher budgets, but diverged sharply as the budget shrank. Humans and AI systems simplify drawings in fundamentally different ways once resources get scarce. 12. Reading a graph is not one single skill. It involves perception, knowing where to look, mapping that visual information onto the actual question being asked, and then translating that mapping into an answer. Each of these steps can independently break down, and people fail for very different underlying reasons even when they land on the same wrong answer. 13. When tested directly against humans on graph reading tasks, leading multimodal AI models, including GPT-4V, showed a meaningful performance gap. Even when a model's overall accuracy approached human levels, its pattern of mistakes looked nothing like how humans actually get things wrong. 14. People choose entirely different types of charts depending on what specific question they are trying to answer, not out of a generic preference for bar charts or scatter plots. Their chart choices closely tracked which visualization would genuinely help someone answer that specific question correctly. 15. Two of the most widely used graph literacy tests in education research turned out to correlate strongly with each other, suggesting they measure overlapping skills. But when researchers dug into the actual error patterns, the standard categories used in textbooks, like "find the maximum" or "identify a cluster," failed to explain why people got things wrong nearly as well as a more basic, underlying four-factor model did. 16. The deepest goal behind all of this research is not just academic curiosity. It is to eventually help students and everyday people develop genuine literacy with the visual tools that science and modern decision-making increasingly depend on, because every generation should be able to see further than the last by standing on the visual tools the previous generation built. Follow @yasminekho for more ideas on thinking better, becoming clearer & building a more intentional life.




