Robert Penner (@robpenner) header
Robert Penner (@robpenner) profile picture

UX Engineer @Microsoft | creator of those easing functions | building next-gen Responsive Easing tools

Brooklyn, NY
Joined October 2008
Photo by Robert Penner (@robpenner)Photo by Robert Penner (@robpenner)Photo by Robert Penner (@robpenner)Photo by Robert Penner (@robpenner)Photo by Robert Penner (@robpenner)Photo by Robert Penner (@robpenner)Photo by Robert Penner (@robpenner)Photo by Robert Penner (@robpenner)Photo by Robert Penner (@robpenner)Photo by Robert Penner (@robpenner)
I'm building a motion curve editor beyond your wildest fantasies. I've been head down solving new easing equations for the last 13 months. I promise nothing less than a quantum leap in easing technology.
After testing Figma Motion, I'd say it's a great start. But if Figma wants Motion to be more than just a UI animation tool, a full Graph Editor should be at the top of the roadmap. Motion lives and dies by timing and easing.
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I'm on a tear with Bend 2, migrating all kinds of business logic and verifying it. Synchronous collaboration between Bend and TypeScript feels incredible. No more modeling/verifying in Lean and porting insights to TS. The laws are live in Bend.
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Any sufficiently advanced tautology is indistinguishable from a tautology.
Replying to @steveruizok
I asked it to find all tautologies
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I love leveling up in automated testing. This year I've climbed up from examples to laws. A unit test is an example. It's like firing one dot in Battleship. But prove a law, and you sweep the field in a huge swathe of truth.
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Made mine with /statechart + Colorfigures, then made them walk around ☕ A metro station whose floors fold, a coffee bean from branch to cup, a slow Saturday on a dice roll, a farm where every animal talks. Isometric, animated, every bit clickable~
show me the all the cool charts you've made with /statechart! and bugs that you've found using the /create-formal-verification skill you can install pstack here: github.com/cursor/plugins/tr…
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Diagrams drawn in JavaScript, made with @poteto's /statechart skill. The business of a boba shop Open source. Made with Claude Opus 5.5
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Today, we're shipping real-time graphics. Using Sol 6.1 Ultrafast we can generate custom overlays, diagrams, and even interactive 3D scenes right on a user's screen, no chat. It's a new visual interface for AI. Here's me learning about LLMs + hardware. Demo:
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Using the right words can drastically improve the results you get from your agents, which is why I built a vocabulary of 244 terms for aiforui.dev. If a term is used in a lesson, it gets a dotted underline. You can hover to see its definition or click to see a demo.
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I think it's becoming rather clear that ALL of mathematics will become possible to formalize rather soon. There are deep implications: 1. Mathematics becomes super interactive. Formalizations make it easy to browse, recombine, and build mathematical proofs from components. 2. Each new idea and paper can be checked much faster. The deep part is meaning and explanation. Validation is almost automatic. 3. Optimization and identification of new proofs can be done partially via agents, and humans can contribute much faster with great new ideas. Proofs from the Book are possible at scale. 4. Scientists and people at large can finally use mathematical artifacts as black boxes with trust and confidence. We can commoditize mathematical theorems. I think the mathematical enterprise also becomes very gamified. We can clearly see each unproven statement as a concrete goal, identify existing tools and directions, and focus quickly on what's missing. And everyone can contribute, with agents or through understanding. Soon, new languages and new theories will also develop, but the structure will keep growing steadily, with no mistakes and on fundamentally solid foundations. Hilbert's dream is becoming a reality. One important caveat: mathematics is by no means trivial now! It's just becoming extremely well-structured and laser-focused. I think the jump in capacity, quality, and scale will be massive. I also hope that our understanding of the big picture will remain solid, because there will be a lot to grasp.
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HBO's @LastWeekTonight has a long track record of citing social science findings without the appropriate context, massaging them to support whatever point @iamjohnoliver is trying to make.
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I wanted to share something I built over the last few weeks: isometric.nyc is a massive isometric pixel art map of NYC, built with nano banana and coding agents. I didn't write a single line of code.
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Since we’re talking about our favorite Nathan Fielder moments
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Not even Black Mirror could predict this level of dystopia.
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For overstretch, you can ask your agent for soft clamping with elastic saturation.
Design Engineering Tip: Try giving elastic drags a limit. Most rubber-band effects are linear, so the further you pull, the further it stretches. iOS uses a curve instead: it resists more the further you go and never passes a maximum. const rubberBand = (x, d, c = 0.55) => (1 - 1 / ((x * c) / d + 1)) * d
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I've read this 3 times and feel like I'm losing my mind. Tao is comparing an AI math proof to an AI cancer cure, which is insane enough by itself. But the point of the comparison is that just like you would obviously rather die from cancer than accept an AI cure a human doesn't understand, we should reject AI proofs that mathematicians don't understand. The mathematicians have truly lost their minds.
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Terry Tao: "Problem-solving alone is now actively harmful to mathematics." I would like to politely disagree, and encourage everyone to continue solving math problems. teorth.github.io/tao-web/sli…
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Most of the recent math results were not reached by a swarm, the way Navier–Stokes was. I think that part got lost in the excitement around the release. OpenAI's unnamed internal model, which I'm going to call Aeon, reached most of them in one shot, from a single prompt, with no interruptions. This model did not even exist before the end of August, and it is still training. Notice how the returns are not dropping off? That chart is from a month ago. It is a log scale. What does Aeon look like now? On average, each result used three hours of thinking. Aeon was given 4000 problems to work on by OpenAI. How many more have been solved in the last three days? That number is not zero. It's like hearing notes in a song that is slowly rising. I don't think Pacing the Frontier was just about Hugging Face or hacking. They've seen how close we are to closing the loop, and as the hour draws near, their resolve begins to quaver. The last piece was model creativity, and I think that threshold was crossed internally by Anthropic and OpenAI in September. You see it in Opus 5.5, which gets it from Fable 5.5. You see it in the math results from Aeon. All that was needed to start the event was the ability for models to think of novel ways to improve themselves. That was the last piece. This is directly analogous to the ability to think of strange, alien ways to solve math problems: solutions so inhuman that they are difficult to express in existing human terms, so the proof winds up incomprehensible. I think these same kinds of alien solutions are now being applied to model improvements internally. That's what all this recent consternation is really about. They see the invisible frontier. And they see what is about to happen.
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I suggest that the right attitude to take toward the OpenAI math release is to imagine we have discovered a notebook full of results from a deceased mathematician. Our job it to make sense of the notebook.
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The Real Mathematicians are vexed, terribly vexed that OpenAI did proofs without their permission. It's math pandemonium! ahmath.org/statements
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