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asked codex to download 106 days of hourly high resolution rapid refresh wind data and make a video where each frame is one snapshot. it made this. (it's upside down. coding agent coordinate system flips are ever-present)

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Codetaur

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100,499 просмотров • 4 месяцев назад •via X (Twitter)

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raymarched viz of 50 vertical layers of 3km resolution HRRR smoke data in a 500km x 500km patch of northern california, in threejs 115 days of hourly snapshots, final data is 167x167x50x2761, all shoved into the GPU so there's no streaming after load and seeking is instant.
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raymarched viz of 50 vertical layers of 3km resolution HRRR smoke data in a 500km x 500km patch of northern california, in threejs 115 days of hourly snapshots, final data is 167x167x50x2761, all shoved into the GPU so there's no streaming after load and seeking is instant.

Codetaur

24,649 просмотров • 4 месяцев назад

my number 1 agent tip is "make the agent prove it understands things". everything boils down to that. the UVs on this book are the best example I have. if you don't do this and then ask the model to do something like "put some text on the front cover" it's going to go wrong
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my number 1 agent tip is "make the agent prove it understands things". everything boils down to that. the UVs on this book are the best example I have. if you don't do this and then ask the model to do something like "put some text on the front cover" it's going to go wrong

Codetaur

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I asked AI to make a video of the 2026 Boston Red Sox and this is what it made
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I asked AI to make a video of the 2026 Boston Red Sox and this is what it made

Boston Globe Pitchbot

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Ever wondered how we come up with combos with new characters and new moves? It’s all in the frame data! Frame data opens up a new perspective to the game. This is one of the most basic levels of frame data 101. Check it out! #streetfighter #streetfighter6 #kimberly #sf6
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Ever wondered how we come up with combos with new characters and new moves? It’s all in the frame data! Frame data opens up a new perspective to the game. This is one of the most basic levels of frame data 101. Check it out! #streetfighter #streetfighter6 #kimberly #sf6

Sherry Nhan

109,419 просмотров • 3 лет назад

Codex is now a full video editor! Our new plugin opens a full built-in NLE right inside the Codex app, where you and your agent can edit together, effortlessly. If you're already paying for Codex, you get this one for FREE. It's available now. Start using it in 1 minute, by just telling your Codex: "Turn Codex into a Video Editor, read
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Codex is now a full video editor! Our new plugin opens a full built-in NLE right inside the Codex app, where you and your agent can edit together, effortlessly. If you're already paying for Codex, you get this one for FREE. It's available now. Start using it in 1 minute, by just telling your Codex: "Turn Codex into a Video Editor, read

ChatCut

1,895,998 просмотров • 3 месяцев назад

building yet another wind data particle layer for mapbox in webgl and while cranking the parameters up to this point probably isn't very useful, it looks preetty cool
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building yet another wind data particle layer for mapbox in webgl and while cranking the parameters up to this point probably isn't very useful, it looks preetty cool

Codetaur

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these models are incredible man. waow. i asked opus 5.5 to make a music video about anything it wanted. it picked voyager 1, which becomes the first human-made object one light-day from earth on nov 18. it wrote the lyrics and drew every frame in code from real nasa data.
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these models are incredible man. waow. i asked opus 5.5 to make a music video about anything it wanted. it picked voyager 1, which becomes the first human-made object one light-day from earth on nov 18. it wrote the lyrics and drew every frame in code from real nasa data.

thoughtcrime

14,184 просмотров • 16 дней назад

AI agents are becoming increasingly capable of taking actions beyond the chat window and interacting with tools in the real world, such as web browsers. We explored this capability by having an AI agent present one of our interactive web-based demos. In this video, it's presenting our election interference demo. This agent is a Cursor coding agent and is equipped with tools to read and interact with the browser (using Playwright) and perform text-to-speech (with ElevenLabs), in addition to the standard Cursor tools.
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AI agents are becoming increasingly capable of taking actions beyond the chat window and interacting with tools in the real world, such as web browsers. We explored this capability by having an AI agent present one of our interactive web-based demos. In this video, it's presenting our election interference demo. This agent is a Cursor coding agent and is equipped with tools to read and interact with the browser (using Playwright) and perform text-to-speech (with ElevenLabs), in addition to the standard Cursor tools.

CivAI

18,451 просмотров • 10 месяцев назад

okay I think I have made the best webgpu wind line LOD system I can. continuous adding / subtracting of lines to maintain screen space density rather than discrete LOD levels, morphing when changing wind layers
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okay I think I have made the best webgpu wind line LOD system I can. continuous adding / subtracting of lines to maintain screen space density rather than discrete LOD levels, morphing when changing wind layers

Codetaur

38,086 просмотров • 3 месяцев назад

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓
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Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

codila

61,294 просмотров • 2 месяцев назад

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Perplexity Computer works so well it's absolutely stunning 🔥 Got it to download, edit and render a video one shot "download the dwarkesh patel and dario amodei podcast and make a clip of where they really start arguing like crazy and make sure it has highlighted captions along with a video of subway surfers at the bottom"

Ananay

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Hermes Agent now has a desktop app! I made a quick video showing how easy it is to install, set up, and get to work! It perfectly synced with my existing agent, or you can use it to make a new one. Plus, a quick tour of the features! It's never been easier to start with Hermes!
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Hermes Agent now has a desktop app! I made a quick video showing how easy it is to install, set up, and get to work! It perfectly synced with my existing agent, or you can use it to make a new one. Plus, a quick tour of the features! It's never been easier to start with Hermes!

tonbi

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The Copilot CLI - how to configure and use it, some tweaks I make and a few of my own workflows / custom agent. I was proud of this one. Pretty much a one shot. It's wild how easy it is to make content when a thing is good.
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The Copilot CLI - how to configure and use it, some tweaks I make and a few of my own workflows / custom agent. I was proud of this one. Pretty much a one shot. It's wild how easy it is to make content when a thing is good.

Burke Holland

34,624 просмотров • 7 месяцев назад

I'm sharing with you one of the best papers I've read on Harness Engineering it takes apart Claude Code (Opus 5.5), Codex, Gemini CLI and more, and boils all of them down to one line: agent = model + harness what you walk away with: > what a harness actually is > how today's coding agents are built under the hood > the patterns every one of them repeats > where the whole category is heading > their checklist for building your own the model gets the headlines. the harness decides whether the agent works loop and graph engineering on top of it - in my article below
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I'm sharing with you one of the best papers I've read on Harness Engineering it takes apart Claude Code (Opus 5.5), Codex, Gemini CLI and more, and boils all of them down to one line: agent = model + harness what you walk away with: > what a harness actually is > how today's coding agents are built under the hood > the patterns every one of them repeats > where the whole category is heading > their checklist for building your own the model gets the headlines. the harness decides whether the agent works loop and graph engineering on top of it - in my article below

Mr. Buzzoni

74,920 просмотров • 7 дней назад

This looks like another step toward removing the data bottleneck in robotics. The bottleneck exists because recording demonstrations is easy, but labeling every grasp, hold, move, and release is slow and expensive. Perceptron Egocentric dropped a new robotics video annotation system. You give it raw robot-camera or first-person/egocentric video. It gives back structured labels that look more like training data: where each small manipulation starts and ends, what the subtask is, what each hand is doing, where the hands are frame by frame, and which hand is left or right. So, Input: raw video of a person or robot doing a task. Output: a machine-readable breakdown of the physical actions inside that video. Basically, it is a machine that converts robot video into policy-training supervision. That means timestamps, subtask boundaries, per-hand actions, and left-right hand grounding. So this will turn large piles of robot or egocentric video into supervision for training robot policies.
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This looks like another step toward removing the data bottleneck in robotics. The bottleneck exists because recording demonstrations is easy, but labeling every grasp, hold, move, and release is slow and expensive. Perceptron Egocentric dropped a new robotics video annotation system. You give it raw robot-camera or first-person/egocentric video. It gives back structured labels that look more like training data: where each small manipulation starts and ends, what the subtask is, what each hand is doing, where the hands are frame by frame, and which hand is left or right. So, Input: raw video of a person or robot doing a task. Output: a machine-readable breakdown of the physical actions inside that video. Basically, it is a machine that converts robot video into policy-training supervision. That means timestamps, subtask boundaries, per-hand actions, and left-right hand grounding. So this will turn large piles of robot or egocentric video into supervision for training robot policies.

Rohan Paul

18,903 просмотров • 3 месяцев назад

As soon as you try to run 2 agents on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:
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As soon as you try to run 2 agents on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:

Santiago

29,268 просмотров • 22 дней назад

I love the NASA astronaut photography map. something about this UI. super fast and clean, extremely easy to filter things down by spatial resolution, cloud cover, date. imagery projected nicely. just a ton of cool images and a really nice way to access them
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I love the NASA astronaut photography map. something about this UI. super fast and clean, extremely easy to filter things down by spatial resolution, cloud cover, date. imagery projected nicely. just a ton of cool images and a really nice way to access them

Codetaur

98,546 просмотров • 1 год назад

🇸🇦 Saudi is taking security at the Hajj VERY seriously Thousands of high-resolution cameras, real-time crowd tracking systems, and rapid-response security teams operating 24/7 aim to make it the safest pilgrimage ever. It’s probably one of the most advanced mass-surveillance and crowd-management systems anywhere on Earth. Source: saudimerijaan on IG
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🇸🇦 Saudi is taking security at the Hajj VERY seriously Thousands of high-resolution cameras, real-time crowd tracking systems, and rapid-response security teams operating 24/7 aim to make it the safest pilgrimage ever. It’s probably one of the most advanced mass-surveillance and crowd-management systems anywhere on Earth. Source: saudimerijaan on IG

Mario Nawfal

403,919 просмотров • 4 месяцев назад

Coding agents waste a lot of tokens just figuring out how a site is structured before they can touch it. Map it once instead. Agentmap crawls any site and turns it into a structured map, pages, flows, and the data on each, that any agent loads in one shot. claude code, cursor, a browser agent, all of them skip the blind exploration and go straight to the task. Open source, powered by hyperbrowser ↓
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Coding agents waste a lot of tokens just figuring out how a site is structured before they can touch it. Map it once instead. Agentmap crawls any site and turns it into a structured map, pages, flows, and the data on each, that any agent loads in one shot. claude code, cursor, a browser agent, all of them skip the blind exploration and go straight to the task. Open source, powered by hyperbrowser ↓

Hyperbrowser

15,640 просмотров • 3 месяцев назад