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Opus 5.5 just filed a pull request that changes the streaming behavior in T3 code. It's so cool that I can send off a prompt with a vague idea of what I want, and the result is a ready-to-merge pull request with a video demo of the changes directly... show more

Theo - t3.gg

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92,627 次观看 • 3 天前 •via X (Twitter)

艺术教育科学技术

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#ChineseGP 🇨🇳 || Lewis Hamilton: “I had so much fun! I mean these cars are a lot of fun to drive.” “The fact that there are the differences between us and Mercedes, make it challenging in the race, we’re able to be a little bit closer, which is fun.” “Obviously, not the result I wanted in the end but a lot learned. Gonna go and see if we make some changes.”

#ChineseGP 🇨🇳 || Lewis Hamilton: “I had so much fun! I mean these cars are a lot of fun to drive.” “The fact that there are the differences between us and Mercedes, make it challenging in the race, we’re able to be a little bit closer, which is fun.” “Obviously, not the result I wanted in the end but a lot learned. Gonna go and see if we make some changes.”

sin ⁴⁴

234,236 次观看 • 6 个月前

While building SEV0, I ran into moments where natural language prompting just wasn't enough - too vague, too indirect. So I started using a method that bridges that gap. I call it Bridged Prompting - a technique where you temporarily step out of the prompt-response loop to manipulate something directly using an AI-generated UI, then step back in. Think of it like GenUI, but more user-driven and transient. AI generates a UI on-the-fly, tailored to your prompt, and lets you manipulate the artifact directly before resuming the conversation. Sure, you could build a full tool, switch tabs, wire it up and round-trip your data. But with Bridged Prompting, the AI just spins up a temporary interface right in the flow. In the video below, I used it to construct the hallway system to closely match the layout of the severed floor. This is something I needed to do for this one project, this one time - I didn't need to make a whole separate app. Bridged Prompting lets you: - Make precise edits visually or structurally - "Hit save" to persist changes to local storage or a backend - Return to your natural language prompt

While building SEV0, I ran into moments where natural language prompting just wasn't enough - too vague, too indirect. So I started using a method that bridges that gap. I call it Bridged Prompting - a technique where you temporarily step out of the prompt-response loop to manipulate something directly using an AI-generated UI, then step back in. Think of it like GenUI, but more user-driven and transient. AI generates a UI on-the-fly, tailored to your prompt, and lets you manipulate the artifact directly before resuming the conversation. Sure, you could build a full tool, switch tabs, wire it up and round-trip your data. But with Bridged Prompting, the AI just spins up a temporary interface right in the flow. In the video below, I used it to construct the hallway system to closely match the layout of the severed floor. This is something I needed to do for this one project, this one time - I didn't need to make a whole separate app. Bridged Prompting lets you: - Make precise edits visually or structurally - "Hit save" to persist changes to local storage or a backend - Return to your natural language prompt

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Just the example itself is so fun to play with! 🔆 I can't wait to pull the code and swap the rendering pipeline to achieve even more cool effects. This is the dream of TypeGPU, AI inference, complex compositing, all on the GPU in a seamless, type-safe pipeline.

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I cracked the code. Most people struggle with AI video prompts. The trick isn't writing more — it's writing structured. I use Claude to turn a simple idea into a full JSON-style prompt: scene, character, camera movement, timing, negative prompts, all mapped out. That structure is the difference between "obviously AI" and something that looks real. Once you have the format, it's stupidly easy to make consistently realistic videos, over and over. Want the exact JSON prompt structure I use? Comment "WANT" and I'll send it over

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10,181 次观看 • 17 天前

50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.

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64,482 次观看 • 2 个月前

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