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this is f*cking insane, Claude Code turned a technical drawing into a 3D model you can edit Dario Amodei (CEO of Anthropic) put 14 agents to work on a CAD application with 620,000 lines of code. the assignment was to make it build parts from drawings and check every...

36,895 просмотров • 24 дней назад •via X (Twitter)

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HydraFusion Explained. Part I: How does the Copilot engine know what to optimize for? Your prompt is evaluated across 4 dimensions: ➡ Does it require deep reasoning? (aka. reasoning depth) ➡ Is it a sophisticated problem? (aka. code generation complexity) ➡ Is it untangling a complicated mess? (aka. debugging difficulty) ➡ Is it dominated by tool-use? (aka. tool orchestration needs) Based on this evaluation, a HyDRA score is assigned to determine the capability profile your task needs the most and to establish a quality bar. Part II: How does it choose a model? Note: It doesn' t pick one model to handle the entire job e2e, (that's Auto mode). Instead, it selects 1 of 3 execution workflows and assigns the best model at different stages based on the HyDRA score: 1️⃣ Single ⚙️ How it works: A single model completes the task from start to finish. ⚖️ Rationale: The task comfortably meets the quality bar with one model. Multi-model orchestration would add latency and cost with no meaningful quality gain. 2️⃣ Cascade ⚙️ How it works: A lightweight, cost-efficient model generates the solution. This draft is evaluated against a quality gate and if it falls short of the quality bar, the entire task escalates to a stronger, frontier model. ⚖️ Rationale: Only bring in the big guns when there is concrete evidence that a lightweight model won't meet the quality threshold. 3️⃣ Critique ⚙️ How it works: A lightweight model drafts the initial code and tool interactions. An independent, read-only frontier model reviews that draft and provides feedback. The original lightweight model then performs any targeted revision(s) before the final response is sent to the user. ⚖️ Rationale: Writing code (output tokens) is expensive while reviewing code (input tokens) is cheap. Instead of incurring the cost of a powerhouse writing hundreds of lines from scratch, a cost-efficient model writes the first draft, and the frontier model just reviews it and points out fixes. HydraFusion is available in experimental preview on the GitHub Copilot CLI: /experimental on, /model and select Hydrafusion (Research Preview)

Julia Muiruri

13,000 просмотров • 2 месяцев назад

Claude Code vs Blender is getting f*cking ridiculous One approach builds a jet engine by hand The other turns a written description into a programmable 3D scene with rotating turbines, mechanical components, realistic materials, and cinematic camera movement And the difference goes way beyond modeling speed Traditional Blender workflow: > MODEL individual mechanical components > POSITION and assemble the geometry > CONFIGURE materials, lighting, and cameras > ANIMATE every moving mechanism > REWORK the scene when specifications change Claude Code + Blender Python: > GENERATE geometry from parameters > ASSEMBLE components programmatically > CONTROL turbine rotation through variables > AUTOMATE lighting and camera movements > REBUILD variations by editing the script The interesting part isn't that AI magically replaces Blender It's that Claude Code can operate Blender through Python, turning repetitive manual work into a reproducible system Want to test the difference? Open Claude Code and ask: "Generate a Blender Python script for a detailed turbofan engine with separate compressor and turbine stages, parameterized blade counts, independent shaft rotation, metallic materials, and a cinematic camera orbit Keep the geometry modular and expose the main parameters so I can regenerate variations without remodeling everything" Blender remains the rendering engine Claude Code changes how you build and control the scene Bookmark this workflow before your next 3D project

Lummox

34,057 просмотров • 19 дней назад