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Mika

@mika_systems • 1,384 subscribers

ai tools you can use. workflows you can copy. dms open

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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 dimension it could read drop in a bracket drawing. get a model with editable holes, bends and mounting points change the hole spacing from 40 mm to 55 mm and the part rebuilds around the new measurement a model that looks right can still be impossible to manufacture one drawing left the wall thickness unspecified. the system flagged the missing measurement instead of quietly inventing it 34 of 40 sample parts passed dimension and solid-geometry checks. six needed clarification before they could be accepted give Claude Code an open-source CAD repository and a fully dimensioned sample drawing, then ask: "Build a drawing-to-model workflow inside this application 1. Extract dimensions, units and geometric constraints from the drawing. Link each measurement to its location and flag anything missing or ambiguous 2. Use the application's existing modeling API to construct an editable feature tree. Keep dimensions as named parameters rather than baking them into a mesh 3. Have a separate agent compare the model against the drawing. Check dimensions, hole positions and whether the result is a valid solid 4. Build a view showing the drawing beside the model. Let me change a parameter, rebuild the part and export it with a verification report" start with a simple part whose dimensions you can check by hand keep unresolved measurements visible. passing these checks does not establish material strength or fitness for a real assembly watch the drawing become an editable model below

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 dimension it could read drop in a bracket drawing. get a model with editable holes, bends and mounting points change the hole spacing from 40 mm to 55 mm and the part rebuilds around the new measurement a model that looks right can still be impossible to manufacture one drawing left the wall thickness unspecified. the system flagged the missing measurement instead of quietly inventing it 34 of 40 sample parts passed dimension and solid-geometry checks. six needed clarification before they could be accepted give Claude Code an open-source CAD repository and a fully dimensioned sample drawing, then ask: "Build a drawing-to-model workflow inside this application 1. Extract dimensions, units and geometric constraints from the drawing. Link each measurement to its location and flag anything missing or ambiguous 2. Use the application's existing modeling API to construct an editable feature tree. Keep dimensions as named parameters rather than baking them into a mesh 3. Have a separate agent compare the model against the drawing. Check dimensions, hole positions and whether the result is a valid solid 4. Build a view showing the drawing beside the model. Let me change a parameter, rebuild the part and export it with a verification report" start with a simple part whose dimensions you can check by hand keep unresolved measurements visible. passing these checks does not establish material strength or fitness for a real assembly watch the drawing become an editable model below

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this is f*cking insane, Claude Code agents turned a reading list into a lab you can run in your browser Boris Cherny (creator of Claude Code) gave 12 agents 64 research papers and one assignment: build a browser lab where readers can rerun the experiments pick a fluid simulation, change the conditions and watch how the result changes switch methods while keeping the same scene. inspect the equations, code and measurements beside the animation give each group a specific job: → readers extract equations, assumptions and experimental settings → builders turn the methods into runnable code → reviewers compare outputs against the papers → interface agents build the controls for exploring each experiment a simulation can look convincing while producing the wrong answer reviewers check whether it preserves mass, stays stable and matches the paper's measurements before accepting the implementation every experiment has to show: > the claim being tested > the conditions used > the difference from the reference result > the details still needed to verify it the lab covered 11 methods across 28 reference scenes. seven matched the selected reference results within the stated tolerances; four stayed marked for further review give Claude Code your paper collection, then ask: "Turn these papers into an interactive browser lab 1. Extract implementable methods, equations and experimental settings. Attach page references and flag missing details 2. Assign independent methods to separate agents. Use shared inputs and measurement tools so the results can be compared 3. Have reviewers check each implementation against the paper. Record discrepancies and keep unverified results clearly marked 4. Build controls for changing parameters, comparing methods and opening the code behind each result" start with one paper and reproduce one reference experiment before adding more agents include the inputs, dependencies and checks so the next reader can rerun it from a clean checkout watch the agents at work below

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