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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...

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Mika

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36,735 görüntüleme • 21 gün önce •via X (Twitter)

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A quick test of using 3d drawing with 6DOF controllers as an "instructor" for a generative AI process. There's so many powerful and fun new ways to create just around the corner.. Here I'm using Dreams, Krea and 3daistudio. The 3d model at the end of the video was generated from the Dreams+Krea output in just around 15 seconds. Only the model on the left is a "true" 3d model. #ai #madeindreams
0:17

A quick test of using 3d drawing with 6DOF controllers as an "instructor" for a generative AI process. There's so many powerful and fun new ways to create just around the corner.. Here I'm using Dreams, Krea and 3daistudio. The 3d model at the end of the video was generated from the Dreams+Krea output in just around 15 seconds. Only the model on the left is a "true" 3d model. #ai #madeindreams

Martin Nebelong

132,954 görüntüleme • 2 yıl önce

Set parent-child hierarchies to accessorize 3D drawing figures! This allows you to change poses freely while keeping the accessory attached. Also use a 3D head model attached to a 3D drawing figure for a custom face. Customize your drawing figures today ↓ #clipstudio
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Set parent-child hierarchies to accessorize 3D drawing figures! This allows you to change poses freely while keeping the accessory attached. Also use a 3D head model attached to a 3D drawing figure for a custom face. Customize your drawing figures today ↓ #clipstudio

CLIP STUDIO PAINT

35,082 görüntüleme • 1 yıl önce

here’s how to make game models with claude opus 5.5 + blender... opus writes python and builds every model in blender, part by part. the trick is treating every model as code, but there’s a smart way to set it up: - step 1: connect opus 5.5 to blender through blender mcp - step 2: send 2-3 refs + a short brief: “copper helmet, navy suit, glass tank. keep the hose and gloves separate” - step 3: opus builds it part by part: boots > suit > helmet > gloves > tank - step 4: rig it in a separate script, with extra bones for props (a hose, a rod, a tank) - step 5: bake materials into one 1024 texture atlas - step 6: animate the clips: idle, walk, capture - step 7: export one fbx with the rig, the mesh and every clip - step 8: test it in the engine: “keep the backpack stable and the hose attached while he captures a ghost” and the rule that saves the project... pick a style that fits code i like to describe how a model should feel, not how to build it
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here’s how to make game models with claude opus 5.5 + blender... opus writes python and builds every model in blender, part by part. the trick is treating every model as code, but there’s a smart way to set it up: - step 1: connect opus 5.5 to blender through blender mcp - step 2: send 2-3 refs + a short brief: “copper helmet, navy suit, glass tank. keep the hose and gloves separate” - step 3: opus builds it part by part: boots > suit > helmet > gloves > tank - step 4: rig it in a separate script, with extra bones for props (a hose, a rod, a tank) - step 5: bake materials into one 1024 texture atlas - step 6: animate the clips: idle, walk, capture - step 7: export one fbx with the rig, the mesh and every clip - step 8: test it in the engine: “keep the backpack stable and the hose attached while he captures a ghost” and the rule that saves the project... pick a style that fits code i like to describe how a model should feel, not how to build it

rewind

74,527 görüntüleme • 19 gün önce

Another big drop from Hunyuan3D by Tencent. Hunyuan3D-Buffalo 1.0 is not just another text-to-3D model. The more interesting part is its approach to 3D understanding and editing. It can understand separate parts of a complete asset, extract them, remove or replace them with prompts, and edit only the selected region while keeping the rest of the geometry unchanged. That feels like a much more useful direction for 3D AI in general: not just generating a mesh once, but actually understanding its structure and letting you continue working with it. The model combines Qwen-VL, TRELLIS and Hunyuan3D, and was trained on an impressive dataset of 87 million 3D samples. Source:
0:18

Another big drop from Hunyuan3D by Tencent. Hunyuan3D-Buffalo 1.0 is not just another text-to-3D model. The more interesting part is its approach to 3D understanding and editing. It can understand separate parts of a complete asset, extract them, remove or replace them with prompts, and edit only the selected region while keeping the rest of the geometry unchanged. That feels like a much more useful direction for 3D AI in general: not just generating a mesh once, but actually understanding its structure and letting you continue working with it. The model combines Qwen-VL, TRELLIS and Hunyuan3D, and was trained on an impressive dataset of 87 million 3D samples. Source:

Stefan 3D AI

36,487 görüntüleme • 3 ay önce

Okay GPT-6 Astra might actually be the most insane thing we've ever seen for 3D. It drives Blender by itself. It clicks the same buttons a real artist would. All it needs is a drawing or an image to start. From there it gives you thousands of fully editable detailed objects with geometry so clean it hurts. Anyone can build an entire 3D world just by talking now. What is happening.
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Okay GPT-6 Astra might actually be the most insane thing we've ever seen for 3D. It drives Blender by itself. It clicks the same buttons a real artist would. All it needs is a drawing or an image to start. From there it gives you thousands of fully editable detailed objects with geometry so clean it hurts. Anyone can build an entire 3D world just by talking now. What is happening.

ImagineArt

21,704 görüntüleme • 2 ay önce

Introducing Pages on Superset is now Paradev mobile Your agents can now deliver interactive demos, mockups, and reports as Pages you can review from your phone. Ask an agent to build a feature, compare designs, or investigate a problem. It can put the result in a Page with the context you need to review it and give feedback. A few ways to use it: • Feature work: try an interactive demo, then inspect the code in the PR. • Design reviews: compare mockups and before-and-after screenshots. • Investigations: explore findings, charts, and recommendations in one place. To try it, ask your agent to build a feature and create a Page for you to review.
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Introducing Pages on Superset is now Paradev mobile Your agents can now deliver interactive demos, mockups, and reports as Pages you can review from your phone. Ask an agent to build a feature, compare designs, or investigate a problem. It can put the result in a Page with the context you need to review it and give feedback. A few ways to use it: • Feature work: try an interactive demo, then inspect the code in the PR. • Design reviews: compare mockups and before-and-after screenshots. • Investigations: explore findings, charts, and recommendations in one place. To try it, ask your agent to build a feature and create a Page for you to review.

Kiet

86,170 görüntüleme • 1 ay önce

One idea, a playable game. Nex-N2.5 Pro drove the whole thing rough idea →2D sketch →Blender 3D scene →finished game. It wasn't prompting from a distance it opened the tools, watched what changed on screen, and kept going the demos keep landing on the same question: can it actually use a computer? not generate an image of a kitchen call Blender MCP, build the kitchen, render it, read the result. Not describe a CAD part script it, adjust parameters, hit an export wall, switch to the GUI, hand off the drawing try it free on openRouter for a limited time: #NexN2 #Blender #ComputerUse #OpenRouter
0:10

One idea, a playable game. Nex-N2.5 Pro drove the whole thing rough idea →2D sketch →Blender 3D scene →finished game. It wasn't prompting from a distance it opened the tools, watched what changed on screen, and kept going the demos keep landing on the same question: can it actually use a computer? not generate an image of a kitchen call Blender MCP, build the kitchen, render it, read the result. Not describe a CAD part script it, adjust parameters, hit an export wall, switch to the GUI, hand off the drawing try it free on openRouter for a limited time: #NexN2 #Blender #ComputerUse #OpenRouter

Nahid

90,852 görüntüleme • 1 ay önce

EXCUSE ME?? IQuest raised a 320B model from its very first token, then gave it over a million exams with three proctors on every single one the exams are real executable containers. one check runs while the environment gets prepped, one while the code executes, and a full trajectory audit after. if the model tries to peek at hidden tests, it gets flagged, so every pass it learns from is earned it's called IQuest-Q1, an open model built to live in your terminal → 15B active out of 320B, only 8 of 256 experts per token → 29T training tokens, context stretched 32K → 128K → 512K → SFT, RL in real environments, then multi-teacher distillation (MOPD) → runs inside Claude Code, Codex and other agent scaffolds my favorite speed trick: an MTP layer gets reused as a drafter that guesses the next tokens, and the main model checks them. long sessions get way cheaper on the same parameter budget the team calls this an early release. a first model with training this strict makes me want to see the second one try it:
0:12

EXCUSE ME?? IQuest raised a 320B model from its very first token, then gave it over a million exams with three proctors on every single one the exams are real executable containers. one check runs while the environment gets prepped, one while the code executes, and a full trajectory audit after. if the model tries to peek at hidden tests, it gets flagged, so every pass it learns from is earned it's called IQuest-Q1, an open model built to live in your terminal → 15B active out of 320B, only 8 of 256 experts per token → 29T training tokens, context stretched 32K → 128K → 512K → SFT, RL in real environments, then multi-teacher distillation (MOPD) → runs inside Claude Code, Codex and other agent scaffolds my favorite speed trick: an MTP layer gets reused as a drafter that guesses the next tokens, and the main model checks them. long sessions get way cheaper on the same parameter budget the team calls this an early release. a first model with training this strict makes me want to see the second one try it:

Nelly;

92,433 görüntüleme • 8 gün önce

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)
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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 görüntüleme • 1 ay önce

Someone used Claude Code with an open-source Blender MCP addon to create most of the 3D assets for a fishing game. → A single Claude Code session controlled both Blender and Unity through the Model Context Protocol → The developer's case study describes a 40-part, 42-bone crab model created in 53 minutes → Claude rendered images from multiple angles so the developer could review and give feedback → The MCP for Blender addon is MIT-licensed and also works with Codex, Cursor and other AI tools Repo:
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Someone used Claude Code with an open-source Blender MCP addon to create most of the 3D assets for a fishing game. → A single Claude Code session controlled both Blender and Unity through the Model Context Protocol → The developer's case study describes a 40-part, 42-bone crab model created in 53 minutes → Claude rendered images from multiple angles so the developer could review and give feedback → The MCP for Blender addon is MIT-licensed and also works with Codex, Cursor and other AI tools Repo:

0xMarioNawfal

56,590 görüntüleme • 18 gün önce

this is f*cking gold Google engineers explained how to make AI rewrite an agent’s system prompt against failing tests. a failed check becomes the next repair job. the regression suite watches for whatever that repair breaks. even the instructions become something you can test and improve. Agent = Model + Harness. the independently compiled page here maps the broader system into six parts: guides carry project rules, constraints and lessons from past failures > sensors check the work through tests, linters and validators > the loop runs the task, checks the result, retries within limits and escalates > memory preserves state, artifacts and decisions across runs > permissions control tools, writes and actions requiring approval > observability records what happened, what it cost and where it failed an agent changes a build file and announces “done.” the check looks for evidence that it actually ran the validator. if that behavior is missing, you have a specific failure to target. adjust the instructions. rerun the evaluations. check whether the improvement holds without breaking existing behavior. now a prompt change has a test history. a recurring mistake has a regression check. the next model upgrade has something concrete to pass. bookmark the diagram. give “done” a test it has to pass.
0:17

this is f*cking gold Google engineers explained how to make AI rewrite an agent’s system prompt against failing tests. a failed check becomes the next repair job. the regression suite watches for whatever that repair breaks. even the instructions become something you can test and improve. Agent = Model + Harness. the independently compiled page here maps the broader system into six parts: guides carry project rules, constraints and lessons from past failures > sensors check the work through tests, linters and validators > the loop runs the task, checks the result, retries within limits and escalates > memory preserves state, artifacts and decisions across runs > permissions control tools, writes and actions requiring approval > observability records what happened, what it cost and where it failed an agent changes a build file and announces “done.” the check looks for evidence that it actually ran the validator. if that behavior is missing, you have a specific failure to target. adjust the instructions. rerun the evaluations. check whether the improvement holds without breaking existing behavior. now a prompt change has a test history. a recurring mistake has a regression check. the next model upgrade has something concrete to pass. bookmark the diagram. give “done” a test it has to pass.

NO1ennn

72,154 görüntüleme • 1 ay önce

Drawing to 3d and animation. Here's how I did this: @Vizcom_ai to make a more "rendered" version of the drawing. Leonardo ai image to image for the "3d" pass. Magnific ai for upscale/detail. Runway Gen-2 for animation. And yes, the soul of the drawing is kinda lost in translation 😬, but imagine once we're able to get that across.. I'm very impressed with how well the process carries over the various elements of the drawing. #ai #art
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Drawing to 3d and animation. Here's how I did this: @Vizcom_ai to make a more "rendered" version of the drawing. Leonardo ai image to image for the "3d" pass. Magnific ai for upscale/detail. Runway Gen-2 for animation. And yes, the soul of the drawing is kinda lost in translation 😬, but imagine once we're able to get that across.. I'm very impressed with how well the process carries over the various elements of the drawing. #ai #art

Martin Nebelong

24,109 görüntüleme • 2 yıl önce

Deadlock heroes new UNIQUE weapon and ability mehanics: Deadmann Danny - weapon overheating (The weapon is fed by a fuel hose, the resource is Heat: alt-fire damage is increased. Headshot items are prohibited for it). Violet - Brushstroke (Abilities consume the Ink resource, and the model contains an ink pot mesh. Flow state, "living ink" animations, and drawing: a state that prohibits shooting (while drawing?))
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Deadlock heroes new UNIQUE weapon and ability mehanics: Deadmann Danny - weapon overheating (The weapon is fed by a fuel hose, the resource is Heat: alt-fire damage is increased. Headshot items are prohibited for it). Violet - Brushstroke (Abilities consume the Ink resource, and the model contains an ink pot mesh. Flow state, "living ink" animations, and drawing: a state that prohibits shooting (while drawing?))

Deadlock News

95,276 görüntüleme • 1 ay önce

Blender just became a prompt box. With Kimi K3 connected through Blender MCP, you can describe a scene like this in plain English and let the model handle the ugly part: terrain, buildings, lighting, materials, camera movement, animation, and the Python scripts holding everything together. The interesting part is not the first render. Kimi can inspect the result, notice that the camera clips through a tree or the city looks suspiciously like plastic, then edit the actual Blender scene and render it again instead of restarting from zero. You still need taste, because “make it cinematic” remains one of humanity’s least useful instructions. But the distance between an empty Blender file and a fully editable 3D world just got embarrassingly small. A few years ago, creating this meant weeks of modeling, scripting, lighting, and animation. Now you can describe the world, watch Kimi build it, and spend your time fixing the final 10% instead of manually constructing the first 90%. Kimi K3 + Blender MCP is basically text-to-3D without trapping the result inside a useless generated video. Every object, material, light, camera, and keyframe stays editable.
0:12

Blender just became a prompt box. With Kimi K3 connected through Blender MCP, you can describe a scene like this in plain English and let the model handle the ugly part: terrain, buildings, lighting, materials, camera movement, animation, and the Python scripts holding everything together. The interesting part is not the first render. Kimi can inspect the result, notice that the camera clips through a tree or the city looks suspiciously like plastic, then edit the actual Blender scene and render it again instead of restarting from zero. You still need taste, because “make it cinematic” remains one of humanity’s least useful instructions. But the distance between an empty Blender file and a fully editable 3D world just got embarrassingly small. A few years ago, creating this meant weeks of modeling, scripting, lighting, and animation. Now you can describe the world, watch Kimi build it, and spend your time fixing the final 10% instead of manually constructing the first 90%. Kimi K3 + Blender MCP is basically text-to-3D without trapping the result inside a useless generated video. Every object, material, light, camera, and keyframe stays editable.

Rina

264,903 görüntüleme • 3 ay önce

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
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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 görüntüleme • 16 gün önce

i gave bonsai, 3.9 gb model one spec to build a working rotary engine autonomously, using hermes agent. all on rtx 3060ti 8gb. watch it go from a black canvas to a running wankel. the epitrochoid housing, a triangular rotor turning at exactly a third of shaft speed, three chambers firing intake, compression, power, exhaust, and ten geometry tests it had to pass to count as done. a provably correct simulation, not a drawing. then i left it alone and it worked. wrote the code, ran its own tests, hit failures, read them, fixed its own lines, until the engine spun and all ten went green. a real agent loop, and i watched every step. it broke three times getting here and clawed back every one. that honest part is in the reply. and this is a sneak. i've been building a show around runs exactly like this, and it's almost ready. more very soon. just getting started.
0:17

i gave bonsai, 3.9 gb model one spec to build a working rotary engine autonomously, using hermes agent. all on rtx 3060ti 8gb. watch it go from a black canvas to a running wankel. the epitrochoid housing, a triangular rotor turning at exactly a third of shaft speed, three chambers firing intake, compression, power, exhaust, and ten geometry tests it had to pass to count as done. a provably correct simulation, not a drawing. then i left it alone and it worked. wrote the code, ran its own tests, hit failures, read them, fixed its own lines, until the engine spun and all ten went green. a real agent loop, and i watched every step. it broke three times getting here and clawed back every one. that honest part is in the reply. and this is a sneak. i've been building a show around runs exactly like this, and it's almost ready. more very soon. just getting started.

Sudo su

35,069 görüntüleme • 3 ay önce

claude opus 5.5 completely solved three.js it took a floor plan and turned it into a 3d house you can walk through home builders pay $5k to $10k per floor plan for this, so buyers can tour a home before it's built and change things if needed here's how to automate client acquisition: > scrape builder sites for plans with no model home > give the plan and their photos to opus 5.5 > it builds the house to scale as a walkable site > send the builder their own plan before you pitch > charge per plan to do the rest of their catalog > rerun it on every new plan they launch give this prompt to your agent to get started: "here's a floor plan and photos for one house. build it as a three.js site i can walk through in the browser, to scale from the plan, with every room, finish and piece of furniture matched to the photos. the bar is the photos themselves. split the house into pieces you can judge separately, and give each one a builder and a separate critic with fresh context. the critic compares a screenshot of our room with the matching photo, picks the better one, names the biggest gap, and sends it back. keep looping until ours wins or i stop you, and tell me anything you couldn't match."
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claude opus 5.5 completely solved three.js it took a floor plan and turned it into a 3d house you can walk through home builders pay $5k to $10k per floor plan for this, so buyers can tour a home before it's built and change things if needed here's how to automate client acquisition: > scrape builder sites for plans with no model home > give the plan and their photos to opus 5.5 > it builds the house to scale as a walkable site > send the builder their own plan before you pitch > charge per plan to do the rest of their catalog > rerun it on every new plan they launch give this prompt to your agent to get started: "here's a floor plan and photos for one house. build it as a three.js site i can walk through in the browser, to scale from the plan, with every room, finish and piece of furniture matched to the photos. the bar is the photos themselves. split the house into pieces you can judge separately, and give each one a builder and a separate critic with fresh context. the critic compares a screenshot of our room with the matching photo, picks the better one, names the biggest gap, and sends it back. keep looping until ours wins or i stop you, and tell me anything you couldn't match."

Chris

320,101 görüntüleme • 23 gün önce

This is f*cking insane. This tip saved me thousands of dollars. run Opus 5.5, Sonnet 5.5, and Fable 5.1 together, and stop burning Opus on work it was never needed for. the whole idea in one line: the strong model plans, the mid-tier model executes, Fable stays quiet until it's actually needed. roles, broken down: Opus 5.5, high effort, owns the plan and ships the final code Sonnet 5.5, medium effort, splits into explorer (reads the codebase), worker (edits files, runs tests), researcher (pulls docs) Fable 5.1, called through /advisor fable, reads everything happening in the session but stays silent unless something's actually wrong three moments where Fable speaks: → a plan goes out: is this actually the right call? → the same failure shows up again: is the search going nowhere? → the task gets marked finished: did something get skipped? Jev engineering does the same thing one level down. the forks that don't need real thought, which file, which tool, keep going or stop, go straight to Jev and come back in under half a second. the big models only ever see the forks that genuinely need a decision. anyone still running one model for everything is paying Opus prices to decide whether a file exists. drop this into Claude Code: "Rebuild my Claude Code setup around this structure: Look through ~/.claude/agents and .claude/agents for subagents already covering explorer, worker, and researcher. Only create new ones for roles that are missing. Set model: sonnet, effort: medium on each. If an existing subagent is locked to a different model, leave it as is and just list it. In ~/.claude/settings.json, set effortLevel to high and advisorModel to fable. Check for anything disabling the advisor, CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY, or anything blocking feature-flag fetches, plus CLAUDE_CODE_EFFORT_LEVEL, which can override subagent effort settings. Report what you find. Don't change any of it yet. Add one line to ~/.claude/CLAUDE.md: check in with the advisor before a big plan, when the same error shows up twice, and before marking a long task done. Show every change as a diff first. Wait for my go-ahead before touching anything."
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This is f*cking insane. This tip saved me thousands of dollars. run Opus 5.5, Sonnet 5.5, and Fable 5.1 together, and stop burning Opus on work it was never needed for. the whole idea in one line: the strong model plans, the mid-tier model executes, Fable stays quiet until it's actually needed. roles, broken down: Opus 5.5, high effort, owns the plan and ships the final code Sonnet 5.5, medium effort, splits into explorer (reads the codebase), worker (edits files, runs tests), researcher (pulls docs) Fable 5.1, called through /advisor fable, reads everything happening in the session but stays silent unless something's actually wrong three moments where Fable speaks: → a plan goes out: is this actually the right call? → the same failure shows up again: is the search going nowhere? → the task gets marked finished: did something get skipped? Jev engineering does the same thing one level down. the forks that don't need real thought, which file, which tool, keep going or stop, go straight to Jev and come back in under half a second. the big models only ever see the forks that genuinely need a decision. anyone still running one model for everything is paying Opus prices to decide whether a file exists. drop this into Claude Code: "Rebuild my Claude Code setup around this structure: Look through ~/.claude/agents and .claude/agents for subagents already covering explorer, worker, and researcher. Only create new ones for roles that are missing. Set model: sonnet, effort: medium on each. If an existing subagent is locked to a different model, leave it as is and just list it. In ~/.claude/settings.json, set effortLevel to high and advisorModel to fable. Check for anything disabling the advisor, CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY, or anything blocking feature-flag fetches, plus CLAUDE_CODE_EFFORT_LEVEL, which can override subagent effort settings. Report what you find. Don't change any of it yet. Add one line to ~/.claude/CLAUDE.md: check in with the advisor before a big plan, when the same error shows up twice, and before marking a long task done. Show every change as a diff first. Wait for my go-ahead before touching anything."

rvaniaaa

255,188 görüntüleme • 1 ay önce