AI Workbench for Designers: How Designers Should Work With AI
Explains why AI tools for designers should behave as durable workbenches, not just chatbots, image generators, or automatic drafting plugins.
ai-design-tools
17 public items tagged ai-design-tools.
Bridge Trace: Why Execution Should Be Planned Before Calling SketchUp
Explains why SketchUp execution should be planned as a bridge trace before calling the Ruby bridge, so agent actions remain inspectable and repairable.
How Codex Can Drive Verifiable SketchUp Modeling
Shows how an agent CLI, MCP server, SketchUp Ruby bridge, runtime skills, and a structured design model turn design intent into verifiable project state.
Why design_model.json Has to Come Before the SketchUp Scene
Explains why the SketchUp scene is an execution view while the structured design model is better for agent reasoning, validation, repair, and replay.
A Designer's Core Value Is Judgment, Not Faster Drafting
Discusses how AI moves designer value from low-level execution toward goals, constraints, feedback, and version judgment.
Designers Should Think, Not Draft
A product and workflow essay arguing that AI should reduce low-level drafting work so designers can focus on intent, judgment, constraints, and tradeoffs.
From Chat Advice to a Durable Design Project
Explains why AI design collaboration needs project workspaces, structured truth, and resumable project state instead of chat history alone.
How to Verify an Agent Harness
Explains why an agent harness needs headless smoke, live bridge checks, release smoke, and installed package verification.
From MCP Server to SketchUp Ruby Bridge
Explains how the MCP server, execution trace, JSON-RPC, Ruby bridge, and feedback loop turn natural language into executable operations.
Making Imports Repairable: From Source Evidence to Working Truth
Explains why plan import should preserve source evidence and generate repairable working truth instead of promising perfect recognition.
Runtime Skill Boundaries in an Agent Harness
Explains the product boundary between product runtime skills, project dynamic skills, and maintainer development skills.
Semantic Component Registry for Agent Placement
Explains why a SketchUp agent needs a semantic component registry that exposes placement intent, constraints, and reuse rules instead of only searching 3D model assets.
Why the Agent CLI Is Only the Entry Point
Explains why Codex and Claude are entry points while the durable SAH core is the MCP server, project workspace, bridge trace, and SketchUp bridge.
How Visual Feedback Becomes Structured Design Changes
Explains how screenshots, renderings, and top views should become structured repair actions instead of another pile of untracked opinions.
Why AI Design Tools Need a Source of Truth
Uses SketchUp Agent Harness to explain why AI design tools need an editable, verifiable, repairable source of truth instead of only generating finished-looking output.
Rule Precedence for AI Design Workbenches
Proposes a rule precedence model for product defaults, designer profiles, project rules, and explicit session instructions in AI design workbenches.
SketchUp Agent Harness
An open-source project connecting agent CLIs, an MCP server, a SketchUp Ruby bridge, structured design models, and runtime skills into a verifiable design workflow.