Open-source product engineering / AI developer tooling
DevMesh.
An evidence-based orchestration framework for ChatGPT, Codex, and future adapters, designed to plan, build, diagnose, verify, recover, and deliver software work without treating assumptions as proof.
Explore public source ↗01 / THE PROBLEM
Make the process visible.
AI-assisted engineering becomes unreliable when planning, implementation, verification, and handoff are treated as one opaque step. DevMesh separates those concerns into explicit skills, evidence states, QA paths, and recovery rules.
02 / THE APPROACH
Turn requirements
into useful work.
The project organizes design-to-code, greenfield website building, full-stack work, website operations, agency operations, WordPress, real-estate workflows, deployment, and QA into composable skill paths. It also distinguishes PASS, FAIL, FIXED, BLOCKED, NOT RUN, and N/A so missing evidence is not presented as success.
- Product architecture and orchestration design
- Evidence contracts and QA state modeling
- Design-to-code workflow design
- Website operations and deployment workflows
- Packaging for multiple AI execution environments
03 / SCOPE & EVIDENCE
What you can inspect.
These notes describe an internal product-engineering project, not a client engagement. Scope and technical descriptions come from the existing WebTayo case study; no performance or commercial outcome is claimed.
Review the linked repository for current implementation details, release history, tests, and documented limitations.
Open source repository ↗