Kodebase

Ship like a team 10x your size. Executable docs compile into working software. AI can't skip quality gates. In alpha.

[Kodebase @Kodebaseai]

Kodebase is live (alpha).

What it does:
→ Write specs in plain English
→ AI validates business logic
→ Artifacts compile into code
→ You review and ship

https://t.co/XzRyWi1ZPp

[Kodebase @Kodebaseai]

Why do you keep re-explaining your codebase to AI? It wrote perfect code yesterday. Today it forgot your architecture, your constraints, every decision from last sprint. AI assistants don't need more intelligence. They need enforced methodology.

[Miguel Carvalho @migcarva]

AI coding assistants have no persistent memory.

They write flawless code in isolation. Then forget everything about your project.

The fix isn't smarter AI. It's a smarter system.

We built three MCP tools (not agents):

Scout → validates business logic before code
Sherpa → guides implementation with full context
Monk → enforces validation, captures learnings

Your LLM calls them. Bring your own Claude, GPT, Gemini. Same chat, enforced methodology.

The key insight: founders should test in their language, not code.

Every task Sherpa creates includes plain-language validation steps:

"Log in as [email protected] � Navigate to Profile � Verify email displays"

Not: "GET /api/users/me returns 200"

Results from dogfooding:

> 54-95x velocity vs industry median
> 1.5% change failure rate
> Zero production rollbacks
> Built solo, using itself