BOBI LABS · DELIVERY INFRASTRUCTURE

bobi-stack

The production scaffold and AI-workflow discipline every Bobi Labs engagement ships on.

Trust through structure. This is the structure.

What it is

bobi-stack is the codebase Bobi Labs starts every build from. Next.js, Supabase, and a typed data layer wired end to end, plus the Claude Code session and memory discipline that keeps AI-assisted delivery consistent across a multi-week engagement. It is a chunk of why a small senior team ships like a larger one. Not a product you buy, the infrastructure behind the work.

Next.js Supabase TanStack Query Tailwind shadcn/ui TypeScript Vercel Claude Code

Architecture

Reads and writes flow through typed registries paired with hooks. Database types flow end to end, so there are no hand-written types for DB data and the same pattern survives from the first table to the hundredth.

READ Component useClientQuery Query registry Supabase + RLS TanStack cache render
WRITE Form (RHF + Zod) useClientMutation Mutation registry Supabase + RLS auto-invalidate

Why it holds up

Session lifecycle

A session-start and session-end checklist mean cross-chat knowledge does not leak between AI-assisted work sessions.

Memory templates

Feedback, project, user, and reference categories with an index. Lessons learned persist across an engagement, not just a chat.

Typed data layer

Query and mutation registries paired with hooks. Schema types reach the component with nothing hand-written.

Fail loudly

Every client-facing action that can fail surfaces a plain-English reason and a path forward. Silent failures erode trust fastest.

Atomic commits

One logical change per commit. The git log reads as a narrative a client can audit, not a pile of mega-commits.

CLI-first ops

Everything runs through one ops script, so CI and local never disagree on how the project builds and tests.

Want this behind your build?

bobi-stack is internal Bobi Labs infrastructure, shared privately for now and opening up once it has proven out further. The fastest way to see what it produces is to put a real problem in front of the team that runs on it.