Jishu Labs
An AI-augmented developer tools suite
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Overview
Jishu Labs is a suite of eight AI-augmented developer tools — code understanding, voice notes, schema design, learning paths, deployment tracking, and more — sharing one backend, one design system, one authentication layer, and one LLM integration surface. Built and shipped solo as a personal platform I dogfood every day: a codebase where new AI tools take days rather than weeks because the foundation is already solved.
The Challenge
Building eight independent tools solo is a recipe for drowning in undifferentiated work. Every tool needs auth, a database, a UI system, billing hooks, AI integration, deployments, and observability. Done eight times, that's eight months before any of them ship anything interesting. The real challenge was making each tool feel independent to users while sharing everything that should be shared under the hood.
The Solution
Invested in one shared backbone — Supabase for auth and Postgres, a TypeScript component library for UI, design tokens for theming, and a set of reusable LLM integration primitives (streaming, structured outputs, prompt versioning, per-feature evals). Each tool is a thin Next.js app on top, deployed independently. A bug in one tool can't take down another, but they all share identity, data contracts, and the AI infrastructure.
Architecture
One shared backbone — auth, data, design, AI primitives — with each tool shipped as an independent Next.js app on top. Independent deploys, shared data, zero cross-tenant leakage.
Key Decisions & Tradeoffs
Independent deploys, shared data
Why: Tools fail differently and iterate at different speeds. Coupling their deploys would have turned every release into a coordination problem. Each tool ships on its own CI; only the data contract is shared.
Row Level Security at Postgres, not the API
Why: Multi-tenant data with AI in the loop is a leak waiting to happen. Enforcing tenant isolation at the database layer means a bug in any API route — or any future tool — can't cross user boundaries. Defense in depth, not trust in one layer.
Component library is the real product
Why: The leverage isn't any one tool — it's the shared library. Investing in primitives (theme, a11y, form patterns, loading states, empty states) made each new tool feel finished on day one instead of needing weeks of UI polish.
One shared auth across all surfaces
Why: Sign in once, use all. Separate auth per tool feels like enterprise IT circa 2010. One identity across the suite is the single biggest UX decision — and it's only possible because of the shared backend.
LLM integration as reusable primitives
Why: Streaming, structured output, prompt versioning, and per-feature evals show up in every AI feature. Codifying them once (with one wrapper, one eval harness, one prompt store) meant new AI features shipped in hours instead of weeks.
What I took away from this project
Lessons that still shape how I build — in my own words.
The shared primitives are the real product
Every tool in Jishu Labs looks like its own app from the outside, but the part I spent the most time on is invisible: one auth flow, one Postgres with RLS, one component library, one LLM wrapper with streaming and structured output built in. When the eighth tool took me a weekend instead of a month, I realized the primitives were the actual product and the tools were just proof that they worked.
Dogfooding is a shortcut disguised as a hobby
I built these because I wanted to use them, not because I had a go-to-market plan. That let me skip the usual dance of user research and ship the version I personally needed on day one. Every tool that's survived is one I still open weekly. The ones I stopped opening got deleted. No better signal exists for a solo project.
Row Level Security is non-negotiable once AI is in the loop
Multi-tenant data and LLMs are a leak waiting to happen — one bad prompt and you're serving someone else's data as context. Enforcing tenant isolation at Postgres (not at the API layer) turned a category of terrifying bugs into an impossible class. Defense in depth isn't paranoid; it's cheaper than the alternative.
Impact
Technology Stack
Frontend
Backend
AI
Infra
Key Features
Want something like this, built for you?
If the Jishu Labs story sounds close to what you're trying to ship, a 30-min call is the fastest way to find out if we're a fit.