The Solo Founder Stack: 6 Products With AI
TL;DR
As a solo founder I run multiple products with AI coding agents, a shared skills layer and rigorous architecture. Here is the exact stack I use to ship.
I run six products as a solo founder at Shahriar Labs — LetX, QuantumSketch, ComiKola, BikroyBuddy, BaghLang and Context-Heavy. The only way this works is a tight, opinionated stack and AI coding agents doing the boring 80%. Here it is.
The layers
| Layer | Tools | Why |
|---|---|---|
| Editor | Claude Code, Cursor | Best-in-class code agents |
| Skills | softco, skill-builder, latex-engineer | Reusable agent capabilities |
| Memory | common-knowledge | Persistent context across sessions |
| Inference | openrouter-free-infer | Free LLM routing for cheap tasks |
| Front end | React, Next.js, TypeScript, Tailwind | Fast, typed, low-maintenance |
| Backend | Go (Gin), Python (FastAPI), Temporal.io | Concurrency + workflows |
| Data | PostgreSQL, Redis, pgvector | One database, many uses |
| Infra | AWS, Terraform, ECS Fargate, R2 | IaC, no surprises |
The pattern: shared skills layer
Every product reuses the same skills. softco handles "act as a complete software firm." latex-engineer handles LaTeX docs across products that need them. I covered the full set in My AI Agent Skills Stack.
Reusable skills are the only reason I can keep six products alive. Custom prompts per repo do not scale.
Front end and backend defaults
I default to React + TypeScript + Tailwind everywhere unless there is a specific reason to switch. Next.js when SEO matters; Vite when the app is primarily authenticated. Backend defaults to Go for HTTP services because the binary is 5–10 MB, GC is predictable, and concurrency is built in.
Building world-class software from Dhaka goes deeper on why footprint matters when you pay for every megabyte.
Data: one Postgres, many uses
Most products use a single PostgreSQL instance plus Redis. pgvector handles embeddings. Context-Heavy extends that pattern to a knowledge graph with recursive CTEs. One database, vector + graph + relational — covered in Building Context-Heavy.
Infra: Terraform or nothing
Every product is one terraform apply away from re-deploying. ECS Fargate for stateless services. R2 for object storage (cheap and S3-compatible). I am not running k8s for a solo shop — covered in Microservices as One Engineer.
What I don't use
- k8s for products under 100k MAU. ECS Fargate is enough.
- Multi-cloud. AWS or nothing.
- Microservice frameworks. Plain Go + Gin.
- Bespoke ORMs. sqlc for Go, SQLAlchemy core for Python.
The constraint is decision fatigue. Every "should I use X?" minute is a minute not shipping.
FAQ
Q: How do you actually keep six products running? A: Aggressive reuse — same backend defaults, same infra patterns, same agent skills layer. New product = new repo + same template.
Q: What is the single highest-leverage tool? A: Claude Code + a curated skills library. It compresses planning, coding, and testing into one loop.
Q: How do you decide what to build next? A: Distribution before novelty. If I cannot picture the first 100 users, I do not build.
Written by Shihab Shahriar Antor. Available for AI engineering and product work — hire me.
Written by
Shihab Shahriar Antor — AI Engineer & Founder of Shahriar Labs. Creator of LetX, QuantumSketch, and more.