Ship an AI Product
For indie hackers and founders shipping AI products solo.
The build → ship → grow arc for a one-person AI startup: the on-site lessons for the engineering, woven together with open-source tools — Inkwell, VolfPack, PromptPlay — for prototyping, finding your first users, and marketing a launch without a team.
- Go from a prompt to a grounded, agentic AI product
- Evaluate quality and keep inference cost in check
- Find your first users and market a launch with open-source tools
- Ship in public, solo, without a team
The Prompting Handbook
Reliable model output is the foundation of any AI product.
PromptPlay
See how fast prompt → shipped artifact can be.
Design a RAG Pipeline
Ground the product in your own data with retrieval.
RAG Chunking Playground
Tune chunking and watch retrieval quality move.
Design an AI Agent System
Let the product take actions, not just answer.
Tool-Schema Designer
Design the tool interfaces your agent calls.
The Agent Evaluations Handbook
Measure quality before your users do.
Context Budget & Cost Planner
Keep token cost predictable as you scale.
Design an LLM Gateway
Route across providers with fallback, limits and caching.
Design an LLM Inference Server
Serve it reliably and affordably.
Inkwell
Find and reach your first users.
VolfPack
Market the launch without a video team.
The Senior AI Engineer Interview Handbook
Harden the engineering as you grow.
The build → ship → grow arc
Shipping an AI product solo means wearing every hat, so this path is deliberately end-to-end. It starts with the engineering — reliable prompting, retrieval to ground the product in your data, agents to let it act, and eval plus cost control so quality and spend stay in hand — then hands off to open-source tools for the parts founders forget: finding first users and marketing a launch without a team.
It's opinionated toward doing, not reading: prototype fast, ground the product, measure quality before your users do, and ship in public. The goal is a real one-person AI product, not a tutorial.