Stanley's AI Systems Lab
Practical AI tutorials for builders who want systems, not hype.
Learn how to apply AI through real workflows: agents with clear boundaries, founder operating systems, product engineering patterns, automation loops, and content systems that earn trust.
I build applied AI systems for founders and product teams. These tutorials turn field notes, production failure modes, and working templates into a learning path you can actually use.
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First three tutorials
What AI Agents Actually Are
A plain-English framework for deciding when an agent is useful and when a normal workflow is better.
02AI Agents vs. Automations vs. Chatbots
A decision guide for choosing the simplest AI pattern that can reliably do the job.
03The Practical AI Stack for Founders
A lean operating stack for research, support, content, sales, and product feedback.
Learning tracks
Build useful AI systems one workflow at a time
AI Agents
Learn how to scope AI agents, give them a small tool belt, inspect their run logs, and evaluate whether they beat simpler automations.
Who it is for: Founders, product engineers, and operators building research, support, QA, or workflow assistants.
What you will build: Design an agent with a clear job, permission boundary, tool list, run log, stop condition, and review loop.
Lesson path
- Define the agent loop — Separate agents from prompts, chatbots, and deterministic workflows before choosing the architecture.
- Choose the simplest useful pattern — Use a decision framework to pick chatbot, automation, or agent based on the job and failure mode.
- Add boundaries and evals — Turn the agent into an inspectable product system with permissions, run logs, stop rules, and regression checks.
AI for Founders
Build founder workflows for customer research, sales prep, support triage, content ops, and weekly decision-making without disconnected tools.
Who it is for: Solo founders, early teams, and product-minded operators who need leverage before they can hire a full function.
What you will build: Create a lightweight founder operating system that captures raw context, structures it, drafts useful outputs, and keeps judgment with the founder.
Lesson path
- Map the founder AI stack — Start with capture, knowledge, workflow, review, and evaluation layers.
- Pick the first workflow — Score repeated work by frequency, stakes, data availability, and reviewability.
- Install the weekly review — Use repeated human corrections as the roadmap for better prompts, schemas, and tools.
AI Product Engineering
Learn how to ship AI features with prompt contracts, structured outputs, fallbacks, evals, and product-quality review loops.
Who it is for: Full-stack engineers, applied AI builders, and product teams adding model-backed features to existing software.
What you will build: Ship AI product workflows with explicit contracts, source handling, failure states, and regression checks.
Lesson path
- Pick the right model interface — Choose between chat, JSON output, retrieval, and tools based on the product job.
- Compare model behavior with real prompts — Use side-by-side prompt tests before committing to a model or prompt contract.
- Design for the failure path — Plan low-confidence states, timeouts, missing context, and human review before launch.
AI Automation
Turn recurring business work into inspectable AI-assisted automations with triggers, validations, exception queues, and human approvals.
Who it is for: Operators, founders, and product engineers automating support, research, reporting, CRM, or content operations.
What you will build: Design automations that run on clear triggers, validate inputs, escalate exceptions, and log enough context for review.
Lesson path
- Choose automation before agency — Identify when fixed steps are enough and where AI should only handle classification, summarization, or drafting.
- Map the trigger and happy path — Write down inputs, validations, outputs, retries, and exceptions before picking tools.
- Add human review at the boundary — Route irreversible, expensive, public, or customer-facing actions to approval.
AI Content and SEO Systems
Build AI-assisted content systems around search intent, proof payloads, internal links, refresh loops, and human editorial review.
Who it is for: Founders, technical marketers, product engineers, and operators turning real work into useful tutorials and search assets.
What you will build: Create a content engine where every brief starts with intent, proof, internal links, and a distribution loop.
Lesson path
- Start from proof, not keywords — Use customer questions, product workflows, examples, and source links as the raw material for content.
- Build a backlog with internal links — Treat every idea as part of a cluster with a funnel stage, proof requirement, and next link.
- Run the weekly brief loop — Generate one review-ready brief, add human judgment, then distribute without auto-publishing.
Get the next AI systems tutorial
I write practical walkthroughs on agents, automation, product engineering, and founder workflows. No generic prompt hacks.