claude-code
AI Product Development
System track
Build AI products like systems, not demos.
This track covers Claude Code workflows, WebMCP agent interfaces, context management, evidence gates, RAG, and the operational decisions that move an AI idea into production.
Guided track Machine-readable 20 implementation notes, case studies, and security-automation architectures
Why this cluster exists
AI product teams get stuck when they confuse model output with system design. This cluster documents the loops that matter: context control, verification, tool orchestration, and shipping discipline.
System object
protocol board
Best for
builders, founders, and engineers shipping with AI
Start here
The best first read in this track.
Core journey
Read these in order if you want the strongest mental model.
Capability overhang
This track is built for agents as well as readers.
The AI product development cluster is where the site’s AAO, AEO, and GEO stack becomes most explicit: answer-ready articles, machine-readable discovery files, and WebMCP tools that agents can call directly.
WebMCP
Callable blog tools
Agents can query posts through structured browser tools instead of scraping screenshots or brittle DOM selectors.
See the implementationAEO
Answer-shaped articles
Definition blocks, FAQ scaffolding, source sections, and track context make these posts easier to extract, cite, and continue.
Read the AEO guideGEO
Discovery infrastructure
`llms.txt`, `ai.txt`, JSON-LD, and internal entity structure give search and AI systems multiple high-trust ways to understand the site.
See the discovery layerFoundation
Claude Was Eating My Tokens. This 3-Tier System Cut Usage 60%.
Progressive disclosure cuts AI token costs by 40%. Learn the 3-tier system that reduced my Claude expenses while improving output quality.
RAG Explained: How to Stop LLMs From Making Things Up
RAG retrieves live data to fix LLM hallucinations. Build accurate AI apps without retraining models.
Deep dive
I Made Claude Code Learn From Its Own Debugging Mistakes
Build a self-improving RAG system where Claude learns from your debugging sessions, captures insights automatically, and reflects to fix issues faster.
I Built a Bot That Builds SaaS Products. It Shipped One in 24 Hours.
MicroSaaSBot automates SaaS building from idea to deployed MVP. Built StatementSync in 7 days with minimal code. See how it works.
Applied / adjacent
I Shipped 5 Products With AI Agents. IDE Plugins Are Dead.
AI agents will replace IDE plugins in product development. Here's how I built MicroSaaSBot to prove it—and what it means for your workflow.
Why I Chose Flat-Rate Pricing Over Per-Transaction for My SaaS
Flat-rate SaaS pricing explained: why it beats per-transaction models, saves heavy users money, and builds customer loyalty in 2026.
unpdf vs pdf-parse: Which Handles PDFs on Vercel Without Crashing
Skip pdf-parse on Vercel. unpdf processes PDFs in 3-5 seconds with zero native dependencies—here's why and how to migrate.
How I Automated Bug Bounty Hunting Without Getting Banned
Build a multi-agent bug bounty system with evidence-gated progression and zero false positives. Full architecture from 3 months of production.
Why Human-in-the-Loop Beats Automation
Keep humans in control when building AI security tools. Full automation sounds impressive until your reputation tanks from false positives.
Supporting angle
Not every important idea belongs in the main reading path.
Use the supporting pieces to deepen the model, test tradeoffs, and connect adjacent ideas without losing the main narrative.
Recommended next
Claude Was Eating My Tokens. This 3-Tier System Cut Usage 60%.
Progressive disclosure cuts AI token costs by 40%. Learn the 3-tier system that reduced my Claude expenses while improving output quality.
Related tools and products
Explore products and experiments
See the tools and MVPs built from the same workflow stack.