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chudi.dev
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

20 implementation notes, case studies, and security-automation architectures
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Start here

The best first read in this track.

Open the guide

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 implementation

AEO

Answer-shaped articles

Definition blocks, FAQ scaffolding, source sections, and track context make these posts easier to extract, cite, and continue.

Read the AEO guide

GEO

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 layer

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 on Vercel: What Actually Works

pdf-parse breaks on Vercel serverless. unpdf processes PDFs in 3-5s with zero native dependencies. Migration guide included.

Bug Bounty Automation Framework: Zero False Positives

A bug bounty automation framework built on multi-agent evidence-gating: how I went from 12 false-positive reports to zero, with the full architecture.

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

Reduce Claude Token Usage 60%: Progressive Disclosure

3-tier progressive disclosure cuts Claude API costs 40-60%. The exact system: tier 1 triage, tier 2 context, tier 3 full pass.

Related tools and products

Explore products and experiments

See the tools and MVPs built from the same workflow stack.

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This is not a general-purpose digest. It follows the same cluster as this page, so the emails continue the reading path instead of resetting the context.

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  • Distribution-ready summaries instead of noisy roundups

Segment: ai-product-development

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