Naive BDD: The Tests Ran, the Tests Passed, the App Didn't Work
13 days, 51 stories, 50+ BDD specs. Most passed. The integrations didn't work. A Potemkin village of green tests over broken functionality.
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Latest Articles
Insights on structured AI development, avoiding LLM-generated technical debt, and building production-ready Phoenix applications.
Prompt and Pray: How I Started With AI Agents, and Why It Broke
AI coding agents contradict themselves on long tasks for a mechanical reason. Every new instruction deprioritizes every prior one. Here's the math.
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Spec-Driven Development: Finally Useful, Still Not Executable
Module spec files gave me and the model the same definition of done. The unit tests passed. The features were still broken. Why.
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BDD Attention Thesis: A Five-Step Workflow for AI Coding That Doesn't Drift
Every long AI coding session collapses for the same mechanical reason. Here's the five-step workflow that holds against it.
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Three Amigos: The Gate That Was Missing
BDD's Three Amigos applied to AI coding agents: confirm scenario titles before any code is generated, plus a sealed-boundary spec module that can't cheat.
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Write It Down: The First AI Coding Workflow That Actually Worked
Get the rules out of the chat and into files the model re-reads at position zero of every session. The first workflow that held against attention drift.
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Dedicated Memory Stores: Most Marketing, Messiest Tradeoffs
MemPalace, mem0, Letta, vendor memory features. Why contamination, opacity, and lock-in outweigh the convenience for coding work. What actually breaks.
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Graph and Structured Memory: Most Ambitious, Most Vaporware
Zep, Graphiti, mem0 graph mode, and the self-evolving knowledge base trap that exploded after Karpathy's LLM-Wiki gist. When graph memory pays off.
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It's Not Memory If You Wrote It: The CLAUDE.md Confusion
The most-starred memory system in AI coding has 87k+ stars and isn't actually memory. Why the CLAUDE.md conflation costs you wrong tool choices.
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Repo-Native Memory: The Boring Answer That Wins
Markdown files in your repo beat MemPalace, mem0, and every dedicated memory system. Cline Memory Bank, Doug's journal, Claude Code auto-memory.
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Retrieval and RAG: The Category Everyone Reaches for First
RAG is over-applied to coding. Why retrieval breaks for most coding work, and the failure modes nobody mentions: wrong retrieval, context rot.
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Transcript-Derived Memory: The Category Nobody's Writing About
Process Claude Code transcripts into durable memory. session-kit, claude-mem, claude-memory-compiler, autoDream. The under-discussed third leg.
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Build In Public: The Three Amigos Problem
My harness was too module-spec heavy and too light on product management. Now prototyping a three amigos process for better BDD specs.
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Why My Harness Produced Incomplete Apps (and What I Changed)
I got specs wrong twice before getting them right. The journey from module specs to BDD specs to executable boundary testing for AI-generated code.
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What Is a Spec? The Most Overloaded Word in Software
Spec means 13 different things in software. If you're doing spec-driven development with AI, most definitions are wrong. BDD specs are the one that verifies.
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Opus 4.7 Migration Guide: What Breaks, What's Better, What to Watch
Claude Opus 4.7 migration guide: three breaking API changes, a stealth 35% cost increase via tokenizer, and what's actually better.
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The Skill Trajectory for Working with AI Agents
Five levels from prompt engineering to platform engineering. Where most developers are, where the leverage is, and how to level up.
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How I Built a Local Embedding Pipeline in Elixir That Searches My Own Docs
Pull docs from compiled BEAM files, embed locally with Ortex, search with sqlite_vec, serve through MCP. No API calls. No network.
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How the CodeMySpec Feedback Widget Captures Screenshots in LiveView
One-click screenshot capture in a LiveView feedback widget using html-to-image, colocated hooks, and presigned S3 uploads.
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What Is the Client? (It's More Powerful Than You Think)
The client isn't just a browser. Browser apps, PWAs, mobile apps, desktop apps - what they are, what they can do, and why it matters.
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What Is It and Where Does It Belong?
Does the toilet belong in the bathroom or the living room? A guide to putting the right parts of your app in the right place.
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What Is Code, Actually?
A plain-English guide to what code is, what JavaScript, React, Supabase, and Vercel actually are, and why your AI picked them.
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What Is a Server and Why Are You Paying For One?
A server is just a computer. Every service your AI signs you up for is someone else's computer. Here's why you're paying $50/month and how to pay $4.
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Your App Has Two Halves and You Need to Know Which Is Which
Your app runs in two places. If you don't know which is which, your data will leak. A plain-English guide for vibe coders.
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ADRs Are the Best Thing You Can Give Your AI Agent
AI agents drift when they don't know what decisions you've already made. ADRs fix that. Here's how to write them for agent consumption.
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Anthropic Just Made the Agent Harness a Product
Anthropic launched Managed Agents in public beta. They're literally selling the harness now. Here's what it is, what it costs, and why it matters.
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Claude Mythos: What We Know, What We Don't, and Why the Harness Still Matters
Anthropic's next model leaked before they were ready. 93.9% SWE-bench claimed. Here's what's confirmed, what's speculation, and why the harness still matters.
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Cursor 3 Isn't an IDE Anymore. It's an Agent Switchboard.
Cursor 3 demoted the IDE for an agent switchboard with 8 parallel workers, mobile control, and Pro at $20/mo. What works and what's marketing.
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What Is Progressive Disclosure and Why It Matters for AI Agents
Progressive disclosure is a 30-year-old UX principle that solves AI agent context bloat. Practical patterns for skills, CLAUDE.md, and MCP tools.
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Architecture for AI Agents: Which Patterns Actually Work?
A 26-point quality gap between AI-only code and human-guided architecture. Here's which patterns produce the best agent output, ranked.
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The Implementation Phase: AI Writes the Code, But Who's Actually Driving?
Devs are 19% slower with AI but perceive 20% faster. Vibe coding has 2.74x more security vulns. Here's what the implementation phase actually looks like.
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Maintenance: Where Agents Actually Earn Their Keep
Bug fixes, dependency updates, security patches, tech debt. Maintenance is 60-80% of software cost and it's where agents deliver the most proven value.
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The Verification Gap: Why Agents Ship Broken Code and What to Do About It
96% of developers don't trust AI code but commit it anyway. The verification gap is the central problem. Here's how to close it.
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Bad Requirements Are Why Your AI Agent Writes Bad Code
The bottleneck moved from writing code to knowing what to build. Here's how AI is changing requirements gathering and why bad specs kill agent output.
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The Agentic Software Development Process
Most teams use AI agents for one phase of development. Here's what the full lifecycle looks like across all eight phases, with data.
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Your AI Agent Is Only as Good as Your Spec
Refactoring dropped 60% and duplication rose 48% after AI adoption. The fix is spec-driven development. Here's how it works.
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Testing AI-Generated Code: The Self-Confirming Loop and How to Break It
When the same AI writes your code and your tests, you don't have tests. You have a mirror. Here's how to break the loop.
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Teaching AI Agents to Deploy: Knowledge Files vs. Direct Access
AI agents can write code but can't deploy it. I close the gap with 4 markdown files instead of giving my agent cloud credentials.
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How I Do Marketing with Claude Code and MCP Tools
Marketing isn't a content problem. It's a system problem. The Claude Code loop I built with Reddit MCP, GA4, Search Console, and 30 minutes a day.
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The Five Layers of an Agentic Coding System
Most developers treat their AI coding tool as one thing. It's five layers. Here's the framework that changes how you evaluate and build with them.
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The Agent Layer: How AI Coding Tools Actually Work
The agent loop is a while loop that changed software. Here's how tool use, context management, and ReAct turn a token predictor into a coding tool.
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The Environment Layer: Where AI Code Actually Runs
CLI, IDE, or cloud? Sandboxed or wide open? The environment determines what your AI coding agent can do. Here's why it matters more than you think.
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The Harness Layer: Why the Wrapper Matters More Than the Model
OpenAI shipped 1M lines with zero manually written source. The secret wasn't the model. It was the harness - constraints, verification, lifecycle.
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The Model Layer: What Your AI Coding Tool Actually Is (and Isn't)
The model didn't write your code. It predicted tokens. Everything else is the harness. Here's why that matters more than benchmarks.
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The Orchestration Layer: Coordinating Multiple Agents
One agent hitting its ceiling? Multi-agent coordination is the next frontier. Here's what works, what doesn't, and why the demo-to-production gap is wide.
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GitHub Copilot in 2026: Features, Pricing, Benchmarks, and Community Sentiment
GitHub Copilot deep dive: $10/mo Pro tier, Coding Agent, 60M+ code reviews, Copilot Memory, and what Reddit developers actually think.
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Aider in 2026: Features, Pricing, Benchmarks, and Community Sentiment
Aider deep dive: 50+ model support, 4.2x token efficiency vs Claude Code, best-in-class git integration, and what Reddit developers actually think.
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Gemini CLI in 2026: Features, Pricing, Benchmarks, and Community Sentiment
Gemini CLI deep dive: 1,000 free requests/day, improving quality with 3.1 Pro, Jules async agents, and what Reddit developers actually think.
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Codex CLI in 2026: Features, Pricing, Benchmarks, and Community Sentiment
Codex CLI deep dive: open source Rust CLI, 2-3x token efficiency, 9,000+ plugins, and what Reddit devs actually think. Pricing, strengths, weaknesses.
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Cursor in 2026: Features, Pricing, Benchmarks, and Community Sentiment
Cursor deep dive: $2B ARR, Background Agents, MCP Apps, credit-based billing, and what Reddit devs actually think. Features, pricing, and assessment.
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Writing Applications for LLMs
Your CLAUDE.md is settings. Your skills are libraries. Your hooks are middleware. Two activities, one progression.
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Claude Code in 2026: Features, Pricing, Benchmarks, and Community Sentiment
Claude Code deep dive: highest-rated for code quality, Agent Teams, MCP ecosystem, and what Reddit developers actually think. Pricing and weaknesses.
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Open Source vs Vendor-Locked AI Coding Tools: The Tradeoffs That Matter
The most-loved tool (Claude Code) is fully closed. The most-starred (OpenCode, 117K) is fully open. Analysis of 21 tools shows when to choose which.
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What Happened to Supermaven, Aide, and Void: AI Coding Tools That Didn't Make It
Supermaven was acquired. Aide is sunsetting. Void went silent. Why AI coding tools die, what patterns predict failure, and which tools are at risk today.
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Building a Markdown API for LLM Collaboration
A web server returning navigable markdown replaces CLAUDE.md stuffing, MCP proliferation, and filesystem sync problems.
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CodeMySpec Specs vs Kiro EARS: Two Approaches to Spec-Driven AI Development
Amazon's Kiro uses EARS notation. CodeMySpec uses BDD. Both bet that specs before code = better AI output. We tested both approaches.
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MCP: The Protocol Connecting AI Coding Tools
Model Context Protocol is USB for AI agents. 1,000+ servers, adopted by Anthropic, OpenAI, Google. What MCP is, who supports it, and what it enables.
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Best Free and Open-Source AI Coding Tools in 2026
9 free and open-source AI coding tools compared. Gemini CLI is truly free. Aider and Cline match paid tools. When is BYOK cheaper than subscriptions?
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AI IDEs Compared in 2026: Cursor vs Windsurf vs Zed vs Kiro
Cursor 3 Glass, Windsurf's price hike, Zed's 1M context BYOK, Kiro's AWS Transform. Updated April 2026 comparison of pricing, philosophy, and fit.
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Claude Code Skills: Writing Apps for Agents
How to write Claude Code skills that actually work. Real examples, common mistakes, and how skills differ from prompts, MCP servers, and hooks.
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The Rise of CLI Coding Agents: Why Terminal-Native AI is Having a Moment
Claude Code accounts for 4% of GitHub commits. Gemini CLI hit 90K stars. The terminal won the AI coding war nobody expected. Here's why.
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The Best CLI Coding Agents in 2026: Claude Code vs Codex vs Gemini CLI vs Aider vs OpenCode vs Goose
6 CLI coding agents compared: independent testing, pricing, and community sentiment. Refreshed April 2026 with Opus 4.7, Codex $100 tier, Gemini restrictions.
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The Five Levels of AI-Assisted Development
From autocomplete to fully autonomous development. A framework for understanding where you are with AI coding tools and where the real leverage is.
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Agentic QA
Unit tests and BDD specs verify pieces. QA verifies the running application — story QA, journey QA, and automated issue filing by AI agents.
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BDD Specs for AI-Generated Code
Unit tests verify your code works. BDD specs verify your app does what users actually want. One scenario per acceptance criterion, traced to user stories.
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How CodeMySpec Built and Verified a Fuel Card App in 5 Days
55 commits, 100K+ lines, 100+ QA issues caught in 5 active development days. How BDD specs and agentic QA verified a fuel card management platform.
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The Part Nobody Talks About: Verifying AI-Generated Code
How CodeMySpec verifies AI-generated code with a 7-stage validation pipeline, dirty tracking, BDD specs, and end-to-end QA journeys.
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Remote Permission Approval: Building Trust Boundaries for Autonomous AI Agents
How we built a full-stack permission approval system for Claude Code that lets you approve tool calls from your phone with Web Push and Phoenix Channels.
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My first serious coding workflow with AI
Learn the architecture, planning, and process iteration that keeps LLMs on track.
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How to write design documents that keep AI from going off the rails
Write one design doc per code file to prevent architectural drift and keep LLMs on track.
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How to design architecture that keeps AI on track
Learn to design Phoenix contexts and vertical slice architecture to keep AI-generated code consistent.
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Why Phoenix Contexts Are Perfect for LLM-Based Code Generation
Phoenix contexts provide self-contained modules, consistent patterns, and built-in testability that make them ideal for AI-assisted development.
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How to manage user stories to get the most out of LLM's
Practical approach to using user stories for AI code generation. Keep LLMs focused on requirements, maintain living documentation, and avoid technical debt.
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Code Generation is About Control, Not Prompts
The best way to get reliable code from an LLM is better control and enforcement through predefined workflows, validation, and test-driven development.
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Design-Driven Code Generation, The Missing Layer Between Specs and Code
Design-driven development adds explicit, reviewable designs that define component architecture before implementation begins.
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