Insights on structured AI development, avoiding LLM-generated technical debt, and building production-ready Phoenix applications.
AI app builders nail the prototype, then break in production: data loss, security holes, credit costs, code you can't own. Why it happens and what to do.
Base44 is fast for prototypes but weakest on ownership and self-host. A fair, ranked list of Base44 alternatives by who each is for, plus the graduation path.
Lovable alternatives ranked by who they fit: Bolt, v0, Replit Agent, Base44, Bubble. Plus the security and ownership reasons people switch, cited.
Replit Agent alternatives for 2026: a fair ranked list (Lovable, Bolt, v0, Base44, Bubble) plus a safer, cost-predictable path when the agent breaks prod.
v0 vs Lovable (2026): Vercel's UI-first Next.js builder vs Lovable's full-stack React plus Supabase app builder. Table, strengths, and which to choose.
Kiro vs OpenSpec (2026): AWS's metered spec-first IDE with EARS vs the free, repo-native brownfield delta tracker. Table and how to choose.
A fair Agent OS review: Brian Casel's free, MIT standards-injection system, the v3 change that dropped durable specs, plus strengths and weaknesses.
Best spec-driven development tools in 2026, ranked by use case: Spec Kit, Kiro, OpenSpec, BMAD, Tessl, CodeMySpec, and the top Spec Kit alternatives compared.
What is the BMAD method? How BMAD-METHOD's two-phase, multi-agent agile framework works for AI coding, its strengths and weaknesses, and where it fits in SDD.
CodeMySpec vs OpenSpec: both are repo-resident and BYO-agent. The split is enforcement and live verification. An honest, specific comparison.
What EARS notation is, the five patterns with examples, why AI coding revived it, and how the Easy Approach to Requirements Syntax differs from BDD.
What is GitHub Spec Kit? A 2026 guide to the /specify -> /plan -> /tasks -> implement workflow, its strengths, the sea-of-markdown critique, and where it stops.
What is Kiro? AWS's spec-first IDE and Q Developer successor. How spec mode, EARS notation, and hooks work, plus the pricing and lock-in trade-offs.
What is OpenSpec? Fission AI's free, brownfield-first spec framework: delta tracking, the propose-apply-archive workflow, and how it compares to CodeMySpec.
OpenSpec vs Spec Kit (2026): lightweight brownfield change-tracker vs the full greenfield toolkit. Table, strengths, and which Spec Kit alternative to pick.
Generic spec-driven and AI coding tools don't understand Phoenix contexts, LiveView, Ecto, or OTP. Here's what Elixir-native spec-driven development looks like.
Vibe coding vs spec-driven development: when each wins, why vibe coding fails on maintained code, and why the real fix is a spec that verifies.
How to do spec-driven development with Claude Code: CLAUDE.md limits, layering Spec Kit or OpenSpec, and CodeMySpec's enforced BDD gate plus live QA.
What is spec-driven development? A 2026 guide to SDD, the spec-first to spec-as-source rigor spectrum, and the top spec-driven development tools compared.
Spec Kit vs Kiro (2026): open agent-agnostic CLI vs AWS's integrated spec-first IDE with EARS and credit metering. Table, pricing, and which to choose.
A fair Tessl review: Guy Podjarny's $125M spec-driven bet, the Tessl Framework and skill registry, the non-deterministic compiler problem, and what ships today.
Phoenix BDD specs the LLM can't cheat past. Compile-time Boundary on the spec namespace plus Credo rules that close every shortcut.
AI agents write Elixir fast but the codebase rots. A verification priority order that keeps Phoenix apps maintainable, from working-app down to code quality.
Write one SKILL.md that works on Claude Code, OpenCode, and Codex. Compatibility matrix, portable frontmatter subset, recommended layout.
The description is the only routing signal. If your skill isn't triggering, this is why. Failure modes, self-check questions, and anti-patterns to recognize.
Step-by-step guide to writing agent skills that trigger reliably. 11-step workflow, the description rubric, and lessons from refactoring 30 agent tasks.
13 days, 51 stories, 50+ BDD specs. Most passed. The integrations didn't work. A Potemkin village of green tests over broken functionality.
AI coding agents contradict themselves on long tasks for a mechanical reason. Every new instruction deprioritizes every prior one. Here's the math.
Module spec files gave me and the model the same definition of done. The unit tests passed. The features were still broken. Why.
Every long AI coding session collapses for the same mechanical reason. Here's the five-step workflow that holds against it.
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.
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.
MemPalace, mem0, Letta, vendor memory features. Why contamination, opacity, and lock-in outweigh the convenience for coding work. What actually breaks.
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.
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.
Markdown files in your repo beat MemPalace, mem0, and every dedicated memory system. Cline Memory Bank, Doug's journal, Claude Code auto-memory.
RAG is over-applied to coding. Why retrieval breaks for most coding work, and the failure modes nobody mentions: wrong retrieval, context rot.
Process Claude Code transcripts into durable memory. session-kit, claude-mem, claude-memory-compiler, autoDream. The under-discussed third leg.
My harness was too module-spec heavy and too light on product management. Now prototyping a three amigos process for better BDD specs.
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.
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.
Claude Opus 4.7 migration guide: three breaking API changes, a stealth 35% cost increase via tokenizer, and what's actually better.
Five levels of working with AI agents: prompt, agent interaction, context engineering, harness engineering, environment engineering. Where the leverage is.
Pull docs from compiled BEAM files, embed locally with Ortex, search with sqlite_vec, serve through MCP. No API calls. No network.
One-click screenshot capture in a LiveView feedback widget using html-to-image, colocated hooks, and presigned S3 uploads.
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.
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.
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.
A plain-English guide to what code is, what JavaScript, React, Supabase, and Vercel actually are, and why your AI picked them.
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.
AI coding agents drift without ADRs. The Nygard format, the pre-made decisions pattern, and Archgate as durable architectural memory for AI agents.
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.
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.
Cursor 3.0 shipped April 2 with an Agents Window running 8 parallel workers across local, worktree, cloud, and SSH. Pro stays $20/mo. A week of real use.
Progressive disclosure is a 30-year-old UX principle that solves AI agent context bloat. Practical patterns for skills, CLAUDE.md, and MCP tools.
A 26-point quality gap between AI-only code and human-guided architecture. Here's which patterns produce the best agent output, ranked.
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.
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.
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.
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.
Most teams use AI agents for one phase. The full lifecycle has eight: requirements, architecture, specs, implementation, testing, QA, deploy, maintenance.
Refactoring dropped 60% and duplication rose 48% after AI adoption. The fix is spec-driven development. Here's how it works.
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.
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.
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.
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.
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.
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.
OpenAI shipped 1M lines with zero manually written source. The secret wasn't the model. It was the harness - constraints, verification, lifecycle.
The model didn't write your code. It predicted tokens. Everything else is the harness. Here's why that matters more than benchmarks.
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.
GitHub Copilot deep dive: $10/mo Pro tier, Coding Agent, 60M+ code reviews, Copilot Memory, and what Reddit developers actually think.
Aider in 2026: Polyglot leaderboard standing, pricing (~$60/mo vs $200 Claude Code), 50+ model support, and the best git integration in its class.
Gemini CLI free tier: 1,000 Flash requests/day, no card. Pro models went paid March 25. Available models, 1M context, Jules, and 429 complaints.
Codex CLI deep dive: open source Rust CLI, 2-3x token efficiency, 9,000+ plugins, and what Reddit devs actually think. Pricing, strengths, weaknesses.
Cursor deep dive: $2B ARR, Background Agents, MCP Apps, credit-based billing, and what Reddit devs actually think. Features, pricing, and assessment.
Your CLAUDE.md is settings. Your skills are libraries. Your hooks are middleware. Two activities, one progression.
Claude Code deep dive: highest-rated for code quality, Agent Teams, MCP ecosystem, and what Reddit developers actually think. Pricing and weaknesses.
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.
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.
A web server returning navigable markdown replaces CLAUDE.md stuffing, MCP proliferation, and filesystem sync problems.
Kiro specs use EARS notation; CodeMySpec uses BDD. How Amazon's spec-driven approach compares to BDD specs for AI-generated code, tested side by side.
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.
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?
Four AI IDEs compared April 2026: Cursor 3 Agents Window, Windsurf $20/mo, Zed 1M context BYOK, Kiro spec-driven. Pricing, models, ownership risk.
How to write Claude Code skills that actually work. Real examples, common mistakes, and how skills differ from prompts, MCP servers, and hooks.
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.
Six CLI coding agents compared April 2026: Claude Code with Opus 4.7, Codex Pro $100, Gemini Flash-only free, Aider, OpenCode, Goose. Pricing and quality.
From autocomplete to fully autonomous development. A framework for understanding where you are with AI coding tools and where the real leverage is.
Unit tests and BDD specs verify pieces. QA verifies the running application — story QA, journey QA, and automated issue filing by AI agents.
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.
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.
How CodeMySpec verifies AI-generated code with a 7-stage validation pipeline, dirty tracking, BDD specs, and end-to-end QA journeys.
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.
Write one design doc per code file to prevent architectural drift and keep LLMs on track.
Learn the architecture, planning, and process iteration that keeps LLMs on track.
Learn to design Phoenix contexts and vertical slice architecture to keep AI-generated code consistent.
Phoenix contexts provide self-contained modules, consistent patterns, and built-in testability that make them ideal for AI-assisted development.
Practical approach to using user stories for AI code generation. Keep LLMs focused on requirements, maintain living documentation, and avoid technical debt.
The best way to get reliable code from an LLM is better control and enforcement through predefined workflows, validation, and test-driven development.
Design-driven development adds explicit, reviewable designs that define component architecture before implementation begins.
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