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Lovable App Builder: When AI Spec-to-App Is Enough
Lovable generates full-stack apps from natural language; effective use means knowing when to escape into hand-coding.
Using AI to pre-mortem an incident runbook, Part 1
Have AI walk through an incident runbook step by step and flag failure modes before a real outage.
Interior Designer in 2026: Renders in Minutes, Taste in Years
Space planning, mood, and 3D viz have collapsed to hours. The designer still has to know what a room should feel like. What AI touches Concept renderings — text-to-image from existing room photos.
Agent Platforms vs Bespoke Builds
Agent platforms accelerate teams; bespoke builds customize fully. Choice depends on capability needs.
AI and TypeScript strict mode migration
Migrate a JS/loose-TS codebase to strict TypeScript with LLM help.
When Agent Loops Go Wrong — Detecting and Breaking Them
Coding agents can spiral: same edit, same test, same failure, forever. Learn to spot agent loops early, the patterns that cause them, and the interventions that actually break the cycle.
Test-Driven Prompting — Failing Tests Are the Best Spec
Test-driven development meets AI: paste a failing test, ask the agent to make it green, iterate. Learn the discipline that makes AI code reliably correct because correctness is now executable.
Security Review of AI-Generated Code
AI happily writes code with classic vulnerabilities. Learn the OWASP-aligned review checklist for AI output, the prompts that catch issues early, and the tools that automate the rest.
Debugging Through MCP — Wiring Agents to Real Data
MCP lets agents query your database, search your logs, and inspect your services. Used right, it dramatically tightens debug loops. Used wrong, it's a security disaster. Learn both sides.
The Second Winter: Expert Systems Collapse
The 1980s AI boom ended when expert systems hit a wall and specialized Lisp machines went obsolete.
Future Jobs: What AI Literacy Means for Your Career
Nobody knows exactly what jobs will look like when you graduate. But the gap between people who can work with AI and people who can't is going to matter — a lot.
AI and designing a board game: make up the rules
Use AI to invent a board game you can actually play this weekend.
AI and write a spy mission: agent X, your assignment
Use AI to invent a top-secret spy mission you can play out at home.
AI Supply Chain Attestation: Knowing What's Actually In Your Stack
Modern AI deployments stack 5-10 vendor models, libraries, and services. When something goes wrong, you need to know exactly what's running where. Here's how to maintain real attestation.
Where Training Data Actually Comes From
You cannot understand modern AI without understanding its diet. Let's map where the data comes from, how it gets cleaned, and what that means.
Narrow, General, AGI, ASI: What We Mean and Why It Matters
The terminology ladder of AI capability is loaded. Clarify your definitions and you clarify your whole view of the field.
AI and acne apps: helpful tracker or anxiety machine?
Spot when an AI skin app helps and when it makes things worse.
Multi-Agent Framework Comparison
Multi-agent frameworks (LangGraph, AutoGen, CrewAI, Swarm) all promise orchestration. Real differences matter.
AI Model Families: Pick an Embedding Model You Can Live With
Embedding choice is hard to reverse — re-embedding millions of documents is expensive — so optimize for retrieval quality on your data and provider stability.
TypeScript Fundamentals With AI
TypeScript is JavaScript with types. Learn how `strict` mode catches bugs at compile time and how AI writes cleaner types than you might alone.
Meta-Prompting and Self-Critique: AI That Improves Its Own Output
Static templates are predictable and cheap. Generated prompts adapt to context. The decision shapes maintenance burden, quality, and team workflow.
Prompt Security: Injection Defense, Jailbreaks, and Refusal Design
Prompt injection isn't solvable by prompting alone. Layered defenses combine prompt design, input filtering, and output validation.
Specification Gaming, Reward Hacking, and the Goodhart Tax
A deep tour of the canonical examples, Goodhart's Law, and why specification gaming is not a bug but a structural property of optimization. That is Goodhart's Law, originally formulated in monetary policy and now the most-cited one-liner in AI safety.
Claude Code Workflows: Beyond Single-Session Coding Help
Claude Code shines when used as a structured workflow, not a single-session helper. Repeatable workflows for code review, refactoring, and incident investigation produce 10x leverage.
RAG Framework Selection: LangChain, LlamaIndex, Custom
RAG frameworks accelerate prototypes and constrain production. Knowing when to use each — vs custom — matters for long-term system health.
Giving Your AI Agent a Clear Stopping Condition (or Watch It Loop Forever)
Without a 'done when X' rule, agents loop until they hit the token limit. Always set the exit.
AI Fractional-Executive Scope Memos: Defining the Engagement Before It Drifts
AI can draft scope memos for fractional CFO/CMO/CTO engagements, but the founder must own the real boundaries.
LangGraph vs Custom Orchestration: When Frameworks Help and When They Hurt
Agent orchestration frameworks (LangGraph, AutoGen, CrewAI) accelerate prototypes and constrain production. Knowing when to adopt and when to roll your own determines architectural longevity.
AI Coding Assistants in 2026: Cursor vs. Copilot vs. Claude Code vs. Windsurf
A 2026 buyer's grid covering speed, agentic depth, repo awareness, and team controls.