#ai
9 articles tagged ai, from AI, AI Engineering. Latest: “How to Find Your AI Business Idea in 60 Seconds”.
How to Find Your AI Business Idea in 60 Seconds
Most people overthink AI business ideas. Three factors decide whether an idea is viable, and you can evaluate all three in under a minute with this free tool.
Designing the AI Request Pipeline: 8 Layers Between User Input and Your LLM
A production architecture blueprint for every layer between a user's message and a safe, reliable LLM response — with Spring Boot code you can actually use.
We Let Claude Code Refactor Our 200K-Line Java Monolith. Here's the Honest Result.
A real post-mortem on 6 weeks of AI-assisted refactoring — what it got right, what it silently broke, and the hybrid model we actually shipped.
Building AI Skills: GitHub Copilot Extensions, Claude Tools, and Reusable Agent Capabilities
One skill, three platforms. Building a custom capability for GitHub Copilot, Claude, and your own AI agents — with real code and a decision framework.
AI Code Review at Scale: How We Use Claude to Review Every PR Before Humans See It
Six months, 4,200 pull requests, and a GitHub Actions workflow that catches real bugs before your team reviews. The architecture, prompt, cost, and lessons.
Feature Flags for AI: The Deployment Pattern That Saved Us From 3 Production Disasters
Why LLM features need flags more than anything you've shipped — the four flag patterns that let us swap models, A/B test prompts, and kill broken features fast.
AI Coding Tools in Enterprise: What 90 Days of Data Actually Shows
A 90-day study of AI coding tools across a 70-engineer team, measuring PR velocity, defect rates, and ROI. The biggest finding had nothing to do with speed.
Agent Memory Patterns: How to Give Your LLM Application a Brain That Persists
Your LLM forgets everything once the request ends. How production systems build short-term, long-term, and semantic memory — with Spring AI and pgvector.
Why Most Enterprise AI Projects Die in Production (And the 5 Patterns That Actually Survive)
Why enterprise AI projects fail in production: it is rarely the model. The failures happen earlier, in scope, data, cost control and ownership.