THE CORE

Chat was the first AI interface. It won't be the last. The next generation of AI products weaves intelligence into the workflow itself — and that requires a different design philosophy.

Everyone talks about language models. Almost nobody talks about embedding models — even though they quietly determine the quality of search, RAG, and classification systems.

AI pipelines fail in ways traditional software doesn't. Here are the patterns, guardrails, and testing strategies that keep production AI systems running smoothly.

The EU AI Act is no longer theoretical. Here's a practical guide to what it means for teams shipping AI products in Europe — without the legal jargon.

Reports of prompt engineering's death are greatly exaggerated. What's changing is what it means — from artisanal craft to systematic engineering discipline.

The vector database market has matured. Here's a grounded comparison based on real-world performance, cost, and operational complexity — not vendor marketing.

Chunking is the most unglamorous and most impactful part of any RAG system. Here's what we've learned about doing it right — and the mistakes that silently destroy retrieval quality.

The open-source vs. proprietary debate generates more heat than light. Here's a clear-eyed comparison based on what actually matters in production.

Leaderboard scores tell you which model is best at benchmarks. They tell you almost nothing about which model is best for your application.

LLMs can review code, but most AI code review tools generate more noise than signal. Here's how to build a review pipeline developers won't ignore.

You can't improve what you can't measure. Here's how to build an LLM monitoring stack that catches problems before your users do.

The AI tooling landscape is exploding. Knowing when to use a vendor and when to build in-house is the highest-leverage decision an engineering leader makes today.

API costs are just the beginning. We break down every line item in a production AI budget — from inference to evaluation to the ops costs nobody talks about.

Governance doesn't have to mean bureaucracy. Here's a lightweight framework for responsible AI that actually works for teams moving fast.

The race to build bigger models dominated 2024. In 2026, the smartest teams are asking a different question: what's the smallest model that gets the job done?

The Model Context Protocol is quietly becoming the USB-C of AI integrations. Here's what it actually does, why it matters, and the patterns emerging around it.

Generating training data with LLMs is now mainstream. But the difference between useful synthetic data and expensive noise comes down to a handful of design decisions.

The decision between fine-tuning and prompt engineering has changed dramatically. Here's a clear framework based on cost, performance, and maintenance burden.

AI agents are no longer a demo-day novelty. After a year of real-world deployments, here are the patterns that work — and the ones that quietly fail at scale.

Semantic search isn't always better than keyword search. Understanding when to use each — and how to combine them — is the key to building search that actually works.

Vision, audio, and document understanding have joined text in production AI. Here's a grounded assessment of multimodal capabilities and where they deliver real value today.