Everyone's talking about AI. Most of it is noise.

I spent 40+ hours this month reading papers, testing tools, and talking to engineers actually shipping AI-powered products. Here's what actually matters:

1. Context windows are a trap. The latest research on retrieval-augmented generation shows that throwing more context at a model doesn't make it smarter — it makes it lazier. The engineers getting the best results are using smaller, more targeted prompts with better retrieval pipelines.

2. Fine-tuning is getting absurdly cheap. We're talking single-digit dollars for domain-specific models that outperform GPT-4 on narrow tasks. If you're still paying per-token for repetitive API calls, you're leaving money on the table.

3. The real bottleneck is evaluation. Every team I talked to said the same thing: building the AI feature takes a week, building reliable evals takes a month. The teams investing in evaluation infrastructure early are shipping 3x faster than everyone else.

I break stuff like this down every week in Neural Brief — free to join. Built for engineers who'd rather read a 5-minute briefing than scroll Twitter for an hour.

What's the biggest AI challenge you're facing in your codebase right now?