Most Dev Courses Don't Work — Here's What Actually Does
Finishing a course and being able to build something are two different skills. Most courses only train the first one.

Finishing a course and being able to build something are two different skills. Most courses only train the first one.

Most teams treat technical debt as something to feel guilty about instead of something to manage. That framing is why it never gets paid down.

Setting up an agent is the easy part. Here's how I actually use one day-to-day inside a real project — planning, reviewing, and keeping it from making a mess.

Model Context Protocol and Agent2Agent solve related but distinct problems in the AI agent stack. Here's how they differ, and when you actually need each one.

Building around a single model felt simpler for a while. It also meant every outage, price change, or deprecation was entirely someone else's decision to make for me.

Anthropic's newest model family brings real gains in coding and long-horizon agentic work. Here's what's different, and how I'm actually using it.

Upgrading to a newer model is rarely a free win. If your prompts and evals aren't versioned, you won't notice what quietly changed until a user does.

With a new model shipping from someone every few weeks, picking one for a task has become its own small skill. Here's the checklist I actually use.

A practical walkthrough of generating a Claude agent, running it locally, and wiring it into platforms beyond the terminal.

'Agent' gets attached to every product announcement now. Most of what's actually reliable today is narrower than the pitch decks suggest.

The debate about whether open models can 'catch up' to the frontier misses why most teams reach for them in the first place.

As per-token costs drop, the interesting shift isn't cheaper bills. It's that entire product ideas that didn't pencil out a year ago suddenly do.

Slower, 'thinking' models get used by default for everything now. A lot of the time, that's wasted latency for no real gain.

The people who get the best results from AI models aren't using secret incantations. They're applying the same rigor they'd apply to any other spec.

The framing that gets people the most value out of Claude or GPT-based coding tools isn't 'autocomplete' or 'autonomous developer' — it's pair programming.

Every release brags about a bigger context window. A bigger window doesn't fix the actual problem most people have with long conversations.

Every new model release comes with a chart showing it beating the last one. Here's why that chart shouldn't drive your tooling decisions.