How developers use AI
The tools developers now code with, how far they run on their own, what the automated reviewer catches, and how AI-shaped work reads in a codebase.
Start here
How do developers use AI to write software?
The one guide to AI-assisted engineering: the tools, how much they do on their own, what automated review catches, and what an AI-heavy workflow leaves behind in the code.
More in this topic
What are Cursor, Copilot, and Claude Code?
The AI coding tool landscape: autocomplete in the editor, chat in the editor, and agents in the terminal. All of them tools a developer adopts in a week.
What is a coding agent, and how much does it do on its own?
The autonomy ladder: autocomplete that suggests a line, a supervised agent that works while a developer watches, and a background agent that takes a ticket and returns a finished change.
What do developers mean by context, skills, and MCP servers?
Why an agent is only as good as what it can see and touch: context as everything the model is shown, skills as packaged instructions, and MCP servers as the sockets to real systems.
Can AI review code, and should it be the only reviewer?
What automated review reliably catches, what it misses, and why teams that automate the first pass keep a person on architecture, intent, and product judgment.
Does AI-written code look different, and can you tell?
What heavy AI assistance leaves behind in a codebase, why AI-assisted says nothing by itself, and the honest state of the argument about whether the tools erode skill.
Will AI replace software developers?
Which tasks compressed, why junior-shaped work compressed first, what a developer still owns, and why lab claims and shipping teams report different things.