By role
What changes for your kind of work.
Adopting an AI coding agent looks different from a backend service, a component library, a CI pipeline and a board meeting. Each page names what actually changes for that reader, points at recipes from their own stack, and ends at the scorecard that fits their role.
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AI coding tools for backend developers
Backend work is where AI coding agents pay off most reliably, because the change usually spans several files at once — a schema, a migration, a handler and its tests. That is the shape an agent handles and an autocomplete cannot. The recipes below cover eight language stacks plus API and database patterns.
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AI coding tools for frontend developers
Frontend work rewards a different tool balance than backend work: much of it is visual and iterative, which is exactly where an editor-based agent with Tab completions and inline edit earns its keep. The recipes below cover React, Vue, Angular, Next.js and Nuxt, plus the mobile-web patterns that sit alongside them.
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AI coding tools for mobile developers
Mobile development constrains what an AI coding agent can check for itself: a simulator build is slower than a unit test, so the self-correction loop that makes agents reliable on the backend runs less often here. The recipes below cover React Native, Expo and Flutter, and the patterns that keep that loop short.
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AI coding tools for DevOps and platform engineers
DevOps is where AI coding agents stop being an editor feature and become a pipeline step. All three run headless, emit structured JSON and support scheduled automations, which makes them usable in CI — and makes sandbox policy and hooks the guardrails that matter, because there is nobody present to approve anything.
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AI coding tools for solo builders
Building alone changes what an AI coding agent has to be good at. There is no reviewer, so the checks an agent runs on itself are the only checks, and the guardrails have to be structural rather than social: a config file the agent always reads, hooks it cannot skip, tests it must pass.
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AI coding tools for tech leads
A team adopting AI coding agents does not have an individual problem repeated N times. Output rises faster than review capacity, and standards that lived in people's habits stop propagating — which makes shared config files, agreed approval modes and a review process that scales the three things worth settling first.
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AI coding tools for CTOs and founders
Adopting AI coding agents across an organisation is a governance question before it is a tooling one. Seat cost is the visible number and rarely the deciding one; what decides it is metered usage, what an agent is permitted to reach, and whether the review process absorbs the extra output it produces.
Every page here is free and needs no account. The recipes they link to are documentation; some of those are free, the rest come with a subscription.