Development

Development: how to choose

Development tools are the category where measured productivity gains are clearest and where the failure modes are most consequential. Code that looks correct and is not compiles, passes a cursory review, and fails later. Choosing well here is mostly about understanding where the tool helps and where it quietly costs you time.

What actually differentiates them

Context awareness is the main differentiator. An assistant that sees only the open file suggests plausible code that ignores your conventions and your existing helpers. One that indexes the repository suggests code that fits. On any codebase past a certain size, this is the difference between useful and irritating.

The autonomy level defines the working relationship. Completion suggests the next lines. Chat answers questions about code. Agents take a described task and modify several files on their own. Each requires a different kind of supervision, and agents in particular need a review discipline that is easy to skip and expensive to skip.

Language and framework coverage is uneven and follows training data. Assistants are strong on mainstream languages with large public corpora and noticeably weaker on niche stacks, internal frameworks and recent library versions. If you work outside the mainstream, test on your actual stack.

Editor integration determines adoption. A tool that requires leaving the editor gets used for a week. Check that your team editors are supported properly rather than nominally.

What to check before committing

Settle the code privacy question first. Whether your source is sent to a third party, whether it is retained, and whether it trains future models are questions with clear answers that vendors publish. For proprietary code or client work under contract, this may eliminate most of the field before any other criterion applies.

Watch for the review shortcut. Generated code arrives looking finished, which discourages the scrutiny you would apply to a colleague pull request. The measurable risk is not that the tool writes bad code occasionally, it is that reviewers relax precisely because the output reads well.

Be aware that suggestions can reproduce patterns from training data, including licensed code. Some vendors offer filtering and indemnification, which matters in a commercial product.

How pricing usually works

Per seat monthly pricing is the norm, with free tiers for individuals and students. Agent style tools increasingly bill by usage on top of the seat, because a task that touches many files consumes far more computation than a completion.

The cost to model is not the subscription but the review time. An assistant that produces more code than your team can properly review shifts the bottleneck rather than removing it, and that shift is where teams lose the gains they expected.