Yandex Code Assistant tested on a secrets-storage task and compared with Cursor

A practical test of Yandex Code Assistant on a secrets-storage task shows the key point: code assistants are no longer just smart autocomplete. They increasingly act as agents that can run development almost like Cursor, but in sensitive scenarios responsibility for architecture, security and final review still rests with the engineer.

Stanislav Denisov, an ML engineer at Infosystems Jet, approaches the debate over vibe coding without the usual split into camps. His position is simple: it is too late to reject AI in development outright, but it is dangerous to trust it unconditionally where the cost of error is high. For MVPs, internal utilities and routine tasks, such tools save weeks of work. For production, especially anything involving access, infrastructure and user data, they are suitable only under strict human control.

In that context, the Yandex Code Assistant test is well chosen. Instead of an abstract example with an algorithm or page layout, the author takes a task that requires storing secrets: tokens, keys, passwords and other sensitive parameters that must not be carelessly hardcoded into code or configs. This scenario tests not only generation quality but the assistant's engineering discipline: does it understand the difference between local development and production, does it suggest environment variables, does it account for key rotation, environment isolation and the risk of accidental leaks into a repository.

The agent shell itself is of separate interest. The author looks not only at what code snippet the model produces, but at the whole workflow: how the tool reads the task, clarifies context, navigates the project, handles nuances, and how confidently it brings the solution to a state that can be reviewed. The text frames this as an attempt to find where the line now runs between useful automation and a false sense of reliability. If an assistant can quickly assemble a working skeleton but misses critical details of secrets handling, the speed gain easily turns into a future incident.

The broader state of the market is telling here. The material cites figures that recently seemed futuristic: Claude Code, according to Semianalysis, already accounts for about 4% of public commits on GitHub, and Google has said that roughly half of the code created there is AI-related. Even if these figures shift from quarter to quarter, the direction is no longer in doubt: assistants are moving from experiments to basic development tools. So the question is no longer whether to use them at all, but which parts of the work can be delegated to them without losing control.

The test's conclusion is fairly sober: in user experience and in the design of its agent layer, Yandex Code Assistant is already close to Cursor, but this similarity does not remove the main limitation. AI can speed up code preparation, suggest a solution structure, highlight typical errors and take over some routine, but choosing a safe secrets-storage scheme, checking compliance with internal policies, final review and accepting risk remain tasks for the developer or the security team. This is where the real line of responsibility runs, and it cannot be delegated to a model just because it writes code confidently.

For the market this is an important signal: Russian teams are getting an increasingly mature local assistant that can compete on user experience with popular Western tools. But the maturity of such a product will be determined not by raw generation speed, but by how carefully it behaves in high-stakes scenarios. Tests on secrets, access and deployment are more useful than any benchmarks, because they show whether an assistant can be trusted with a working environment or only with a draft.