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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsSolver is presented as a repository-level coding agent: it can inspect a codebase, propose edits and tests, and attempt to run those tests. That goes beyond inline code suggestions, but it does not mean the agent can reliably finish arbitrary engineering work on its own. A documented example from a 2025 review ended with Solver’s newly added test failing, so developers should treat its changes as proposals to inspect and validate.
What is Solver AI?
Solver is an AI software-engineering agent designed to work on tasks within a repository, rather than only completing the next line of code in an editor. InfoWorld’s February 2025 review described tasks including bug fixes, security scans and fixes, feature work, speed improvements, refactoring, test and documentation improvements, and maintenance. The distinction matters: an agent operating at repository level may need to locate relevant files and understand how they fit together before making a change.
That broader scope also creates more room for error. A plausible code edit is not proof that the change works, and a task description does not remove the need to review the result in the context of the project.
How does Solver handle a coding task?
The workflow reported in InfoWorld’s review involved inspecting repository files, proposing code changes and tests, then attempting to run a test. In its example, Solver worked in the Python requests repository on an issue involving URLs with multiple slashes. It proposed a test and a code change, but the test failed; the example output included a malformed URL containing null characters. The reviewer also observed that Solver could go off on tangents and sometimes recover.
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This is one historical example, not a controlled reliability study. It demonstrates why test execution and human inspection are important, but it does not establish how often Solver succeeds or fails across repositories and task types.
What can developers infer from the available coverage?
Repository-level work is the central distinction
The reviewed coverage positions Solver as an agent that can take on a bounded task across repository files, rather than an autocomplete feature limited to the code being typed. That makes it potentially useful for delegating investigation and implementation steps, provided the task is specific and the developer can evaluate the resulting diff.
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Runtime and integrations changed after the first review
In February 2025, InfoWorld reported that Python was the only language in which Solver could run test code, and that its API was not yet available to customers at that time. Those are historical details, not reliable statements about current availability or support.
A later InfoWorld survey reported that Solver had expanded its preconfigured runtime images from Python to more than 20 images, including multiple versions of some languages—what the author characterized as closer to 10 distinct language families. That survey also reported MCP server support through Smithery, Slack integration, repository-dependent memories for instructions and preferences, and image input. These are secondary-source reports; the available coverage does not establish that each feature remains available today.
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Automation claims need attribution
The later survey says Solver claims a Level 4 automation rating, described there as high automation with little to no human supervision. That is Solver’s claim as reported by InfoWorld, not an independently validated measure. The article does not establish the rating’s methodology, so it should not be read as proof that review or supervision is unnecessary.
What should you check before accepting Solver’s changes?
Use the same review discipline you would apply to a human-authored change, with particular attention to the agent’s assumptions and test results:
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- Inspect the diff: Check which files changed, whether the edits fit the task, and whether unrelated code was modified.
- Examine new or changed tests: Confirm they test the intended behavior rather than merely matching the implementation.
- Run the project’s relevant checks: Verify test results in the environment and configuration that matter for your repository. A test that fails, as in the reported example, is a signal to investigate—not a successful fix.
- Review edge cases and security implications: Look for malformed inputs, regressions, unintended behavior changes, and assumptions the task did not specify.
- Keep approval with the developer: Do not merge or deploy a change solely because an agent reports that it has completed the task.
Is Solver’s performance or current availability established?
No like-for-like, controlled reliability comparison is established by the cited coverage. InfoWorld’s February 2025 review reported Solver at #16 on SWE-bench Verified, with 50% of problems resolved at the time of writing, while noting that rankings change frequently. That dated figure is not a current ranking or a durable measure of how Solver will perform on your codebase.
The available articles also do not establish Solver’s current availability, pricing, usage limits, supported environments, data-handling terms, or current integration status. Developers considering the service should confirm those details in current official materials before sharing repository code or relying on a particular capability.
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Who might benefit from trying Solver?
Solver may be worth evaluating when a developer can delegate a clearly bounded repository task, inspect the proposed changes, and run meaningful checks before accepting them. Its repository-level approach is more relevant to that workflow than inline completion alone. The evidence available here does not show that it consistently completes broad or ambiguous engineering work without supervision, or that it outperforms other agents under controlled conditions.
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