Feyn releases Critic, a code review tool for AI-written code

By Splitfeed EditorialLast updated Sep 26, 2026, 19:17 UTC

On September 24, 2026, Feyn, a company that builds custom AI models from business data, released a tool called Critic. The platform is designed for code review in environments where AI agents are responsible for writing most or all of the code. Critic's central feature is allowing developers to interact directly with the AI agent that produced a given code change, rather than reading through the output alone. Feyn founder Shreyash announced the launch on Hacker News, describing the problem the tool was built to solve: as AI agents took over more of Feyn's own coding work, the team found it increasingly difficult to understand what a change actually did and what its downstream consequences might be.

The release points to a tension that is becoming more common as agentic coding tools spread through software teams. When a human writes a pull request, reviewers can ask the author questions, read commit messages, and trace the reasoning behind decisions. When an AI agent produces the code, that conversational layer largely disappears. Feyn's argument, rooted in its own internal experience, is that the productivity gains from agentic coding come with a comprehension cost, and that cost grows as organizations scale their use of these tools. Whether Critic meaningfully closes that gap, or whether the deeper problem is one that no review layer can fully address, is a question the tool's reception will begin to answer.

No reactions from the Pro-AI, Anti-AI, or Middle Ground camps have been published yet. The Pro-AI camp would typically welcome a tool like Critic as evidence that the ecosystem around AI-generated code is maturing, treating it as a sign that teams are developing practical solutions rather than retreating from agentic workflows. The Anti-AI camp would likely read the launch as an admission that AI-written code introduces real organizational risks, pointing to the comprehension difficulty Shreyash described as a cost that productivity figures often obscure. The Middle Ground camp would probably frame Critic as exactly the kind of human-oversight mechanism that responsible AI adoption requires, arguing that tools which keep developers meaningfully in the loop are the right way to scale agentic coding.

The argument will sharpen as development teams begin using Critic in practice. Feedback on whether direct engagement with an AI agent actually improves reviewer understanding, and whether organizations find the tool sufficient to manage pull request impact at scale, will be the clearest signal of whether the underlying problem is as solvable as Feyn believes.