标签：#codereview

You ask Claude to review your code. It says "looks good, clean, well factored". Of course it does. It wrote that code five minutes ago. You just asked the author to grade his own paper, and he gave himself an A. Having an AI review code works. But not by asking the one who just wrote it. Quality doesn't come from a smarter model, it comes from an architecture where no role checks itself. The self-preference bias This isn't a hunch, it's measured. A model evaluating its own output rates it higher than others' at equal quality: the self-preference bias , documented by Panickssery and co-authors in 2024, and it's causal, not correlational. The model recognizes its own style and prefers it. In practice that means the naive loop "write, then review what you just wrote" is broken by construction. You don't get a review, you get a justification. The agent already decided its code was good the moment it produced it; asking again only confirms. The blind reviewer So the first rule: the reviewer is never the author. In my config, the review agents run in a clean context . They don't see the implementation prompt, they don't know what constraints the author set, they meet the diff like a colleague on Monday morning. And when the author is a known model, the reviewer is from a different family , to break style recognition. One detail matters as much as the rest: the developer's name never enters the reviewer's prompt. No "this was written by a senior", no "review this model's work". The author's identity is exactly the information that triggers the bias. We take it off the table. No finding without a receipt The second trap is the opposite of the first. An AI reviewer, especially in a clean context, tends to over-flag: it invents problems to look useful, it flags "vulnerabilities" that aren't. A review that cries wolf on every line is no better than a complacent one: either way, you stop listening. Hence the receipt rule. Every finding must cite a file:line and pass a check bef

Revisar código es una de las actividades más subestimadas del desarrollo de software. La mayoría de los equipos la tratan como un trámite, algo que hay que aprobar antes de mergear. El resultado es que los PRs se aprueban con un “LGTM” después de dos minutos de scroll, y los problemas reales pasan de largo. Una revisión bien hecha no es leer línea por línea buscando typos. Es entender qué intenta hacer ese código, si lo hace de la manera correcta, y si introduce riesgos que no existían antes. Eso requiere un proceso, no un instinto. Este artículo cubre cómo estructurar ese proceso: qué revisar, en qué orden, qué preguntas hacer, y cómo detectar problemas de seguridad sin ser un experto en ciberseguridad. Empieza por el contexto, no por el código El error más común en code review es abrir el diff y empezar a leer desde la primera línea modificada. Antes de ver una sola línea, necesitas entender qué problema resuelve este cambio. Lee la descripción del PR. Si no hay descripción, o si dice “fixes bug”, ya encontraste el primer problema. Un PR sin contexto obliga al reviewer a reconstruir el razonamiento del autor desde cero, y eso aumenta la probabilidad de aprobar algo que no debería aprobarse. Lo que un buen PR debe explicar: Qué cambia no cómo, sino qué problema resuelve Por qué este approach si hay alternativas que se descartaron, decirlo Cómo probarlo, pasos para verificar que funciona Qué no cubre, scope explícito evita confusiones Si tienes esa información antes de ver el diff, tu revisión va a ser significativamente más efectiva. Qué revisar y en qué orden No toda línea de código merece el mismo nivel de atención. Un buen reviewer distribuye su energía de forma inteligente. Primero: arquitectura y flujo de datos. ¿El cambio tiene sentido a nivel de diseño? ¿Agrega una dependencia innecesaria? ¿Rompe alguna abstracción existente? Esto es lo más difícil de cambiar después. Segundo: lógica de negocio. ¿El código hace lo que dice que hace? ¿Los edge cases están cub

I think a lot of people still imagine AI coding as opening ChatGPT, asking for code, and copy-pasting the result. That's not really how I work anymore. The biggest shift for me is that planning matters far more than coding. Earlier, execution was expensive, so most of the effort went into writing code. Now execution is cheap. I can have an agent implement something in minutes. The hard part is making sure the plan is correct. Most of my effort goes into thinking through the architecture, edge cases, failure modes, test strategy, and how the change fits into the broader system. If the plan is vague, the agent will confidently implement the wrong thing. The quality of the result is mostly determined by the quality of the plan. Once I have a plan, I break it into small independent pieces. Each piece should be executable without additional clarification. If an agent needs to stop and ask questions, the task probably isn't broken down enough. Those pieces become tickets. Then an agent picks up a ticket and implements it. The important thing is that the agent isn't operating in a vacuum. I try to give it a good environment to work in: Clear architectural rules Reusable skills and workflows Guardrails Hooks for things that must always happen One lesson that really stuck with me is that instructions are guidance, not guarantees. At one point I had "always use a git worktree" written in AGENTS.md. The model still ignored it occasionally. When I dug into it, the answer was simple: models can drift from instructions. So if something absolutely must happen, don't rely on instructions. Enforce it. Put it in a hook, script, validation step, CI check, or some other deterministic mechanism. If it is important, make it impossible to skip. Once the implementation is done, the agent opens a PR. This is where another useful pattern comes in: don't let the same model review the code it wrote. I usually have one model implement and another model review. Different models catch different t

GitHub moved Copilot Chat's richer pull request experience to general availability this week — side-by-side chat with diffs, inline editing, and context-aware answers without leaving the review view. Previously in public preview, it is now live for all Copilot license holders. It is a real improvement for reviewing changes inside a single pull request. But it highlights a gap that per-PR AI tooling structurally cannot close: knowing what is open across the rest of your organisation. The Problem That Lives Outside the PR Most engineering teams don't work in one repository. They ship across services, libraries, and infrastructure — often with related PRs open in multiple repos simultaneously. A reviewer approving a payments service change without knowing that a dependent auth-service PR is still in draft is reviewing without full context. This is not a quality-of-feedback problem. It is a visibility problem. No amount of intelligence surfaced inside a PR tells you what is happening across your repositories. Gartner's 2026 assessment of AI coding agents makes the point clearly: the bottleneck has shifted from generating code to reviewing, securing, and governing it. Better per-PR AI raises the floor on feedback quality. The teams that pull ahead will be the ones who also solve the coordination layer — which PRs are open, which are stale, which are blocked on a dependency in another repo. What Changes With Better In-PR AI GitHub's GA release makes the review experience faster and less disruptive for individual PRs. That matters. But as per-PR intelligence becomes table stakes, the differentiator shifts toward cross-repo awareness: who is waiting for review, what related work is in flight, and where the actual bottlenecks in the delivery pipeline are. Engineering leaders should be watching PR age distribution and review load across all repositories — not just the ones that happen to be open in a browser tab right now. For teams already dealing with multi-repo sprawl, Cod

TL;DR — AI assistants are producing C# that looks correct and passes review, but reintroduces production regressions we spent years training out of teams. I'm trying to find out whether other .NET teams see the same patterns — and what's actually catching them before merge. More AI-generated C# is landing in pull requests. Most of it is fine. But a specific category keeps slipping through — and it's the dangerous one, because it compiles, tests pass, and a human skim says "looks good." The pattern The code compiles. Tests pass. Review approves. Production finds out. These aren't syntax errors. They're architectural intent violations — the kind of thing a senior dev would have caught in review before PR volume tripled. Five regressions I keep seeing 1. EF Core read paths without AsNoTracking() Fine in dev. Expensive on a hot read path in prod. // ❌ Looks reasonable. Tracks entities you never mutate. var orders = await _db . Orders . Where ( o => o . CustomerId == id ) . ToListAsync ( cancellationToken ); Fix direction: AsNoTracking() on read-only queries, or a team convention documented in CLAUDE.md / Copilot instructions. 2. Captive dependency (scoped service in a singleton) Compiles. Runs. Wrong state across requests. // ❌ Singleton lives forever; scoped dependency does not. services . AddScoped < IOrderRepository , OrderRepository >(); services . AddSingleton < ReportCache >(); // ctor takes IOrderRepository Fix direction: align lifetimes, or inject IServiceScopeFactory instead of capturing scoped services. 3. Dropped CancellationToken The method accepts cancellation. The downstream call ignores it. // ❌ Signature honours cancellation; body doesn't. public async Task RunAsync ( CancellationToken cancellationToken ) { await Task . Delay ( 500 ); // overload with token exists } Fix direction: forward cancellationToken to every downstream async call that accepts one. 4. Swallowed exception Failure disappears. Monitoring stays green. // ❌ "Handle errors gracefully" —

This article was originally published on aicoderscope.com Most AI coding tools are generalists—they write code, answer questions, and somewhere in the feature list, review pull requests. CodeRabbit is the opposite: one thing, done obsessively. Every feature, every design decision, every pricing tier revolves around making PR review better. After reviewing the pricing, benchmarks, and comparing it to GitHub Copilot's native code review, here's the honest assessment. What CodeRabbit actually is (and what it isn't) CodeRabbit sits between your developer's git push and the merge button. You connect it to your repository host—GitHub, GitLab, Azure DevOps, or Bitbucket—and it automatically reviews every pull request. No button to click. It reads the diff, checks it against your full codebase for context, runs 40+ static analysis tools, then uses a multi-model AI stack to flag bugs, security issues, and style violations directly in PR comments. What it cannot do: generate application code, scaffold features, or replace a coding assistant. It is review-only. That constraint shapes everything about the product. At $40M ARR as of April 2026 (up 700% year-over-year from $5M ARR in April 2025), with 2 million repositories connected and more than 13 million pull requests reviewed, CodeRabbit has clearly found a market. It currently holds the #1 position among AI apps on GitHub Marketplace. How the review actually works Every CodeRabbit review runs in three stages. Stage 1: Context engine. Before analyzing the diff, CodeRabbit indexes your codebase using a retrieval system similar to what backs its code reviews across millions of repositories. It uses NVIDIA Nemotron for this context-gathering and summarization stage—a lightweight open model optimized for retrieval rather than generation. This is why CodeRabbit catches cross-file issues that pure diff-reviewers miss. Stage 2: Static analysis. A deterministic SAST layer runs linters that don't need AI inference: Biome, ESLint, Ruf