Korbit AI

What engineers see: clear suggestions in the PR. What leaders see: trends and quality improving. Deliver Better Code Faster with Korbit AI #AICodeReview #DevTools #Developerproductivity #EngineeringLeadership #Devops #CodeReview

We asked our customers what it's actually like using Korbit AI and here's what they said: fewer bugs, faster reviews, and devs learning more with every PR. #AICodeReview #DevTools #Developerproductivity #EngineeringLeadership #Devops #CodeReview

Security issues hiding in your PRs? Korbit AI spots them before they hit main. Korbit Security Reports plug into your existing flow and give your team clear, real-time guidance on what to fix and why it matters. What you get: ✔️ Automated PR scans: catch vulnerabilities early in GitHub, GitLab, or Bitbucket. ✔️ Plain-English explanations: what the issue is, why it’s risky, how to resolve it. ✔️ Actionable recommendations: practical steps your devs can apply right away. ✔️ In-PR feedback: fix it before merge, not after a firefight. ✔️ Compliance friendly: audit-ready history of issues and resolutions. The Results: Stronger code security Lower breach risk Faster reviews and happier developers If “ship faster and safer” is on your roadmap this quarter try korbit here: https://www.korbit.ai/ #CodeReview #DevSecOps #SoftwareSecurity #EngineeringLeadership

2025 is the year AI code review stops being a pilot and starts being policy. Why now? 1. Release velocity is up, reviewer bandwidth is not 2. Security debt from legacy code keeps piling up 3. Senior reviewer hiring is pricey 4. Audit readiness needs consistent, documented reviews What AI review does beyond linting: it understands context. Not just “is this syntactically correct,” but “does this make sense for our feature flags, architecture, and team conventions.” That is where the real gains show up. Read our full blog post on The Benefits of AI Code Reviews for Enterprises in 2025: https://lnkd.in/esdYEjSB

This is an important habit of high-performing engineering teams. Fast PR reviews. The more time we let the PRs stay unreviewed, the more context we need to keep holding in our heads and the harder it is to move to other things. Slow reviews drain both the author and reviewers. The best engineering teams take pride in reviewing code as fast as possible and unblock the author to ensure they can check that thing off the list. It's much easier to focus on 1 thing at a time than working on 3 things simultaneously, and nothing gets done at the end of the day. And good tooling helps a lot with this. Korbit AI is one of them because it makes the PR review process easier and faster. I've personally tried it and it's easy to set up -> create the account, connect the repository, and that's it. After that, with every PR you create, it: - fills the PR description based on the changes made in the code, - reviews the code for any issues, security, performance and you can also set up your own rules as well, and - also understands the whole project context and learns from past reviews. It's much easier to review code when you automatically get a good overview of the state before you start. Don't forget that the accountability as a reviewer is still on you, but tools like this make your job easier. P.S. In all transparency, this is a sponsored post, but I definitely recommend to check out Korbit AI and try it out here: korbit.ai

Clean code isn't about following every best practice. It's about making code that your team can understand and maintain six months from now. Sometimes that means breaking a few 'clean code' rules. 4 signs your clean code is actually hard to maintain. 1. Over-abstracted classes 2. Excessive one-line methods 3. Too "DRY" code 4. Premature optimization for change Static linters enforce style, but they are blind to logical context and business intent. This forces your most experienced engineers to act as human linters for logic in every PR, time stolen from critical work like system design, mentoring, and innovation. It’s a bottleneck that slows down your entire team. That's why top-performing teams are adopting an AI-powered 'Zeroth Reviewer.' Before a human ever sees the PR, Korbit AI performs a deep analysis to - 1./ Find critical bugs - Catches logic flaws and edge cases that static tools miss. 2./ Fix maintainability traps - Flags risky patterns with clear, actionable explanations. 3./ Automate grunt work - Instantly writes PR descriptions and summaries. The result is simple: Cleaner pull requests, faster review cycles, and senior engineers focused on what truly matters. Which of these 'clean code' traps hits closest to home for your team? korbit.ai

Code reviews are often the most frustrating part of a developer’s day. They’re supposed to catch bugs and improve quality, but too often they drag on, with vague comments, missed issues, and endless back-and-forth. The truth is, reviewing code isn’t just about spotting syntax errors, it demands understanding security risks, performance pitfalls, architecture, and complex logic. But with tight deadlines and constant context switching, even the best reviewers can miss critical problems or give inconsistent feedback. Korbit AI solves this problem really well. I recently started exploring it, and here’s what stood out from a technical perspective: ◾ Korbit goes beyond syntax, analyzing the logic and architecture behind code changes. ◾ It flags subtle security risks often missed by basic static analyzers. ◾ Identifies inefficient code patterns and potential bottlenecks early in the review process. ◾ Provides detailed reasoning for each issue, helping developers understand why it matters, not just what is wrong. ◾ Detects complex bugs that standard linters typically overlook. ◾ Works with GitHub, GitLab, and Bitbucket without disrupting existing processes. ◾ Reduces reviewer fatigue by handling repetitive quality tasks. ◾ Early identification of problems helps cut down overall review time. ◾ Let reviewers concentrate on design and architectural feedback rather than minor nitpicks. ◾ Detailed, contextual feedback aids junior developers in learning best practices faster. Integrating it into our GitHub workflow was seamless and immediately helped reduce bottlenecks and improve review quality. Bottom Line: It’s not about replacing humans but augmenting their capabilities to make code reviews smarter and more efficient. So, If slow, inconsistent code reviews are a challenge for your team, exploring AI-assisted tools like Korbit is a practical first step toward improvement. Check here: korbit.ai

Wish someone had told me this about code review etiquette 🚀 The goal is shipping reliable code, not winning arguments. 😊 𝐀𝐬 𝐚 𝐑𝐞𝐯𝐢𝐞𝐰𝐞𝐫: 𝐃𝐨'𝐬 ✅ ◾ Review the code within 24 hours — blocking teammates kills productivity ◾ Start with "What problem is this code solving?" ◾ Look for security vulnerabilities first, then architecture, then style ◾ Ask questions instead of making accusations ("What's the reason for...?" vs "This is wrong") ◾ Suggest alternatives with code examples when possible ◾ Acknowledge good patterns and clever solutions 𝐃𝐨𝐧'𝐭𝐬 ❌ ◾ Don’t nitpick about style if there's an automated linter ◾ Don’t rewrite the code in your preferred style ◾ Never make it personal, critique the code, not the coder ◾ Don’t approve without actually reviewing ◾ Don’t block PRs for minor issues 𝐀𝐬 𝐚 𝐂𝐨𝐝𝐞 𝐀𝐮𝐭𝐡𝐨𝐫: 𝐃𝐨'𝐬 ✅ ◾ Keep PRs small (under 400 lines when possible) ◾ Add context in PR description (screenshots for UI changes) ◾ Self-review before requesting others ◾ Break down large changes into smaller PRs ◾ Respond to comments within one business day ◾ Add tests for new code ◾ Document non-obvious decisions 𝐃𝐨𝐧'𝐭𝐬 ❌ ◾ Don’t take feedback personally ◾ Don’t push back without explanation ◾ Don’t mark conversations resolved without addressing them ◾ Don’t submit PRs without testing locally ◾ Don’t expect instant reviews for massive changes You can add more, based on your experience. 👍 But let me leave you with one final thought. Code reviews are still mostly manual, but the landscape around us is shifting fast. With tools like Cursor, Replit, Devin, and others, AI-generated code is becoming the norm. Teams are shipping faster, and the volume of code is growing. But our review processes haven’t caught up. And this gap is only going to widen. That’s why I find tools like Korbit AI interesting. Instead of reviewing PRs in isolation, it brings full codebase context into the review. It also helps engineering managers track things like security risks, code health, and developer insights, all of which get harder as AI-generated code scales. korbit.ai

How AI Developer Tools Strengthen Code Consistency by 300% 🔥 AI-assisted workflows are a game-changer for modern dev teams facing: • 41% AI-generated commits fueling a 4× spike in duplicated code • Divergent style guides - centralized bots deliver 81% quality gains • Review fatigue - 17% of AI-made PRs still ship critical bugs Discover how LLM-powered linters, AI PR agents, and shift-left feedback cut noise, speed review cycles, and triple consistency. Dive into the full insights and metrics in our latest blog: https://lnkd.in/e8-ViigA #AI #CodeReview #DevOps #KorbitAI #SoftwareEngineering