Qodo

DevOps.com covers how the DevOps bottleneck is shifting from deployment to code review...as AI generates more code faster, review becomes the critical checkpoint. Key points from the article: - The Challenge: 60% of organizations now use AI to build and deploy software (Futurum Group). But as flawed AI-generated code increases, DevOps engineers responsible for quality are getting overwhelmed. - The Multi-Agent Approach: Qodo 2.0 uses specialized AI agents trained for specific review tasks, each operating with the precision of a senior engineer. - The Benchmark: 580 defects across 100 real production PRs. Qodo achieved 54% F1 score, outperforming 7 other platforms. As the article notes: "No matter how fast code is written, it's not going to make a meaningful impact if quality is sacrificed at the altar of speed." Read the full DevOps.com article → https://lnkd.in/eViq-mEn

Congrats to our partners at Snyk on the AI Security Fabric launch. Their new State of Agentic AI Adoption report reveals what many teams are just starting to realize: - For every model you deploy, you inherit 3x more hidden components - 82% of AI tools come from third-party packages - Traditional security tools can't see this attack surface This is the infrastructure security layer the AI era needs!

Passing tests is not proof the code is safe or high quality. This piece from Saqib J. at Deep Engineering nails the blind spot in AI coding benchmarks. SWE-bench and similar benchmarks measure if AI can generate code that passes tests. They don't measure if that code is maintainable, secure, or aligned with your architecture. Teams optimize for speed and end up with quality rot. Generate 10x more code, you also generate 10x more bugs. All while the test suite stays green. The analogy we use internally: you need separate agents for bookkeeping and auditing. The same model that wrote the code shouldn't be the one reviewing it for safety. That's the system intelligence approach we built Qodo 2.0 around. Specialized review agents that understand your codebase, enforce your standards, and catch what generalist models miss. Speed matters. But speed without quality isn't velocity; it's just more code to fix later. Read the full piece article below -

AI generates code faster than teams can ensure its quality and enforce organizational governance. Today, we launched Qodo 2.0 to close that gap! The problem: Most AI code review tools either flag everything (noise) or miss critical issues (blind spots). High precision with low recall, or vice versa. Neither scales. What we built: A multi-agent review architecture where specialized agents handle different review responsibilities, including bugs, standards, risk, architecture; each with dedicated context instead of competing for attention in a single pass. A judge agent evaluates findings across agents, removes duplicates, filters noise, and surfaces only high-confidence issues. A context engine that extends beyond the codebase to include full PR history, so agents understand not just what the code is, but how it got there. The results: We built a benchmark on real PRs with verified bugs to test this approach. Qodo 2.0 achieved the highest F1 score overall, +11% better than the next-best solution. Precision can be tuned through filtering. Recall can't. If your system doesn't detect an issue, no amount of post-processing recovers it. If AI is going to write more of the code, review has to do more of the thinking. Qodo is here to help with that → www.qodo.ai/2.0

How do we think about Code Review? Fast, precise, and kinda fun. So we built a game! The Qodo Bug Chase puts you in the shoes of an code reviewer racing against time to find issues before they hit production. Think you've got sharp eyes for buggy code? Here's your chance to prove it: - Scan code snippets for real issues - Race against the clock - See how you stack up against other developers Tune in for weekly winners. No sign-up. No download. Just you vs. the bugs. Play now → www.qodo.ai/bugechasegame Drop your high score in the comments 👇

This is how developers are using AI development tools in 2026... Nnenna Ndukwe ~ AI and Emerging Technology shared her workflow for building and reviewing code with paired AI models: → Claude Code+ Opus 4.5 to expand test coverage (hit 88% coverage with 917 statements tested) → Qodo+ GPT 5.2 to review unstaged changes before commit The insight: Don't commit to a single model or tool. Use different strengths for different tasks. Write the code with Claude. Expand the tests. Review the changes with Qodo. Fix the issues. Repeat. All from the CLI. Faster feedback loops. Stay in context.

Join us today (Jan 29 at 12pm ET) with Qodo Co-Founder Dedy Kredo to hear more about breaking down the architecture behind high-signal AI code review. We're deconstructing how multi-agent systems work, where each agent has exactly one job and one definition of done. Think of it how senior teams review: one person checks correctness, another looks at security, someone focuses on performance. Specialized roles, not a single overwhelmed reviewer. You'll learn: - Why single-agent bottlenecks kill review quality on complex diffs - How to architect multi-agent swarms with specialized roles - The engineering behind reducing false positives - How context and tools turn generic feedback into actionable insights - The anatomy of quality: correctness, security, performance, maintainability, tests Join the conversation! Register → https://lnkd.in/dZynit2V