The AI code quality and governance platform for developers.

Joined December 2022
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Introducing Qodo 3.0: the quality and governance layer for the software factory. Engineering teams are building agentic software factories. Output is up, but code review hasn't kept up. And a factory with no quality control is just a faster way to ship defects. Qodo 3.0 governs your software factory from the first line an agent writes through review and into production. Instead of individual PRs, group your code into work packages as connected changes across repos. Reviewed, ranked, and measured together, with a clear map of impact.
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When does fine-tuning a model make sense? Gil Elbaz (@gil_elbaz_) discusses latency, scale, and domain knowledge as reasons to train, alongside the expertise that requires. Listen to The Agentic Review podcast with @nnennahacks and @itamar_mar.
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31.7% of engineering leaders in our 2026 survey say quality depends on exhaustive manual work. Which controls rely on someone remembering to intervene? Record why a retry was disabled or who owns a data contract, and keep that reasoning current.
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What breaks when AI agents reach production? Tomorrow in NYC, we're bringing builders and OSS maintainers together to talk reliability, code review, agent loops and observability. Agentic AI in the Wild Oct 22 · 5–8 PM EDT Corgi Cafe, 121 E 27th St
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A reviewer needs to challenge the agent writing the code. Inside Qodo's Dark Factory, the Reviewer never edits the implementation. Shared context lets it question the plan while keeping that responsibility separate.
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In our 2026 survey, developers use AI for code generation (60%), review (57.6%), and tests (51.4%). Code and generated tests can share a misunderstood requirement. Give verification an agreed acceptance criterion it can check.
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I don't read code anymore. The abstraction layer for humans has changed. AI agents are frontline coders now. I define tasks and constraints, and I work with my team lead agent, but my role is that of a product engineer, a general of my robot army: designing logistics, providing provisions (context) and offering judgement for key decisions. I use multiple models to help me, from different model families: - A team lead agent that can either implement work, or assign it to other agents - Automated reviewers to check for biases - Multi-model cross family review for key changes Intentionally, I create smaller iteration loops within the overall code review loop: - Cheap + fast checks and verification from the implementing coding agent can send the change back for additional work - Standardized reproduction of findings - Re-review of final commits post fixes by lead models - Evidence backed merge decisions You need a code review loop that unblocks you as a human, keeping your judgement for the most important decisions, and unblocks your AI factory to move at agent speed. Agents code. Humans lead. Full breakdown featuring @coralbricksdev, @QodoAI, examples from my OSS project @avoidaiwriting , insights from @AnushElangovan, @kimblgn & much more below👇
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Nuance is one of the hardest frontiers in agentic engineering. Making it available to agents is work practitioners will need to do: explain it, codify it, and embed it into the software factories we’re building. In her latest article, @nnennahacks explores how to codify that nuance, and why practitioners who learn to do it well will become increasingly valuable. Read “How to Codify Nuance in Agentic Engineering”.
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When AI writes a change, waiting until the pull request to review it leaves the rework for later. Qodo's Agentic Toolbox brings review into the coding session, using the same review engine and codebase context as PR review. The pull request remains a final gate.
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Qodo 3.1 | October changelog • Automatic muting for consistently ignored rules • Analytics dashboard now generally available • Configuration search with ⌘K / Ctrl+K • Clearer setup progress, indexing retry status, and links to affected repos
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