Jotlin

Qodo (formerly Codium) analyzes your code and generates meaningful tests to catch bugs before you ship. Qodo maps your code’s behaviors, surfaces edge cases, and tags anything that looks suspicious. Then, it generates clear and meaningful unit tests that match how your code behaves. Get full visibility of how your code behaves, and how the changes you make affect the rest of your code. Code coverage is broken. Meaningful tests actually check functionality, giving you the confidence needed to commit. Spend fewer hours writing questionable test cases, and more time developing useful features for your users. By analyzing your code, docstring, and comments, Qodo suggests tests as you type. All you have to do is add them to your suite. Qodo is focused on code integrity: generating tests that help you understand how your code behaves; finding edge cases and suspicious behaviors; and making your code more robust.

NEO is an autonomous machine learning engineer: a multi-agent system that automates the entire ML workflow so that teams can delegate data engineering, model development, evaluation, deployment, and monitoring to an intelligent pipeline without losing visibility or control. It layers advanced multi-step reasoning, memory orchestration, and adaptive inference to tackle complex problems end-to-end, validating and cleaning data, selecting and training models, handling edge-case failures, comparing candidate behaviors, and managing deployments, with human-in-the-loop breakpoints and configurable enablement controls. NEO continuously learns from outcomes, maintains context across experiments, and provides real-time status on readiness, performance, and issues, effectively creating a self-driving ML engineering stack that surfaces insights, resolves standard settlement-style friction (e.g., conflicting configurations or stale artifacts), and frees engineers from repetitive grunt work.