AI coding agents write a lot of code now. Codex on the ChatGPT desktop app, Claude Code in your terminal, OpenCode wherever you like it. They are fast, they are cheap to run, and they will happily introduce a hardcoded API key, a vulnerable dependency, or a pull_request_target workflow without breaking stride. The question is not whether your agent will write insecure code. It is what catches it before merge.

This post walks through the workflow we use: your agent writes the code, Achilles reviews it, and the two talk to each other over MCP. You can also skip the agent loop entirely, run Achilles yourself, and send a one-click prompt back into your harness when you find something. Both paths work, and both are free.

If you ship Apache ECharts from Python and you care where your static assets come from, this post is for you.

We built vizly, a high-performance, production-ready Python visualization library over Apache ECharts. It is the culmination of a few months of consistent building. This post walks through how vizly compares to PyECharts, the most popular Python ECharts wrapper, and why we think vizly is the better default for teams shipping charts to production.