As a solo maker, I've been diving deep into the world of autonomous AI agents (LangChain, LlamaIndex, etc.). One of the biggest bottlenecks I kept hitting was code execution.
When an agent needs to analyze data, scrape a site, or run a simulation, it writes code. But where do you safely run it?
exec() or subprocess on the host machine? Terrifying. One bad prompt injection and the LLM accesses your .env files or exfiltrates data.
Standard Docker containers? Too slow and heavy for rapid-fire agent tool calls.
Cloud sandboxes? Great, but I didn't want to send my local data or proprietary agent logic to a 3rd party API just to run a simple pandas script.
A free calculator that converts your birth date into a Mayan Tzolk'in reading, including your Nawal day sign, Galactic Tone, daily Mayan date, and compatibility insights.
Something I built led to a design decision I want to get feedback on.
LineageLens is a free VS Code extension that captures every AI code insertion and scores it for risk on a 0 100 scale. Works with Cursor, Copilot, ClaudeCode, Gemini CLI. Zero config on install just start using your AI tools and your insertions start showing up in the sidebar.
The scoring is deterministic rules: +28 for credential-like material, +24 for eval/exec patterns, +22 for subprocess calls, +14 for landing in an auth or payments file, and so on. Fully traceable. No ML, no black box.
The design decision that surprised me: missing prompt capture when the extension records a file insertion but has no record of what was asked adds +24 to the risk score. Same weight as detecting an eval() call.
