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Agentic AI moves fast. A few lines of code, a powerful LLM, and suddenly an agent is doing something that looks impressive. The rapid iteration is addictive, but it leads to a development style I call "vibe coding" -- tweak a prompt, rerun, and if the output feels right, ship it.
This works for a demo. It is a recipe for disaster in production.
You're paying $20-200/month for an LLM subscription. You have access to Claude, GPT, or Gemini. You have Playwright. You have Python. So why are you also paying for Crawl4AI, Moltbot, Firecrawl, or whatever the "AI-powered web scraping" flavor of the month is?
Because marketing works. That's why.
I built a WordPress 7.0 compatibility scanner CLI that detects deprecated editor integrations and iframe-unsafe code patterns in plugins/themes. It gives rule IDs, migration replacements, and CI exit codes so teams can block risky code before release windows.
Gemini Ollama CLI Bridge chains a local Ollama model with Google's Gemini into a two-stage code analysis pipeline. The first version piped data to Gemini through a shell subprocess -- fragile, hard to test, and impossible to cache. This upgrade replaces the shell integration with the google-generativeai Python SDK, adds result caching, and grows the test suite from 4 tests to 22.
Simon Willison shared research on running Pydantic's Monty in WebAssembly. Monty is a minimal, secure Python interpreter written in Rust, designed specifically for safely executing code generated by LLMs. I put together a demo project to test both the Python integration and the WebAssembly build.
This is one of the few sandboxing approaches I have seen that actually addresses the real problem.