Local-Ai

Previously, I reported that I vibe-coded a tool that reads a blog post I’ve written and generates platform-specific promo copy using a local Ollama model. I chose local models because I’m curious about them. They seem to be the future of AI, at least for use cases like this… and it works… sort of. Now, the continued story of how I trusted three local AI models and Python had to clean up after them. The truth is that I was asking too much of them, and they returned occasionally insightful and often malformed and hallucinatory results. ...

Every Monday, I publish a blog post. Then I write five slightly different versions of “hey, I wrote a thing” for LinkedIn, Twitter, Bluesky, and Mastodon. Each platform has different character limits, different audiences, and different best practices. It’s tedious. I wanted to automate it. Not with a frontier model, but with a small local one running on my laptop. Something like phi or llama, through Ollama. I didn’t need a polished production app. I needed a quick prototype to test my theory. My theory was that a small local model can handle a real, recurring task. …and it can do it well enough to be useful. ...