Last week, I spent three hours trying to get a RAG pipeline running for a client project. I needed a model with a massive context window, a decent inference API, and a place to host the custom dataset we were pulling from. I started on Hugging Face, as most of us do, hunting through thousands of fine-tuned variants, spinning up an Inference Endpoint, and wrestling with tokenizer configs. Then a colleague dropped a link to GLM 5.2 in our Slack.
Last month, I was running a project that required spinning up dozens of automated coding agents to refactor a massive monolithic codebase. Within 48 hours, my OpenAI bill had shot past $300. Claude So
I was deep in a literature review last month, juggling 14 different PDFs across two monitors, and I kept hitting the same wall. I'd feed a 40-page paper into my usual model, ask a specific question ab
# How to Use Hugging Face for Model Deployment: Step by Step I've been deploying machine learning models with Hugging Face for over two years now, and I can confidently say it's one of the most strea