Section
Ollama
The latest Ollama coverage from Bullquill: sourced, dated and explained.
4 POSTSUPDATED
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LLM quantization explained: 70B model from 141 GB to 43 GB
Quantization is how a 70-billion-parameter model fits on one workstation. Here is what it does to the weights, how much memory each GGUF level needs, what the perplexity tests show and how to choose a quant in Ollama or llama.cpp.
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How to build an AI agent in Python with tool calling
A framework-free guide to building an AI agent in Python: describe tools with JSON Schema, write the loop that runs tool calls, switch to a local model via Ollama, and enforce guardrails such as allow-lists, step limits and human confirmation.
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How to Build a RAG App in Python with Ollama and Chroma
A hands-on, fully local retrieval-augmented generation walkthrough: split your documents into chunks, embed them with Ollama, search them in Chroma and make a local LLM answer with citations, plus chunk-size trade-offs and a simple evaluation checklist.
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How to run a local LLM with Ollama on macOS, Windows or Linux
A hands-on guide to running a private, offline AI model with Ollama: what hardware you need, how to install it, pick a quantized model, save a custom system prompt and call the local API from curl and Python.
