Building AI Tools with Ollama
A practical guide to experimenting with local LLMs using Ollama: model selection, prompt design, latency tradeoffs, privacy benefits, and how local models can work alongside cloud APIs like OpenAI and Claude.
Technical article draft
Overview
Local AI tools are useful when the goal is speed, privacy, and experimentation. Ollama makes it possible to run models locally and test assistant workflows without treating every idea as a cloud API call. This article will break down how I think about local models, where they fit, and how they can power personal productivity systems like Jarvis.
Planned Structure
Part 1
When local LLMs are useful and when cloud models are still better
This section will turn the topic into practical, founder-level documentation with clear decisions, constraints, implementation notes, and lessons that can be reused in future products.
Part 2
Setting up Ollama for personal productivity and assistant workflows
This section will turn the topic into practical, founder-level documentation with clear decisions, constraints, implementation notes, and lessons that can be reused in future products.
Part 3
Designing prompts for planning, summarization, and automation tasks
This section will turn the topic into practical, founder-level documentation with clear decisions, constraints, implementation notes, and lessons that can be reused in future products.
Part 4
Connecting local AI experiments to real desktop or workflow tools
This section will turn the topic into practical, founder-level documentation with clear decisions, constraints, implementation notes, and lessons that can be reused in future products.
Publishing Goal
The goal for this article is to show how I think, build, make tradeoffs, and learn from real execution. It should help clients, collaborators, and hiring teams understand the quality of my product thinking, not just the tools I can use.