This article discusses how to effectively prompt local Large Language Models (LLMs) like those run with LM Studio or Ollama. It explains that local LLMs behave differently than cloud-based models and require more explicit and structured prompts for optimal results. The article provides guidance on how to craft better prompts, including using clear language, breaking down tasks into steps, and providing examples.
An analysis of the current LLM landscape in 2026, focusing on the shift from 'vibe coding' to more efficient and controlled workflows for software development and data analysis. The author advocates for tools like AI Studio and OpenCode, and discusses the strengths of models like Gemini 2.5 Pro and Claude Sonnet.
