My local agentic dev setup today (willemvandenende.com)

In a recent post, a developer outlined their local AI development setup, revealing a shift from a Claude Max cloud plan to a personal, efficient operation on a refurbished MacBook Pro M3 Max with 64GB of RAM. The choice was driven by growing local model performance, leveraging frameworks like llama.cpp, which allows for reduced RAM usage and faster processing. The author employs Pi.dev as a coding agent and GPTEL in Emacs for brainstorming tasks, demonstrating a preference for local open-source models that prioritize data privacy and personal control over out-of-the-box solutions like Claude Code or Codex. This setup reflects significant advancements in open LLMs, with recent improvements nearly doubling running speeds and resource efficiency. The developer highlights the importance of a robust harness for integrating skills and extensions and shares insights on model configuration and usage, particularly around the Qwen 3.6 models. These developments suggest a growing trend in the AI/ML community toward customizable and efficient local solutions, empowering developers to optimize workflows while maintaining autonomy over their tools and data. As AI capabilities continue to evolve rapidly, such setups may become increasingly mainstream, emphasizing a balance between performance, privacy, and user control in AI development.