The software development retreat highlighted growing interest in Harness Engineering, focusing heavily on context management for LLMs to ensure model attention. Computational sensors are also gaining traction, with a shift towards languages like Rust and enhanced validation techniques. The future utility of harnesses remains uncertain but currently offers benefits like reduced token usage and enabling weaker models. Self-hosting open-weight models is increasingly attractive due to rising token costs and a desire for independence from frontier model providers. Factors like information security and data sovereignty further drive this trend. However, self-hosting presents challenges in GPU talent and significant operational costs, similar to the early days of private clouds. Effectively managing LLMs, whether self-hosted or external, hinges on teaching users to select appropriate models and potentially using LLMs as brokers for task distribution. Fine-tuning models for specific domains is expected to increase, leading to more efficient and cost-effective operations. A central theme emerged: determining the appropriate unit of work to delegate to AI agents and maintaining confidence in their output. The concept of "bringing me a rock" was re-framed, suggesting that with AI's patience, iterative refinement could become a defensible workflow. The discussion around who should steer LLMs evolved to focus on managing them by objective rather than method, emphasizing skills beyond just engineering. Ultimately, building software with AI involves exploration and human guidance, as AI cannot entirely outsource the creation and understanding of models. Local models, like Qwen 3.6, are proving viable for agentic programming, and strategies like directing powerful models to use cheaper ones for simpler tasks can control costs. The rise of AI is impacting developer education, with concerns about the future viability of paid courses and the sustainability of free, high-quality content creation.