Daily Reading List – November 5, 2025 (#659)

Full day today, with another good set of reading list items that touch on AI coding, AI projects, and advances in AI technologies.

[blog] Conductors to Orchestrators: The Future of Agentic Coding. Important piece here. Maybe we all find our way to orchestration, or pull back to something else. But the attempt to initially get there will not stop.

[blog] Why I code as a CTO. What and why should leaders code? I liked this, as there is real value in maintaining a strong context, and doing what you love.

[blog] The Agentic Manifesto: Engineering in the Era of Autonomy. Casey outlines the new rules for engineering leaders who are going to build with AI.

[blog] Quick Guide to ADK Callbacks. The ability to intercept and intervene before/after an agent, model, or tool does something is powerful.

[blog] Is it worrying that 95% of AI enterprise projects fail? I find that the most important question to ask someone when the raise an issue of cost, success, failure, complexity, or whatever is “compared to what?” That’s a question Sean asks here.

[blog] Driving a web browser with Gemini’s Computer Use model in Java. Guillaume’s experience wasn’t perfect, but he shows the potential of working with AI that can programmatically interact with your computer.

[article] A fresh look at the Spring Framework. Speaking of Java, Spring has remained relevant and modernized along the way.

[article] Does Giving Developers Control Over AI Improve Trust and Adoption? Hmm. This seems like very actionable data if you’re trying to figure out how to establish sustainable adoption of AI tech at your business.

[blog] Cloud CISO Perspectives: Recent advances in how threat actors use AI tools. Make yourself aware of how bad actors are using AI too.

[blog] The Smart Shopping Cart: AI Agents With Gemini, MongoDB Atlas Vector Search, and the MCP Toolbox. Good mashup of tech here that demonstrates how to use natural language to find similar data.

[blog] Code execution with MCP: Building more efficient agents. Don’t fall in love with too many AI patterns. They change too often.

[blog] Beyond Standard LLMs. Also, don’t fall in love with one type of model, as there’s constant innovation happening here too.

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