Daily Reading List – September 9, 2025 (#624)

Big reading list today, with some point-counterpoint pieces that look at different perspectives. Enjoy!

[article] Survey: Most IT Teams Not Prepared to Manage AI Workloads. Honestly, have most IT teams been ready for any of the tech advances of the past couple decades? Gotta learn on the fly!

[blog] AI Mode is now available in five new languages around the world. Free, easy to use, and actually helpful. Give it a whirl.

[blog] A new experimental Go API for JSON. I not only like the Go language, I also like how this team (and community) approaches updates to the language.

[blog] IP Address Planning for Large-Scale GKE Deployments. This is the least flashy topic I could have possibly read about today. But also kinda interesting and relevant.

[article] How to Keep Your Team’s Spirits Up in Anxious Times. Whether folks are anxious or not—and it seems like there have been anxious folks for a long time now—this is good advice for stable, inspiring leadership.

[blog] Calling Nano Banana from Java. Excellent post that shows off image generation, image alteration, and image combination with our fantastic new AI model.

[article] A “Beam Versus Dataflow” Conversation. Run data processing workflows on self-managed infrastructure, or with a managed service? Age old debate, with some thoughts here.

[blog] Now available: Rust SDK for Google Cloud. Rust is growing in popularity, and I’m glad we now have GA support for an official SDK that covers most of our cloud APIs.

[article] Conflicting opinions on the ROI of AI. Is everyone failing with AI projects, or succeeding? It can be true that many struggle for a variety of reasons. And also true that many have the right sponsorship, tech foundation, and customer-focus to deploy AI agents successfully!

[report] The ROI of AI 2025. Here’s the report that David refers to the previous piece. I’ve gone through the report. It’s got a ton of data from a very large survey population of IT leaders.

[article] 5 Times LLMs Help You Code… and 5 Times They Fail. These can be success and failure points. Or not. Depends on the user, the situation, the product, and the expectations!

[blog] Build with your Favorite Models from the Vertex AI Model Garden with LiteLLM. This is a handy proxy that can sit in front of multiple LLMs. Karl’s examples—direct calls, via AI coding tools, and as a persistent OS service—are very tangible.

[article] Developer reliance on AI worries IT leaders. On one hand, I see that execs want their teams using AI more aggressively. Then I see these results that show leaders are concerned about the level of AI use. WHAT TO BELIEVE.

[blog] Agent Middleware. Agents need context, and often, a refresh along the way. LangChain is adding a new middleware construct to help with that. Note that ADK has something similar.

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