Daily Reading List – May 13, 2026 (#783)

Today's links look at what to do when your brain runs out of RAM, why your AI problem is a data problem, and how to escape from the (human in the) agentic loop.
Daily Reading List – May 13, 2026 (#783)

My day reflected some of the articles below. My brain can’t hold what it needs to hold, and I need fewer interruptions by technology. There are some suggested fixes in today’s list.

[article] Escape from agentic loop (https://www.proofofconcept.pub/p/escape-from-agentic-loop). This proposes that the human-in-the-loop workflow of AI is exhausting and fake productivity. Instead, be on-the-loop and use AI managers that follow your guidance.

[blog] Meet the latest Database Center, now with Gemini-powered fleet intelligence (https://cloud.google.com/blog/products/databases/database-center-improvements-from-next26/). Can’t just use one database engine? Ok, but now you have a problem trying to manage all these distinct engines. Our Database Center pulls it together.

[article] 12 model-level deep cuts to slash AI training costs (https://www.infoworld.com/article/4168496/12-model-level-deep-cuts-to-slash-ai-training-costs.html). Smart list of ways you can be more efficient with training and make good architectural adjustments in your ML pipeline.

[article] The engineering management memory crisis (https://newsletter.manager.dev/p/the-engineering-management-memory-crisis). Is your brain running out of RAM? Mine is. this is a good lesson about having an LLM that points to personal context.

[article] Your AI Problem Is a Data Problem (https://www.oreilly.com/radar/your-ai-problem-is-a-data-problem/). Some good data points here, and reminders that AI isn’t a procurement decision; you need a strong data layer.

[blog] Tutorial Series : Gemini Enterprise Agent Platform (https://medium.com/google-cloud/tutorial-series-gemini-enterprise-agent-platform-e3bd6373d486). Terrific five part series from Romin that lays out how you build, scale, govern, and optimize agents.

[article] Why agent harnesses fail inside cloud-native systems (https://thenewstack.io/agent-harness-distributed-feedback-problem/). Can your AI agent harness do real work within distributed systems? Or is the lack of a realistic and isolated test bed giving you false confidence?

[blog] Why Real-Time Authorization Is Best For Agentic AI (https://nordicapis.com/why-real-time-authorization-is-best-for-agentic-ai/). Long argument for giving agents short-lived creds and specific access.

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