Daily Reading List – August 12, 2025 (#607)

Sometimes I choose the “hard way” for no good reason. I’m building an MCP server for educational purposes, and instead of choosing a full-featured library, I choose a capable, but loosely documented one. So I spend most of my time fiddling with ambiguous problems that even agentic AI isn’t helping me with. Forget sunk-cost fallacy. Tomorrow I start over with Python or TypeScript!

[blog] Why Can’t I Just Use an API? Because Your AI Agent Needs MCP. Strong post that explains why handing a pile of API endpoints to an AI agent isn’t the smartest strategy.

[blog] Supercharge your AI: GKE inference reference architecture, your blueprint for production-ready inference. This links to an executable blueprint with a performant, scalable, cost-effective, and observable Kubernetes foundation for AI inference.

[blog] You need evals to ship AI features. Enough folks are saying this that you should pay attention. If you’re going to be successful with AI, you’ll need strength at evals.

[blog] How Yahoo Calendar broke free from hardware queues and DBA bottlenecks. Detailed story about a careful move of critical infrastructure for a global service.

[blog] How I coded the Google Style Guide into a Gemini CLI Custom Commands Workflow. Our tech writing team is getting into the action. Shweta has a good post about using AI as a content checker.

[blog] Early Adopters Share AI-Centric Service Desk Results. There’s no debate that AI can make summarization easier. And in some cases, help close very routine support cases.

[article] Multi-agent AI workflows: The next evolution of AI coding. Take it one at a time. Don’t thrust a dozen agent personas into your team and expect anything except chaos.

[blog] The Google Developer Program is evolving. The perks for the freebie edition are good. If you want more things (and a good value), there’s now a paid Premium tier where you get more AI credits and higher usage limits, along with cert vouchers.

[blog] Bottleneck or Bisect: AI-Assisted Coding Will Change Product Management. Good post. A subset of PMs are going to need to adapt to faster engineering turnarounds and public experiments.

[blog] Claude Sonnet 4 now supports 1M tokens of context. I’ve been somewhat shocked that it’s taken this long to get another model to 1 million input tokens. Google Gemini’s had this for quite a while, but it requires some sophisticated models and underlying infra to support.

[blog] Google is a Leader in the 2025 Gartner® Magic Quadrant™ for Container Management. Your choice of container management runtime(s) matters a lot. This year’s assessment was super thorough, and you should look through the report.

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