Daily Reading List – January 7, 2026 (#695)

I visited my Open Source Program Office team today in our San Francisco office, and also had other fun chats. In between, I tried my hand at getting a single LLM prompt to do something I wanted. A dozen experiments later, still hunting for the right instructions!

[blog] On Friday Deploys: Sometimes that Puppy Needs Murdering. We’re starting off hot. Charity shares wisdom on deployment freezes.

[blog] Reclaiming Excellence: A Manifesto. The pursuit of excellence, and celebration of it, makes society better.

[blog] When Agents Talk to Each Other. Don’t know your A2A from your ACP? Allen looks at the need for agent communication, and then explores the protocols that have taken hold.

[blog] Creating space. Terrific post with a couple dozen statements about where you need to focus yourself so that you’re doing the most important things.

[article] A Step-by-Step Guide To Deploying ADK Agents on Cloud Run. Go past localhost and get that agent onto an accessible runtime.

[blog] The Next Two Years of Software Engineering. These are five critical questions to ask of yourself and your team. The recommended actions are solid.

[article] When There’s Nowhere to Promote a Star Employee. Advancement can’t always be about promotion. Sometimes there’s no spot available. But there are plenty of ways to make someone’s job more challenging and impactful.

[blog] The Art of Fast Agents: 17 Strategies to Fix Latency. Maybe latency is a concern after you’ve already gotten agents figured out functionally, but I thought these were good areas to pay attention to at some point.

[article] Why enterprise AI pilots fail. I’m not sure if the failure rate here is much different than any other IT initiative—the track record for projects in our industry is lousy—but the advice here seems reasonable.

[blog] A Year Of Vibes. This post has all sorts of hard-fought insights. The one that jumped out to me was yet another person emphasizing that git might not be the right answer for code in the AI era.

[article] Generative AI and the future of databases. Here’s some insight into the challenges of going from natural language to SQL, and how databases are evolving to match what’s needed from AI workloads.

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