Looking back through today’s list, there’s something that jumped out at me. You should ask a question after reading the headline. Why didn’t OKRs work? Why are predictability and outcomes at odds? How many pilots of any sort make it all the way to production? Why rewrite all your code? Let’s be good question-askers, and seek answers.
[blog] “You didn’t do it right.” Every measurement framework isn’t right for every team. Jason and team tried OKRs, and they just didn’t land. It’s often not a knowledge problem, but just the wrong context for it to work. That’s ok.
[article] 38 things we’ve learned about building AI products. Sheesh, what a fantastic list of lessons learned. You’ll have your own, but digest these too.
[blog] One Agent Development Kit, five languages. If you’re going to build a code-based agent—and I’m not positive that’ll be the dominant way as we see more pre-built, general agents that get fed custom skills instead—it’s useful to have a common framework across programming languages.
[blog] Predictability vs Outcomes. This made me stop and think. It’s not output versus outcomes, but the decision between predictability and outcomes.
[article] How Anthropic scales telemetry and billing data with Pub/Sub. Pretty wild stats, and a testament to using an at-scale cloud messaging engine.
[blog] How to Manage Context in AI Agents. This all feels very unsettled to me. Shir does a good job articulating the options. But I do see people debating the merits of all these approaches!
[blog] Previewing Gemini 4 Argon by Building an AR Game for My 4-Year-Old. Thats a tough customer. How great is it that we can realize our ideas so quickly now?
[article] Why 88% of AI pilots never make it to production. Noted. But also recognize that 20-30% of ALL pilots make it production. It’s hard for technology to go from a perfectly curated clean room to the rough-and-tumble production world.
[blog] 6 Years of Tech Debt, 1 Prompt: Modernizing a Legacy App with Gemini 4. Excellent demo scenario from Mete. A six-year full-stack upgrade in one shot? Remarkable.
[blog] How to effectively debug when you no longer understand the code. A small novel in length, this piece from Cindy is a wonderful look at expertise, assumptions, and how to rethink our directions to AI agents.
[article] It’s time to rewrite all that old code. Sure. But first ask “why?” And “in what language?” And “which tool?” Maybe “targeting which platform?”
[blog] Better HTTP/1 connection reuse. Geeky topic? Yes. But I like knowing my coding language takes care of stuff like this on my behalf.
[blog] Predictable AI spend in Big Query: Introducing cost management for generative AI functions. I like being able to embed LLM calls inside my queries, but it’s probably easy to go overboard. I’m glad we have quotas in place for you.
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