Hey, I'm a political science major. Don't let anyone tell you that you need certain credentials to do big things with technology. In this industry, the most curious people win.

Let's talk about Jev! I got interested in this new model when I saw folks here on X talking about "JevOps". I think there are a lot of promising use cases for a simple, fast, cheap, flexible decision-making model. g.dev/cloud/typesafe-jev

Karl's video on Jev goes to a really nice level of depth and he goes over useful context to help you understand where the model has come from and where it fits into the ecosystem today.

When you implement AI in production, you don't always need to generate text. Sometimes, you just need a fast decision with a confidence level. Here's my blueprint to turn an open-weight text diffusion model into a solution like Jev.

The great thing about Open Source - YOU can have a say in its direction!

I've been looking into Jev and I like this explanation of it. Billy uses several demo examples from different developers to show how jev might be useful for different types of tasks, and explains why Jev is different from LLMs.

Googlebook is available for preorder today. Built on the @Android technology stack and paired with desktop foundations from ChromeOS, it’s engineered to work with your phone right out of the box and is designed for Gemini Intelligence.

Omg it's so cute!! 😍

If anyone wants to play with Agent Substrate, we recently opened an environment abstraction. It's useful to play with Substrate without writing code. It also provides an MCP server with fs and shell tools. You can try it with anything that supports MCP. github.com/agent-substrate/e…

Honestly, I’m working 5x harder since coding agents actually became useful. Weren't these supposed to take software engineers out of the critical path? Now it just feels like every part of the stack needs to be rebuilt, and it has to happen now.

Ho. Ly. Shit. This is going to be good. Very good.

If you want to check out Agent Substrate this weekend, check out our installer. We are going to make installation and onboarding super easy, see github.com/ai-on-gke/substra…

🥳 This week we announced Agent Substrate. It's the compute runtime (that can work on Kubernetes) that enables everything you've seen from me. We can suspend and resume so fast that you can do it per tool call. 👉 github.com/agent-substrate/s… 👉 github.com/agent-substrate/e…

🥳 Excited to start revealing what we've been working on in the last few months. First, we decided to reinvent Kubernetes for agentic workloads with statefulness and fast resumption. Secondly, we are building an agentic orchestrator that will be Google's open agentic orchestrator and runtime. github.com/google/ax

After going on a global AI conference tour from Tokyo to Amsterdam, speaking about agent harnesses and using Claude Code as my primary example: Anthropic: ew. You are an ambassador with that *other* AI platform. Your ambassadorship role here has been terminated, effective immediately. Me: also just used MY connections with an Executive Fellow at Harvard to secure Harvard Business School as the venue for the newest Claude Boston meetup… and was up at 4am with Claude ambassadors planning extensively (who are fantastic ppl btw). Anthropic: leave and never come back. I was already proud to be an ambassador for both Agentic AI Foundation and Codex. But this unique encounter made it all the more clear to me the difference between genuine community for the people and arrogant marketing levers disguised as community. I will continue leaning heavily into AAIF and Codex, which is where I’m treated well and where I’m wanted.

"why do you still draw if AI can do it for you?" "why would you still assemble a PC" because some things I do for fun, creative outlets, and flow state I use AI to make my life easier, not to replace the joy

😳 wow... I can't believe what I'm seeing. Firebase has spending caps. Like honest to god limits, not warnings. It's not for every service but wow what a start.

Great to see Google Cloud's continued movement into being a leader and the recognition from @Gartner_inc

When you fix a bug, AI agents usually add a test in the final step. The problem is, the generated test often doesn't replicate the original bug. I use TDD, or test-driven development, to ensure that I'm not just adding code coverage. My next problem is: when agents do TDD, they tend to add a specific, brittle test for that issue. Over time, my test files become a disorganized and duplicative mess. So, lately I've been adding something like this to my prompt: "Use TDD, and don't just bolt on a new test. Where helpful, refactor the test suite as you go. Consider the structure, DRY principles, and whether this should be a parameterized test case." This should be in my AGENTS.md file, but I'm still fine-tuning the approach. What are your tips on writing tests with AI agents?

The GKE engineering team has designed a multi-agent system for managing Kubernetes clusters and released it on GitHub. If you're interested in how agents might help with cluster management, you can check it out yourself! github.com/gke-labs/kube-age…