Author: Richard Seroter

Finished this trip to Google Cloud HQ with a productive day. My team and scope changed this week, so I’m focused now on landing all that while keeping us running at full speed. Easy, right?

[article] The Learning Loop and LLMs. Don’t skip the crucial stage of actually learning something, in favor of doing it faster.

[blog] Yes, we can have nice things! Using Gemini CLI in an Enterprise Environment. This is a much-needed post about the “responsible” way to use something like our agentic CLI.

[blog] A Reference Architecture for Composable Enterprise AI Systems. I saw this because of a tweet from Keith and his argument about what you get for ‘free” in a cloud versus DIY infrastructure. Orchestration (agentic and infra) can be taken for granted!

[blog] Announcing Ironwood TPUs General Availability and new Axion VMs to power the age of inference. Everyone seems to want to build their own chips now, and I’m happy we have such a head start. This new TPU is crazy powerful, and already depended on by key customers.

[blog] From silicon to softmax: Inside the Ironwood AI stack. Tons of details about our AI stack woven into this cutting-edge TPU.

[article] 21 Git Commands for Software Engineers. I’m somewhat glad that AI is allowing me to forget some of this, but we should still know what’s possible in Git (even if we forget the exact commands).

[blog] How I use AI (Oct 2025). I like these kinds of reports. This doesn’t feel too different from how I use AI things. I do like tagging git commits with “AI” if AI did most of the work. Good idea.

[blog] More ways to build, scale, and govern AI agents with Vertex AI Agent Builder. Our agent stack is legit. And now Go devs get an Agent Development Kit and our Agent Engine gets a free tier along with new observability experiences.

[blog] Architectural debt is not just technical debt. Where should enterprise architects focus and how does architectural debt propagate? Insightful post.

[blog] From Skeptic to Believer: A Process-Driven Approach to Vibe Coding. We’re collectively learning what works and what doesn’t with AI coding. Mollie shares her recent “aha” moments.

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Full day today, with another good set of reading list items that touch on AI coding, AI projects, and advances in AI technologies.

[blog] Conductors to Orchestrators: The Future of Agentic Coding. Important piece here. Maybe we all find our way to orchestration, or pull back to something else. But the attempt to initially get there will not stop.

[blog] Why I code as a CTO. What and why should leaders code? I liked this, as there is real value in maintaining a strong context, and doing what you love.

[blog] The Agentic Manifesto: Engineering in the Era of Autonomy. Casey outlines the new rules for engineering leaders who are going to build with AI.

[blog] Quick Guide to ADK Callbacks. The ability to intercept and intervene before/after an agent, model, or tool does something is powerful.

[blog] Is it worrying that 95% of AI enterprise projects fail? I find that the most important question to ask someone when the raise an issue of cost, success, failure, complexity, or whatever is “compared to what?” That’s a question Sean asks here.

[blog] Driving a web browser with Gemini’s Computer Use model in Java. Guillaume’s experience wasn’t perfect, but he shows the potential of working with AI that can programmatically interact with your computer.

[article] A fresh look at the Spring Framework. Speaking of Java, Spring has remained relevant and modernized along the way.

[article] Does Giving Developers Control Over AI Improve Trust and Adoption? Hmm. This seems like very actionable data if you’re trying to figure out how to establish sustainable adoption of AI tech at your business.

[blog] Cloud CISO Perspectives: Recent advances in how threat actors use AI tools. Make yourself aware of how bad actors are using AI too.

[blog] The Smart Shopping Cart: AI Agents With Gemini, MongoDB Atlas Vector Search, and the MCP Toolbox. Good mashup of tech here that demonstrates how to use natural language to find similar data.

[blog] Code execution with MCP: Building more efficient agents. Don’t fall in love with too many AI patterns. They change too often.

[blog] Beyond Standard LLMs. Also, don’t fall in love with one type of model, as there’s constant innovation happening here too.

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Great day at the office, and a terrific reading list (if I may say so) today. Many good “think” pieces versus product announcements.

[blog] Most of What We Call Progress. Wonderful post on the things that many of us tend to learn over time while building systems.

[blog] Leaving the Cloud Isn’t for Everyone. Right on. For a subset of folks, running their own hardware and systems is the right thing competitively, and from a business sense. For the other 93%, you’re wasting time and money doing it yourself.

[article] Generative AI in the Real World: Chris Butler on GenAI in Product Management. Good discussion (and transcript for those who read faster than they listen). Product management is ripe for taking advantage of AI.

[blog] What have we learned about building agentic AI tools? I agree with most of these, but not sure RAG-for-codebases being completely wrong. Do repo search tools replace that? Not sure.

[blog] The Data Engineering Agent is now in preview. Looks cool. It doesn’t look like it replaces a data engineer, as you still need to know what to ask for. But this type of assistance makes smart people better.

[article] What Good Software Supply Chain Security Looks Like. Not a “hot” topic like it was a few years ago, but no less important.

[blog] Why Log Data Management Is a Thing. It’s a question of data, says James. Here’s a good roundup of some of the players trying to get log management under control.

[blog] Medium CEO explains how AI is changing writing. Writing to learn and think is important. It’s why I don’t use any AI on any of this, or other newsletters I write. But other circumstances don’t require that so much “thinking” to occur.

[blog] How Design Teams Are Reacting to 10x Developer Productivity from AI. Good timing here, as I’m talking to a group of designers today about the expected impact of AI.

[blog] Accelerating the magic cycle of research breakthroughs and real-world applications. We’re going faster and discovering more things in Google Research because of AI. Great to see.

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I’m in Sunnyvale this week for four different Google offsites. It’s a key time for ensuring momentum through 2025, and identifying our biggest (initial) priorities for 2026.

[report] State of Developer Ecosystem Report 2025. Some excellent data in this survey of 24k+ developers. What languages have momentum, where are developers using AI, and more.

[article] Google ADK: A complete toolkit for production-ready AI agents. It’s a great framework for those that want baked-in opinions when building AI agents.

[blog] How fast can an LLM go? Maybe most appealing to ML engineers, this post was still interesting to me.

[blog] The real problem with AI coding. Are we piling up “comprehension debt” into our repos as we stuff in code that wasn’t written by humans? How do you avoid it?

[blog] Beyond Request-Response: Architecting Real-time Bidirectional Streaming Multi-agent System. This post presents a sophisticated topic, but basically, it explains why the old “take-turns” method for AI agents is too slow. It describes a new, faster way that allows agents to process data in real-time, much like a continuous, natural conversation.

[article] Stop Writing Code, Start Writing Docs. My boss Keith has a long history with dev tools, and continues to use them on a daily basis. Here’s some perspective worth listening to (or reading).

[blog] Evolving Ray and Kubernetes together for the future of distributed AI and ML. These projects are working better together, which is good news for AI/ML folks who depend on Kubernetes. Also, great new TPU support.

[blog] The Green Tea Garbage Collector. Do you geek out over low level functions like garbage collection? If so, you sound interesting. Let’s be friends.

[blog] The New Calculus of AI-based Coding. Good thoughts here about a productive approach to AI coding, but with cautions about what breaks at this new velocity.

[blog] New tools in Google AI Studio to explore, debug and share logs. Better insights and tracking capabilities are baked into this must-have Ai tool.

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That’s a wrap on the week, and the month. Tonight I’ll chase my kids around the neighborhood for Halloween, and “borrow” some of the top-tier candy they acquire. Don’t judge me.

[blog] Spec-Driven Development: 10 things you need to know about specs. This post contains some very good questions to consider. It’ll be easy for us to over-index on specs and accidentally re-create waterfall development!

[blog] What do we think of GitHub saying there are 180m developers in the world? It’s fine. They’re counting anyone with a GH account which is generous. But the macro point is that there are more builders out there, using all sorts of tools.

[blog] Analyze anything with AI-powered SQL in BigQuery. I really love this feature. So much that I tried it out today and did a tweetstorm about it.

[blog] Build a Retail Agent with MCP Toolbox, YugabyteDB, and Google ADK. You can fulfill one of these sorts of sneaky-powerful use cases in minutes and hours nowadays. Wild.

[blog] StreetReaderAI: Towards making street view accessible via context-aware multimodal AI. We’ll keep doing commercial stuff to pay the bills, but here’s another great case of AI research that might have a massive impact on those who are blind or vision impaired.

[blog] Google Cloud Assist Investigations: AI-Powered Root Cause Analysis. “I don’t know where to start” is going to be a question we ask ourselves less and less often. Ops team RIGHT NOW can get an AI assist before they even start troubleshooting.

[blog] Survey: Two-thirds of AI-native startups let AI write most of their code. I’d assume these shops will need that AI-assisted troubleshooting if they run these apps long enough for it to matter!

[blog] Tool descriptions are eating up all your AI tokens (but they don’t have to). Good points by Craig here. It’s super easy to eat up 100k tokens just on processing the available MCP tools.

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There’s a good mix of advice, industry news, and tech insight in the reading list today. Enjoy!

[article] The 4 Seasons Organizations Go Through—and How to Lead in Each. It took me too long to realize that leadership isn’t a blunt hammer that applies to every circumstance and person the same way.

[blog] Java relevance in the AI era – agent frameworks emerge. No shortage of frameworks for Java developers. Besides Python, it might have the best options for AI app/agent builders.

[article] Surviving the AI Sprint: up close with Google Cloud and Cisco. Fascinating discussion that shows how much innovation is happening in the infra tier, and why demand is outstripping supply.

[blog] Introducing ConfigHub. Maybe lost in all the AI stuff, there’s innovation happening up and down the stack. Brian and team are offering a new way to store configurations.

[article] What’s the Go language really good for? Most things, nowadays. But like every language, there are sweet spots and weaker areas.

[blog] Master multi-tasking with the Jules extension for Gemini CLI. Every developer is a “lead” now and coordinating the work of multiple parties.

[blog] How many pillars of observability can you fit on the head of a pin? Charity lays it down on people obsessed with pillars of observability, versus focus on unified storage model for signals.

[blog] Chat in NotebookLM: A powerful, goal-focused AI research partner. While wildly popular, NotebookLM is still underrated. And now it’s even more useful.

[article] GitHub Embraces the Coding Agent Competition With Agent HQ. Smart to drag agents closer to the source code in GitHub. We’ll see if that’s the true center of gravity moving forward!

[blog] Announcing docs.cloud.google.com: The new home for Google Cloud documentation. It’s not trivial moving 9m pages of docs and making a series of updates on the way. My team finished this first milestone, and it’s a great accomplishment.

[blog] Why do AI models use so many em-dashes? Nobody knows for sure, and I refuse to change how I write just because AI is stealing my style.

[blog] How I Made an MCP Server That Saves Me an Hour per Week. Aim lower. Not EVERY use of AI has to change the world. Making a particular task a little better is still a win.

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What a wild year or so of tech changes. Cloud provider preferences aren’t the same, toolchains aren’t the same, and well-established jobs (like product manager) aren’t the same. Today’s reading list exemplifies that.

[blog] Google Cloud Q3 revenue surges 34% as backlog hits $155 billion. The fundamentals look good, and customers are trusting us to help them do their next big thing.

[article] Amazon faces a ‘fundamental’ shift in how AI startups spend on the cloud, internal documents show. Behind a paywall, but if you have access, it’s a good read about the shifting preferences of startups who look at a new style of tech provider. Backed up by public data here.

[article] 7 Must-Know Agentic AI Design Patterns. If you’ve been keeping an eye on this space, these won’t feel brand new. But still a useful overview of some important ideas.

[blog] Introducing the Jules extension for Gemini CLI. This feels like a big deal. Offload background work to Jules and stay in the flow within Gemini CLI.

[blog] The Role of a Product Manager in the Age of AI. Building-first culture may spark the most disruptive cultural change within companies in a decade.

[blog] Introducing Spring AI Agents and Spring AI Bench. The Spring team never rests. This looks like an interesting pair of new projects for those using agentic coding tools.

[blog] Gemini CLI Tips & Tricks. Speaking of AI coding tools, you’ll be an absolute pro after you apply many of these expert-level tips from Addy.

[blog] The “10x” Commandments of Highly Effective Go. Good specific advice on the Jetbrains site. This is obvious for Go devs, but much of it applies to any programming language.

[blog] How Model Armor can help protect your AI apps from prompt injections and jailbreaks. This is a model-agnostic and cloud-agnostic service that offers a handful of very important dimensions of protection for your AI models and apps.

[blog] Introducing Cursor 2.0 and Composer. Big update for the AI coding tool with the most momentum. Folks love Cursor, and this won’t change that. More from Simon on their unique coding model.

[blog] Context engineering is sleeping on the humble hyperlink. Are we missing hyperlinks as an obvious source of context for your agents?

[blog] Comparing Agent Frameworks: PydanticAI, LangChain 1.0 and Google ADK. Genuine praise and criticism for popular agent frameworks, including ours.

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I had lunch with a friend today and we talked about tech, and leadership in general. I mentioned that if you’ve ever been described by a manager as a “hidden gem”, that’s a red flag. Maybe once, but if you’re continually “hidden” that seems like a bad manager who doesn’t give you the spotlight. Thoughts?

[article] The future of the web is the history of YouTube. Are we under-valuing what’s about to happen to software, now that everyone is a creator? Thought-provoking take.

[article] AI Is Reshaping Developer Career Paths. Deep, specialist knowledge will always matter in certain places. But if that’s your calling card as a developer, it might not be as valuable now as you think.

[article] Tame Python Chaos With uv – The Superpower Every AI Engineer Needs. I’ve begrudgingly used Python to build some things lately. I lot of people I know have been raving about uv.

[blog] Building a Multimodal AI Shopping Workflow with Gemini 2.5 Computer Use, Gemini Nano Banana, and Veo. We’re at the very beginning stages of some wild new software experiences. This mashup of AI tech is awesome.

[article] Wyndham puts 250 AI agents into production. Sure there’s hype, but clearly folks are starting to see some real value from AI investments.

[blog] A practical guide to Google Cloud’s Parameter Manager. Where do you stash config values like API keys, certs, and connection strings? Our Parameter Manager offers a pretty solid solution for this.

[article] What are the 2025 benchmarks for the key DORA metrics? What’s “good” changes each year. This post explores the latest benchmark for top performance in software delivery.

[article] CEO of Alphabet’s X, Astro Teller, on what makes a moonshot. I like working at a place that isn’t afraid to take risks, fail, and try something else.

[article] 8 platform engineering anti-patterns. This looks like a very respectable list featuring opinions from people who know what they’re talking about.

[blog] HTTPS by default. Platforms should make it easy to do the right thing. We’re doing that with Chrome.

[article] Valkey 9.0 Debuts Multidatabase Clustering for Massive-Scale Workloads. We’re definitely seeing divergence now between Redis and the one-time fork, Valkey.

[blog] Introducing Google Gen AI .NET SDK. Why can’t C# devs get in on the fun? Add a strategic Gemini call or two to your .NET apps with our new SDK.

[article] The Pragmatic Engineer 2025 Survey: What’s in your tech stack? Part 3. Good data here about what people are using for observability, logging, feature flags, incident management, and more.

[article] HashiCorp Previews “Agentic Infrastructure” Future with Project Infragraph. Even infrastructure management is getting the AI shakeup.

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It’s good to be back after a fantastic week off. I stayed in town, but had some solo time, time with friends and family, and time to simply relax. It’ll take a while to clear out the reading queue, so expect some chunky reading lists this week!

[article] Code Generation and the Shifting Value of Software. Use packaged software and pre-built OSS libraries, or just use AI to generate exactly what you need? This is absolutely a discussion you’re going to have with teammates.

[blog] Some people that will make you smarter about the practical uses of AI. Nice to be included, but be sure to follow those other folks for actual insights.

[article] Meetings and interruptions are still the biggest obstacles for developers, even with AI. Are we losing time in places where AI coding tools will help? Not necessarily.

[blog] How to Optimize API Documentation for AI Discoverability. Keep building for humans, search bots, and now AI readers.

[blog] Introducing vibe coding in Google AI Studio. I like the ability to point at areas you want changed, and the model can target updates to your app. More on the improved dev experience.

[article] The price of mandatory code reviews. Can you skip the code review stage if you’ve got smart devs? If you want more speed and more bugs, then yes.

[blog] Scripts I wrote that I use all the time. Cool list. I can imagine giving these to my agentic coding tool so that it makes smarter choices.

[article] ‘AI is tearing companies apart’: Writer AI CEO slams Fortune 500 leaders for mismanaging tech. This is about companies using AI like another tool for IT versus embracing the bigger opportunity.

[article] InfoQ Cloud and DevOps Trends Report – 2025. One of my takeaways here is that there are a LOT of mature practices in this domain right now.

[article] How to improve technical documentation with generative AI. Good topic, and something we’re super focused on right now. We’re improving the authoring experience for writers, and the delivery of docs for developers.

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Heading home after a solid trip to Texas. Glad to be done this stretch of travel, and taking off a few days to try and recharge. No reading list next week!

[article] Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl. There seem like a dozen ways to do everything in the AI world. Here’s a look at a few options for those creating agent-friendly specs.

[paper] A Survey of Vibe Coding with Large Language Models. Good look at the state of vibe coding and what’s still true, regardless of how fast you can crank out code.

[blog] Claude Skills are awesome, maybe a bigger deal than MCP. Super interesting! We’re also drowning in techniques now (Skills, MCP, tools, agents, sub-agents, etc), but that’ll happen until we hit a logical consolidation point.

[blog] What is Configuration as Data? Brian was an early pioneer of this approach which still isn’t mainstream. But super relevant and maybe even easier to deliver now.

[blog] The ultimate prompting guide for Veo 3.1. You don’t need to be a train cinematographer to use a video generation model like Veo. Great advice here.

[article] The Best CEOs Can Spot Where Their Organization Is Under the Greatest Pressure. I liked the “three fronts” that leaders have to always protect.

[blog] Save costs and decrease latency while using Gemini with Vertex AI context caching. You don’t care about this when prototyping or working with small apps. But as soon as you start dealing with large amounts of context, you’ll want a way to reuse what you have and keep cost down.

[article] Deep Agents. Seems like a topic of conversation lately. We’re already moving past the first stage of simplistic agents to something more.

[article] The “AI is a Bubble” Narrative is Stupid, Wrong, and Dangerous. I’ll admit that I’ve found some of the recent AI funding decisions to be curious. But this is a sober take into what’s actually going on.

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