Karl Weinmeister

@kweinmeister.bsky.social

Cloud Developer Advocacy @ Google. AI/ML/Data, Blue Devil & Longhorn, wanna-be at home improvement. Opinions are my own.

I love vibe coding as much as anyone, but what's the one thing you would NEVER let your agent hand-roll from scratch? 5 things I always use a service or library for: 1. Authentication 2. Payments 3. Dates & time zones 4. Cryptography 5. Job management What's missing?

We just shipped Gemini 3.8 Flash! It's our third Flash model in six weeks. It really shines at long-horizon coding and autonomous agent tasks. To showcase the improved reasoning and tool-calling, check out this game written with a single looping prompt. Prompt and launch post are in the thread 👇

I was about to build a multi-agent system to assemble my monthly newsletter. I wrote a skill file instead. A year after AI agents hit the enterprise, a lot of developers (myself included) fall into the over-engineering trap for personal workflows. medium.com/google-cloud...

Why I write skills instead of agents for knowledge work

Are there tasks you find yourself doing repeatedly, wondering if there is a better way? For me, one of those is writing a bi-weekly…

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You don't need a math degree to vibe code with AI, but knowing just enough will save your app from freezing up or giving you unexpected results. I wrote about 4 math concepts every vibe coder needs: probability, tokens, vectors, & Big-O: www.linkedin.com/pulse/four-m...

Four math concepts to improve your vibe coding

The power of vibe coding is undeniable. Describe what you want in Google AI Studio, Antigravity, or your favorite agent, and you'll get it! You don't need to know the math behind why things work the w...

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Matt Pocock's Skills 1.1 just dropped: 🧭 Wayfinder: progressive discovery for multi-session work 🔬 Background research agents 📋 Consolidated planning pipeline 🔍 12 Fowler smells in code review 🎯 Grilling won't answer its own questions ✂️ TDD trusts the agent npx skills@latest add mattpocock/skills

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2 major model releases today! You can generate cost-efficient images in 4 seconds with Nano Banana 2 Lite (GA). And with Omni Flash, you can generate video with real-world knowledge and perform conversational video editing (Preview). And yes, the infographic was created with Nano Banana 2 Lite!

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Most developers don't think of AI agent skills as dependencies. But they behave just like them. An agent skill is a package of instructions and scripts that help an agent accomplish tasks. Just like code libraries, these instruction sets drift as the underlying APIs and systems evolve.

Many developers treat testing as an afterthought when using AI coding agents. "Build this feature. Now write unit tests." The result? Green test suites that give a false sense of security. Here are 5 essential tests your agent should create instead. www.youtube.com/shorts/3mm_8...

Essential Testing Strategies for AI Agents

I share why most developers fall into the coverage trap when prompting AI coding agents and how I treat test suites as architectural guardrails. In this walkthrough, I break down five essential…

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Infrastructure setup is the ultimate "stop energy" for new ideas. How many times have you abandoned a side project because configuring billing accounts, APIs, and security poliicies stalled your momentum? The Starter Tier on Google Cloud demolishes this friction 👇

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"Knowledge-as-wiki" is an emerging pattern Karpathy made popular with LLM Wiki. Google has formalized this with the Open Knowledge Format. OKF gives agents a common language to understand your APIs, data schemas, and more. My latest article breaks it down. www.linkedin.com/pulse/your-a...

Your AI agent reads files. It should use OKF to read systems.

Today's AI coding agents don't hallucinate the way they used to; we're generally past the "inventing fake APIs" stage. The failure mode now is subtler: they over-index on part of the codebase and miss...

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I will show you how to prevent your AI coding agent from building the wrong thing. Use the /grill-me skill so it knows what you really want. A few extra questions upfront saves lots of refactoring and frustration later.

When evaluating a long-running AI agent, the top 3 metrics to watch are: ✅ Success rate: was the goal achieved? ⏳ Duration: how long & how many steps? 💸 Cost: how many tokens were consumed? What would you add to the list? www.youtube.com/shorts/rebAE...

AI Agent Metrics that Matter

At 95% per-step reliability, a 20-step agent succeeds less than 36% of the time. I cover the three metrics — success rate, token usage, and duration — that tell you where your agent is actually…

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