Daily Reading List – March 10, 2026 (#738)

We shipped some sweet AI updates today—AI app framework, unique embedding model, smarter AI in Workspace—but I read through more than just that 🙂

[blog] Gemini Embedding 2: Our first natively multimodal embedding model. Wow. Map text, images, video, audio, and documents into as ingle embedding space. Unique, and powerful.

[blog] Gemini in Google Sheets just achieved state-of-the-art performance. We can all become spreadsheet masters now. Check out what’s now in your broader Google Workspace toolbox. I should use more of this!

[article] Dynamic UI for dynamic AI: Inside the emerging A2UI model. This is a good dive into the new frontend paradigms to think about now, and how A2UI works.

[blog] Your Data Is Made Powerful By Context (so stop destroying it already). You’re screwing up key data needed by your AI systems by separating observability signals in different “pillars”, says Charity.

[blog] Extend your coding agent with .NET Skills. Cool. This should become standard son as languages provide out -of-the-box skills to use them correctly.

[blog] Announcing Genkit Dart: Build full-stack AI apps with Dart and Flutter. I find this VERY intriguing. Model agnostic, run anywhere, and built-in AI flows for your apps.

[blog] Cracking the code on corporate visibility. You’re doing some great work. How come you’re not getting the right credit for it? Consider being more visible by creating and sharing content.

[article] A2A vs MCP – How These AI Agent Protocols Actually Differ. Impressive level of detail in this new tutorial from DigitalOcean.

[blog] Fixing AI Slop with a Skill in Gemini CLI. I don’t get super riled up if I know I’m reading AI generated text. What I want is text that sounds like a human. This skill fixes the default mode of text generation.

[article] AI coding assistants may influence which languages developers use. I could see that. I’m functional in four programming languages, but I tend to generate AI code in only one or two of them.

[article] The “Last Mile” Problem Slowing AI Transformation. Even enthusiastic adopters of AI tools hit issues. Where do they struggle to get through the last mile, and how can we all learn from them? Some tips here.

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