Daily Reading List – November 13, 2024 (#440)

Another day of interesting content. There’s a fair bit of AI, but plenty of other topics as well.

[blog] Our Machine Learning Crash Course goes in depth on generative AI. This is a remarkable free resource. It’s a self-study course taken by millions, and now refreshed with the latest AI advances. I might take it myself!

[blog] 65,000 nodes and counting: Google Kubernetes Engine is ready for trillion-parameter AI models. Wow. Just wow. This record-breaking cluster size is only possible because of the uniquely awesome platform foundation in Google Cloud.

[blog] Introducing enhanced Dataproc Serverless runtimes for a streamlined ML development experience. We’ve added a fresh set of libraries to our serverless Spark service so that there’s less for you to do.

[blog] Deployment-Driven Development. Tyler says that we should change our thinking. Instead of treating deployments as a destination, it should be prioritized from the start.

[article] Employee AI adoption cools globally. Temporarily. This survey from Slack shows that folks don’t want to be perceived by management as lazy for using AI. We can fix that with better education of the management ranks, and better sponsorship of AI initiatives from exec teams.

[blog] Computer Use with Anthropic’s Newest Model on Vertex AI. I’m familiar with this cool Anthropic feature, but Nikita helps us really see how to use it.

[blog] Virtual Personas for Language Models via an Anthology of Backstories. The AI research team at Berkeley proposes an interesting idea by giving LLMs a backstory.

[blog] Why Developers Are Unresponsive to Traditional Marketing. Adam gives some candid, necessary truth to those who are trying to coax developers into their universe.

[article] Memory in Agent Systems. This is a good topic to learn about. With all this talk about agents, how do we think about durable storage of “memory”?

[article] Google DeepMind open-sources AlphaFold 3, ushering in a new era for drug discovery and molecular biology. A lot of our work is for commercial gain, and I make no apologies for it. But I love how much we do that’s just about discovery and advancing ideas.

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