Daily Reading List – June 10, 2024 (#336)

Happy Monday! I had a weekend of baseball games, playing outside, and running errands. So, mostly kid-like activities with a little bit of adulting. I read through a lot today which I hope you enjoy.

[blog] AI Patterns. I read everything that Steve writes, as he’s one of my favorite industry analysts. This perspective on AI interface, model sizes, and deployment locations may spark some thoughts of your own.

[blog] TinyAgent: Function Calling at the Edge. The Berkeley AI folks wrote up a good post about running model inference locally. Neat stuff here.

[blog] Lightning-fast decision-making: How AI can boost OODA loop impact on cybersecurity. This is about security teams, but most can benefit from these rapid learning loops.

[article] Tech Works: How Can I Make Myself More Productive? It’s ok to be a little selfish sometimes. How do you advocate for your own growth?

[blog] What To Expect In OPA 1.0. Open Policy Agent is fairly widely used as a way to define and implement policy in your Kubernetes clusters. Check this out for what’s new in the 1.0 release.

[blog] Exploring Google Cloud Reasoning Engine. This managed service makes it easier to build generative AI apps that use LangChain to supplement results with function calling.

[article] Apple Intelligence is the company’s new generative AI offering. Apple made a ton of exciting announcements today, and will make AI accessible to tons of folks.

[blog] Hands on with Gemini models in BigQuery: Decoding sentiment in customer reviews. Lots of depth in this post. Read through for how to invoke LLMs from data warehouse queries.

[blog] Everything you need to know about Istio installation. Tons of details here. One option not mentioned here are the installers that come from vendors who package Istio.

[docs] Cross-Cloud Network for distributed applications. This four-part doc set offers some terrific guidance for those developing cross-cloud systems with responsible networking.

[blog] Kubernetes: 48% of Users Struggle With Tool Choice. Bigger deployments, more complexity, and trouble picking tools in the ecosystem. That’s inevitable with the mainstreaming of a platform like this, but still a reason to look at practices like platform engineering.

[blog] Straightforward, Gemini powered sentiment analysis with Langchain4J. Calling an LLM isn’t super difficult now, and Aaron shows how to do sentiment analysis in a Spring Boot app.

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