Daily Reading List – September 5, 2024 (#391)

It was a good day. I had productive meetings, one epiphany, and a chance to write. In the reading list below, you’ll find some tech deep dives, but also a few pieces that’ll help you with strategic thinking.

[blog] Using Node-based pricing on GKE Autopilot. Fully managed Kubernetes is a good deal. William talks about the couple of ways (pod based, node based) to pay, and how the new Custom Compute Class gives you a very flexible way to define workload priorities.

[article] TikTok Releases Tool to Improve Monorepo Performance. Google famously has a monorepo, but there’s work to be done to make it usable for every developer. This article explores a new tool that helps devs pull subsets of files.

[blog] Building LLMs from the Ground Up: A 3-hour Coding Workshop. Folks are learning AI from lots of places, including YouTube. Great video here from Sebastian.

[blog] Looks Matter (When It Comes to Software Products). Good product design isn’t a nice-to-have; it’s critical for long term success.

[blog] Best practices for cost-efficient Kafka clusters. Lots of details here, whether you’re self-hosting or using a managed environment for event stream processing.

[blog] AI Security frameworks in a nutshell — Part 1. Sita does a great job looking into the industry and government-led frameworks that matter for security folks. Also check out part 2.

[blog] From Idea to Reality: Building the Instant Web with Gemini (Part 1). How can you go from wireframe to website with the help of an LLM? Thu Ya has a good example of the iterative process.

[blog] Why “AI” projects fail. The amusing rant here claims that AI projects fail because folks “do AI” to avoid the harder work of identifying and fixing real problems.

[blog] Telemetry in Go 1.23 and beyond. I like the transparency and insights provided by the Go team related to user-provided telemetry about Go usage.

[blog] How to consistently output JSON with the Gemini API using controlled generation. Gemini was among the first LLMs to offered structured output in JSON, and this post explains why it matters, and how to use it.

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