Daily Reading List – October 2, 2024 (#410)

I had a day trip to San Francisco today, so a shorter reading list overall. Still some good ones in here!

[article] Using AI to jump-start code samples. Good experiment! It seems that AI generated code samples is one way to scale the work. But did it work and did the engineers like it? Tom shares his experience.

[blog] Fundamental challenges with Infrastructure as Code imply the language doesn’t matter. Brian thinks that the language you use—HCL, Java, whatever—doesn’t necessarily solve some of the trickier challenges of infrastructure as code.

[blog] Announcing Avien for AlloyDB Omni on Google Cloud, AWS, and Azure. This is a fun twist on multi-cloud. Avien took our run-anywhere PostgreSQL software (AlloyDB Omni) and made a service of it.

[article] ML Engineer comparison of Pytorch, TensorFlow, JAX, and Flax. There are lots of frameworks out there for ML engineers. This post looks at a handful of the most popular.

[blog] Using HBase Quotas to Share Resources at Scale. This is a post from HubSpot and goes into a lot of depth into their design decisions.

[blog] Understand your Cloud Storage footprint with AI-powered queries and insights. Here’s a good application of AI where it naturally fits into the existing product. Get smart recommendations and engage with your content.

[blog] Users engage with only 6% of product features: Product benchmark findings. Oof. I can believe it! Only a subset of functionality in what you’ve built gets the majority of usage.

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