Today was a good day. A lot got done, and I’m excited about the things my team is doing. I’m off tomorrow as part of a long weekend staycation, but will be back Monday. In the meantime, enjoy a fun list of things to read!
[blog] Infrastructure as Code reminds me of “make run-all”. Brian offers a mix of a history lesson along with a look forward at generated/rendered config files.
[blog] Announcing Anthropic’s Claude 3.5 Sonnet on Vertex AI, providing more choice for enterprises. This looks like an excellent step forward from Anthropic, and you can already access this model in most cloud platforms.
[article] How do work environments impact developer focus and productivity? Engineers want space for deep work and private spaces to avoid interruption. Makes sense!
[blog] From notebook to Cloud Run service in 10 minutes: applied to Gemini Function Calling. Good post from Karl that shows you, step by step, how to move from a Jupyter notebook to a running app.
[blog] Enhancing Cloud Usage Forecasting, Monitoring & Optimizing. I like how Etsy takes a proactive approach to FinOps and optimizing their cloud infrastructure around their target metric.
[article] What Drives Ninety-Nine Percent of Performance. Steve makes a strong point about the mistake of focusing on the minor details and missing the “big holes” in our fundamentals.
[blog] Bringing file system optimizations to Cloud Storage with hierarchical namespace. This service is widely used, and still underrated. While it’s offered “folders” for a while, these were simulated structure, not a real one. With hierarchical namespaces, it’s even more powerful.
[blog] Putting Go’s Context package into context. It’s helpful to understand this important Go package. This post goes into a lot of useful details.
[blog] Pay The Creators: A Manifesto. Here’s the first post from the “Freeman and Forrest” team that is looking to help tech creators get compensated for their work.
[blog] Benchmarking results for vector databases. The Redis folks are feeling frisky and punching back a bit. Here, they take on all comers and say they’ve got the fastest option for vector databases.
[blog] PostgreSQL pgvector: Eliminating the Need for a Dedicated Vector Database. Speed isn’t the only factor in your vector database choice, and maybe you want to use your existing PostgreSQL database to support those workloads. Yugabyte offers a good deep dive.
[article] The Most Strategic Leaders Excel in 4 Disciplines. This is good, and represents a look at what a self-aware and mature leader is comfortable doing. I’m barely either of those.
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Richard Seroter's Architecture Musings 