Daily Reading List – November 11, 2024 (#438)

It’s getting dark by 5pm here. Not my favorite time of year. Fortunately, it’s still 63 degrees F so I can’t complain too much. Do you find yourself working different hours when it’s light early and dark early?

[blog] How Google supports veterans and military families. Good stuff. I think most every large tech company offers useful programs for veterans adjusting to civilian life.

[article] Boost Your Team’s Productivity by Hiring Force Multipliers. Are you building “talent density”? It comes in handy when you’re in a dynamic industry and need folks who can pivot to new challenges quickly.

[blog] ML in Go with a Python sidecar. Great blog post from Eli. He looks at how you might interact with models that don’t support your preferred language.

[blog] the death of the architect. Is big upfront architecture a prison of your own making? This post looks at the thinking behind lightweight design and incremental architecture.

[repo] LLM Prompt Tuning Playbook. Want to get better at prompting? This playbook helps you ask better questions of LLMs, including how to best use system instructions.

[blog] Introducing your new JavaScript package manager: Deno. Keep an eye on this JavaScript runtime. They’re iterating at a good speed.

[paper] LoRA vs Full Fine-tuning: An Illusion of Equivalence. Go deeper into these two approaches that may perform similarly, but have different side effects.

[blog] Go Turns 15. Happy birthday to the Go programming language. It’s still my first choice nowadays when coding up most anything.

[blog] Welcome to the era of Services-as-Software… where the lines between services and software are blurring. The HFS folks point out that companies are looking to software (AI powered) to replace labor provided by professional services companies.

[site] Modern Java. Here’s an online book for learning Java from scratch. It’s updated regularly, and has some great foundational content for learning the language.

[article] Paligemma Performance. DigitalOcean posted a tutorial that shows how to fine-tune this open Google model.

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