How we built Text-to-SQL at Pinterest – Text-to-SQL and vice versa became one of the canonical examples of LLM, and every product needs one. The post described a very interesting work that can be implemented relatively easily. I relate the most to the closing paragraph, which emphasizes the gap between demos, tutorials, benchmarks, and real-world use cases. – “It would be helpful for applied researchers to produce more realistic benchmarks which include a larger amount of denormalized tables and treat table search as a core part of the problem.”
https://medium.com/pinterest-engineering/how-we-built-text-to-sql-at-pinterest-30bad30dabff
(p.s I mentioned post in a recent LinkedIn post – LLMs in the enterprise – looking beyond the hype on what’s possible today)
How an empty S3 bucket can make your AWS bill explode – this story completely blew my mind (and gladly not my account). I was happy to see that AWS is looking into this issue and wondered if in bigger accounts, such anomalies could get unnoticed.
The Design Philosophy of Great Tables – great_tables is a Python package for creating wonderful-looking tables. This post shares its visual design philosophy and is worth reading if you create tables even if you will not use this package.
https://posit-dev.github.io/great-tables/blog/design-philosophy/
1-measure-3-1 – a variation of the 1-3-1 problem-solving method for making proposals. I found it specifically effective for engineers as it is structured and focused.
https://www.annashipman.co.uk/jfdi/1-measure-3-1.html
On Making Mistakes — I love it when people combine experience or knowledge in one field or domain with another. For example, someone brings her experience as a soccer player to managing a team, or someone uses lessons he learned as a supermarket cashier to software architecture. This post discusses making mistakes and working through them and refers to several domains, including improv, chess, and F1 team management.
Tom Ron 