2024 Tech Wrapups¶
Databases¶
I really enjoyed this post from Andy Pavlo, Databases in 2024: A Year in Review. I’ve been looking at DuckDB for a few use cases, and always love the ability to add things to Postgres instead of spinning up a new service!
In the same way that Postgres is the default choice for anyone starting a new operational database, DuckDB has entered the zeitgeist as the default choice for someone wanting to run analytical queries on their data. Pandas previously held DuckDB’s crowned position. Given DuckDB’s insane portability, there are several efforts to stick it inside existing DBMSs that do not have great support for OLAP workloads. This year, we saw the release of four different extensions to stick DuckDB up inside Postgres.
Large Language Models¶
This post from Simon Willison, Things we learned about LLMs in 2024 is another great overview. I’ve been following his blog for years, since we hang out in similar Python circles, and it’s been great to see the traction he’s gotten by being a solid experimenter in the AI ecosystem.
A welcome result of the increased efficiency of the models—both the hosted ones and the ones I can run locally—is that the energy usage and environmental impact of running a prompt has dropped enormously over the past couple of years.
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For less efficient models I find it useful to compare their energy usage to commercial flights. The largest Llama 3 model cost about the same as a single digit number of fully loaded passenger flights from New York to London. That’s certainly not nothing, but once trained that model can be used by millions of people at no extra training cost.
