iwd

I just got to see a different species of kleptoplastic sea slugs in the wild last month, on a kayak tour of the mangroves around Key West. Our guide scooped some lettuce sea slugs up in a plastic container (and then returned them safely). They were bigger, about 3 inches long, with a wavy/frilly green border. It made my biologist heart very happy!

Meh. My 16 year old writes shaders for her own games, writes for her VR headset, programs her graphing calculator, etc etc. The next generation will have plenty of self-taught SWEs, just like ours. Most kids couldn’t use computers in the 80s or now, and it wasn’t a disaster either time.

I’ve been trialing a bunch of these models at work. They basically learn where the DNA has important functions, and what those functions are. Its very approximate, but up to now that’s been very hard to do from just the sequence and no other data.

Do you have compression enabled? At least from Pandas, Parquet defaults to compressed and Arrow/Feather default to uncompressed. When I enable zstd compression, I get similar file sizes, and sometimes Arrow is smaller.

Not an expert, but I believe many papers on other video games make a single decision for the next X frames at once, possibly including a delay factor that governs exactly when to act. I think OpenAI’s Dota2 agent does this.

If the hours you spend implementing and maintaining your DevOps exceed the hours of downtime you prevent, you're probably not making good use of your time. The less you build, the less you have to maintain.

I've been running a Python-based, highly custom web store solo since 2007, and it supports multiple people doing fulfillment. I host on Opalstack so as to outsourcing patching, email config, database maintainence, etc. I run directly in a Git repo (actually Hg, it's that old) and releases are "git pull && ./restart.sh". Rollbacks are "git checkout ...".

I've had to migrate/rebuild the VM about every 5 years. Tech changes enough in that time that no automation will still work unmodified. So I just keep good notes about what I did last time, and figure out what the new equivalents are when I finally have to do it again (updating the notes, of course). Database and Conda are easy to port. It's usually DNS and email integrations that are a pain.

As others have said, KISS is key. Industry DevOps is for a work setting with a decent-sized team, where you can afford the overhead of maintaining it all in order to make the overall team more efficient.

This seems like a “problem” that shouldn’t be solved. If the thing keeping you from algorithmic trading is difficulty in deploying, you probably have no business doing it.

Cynically, this seems like a way to draw more naive novices into the market. As the old saying goes, if you don’t know who the mark is, it’s you.

Agriculture PhD plus even modest data science skills is hugely in demand at the giant ag-biotech company where I work. Many of the data engineers I lead are former science PhDs. Literally all of our people strategy discussions in R&D are about how we need more people like you. There are only a handful of big science companies left in ag, but I bet the other ones have similar needs.