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 you’re doing it for fun, one option is to start with a simplified version of the game. It’s faster to implement and faster to run. And you’ll get insights you can apply to the full game.

That’s what I did when I applied RL to Dominion, because the complexity of the game depends heavily on the cards you include! See part 3 of https://ianwdavis.com/dominion.html

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.

I've written a lot about this as a resource for my colleagues at work, perhaps you would find it useful as well. There's a list of recommended books embedded in there, but a lot of other info as well: https://ianwdavis.com/advice-new-lead.html

There's no perfect answer here, because different people will have different reactions to whatever approach you take.

So what follows is just an opinion. I work at a large biotech company, and sometimes hire engineers. Personally, I would like to see an entry in your job list in appropriate chronological order, with a very brief explanation. Many people have to take a break, whether for raising children, caring for family, or dealing with health issues. As a hiring manager, a gap in your job history with an explanation like that is less concerning to me than a completely unexplained gap.

If you can truthfully indicate the issue is resolved and won't impact you in a future job, great; if not, just stop with the explanation of the gap.

BTW, I just stumbled across the AWS Chime SDK: https://aws.amazon.com/blogs/business-productivity/customers...

Sounds similar to what Twilio offers, but at about 1/6th the price -- might be worth a look if costs are a concern...

Hadn't planned on the random feature, but totally open to pull requests!

My Python code is up on Github under a BSD license, if you want to play with it. https://github.com/iwd32900/happyhour

I didn't realize Twilio did video -- I'll have to take a look!

Thanks for building this! Curious to know why you went with random mixing, rather than self-sorting. I expect the timed random mixing would feel sort of heavy-handed, though I haven't tried it so maybe I'm wrong.

Also -- what did you use for the video chat component? I'm assuming you incorporated some existing software/service?

Interesting! I built something really similar last month, though less polished: https://happyhour.ianwdavis.com/

In my case, I let people create groups ("tables") on the fly and hop between them at will, rather than forced mixing. There's a list along the side that shows which people are at each group. Groups with fewer people sort to the top, to encourage people to join those.

I built on top of Jitsi and the meet.jit.si public servers for video chat. The quality hasn't been as good as something like Zoom, but I love how easy it is to embed and customize!