GLP-1 and GIP are both hormones the human body makes in the gut. The famous drugs are mimicking those hormones. This is more akin to taking supplemental testosterone than it is to taking Fen-Phen or whatever.
That's awesome. Before ~2007 they allowed you to use open-source Pidgin to connect to the Domino servers. A friend of mine and I used it to make a bot: if you sametimed me, you got Zork.
It reminds me of another IBM IT rule: they wanted your chat history (and email) older than two years to be all deleted for legal liability reasons. It was important to save your sametime chat history (an XML file) and export your email periodically if you wanted to keep this stuff.
This was actually better than Slack in one way- you could grep the files for things, and not have to rely on search within the tool.
You may want to look into https://exist.io/. It's a very indie developer duo out of Australia (IIRC). And also IIRC they were looking for a buyer on Twitter some time ago.
I'm an ML Engineer who's really closer to an MLOps role. I'm weak on ML and strong on data, scaling, cloud stuff, infra as code, making processes not suck, kinda everything _but_ the ML. So take my opinions for what they are worth, and keep in mind that the role of ML Engineering at company A != ML Engineering at company B.
I've described ML Engineering as putting the "science" in Data Science because we help introduce reproducibility. For example, I can take your model training and make it a robust process that happens over a huge amount of data on a daily basis with all the monitoring, logging, and reliability stuff surrounding that.
Some topics I would personally want to see for an ML Engineer on my team (and again, "ML Engineer" has less of a solid definition across the industry than "frontend engineer" or other roles that have been around longer)
- Docker: can you containerize your code? Can you interact with a local container?
- Model serving: at a basic level, can you wrap an API around a model? There's lots more systems design stuff here if you want to go deeper on model serving platforms.
- CI/CD: do you know what Jenkins does? (Or equivalent) Can you talk about a coherent code testing strategy for ML code? How would you deploy a model service using a system like Jenkins?
- Cloud stuff: you don't need to be an expert, but can you interact with cloud APIs directly or through Terraform, spin up instances, know the difference between object storage and databases, and do you have some Kubernetes experience (run a pod, get the logs, take some debugging steps when something's wrong).
- Modern MLOps: model registry systems like MLFlow, feature stores (DIY preferred but vendors ok)
- Scheduling and Pipelining: Airflow, Vertex Pipelines, lots of options here but those are the biggies. Know how to use these for basic data pipelines, model training, service deployment, and why and how you can deploy these via CD
- Monitoring: know the difference and have strategies around monitoring systems metrics (cpu usage, etc) and model metrics (data drift, etc)
A lot of this stuff is harder to learn on your own because it often comes up in the context of larger teams and enterprise scale, where monitoring and reliability turn into KPIs that execs look at, but this is, to me, the stuff that defines the difference between a Data Scientists and an ML Engineer.
Thanks, this is useful information (and also fairly overwhelming). I have a basic idea of some of these because of having taken CS courses but no hardcore experience in any of them. Even though I'd like to work on these, it does sound like I need to get into a tech company that does this in the first place. Having had a life revolve around university for a while, looks like I have a hill to climb.
Changing duvet covers! I learned the Burrito Method [1] and never looked back. When I send this to people, the reactions vary from "OMG WHAT YOU CHANGED MY LIFE" to "you srsly didn't know that?"
In some ways I feel lucky that tech was an early career change for me. I was trying to get a career in the arts off the ground, and it was going poorly. I was a recent grad millennial in the economy of the 2008 recession.
The decision to turn my back on what I thought was my passion was a profound spiritual experience. The decision to change came from outside of me. The decision of what path to follow was up to me though.
Tech was hiring and hiring like crazy, and I wasn't going to do an unprofitable degree twice so CS it was. I had a job before I graduated making 4x what my mom was making at her non-profit admin job.
If I hadn't pursued my art career first and had the chance to get deeply disillusioned with it, I would definitely be sitting at my desk trying to write code and thinking "what if... I'm not made for this... there's something else..." The truth is that I'm not cut out for the arts industry. I like stability, I like being salaried, I like having the upper hand in the hiring market (I know Big Tech is doing layoffs, but try spamming applications for a year to everything you can think of until the only place that calls you back is a cashier position at a grocery store. I have skills that are in demand now.) I like work that is decent and stimulating enough but which is definitely not "my passion" because that helps me keep boundaries on it.
I feel for folks who didn't get that chance to try out that other thing, who went straight into this career maybe because they wanted to, maybe because they didn't have the safety net I had that allowed me to do a second degree, maybe because life has held them down and change doesn't feel like an option. I've been out there with my chosen field and gotten burned hard by it so I'm content to stay put. It's definitely one of the cliche sayings about how the lows make the highs much higher.
I have no useful advice for anybody beyond their very early 20s facing this question. I know I would be eaten by this question if I hadn't already gotten my answer at the start.
I feel very fortunate I had a similar experience. When I was younger I joined the Army and after that did construction. I look back at those experiences and I realize how lucky I am to be able to work in Tech. Unfortunately a lot of people jump into tech at 22/23 fresh out of school. They work 10 years and start to become disillusioned that tech work is bad because they have nothing to contrast it to.
The connection of lead poisoning and narcissism does seem plausible.
It's also worth noting that, broadly speaking, the parents of Boomers (depending on their age) went through 1 or 2 world wars, the great depression, the spanish flu, etc. and probably didn't come out unscathed. The trauma of the parents is bound to have ripple effects on the mental health of the kids.
There's more than a few stories of dads who went off to war and came back silent, drinking, and traumatized.
I avoided learning anything having to do with front-end for a long time, and I'm just now catching up. I've been doing a course on Responsive Web Design with scrimba.com, and that starts with a really comprehensive overview of CSS basics, including margin collapse, which I'd certainly been frustrated by in the past without having a name for it.
Just a little local perspective on the lines in Georgia. The waits that some folks had to endure in the primaries were a huge problem. Georgia absolutely has history of voter suppression like many post-confederate states, and some seriously shady stuff has gone down in the past.
However, the really long lines yesterday were more attributable to incredible turnout on the first day of early voting, which also happened to be a federal holiday so many people had the spare time to come out and vote. There are new voting systems, new volunteers for whom that was their first day running the polls, and a very motivated electorate.
For comparison, today, two days later, I know of at least two people who were in and out in 15 minutes in some of the same areas that had incredible lines on Monday.
There are legit voter suppression stories of great concern in GA, but I don't think the lines of Oct 12 qualify. I'm totally open to being corrected on this opinion though.
