The online version will remark free and available. The in-person, for credit course is being discontinued. Unless you are an incoming Harvard student, this won’t affect you.

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Up

My use case will be to remove the looping bland house music from tech product demos. I really don’t understand why every tech company needs to make me feel like I’m at a club when I’m just there to learn about a new feature in their product and how I can use it.

Sure, but $120k is definitely not FAANG-tier base comp in SF. Not even close. Maybe it’s on the low side of scrappy startup/scaleup comp.

The UPS driver that delivers to my home office a bag of electronic goodies every week couldn’t care less about what OS I use. I mean I even tried to tell him about all the awesome Minty Pops and Arches and all he had to say was “that Fedora looks fucking dope, bro. Say, do you listen to Hannah Montana?”

Absolutely not true. Disclaimer, I do work for NVIDIA as a forward deployed AI Engineer/Solutions Architect—meaning I don’t build AI software internally for NVIDIA but I embed with their customers’ engineering teams to help them build their AI software and deploy and run their models on NVIDIA hardware and software. edit: any opinions stated are solely my own, N has a PR office to state any official company opinions.

To state this as simply as possible: I wouldn’t have a job if our customers weren’t seeing tremendous benefit from AI technology. The companies I work with typically are very sensitive to CapX and OpX costs of AI—they self-serve in private clouds. If it doesn’t help them make money (revenue growth) or save money (efficiency), then it’s gone—and so am I. I’ve seen it happen; entire engineering teams laid off because a technology just couldn’t be implemented in a cost-effective way.

LLMs are a small subset of AI and Accelerated-Compute workflows in general.

We’re looking at this from opposite sides of the same coin.

The NN graph is written at a high-level in Python using frameworks (PyTorch, Tensorflow—man I really don’t miss TF after jumping to Torch :) ).

But the calculations don’t execute on the Python kernel—sure you could write it to do so but it would be sloooow. The actual network of calculations happen within the framework internals; C++. Then depending on the hardware you want to run it on, you go down to BLAS or CUDA, etc. all of which are written in low-level languages like Fortran or C.

Numpy fits into places all throughout this stack and its performant pieces are mostly implemented in C.

Any way you slice it: the post I was responding to is to argue that AI IS CODE. No two ways about that. It’s also the weights and biases and activations of the models that have been trained.

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Neural nets are typically written in C; then frameworks abstract on top of that (like Torch, or Tensorflow) providing higher-level APIs to languages like (most commonly) Python, or JavaScript.

There are some other nn implementations in Rust, C++, etc.

Costco’s soft-serve is way better than McD’s and actually is cheap.

Bullshit. Developers never make mistakes. N.E.V.R.

Can’t wait for Nintendo to sue Microsoft because VS Code can be used to edit save files.

Fair enough. I agree for what it’s worth—just have yet to find a browser that meets my needs for both usability and privacy. Always happy to explore options and I do sometimes. Just always end up back with Brave because everything else I try ends up annoying me in some way or the other.

You can turn the crypto part off you know. They even tell you how to do it.

Gonna go ahead and be downvote sponge here: Brave. Its privacy features and integrated Adblock have no peer that I’ve found yet, and easy bookmark/history syncing across multiple devices.

Yeah the CEO is a POS. Find me a tech CEO that’s not, besides Meredith Whitaker.

The most annoying thing about a lot of these is that tutorials are “minimal viable setup” sorta things. Like “now you have it setup, make sure you tune it for production”

Dude I’m already in pain from trying to serve these models and you just have to go rub salt into my eyes. “Simplify your stack with <Tech>” they said. “Share your resources effectively and easily with <Tech>” they said. “Here’s your fuckin’ ‘Hello, World’ now GRTFM and buzz off” they said.

Working close to the metal do be like that.

Can’t wait to see this project too in Google’s graveyard.

I work for a large enterprise and build ML model monitoring pipelines fairly frequently—this will be a more in depth but similar use case to what you’re asking.

We use Grafana (visualization) and Prometheus (timeseries db)—they’re built for this use case exactly. Tons of info out there on how to build, configure, connect to your sensors, and deploy it.

Nah, I work with real big data all the time—I’m a ML engineer/DataSci depending on the day.

It’s not crashing because I put a trivial couple hundred rows of data into a spreadsheet.

It crashes because there’s some conflict between its Java core and the Linux kernel I’m running it on. It’s been like this across many versions; I keep everything updated, etc. Tried many versions of Java, and OpenJDK because FuckOracle. I’m no Java developer though, so Inwouldnt be able to contribute unless they want to refactor the entire core to Rust in which case I’d love to help.

I send bug reports and it’s always just crickets—either they don’t know and don’t communicate that they don’t know, or don’t care, or more likely are just too busy with their realjobs to go on the hunt for a solution to a corner-case bug/crash scenario like mine probably is.

I use office programs so infrequently that I just deal with it. But if I was like my directors and managers who live and die by office productivity apps then I’d have to abandon LibreOffice and go to the closed-source solution.