481–490 of 725 posts
Earlier quoted context omitted.
Sure, but I'm sure most of their lead devs are well paid now. In general C++ work and similar, if not in Chrome development.
cause != effect
Earlier quoted context omitted.
Most reasonable advice I've seen under this post. Having graduated in 2025 I've experienced the horror of competing with infinitely many third worlders in my small country, who will gladly take 1/3 my pay and be serfs. I really hope we get another hiring boom like in 2020 when I decided to study CS. Otherwise my career will be very rough. I love it and can't imagine doing anything else.
Horrible. Which country is this? Canada maybe?
Earlier quoted context omitted.
True. I'm a big fan of Cactus's work on the needle family of Simple Attention Network: https://github.com/cactus-compute/needle
That looks like a really nice and well put together library. But it feels like it sort of backs up my point about there being good models at every size class. Fine tuning Needle looks automated. Yes, you need to know basics like what validation loss means and how to use Python, but otherwise it's all about creating the dataset.
Earlier quoted context omitted.
In that regard, it's not too different from mobile telephony. Mobile phones drove the electronics industry 20 years ago, but there is limited demand for people who really know how to build a phone (ie. build the hardware and write all the signal processing from scratch), as there aren't that many companies that do phones at the lowest level. A few of the engineers got rich (eg. Viterbi), but most 'just' made a good l…
I was there at the start of the smartphone boom. I built a demo Android device that was capable of telephony/data, 3D rendering, etc. all before Google open-sourced the OS, for a SoC vendor that wasn't in Google's inner circle. Yet the industry was not interested in my junior profile during the subprime crisis.
There's this dilemma where in theory there's a ton of demand for engineers that can do real LLM machine-learning, but in practice there are very few available positions and entrepreneurship opportunities. The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM comp…
This is a wildly incorrect and myopic view on the world. Finetuning model is cheap and incredibly useful for deployment. You don't need to pre-train a frontier llm from scratch to make useful models. There is tons of domains where you and fine-tune llms and deploy them for value in companies and for your own entrepreneurship ambitions. I have made this a big part of my career for the last few years and now I'm workin…
Earlier quoted context omitted.
I would advise any somewhat ambitious 17 year old to avoid tech and get into healthcare if they can stomach human interactions and bodily fluids. Sure, it is not all sunshine and rainbows, but there will still be plenty of work helping people who are ill or elderly. Even in the worst-case economic scenario, medicine will be a more socially rewarding and stable life path.
This seems like a much more interesting question to me. Telling other people's children what to do is easy and basically doesn't have any downside to being wrong. With your own children things are a bit different. So: what are people here with school age children telling their own kids about the future? If their kids ask, what kind of careers would they encourage them to pursue, assuming they have the skills and inte…
I'm (more than) twice that age, but I've spent time learning this exactly this from videos by Andrej Karparthy and from books by Sebastian Raschka. I didn't do it because it was useful to me in a practical sense. It's because LLMs are fascinating and I want to know how they work. From that perspective it's been a great experience. I have afirm grasp of the basics. This makes it much easier to understand frontier conc…
There's this dilemma where in theory there's a ton of demand for engineers that can do real LLM machine-learning, but in practice there are very few available positions and entrepreneurship opportunities. The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM comp…
Learning should not be done only as a direct path to getting paid.
Learn to create pattern matching and intuition to solve future problems.
When you are 17 it is a good time to understand how the world works so you can build on top of it in the future. If we assume most tech is going to have an LLM as part the stack, a solid basis in how LLMs work is likely to help you in future endeavors the same way a solid basis in how the web works helps you today.
Maybe a 17 year old should learn both. As a small anecdote when I was 17 I learned a lot about load balancers, failover, and building self-healing systems running small hosting company that had to be fault tolerant when I was attending high school. This wasn't at state of the art levels (e.g. I wasn't configuring gigabit routers or global CDNs -- but it was useful pattern matching for future problems)
I currently don't touch any of that tech, but I have working knowledge that still serves me today.
Think long term.
As an aside, for someone interested and who's an absolute beginner, can someone please recommend good resources on how to build LLMs from scratch? Thank you in advance.
1. Build an LLM from Scratch by Sebastian Raschka ( https://sebastianraschka.com/llms-from-scratch/ ) 2. LLM from 0 to Hero, and nanoGPT by Andrej Karpathy
There's this dilemma where in theory there's a ton of demand for engineers that can do real LLM machine-learning, but in practice there are very few available positions and entrepreneurship opportunities. The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM comp…
Yes. It's like looking at the (Apollo) moon rocket launch and then suggesting teenagers should learn to build rockets in their garages for the coming space age. It is viable as a toy project, but there are vanishingly few career opportunities.
But that was way back in the early 1970's and all I had to work with was a mainframe.
Well the mainframe itself wasn't bad, the real show-stopper was that I didn't own the computer outright, no strings attached, no debt, etc.
>I'd probably try to make an LLM that I could use on some specific problem.
I thought so too back then, still do so I guess this is one of those things that could stand the test of time. I always wanted to start with something a lot simpler than a Moon mission myself. At 17 I already had a significant breakthrough in the chem labs and it was from alternatives to a single processing step plus everything that descended from that, rather than trying to tackle a much more complex detailed multi-step synthesis. I was only 17 but I was not trying to be a slouch, I don't think pg was either at that age but his advice is not for just anybody. I couldn't have done it if I hadn't made major progress since being 16, and it really emphasized at the time how much maturity can make a difference. My imagination ran wild as I extrapolated :)
In a reply from LeCun to pg:
>>I'll figure out a set of methods and architectures beyond LLMs that can quickly learn to perform physical tasks as efficiently as humans and animals. That last item is also what I would if I were 30, 40, 50, or 66 years old
I see no reason to stop at 66 either ;)
But I figured that people owning more computer power than I could ever afford were going to be doing something like this as soon as they could, without having to wait for something like an LLM to arrive before getting peoples' attention.
It did seem like things were going to take longer than you expect, so it's pretty good to have a lifetime of concentrating on the specialized natural science domain expertise, focused now for 50 full years on how it would combine if AI ever got good enough.
Both the natural science and the AI need to be a major cut above, I still see dramatic room for improvement in my own work. If I'm going to have to rely on "other peoples' AI" then that natural science component is going to have to pull a lot of weight to keep up with the kind of computers that only rich-as-hell high-rollers have access to.