371–380 of 1001 posts
There are 4.5 million SWEs in USA alone. of those how many are great at what they do? 0.5% at best. how many are good? 5% tops. average/mediocre 50%. below average to downright awful - the rest.
while LLMs won’t be touching the great/good group in any near future they 100% will the awful one as well as average/mediocre one
This is a path I would recommend with or without LLM's in the picture.
Working in ML my primary role is using new advances like LLMs to solve business problems.
It is incredible though, how quickly new tools and approaches turn over.
Nothing because I’m a senior and LLM’s never provide code that pass my sniff test, and it remains a waste of time. I have a job at a place I love and get more people in my direct network and extended contacting me about work than ever before in my 20 year career. And finally I keep myself sharp by always making sure I challenge myself creatively. I’m not afraid to delve into areas to understand them that might look “…
When you consider LLMs to be building blocks in bigger, more complex systems, their potential increases dramatically. That's where mid/senior engineers would chip in and add value to a company, in my point of view. There's also different infrastructure paradigms involved that have to be considered carefully, so DevOps is potentially necessary for years to come.
I see a lot of ego in this comment, and I think this is actually a good example of how to NOT safeguard yourself against LLMs. This kind of skepticism is the most toxic to yourself. Dismiss them as novelty for the masses, as bullshit tech, keep doing your same old things and discard any applications. Textbook footgun.
Even if these LLM tools do see massive improvements, it seems to me that they are still going to be very happy to take the set of business rules that a non-developer gives them, and spit out a program that runs but does not do what the user ACTUALLY NEEDS them to do. And the worst thing is that the business user may not find out about the problems initially, will proceed to build on the system, and these problems become deeper and less obvious.
If you agree with me on that, then perhaps what you should focus out is building out your consulting skills and presence, so that you can service the mountains of incoming consulting work.
Earlier quoted context omitted.
emmmmm... i think your argument is not valid any more: https://chatgpt.com/c/6760a0a0-fa34-800c-9ef4-78c76c71e03b
Seems like they caught up because I have posted this before including in chatGPT. All that means is you have to change it up slightly. Unfortunately “change it up slightly” is not good enough for people to do anything with, and anything more specific just trains the LLM eventually so it stops proving the point. I cannot load this link though.
Nothing because I’m a senior and LLM’s never provide code that pass my sniff test, and it remains a waste of time. I have a job at a place I love and get more people in my direct network and extended contacting me about work than ever before in my 20 year career. And finally I keep myself sharp by always making sure I challenge myself creatively. I’m not afraid to delve into areas to understand them that might look “…
For those less confident: U.S. (and German) automakers were absolutely sure that the Japanese would never be able to touch them. Then Koreans. Now Chinese. Now there are tariffs and more coming to save jobs. Betting against AI (or increasing automation, really) is a bet against not against robots, but against human ingenuity. Humans are the ones making progress, and we can work with toothpicks as levers. LLM's are ou…
Truly hit the nail on the head there. We HAD no business with these side-quests, but now? They're all ripe for the taking, really.
I spent a lot of time with traders in early '00's and then '10's when the automation was going full tilt.
Common feedback I heard from these highly paid, highly technical, highly professional traders in a niche indusry running the world in its way was:
- How complex the job was - How high a quality bar there was to do it - How current algos never could do it and neither could future ones - How there'd always be edge for humans
Today, the exchange floors are closed, SWEs run trading firms, traders if they are around steer algos, work in specific markets such as bonds, and now bonds are getting automated. LLMs can pass CFA III, the great non-MBA job moat. The trader job isn't gone, but it has capital-C Changed and it happened quickly.
And lastly - LLMs don't have to be "great," they just have to be "good enough."
See if you can match the above confidence from pre-automation traders with the comments displayed in this thread. You should plan for it aggressively, I certainly do.
Edit - Advice: the job will change, the job might change in that you steer LLMs, so become the best at LLM steering. Trading still goes on, and the huge, crushing firms in the space all automated early and at various points in the settlement chain.
Earlier quoted context omitted.
> I am constantly surprised how prevalent this attitude is. ChatGPT was only just released in 2022. Is there some expectation that these things won't improve? Is there any expectations that things will? Is there more untapped great quality data that LLMs can ingest? Will a larger model perform meaningfully better? Will it solve the pervasive issue of generating plausibly sounding bullshit? I used LLMs for a while, I…
> Is there any expectations that things will? Seems like an odd question. The answer is obviously yes: There is a very pervasive expectation that LLM's will continue to improve, and it seems odd to suggest otherwise. There is hundreds of billions of dollars being spent on AI training and that number is increasing each year. > Is there more untapped great quality data that LLMs can ingest? Why wouldn't there be? AI's…
It isn't odd at all. In the early 21st century there were expectations of ever, exponentially increasing processing power. This misconception partially gave us the game Crysis, which, if I'm not mistaken, was written with wildly optimistic assumptions about the growing power of computer hardware.
People are horrible at predicting the future of technology (beyond meaninglesslessly or trivially broad generalizations) and even when predictions turn out to be correct, they're often correct for the wrong reasons. If we were better at it, even in the shortest term, where such predictions should be the easiest, we'd all be megamillionaires, because we'd have seen the writing on the wall and invested in Nvidia before the AI craze reached its current fever pitch.
Earlier quoted context omitted.
Not parent poster, but I imagine it will be a bit like the horror stories of companies (ab)using spreadsheets in lieu of a proper program or database: They will use an LLM to get half-working stuff "for free" and consider it a bargain, especially if the detectable failures can be spot-fixed by an intern doing data-entry. I think we'll see it first in internal reporting tools, where the stakeholder tries to explain so…
> Not parent poster, but I imagine it will be a bit like the horror stories of companies (ab)using spreadsheets in lieu of a proper program or database: They will use an LLM to get half-working stuff "for free" and consider it a bargain, especially if the detectable failures can be spot-fixed by an intern doing data-entry. When I read things like this I really wonder if half of the people on HackerNews have ever held…
Do you perhaps have any friends at companies which hired overseas contractors? Or system-admins working at smaller companies or nonprofits? They're more-likely to have fun stories. I myself remember a university department with a master all_students.xls file on a shared drive (with way too many columns and macros in it) that had to be periodically restored from snapshots every time it got corrupted...