by InfamousRece on 8/23/24, 11:31 PM with 52 comments
by mianos on 8/24/24, 11:00 AM
by somethoughts on 8/24/24, 8:38 AM
In other words, if you were starting college today, would you still do tech? Is it still better than getting any other job for equivalent pay bands?
Specifically comparing early career job seekers:
* Easier/harder to get $50-100K tech job versus $50-100K job in other fields?
* Easier/harder to get $100-150K tech job versus $100-150K job in other fields?
* Easier/harder to get $150-200K tech job versus $150-200K job in other fields?
Other fields are things like marketing, accounting, law, medical, biotech. A quant/finance job is probably CS or CS adjacent these days so its basically a tech job.
My take is that by the time you get above $130K there probably is not any other field outside of tech where this is possible unless there is some very unique skill.
And instead of annoying but somewhat objective leetcode/live coding whiteboard interviews; hiring is way more subjective, credential based, network based and gate kept.
by tropicalfruit on 8/24/24, 7:00 AM
the derivative and repetitive nature of most coding tasks.
when I ask GPT for some code and it spits out 200 lines of boilerplate in 2 seconds, i feel sick. what am i doing with my life.
by jarsin on 8/24/24, 12:21 AM
The fed just made it official that the easing cycle is beginning. The 10yr has already front run them by about 1.7%.
So if the article conclusion is correct tech job market could look good going into the new year.
by PeterStuer on 8/24/24, 2:12 PM
In my experience a lott of LLM usage in B2B is fairly basic (translation, summerization, data extraction, categorization) and fairly well handled by current SOTA foundation models. Could this be improved by custom/finetuned models? Sure, but in nearly all cases I have seen the ROI of improving other parts of the application is way higher than the investment in developing and maintaining custom models.
by crackalamoo on 8/24/24, 3:51 AM
> Algorithms are tiny parts of large systems. The algorithm part can usually be abstracted as an API call that provides some value(s) with inherent uncertainty; an ML endpoint is in essence an API call with few or no side-effects that returns an uncertain value. It turns out, the system design surrounding this almost always matters more than the algorithm and the dumb algorithms tend to do well enough anyway.
I think this is basically dismissing almost all APIs and algorithms in favor of system design. But system design is pretty similar in many apps, while the value of each app is very different. So clearly the API/algorithm is very important, and often the most important part.
I also disagree about AI not taking jobs. GitHub copilot definitely makes programming about 30% faster. To keep up the jobs, we would have to write 30% more code. But it seems innovation is the limiting factor more than typing code, so maybe we'll only write 15% more code, and we can have less devs.
by eclectic29 on 8/24/24, 3:57 AM
by tekla on 8/24/24, 12:48 PM
by yungporko on 8/24/24, 12:53 PM
i update my cv, upload it to the big recruitment websites, recruiters do all of the work, my phone rings 3-5 minutes later and the calls barely stop for a second until i've been in my new role for 2-3 months. interviews are always laid back conversations over the phone or a teams call with no curveballs, just the standard questions like what kind of stuff do you work on in your current job, what tech stack do you use, what do you think about [current popular thing], etc. i'd say i get an offer from about half of the interviews i do.
my only real complaint other than the amount of recruiters who deliberately waste mine and their own time for reasons known only to them (which has always been a thing), is the very obvious coordinated push to get people "back in the office" spilling over and affecting developers, which means there are way less fully remote roles than there were even before covid.