The thing that always confuses me about "learn AI" in reference to Chat-GPT is... What exactly is there to learn? It's a web terminal that you type queries into and it returns results. Learning how to coerce it into giving you what you want is certainly an art, but no more esoteric an art than using Google to correctly return a useful result to your query - it's a lot of trial/error and simple logic about how to get it to respond with something useful.
In the case where people think that they either already are or can rapidly become experts in NLP, language modeling, or other topics related to Machine Learning... Well that's just the Dunning-Kreuger effect on full display. While you may be able to develop rudimentary tools based on machine learning w.r.t. NLP, none of these lifetime React devs with "interest in the AI space" are doing anything close to that, or if they are, it falls amazingly short of a project like OpenAI's. At this point, AI is the new "data science" of 2023, a handy buzzword for laypeople to invoke when they want to gesture towards "high tech", and one that's frequently divorced from an advanced technical understanding about how training/using a service like ChatGPT works.
As a closing anecdote, a friend of mine (would be startup founder) recently showed me her business plan for a "AI powered music generation service" where users could use a ChatGPT like interface to compel the computer to give them a "lofi beat to relax and study to" or an "upbeat electronic track for a space exploration video game stream" for YouTube videos or producing other forms of license-free music. When I started digging into how this was going to be done, the furthest we got is:
> "You need to train a neural network on a tagged and curated list of music samples, and develop instruments to allow for additional human training/tagging and re-processing, in addition to developing a suite of MIDI -> audio tools (essentially a headless/distributed DAW) to actually produce music. "
Their response?
> We'll build the web interface first then we can iterate from there.
> It's a web terminal that you type queries into and it returns results
Even just chatGPT in its current form is more than that. It is a full-fledged virtual assistant - but an esoteric one. Using it as better search is one use case, but how about to write a novel? It isn't mature enough to write _good_ prose sufficient for the the whole length of a publication. But it is good enough to generate ideas for the plot, edit, identify continuity errors, flush out backstories, generate maps, new ideas, write starter content that you modify. It could seriously change how productive one is. Similarly for writing code, or putting together home remodel plans or whatever. Learning how to have an AI "assistant" is a new thing because the intuitions about what it will be good and or bad at, especially for folks less familiar with the underlying way LLMs are built is not something that you get automatically.
> AI powered music generation service
Your friend does sound a bit niave about the complexity of such a service. But even right now, you can ask ChatGPT to generate coord progression, or representative example pieces of music that match the prompts you gave that would could use with some downstream system without a full "music model" along the lines you described.
Plenty of us are finding dozens of new applications every day. But they don't all work 100% out of the box. Most don't. So understanding the limitations and what kind of integrations are needed is really key to leveraging the value.
Yeah I had the same experience. The point of ChatGPT is that you don't have to learn anything. GPT4 is so good that I can type short words in chatting lingo with typos and it still works. I don't know what people are learning here (other than how freaky good AI got so quickly).
I'm not sure you can say that their attraction of youth is actually "natural" - as other subthreads here have suggested, Gen Z is considerably less interested in computing than my (millenial) generation was. When I was a teenager I was torrenting 100% of my media, tweaking and installing VST's in Ableton's Max for Live to make music, hacking my school district's crappy computer security system to get onto a proxy and play video games in the library, and then figuring out how to code in Powershell so I could bot on those games.. the list goes on. Me and my friends thought we were Epic Trolls and hackers - I think that image has been relegated to the "cringe" bin of history, the same way that people look at pictures of themselves wearing leather jackets with teased hair from the 80's and go - Look! Look how ridiculous I was back then! Times have changed.
I think for a certain segment of the population born between 1980 and 1995, mostly male gamer/geek type personalities (and believe me, I do cringe at the person I believed I was in 2006 when I was doing all of this stuff), computers were and remain attractive because of the stereotypical associations with the Hacker/slacker archetype - the cultural milieu consistent with The Matrix, online video games, shows like the I.T. crowd, musicians like Aphex Twin and Radiohead, fantasy and sci-fi ranging from the more primitive Dwarf Fortress and Lord of The Rings to the fast-paced cyberpunk of Counter Strike and Snow Crash.
I'm not particularly "hip" but a lot of the mystique and glory of the Hacker seems to have worn off with kids these days - gaming and mobile phones are ubiquitous and easy to use, where they used to only be for the "hardcore nerds". Tech startups full of people like me when I was 18 have taken over the world (unfortunately, I was not one of them) and they seem to have done their fair share of harm.
I know that computer science remains popular as a career avenue to make money for young people, but I'm not sure that they're particularly passionate about it in the way that some people my age and older are. When I was into it as a self-styled "outcast" neckbeard teenager, I was into it because of the cultural connotations and the associations marketed to me as a young man in the "hacker" heyday. Imagine telling me or any young "geek" type guy in 2005 that when I was 35 that I would be a systems engineer working with ex-frat bro type guys on the hardest problems I have ever approached - I probably would have scoffed and told you that there's no way such a small mind could ever approach the mathematics and algorithms needed to REALLY be an engineer (again, cringe, but true). Yet here I am, and it's great.
Suffice it to say that I don't think there's anything uniquely young or male about computing or computers, and that the culture around them is completely free to morph and shift as
Between hardware and underlying software becoming more capable and reliable, software removing more and more avenues for tinkering with a computer's innards, passion getting replaced with profit, and straight up computing becoming "stuff my mom and dad does", the young of today are rightfully finding greener pastures for inspiration.
I know in my day I had a blast just messing with Windows 95 and Microsoft Office. Yes, really. Just messing in them was as fun, or even more fun than, playing video games. Microsoft Access was my favorite "game". The sheer potential that even my kid brain could see was just mindblowing.
Now? It's not. No more are developers writing for passion, most are writing for profit. No more are we allowed to tinker; to be clear the likes of Windows and Linux still let us, but iOS and Android refuse tinkerers. No more is computing "fashionable" to young minds, it's the stuff old people (read: us) do and that is so not young, come on man!
Times are changing, and we're simply no longer in the front row seats.
I agree, quite accessible, despite its length. I personally would not recommend skipping the footnotes. Though they are a pain, they frequently add so much color and deeply-nested, parenthetical humor to the book. Occasionally you need to look up a word (which is always worth it, because he really knows how to pick the right word), occasionally you get bored in the middle of one of "those" chapters (likely an inevitability that you get some ups and some downs in a 1,000+ page book).
But I totally agree that it just gets more and more relevant and poignant. And completely hilarious. I think that part of the book (and his writing in general0 is undersold. Some of the passages are amusing because of their literary references and wordplay, some are laugh-out-loud funny, the type of stuff that you'll have to read back to someone else immediately because of the extreme mirth you just experienced reading it.
As Dave Eggers says in his introduction to the 2006 version of the book:
> A Wallace reader gets the impression of being in a room with a very talkative and brilliant uncle or cousin who, just when he's about to push it too far, to try our patience with too much detail, has the good sense to throw in a good lowbrow joke.
honestly people on here are not spending enough time telling you that if your job is shitty and your career feels dead that you will be angry, depressed, anxious. not that having a job and a career is everything, but speaking from the experience of having done that once or twice before, 8+ hours of terrible job will get to you and start to fray anyone's patience and sap their life force. for the love of yourself and your own family, go find something to do all day that you don't hate.
This shit is so difficult. I took technical interviews in gas station parking lots on lunch breaks, used my 1 sick day per month to try to get a break out of my terrible entry-level law office job, where I too would be fired if I needed to take next Tuesday afternoon off without explanation. It took leaving that job and a little bit of a leap of faith to finally get some flexibility to get those interviews that were worth it.
It's undoubtedly got to be hard for the "entrepreneur" without novel ideas or an ability to notice market shortcomings and imagine patches to them. Like you, I read this post with a bit of incredulity - sure, you might want to "be your own boss" and "launch a successful product" but in order to actually do that... you kinda have to do something novel. I don't want to crap on OP's dreams, but I don't see the link manager thing turning into a billion dollar unicorn.
I feel like this is the same thing that happens with musicians and artists - it's trend hopping and hoping to get a second of the spotlight for what's in vogue. One year it's brostep, then it's NFT, then its your vanlife miniseries on Youtube, then it's your hyperpop EP, then it's your "metaverse" art project, etc. I know a lot of people like this and few of them seem to find lasting success because they either aren't sticking in a certain domain long enough to really excel, or have no real passion other than chasing "success" which results in ventures that are half-baked and obvious clones of things that already exist.
> but I don't see the link manager thing turning into a billion dollar unicorn.
I suspect for every $1000M unicorn, there are 1000x $1M businesses.
1. Many of those 1000 $1M businesses can be very derivative, but with some focus that is just too specific for competition (vertical, market, # of clients, feature focus, whatever). That link manager could easily be a yummy small business.
2. I suspect one can make as much money *adjusted-for-risk* with a $1M businesss as one can trying to make a $1000M business (cap-table and yearly profits are also often ignored, and one might make little especially if one is not the founder of a unicorn).
Sure, then I guess the question is how likely any of that is and if we’re adjusting for risk, what’s the opportunity cost vs just working for someone else? My guess is that in 10+ years of saving or investing zero dollars, each subsequent year of dream chasing is going to have an opportunity cost so high that the big payout ending would be the only thing that could balance the scales
The “unicorn plan” is highly leveraged, high risk, and founders ignore the Kelly Criterion for their costs (especially their time).
I would love to see the average return for the founders of ycombinator startups. Given the power law (Pareto) distribution, it can probably be calculated by knowing the number of founders from 2005 to 2010, and some rough estimates of returns for the major ycombinator success stories.
VC as an fund category has poor returns (“the Cambridge Associates U.S. Venture Capital Index averaged just 5.06% per year between 2000 and 2020”). Also VC returns compared with founders equity: have higher seniority (preferential), lower volatility, diversity, and the chance of getting the necessary outlier success story returns.
The median/average founder in the market is a loser.
Do a funded startup for social status, to learn, to gamble, or for kicks. Don’t become a funded founder because it makes financial sense for an individual.
> k8s is a terrible experience and a terrible abstraction. I'm not smart enough to do better but I'm smart enough to know it terrible. It's snake-oil
If you're running Kubernetes on bare metal then I don't blame you for having a bad impression. I can't help you understand that your problems are almost certainly to do with a group of "hardcore Ops" guys trying to do everything GKE or EKS gives you for free while neglecting your actual needs, not to do with Kubernetes itself, which is most useful when Google just creates a cluster for you and you can immediately start dumping manifests into it.
> at my company, a bunch of devops folks that deployed k8s 80% of the way left soon later to make 2x the pay as contractors at other companies to help them push through their last 80%. This is another reason that there is a lack of parity on the new infrastructure. Unfortunately, everyone is still stuck at 80%. Strange.
Again, this isn't really to do with k8s is it? Probably better to point the finger at management for not retaining the people who they needed to run k8s or listening to your complaints trying to work with something 80% done instead of 100%. At my company we used k8s 110% for everything and have amazing support for it, and as a dev it's a dream come true. For people who don't understand Ops, it can be difficult, but when you open the "DevOps" box of saying "Devs own their services in production" for the first time, you're bound to do a lot of learning. I see this in my teammates a lot, who have basically zero understanding of how things actually deploy or run once they've left their local machine.
In the case where people think that they either already are or can rapidly become experts in NLP, language modeling, or other topics related to Machine Learning... Well that's just the Dunning-Kreuger effect on full display. While you may be able to develop rudimentary tools based on machine learning w.r.t. NLP, none of these lifetime React devs with "interest in the AI space" are doing anything close to that, or if they are, it falls amazingly short of a project like OpenAI's. At this point, AI is the new "data science" of 2023, a handy buzzword for laypeople to invoke when they want to gesture towards "high tech", and one that's frequently divorced from an advanced technical understanding about how training/using a service like ChatGPT works.
As a closing anecdote, a friend of mine (would be startup founder) recently showed me her business plan for a "AI powered music generation service" where users could use a ChatGPT like interface to compel the computer to give them a "lofi beat to relax and study to" or an "upbeat electronic track for a space exploration video game stream" for YouTube videos or producing other forms of license-free music. When I started digging into how this was going to be done, the furthest we got is:
> "You need to train a neural network on a tagged and curated list of music samples, and develop instruments to allow for additional human training/tagging and re-processing, in addition to developing a suite of MIDI -> audio tools (essentially a headless/distributed DAW) to actually produce music. "
Their response?
> We'll build the web interface first then we can iterate from there.