Forgettable fodder, as so many of these recent allegedly groundbreaking talks are. I don't know if it's my increased exposure to them or something else.
// personal opinion: I think machine learning as it currently stands is widely overhyped
How is this the top comment?
> I am starting to notice a pattern in these papers - Writing hyper-specific tokenizers for the target problem.
This is merely expressing what they consider as part of a game state, which is entirely needed for what they set out to do.
> I argue this is just ordinary programming
"Ordinary programming" (what does that mean?) for such a task implies extraordinary chess intuition, capable of conjuring rules and heuristics for the task of comparing two game states and saying which one is "better" (what does better mean?).
> How would this model perform if we made a small change to the rules of chess and continued using the same tokenizer?
If by "small change" you are implying i.e. removing the ability to castle, then sure, the tokenizer would need to be rewritten. At the same time, the entire training dataset would need to be changed, such that the games are valid under your new ruleset. How is this controversial or unexpected?
It feels like you are expecting that state of the art technology allows us to input an arbitrary ruleset and the mighty computer immediately plays an arbitrary game optimally. Unfortunately, this is not the case, but that does not take anything away from this paper.
I wanted to label this as a “nothingburger”, but all the pieces of advice are either tautologies, vague general dating tips (i.e. "make sure the vibes are not off"), or straight up untrue: "They want to work on this part-time", "They want to quit their job but haven't set a date to do it", "They're going to quit their job but only after the YC interview". The latter are all very context dependent and are impossible to label as a "red flag" without further information. Possibly SEO bait?
> don't see it as any less dignified than any other work
You do not, and that is your moral judgement. Rationalizing earning money by any means necessary is a very slippery slope, and the discussion is much more nuanced than popular media would lead you to believe.
tried it quickly on a personal machine running windows; all attempts at submitting `BUY`s for popular tickers (regardless of price, tax, quantity, ticker, date) seem to result in an unknown error. Notably, I tried having the account match the ticker's currency, but that does not fix it.
> The answer is it's faster and (in my experience) generally accurate for common command syntax.
If you don't mind, around what level of complexity are you querying it for? Are the queries along the lines of "how do I find a keyword in a directory in UNIX?" or more along the lines of "write a bash script that will do foo recursively and treat edge case bar by doing baz"?
In my experience, if the queries are closer to the latter, it takes less time to "man page" the holes in my knowledge than to identify and to fix whatever is wrong with the LLM's initial guess. If you more often than not receive correct answers, even in non-trivial cases, could you please provide the LLM and any custom parameters if they exist? I'd be happy to be proven wrong and learn something in the process.
> Googling is very hit or miss these days you end up with a load of sponsored results and then have to try and find an example with ideally the exact syntax you're looking for.
May age be a factor here? I grew up as search engines were becoming a thing and ignoring "fake" results has become second nature.
I am honestly asking and not trying to be a smartass:
What are the advantages of LLM summaries over man pages/google searches/stackoverflow threads?
I could maybe empathize if an engineer only had 30 seconds to understand a code snippet or a large shell command, but how often is that really the case? Is the time gained worth the risk of hallucination or incompleteness?
In this particular discussion, I am not looking for the best process for the task. I am looking for a process that will not get deemed a "red flag" by candidates.
No, because I feel like mentioning previous employers AND mentioning the languages I speak would get quite specific.
You are interested in Eastern Europe, so you should be familiar with olympiads; ask around any circle of ex-olympiad participants and you are bound to find something.
I am not sorry if you took offense; you're either from one of these countries and are clueless to the circles that exist next to you, or you are not from one of these countries and are trying to be offended on behalf of others.
realistically we're looking for much higher (sub 1%) cognitive ability. I understand this is highly sought after, so we incentivize by paying a few times what FAANG does locally.
I was genuinely wondering how OP preferred to be approached vis-a-vis this sort of assessment, since they suggested that they would walk out on conventional approaches. I mentioned cognitive ability and ability to learn as I feel those are harder to extrapolate from one's existing publications/contributions/take-home assessments, compared to in-person discussion(s).
I think this is where our different opinions come from, while we agree on the other aspects.
In my personal experience, I have never felt that the hire/no-hire decision relied exclusively on my ability of solving the presented problem; I have passed interviews where I did not solve the LC-style problem optimally but I communicated clearly, picked up on hints, was aware of when I hit "walls" and provided working but less than ideal alternatives when I could not figure out the neat tricks.
Reading through the thread it seems that my experience is not universal, and the majority here have had less pleasant interviews, so I understand where you are coming from.
I am starting a company, and I need smart people; I do not care how good they are at programming language X, or technology T - all the skills my employees will need can be learned on the job.
I want to optimize the time it takes for an arbitrary hire to become independent, to have learned the basics, and to make meaningful contributions. They would write code at most 20% of the time, and the job has many other nuances.
In your opinion, what would a process that you wouldn't refuse look like? Would a learning interview (I present something that you have not seen before, act as an oracle, provide docs, evaluate how quickly you pick up concepts) be so insulting?
Question banks that are too big: huge variance, and OP's point stands.
Question banks that are too small: leaked on eastern forums immediately, candidates show up reading answers out to you (some of the guides include guidance on when to pretend to think, I am not kidding).
The idealized version of "question banks" might work. The real one does not; you'd require employees constantly scouring forums in every language known to mankind, immediately removing anything that gets leaked. On top of that you'd probably require a competent committee overseeing all questions in the bank constantly and ensuring the lack of variance in difficulty.
I think it's objective truth to note that "Bitcoiners" are predominantly comprised of:
a) people conducting illegal affairs (don't read this as "illegal" in the sense of jaywalking)
b) technically illiterate people, down on their luck and hoping to get rich quickly
With those in mind, I would argue that "Bitcoiners" are instead drawn from the left side of most distributions.