I've seen that problem in an interview before, and I thought the solution I hit upon was pretty fun (if dumb).
class Solution:
def findMin(self, nums: List[int]) -> int:
class RotatedList():
def __init__(self, rotation):
self.rotation = rotation
def __getitem__(self, index):
return nums[(index + self.rotation) % len(nums)]
class RotatedListIsSorted():
def __getitem__(self, index) -> bool:
rotated = RotatedList(index)
print(index, [rotated[i] for i in range(len(nums))])
return rotated[0] < rotated[len(nums) // 2]
def __len__(self):
return len(nums)
rotation = bisect_left(RotatedListIsSorted(), True)
print('rotation =>', rotation)
return RotatedList(rotation)[0]
I think it is really interesting that you can define "list like" things in python using just two methods. This is kind of neat because sometimes you can redefine an entire problem as actually just the questions of finding the binary search of a list of solutions to that problem; here you are looking for the leftmost point that it becomes True. Anyway, I often bomb interviews by trying out something goofy like this, but I don't know, when it works, it is glorious!
Note: I haven't done any tech interview in 6 years.
I'm kind of surprised they still do leetcode-style questions on remote interviews these days. I thought those types of interviews would be 100% gamed by now.
They don't, at least in the SRE space. I have been interviewing for 6 months without a single coding challenge or LeetCode-type challenge. Though I would passively avoid companies that offer them, as in I would avoid them if offered, I have yet to be offered the chance to avoid it in an interview.
Unfortunately this practice is prevalent still. Recently I’ve been applying to jobs in the two industries I have experience in (algorithmic robotics and fintech) and nearly half of the companies that I’ve heard back from start with either a timed leetcode problem or an HR interview which is immediately followed by a timed leetcode problem. It’s exhausting.
Interesting. I am going for a broad search rather than being targeted. Maybe it's as you say, an industry specific problem. At my last fintech job they just quizzed me a bit on Terraform and asked me about experience, though that role ended up being a disaster later on.
Your "no compiler" rule on day 3 taught you more than the LLM did. The LLM made concepts click. But the binary search vanishing under interview stress proves that understanding something and being able to produce it under pressure are totally different skills. Nobody talks about this enough in the "just use ChatGPT to learn" discourse.
You are 100% right. For me, the most important thing is that the LLM teacher allowed me to break through my algorithmic ignorance in just one week.
The rest is somehow orthogonal to the LLM and is just pure practice. It is very easy to procrastinate with an LLM without actual practice.
It allowed me to actually see the problem space and something like the "beauty of classical algorithms".
It shifted my "unknown unknowns" into "known unknowns".
I had failed so many times to achieve exactly that without an LLM in the past.
Yeeah, LLMs are the perfect procrastination tool because they feel productive. You're "learning", you're "exploring", you're having this great conversation about the problem. And then you close the tab and realize you never actually wrote anything yourself.
The best procrastination device ever built because it validates you the entire time. Great post, even beyond the algorithms example.
Interesting article - but perhaps a bit light on details in some places, like:
> I generated a list of the most common interview tasks
How? I suppose they mean gathered, or searched for, not strictly generated?
Also a little light on details of the actual interview.
I'm also a little confused about the listing of "problems" - do they refer to some specific leet-code site's listing of problems?
It seems like half-way between naming an actual algorithm/problem and naming a concrete exercise.
As for:
> How is it that we do not use this "forgotten and forbidden" coding in our daily production code, even though all highly reusable, useful code is essentially an exploitation of the intersection between classical algorithmic thinking and real-world problems?
I'm not sure what to say - most of this stuff lives in library code and data structure implementations for any language in common use?
Indeed the one saving grace of leet code interview is arguably that it shows if the candidate can choose sane data structures (and algorithms) when implementing real-world code?
You are right, I missed some crucial details in the blog entry.
I will definitely take your feedback into account for Part 2, where I want to do a more detailed deep dive into the prompting protocols (with maybe some exact examples) and my learning strategy.
To answer your questions:
1. By "generated" I mean that I prompted the LLM incrementally to provide me the list of the next LeetCode problems to do (without the deep research/search function)
2. Yes, the problem names are the exact names from LeetCode.
Initially, the LLM suggested this format, and I later forced it to stick to real LeetCode problems.
This allowed me to verify some output independently of the LLM (avoiding hallucinations), cross-check solutions with other materials, and track my progress.
Interestingly, I realized later that the LLM was mostly pulling from the standard Blind 75 problem set, and almost all the problems are from that list.
3. About the "forgotten and forbidden" code: I probably phrased it poorly in the article. As you said, this algorithmic logic is abstracted away in standard libraries and data structures. The disconnect for me (and I suspect for many "business logic" developers too) is that our daily production code rarely requires writing these fundamental structures from scratch, so we do not see the patterns that can also be applied in more high-level business logic. But this is still an in-progress hypothesis in my mind, without detailed examples.
Very cool, I have personally been studying zk-cryptography with a similar approach, works really well with some caveats. Will save this article and try this version as well when the time comes!
This is very interesting, I've been using LLM to learn new things that way and it really worked. To some extent, learning with LLM is better than taking any course, even with a tutor, as I am getting something prepared for me, in terms of my experience, progress level, etc.
LLM is going to change schools and universities a lot, teachers, tutors will have to find themselves in the new reality, as they have a strong competitor with infinite resources and huge knowledge, patient and ready to work with every student in a distinct way, according to student's needs, level, intelligence, etc.
Instruction-based tutoring is dead from that perspective, why should I follow someone reciting a book or online tutorial, while there is a tool that can introduce me into subject in a better and more interesting way?
Sure, there are great teachers, who are inspiring people, who are able to present the topic in a great way, the point is, they are minority. Now, everyone can have a great tutor for a few dollars a month (or for free, if you don't need generating too much data quickly).
To some extent. I had Claude (Sonnet 4.5) generate some homework problems for students I was teaching to code, and the problem/answers weren't actually right. They were subtlety wrong, which makes me worry about using it for other subjects.
> LLM is going to change schools and universities a lot, teachers, tutors will have to find themselves in the new reality, as they have a strong competitor with infinite resources and huge knowledge, patient and ready to work with every student in a distinct way, according to student's needs, level, intelligence, etc.
No it won't. It really, really wont. You clearly don't have any university professors amongst your friends or acquaintances.
What you wrote is what the STUDENTS think. The students think they have found a cheat code.
No university professor considers LLM "a competitor". They see the slop output every day on their desk.
The reality is just like LLMs will confidently push out slop code, they will also push out slop for everything else. Because the reality is that LLMs are nothing more than a party trick, a stats based algorithm that gives you answers within a gaussian curve.
The students come to the professors with stupid questions because they've been trusting the AI instead of learning properly. Some of the students even have the audacity to challenge the professor's marking saying "but the AI said it is right" in relation to some basic math formula that the student should know how to solve with their own brain.
So what do my university professor friends end up doing ?
They spend their evenings and weekends thinking up lab tasks that the students cannot achieve by simply asking the LLM for the answer. The whole point of university is you go there to learn to reason and think with your own damn brain, not paste the question into a text box and paste the answer to your professor.
Trying to cheat your way through university with an LLM is a waste of the students time, a waste of the professors time and a waste of the university's infrastructure.
I’m an unusually good programmer, I’ve worked in over 25 different programming languages and have been doing it since I was 6. I’ve spent most of my career as an applied researcher in research orgs where my full time job is study.
Finding new relevant things to learn gets progressively more difficult and LLMs have blown that right open. Even if they haze zero new ideas the encoding and searching of existing ideas is nothing live I’ve seen before. If they can teach me things they can definitely teach less experienced people things as well. Sometimes it takes a bit of prodding, like it will insist something is impossible but when presented with evidence to the contrary will resume to give working prototypes. Which means in these very long tail instances it does still help to have some prerequisite knowledge. I wish they were more able to express uncertainty.
I think the primary reason Ed Tech hasn’t been disrupted is that an expensive education is a costly signal and a class demarcator, making it cheaper defeats the primary purpose. Grade creep, reproducibility crisis, plagiarism crisis, cheating scandals fail to undermine this purpose. In fact the worse it gets the more it becomes a costly signal. As inequality increases so does the importance social signals.
In many countries Universities are given special privileges to act as a gateway to permanent residency which is extremely profitable. If anything is to replace education it would have to either supplant this role as a social signal or the reward for the social signal will need to be lost and I don’t see either happening anytime soon short of a major calamity.
> No it won't. It really, really wont. You clearly don't have any university professors amongst your friends or acquaintances.
Maybe some fancy professors in their cushy Ivy league ivory tower won't, but a lot of teachers that work for minimal salary sure will.
> Because the reality is that LLMs are nothing more than a party trick, a stats based algorithm that gives you answers within a gaussian curve.
A lot of humans can't even do that.
> Some of the students even have the audacity to challenge the professor's marking saying "but the AI said it is right" in relation to some basic math formula that the student should know how to solve with their own brain.
Students challenge professors over some stupid assumption, more news at 11.
> Trying to cheat your way through university with an LLM is a waste of the students time, a waste of the professors time and a waste of the university's infrastructure.
Who even said anything about cheating? Witch hunting too much? For majority of layman topics LLM will be a far superior offering precisely because LLMs have no ego and will reply to their best abilities instead of chastising students about, oh God forgive, HAVING AUDACITY to disagree over a topic.
Which includes hallucination, reward-hacking, over-confident delivery of completely wrong answers etc.
> HAVING AUDACITY to disagree over a topic.
When we are discussing a long-standing centuries-old textbook mathematical formula which is internationally recognised there is no disagreement to be had.
If an LLM hallucinates and tells a student that the textbook mathematical formula is wrong, and the student has the audacity to complain to the professor on that basis, I see no issue with the professor firmly challenging the student. University is there to foster learning and reasoning using your own brain, not outsourcing it to a hallucinating LLM.
He said:
"LLM is going to change schools and universities a lot"
You said:
"No it won't. It really, really wont."
With the explosive development of LLMs and their abilities, it seems your point of view is probably the hopeful one while the other poster has the realistic one.
It seems that you simply can't say anything about what LLMs will not be able to do. Especially when you try to use current "AI slop" as your main reason, which is being more and more eradicated.
> "AI slop" as your main reason, which is being more and more eradicated.
The slop is the hard truth.
As I made perfectly clear in my original post. My university professor friends get handed AI slop by their students each and every day.
There is no "eradication of slop" happening. If anything, it is getting worse. Trust me, my friends see the output from all the latest algorithms on their desk.
The students think they are being very clever, the students think the magical LLM is the best thing since sliced bread.
All the professor sees is a wall of slop on their desk and a student that is not learning how to reason and think with their own damn brain.
And when the professors tries politely and patiently to challenge them and test their understanding as you would expect in a university environment, the snowflake students just whine and complain because they know they've been caught out drinking the LLM kool-aid again for the 100th time this week.
Hence the student is wasting their time and money at university, and the professor is wasting their time trying to teach someone who is clearly not interested in learning because they think they can get the answer in 5 seconds from an LLM chatbot.
My professor friends chose the career they did because they enjoy the challenge of helping students along the way through their courses and watching them develop.
They are no longer seeing that same development in their students. And instead of devoting time to helping students, they are wasting time thinking up over-engineered fiendishly-complicated lab-tasks and tests that the students cannot cheat using LLM.
It is honestly a lose-lose situation for everybody.
I think you're missing the point.
The conversation is not about what students give the professors, it's about how students learn. This obviously requires someone that wants to learn.
Sweep aside the misunderstanding about students trying to "cheat" with LLM output instead of engagement in the topic at hand. I think there is a secondary debate here, even when you understand the original intent of the post above. It still boils down to the same concerns about "slop". Not the student presenting slop to the existing teaching system, but the student being led stray by the slop they are consuming on their own.
Being an auto-didact has always been a double-edged sword. You can potentially accelerate your learning and find your own specialization, but it is an extremely easy failure mode to turn yourself into some semi-educated crank. Once in a while, this leads to some renegade genius who opens new branches of knowledge. But in more cases, it aborts useful learning. The crank gets lost in their half-baked ontology and unable to really fix the flaws nor progress to more advanced topics.
The whole long history of learning institutions is, in part, trying to manage this very human risk. One of a teacher's main roles is to recognize a student who is spiraling out in this manner and steer them back. Nearly everyone has this potential to incrementally develop a sort of self-delusion, if not getting reality-checked on a regular basis. It takes incredible diligence to self-govern and never lose yourself in the chase.
This is where "sycophancy" in LLMs is a bigger problem than mere diction. If the AI continues to function as a sort of keyhole predictor, it does not have the context to model a big-picture purpose like education and keep all the incremental wanderings on course and bound to reality. Instead, it can amplify this worst-case scenario where you plunge down some rabbit-hole.
> it's about how students learn. This obviously requires someone that wants to learn.
FINALLY ! Someone who gets the point I was trying to make. I wish I could upvote you a million times.
This is precisely the point. Professors are happy to help people who want to learn.
Students who prefer to copy/paste into LLMs do not want to learn. University is there to foster learning and reasoning using your own brain. An LLM helps with neither.
I sure hope those "university professor friends" exist, and you're not self-distancing. Because you really need help with the mindset like that. Students are not your enemies and LLMs are not ought to get you. Seek help.
> as if he wasn't the one who clutched at some mythical "the university"
Next you'll tell that anything below Ivy league isn't considered university and kids should've known better and used their damn head™ really hard to be born in better families to be able to get real university™ education.
(not OP but..) I personally am not very into "prompting", you just need to figure out how these models work
it's best when you ask a well known problem/thing they can reference (vs. a niche way to solve exactly what you want to solve)
then you work backwards, I e. why is it like this, what is this for, what are the alternative ways to accomplish this etc...
it's a big query engine after all.
don't try to ask like "what is the exact right way" or etc. because it will try to generate that and likely hallucinate if there is no such answer in its training corpus.
LLMs aren't any of these things: infinite, knowledgable, patient, or ready. They are a compressed representation of all of the misstatements and misunderstandings in the history of Reddit. If you think you've been using LLMs to "learn new things" it could be because you aren't already familiar with the domain and you can't see where it's misleading you.
I mean, you've collapsed a complex, mixed system into a single negative narrative.
Examples of how I learn with LLMs:
- Paste sections from reading and ask questions / clarify my understanding / ask it to quiz me
- Produce Anki cards by pasting in chapter text and the culling out the goods ones
- Request resources / links for further learning
Basically, LLMs serve as a thinking partner. Yes, it's a fallible tool, not an oracle. But dismissing the idea that you can learn (and learn faster / more efficiently) with LLMs is reductionist
another POV is, it used to be cool to work for Google
it's been so uncool and harrowing for a while now, to deal with their leetcode BS. i mean obviously this guy is well meaning but didn't learn anything, other than for the paycheck and whatever desperate circumstances require that.
the LLM stuff being used to solve their interview process is an inflection point where it really steeply goes down to want to work for Google for any reason other than money
maybe this is why Google deepmind researchers keep leaving to start their own successful companies
I've seen that problem in an interview before, and I thought the solution I hit upon was pretty fun (if dumb).
I think it is really interesting that you can define "list like" things in python using just two methods. This is kind of neat because sometimes you can redefine an entire problem as actually just the questions of finding the binary search of a list of solutions to that problem; here you are looking for the leftmost point that it becomes True. Anyway, I often bomb interviews by trying out something goofy like this, but I don't know, when it works, it is glorious!Good luck on your second round!
I'm kind of surprised they still do leetcode-style questions on remote interviews these days. I thought those types of interviews would be 100% gamed by now.
It is also odd that this article appears here after someone complained about vibe coding killing the interest in algorithms.
This game is played often. People have valid complaints, then someone posts a "rebuttal" ("LLMs are not bad for $X---they are good for $X").
Anyway, he uses LLMs more in the search capability, which is less controversial than generative AI and vibe coding.
The rest is somehow orthogonal to the LLM and is just pure practice. It is very easy to procrastinate with an LLM without actual practice.
It allowed me to actually see the problem space and something like the "beauty of classical algorithms". It shifted my "unknown unknowns" into "known unknowns". I had failed so many times to achieve exactly that without an LLM in the past.
The best procrastination device ever built because it validates you the entire time. Great post, even beyond the algorithms example.
> I generated a list of the most common interview tasks
How? I suppose they mean gathered, or searched for, not strictly generated?
Also a little light on details of the actual interview.
I'm also a little confused about the listing of "problems" - do they refer to some specific leet-code site's listing of problems?
It seems like half-way between naming an actual algorithm/problem and naming a concrete exercise.
As for:
> How is it that we do not use this "forgotten and forbidden" coding in our daily production code, even though all highly reusable, useful code is essentially an exploitation of the intersection between classical algorithmic thinking and real-world problems?
I'm not sure what to say - most of this stuff lives in library code and data structure implementations for any language in common use?
Indeed the one saving grace of leet code interview is arguably that it shows if the candidate can choose sane data structures (and algorithms) when implementing real-world code?
To answer your questions:
1. By "generated" I mean that I prompted the LLM incrementally to provide me the list of the next LeetCode problems to do (without the deep research/search function)
2. Yes, the problem names are the exact names from LeetCode. Initially, the LLM suggested this format, and I later forced it to stick to real LeetCode problems.
This allowed me to verify some output independently of the LLM (avoiding hallucinations), cross-check solutions with other materials, and track my progress.
Interestingly, I realized later that the LLM was mostly pulling from the standard Blind 75 problem set, and almost all the problems are from that list.
3. About the "forgotten and forbidden" code: I probably phrased it poorly in the article. As you said, this algorithmic logic is abstracted away in standard libraries and data structures. The disconnect for me (and I suspect for many "business logic" developers too) is that our daily production code rarely requires writing these fundamental structures from scratch, so we do not see the patterns that can also be applied in more high-level business logic. But this is still an in-progress hypothesis in my mind, without detailed examples.
LLM is going to change schools and universities a lot, teachers, tutors will have to find themselves in the new reality, as they have a strong competitor with infinite resources and huge knowledge, patient and ready to work with every student in a distinct way, according to student's needs, level, intelligence, etc.
Instruction-based tutoring is dead from that perspective, why should I follow someone reciting a book or online tutorial, while there is a tool that can introduce me into subject in a better and more interesting way?
Sure, there are great teachers, who are inspiring people, who are able to present the topic in a great way, the point is, they are minority. Now, everyone can have a great tutor for a few dollars a month (or for free, if you don't need generating too much data quickly).
> “everything you read in the newspapers is absolutely true, except for the rare story of which you happen to have firsthand knowledge”.
No it won't. It really, really wont. You clearly don't have any university professors amongst your friends or acquaintances.
What you wrote is what the STUDENTS think. The students think they have found a cheat code.
No university professor considers LLM "a competitor". They see the slop output every day on their desk.
The reality is just like LLMs will confidently push out slop code, they will also push out slop for everything else. Because the reality is that LLMs are nothing more than a party trick, a stats based algorithm that gives you answers within a gaussian curve.
The students come to the professors with stupid questions because they've been trusting the AI instead of learning properly. Some of the students even have the audacity to challenge the professor's marking saying "but the AI said it is right" in relation to some basic math formula that the student should know how to solve with their own brain.
So what do my university professor friends end up doing ?
They spend their evenings and weekends thinking up lab tasks that the students cannot achieve by simply asking the LLM for the answer. The whole point of university is you go there to learn to reason and think with your own damn brain, not paste the question into a text box and paste the answer to your professor.
Trying to cheat your way through university with an LLM is a waste of the students time, a waste of the professors time and a waste of the university's infrastructure.
That, my friend, is the reality.
Finding new relevant things to learn gets progressively more difficult and LLMs have blown that right open. Even if they haze zero new ideas the encoding and searching of existing ideas is nothing live I’ve seen before. If they can teach me things they can definitely teach less experienced people things as well. Sometimes it takes a bit of prodding, like it will insist something is impossible but when presented with evidence to the contrary will resume to give working prototypes. Which means in these very long tail instances it does still help to have some prerequisite knowledge. I wish they were more able to express uncertainty.
I think the primary reason Ed Tech hasn’t been disrupted is that an expensive education is a costly signal and a class demarcator, making it cheaper defeats the primary purpose. Grade creep, reproducibility crisis, plagiarism crisis, cheating scandals fail to undermine this purpose. In fact the worse it gets the more it becomes a costly signal. As inequality increases so does the importance social signals. In many countries Universities are given special privileges to act as a gateway to permanent residency which is extremely profitable. If anything is to replace education it would have to either supplant this role as a social signal or the reward for the social signal will need to be lost and I don’t see either happening anytime soon short of a major calamity.
Maybe some fancy professors in their cushy Ivy league ivory tower won't, but a lot of teachers that work for minimal salary sure will.
> Because the reality is that LLMs are nothing more than a party trick, a stats based algorithm that gives you answers within a gaussian curve.
A lot of humans can't even do that.
> Some of the students even have the audacity to challenge the professor's marking saying "but the AI said it is right" in relation to some basic math formula that the student should know how to solve with their own brain.
Students challenge professors over some stupid assumption, more news at 11.
> Trying to cheat your way through university with an LLM is a waste of the students time, a waste of the professors time and a waste of the university's infrastructure.
Who even said anything about cheating? Witch hunting too much? For majority of layman topics LLM will be a far superior offering precisely because LLMs have no ego and will reply to their best abilities instead of chastising students about, oh God forgive, HAVING AUDACITY to disagree over a topic.
Which includes hallucination, reward-hacking, over-confident delivery of completely wrong answers etc.
> HAVING AUDACITY to disagree over a topic.
When we are discussing a long-standing centuries-old textbook mathematical formula which is internationally recognised there is no disagreement to be had.
If an LLM hallucinates and tells a student that the textbook mathematical formula is wrong, and the student has the audacity to complain to the professor on that basis, I see no issue with the professor firmly challenging the student. University is there to foster learning and reasoning using your own brain, not outsourcing it to a hallucinating LLM.
You said: "No it won't. It really, really wont."
With the explosive development of LLMs and their abilities, it seems your point of view is probably the hopeful one while the other poster has the realistic one.
It seems that you simply can't say anything about what LLMs will not be able to do. Especially when you try to use current "AI slop" as your main reason, which is being more and more eradicated.
The slop is the hard truth.
As I made perfectly clear in my original post. My university professor friends get handed AI slop by their students each and every day.
There is no "eradication of slop" happening. If anything, it is getting worse. Trust me, my friends see the output from all the latest algorithms on their desk.
The students think they are being very clever, the students think the magical LLM is the best thing since sliced bread.
All the professor sees is a wall of slop on their desk and a student that is not learning how to reason and think with their own damn brain.
And when the professors tries politely and patiently to challenge them and test their understanding as you would expect in a university environment, the snowflake students just whine and complain because they know they've been caught out drinking the LLM kool-aid again for the 100th time this week.
Hence the student is wasting their time and money at university, and the professor is wasting their time trying to teach someone who is clearly not interested in learning because they think they can get the answer in 5 seconds from an LLM chatbot.
My professor friends chose the career they did because they enjoy the challenge of helping students along the way through their courses and watching them develop.
They are no longer seeing that same development in their students. And instead of devoting time to helping students, they are wasting time thinking up over-engineered fiendishly-complicated lab-tasks and tests that the students cannot cheat using LLM.
It is honestly a lose-lose situation for everybody.
Being an auto-didact has always been a double-edged sword. You can potentially accelerate your learning and find your own specialization, but it is an extremely easy failure mode to turn yourself into some semi-educated crank. Once in a while, this leads to some renegade genius who opens new branches of knowledge. But in more cases, it aborts useful learning. The crank gets lost in their half-baked ontology and unable to really fix the flaws nor progress to more advanced topics.
The whole long history of learning institutions is, in part, trying to manage this very human risk. One of a teacher's main roles is to recognize a student who is spiraling out in this manner and steer them back. Nearly everyone has this potential to incrementally develop a sort of self-delusion, if not getting reality-checked on a regular basis. It takes incredible diligence to self-govern and never lose yourself in the chase.
This is where "sycophancy" in LLMs is a bigger problem than mere diction. If the AI continues to function as a sort of keyhole predictor, it does not have the context to model a big-picture purpose like education and keep all the incremental wanderings on course and bound to reality. Instead, it can amplify this worst-case scenario where you plunge down some rabbit-hole.
FINALLY ! Someone who gets the point I was trying to make. I wish I could upvote you a million times.
This is precisely the point. Professors are happy to help people who want to learn.
Students who prefer to copy/paste into LLMs do not want to learn. University is there to foster learning and reasoning using your own brain. An LLM helps with neither.
I'm talking about university education here. Where the hell does the clutching at straws "99% of children in the world" argument come into it ?
> as if he wasn't the one who clutched at some mythical "the university"
Next you'll tell that anything below Ivy league isn't considered university and kids should've known better and used their damn head™ really hard to be born in better families to be able to get real university™ education.
I know professors from across the spectrum and I most certainly do not consider Ivy League should be placed on a pedestal.
That is all I am willing to say on that subject. Let's not drift off into personal attacks, please.
it's best when you ask a well known problem/thing they can reference (vs. a niche way to solve exactly what you want to solve)
then you work backwards, I e. why is it like this, what is this for, what are the alternative ways to accomplish this etc...
it's a big query engine after all.
don't try to ask like "what is the exact right way" or etc. because it will try to generate that and likely hallucinate if there is no such answer in its training corpus.
instead ask what the model does know, or doesn't.
Examples of how I learn with LLMs:
- Paste sections from reading and ask questions / clarify my understanding / ask it to quiz me
- Produce Anki cards by pasting in chapter text and the culling out the goods ones
- Request resources / links for further learning
Basically, LLMs serve as a thinking partner. Yes, it's a fallible tool, not an oracle. But dismissing the idea that you can learn (and learn faster / more efficiently) with LLMs is reductionist
it's been so uncool and harrowing for a while now, to deal with their leetcode BS. i mean obviously this guy is well meaning but didn't learn anything, other than for the paycheck and whatever desperate circumstances require that.
the LLM stuff being used to solve their interview process is an inflection point where it really steeply goes down to want to work for Google for any reason other than money
maybe this is why Google deepmind researchers keep leaving to start their own successful companies