But maybe I should read this to see if there's anything that applies to my current work.
IMO, it’s much more practical to thoroughly learn and understand the simpler techniques. There is often more depth to it, than you’d think.
Analogy: Many (most?) MMA matches are won with techniques you learn in the first year.
Aside: I’ve gotten much more mileage out of simple statistics in the 16 years since I graduated, than advanced algorithms.
EDIT:
IMO, theories and techniques can be easy enough to learn. Figuring out applications can be much more difficult. It requires some creativity.
I remember to this day the exact location I was when being introduced to construct hypercubes, to the exact bench in our uni cafeteria. It changed my understanding of multidimensionality completely.
For starters the whole street network is represented as a directed graph. The most widely used algorithm is the venerable A* to find paths through the vertices of the graph, not necessarily the shortest distance but the most optimal distance given all the trade-offs you wish based on the heuristics you define. There are lots of variations of A* and today most implementations would use its faster cousin: bidirectional A* which attacks the point-to-point calculation from both ends at the same time.
Then you have Dijkstra which is used for flood searches to e.g. give the best cost back home from every vertex so you know when to turn back the search if you have a search budget.
Menger’s theorem I’ve used in order to determine whether we can create a loop back. Basically search outwards, then search back without visiting those same edges.
Tarjan to ensure we identify all subnetworks so we don’t get stuck in a disconnected island of vertices with no way out to join the wider network.
A lot of these algorithms were conceived decades ago and are still the backbone of modern routing software.
One thing that’s constantly been in the back of my mind is, since routing is a highly deterministic problem space, it doesn't seem plausible that LLMs can take this over. It seems to me that it's a good example of a tool that LLMs would call out to rather than implement themselves.
In fact, see this convo I had with @simonw wherein he had Astra generate a jogging route. It setup a whole GIS stack and took about half an hour. That seems like a boil the ocean kind of approach and I’m not sure if that’s ever going to be optimal. https://x.com/baz_shkara/status/2099118620647305630
Finding the most optimal traveling salesman problem for 20 cities by brute force will take a few universes lifetimes and you still won’t be halls way. You’ll need to compromise with heuristics but that usually leads to near optimal
A good graph theory course abstracts away the details of code and pursues the goal of understanding how many problems can be expressed concisely and clearly through graphs.
A good leetcode practice ... honestly just hones how patient you are in debugging and memorising various code patterns.
If you want to pursue money, it's almost always better to pursue leetcode exclusively
Graph theory and algorithm engineering overlap a great deal. And not only in the directly graph theoretic terms like BFS, Dijkstra, Flow, etc, but also in that you need a good grasp of greedy algorithms, dynamic programming, divide and conquer, and several other general techniques.
[1] https://en.wikipedia.org/wiki/PageRank
There are plenty of ways to get a copy.
(Edit) But I understand that goal of a book can be completely different. So it's just problem of my expectations.
And for the bleeding edge part: My godchild wanted to know the name of a Pokémon from his licensed shirt. One of the first ones, found in every training set. Couldn't get it right, was certain it was the wrong one.
how did you even ask an AI this question? Took a picture of the shirt, uploaded it, and asked the AI what it was? That's a different class of problem than "answer this graph theory question and include your references: <paste text here>"
You don't ask for proofs to print them out, put them in your drawer, and brag around that you own the proofs for these exercises.
You ask Claude for proofs to help your understanding. Understanding is the exact same activity as verifying.
Related HN thread: https://news.ycombinator.com/item?id=17745779
Please prove me wrong.
This author has done nothing to deserve that scrutiny, but I can’t escape the feeling now of having to be guarded when I see a new book online with a relatively short history (spring 2026 is the earliest version of this text), as I can’t be sure it’s either “AI-enhanced” or AI-written.
I don’t like this feeling and it dampers any enthusiasm I once had for being excited about stuff like this.
That aside, if the author did write this themselves without AI, kudos to them and their acheivement!
This book aside, though, the fact that writing is no longer definitive evidence of human thought is one of the things I dislike the most about the LLM era. I very much share your sadness about that.
I vocalize this feeling I have, not because the author has done anything to warrant it, but here, in this forum, we commonly have folks saying “so what if it’s AI?”, and by bringing up these feelings we have around the industry, we can have the conversation and (hopefully) have other folks think twice about whether they would use AI to write their book.
I want to read this book and have added it to my list of things to read; and your vouching for the author does help. One underlying issue I have (that is a me problem) is that I know nothing about this particular area, and therefore know nothing about the experts in this area, so I rely on human vouching.
The author put his name and reputation to it. If it's excellent work, it's excellent work. If it's slop, it's slop.
This book is presented as being written by a human. (In this case, a human I happen to know, which would add another dimension to my complaint, so let's imagine that weren't the case.) Maybe someday we'll live in a world where sticking a person's name at the top of a book doesn't carry the implication that they wrote the words in the book. That would make me sad for other reasons, but in any event, we don't live in that world right now, so for now, if you put a person's name on your book, you are claiming that that person wrote the book. If that's not true, you are lying, and like most people, I don't like being lied to.
It's not a perfect proxy, but I'm usually going to trust a domain expert over the confident idiot who constantly lies to me, hides and downplays mistakes, and flits around opposing positions like a drunken moth.
At this time, if I see a text is AI generated, I have to assume it is unsound and possibly even nefarious. There is way too much information assailing my daily life that I have no other rational choice.
I think we're quickly getting to a point where AI is going to be able to do a better job teaching things than humans can (especially stuff with heavy associations with paper/screens, like programming and math). AI can augment for personal styles and spin up whole entire educational widgets on demand. Human's can't scale like that.
Quite frankly this is.. fucking terrible? Even worse, the end of something for humanity..?
Also, saw your comment response, I do feel this very same guarding and unfortunately my instinct is to assume the content is AI, which is a disservice to all the people who are putting their hard effort into putting out quality things.
For sure it was less to review, less options but still.
So what did you use? I used things like Reviews, skimming through, getting a referal through a blog or hn and buying from reputational sources.
HN is a filter. Same as all the other things we have.
But who reviews the reviewers?
1. AI generated text works off of whatever model it came from, whatever prompt was used, and probabilities. It’s been described as a “next token predictor” and that, for the purposes of this answer, is how I’m describing it as well. First problem: it’s only as good as the above.
2. Because of the above, its accuracy relies on probabilities. It can confidently give me wrong information, and as someone who would read this book to learn a topic I don't already know, I have no frame of reference to know whether I’m being fed accurate information.
3. Since it greatly speeds up the time it takes to physically write material, there is a possibility the author didn’t closely vet the information provided by the LLM as a next token predictor. That means it could be wrong in subtle ways, and the only way for me to know is to rely on knowledge I don’t have.
4. Had AI not been used, I could conceivably go on the past work of the author, rely on the person who has the expertise who wrote the forward, or the fact that physically writing a book takes prohibitively long enough that the dopamine from having written a book doesn’t outweigh the amount of time it takes, and if you are going to take months to years to write a book, you’re going to not want that effort to be in vain. You’re going to do your diligence and not just “wing it”.
AI makes it relatively trivial to appear knowledgeable and to get that dopamine rush from saying “I’m an author” and appearing to be an expert in a field without being an expert in a field. For some, that temptation is too great and there’s no way to know without the passage of time. Time for folks to review the book who know the subject matter. Time for reviews. Time for folks to put signs up that say “don’t trust this material.” In the meantime we still have that content out there, further infecting new AI models and people into believing it’s accurate.
That’s why the use of AI is dangerous, and is especially dangeorus without disclosure.
Tangent: I use AI for code, but I've had to ban it from writing readmes and doc comments; the AI models I've used produce text I'm unable to parse, and assume my readers would not be able to either.
I don't understand why HN cannot ever say the first sentence without the second one.
Most people agree that using AI for research or finding better words is fine, but then can't upvote or have a meaningful conversation because of the performative politics and wild delusions about a scifi future.
Dude, all we have with LLMs is an NLP search engine. Get over it. It's been around half a decade of this nonsense. There is no "step change". There is no "hockey stick" curve. The models have not "dramatically improved compared to last year" regardless of what year it currently is. There is no big bad overlord coming for you. Nobody is getting replaced by AI. Even frontier AI can't replace local AI. It's just the logical next step towards better search that assumes you want the "I'm feeling lucky" button a little too often.