Ask HN: Is ChatGPT making tech publishing obsolete?
By tech publishing I mean the books from Manning and Oreilly. I was shopping those books of late, but somehow I found it is harder to convince myself they are useful comparing to ChatGPT
If you want a quick reference on a well-known topic, then sure, use an LLM. Or a search engine. Chances are it will even answer you correctly (but also chances are it won't, and if it is a new topic for yourself - strap in, you're in for a ride).
But if you want to really understand something, then you will have to do your research, and a lot of this research has already been summarized into tangible artifacts optimized for your consumption which LLMs would never be able to replicate.
Even if you can convince one to regurgitate a book verbatim, the narrative thread would be lost unless you weave it yourself with your prompts - but would you, the learner, be able to do re-enact the narrative better or even on the same level than the original author who posessed the knowledge on the topic?
Maybe. The author needs to write in a way that makes the book digestible to most people. Prompts allow me to get a version that's tailor-made for me.
LLMs are making the quick jot to Stack Overflow obsolete, which solves your immediate problems using the least amount of brainpower.
They are also making reference-style documentation and long-form books more important than ever, since you still need to learn things and correct your own knowledge biases.
Not for me they're not.
I've found it to be wholly inadequate at answering the kinds of questions I do have a lot of - stuff like "How to watch a list of objects using kubernetes controller-runtime?". The answer I got is entirely hallucinated.
For those I find myself still searching GitHub for example code and sometimes using stack overflow.
At one point in time, > 50% of people used to smoke cigarettes too.
There are other books to the tune of “Docker Cookbook” “Java Pocket reference” etc, that will lose their relevance when LLMs could do that job of digesting existing info into easily usable form.
But I think many tech books carrying some of flavor of being a manual, especially those centered around some particular library/framework, thinking of 'XX in Action' series.
Ones that talk about more abstract ideas, like on the art of developing/testing software, will continue to thrive. However, I think the bread and butter for the tech publishing industry are those manual books, which follow a relative mature format of writing, and are produced in bulk each year. If they are going away, this might speak trouble for the whole industry.
But that is just my hypothesis for now, I hope the industry find a way to sustain itself.
- When you're first learning a topic, ChatGPT isn't as useful because you don't really know what questions to ask. It's useful to get a broad overview guided by an expert so you can learn the concepts, and more importantly the vocabulary, which you can then use to dive deeper into parts you're interested in with ChatGPT.
- For really new stuff, ChatGPT obviously won't have any training data. So the books will be the only real resource until the models are updated.
Even before CGPT existed, I've always felt like online searches/examples can get me 75% of the way there (i.e. good enough) in way less time than reading a book. As nice as the O'Reilly media is, I've got too many tools to learn on the job and not enough time to read dozens of books.
This is now SOP for plenty of PMs and TechDoc teams at a couple large companies, and on the roadmap for others.
The act of transcribing is low value and should be automated, but the act of creating and moderating abstract ideas is still something in the human domain.
Reuters has been doing this since the early 2000s btw at their R&D lab in Bangalore.
An LLM has no charisma and the time of buying a book from an AI being "cool" has already come and gone.
There are just ML algorithms and humans who (mis)use them to benefit or harm.
Where you may be right is that it will certainly help a small number of already rich and influential people concentrate even more money and influence. However, whether it will lead to any qualitative change is dubious as far as I’m concerned.
You don't need to understand all this but simply trying to make money will contribute towards achieving the AGI so work as hard as you can to make as much money as you can and the rest will take care of itself.
At runtime. When trying to do something I know is even slightly unusual I don't even bother with ChatGPT and friends.
But when I see people say things like "Look! I used GPT to write a functioning webapp!" - I worry that people get a false sense of "It works!" from pasting GPTs code into their compiler and seeing roughly the results they expect. That's great, but GPT in its current form spends exactly zero time "thinking" about corner cases - It's just a black box that repeatedly spits out "most likely next token". So maybe that app works 90% of the time. Or 95%. Or 99%. But you don't have much of a way to tell the difference without rigorous testing that includes thorough and well-articulated test cases. But in order to do that, you need to understand the problem you're solving in a very detailed way, and how your code reacts to it. And in order to do that, you need to... know how to write the program.
I think this latest wave of LLMs and generative AI is really awesome tech, and I play with it every day, because it's just so cool. But seeing people trust programs written with them worries me. Some day someone is gonna copy/pasta some LLM generated code into mission critical software, trusting it implicitly, and cause a tragedy.
I tend to read along with the code it's writing and make suggestions when I see it's missing stuff, or I fill it in myself afterwards, depending. For one it can type out the annoying bits much faster than I can!
At the moment I do keep the general plan in my head myself though, and I thoroughly read anything it generates before I run it.
LLMs will help on specific problems sure. But there can be entire swaths of options and entire areas you may not have considered.
Java for example has some nice native support for multithreading. However, threads have never been the only option for asynchronous work, and many of the options are situational. Knowing those options can help tremendously in implementation.
OFC, if video makes tech publishing obsolete, then it would've made it obsolete a decade ago.
A more realistic expectation is to divide learners into readers, video consumers, course-takers, etc. ChatGPT could integrate into any of those workflows, albeit with varying results. If using an AI becomes as second nature as watching a video, it might have some effect.
So basically the expert/author will help fine tune the model.
You still have time to derive some utility from them. Additionally, you can use ChatGPT to help you learn the material faster.
It doesn't have to be mutually exclusive.
Sure CGPT can generate a table of contents if you want to learn a programming language for instance, tell you what to do first, what to try, delve deep in concepts, etc., but I think it's still hit or miss, as opposed to great tech authors and great publishers, where you can be sure you're getting your money's worth.
Also, IMO people tend to have a bias to go slower with paper material, because they committed to reading a book, they are less likely to skip sections, which means the end result is that you get more out of a book than the Html/pdf same content, might be different if you need to try things out on a computer while reading though.