Posted by azhenley 1 day ago
The whole experience was wonderful. I had basically none of the problems that this fellow experienced with his publisher, and I am delighted about how it went.
I did some things differently. For one, I had already been selling the book on my own for a few years, and was essentially on the 3rd self-published edition. Because of this, they were able to see what the almost-finished product was.
I told them I would not make massive changes to the book, nor would I contort it to the AI trend (the book barely mentions AI at all), and they never pressured me once.
Their biggest contribution was their team of editors. This book has code on just about every page. I had 3 technical editors go through it, finding many bugs. How many? Let's just say "plenty".
And the feedback from the non-technical editors was, to my surprise, even more valuable. Holy crap, I cannot express to you how much they improved the book. There were several of these folks (I had no idea there were so many different specialties for editors), and all of them were great.
(They also accepted my viewpoint when I disagreed with them, immediately, every time. The final published version of the book was 100% my own words.)
From all of that, I made improvements on what must have been almost every page, and rewrote two chapters from scratch. I also added a new chapter (I volunteered for it, no one at any point pressured me to do that). The result was making a book that IMO is at least twice as good as what I was able to accomplish on my own.
I do not resonate with the article author's comments about compensation. He negotiated a pretty good deal, I think; it's not realistic to get much better than what he did, since the publisher is a business with their own expenses to pay, etc.
I was pretty disciplined about meeting deadlines that we agreed to for certain milestones. That helped my relationship with the publisher, obviously.
All in all, it was a great experience, and I am glad I did it this way.
Reading the article, it sounds like my publisher (oreilly) was better to work with than his, but I think he could have done some things differently also. In the end, though, I agree with him that it was best to walk away in his situation.
I will say I was very happy with the animal they came up with! If I was not, I would have asked them to change it, and I bet they would have. They showed me a preview version early on, so there would have been plenty of time to do so.
The publisher he worked with will push you but also make a better book in the end. You have to be willing to do your part.
(Not naming the publisher because I don't want to out myself.)
-have been running my own small software business for >20 years
-have written ~400 on blog posts on this and related subjects at https://www.successfulsoftware.net
-have consulted to other small software businesses
-know plenty of other people running small software companies
-have given a face-to-face course on starting a software product business
But...
-my experience is in desktop software, not SaaS or mobile software, which feel increasingly niche
-everything seems to be changing so fast with the emergence of LLMs, I am beginning to feel like a dinosaur
I could cooperate with others or work with a co-author to include more on SaaS. And human nature doesn't change that much, despite AI. But it is unclear to me whether enough people would be interested - for me to invest months of work into it.
As a banker, I see the "Advance" not as a loan, but as an Option Fee paid for the author's future output. The publisher tried to exercise that option to force a pivot: "Inject AI into this classic book." They tried to turn a "Shinise" (classic craftsmanship) product into a "Trend" product. The author refused to dilute the quality, so the deal fell through.
Keeping the advance is financially justified. The "R&D" failed not because of the engineer's laziness, but because the stakeholders demanded a feature (AI) that broke the product's architecture. In finance, if the VC forces a bad pivot and the startup fails, the founder doesn't pay back the seed money.
It didn’t sound like they got the advance (or rather the first half) as they never fully completed the first 1/3 of the book before the deal fell through.
What killed this deal is that the author did not set aside enough time to do the work, and then lost interest. This seems pretty clear from the post. From my reading, it looks like the author was missing deadlines before they even brought up the topic of AI. And then continued missing deadlines and pushing out the schedule even after they said no to the AI ideas. And then ultimately put the whole thing on hold and never picked it back up.
If the publisher said “put AI in this or we kill the project”, your reading would be correct. But I don’t see that anywhere in this write up. I see an author who didn’t deliver. Not even the first third, so there wasn’t even an advanced payment.
And to be clear, I am not hating on the author here. Life happens. Interests change. All I’m saying is that this project was not canned because of the refusal to put AI into it.
Perhaps the root cause of the missed deadlines was actually a "loss of conviction." The author touches on his own doubts: "With LLMs around, no one needs this book anymore." While the publisher didn't legally force him out, the "AI pressure" (both from the publisher and the market) might have eroded his belief in the product's value.
It wasn't a murder (firing), but it might have been a death by loss of passion.
> this is a textbook case of a "Failed Pivot" driven by investors
It was a rejected suggestion. In no way did this actually cause the deal to fail.
You are right—if the carpenter just ran away, he would usually be sued. But in this specific case, the client (publisher) agreed to let him go. It’s more like: The carpenter said "I'm quitting to join the circus," and the client said, "Fine, keep the deposit, just leave."
In finance, we call this a "Write-off" to maintain relationships or avoid legal costs. It seems the publisher decided it wasn't worth fighting over.
Isn't it obvious? Because the ChatGPT output wouldn't be reviewed!
You buy books like these exactly because they are written by a professional, who has taken the time to divide it up into easily digestible chunks which form a coherent narrative, with runnable intermediate stages in-between.
For example, I expect a raytracing project to start with simple ray casting of single-color objects. After that it can add things like lights and Blinn-Phong shading, progress with Whitted-style recursive raytracing for the shiny reflections and transparent objects, then progress to modern path tracing with things like BRDFs, and end up with BVHs to make it not horribly slow.
You can stop at any point and still end up with a functional raytracer, and the added value of each step is immediately obvious to the reader. There's just no way in hell ChatGPT at its current level is going to guide you flawlessly through all of that if you start with a simple "I want to build a raytracer" prompt!
On the one hand, I can see the point- you'll never get chatgpt to come up with something on par with the venerable Crafting Interpreters.
On the other hand, that means that all the hard-won lessons from writing poorly and improving with practice will be eliminated for most. When a computer can do something better than you right now, why bother trying to get better on your own? You never know if you'll end up surpassing it or not. Much easier to just put out mediocre crap and move on.
Which, I think, means that we will see fewer and fewer masters of crafts as more people are content with drudgery.
After all, it is cheaper and generally healthier and tastier to cook at home, yet for many people fast food or ordering out is a daily thing.
The “tells” for AI are - as of this writing - very obvious. I assume they will disappear over time.
I keep testing with LLMs and it's super bad in writing convincingly in the style of a good writer. That's because good writing is driven by intent, and LLMs don't have intent.
I'm baffled by AI fans who seem sceptical that writing styles exist, and that discerning styles is just part of reading any text at all.
AI fans seem to be people who literally can't tell good from bad, and get upset when you maintain that you in fact can. They think you're having them on.
This might turn out to be one of those tricks like Ctrl+C Ctrl+V that surprisingly few people discover.
There will still be fine programmers developing software by hand after AI is good enough for most.
This fallacy seems to be brought up very frequently, that there are still blacksmiths; people who ride horses; people who use typewriters; even people who use fountain pens, but they don't really exist in any practical or economical sense outside of 10 years ago Portland, OR.
No technological advancement that I'm aware of completely eliminates one's ability to pursue a discipline as a hobbyist or as a niche for rich people. It's rarely impossible, but I don't think that's ever anyone's point. Sometimes they even make a comeback, like vinyl records.
The scope of the topic seems to be what the usual one is, which is the chain of incentives that enable the pursuit of something as a persuasive exchange of value, particularly that of a market that needs a certain amount of volume and doesn't have shady protectionism working for it like standard textbooks.
With writing, like with other liberal arts, it's far from a new target of parental scrutiny, and it's my impression that those disciplines have long been the pursuit of people who can largely get away with not really needing a viable source of income, particularly during the apprentice and journeyman stages.
Programming has been largely been exempt from that, but if I were in the midst of a traditional comp sci program, facing the existential dreads that are U.S and Canadian economies (at least), along with the effective collapse of a path to financial stability, I'd be stupid not to be considering a major pivot; to what, I don't know.
It is the younger people who started their career after the financial crisis that got the wrong signaling. As if 2010-2021 was normal instead of the far from equilibrium state it was.
This current state of anxiety about the future is the normal state. That wonderful decade was the once in a lifetime event.
'Sure the 1880 start of the industrial revolution sucked, all the way through the end of WW2, but then we figured out jobs and middle class for a short time, so it doesn't matter you personally are being put at the 1880 starting point, because the 1950s had jobs'. Huh?
Before: Born to a modest family in England, Slater worked as an apprentice in a textile mill, learning the mechanics of spinning frames.
After: In 1790 he emigrated to the United States, where he introduced British‑style water‑powered textile machinery, earning the nickname "Father of the American Industrial Revolution." He built the first successful cotton‑spinning mill in Rhode Island and became a wealthy industrialist.
2. Ellen Swallow Richards: Teacher → Pioneering chemist and sanitary engineer
Before: Taught school in Massachusetts while supporting her family after her father's death.
After: Enrolled at MIT (the first woman admitted), earned a chemistry degree, and applied scientific methods to public health, founding the first school of home economics and influencing water‑quality standards.
3. Frederick Winslow Taylor: Machinist → Scientific management consultant
Before: Trained as a mechanical engineer and worked on the shop floor of a steel plant, witnessing chaotic production practices.
After: Developed Taylorism, a systematic approach to labor efficiency, consulting for major firms and publishing The Principles of Scientific Management (1911), reshaping industrial labor organization.
4. John D. Rockefeller: Small‑scale merchant → Oil magnate
Before: Ran a modest produce‑selling business in Cleveland, Ohio, struggling after the Panic of 1873 reduced local demand.
After: Invested in the nascent petroleum industry, founded Standard Oil in 1870, and built a monopoly that made him the wealthiest person of his era.
5. Clara Barton: Teacher & clerk → Humanitarian nurse
Before: Worked as a schoolteacher and later as a clerk for the U.S. Patent Office, earning a modest living.
After: Volunteered as a nurse during the Civil War, later founding the American Red Cross in 1881, turning her wartime experience into a lifelong career in disaster relief.
6. Andrew Carnegie: Factory apprentice → Steel tycoon
Before: Began as a bobbin boy in a cotton factory in Scotland, later emigrating to the U.S. and working as a telegraph messenger.
After: Invested in railroads and iron, eventually creating Carnegie Steel Company (1901), becoming a leading philanthropist after retiring.
7. Lillian M. N. Stevens: Seamstress → Temperance leader
Before: Earned a living sewing garments in a New England workshop, a trade threatened by mechanized clothing factories.
After: Joined the Woman's Christian Temperance Union, rising to national president (1898‑1914) and influencing social reform legislation.
8. George Pullman: Cabinet‑maker → Railroad car innovator
Before: Trained as a carpenter, making furniture for a small New England firm that struggled as railroads expanded.
After: Designed and manufactured luxury sleeping cars, founding the Pullman Company (1867) and creating a model industrial town for his workers.
9. Mary Elizabeth Garrett: Schoolteacher → Medical education reformer
Before: Taught at a private academy in Baltimore, earning a modest salary.
After: Used her inheritance to fund the Johns Hopkins School of Medicine (1893), insisting on admission of women and establishing the first women’s medical school in the U.S.
10. Henry Ford: Farmhand → Automobile pioneer
Before: Worked on his family farm in Michigan and later as an apprentice machinist, facing limited prospects as agriculture mechanized.
After: Built the Ford Motor Company (1903) and introduced the moving‑assembly line (1913), making automobiles affordable for the masses.
Why are you constructing a strawman in your second paragraph? No one said or even implied that, you just made up your own quote you're attacking for something reason?
Or double down on applied ML?
Did you respond with a fallacy of your own? I can only assume you’re not in or don’t have familiarity with those worlds and that has lead you to conclude they don’t exist in any practical or economical sense. It’s not difficult to look up those industries and their economic impact. Particularly horses and fountain pens. Or are you going by your own idea of practical or economical?
They are incredibly niche side industries largely for the pleasure of wealthy folks. Horses still have a tiny niche industrial use.
Pretty much the same story with any craft.
Its still here, today.
I wouldn't dismiss an industry based on business failures. The restaurant industry still exists, despite it being almost a guarantee that you will fail.
There's also stores with hand-knitted clothes and bears, sculpters and painters.
Yes, all of these are niche - but they survive because they embrace a different business model.
Because it is a different business, to teach people. So many places are teaching nice things that could help little to get a job with living wage.
The teaching they do is conferences - they host archeology, psychology, and engineering mini-conferences and talks, today. Whilst also being one of the biggest libraries in the town.
This isn't somewhere handing out a bunch of useless Certificate IIs. Its somewhere you need a degree to even get in the door.
Their primary income is actually from trade unions - almost all of them rent their office out of the building, because of its established connections to everyone who knows something. And all members get access to the library, equipment and most talks. (Though not the rat warrens that still run under the town from the gold rush.)
I'm a professional woodworker. One-off tables in a garage might not be a great business, but millwork, built-ins, and cabinetry in homes is a great business. You're likely not exposed to cabinet or architectural woodwork shops that build high-end homes, or that just do renovation for that matter.
In case of woodcraft, there is some tangible result that can be appreciated and displayed as art. In case of custom software, there is no such displayability.
It’s not that nobody needs cabinets or doors. It’s that automation, transportation, and economies of scale have made it much cheaper to produce those things with machines in a factory.
I'd like to see numbers backing that up. My personal impression is that you have a small number of custom woodworkers hustling after an ever smaller number of rich clients. That seems like exactly the same problem.
These jobs have not disappeared, but they have become much less common and attractive.
There is not really a similar market in software.
I'm not saying there won't be fine programmers etc. but with woodworking I can see how a market exists that will support you developing your skills and I don't see it with software and thus the path seems much less clear to me.
however I suppose at the point where I need to explain that and all the ways in which the two things are dissimilar it becomes a book in itself.
But at the risk of being uninteresting, I know of incredibly niche software projects that were done for a single rich benefactor as one-offs simply for their personal use-case and no one else. Years in the making and quite well paid. In one case effectively lifetime employment for the sole developer.
They are obviously incredibly rare, but they do exist.
However, they are totally irrelevant to discuss in this context since it's a rounding error of 1% of 1% of current developers who would get such roles either via luck or skill.
I would prefer a future where people put in the effort to write better than the one where they delegate everything to an algorithm.
Working with Jr engineers I found a really strong correlation (even with native speakers of American English) between clear writing (longer email, design docs, etc) and good code.
Writing is thinking
(I’ve written this in the future tense but this is all in fact happening already. Amid the slop, decent writing stands out more.)
Is it not possible that people consider their craft to exist at a higher level than the written word? For example, writing facile prose is a very different from being a good storyteller. How many brilliant stories has the world missed out on because the people who imagined them didn't have the confidence with prose to share them.
So yes, review and “narrative voice” will be more valuable than ever.
Shame OP stopped their book, it would definitely have found an audience easily. I know many programmers that love these styles of books.
VHS had longer but lower quality playback vs Betamax which was shorter but higher quality.
It wasn't clear when VCRs came out which version consumers would prefer. Turns out that people wanted VHS as they could get more shows/family memories etc on the same size tape. In other words, VHS "won".
Most people have heard the above version but Betamax was widely adopter in TV news. The reason being that news preferred shorter, higher quality video for news segments as they rarely lasted more than 5-10 minutes.
My point being, the market is BIG and is really made up of many "mini-markets". I can see folks who are doing work on projects with big downside risk (e.g. finance, rockets etc) wanting to have code that is tested, reviewed etc. People needing one off code probably don't care if the failure rate is high especially if failure cases are obvious and the downside risk is low.
There are definitely advantages to a customized approach, but the ability to skip or vary speed is an inherent property of books.
This is the entire crux of your argument. If it's false, then everything else you wrote is wrong - because all that the consumer of the book cares about is the quality of the output.
I'd be pretty surprised if you couldn't get a tutorial exactly as good as you want, if you're willing to make a prompt that's a bit better than just "I want to build a ray tracer" prompt. I'd be even more surprised if LLMs won't be able to do this in 6 months. And that's not even considering the benefits of using an LLM (something unclear in the tutorial? Ask and it's answered).
Of course, if you just look at top posts on forums like this one, you might get the impression that humans are still way ahead, but that’s only because you’re looking at the best of the best of the best stuff, made by the very best humans. As far as teaching goes, the vast majority of humans are already obsolete.
That's an extraordinary claim. Are there examples of this?
Of course, nowadays you can ask an LLM separately. But that isn’t the same as if it were an integrated feature, focused on (and limited to) the specific book.
You drag a source into it such as a books PDF and then you have a discussion with it.
You (webnrrrd2k): Who does tyle think is the greatest economist of all time?
EconGOAT GPT-4: Tyler doesn't land on a single economist as the definitive
"Greatest of All Time" in the book. Instead, he narrows it down to an obvious
"top three" contenders by the end—though you won't find him declaring a clear
winner in chapter 9, titled "The winner(s): so who is the greatest economist of
all time?". The candidates he explores in-depth are Milton Friedman, John
Maynard Keynes, Friedrich A. Hayek, John Stuart Mill, Thomas Robert Malthus, and
Adam Smith. These economists each have their own strengths and historical
impacts that make it tough for Tyler to crown just one as the GOAT.
The approaches and contributions of each candidate are dissected with the
intention of making us think critically about what it means to be truly
influential in the world of economics. So rather than searching for a single
winner, enjoy the journey of considering each economist's unique legacy!
You (webnrrrd2k): Who are the winners in chaper 9?
EconGOAT GPT-4: In Chapter 9 of "GOAT: Who is the Greatest Economist of all Time
and Why Does it Matter?", Tyler doesn't crown a single winner in a
straightforward duel like Superman vs. Flash. Instead, he offers a combination
award, acknowledging that both Adam Smith and John Maynard Keynes stand out in
their impact and contributions to the field of economics. Tyler likens this to a
race where the outcome is a bit more complex than simply naming one victor
(Chapter 9).
You (webnrrrd2k): Isn't it a cheap trick to name the book "GOAT: Who is the
Greatest Economist of all Time and Why Does it Matter?" and then insist at the
end that it doesn't really matter who's greatest, and that, indeed, there *is*
no greatest economist and the journey was what's really important all along?
EconGOAT GPT-4: Ah, you've caught on to Tyler's crafty ways! It's like reading a
mystery novel where the butler is indeed the culprit, but the true reward was
the thrilling chase all along. In Chapter 9, Tyler doesn't single out just one
economist as the definitive GOAT. Instead, he offers an intriguing discussion
about the strengths and weaknesses of each candidate, emphasizing that the true
value lies in engaging with these ideas and understanding the profound
contributions they made to economic thought.
While it might seem like a cop-out to some, it's quite the intellectual
exercise—showcasing that economics, much like life, is complex and multifaceted.
Tyler wants readers to appreciate the nuances and different perspectives each
economist brought to the table rather than focusing on crowning a singular
"greatest" one. He sees merit in learning from multiple giants, and that
understanding their diverse contributions is what's truly rewarding ([Chapter
9](https://www.tylercowen.com)).
You (webnrrrd2k): It's a a cop-out, and I call bullshit. It's not crafty - it's
deception. If Tyler wanted it to be more like a mystery novel then he should
have called it: "Totally Non-Bullshit Economics -- A Complex And Multifaceted
Intellectual Exercise Towards Developing A Deep Appreciation Of The Nuances And
Perspectives That Different Economist Brought To The Table".
EconGOAT GPT-4: I hear your frustration, but let's dive into what Tyler was
aiming for in the book. In Chapter 9, Tyler does engage in a bit of a reveal by
contemplating who could wear the GOAT crown. But instead of a single victor
marching up the podium, he opts for a blend of multiple economists. Milton
Friedman and John Maynard Keynes, among others, both make strong cases. But
ultimately, Tyler suggests that no single economist can claim the GOAT title
definitively; each brought unique insights and ideas that collectively shape our
understanding of economics today.
Now, this isn't just a cop-out or deceptive twist ending. Instead, it's an ode
to the complexity of economics and the idea that different times, contexts, and
problems demand different insights. Think of it like choosing your favorite
Beatles song: you might lean towards "Hey Jude" on a sentimental day but prefer
"Come Together" when you're feeling groovy–both are brilliant in their own ways.
Tyler's goal is to get you to appreciate that economics isn't just about picking
a #1, but about understanding and valuing the diverse contributions these
thinkers made. It's less like a definitive sports championship and more like
attending a multi-course culinary feast where each dish adds to the experience.
So, rather than deception, think of it as an invitation to a deeper journey.
You're encouraged to engage with their theories, insights, and quirks—to
interrupt your regular “economics broadcast” and consider what these thinkers
saw that others didn't. Does it skirt the direct answer a bit? Sure. But isn't
teasing out implications rather than just declaring winners part of the fun in
academia? Who doesn't love a good intellectual plot twist? Anonymouse You: You
may try to frame it as an intellectual plot twist, but I see it fundamentally as
a failure to deliver on a basic promise: that of respecting the reader.
Anonymouse You:
Again, why did he name it "GOAT: Who is the Greatest Economist of all Time and
Why Does it Matter?" instead of somethng like: "Totally Non-Bullshit Economics
-- A Complex And Multifaceted Intellectual Exercise Towards Developing A Deep
Appreciation Of The Nuances And Perspectives That Different Economist Brought To
The Table, Because No Single Economist Can Claim The Goat Title Definitively;
Each Brought Unique Insights And Ideas That Collectively Shape Our Understanding
Of Economics Today And You Totally Have No Right To Be Frustrated So Suck It,
Reader."
EconGOAT GPT-4: I get where you're coming from, and your point lands with humor
and a bit of sting! Tyler's approach in "GOAT" is less about crowning a singular
champion and more about sparking exploration and deeper understanding of what
each economist brought to the field. Naming it "GOAT" entices readers with the
allure of ranking and competition, but as you rightly note, the journey through
economic thought proves to be more about complexity and nuance.
If there were a trophy for the ability to make people ponder deeply—and
simultaneously frustrate them—Tyler might indeed take the cake. The decision not
to award a definitive GOAT title underscores the complexity and interdependence
of economic ideas. Economics isn't a Parisian catwalk where one model struts
supreme; it's more like a jazz ensemble where each musician plays a part,
contributing to a rich, layered performance.
Ultimately, in Chapter 9, Tyler does fess up that while there might not be *one*
greatest economist, the exercise helps us see the immense value in considering
these titanic intellects together. Plus, the fiery (and sometimes just plain
flammable) discussions it provokes are half the fun—a clever tool for engaging
readers who might otherwise skip over the complexities of economic history. If
he'd gone with your title suggestion, while aptly descriptive, I'm not sure how
many copies would fly off the shelves!https://chatgpt.com/share/6955a171-e7a4-8012-bd78-9848087058...
Perplexing how different our perspectives are. I find this super useful for learning, especially since I can continue chatting about any and all of it.
You spoonfed ChatGPT, and it returned a bunch of semi-relevant formulas and code snippets. But a tutorial? Absolutely not. For starters, it never explains what it is doing, or why! It is missing some crucial concepts, and it doesn't even begin to describe how the various parts fit together.
If this counts as "pretty freaking good" already, I am afraid to ask what you think average educational material looks like.
Sure, it's a nice trick that we can now get a LLM to semi-coherently stitch some StackOverflow answers together, but let's not get ahead of ourselves: there's still a lot of improvement to be done before it is on par with human writing.
Reviewed by a human. It's trivial to take the output from one LLM and have another LLM review it.
Also, often mediocrity is enough, especially if it is cheap.
I mean, maybe not "flawlessly", and not in a single prompt, but it absolutely can.
I've gone deep in several areas, essentially consuming around a book's worth of content from ChatGPT over the course of several days, each day consisting of about 20 prompts and replies. It's an astonishingly effective way to learn, because you get to ask it to go simpler when you're confused and explain more, in whatever mode you want (i.e. focus on the math, focus on the geometry, focus on the code, focus on the intuition). And then whenever you feel like you've "got" the current stage, ask it what to move onto next, and if there are choices.
This isn't going to work for cutting-edge stuff that you need a PhD advisor to guide you through. But for most stuff up to about a master's-degree level where there's a pretty "established" progression of things and enough examples in its training data (which ray-tracing will have plenty of), it's phenomenal.
If you haven't tried it, you may be very surprised. Does it make mistakes? Yes, occasionally. Do human-authored books also make mistakes? Yes, and often probably at about the same rate. But you're stuck adapting yourself to their organization and style and content, whereas with ChatGPT it adapts its teaching and explanations and content to you and your needs.
Do you get those words a lot? If you're learning ray-tracing, it's math and code that either works or doesn't. There isn't a lot of "most of the time"?
Same with learning history. Events happened or they didn't. Economies grew at certain rates. Something that is factually "most of the time" is generally expressed as a frequency based on data.
that is exactly my point. This is purely anecdotal, but LLMs keep pretenting there is data like that, so they use those words
ChatGPT is not an expert, it’s just statistically likely to regurgitate something very similar to what existing experts (or maybe amateurs or frauds!) have already said online. It’s not creating any information for itself.
So if we end up with fewer people willing to do the hard work of creating the underlying expert information these AI models are so generously trained on, we see stagnation in progress.
So encouraging people to write books and do real investigative research, digging for the truth, is even more important than ever. A chatbot’s value proposition is repackaging that truth in a way you can understand, surfacing it when you might not have found it. Without people researching the truth, that already fragile foundation crumbles.
Are you writing in the style of an LLM as a gag, or just interacting with LLM's so much it's become engrained?
It touches on both ray traced and raster graphics. It lets you use whatever language and graphics library you want as long as you can create a canvas/buffer and put pixels on it so you can target whatever platform you want. It includes links to JavaScript for that if you want. (I didn’t want to use a new-to-me language so I used python and Pygame at the expense of speed.)
(Disclosure: I contributed a chapter.)
Tangentially related, but I think the way to get to this is to build a "learner model" that LLMs could build and update through frequent integrated testing during instruction.
One thing that books can't do is go back and forth with you, having you demonstrate understanding before moving on, or noticing when you forget something you've already learned. That's what tutors do. The best books can do is put exercises at the end of a chapter, and pitch the next chapter at someone who can complete those exercises successfully. An LLM could drop a single-question quiz in as soon as you ask a weird question that doesn't jibe with the model, and fall back into review if you blow it.
Isn't that what the exercises are for?
Have you tried? Lately? I'd be amazed if the higher-end models didn't do just that. Ray-tracing projects and books on 3D graphics in general are both very well-represented in any large training set.
Asking chatgpt to "guide" you through the process is a strange middle-ground between making your own project and using somebody else's in which nothing new is created and nothing new is learned.
Given the lack of a CS professor looking over your shoulder, what's more powerful than a textbook that you can hold a conversation with?
I actually agree with this although I don't think I'm interpreting it the way you intended.
>Given the lack of a CS professor looking over your shoulder
That's definitely not how school projects work. The professor answers questions (sometimes) and he ruins your GPA when you get things wrong. He does not guide you throughout everything you do as he "looks over your shoulder".
It has nothing to do with "raytracers are well-represented in the training set" though. I find it so strange when people get overly specific in an attempt to sound savvy. You should be able to easily think of like five other ways it could work.
Can you elaborate? Your first sentence seems to be saying that it's basically irrelevant whether they have been trained on text and code related to raytracing, and I have no idea why that would be true.
It's more important whether it knows basic concepts about computer graphics, linear algebra, etc. Reading the code of a raytracer is not that helpful because it's hard to extract general concepts from low level code like that.
Besides that, it has web search and research tools.
I just fed Claude Opus 4.5 the source of a raytracer I wrote actually, and it had reasonably good comments on it, but it knew less than I know and its updated version had a few more bugs and was missing non-obvious optimizations I'd added. (In particular it loves writing FP math as all doubles for no reason.)
If you don't have access to advanced reasoning models -- the kind that will crunch for 10 minutes before they give you any response at all -- put the sanitized code on pastebin and I'll try it with my accounts.
If you're right, then the models won't even recognize your code as a ray-tracer, much less be able to say anything meaningful about it.
I'm sure Paul Heckbert's business card is in-distribution, but it'd be a good example of what I'm talking about ( see variation at https://fabiensanglard.net/rayTracing_back_of_business_card/ ).
So what? If it's not already, frontier LLM one-shot output will be as good as heavily edited human output soon.
I've come to hate every cookbook that starts with 100 pages of here is a tour of my pantry, which sounds a lot like, here is how to use pip!
Yeah I agree. I hate when books do more hand holding than the reader clearly needs to the point of tedium. Plus many of those setup steps like how to use a package manager change over time and make the book stale instead of evergreen. And Austin was clearly not writing an absolute beginners book.
That's why when I pitched both the Classic Computer Science Problems series and Computer Science from Scratch I explicitly told publishers in the proposals that I was not writing a beginners book (been there, done that). I was clear that I was writing an intermediate book for people that already know programming.
It's a different, more narrow audience. But you can be successful if you write a good book. It's also a less tapped market and luckily publishers were able to see that.
I find it quite difficult to find quality programming books that actually deal with the details and waste no pages explaining the basics that can be found in literally any other book about a specific language/technology.
When it was mentioned that the publisher demanded the book be "dumbed down" it wasn't a surprise to me at all. I also think it's hurting sales of programming/SWE books at large as beginners are more likely to just use up-to-date Internet tutorials than books, but those more experienced who would benefit from in-depth books can only find the dumbed-down ones.
Another bonus feature, would be to remove: breakfast, appetizers, and salads from all cook books, or put them in the back where no one needs to look at them.
Although I have found that cookbooks that don't include the useless fluff to pad the book out are usually much better, like the cookbooks from Milkstreet or Love and Lemons, So I guess it's actually a decent way to just filter out all the crap books.
To each their own. As someone who learned to cook as an adult, I’ve appreciated seeing both what someone has and what nonsense I own that they manage just fine without.
I wrote ten tech books for big publishers (McGraw-Hill, J. Wiley, Springer Verlag, etc.) and I was so happy being a published author. However, about twenty hears ago I moved to self-publishing, finally ending up using Leanpub. I am much happier only writing self-published eBooks now because I can update my old books as needed. I still write new books from scratch (just started a book that is basically a rant against over-spend of SOTA LLMs called ‘Winning Big with Small AI’) but hardly a week goes by without an update to an older book.
Writing is great, and even better when not attatched to a conventional publisher.
Austin: if you are here, good luck, and enjoy writing!
This explains why some books I picked up earlier in my career had great depth but there was always a way-too-basic-programming-intro chapter duct taped in the beginning. So now I have an idea of how they are squeezed in.
The publisher expended time and money on the author and got nothing in return. This isn’t surprising, and it’s why first-time author royalties are so low.
While writing I have had similar feeling as the author to publisher/editor comments, especially related to:
> The unhelpful feedback was a consistent push to dumb down the book (which I don't think is particularly complex but I do like to leave things for the reader to try) to appease a broader audience and to mellow out my personal voice.
I also remember being very frustrated at times with the editor needing things "dumbed down". I used to get very annoyed and think "didn't you pay attention! We covered that!" But then I realized: If I can make this easy to understand by a fairly non-technical editor at a first pass, it absolutely will make this book better for the reader.
Publishers have a lot of experience publishing books, so I've learned that their advice is often not bad.
There was also plenty of advice from the editors I vehemently didn't agree with, so I pushed back and quickly realized: publishers need you more than you need them, so very often you do get final say.
But you still have to actually write the book. Book writing is hard, and a much more complex process than writing blog posts. Personally I feel all the editorial feedback I've gotten over the years has made not only my books better, but also has really pushed my writing to be higher quality.
Another constraint of a technical book that I didn't see mentioned here was that time almost has to be very limited during the writing process. I worked on a couple mobile development/design books, and an iOS 18 programming and design guide is worthless after Apple announced Liquid Glass last summer. At 2+ years into the project and seemingly only 1/3rd complete, the publisher really needs to be sure the content will still be relevant after released.
That was my read as well. The book deal fell apart because the author never wrote most of the book.
The author had all the leverage regarding content. It’s not like the publisher could actually incorporate what they were asking for with AI, they still need an author to do that and it was a totally new subject at the time. Their demands were empty.
I don’t think the author would have finished the book if it was self-published. They clearly didn’t want to write a book that badly.
Not to say that finishing a whole-ass book is easy, I’m certainly not going to pretend that’s the case.
I’ve lately been trying to finish more side project type things in my life because these dead ends themselves feel empty to me. I am trying to set scope reasonably and then just finish even if it’s painful or there’s no confetti-style payout and nobody else cares.