Interestingly, this was my exact experience when working with a publisher (Manning, in my case), and it was the main reason I decided to part ways when writing my book (The Software Engineer’s Guidebook). While I did appreciate publisher’s desire to please a broader crowd by pushing a style they thought would broaden the appeal: but doing so makes technical books less attractive, in my view. And even less motivation to write!
In my case, self publishing worked out well enough with ~40,000 copies sold in two years [1], proving the publisher’s feedback wrong, and that you don’t need to dumb down technical books, like this specific publisher would have preferred to do so.
Even if it wouldn’t have worked out: what’s the point writing a book where there’s little of the author (you!) left in it. Congrats to OP for deciding to stick to your gut and write the book you want to write!
[1] https://newsletter.pragmaticengineer.com/p/the-software-engi...
I wrote a similar blog post a month ago describing the process of creating the book and getting it published called "Writing Computer Science from Scratch":
https://www.observationalhazard.com/2025/12/writing-computer...
Some in this thread have wondered what publisher Austin was working with. Based on my experience working with three different technical publishers and the setup and terms Austin was offered, my educated guess would be Manning.
I will critique the blog post a little bit. It's presented as a critique of the experience of working with the publisher, but ultimately I'm reading between the lines that the book failed because he was missing deadlines. He wrote that "life got in the way" and I think he lost his motivation only partially because the publisher wanted AI in more of the book. Many of the trials he had along the way: dealing with a development editor who wants to tailor your style to a particular audience, a technical editor who needs a couple chapters to warmup, back and forth on the proposal, etc. these are all really par for the course when writing a technical book. Ultimately you have to be self-motivated to finish because of course the development editor, technical editor, etc are going to disagree with you from time to time and try to push you in different directions. If that alone is so demotivating to you, it's just not for you to work with a publisher.
PS I think his blog is really good and he should think about self publishing under a time frame and terms he is more comfortable with.
In fairness to the author, he presents a reasonably balanced view and it did not read to me like “my publisher sucked.”
I assume that was about competing with LLMs writing content, rather than including LLM-related technical projects.
I’m glad I did it but I’m not sure how much the publisher added beyond some prestige and a few bucks. The first edition in particular I felt I needed to pad out a bit to meet length requirements.
Glad the author got out relatively unscathed.
Self publish - especially with AI available to get you through the stuff where you just need superficial or process knowledge, like which firms to hire and how to market a self-published work, what boilerplate legal protections you need. You'll get 99% of the value of a big publishing firm at a small fraction of the cost, and you won't have to put up with someone else taking a cut just because they know a few things that they don't want to tell you in order to justify taking your money.
Putting aside for a moment that nobody should be trusting a frequently-hallucinating AI algorithm with any of the above...
Your world-view is one of those that returns to the old adage "it only works if you value your time at zero".
Its the sort of thing we see in tech the whole time. Some dude saying "oh, I can just fix my motherboard myself".
Or in the automotive sector, someone with experience and kit fixing their own engine block.
Well, sure you can dude. Because you've got the domain expertise, you've got the kit AND you are willing to value your time at zero.
However in the majority of cases, if you do not value your time at zero, then spending even just a few hours waving an oscilloscope and soldering iron over the proverbial motherboard is time better spent on other tasks and the "more expensive" option suddenly does not look that expensive any more.
And that is all before we address the other elephant in the room.... Your suggestion that it is easy to self-market a self-published work.
Maybe if you are a well known and respected author, such as Mr Performance (Brendan Gregg) or Mr Oracle (Tom Kyte) etc.
But if you are just Joe Schmoe. And perhaps especially if you are Joe Schmoe who's just written your first self-published book. The outcome is unlikely to be the same.
-Do you release a physical book? If so, what are the mechanics of that and how much does each book cost?
-Do you release it in an electronic format? If so, what format and how do you stop it being mercilessly pirated?
Bandcamp learned this lesson. GOG learned this lesson. They both provide services users love, without DRM, and just accept that there is no capitalistic scarcity inherent to digital goods like there is to physical ones. An indie ebook publisher would be wise to heed those teachings.
Happy New Years, HN.
Ironically though, your reply has another similar one. You read books every day; reading books is an everyday activity for you.
Write a sentence and ask it it is correct, if it is idiomatic, and to explain rules behind it.
- set up [1] (notice that it's a verb)
- setup [2] (notice that it's a noun)
- Phrasal verbs [3]
Unfortunately, I'm afraid it's mostly stuff one needs to know by heart, but I think it's often that the noun is the one that is all in one word and the verb is the phrasal one (composed of "base" and the particle, in several words). Note: I'm not a native English speaker.
[1] https://en.wiktionary.org/wiki/set_up#English
On the other hand, LLMs are very bad writing partners, they are sycophants and very rarely give substantial criticism, the kind of feedback an editor would give and is mentioned in the article.
This is the substantial service an editor will provide going forward in the AI slop era, where everyone and their grandma will self publish some personal masterpiece: a contact with the real world and setting the bar high, to the point you need to struggle to achieve the required quality. Writing a book, especially finishing a great book, is not supposed to be enjoyable, it's hard, grueling work.
Edit: actually, either way works, except when using with a pronoun. So, you can 'set it up', but you can't 'set up it'.
You can, however, set up us the bomb.
If you can't finish a partial manuscript when you have people reaching out to you and reviewers ready to provide feedback, how confident can I be that you'll actually write when you have a faceless pre-order instead? Or will life just get in the way again?
Considering all the confusion and questions in this comment section, maybe he should have been more open to an editor.
I hope this trend is not industry wide. A publisher chasing fads and trends over enduring quality, so sad. I wish I knew who the publisher was to avoid but I can foresee their pivot to AI authors with titles like "From Zero to Hero, ChatGPT 5.2 Top Prompting Secrets for Dummies"
Publishers are going to demand chasing the hot-new-thing which will most likely be irrelevant by the time the book is on the shelf.
"How to write x86 ASM... with the Copilot Desktop app! - Build your bootloader in 15 seconds!"
It's exactly the sort of financial pressure that will make them chase fads and trends, and it gets worse in difficult economic times.
That is true of many industries, including films, vc software startups, games and books. As the Internet increases competition and opportunity, it is likely to become more true.
The thing is, it’s not about getting chapters published on AI. The publishers are keenly aware that AI is using their content to steal their market and so anything they publish on AI will be obsolete before the final manuscript is published. It’s about getting potentially difficult first time authors to quit before their first third gets approved - that’s when the author is owed their first advance.
It’s a lot easier to slaughter sheep if the most docile select themselves.
If any folks want to talk about nonfiction publishing, I'm always happy to chat as many people were incredibly generous with their time for me and I'd like to try to pay it forward.
Some of them already replied saying the proposal seems interesting but they want to read a few chapters. I don't know if I am in the right path or not, but I'd love to read more about your experience and what can be shared!
The prestige probably isn't what you'd expect. Having an ISBN to one's name carries ~zero weight for the people that actually matter for your career (it may mildly impress some future coworkers in a decade's time, though). The real value of having written a book is that then you have written a book.
Having a publisher carries one extra benefit that was merely implied in the post: you get assigned a professional editor. If you're lucky (I was), the editor has a really good understanding of how to wield language and the lessons you get from the editing process are going to far outweigh any direct financial benefits. When I wrote mine, I had been doing freelance writing for a large IT magazine for nearly ten years - and as a direct result had been taught how to use written language as a weapon by a good number of old-guard journalists. The year I spent on the book project taught me a LOT more still, because I was assigned an editor who herself was studying (in a university) to become a language teacher.
The skills I picked up from that process are with me to this day, and ironically have been the single most valuable asset I have as an engineer and/or engineering leader. Being able to write well to a varying audience is a superpower. You also learn to appreciate professional authors, because what they do is decidedly not easy.
In the end my book sold well enough to earn out its advance, so I guess it was a non-failure for the publisher as well. I also picked up a lesson for all aspiring authors:
Writing a book is easy. You sit down by the keyboard, slit your wrists and pour it all out.
I think I lack all the last parts (that some publishers are requiring for) such as a social media platform to reach your potential readers. I find that a bit unfair because it means you first have to play the Instagram game and once you are popular there, you can write a book.
If you give me an email address I'd love to tell you more about my book!
It's not always a dealbreaker, I didn't have any social media following or anything -- the way I pitched it was by figuring out a bunch of conferences, niche podcasts, etc. and highlighting that there was an audience there I could activate (and marketing is a big part of the book process I've learned).
My bio has my email now!
Seriously, I’ve seen this exact genre of comment daily on hn lately and I don’t get what you’re gaining by trying to sniff out bots. Not what anyone has to gain by truly botting on here. Nobody is selling their HN accounts right? And how many pretty run-of-the-mill comments like the above would it take to have an account worth selling anyway even if there was a market for that? 100,000?
Goodreads seems to think so. https://www.goodreads.com/author/show/14291276.Joel_Burke
Edit: Added some context and I'd also mention that one thing that was quite helpful is that at the start of the writing process I created a massive spreadsheet where I'd add in quotes, writing, and anything interesting I thought I might pull from (some of it manually written, like when watching documentaries). This was hugely helpful when I was going back but also during the writing process so I had a single source of truth I could keyword search. I've just checked it and its got 4787 rows, with most entries being about a paragraph long
Rebooting a Nation: The Incredible Rise of Estonia, E-Government and the Startup Revolution Paperback by Joel Burke
The upshot is, they don't want to do the hard part - continuity editing, developmental editing, hell, just finishing the dang thing. Even the boring chapters you didn't really have any idea what was going to go on there.
Writing, as an occupation, is a whole lotta schmoozing, attending conferences, volunteering, promoting. Maybe 1 month of writing a year, for 11 months of the hard stuff.
I have a buddy who says he always wanted to start a bar. I said, You like budgeting? Taxes? Hiring? Firing? Stocking? Remodeling? Promoting?
Nah; turns out, he just likes to hang out in bars.
The only reason you start a business is, because you like to run a business.
The only reason you become a writer is, because you like the business of writing.
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.)
Does this mean they get to keep the advance, and all the feedback from the editors, as well, for free? That seems like a pretty good deal - the publisher put resources into this project and got exactly zero in return.
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.
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.
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.
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?
The classic old person advice is to just walk in somewhere, give the owner a stern handshake, and you got a job, and if that job could pay your mortgage, then problem solved. Whereas now, to become a buggy whip maker (or whatever), we've developed yhe expectation that you go to school for 4 years and start out at the bottom of the income ladder. If the income we need to pay for the basics (which admittedly are different) requires a lifetime of experience, then it's impossible to pivot
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.
The guys that ended up spending their lives living in boarding houses with other men, never starting a family. The guys that ended up living in tramp camps traveling the country looking for work. The families that ended up as migrant workers. The people that broke and lived in flop houses or those long term hotels in downtowns.
That is the real picture no one shows. What does 'find new work' after the industrial revolution look like for the average person, and the answer isn't 'become Henry f'ing Ford' now is it post WW2 style middle class employment.
1. Blacksmith → Railroad Carriage Foreman
Before: Local village blacksmith, shoeing horses, forging tools; works long hours in a small shop; income modest but steady; reputation tied to local farmers.
After: Takes a job at a nearby railway workshop as a metalworker assembling and repairing iron carriages; works in shifts under foremen; more regular wages and slightly higher pay; moves to a rail town, his kids gain access to a school; he loses some independence in his new setting but has marked gains in job security and cash pay.
2. Handloom Weaver → Textile Factory Machine Minder
Before: Home-based handloom weaver producing cloth on commission; schedule flexible but income fluctuates with orders; household-centered labor.
After: Employed in a textile mill running power looms; fixed hours, supervised by overseers; steady wage, production quotas; exposure to somewhat harsher factory conditions, but more reliable monthly income and potential for overtime pay.
3. Sailmaker/Canvas Artisan → Industrial Tent or Sail Factory Worker
Before: Small workshop making ship sails and canvas goods for local ships; skills highly specialized and tied to maritime trade.
After: Joins a larger factory producing canvas goods and tents for military contracts or industrial customers; moves from bespoke work to standardized production; learns new machine operations; steadier demand, less artisanal pride but higher throughput.
4. Small-Scale Farmer → Agricultural Wage Laborer / Seasonal Harvester
Before: Owner-operator of a few acres, subsistence mixed with small-market sales; vulnerable to crop failures and market price swings.
After: Sells or loses land, takes wage labor on larger farms or in orchards; seasonal work, long hours during harvests; cash wages replaced self-provisioning, children sometimes work; potential to move seasonally to find work.
5. Canal Boatman → Dockyard or Stevedore Worker
Before: Independent canal boat operator transporting goods along waterways; income from tolls and freight; lifestyle itinerant but autonomous.
After: With canal traffic declining due to railways, he becomes a dock laborer unloading goods at a port; work is bunched into long, physical shifts; income less variable but less autonomy; often joins laborer networks or unions.
6. Carpenter (Craft) → Construction Gang Member on Urban Projects
Before: Skilled carpenter building houses and furniture for local clients; runs small crew, has flexible custom work.
After: Migrates to a city for large-scale building projects (tenements, bridges); works as part of an organized gang, specializing in one repetitive task (e.g., formwork); wages steadier including opportunities for overtime, though with less creative control.
7. Tanner/Leatherworker → Factory Leather Stitcher / Machine Operator
Before: Family tannery producing saddles and boots for local markets; knowledge-intensive, smelly but respected craft.
After: Enters a leather goods factory operating stitching or cutting machines for mass-produced footwear; learns machine maintenance; working environment more regulated, discipline stricter, pay more regular.
8. Rural Cooper (Barrelmaker) → Brewery or Canning Works Employee
Before: Independent cooper making barrels for local brewers and farms; demand falls as metal containers and standard packaging rose.
After: Hired by an urban brewery or canned-goods plant to maintain wooden vats or operate filling machinery; moves closer to urban amenities; job is less entrepreneurial but offers steady pay and sometimes benefits.
9. Watchmaker/Clock Repairer → Precision Machine Operator or Assembler in Electrical or Watch Factories
Before: Skilled artisan repairing and crafting clocks, serving local clientele; income from repairs and small commissions.
After: Joins an urban precision workshop or early watch factory assembling parts or operating lathes; narrower tasks but higher volume and a connection to emerging industrial technologies; more predictable pay and potential for apprenticeships for children.
10. Fisherman (Small-Boat) → Cannery Worker in Coastal Town
Before: Independent small-boat fisherman selling catch to local markets; income seasonal and weather-dependent; family-based operation.
After: Cannery expansion offers steady work in fish-packing plants, processing, salting, or canning; shift work with set hours and piece-rate opportunities; his income is quite a bit steadier, though the job is somewhat repetitive and sometimes hazardous.
Here's a sketch of the bigger picture we can see from these scenarios:
Greater wage stability: Moving from piecework, self-employment, or seasonal income into factory, railway, or dock wages usually meant steadier cash pay and more predictable household budgeting.
Larger scale means less autonomy: Many tradesmen lost control over hours, pace, and methods, trading independent decision-making for supervised, repetitive tasks.
Increased urbanization and mobility: Job shifts often prompted moves to towns or industrial centers, changing family networks and access to services (schools, markets, hospitals).
Routine and discipline: Factory and industrial work imposed fixed schedules, time discipline, and stricter workplace rules compared with artisan or farm life.
Skill reorientation: Some workers adapted existing skills to machines and assembly roles, while others lost craftsman status as specialized knowledge gave way to standardized production.
Household changes: Women and children were drawn into wage labor more frequently; domestic production declined as cash purchases rose.
Health and safety trade-offs: Improved incomes sometimes came with worse working conditions, crowding, and new occupational hazards.
Social mobility mixed: For some, steady wages enabled upward moves (education for children, home purchase); for others, loss of independence and precarious labor markets limited long-term mobility.
Community and identity shifts: Occupational identities tied to craft or land weakened; new worker solidarities and urban social institutions (mutual aid, unions, churches) grew in importance.
Consumption patterns: Predictable wages led to more regular purchase of manufactured goods, changing diets, clothing, and household items.
What did losing your stables/small scale manufacturing/family farm/etc and adjusting to post industrial revolution 'finding a new job' look like? Saying 'Henry Ford existed' doesn't add anything to understanding that.
You're the one who asked the question...
Sorry but if you're going to mean something other than what you say, then you should specify that in what you say rather than have people guessing or answering questions you yourself asked then saying, but no that's not what I mean. Well, yeah, we don't know unless you say so, and that's exactly where the goalpost moving is, you literally did it in the above comment.
'new jobs come' is very much pushed by the AI set when people bring up concerns. I was simply trying to define what that looks like. Apparently it's 'just become Henry Ford or a Rockefeller'. Good luck with that talking point.
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?
Tramps/homeless/tramp camps was a part of life. Huge groups of men lived in 'boarding houses' their entire lives. Most people today don't even know what boarding houses were. There were huge populations that traveled as migrant field workers. The reality looked much different than post WW2 employment.
Sure you said people found other jobs. But really, lots of people didn't. Or didn't find jobs that allowed them to live outside boarding houses and have families. There is a whole big picture waved away with 'found jobs' that implies post WW2 jobs/lifestyles, when in fact that is not what happened post industrial revolution, and did not happen until much later, and was not guaranteed to happen ever.
Ratelimited so editing:
People handweaving away legitimate fears with 'people found new work' are implying it is on the level/quality of life. My 'strawman' is pointing out their 'found new work' was shittier work and a shittier life and that the handwaving/minimizing peoples fears with such a hollow statement is bullshit and should be called out as such. 'found new work' is nothing more than 'people didn't all die'.
No, you are the only one implying this, no one said anything about this which is why I said it is a strawman. I said nothing about the quality of work found, only that new work was found. That may or may not be the case for today's situation, no one can know and I am not in the business of prognosticating so.
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.
A rhetorical question is a question asked for effect rather than to get an answer.
If you have to explicitly answer the question it is not rhetorical.
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.
Despite that, both are incredibly useful if used correctly.
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.
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?
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!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/ ).
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.
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.
It's a good tool to learn stuff, I'm not trying to argue that, but one has to be fully aware of its shortcomings and put in extra work. With actual tutorials or books you have at least some level of trust.
A lot of it is just, are its explanations consistent? Does the code produce the expected result?
Like, if you're learning ray-tracing and writing code as you go, either it works or it doesn't. If the LLM is giving you wrong information, you're going to figure that out really fast.
In practice, it's just not really an issue. It's the same way I find mistakes in textbooks -- something doesn't quite add up, you look it up elsewhere, and discover the book has a typo or error.
Like, when I learn with an LLM, I'm not blindly memorizing isolated facts it gives me. I'm working through an area, often with concrete examples, pushing back on what seems confusing, until getting to a state where things make sense. Errors tend to reveal themselves very quickly.
that is exactly my point. This is purely anecdotal, but LLMs keep pretenting there is data like that, so they use those words
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.
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.)
So what? If it's not already, frontier LLM one-shot output will be as good as heavily edited human output soon.