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- Hacker News
- Speaking as someone who never learned well from courses and always preferred documentation and 'just doing it': I wonder how much of this drop is 100% LLM usage by devs vs. devs realizing a lot of video content was just padded out anyway.by drewbitt
- I'm taking courses at my local college (heavy duty diesel tech) and I'm finding how little use the teachers is for me. I got a digital copy of the textbook, and downloaded all the courses notes, pumped that into claude and made a discord bot that generates me quizes, tracks all my progress, and builds me a study guides off the results.
Superior to any teacher I've ever had, no matter how involved they are. I genuinely don't even see what my teacher is needed for beyond a requirement for me to be there. I can ask the bot "can you clarify X section" at 3am and it'll do in 1 second. In class, the teacher reads the slides, adds some extra points and takes questions. But my bot does all this, completely personalized to me.
Times move on, and I don't see teachers surviving. I'm hoping by next year the college will offer an "at home, self paced course" option. Theres no reason for me to be there.
by Ralo - So I teach a couple of web architecture classes, and it’s been my experience that there is still a desire to learn how to truly build quality systems because they want to learn how to build quality systems.
I think our industry needs to transition to figure out what does it mean to build a quality system in the age of AI? It’s one thing to vibe code an application that works. But would you depend your livelihood on it? Would you get business funding and base the future of your team on it?
I think the challenge of the educational space is to figure out how to teach the judgement to build solid systems without relying on “hand coding”. For me I’ve been working on technology agnostic composable frameworks that help build that judgement. But honestly we are all just figuring this out together.
There will still be a need to be able to train system builders, the question really is what do they actually need to learn to be solid system builders in this AI era. And there’s a strong chance that pure programming language or tech stack learning may not be enough. This is what we educators need to figure out.
by kayashaolu2 - Founder of Boot.dev here: the industry is absolutely having a VERY rough time in 2026. Our revenue in particular is actually up in 2026 vs 2025, but modestly low double digit percent) vs 2025 being nearly double 2024. That said, I feel we're an exception in that we have been able to grow, mostly due to a complete doubling down on high quality human content, and trying to build experiences you just can't get from plain text (interactivity, animations, etc). time will tell.by wagslane
- Around 10 years ago I used to worry a little about keeping up with younger folks. I only really started using computers when I was 9-10, and didn't get the internet at home until my mid-teens. I thought there's probably kids learning JS at age 6 these days... These kids are going to be almost super-human in their technical skills compared to someone like myself who didn't learn HTML until their early teens.
But then around 2020 I realised kids actually have no clue how to use computers because their primary computer is a phone or a tablet. And there only experience with desktop computers are Chromebooks at school.
Now I suspect my generation will be the most knowledgable technical generation by far. The only issue for us is that no one will care.
As someone who used to write a lot of tech tutorials and has published a technical book, for me it's much less a financial thing and much more just sadness that something I spent a lot of my life doing, that used to generate emails every week from people saying thank you for helping them is now just completely worthless. I've spoken about this before here, but I feel like I've lost a large part of myself over the last couple of years because almost everything I used to do in my spare time now seems completely pointless.
by kypro - As an educator and author, I feel Molly's pain (Anthropic owes me $60k for pirated books). My course and book sales have dropped significantly. I feel like much of the value in my book-based knowledge is harder to discover in AI agents. (One example might be inspecting SHAP values from XGBoost models and using that as feedback to feature engineering for a linear model (like Logistic Regression)). So I'm afraid more advanced techniques might go by the wayside in the name of delivery speed.
I use AI both in the cloud (I tend to avoid Anthropic...) and with local models.
I've probably written more code in the past year than in my whole career. I can now create what I desire (I just created a telemark skiing game over the weekend based on my Strava segments). I think this has helped my teaching as well. In a recent ML course, I created many interactive examples that in the past were just doodles on my whiteboard, but are now embedded in the notebooks I give students.
I've also taught a few AI courses to clients as well. However, with the inevitable rust/ASM-ification of everything, I'm not sure how long those skills will be valid. I do, however, think that having a human in the loop for ML and data analysis is still important.
- I'm on the same boat, negatively affected by AI. I'm the CEO and founder of an EdTech company and our B2C revenue has greatly decreased as a result of Gen AI.
But unlike the author of this post (or cited authors) I'm not sore. AI is providing a much better education model for students. So it's us that we have to adapt, not complain that the world is changing.
For example, in Machine Learning our projects are sometimes extremely long and repetitive. Because before AI, it was a valuable skill to be able to recall APIs quickly (what is the method to drop duplicates in a dataframe again?).
But today that's no longer the truth, and for the better. I am happy students don't have to go over the same repetitive low-value processes that I had to go to. Students can now focus on more high level valuable tasks: understand the domain, compare evaluation methods, compare models, balance tradeoffs, present solutions/reports, etc.
It's like in the 50s when students had to memorize tables of logarithms. Nobody misses that.
We are in the process of recreating all our projects to adapt to the new world. We'll see how it goes.
- I am genuinely starting to fear an almost universal dumbing down of humanity as a result of AI. It feels like so many people have just stopped caring about what they do or what they create. They no longer believe they need to know what they are doing or care about the results. The machines will do the knowing and caring for them. Or they won't - but what they produce without knowing or caring will be good enough to tick whatever boxes are necessary so it doesn't matter.
Over the years I have enjoyed many deep and interesting technical discussions online - both here on HN and elsewhere. I can't help feeling that those discussions have also all but disappeared over the past few months - alongside high quality free material that some experts have kindly given away but increasingly no longer do. I rarely find myself contributing any more because there is little on any of my usual online hangouts that is interesting enough to spark a deep technical discussion any more. It's 90% AI stuff - either written about AI or (usually poorly) written by AI - and most of the discussions that do initially catch my interest at all seem to end being very negative and depressing. (I appreciate the irony that I'm perpetuating that cycle right now. Looking through my relatively sparse recent contributions to HN is actually quite depressing. Although at least I actually am getting out more!)
I wouldn't mind so much if I believed the hype about the benefits of AI-generated work and thought that overall this period would be a net win for humanity. Unfortunately I don't really believe that. I fear the LLM era will eventually be remembered for producing tools that could do average/routine tasks quickly and to an acceptable standard but that were actually used to (try to) do good/innovative tasks with substandard results. But by the time enough decision-makers realise this it might be too late and the damage might already be done and hard to reverse.
by Silhouette