Researching concepts, for one, has become so much easier, especially for things where you don’t know anything yet and would have a hard time to even formulate a search engine query.
LLMs are really valuable for finding information that you aren't able to formulate a proper search query for.
To get the most out of them, ask them to point you to reliable sources instead of explaining directly. Even then, it pays to be very critical of where they're leading you to. To make an LLM the primary window through which you seek new information is extremely precarious epistemologically. Personally, I'd use it as a last resort.
I use LLM to do things like brainstorm, explaining programming concepts and debug. I will not use it to write code. The output is not good enough, and I feel dumber.
I only see the worst of my programming collegues coding with AI. And the results are actual trash. They have no actual understanding of the code "they" are writing, and they have no idea how to actually debug what "they" made, if LLM is not helpful. I can smell the technical debt.
I used to be a bit more open minded on this topic but im increasingly viewing any programmers who use AI for anything other than brainstorming and looking stuff up/explaining it as simply bad at what they do.
You know, aside from AI making it super easy and fast to generate this tech debt in whatever amounts they desire?
AI removes that need. You don't need to know what the function does at all, so your brain devotes to energy towards remembering or understanding it.
Not a billion dollar business yet, maybe, but 300 cars generating five or six figures revenue per year each isn't far off.
(And I say this as someone who is skeptical that totally autonomous cars worldwide will ever be a thing, but you can get to £10Bn far, far before that point. Become the dominant mode of transport in just ONE major American city and you're most of the way there).
Because geo fenced driving in a few select cities with very favourable conditions is not what was promised. That's the crux. They promised us that we have self drive anywhere at anytime at the press of a button.
> Not a billion dollar business yet, maybe, but 300 cars generating five or six figures revenue per year each isn't far off.
I'm not sure how you get to 6 figures revenue. Assuming the car makes $100 per hour for 24x7 52 weeks a year we still fall short of 1 million. But let's assume you're right $300M revenue (not profit, are they even operating at a plus even disregarding R&D costs?) on investment of >10 billion (probably more like 100), seems like the definition of hype.
> (And I say this as someone who is skeptical that totally autonomous cars worldwide will ever be a thing, but you can get to £10Bn far, far before that point. Become the dominant mode of transport in just ONE major American city and you're most of the way there).
What I don't understand with this argument, how are you proposing they become the dominant mode of transport. These services are competing with taxis, what do they offer over taxis that people suddenly switch on mass to self driving taxis? They need to become cost competitive (and convenience competitive) with driving your own car, which would significantly drive down revenue. Secondly if robotaxi companies take over transport, why would the public continue to build their infrastructure and not demand that these robotaxi companies start to finance the infrastructure they exclusively use?
So yeah, right now they'd have to be at ten cities x 300 cars each to hit 300M revenue, but there's still plenty of room for growth. Or should be, assuming the Waymo model isn't maxed out supporting the current handful of cities.
But I'm not convinced they have to hit cost parity with personal cars, because the huge advantage is you can work and drive (or be driven). If NYC and LA rush-hour congestion time becomes productive time, there's your billions.
I drive but prefer to take transit for this reason - some of my colleagues are able to join work calls effectively while driving, but for whatever reason my brain doesn't allow that. Just paying attention to calls is enough, you want me to pay attention to the road AND the call?
Specifically, natural language is:
- ambiguous (LLMs solve this to a certain extent)
- extremely verbose
- doesn't lend itself well to refactoring
- the same thing can be expressed in way too many different ways, which leads to instability in specs -> code -> specs -> code -> specs loops (and these are essential to do incremental work)
Maybe LLMs can be sued to design such a thing ?
nice misspelling (or a joke?), related to all the lawsuits around LLMs.
Joking aside, it’s already there in a sense. Several times I started with a brief outline of what the prototype should do (an HTML/CSS/JS app), and sure enough, refinements and corrections followed. When the final version worked more or less as expected, I asked the LLM to create a specification (a reproducing prompt) of everything we made together. Even if the vibe-coded prototype is dropped, the time wasn’t wasted, I probably would never have come to the same bullet list specification without having an actual working app at my disposal to test and evaluate. So paradoxically this specification even might be used by a human later
True in the long run. Like a car with a high acceleration but low top speed.
AI makes you start fast, but regret later because you don't have the top speed.
Chill. Interesting times. Learn stuff, like always. Iterate. Be mindful and intentional and don't just chase mirrors but be practical.
The rest is fluff. You know yourself.
I am not defending we should drop AI, but we should really measure its effects and take actions accordingly. It's more than just getting more productivity.
I’m could have used an LLM to assist but then I wouldn’t have learned much.
But I did use an LLM to make a management wrapper to present a menu of options (cli right now) and call the scripts. That probably saved me an hour, easily.
That’s my comfort level for anything even remotely “complicated”.
Personal experience (data points count = 1), as a somewhat seasoned dev (>30yrs of coding), it makes me WAY faster. I confess to not read the code produced at each iteration other than skimming through it for obvious architectural code smell, but I do read the final version line by line and make a few changes until I'm happy.
Long story short: things that would take me a week to put together now take a couple of hours. The vast bulk of the time saved is not having to identify the libraries I need, and not to have to rummage through API documentation.
if you know the fundamentals really well. AI coding actually speeds up development process.
source to the article: https://geohot.github.io//blog/jekyll/update/2025/09/12/ai-c...
are you planning to stream it? @ThePrimeagen
I'm not.
I can build anything, but often struggle with getting bogged down with all the basic work. I love AI for speed running through all the boring stuff and getting to the good parts.
I liken AI development to a developer somewhere between junior and mid-level, someone I can given a paragraph or two of thought out instructions and have them bang out an hour of work. (The potential for then stunting the growth of actual juniors into tomorrow's senior developers is a serious concern, but a separate problem to solve)
Shhh, WIP blog post (on webpipe powered blog)
https://williamcotton.com/articles/introducing-web-pipe
Yes, I wrote my own DSL, complete with BDD testing framework, to write my blog with. In Rust!
GET /hello/:world
|> jq: `{ world: .params.world }`
|> handlebars: `<p>hello, {{world}}</p>`
describe "hello, world"
it "calls the route"
when calling GET /hello/world
then status is 200
and output equals `<p>hello, world</p>`
In some cases, especially with the more senior devs in my org, fear of the good parts is why they're against AI. Devs often want the inherent safety of the boring, easy stuff for a while. AI changes the job to be a constant struggle with hard problems. That isn't necessarily a good thing. If you're actually senior by virtue of time rather than skill, you can only take on a limited number of challenging things one after another before you get exhausted.
Companies need to realise that AI to go faster is great, but there's still a cognitive impact on the people. A little respite from the hardcore stuff is genuinely useful sometimes. Taking all of that away will be bad for people.
That said, some devs hate the boring easy bits and will thrive. As with everything, individuals need to be managed as individuals.
I find this hilarious. From what I've seen watching people do it, it changes the job from deep thought and figuring out a good design to pulling a lever on a slot machine and hoping something good pops out.
The studies that show diminished critical thinking have matched what i saw anecdotally pairing with people who vibe coded. It replaced deep critical thinking with a kind of faith based gambler's mentality ("maybe if i tell it to think really hard it'll do it right next time...").
The only times ive seen a notable productivity improvement is when it was something not novel that didnt particularly matter if what popped out was shit - e.g. a proof of concept, ad hoc app, something that would naturally either work or fail obviously, etc. The buzz people get from these gamblers' highs when it works seems to make them happier than if they didnt use it at all though.
To pair this with the comment you're responding to, the decline in critical thinking is probably a sign that there's many who aren't as senior as their paycheck suggests. AI will likely lead to us being able to differentiate between who the architects/artisans are, and who the assembly line workers are. Like I said, that's not a new problem, it's just that AI lays that truth bare. That will have an effect generation over generation, but that's been the story of progress in pretty much every industry for time eternal.
1) you try to explain what you want to get done
2) you try to explain what you want to get done and how to get it done
The first one is gambling, the second one has very small failure rate, at worst, the plan it presents shows it's not getting the solution you want it to do.
The issue of senior-juniors has always been a problem; AI simply means they're losing their hiding spots.
Very true. I think AI (especially Claude Code) forced me to actually think about the problem at hand before implementing the solution. And more importantly, write down my thoughts before they fleet away from my feeble mind. A discipline that I wished I had before.
These days the only difference is that I feed my ideas to a few different LLMs to have "different opinions". Usually they're crap but sometimes they present something useful that I can implement.
That looks like plenty of hours of fun! Thanks for the link :)
There's also the fact that, while you're coding the easy stuff, your mind is thinking about the hard stuff, looking things up, seeing how they articulate. If you're spending 100% of your time on hard stuff, you might be hurting these preliminaries.
My life quality (as a startup cofounder wearing many different hats across the whole stack) would drop significantly if Cursor-like tools [1] were taken away from me, because it takes me a lot of mental effort to push myself to do the boring task, which leads to procrastination, which leads to delays, which leads to frustration. Being able to offload such tasks to AI is incredibly valuable, and since I've been in this space from "day 1", I think I have a very good grasp on what type of task I can trust it to do correctly. Here are some examples:
- Add logging throughout some code
- Turn a set of function calls that have gotten too deep into a nice class with clean interfaces
- Build a Streamlit dashboard that shows some basic stats from some table in the database
- Rewrite this LLM prompt to fix any typos and inconsistencies - yeah, "compiling" English instructions into English code also works great!
- Write all the "create index" lines for this SQL table, so that <insert a bunch of search usecases> perform well.
[1] I'm actually currently back to Copilot Chat, but it doesn't really matter that much.
Obviously... in what way? I feel the anti-ai pattern is clear.
Self-driving cars don't work in my city so the whole concept is a hoax. LLMs don't code my proprietary language so it's a bubble.
> From this study (https://arxiv.org/abs/2507.09089)
I can tell this is going to be the most misquoted study in blogs and pop-sci books after the 10,000-hour mastery study. And it's just a preprint!
> This isn’t to say “AI” technology won’t lead to some extremely good tools. But I argue this comes from increased amounts of search and optimization and patterns to crib from, not from any magic “the AI is doing the coding”
* I can tell claude code to crank out some basic crud api and it will crank it out in a minute saving me an hour or so.
* I need an implementation of an algorithm that has been coded a million times on github, I ask the AI to do it and it cranks out a correct working implementation.
If I only use the AI in its wheelhouse it works very well, otherwise it sucks.
Did geohot not found one of these?
In any case I don't fully understand what he's trying to say other than negating the hype (which i generally agree with), but not offering any alternative thoughts of his own other than- we have bad tools and programming language. (why? how are they bad? what needs to change for them to be good?)
> It’s why the world wasted $10B+ on self driving car companies that obviously made no sense. There’s a much bigger market for truths that pump bags vs truths that don’t.
This reeks of bias-dismissing massive investments as ‘obvious’ nonsense while hyping its own tinygrad as the ‘truth’ in AI coding.
Author is allowed to claim ‘most people do not care to find the truth’ but it’s hypocritical when the post ignores counterpoints, like PyTorch’s dominance in efficient coding benchmarks.
Author doesn’t seem to care about finding the full truth either, just the version that pumps its bag.
- Autocomplete in Cursor. People think of AI agents first when they talk about AI coding but LLM-powered autocomplete is a huge productivity boost. It merges seamlessly with your existing workflow, prompting is just writings comments, it can edit multiple lines at once or redirect you to the appropriate part of the codebase, and if the output isn’t what you need you don’t waste much time because you can just choose to ignore it and write code as you usually do.
- Generating coding examples from documentation. Hallucination is basically a non-problem with Gemini Pro 2.5 especially if you give it the right context. This gets me up to speed on a new library or framework very quickly. Basically a stack overflow replacement.
- Debugging. Not always guaranteed to work, but when I’m stuck at a problem for too long, it can provide a solution, or give me a fresh new perspective.
- Self contained scripts. It’s ideal for this, like making package installers, cmake configurations, data processing, serverless micro services, etc.
- Understanding and brainstorming new solutions.
- Vibe coding parts of the codebase that don’t need deep integration. E.g. create a web component with X and Y feature, a C++ function that does a well defined purpose, or a simple file browser. I do wonder if a functional programming paradigm would be better when working with LLMs since by avoiding side effects you can work around their weaknesses when it comes to large codebases.
He is right, however AI is still darn useful. He hints at why: patterns.
Writing a test suite for a new class when an existing one is in place is a breeze. It even can come up with tests you wouldnt have thought of or would have been too time pressed to check.
It also applies to non-test code too. If you have the structure it can knock a new one out.
You could have some lisp contraption that DRYs all the WETs so there is zero boilerplate. But in reality we are not crafting these perfect cosebases, we make readable, low-magic and boilerplatey code on tbe whole in our jobs.
But, what's with the self driving hate? I take Waymos on a regular basis, and he is basing his credibility on the claim that they are not a thing. Makes him sound bitter more than insightful.
I don’t live in California (like most of the population of the planet) - Toronto for 18 years and now the American side of the Great Lakes.
Ice storms, snow, sleet, cold weather 5-6 months out of the year. Batteries suck in the cold, sensors fail or under-perform. Hell, door handles and windows struggle in this weather.
Waymo is not a thing in NY or Chicago or Minneapolis or Philadelphia (I could go on).
My teammate shared 3 phase workflow we are using on our team to deliver project at rapid phase.
It's shared on ClaudeCode subreddit https://www.reddit.com/r/ClaudeCode/s/iy058fH4sZ
I've been using it for months with great success
* Getting good results from AI forced me to think through and think clearly - up front and even harder.
* AI almost forces me to structure and break down my thoughts into smaller more manageable chunks - which is a good thing. (You can't just throw a giant project at it - it gets really far off from what you want if you do that.)
* I have to make it a habit of reading what code it has added - so I understand it and point to it some improvements or rarely fixes (Claude)
* Everyone has what they think are uninteresting parts of a project that they have to put effort into to see the bigger project succeed - AI really helps with those mundane, cog in the wheel things - it not only speeds things up, personally it gives me more momentum/energy to work on the parts that I think are important.
* It's really bad at reusability - most humans will automatically know oh I have a function I wrote to do this thing in this project which I can use in that project. At some point they will turn that into a library. With AI that amount of context is a problem. I found that filling in for AI for this is just as much work and I best do that myself upfront before feeding it to AI - then I have a hope of getting it to understand the dependency structure and what does what.
* Domain specific knowledge - I deal with Google Cloud a lot and use Gemini for understanding what features exist in some GCP product and how I can use it to solve a problem - works amazingly well to save me time. At the least optioning the solution is a big part of work it makes easier.
* Your Git habits have to be top notch so you can untangle any mess AI creates - you reach a point where you have iterated over a feature addition using AI and it's a mess and you know it went off the rails after some point. If you just made one or two commits now you have to unwind everything and hope the good parts return or try to get AI to deal with it which can be risky.
Think about this. Before there were cars on roads, people were just as much happy. Cars came, cities were redesigned for cars with buildings miles apart, and commuting miles became the new norm. You can no longer say cars are useless because the context around them has changed to make the cars a basic need.
AI does same thing. It changes the context in which we work. Everyone expects you use AI (and cars). It becomes a basic need, though a forced one.
To go further, hardly anything produced by science or technology is a basic need for humans. The context got twisted, making them basic needs. Tech solutions create the problems which they claim to solve. The problem did not exist before the solution came around. That's core driving force of business.
Once we realize that what we actually want is turning specifications into software, I think that English will become the base for a new, high level specification language.
--
I've got a cognitive hammer that I tend to over-use, and that is seeing the world through the lens of a Ham Radio operator, and impedance matching[2]. In a normal circuit, maximum power flows when the source of power and the load being driven have the same effective resistance. In radio frequency circuits (and actually any AC circuit) there's another aspect, reactance. It's a time shifted form of current. This is trickier there are now 2 dimensions to consider instead of one, but most of the time, a single value, VSWR is sufficient to tell how well things are matched.
VSWR is adequate to know if a transmitter is going to work, or power bouncing back from the antenna might destroy equipment, but making sure it will work across a wide range of frequencies, yields at least a 3rd dimension. As time progresses, if you actually work with those additional dimensions, it slowly sinks in what works, and how, and what had previously seemed like magic, becomes engineering.
For example, vacuum tube based transmitters have higher resistances that almost any antenna, transformers and coupling through elements that shift power back and forth between the two dimensions allow optimum transfer without losses at the cost of complexity.
On the other hand, semiconductor based transmitters tend to have the opposite problem, their impedances are lower, so different patters work for them, but most people still just see it as "antenna matching", and focus on the single number, ignoring the complexity.
{{Wow... this is a book, not an answer on HN... it'll get shorter after a few edits, I hope, NOPE... it's getting longer...}}
Recently, a tool that used to cost thousands of dollars, the Vector Network Analyzer, has become available at less than $100. It allows for measuring resistance, reactance, and gain simultaneously across frequency. It's like compilers, making things manageable in scope that otherwise seemed too complex. It only took a few times playing with a NanoVNA to understand things that previously would have been some intense EE classwork with Smith Charts.
Similarly, tools like Software Defined Radios for $30, and GNU Radio (for $0.00) allowed understanding digital signal processing in ways that would have been equally difficult without professional instruction. With these tools, you can build a signal flow graph in an interactive window, and in moments have a working radio for FM, AM, Sideband, or any other thing you can imagine. It's magic!
-- back to computing and HN --
In the Beginning was ENIAC, a computer that took days to set up and get working on a given problem by a team with some experience. Then John Von Neumann came along, and added the idea of stored programs, which involved sacrificing the inherently parallel nature of the machine, losing 70% of its performance, but making it possible to set it up for a task simply by loading a "program" onto it's back of instruction switches.
Then came cards and paper tape storage, further increasing the speed at which data and programs could be handled.
It seems to me that compilers were like one of the above tools, they made it possible for humans to do things that only Alan Turing or others similarly skilled could do in the beginning of programming.
Interactive programming increased the availability of compute, and make machines that were much faster that programmers, more easily distributed among teams of programmers.
IDEs were another. Turbo Pascal allowed compile, linking, and execution to happen almost instantly. It widely opened the space for experimentation by reducing the time required to get feedback from minutes to almost zero.
I've done programming on and off through 4 decades of work. Most of my contemplation is as an enthusiast, instead of professional. As far as compilers and the broader areas of Computer Science I haven't formally studied, it seems to me that LLMS, especially the latest "agentic" versions, will allow me to explore things far easier than I might have otherwise done. LLMs have helped me to match my own thoughts across a much wider cognitive impedance landscape. (There's that analogy/hammer in use...)
Compilers are an impedance matching mechanism. Allowing a higher level of abstraction gives flexibility. One of the ideas I've had in the past for helping with better interaction between people and compilers is to allow compilers that also work backwards.[1] I'm beginning to suspect that with LLMs, I might actually be able to attempt to build this system, it's always seemed out of reach because of the levels of complexity involved.
I have several other ideas that might warrant a new attempt, now that I'm out of the job market, and have the required free time and attention.
{{Sorry this turned out to be an essay... I'm not sure how to condense it back down right now}}
--- tl;dr; ----
LLMs are a tool to help match human thought to what computers can do. People would like them to have exact reproducible results, but they're on the other end of the spectrum, more like people than tools. George correctly points out there is a vast space to explore closer to the compute hardware that might profitably be explored.
[1] https://wiki.c2.com/?BidirectionalCompiler
[2] https://en.wikipedia.org/wiki/Impedance_matching
[3] https://garden.joehallenbeck.com/container/mappers-and-packe...
Geohot is easily a 99.999 percentile developer, and yet he can’t seem to reconcile that the other 99.999 percent are doing something much more basic than he can ever comprehend.
It’s some kind of expert paradox, if everyone was as smart and capable as the experts, then they wouldn’t be experts.
I have come across many developers that behave like the AI. Can’t explain codebases they’ve built, can’t maintain consistency.
It’s like a aerospace engineer not believing that the person that designs the toys in an Kinder egg doesn’t know how fluid sims work.
That's the point - it's a higher level of abstraction.
>highly non-deterministic
...not unlike say a boss telling a junior to change something?
The bet here isn't that AI can be as precise as something hand coded but rather that you can move up a step in the abstraction layer. To use his compiler example...I don't care what the resulting assembly instructions look like, just whether it works. It's the same thing here just one level higher
//now I will get rows X, Y, Z from ContentsProvider
then tab tab complete. You can then even tweak the generated code, very useful!
Not everything should make sense. Playing , trying and failing is crucial to make our world nicer. Not overthinking is key, see later what works and why.
The llm prompt has even fewer bits of information specifying the system than code. The model has a lot more bits but still finite. A perfect llm cannot build a perfect app in one shot.
However AIs can research, inquire, and iterate to gain more bits than when you started.
So the comparison to a compiler is not apt because the compiler can’t fix bugs or ask the user for more information about what the program should be.
Most devs are using ai at the autocomplete level which is like this compiler analogy which makes sense in 2025 but that isn’t where we will be in 2030.
What we don’t know is how good the technology will be in the future and how cheap and how fast. But it’s already very different than a compiler.
ChrisMarshallNY•1h ago
Adderall is similar. It makes people feel a lot more productive, but research on its effectiveness[0] seems to show that, at best, we get only a mild improvement in productivity, and marked deterioration of cognitive abilities.
[0] https://pmc.ncbi.nlm.nih.gov/articles/PMC6165228/
luckylion•1h ago
ChrisMarshallNY•1h ago
If legitimate research had found it to be drastically better, that study would definitely have been published in a big way.
Unscientifically, I personally know quite a number of folks that sincerely believed that they couldn’t function without it, but have since learned that they do far better on their own. I haven’t met a single one that actually had their productivity decline (after an adjustment period, of course), after giving up Adderall. In fact, I know several, that have had their careers really take off, after giving it up.
luckylion•46m ago
"Antibiotics don't improve your life, but can damage your health" would likely be the outcome on 13 randomly selected healthy individuals. But do the same study on 13 people with a bacterial infection susceptible to antibiotics and your results will be vastly different.
ChrisMarshallNY•29m ago
They'll need to learn, the same way I see lots of people learn.
It's been around long enough, though, that all the Adderall-eating people should have established a Gattaca-like "elite," with all us "undermen," scrabbling around at their feet.
Not sure why that never happened...
Eikon•1h ago
It seems like that with such small groups and effects you could run the same “study” again and again until you get the result that you initially desired.
ChrisMarshallNY•1h ago
raincole•1h ago
https://pmc.ncbi.nlm.nih.gov/articles/PMC3489818/table/tbl1/
diarrhea•1h ago
ChrisMarshallNY•1h ago
People without ADHD take it, believing that it makes them “super[wo]men.”
bdcravens•1h ago
That said, I'll leave the conclusions about whether it's valuable for those with ADHD to the mental health professionals.
gobdovan•1h ago
joefourier•1h ago
ChrisMarshallNY•1h ago
The ADHD was caught early, and treated, but the dyslexia was not. He thought he was a moron, for much of his early life, and his peers and employers did nothing to discourage that self-diagnosis.
Since he learned of his dyslexia, and started treating it, he has been an engineer at Intel, for most of his career (not that I envy him, right now).
hereme888•6m ago
Their benefits when used as intended are solidly documented in research literature.