HN user
Programmer.
Currently located in the US.
SK Hynix stock went up over 9% - today. Up 72% in the last month. 323% in the last six months. 978% in the past year.
Micron up 3% today, 76% last month, 292% last six months, 863% in the past year.
I bought Micron in mid-March when it dipped. I looked at SK Hynix last week with thoughts to buy, but it had gone up so much in the past month I figured too late. Nope, up 9% today.
Tangential to the theme, here is the HN post about the (AFAIK) first public success of deep learning techniques with SuperVision's AlexNet. You can read what their prognosis on the future success of deep learning was (hint: same prognosis as Dropbox)
With Gemini 3, I wrote an Emacs Lisp which can tell if a number is prime or not using only primitive recursive functions. That was done at the end of last year, and none of the frontier LLMs were able to do it earlier in 2025.
I had some test functions where minimization could be optionally used, but wanted to do one where minimization was needed, like the Ackermann function. Most of the frontier models struggled with doing this, although I may have been prompting incorrectly. Although - if I had been prompting totally correctly, I probably could have gotten what I got out of a frontier LLM in early 2025 and before.
Incidentally the test function that tells you if a number is prime in Emacs Lisp with primitive recursion is
(defalias 'prime (c (c (c (r 's (c 'z (p 1))) (p 1) 'z) (c (r (p 1) (c 's (p 2))) (c (c (c (r 'z (c (c 's 'z) (p 1))) (p 1) 'z) (c (r (p 1) (c (c (r 'z (p 1)) (p 1) 'z) (p 2))) (p 1) (p 2))) (p 2) (p 1)) (c (c (c (r 'z (c (c 's 'z) (p 1))) (p 1) 'z) (c (r (p 1) (c (c (r 'z (p 1)) (p 1) 'z) (p 2))) (p 2) (p 1))) (p 2) (p 1)))) (c (c (r 'z (c (r (p 1) (c 's (p 2))) (c (c (r 'z (c (r (p 1) (c 's (p 2))) (p 2) (p 3))) (c (c (c (r 's (c 'z (p 1))) (p 1) 'z) (c (r (p 1) (c 's (p 2))) (c (c (c (r 'z (c (c 's 'z) (p 1))) (p 1) 'z) (c (r (p 1) (c (c (r 'z (p 1)) (p 1) 'z) (p 2))) (p 1) (p 2))) (p 2) (p 1)) (c (c (c (r 'z (c (c 's 'z) (p 1))) (p 1) 'z) (c (r (p 1) (c (c (r 'z (p 1)) (p 1) 'z) (p 2))) (p 2) (p 1))) (p 2) (p 1)))) (c (c (r (p 1) (c (c (r 'z (p 1)) (p 1) 'z) (p 2))) (c (r 'z (c (r (p 1) (c 's (p 2))) (p 2) (p 3))) (p 2) (c (r 'z (c (r (p 1) (c 's (p 2))) (p 2) (c (c (r 's (c 'z (p 1))) (p 1) 'z) (c (r 'z (c (r 'z (c (r (p 1) (c 's (p 2))) (p 2) (p 3))) (c 's (p 2)) (c (c (r 's (c 'z (p 1))) (p 1) 'z) (c (c (c (r 's (c 'z (p 1))) (p 1) 'z) (c (r (p 1) (c 's (p 2))) (c (c (c (r 'z (c (c 's 'z) (p 1))) (p 1) 'z) (c (r (p 1) (c (c (r 'z (p 1)) (p 1) 'z) (p 2))) (p 1) (p 2))) (p 2) (p 1)) (c (c (c (r 'z (c (c 's 'z) (p 1))) (p 1) 'z) (c (r (p 1) (c (c (r 'z (p 1)) (p 1) 'z) (p 2))) (p 2) (p 1))) (p 2) (p 1)))) (c 's (p 2)) (p 3))))) (c 's (p 1)) (p 3))))) (p 1) (p 2))) (p 1)) (p 1) (p 2)) (c 'z (p 1))) (c (c (r 'z (c (c 's 'z) (p 1))) (p 1) 'z) (p 1))) (p 3) (c 's (p 1))) (p 2))) (p 1) (p 1)) (p 1)) (c 's (c 's 'z))))
Stock market last 365 days:
S&P 500 - up 16%
Nvidia - up 25%
ICLN (global clean energy ETF) - up 46%
Micron - up 246%
Seagate - up 270%
Western Digital - up 342%
Dijkstra's notions about provable functions are probably more important during these times where LLMs are churning out hallucinated code.
I am on the free tier of Gemini 3. With some intervention on my part, I got it to build, in Emacs Lisp, a primitive-recursive function for determining if a number is prime (by mu-recursive I mean a function built from the building blocks of a constant, successor and projection function, as well as a primitive recursive function and compositional function/macro). I was impressed, as previous models (including Anthropic and OpenAI) could not do this.
For the past few days I asked it to built a mu-recursive Ackermann function in Emacs Lisp (built on the primitive-recursive functions/operators, plus an extra operator - minimization). I said that the prime detector function it already built should be able to use the same functions/operators, and to rewrite code if necessary.
So far it has been unable to do this. If I thought it could but was stumbling over Emacs Lisp I might ask it to try in Scheme or Common Lisp or some other language. It's possible I'll get it to work in the time I have allotted from my daily free tier, but I have had no success so far. I am also starting with inputs to the Ackermann function of 0,0 - 0,1 - 1,0 - 1,1 to not overburden the system but it can't even handle 0, 0. Also it tries to redefine the Emacs Lisp keyword "and", which Emacs hiccups on.
A year ago LLMs were stumbling over Leetcode and Project Euler functions I was asking it to make. They seem to have gotten a little better, and I'm impressed Gemini 3 can handle, with help, primitive recursive functions in Emacs Lisp. Doesn't seem to be able to handle mu-recursive functions with minimization yet though. The trivial, toy implementations of these things. Also as I said, it tried to redefine "and" as well, which Emacs Lisp fell over on.
So it's a helpful helper and tool, but definitely not ready to hand things over to. As the saying goes, the first 90% of the code takes 90% of the time, and the last 10% of the code takes the other 90% of the time. Or the other saying - it's harder to find bugs than write code, so if you're coding at peak mental capacity, finding bugs becomes impossible. It does have its uses though, and has been getting better.
It's The Mythical Man Month idea. Programming software is a different thing than working on an assembly line, or a call center, or in retail sales. You're much better off having four programmers who are worth paying $200k a year than ten programmers who are worth paying $75k a year.
Yes...NVDA closed at $188.15 yesterday, a price it was never at until October. It did hit $212.19 last week, but retreated.
After spring 2023, Nvidia stock seems to follow a pattern. It has a run-up prior to earnings, it beats the forecast, with the future forecast replaced with an even more amazing forecast, and then the stock goes down for a bit. It also has runs - it went up in the first half of 2024, as well as from April to now.
Who knows how much longer it can go on, but I remember 1999 and things were crazier then. In some ways things were crazier three years ago with FAANG salaries etc. There is a lot of capital spending, the question is are these LLMs with some tweaking worth the capital spending, and it's too early to tell that fully. Of course a big theoretical breakthrough like the utility of deep learning, or transformers or the like would help, but those only come along every few years (if at all).
From spring 2019 to July of this year, I worked at IT at a Fortune 100 retailer.
The project I worked on was enormously successful in terms of revenue growth. I, and people on my team had a huge nationwide impact, which started when we were about a dozen people (it has grown now to several dozen).
Whereas even a manager of one of the big box stores would only have a limited geographical impact. Whereas my work would always have nation-wide impact (and at some companies programmers would have world-wide impact). I turned on a payment option for my platform one quarter, and very quickly people were using it for one million a month in purchases. Which kept going up.
The book Capitalism without Capital talks about this. Some aspects of it are alluded to in Fred Brooks 1975 book The Mythical Man Month.
To build a car, a lot of effort has to be made in making the car - not just the end result, but the making the glass, tires and so forth. Whereas with programming, I write an app, or a feature for an app, and the end result is duplicated and distributed around the country (or even around the world) for free, or virtually free. I'm not helping make commodities one at a time like someone on an automobile line is. It is something different.
If so, why wouldn't OpenAI or Anthropic etc get there first given their resources and early access to leading technology?
innovator's dilemma
It lists 118698 installs of firefox-esr.
NVDA had a surprise earnings on May 24, 2023, closing at $305 and opening at $385 (before the 10 for 1 split). Pretty much every earnings day since then has been the same - the numbers come out, they beat what they estimated, the stock goes down a little. People have been doom and glooming it every earnings call - you can read the threads here from May 2023, people were saying it was a bubble then, and it's over four times what it was then.
It sure wasn't when AlexNet won the ImageNet challenge 13 years ago
The ten most valuable S&P 500 companies are, in order of market cap:
Nvidia, Microsoft, Apple, Alphabet/Google, Amazon, Meta/Facebook, Broadcom, Tesla, Berkshire Hathaway, and Walmart.
A commonality to most of them (and to a lesser extent all of them) is they write software.
If a company not on the list like Ford has an F-150 truck come off the assembly line, some of that $40,000 cost is in the capital expenditure for the plant, any automation it has, the software in the car and so on. But Ford has to pay for the aluminum, steel and glass for each truck. It has to pay for thousands of workers on the assembly line to attach and assemble parts for each truck.
Meanwhile, at Apple a team writes iOS 18, mostly based on iOS 17, and it ships with the devices. Once it is written that's it for what goes off on iPhone 16. There may be some additional tweaks up until iOS 18.6. The relatively small team working on iOS has it going out with tens of millions of units. Their work is not as connected to the process of production as the assembly line people attaching and assembling the F-150 truck. If some inessential feature is not done as a phone is being made, it will be punted to next release. This can't be done with an F-150 truck.
Software properly done is just much more profitable than non-software work. We can see this here. Yes, some of the latest boost is due to AI hype (which may or may not come to fruition in the near future), but these companies got to this position before all of that.
I was watching a speech by Gabe Newell talking about the (smaller) software industry of the 1990s, and the idea back then to outsource and try to save on salary costs. He said he and his partners went the other way and decided to look for the most expensive and best programmers they could find, and Valve has had great success with that. Over the past 2 1/2 years we've seen a lot of outsourcing to cheaper foreign labor, FAANG layoffs (including Microsoft's recent Xbox layoffs), and more recently attempts to lower costs by having software produced by less experienced vibe coders using "AI". I have seen myself at Fortune 100 companies, especially non-tech ones, that the lessons of the late 1960s NATO software engineering conferences, or the lessons learned by Fred Brooks while managing the OS/360 project in the 1960s haven't been learned. Software can be a very, very profitable enterprise, and it is sometimes done right, but companies are still often doing things in the same way they were attempting such projects in the early 1960s. Even attempts to fix things like agile and scrum get twisted around as window dressing to doing things in the old-fashioned corporate way.
I've been reading this website for probably 15 years, its never been this bad.
People here were pretty skeptical about AlexNet, when it won the ImageNet challenge 13 years ago.
Cisco stock (which I thought about buying in 1992 and didn't, unfortunately) doubled in 1990, tripled in 1991, doubled in 1992, and kept going up every year - in 1995 it doubled, in 1998 it doubled, in 1999 it doubled. So it had a long run (and is also still worth over $250 billion).
The monetary push is very LLM based. One thing being pushed that I am familiar with is LLM assisted programming. LLMs are being pushed to do other things as well. If LLMs don't improve more, or if companies don't see the monetary benefits of using them in the short/medium term, that would drag Nvidia down.
Nvidia has a lot of network effects. Probably only Google has some immunity to that (with its TPUs). I doubt Nvidia will have competition in training LLMs for a while. It is possible a competitor could start taking market share on the low end for inference, but even that would take a while. People have been talking about AMD competition for over two years, and I haven't seen anything that even seems like it might have potential yet, especially on the high end.
Nvidia's trailing P/E ratio is 53 (stock hitting a new high today). Its forward P/E ratio is 38.
A year ago both its trailing and forward P/E were higher. So the stock is relatively a bargain compared to what it was a year ago.
The price implies that revenues and profits are expected to continue to grow.
My intuition is that the absence of the rapid, generationally transformative, advances in tech and industry that were largely seen in the latter half of the 20th-century (quickly followed with smartphones and social networking), stock market investors seem content to force similar patterns onto any marginally plausible narrative that can provide the same aesthetics of growth
I wouldn't disagree with this.
Yes. In 2021, Nvidia was actually making more revenue from its home/consumer/gaming chips than from its data center chips. Now 90% of its revenue is from its data center hardware, and less than 10% of its revenue is from home gpus. The home gpus are an afterthought to them. They take up resources that can be devoted to data center.
Also, in some sense there can be some fear 5090s could cannibalize the data center hardware in some aspects - my desktop has a 3060 and I have trained locally, run LLMs locally etc. It doesn't make business sense at this time for Nvidia to meet consumer demand.
The book Androids by Chet Haase talks about how the early Android team had a lot of ex-Palm people on it.
Most human ten year olds in school can add two large numbers together. If a connectionist network is supposed to model the human brain, it should be able to do that. Maybe LLMs can do a lot of things, but if they can't do that, then they're an incomplete model of the human brain.
People have different opinions about this, but I think one problem is there are different questions.
One is - Google, Facebook, OpenAI, Anthropic, Deepseek etc. have put a lot of capital expenditure into train frontier large language models, and are continuing to do so. There is a current bet that growing the size of LLMs, with more or maybe even synthetic data, with some minor breakthroughs (nothing as big as the Alexnet deep learning breakthrough, or transformers), will have a payoff for at least the leading frontier model. Similar to Moore's law for ICs, the bet is that more data and more parameters will yield a more powerful LLM - without that much more innovation needed. So the question for this is whether the capital expenditure for this bet will pay off.
Then there's the question of how useful current LLMs are, whether we expect to see breakthroughs at the level of Alexnet or transformers in the coming decades, whether non-LLM neural networks will become useful - text-to-image, image-to-text, text-to-video, video-to-text, image-to-video, text-to-audio and so on.
So there's the business side question, of whether the bet that spending a lot of capital expenditure training a frontier model will be worth it for the winner in the next few years - with the method being an increase in data, perhaps synthetic data, and increasing the parameter numbers - without much major innovation expected. Then there's every other question around this. All questions may seem important but the first one is what seems important to business, and is connected to a lot of the capital spending being done on all of this.
If I look at Nvidia stock from mid-June of last year, or the IYW index (Apple, Microsoft, Facebook, Google) - NVDA is down 10%, IYW is down maybe 2-3%. It doesn't feel like I'm in the middle of a huge bubble like, say, the beginning of 2000.
By the late 1960s, software projects had finally reached a certain size, and the particularities of managing these large projects were discussed at NATO software engineering conferences, and codified by Fred Brooks in 1975 in the Mythical Man Month. The upshot being, software projects at corporations can't be run in the same manner as non-software projects. Yet at Fortune 500 companies today, and in the past two years that includes at least some of the FAANGs, you see them trying to run software projects and treating programmers in the same manner they treat non-programmers, and it doesn't work.
I just happened to watch some old talk or interview with Gabe Newell recently, and he said industry thinking at the time Valve was founded was of a certain kind, and Valve took the opposite approach from the industry - the industry was looking to get cheaper programmers, and Valve went the other way and looked for the most expensive programmers, and so forth. Valve has probably less than 400 people working there (don't know the 2025 headcount), but makes billions a year in profit.
Yes, the average VP at JP Morgan makes less than $200k. The VP title at banks does not mean what it means at other companies.
I learned how to do some things in a Unix shell in 1989, like cat, sort, uniq, and piping them together. Now it is 2025, and I am still doing those things, on the Linux box I am typing on now, or some servers I log onto, or in the shell of the MacBook Pro I sometimes use.
Whereas I use an IDE to program Android - in 2011 I was using Eclipse with an Android Developer tool plugin. Then in 2014 Android Studio became the favored IDE, so I had to learn a whole new IDE to do what I was doing before. Speaking of my Linux box and MBP, to go to a line in Android Studio with Linux is Control-G, whereas on an MBP it is Command-L ( https://developer.android.com/studio/intro/keyboard-shortcut... ).
Over the years I learned how to do more things (not enough!) with awk, sed, redirecting STDIN, STDOUT and STDERR, various shell things. It is nice as I accumulated this knowledge over 35 years that I can still use it, and it isn't just effectively tossed out like learning Eclipse IDE keybindings was (and mapping them to AS didn't make much sense to me).
With regards to what they're saying, the first thing to do is reverse the perspective. The applicant is hearing a yes or no, so if it's a no they want to know what they did wrong to improve themselves.
From the interviewer's perspective - we get someone who is average, then another person who is average, then someone who has trouble with basic questions, then we get this person who may be as average as the first two, then we get someone who answers every question correctly, and has a deep knowledge of the domain if you drill down, then you get an average person again.
There's nothing really wrong with the person, they did as well as four other people. It's just that someone else came in who was a standard deviation above the majority of the people in the bell in the normally distributed Gaussian curve.
Samsung has its own bootloader. Some people get it to work with AOSP/LineageOS, but it is an extra pain. So I avoid Samsung for LineageOS.
This is a list of support devices
https://wiki.lineageos.org/devices/
I installed LineageOS on a Motorola Edge a bit back.
One problem is it takes a bit of time for a device to get officially supported by LineageOS. By the time it is, stores are often selling the next generation of devices.
That was not the case when I bought an Edge and put LineageOS on it in 2021.
Last week there was a "Switched Back to Windows After over 10 Years on Linux" thread ( https://news.ycombinator.com/item?id=42496032 ), and this story shows one of the reasons I prefer Linux to Windows.
I am saying several different things. For your question, the farther out in the future, the more uncertain it is. The stock analyses from the middle of last year which talked about the threat of AMD in a more immediate tone were obviously wrong. Some of the analysis now that talks about the short-term threat of AMD does not jibe with what programmers here working with AMD have said in threads this week. Or what George Hotz or others are saying about buggy AMD chips.
One point is we here know certain things here that stock analysts looking at the last quarterly report do not. The short-term threat of AMD was certainly overstated by Wall Street last year, and in my opinion still is.
If we stop talking in terms of months and start talking in terms of years, it is harder to tell. Maybe AMD will get its act together and give Nvidia a run for its money. Maybe not. For 2025, I don't think AMD will have much effect on Nvidia though.
Expectations are priced in, and others probably have more information than you anyway.
I have always made money on the stock market. I bought Nvidia in December 2022 and then again in April 2023. Yes the information was out there - anyone reading this board back then could see it. Or who saw Emad Mostaque's tweets that Stable Diffusion was trained on Nvidia, or who knew the semi-public information that OpenAI trained on Nvidia. I bought and it quadrupled in two years when averaging out. The expectations on this board were not priced in the market then.
When FAANG and more b2b tech companies (Salesforce, Oracle) tanked in 2022 I poured money into tech indexes like IYW and IGV. Not as much as into Nvidia, but they were good companies and finally and unusually cheap. They went way up too.
I was making money in the 1990s buying SGI, Sun Microsystems, Netscape, Cisco etc. No brainers for me.
Only two losses I recall - Cascade, a router company whose stock dipped and was bought ny US Robotics on the dip (1990s?). Also some ad company which was crushed by competition with Google.
I completely disagree with your premise. I don't even know how to make a neural network in Pytorch, nor can I do matrix math with NumPy. But in December 2022 Nvidia seemed worth the risk, and by April of last year a lot of that risk evaporated with a huge potential upside. I knew the stock was cheap and all the PhDs on Wall Street did not. Also, they are stuck in a bureaucracy - I used to work in IT for a major investment bank in Manhattan. Watch the Big Short on the pressure the people who were right got from their management and investors. I don't have that pressure, it is my money.
We, the people here, do get insights which are not priced in. It's not that there is no risk, but the coin flip sometimes goes 51% in our favor. Sometimes even 52% (or 53%...or 60%). This is how Warren Buffett, Peter Lynch etc. made money.
Read the Wall Street analyses from the middle of last year on AMD being a big Nvidia competitor, then read the posts here this week about how AMD engineers don't have boards to fix bugs. "We the customer had to mail the AMD engineer a spare board to fix the bug". The cobbler's children wear no shoes at AMD. Then go read some Wall Street report about the stiff competition Nvidia faces from AMD. The information is here, not in the Wall Street analysis report by people who know less than me about how transformers work.