Nvidia was 226 in August (14th)
Today 161... So is also massively down, and not just since the last few days, it's been trending downward for months.
But Jensen also sold 1B of his stock over that same time period, that would also deprecate the value through higher availability. And he may continue to, especially now.
Buying the dip is a strategy here that'd be incredibly spicy, but ymmv. It's also possible that it gets a new height, unlikely but markets aren't rational so impossible to predict
Given current market tends, if that becomes more frequent the likelihood of consumers actually getting GPUs goes up, as these will at least end up on various markets
No, nanite is just having different levels of detail for objects, and which LOD gets selected is done automatically by the engine. So users can use ultra-high-def models (including 3d scans of real-world objects), and the engine automatically generates and uses lower polygon versions for when the object is far away or whatever.
Strange, when I looked into it that seemed to be just a desirable side effect with the rendering being done via the triangles - which was also supposedly the reason why you could directly use scanned models, despite them having millions of triangles in their model
But again, I've really only super superficial understanding of this, I just understood it like that back then
I learned about gaussian splattering mere months before nanite was introduced and their technical talks and articles always sounded exactly like what I just learned about the splattering, that's why I got that impression
Fwiw, that's not what the parent was talking about.
You're trying to middle road it. He was talking about actually treating it as a black box, so just give the model your expectations and goals it needs to meet, not tell it a rough solution for it to apply in detail.
The expectation/goal approach in a ai edited project works better then the middle road since opus5.5 ime. Depending on your project structure, it may not be viable however without large refactorings
For me it's really hard to find a balance with LLMs. Even trying one to add .NET IR codegen to my compiler that already had a working C++, JS, and C# backend for it to use as a reference. It just kept doing really odd stuff and when we got to generics it had done such a bad job with the prior work it struggled to collect the information it needed from .NET to do generic calls correctly, and mid session decided to just make everything reflection based with no notice to me. It had a doc of the AST with notes on what the nodes mean and how different constructs are represented, it's a pretty typical ML family language. Plus a working C# backend to reference.
Other times it will one shot things near perfectly that I was expecting to just use as a reference. Demo pages for work I've done its so fast and gets good enough right out the door
Every time I've tried to use it to write something small in my compiler? :"( pain. It's the perfect intersection of looks good and covers 90% of cases fine but explodes far away because some subtle invariant is wrong. I can't always think of the invariants up front without writing the code myself
When did you try that? The rate of improvement of models has been hard to keep up with.
I recently asked Gemini 4 Argon a question about a gcloud api, and to answer the question it decompiled the Google Java client library (without me asking) and figured out how it worked.
I do wonder if models struggle with .NET IR because there may be less info about it in its training data. When you’re working with open source systems it has much more to go on.
I’ve used models to generate ONNX IR (executable format for deep learning models), and that worked well. But the ONNX IR is much simpler.
This was about 2 months ago using 5.6 Sol and 6.0 Astra (I used Astra for the initial steps and setup and discussion and used separate prompts with Sol in the Codex desktop app afterwards).
The issue that it ran into is that it needed to keep track of .NET assembly and type information to do do generic calls (you need a reference to the actual type/method information, which you would need to collect and keep for lookup when compiling your AST into .NET). I didn't ask it to do generics until a handful of prompts in, and the codebase was not terrible but sketchy enough that once the LLM realized it needed type information it hadn't been tracking, it tried, flopped a couple times, and reached for reflection to avoid the problem
This is the kind of thing I might have considered to flag to the LLM ahead of time if I had written a .NET IR generator before, but I'm just a long time user/typically focus on native code or transpilation. If I was writing it myself I would have noticed early on that generics should be filed under "weird shit to verify" given it requires me to track info that is not in my AST, but when I'm prompting an LLM my mind is not on that, I'm focused on the higher level constraints + semantics (ie how suspend/resume works or rules around product types etc), and not details like that
I think it could been affected by a lack of training data, I don't know how many feature complete compilers for typed languages exist out there for .NET. C# and F# of course, and there are compilers for SML, Koka, and maybe some others I cant think of, but most were for dynamic languages written many years ago.
.NET IR was standardized before .NET core even IIRC and everything I use in the .NET stack has been MIT for a long time now
I wanted to reply to this point separately instead of one massive reply
> I recently asked Gemini 4 Argon a question about a gcloud api, and to answer the question it decompiled the Google Java client library (without me asking) and figured out how it worked.
I've noticed the same sorts of things happen and its kind of funny, because at times it is the right move, and others I have to interrupt the model and remind it that there is a uh, documentation, it can just skim real quick
It is super handy but also a little funny how quick they are to "let me just reverse engineer this real quick" before considering a short search
Expectations and goals can be technically nonsensical. Agents have gotten really good, but they can’t magically solve intractable problems. If you’re out of your technical depth it can be easy to set unrealistic expectations without realizing it
Then you're still middle-roading it from my perspective. Treating it as a black box means you're only specifying what comes out, essentially.
But to preempt a "I can't do that in my brownfield distributed monolith I'm maintaining at $dayjob": yes, that's why I said it may not be possible without refactoring.
I think we are talking past each other a bit? I’m curious about GP’s perspective here
My point is that you can ask for unrealistic outputs even if you’re treating the system as a black box. This is the classic case of higher level managers asking for infeasible results from their engineering team because they haven’t considered they’re trying to solve the wrong problem. Depends what you’re doing but I don’t think agents tend to push back on badly specified goals unless you’re proactive about it
Yeah I think this is the main issue at play. Bad engineers are now 10x bad engineers with the ability to hide behind the mask cause they can output something that looks right, but they don't understand it. You can "make" an agent do ANYTHING. There is no rigor, there is no taste, there is no risk. Anecdotally I have seen this play out IRL. I worked with a 3-month boot-camper that somehow stumbled into a software developer role at a small company. The amount of slop that he was creating was unprecedented. No formal training, no idea of abstraction, letting/making the agent reinvent the wheel for everything. The LLM was doing the whole "you're absolutely right" so he was also under the impression that he was a real engineer doing the "right thing". Concretely he convinced our manager that we need to re-write our web-app in Go because it was so much faster(0 users btw) and safer than python. He had an agent build out this test suite and there was a metric like Go vs Python DB operations, Go being 107x better. Seemed sus, looked into it and he(his agent) removed connection pooling for the python side but for the Go side, 100 concurrent connections, 10 idle and a connection lifespan of 1 hour. His reason for that was that the python code kept crashing every time his agent adding pooling so he had the agent removed it. This sheet got printed as is and distributed to our C-suite to much praise. I know this is a very specific example but I fear this is happening at some level everywhere. Where in the past a bad engineer to this degree would stick out like a sore thumb if they were surrounded by competent people that have rigorous standards. An LLM/AI doesn't have to worry about what happens if things don't work and when it's confirming that YOU are a genius every step of the way. It's going to be a disaster when the reckoning comes. I am not a AI luddite but software engineering is just as much as a culture as it is art and science. Without that backstop I just think that we will see a huge degradation in quality because there is no incentive for LLMs/AI to write "good software".
You don't give it tasks. You give it what you want. It decides the tasks, and then executes. Then you give it feedback. "black-box approach" here is just a fancy word for vibe coding.
I want to be rich, healthy, happy, young, smart, beautiful. Same for all my friends and family please. How many tokens?
PS: make no mistakes and tell me in advance if I forgot anything that I could regret later.
Or on a more technical side: I would like all problems that any app/company/product could solve to be solved in the best possible way, so that nobody has to even think about what kind of problems even could be solved. Go iterate on that but do not make any mistake, do not ask any questions, be done by tomorrow, do not interpret what I said in any bad way.
Eh, the industries went pretty long with the title being programmer? Didn't it only start with faang that things kept getting sillier? Tbf, I wasn't yet in the job market before the dotcom bubble, so maybe it only seems like it to me who started in the 2010s
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