I wonder if red choosers really don’t understand that they are choosing to live in a world where half of all people, the more selfless half, are dead. It’s like living through a nuclear war except all of the nice people are gone, not just a random sample
Nobody actually understands what they're doing. When you're learning electronics, you first learn about the "lumped element model" which allows you to simplify Maxwell's equations. I think it is a mistake to think that solving problems with a programming language is "knowing how to do things" - at this point, we've already abstracted assembly language -> machine instructions -> logic gates and buses -> transistors and electronic storage -> lumped matter -> quantum mechanics -> ???? - so I simply don't buy the argument that things will suddenly fall apart by abstracting one level higher. The trick is to get this new level of abstraction to work predictably, which admittedly it isn't yet, but look how far it's come in a short couple of years.
This article first says that you give juniors well-defined projects and let them take a long time because the process is the product. Then goes on to lament the fact that they will no longer have to debug Python code, as if debugging python code is the point of it all. The thing that LLMs can't yet do is pick a high-level direction for a novel problem and iterate until the correct solution is reached. They absolutely can and do iterate until a solution is reached, but it's not necessarily correct. Previously, guiding the direction was the job of the professor. Now, in a smaller sense, the grad student needs to be guiding the direction and validating the details, rather than implementing the details with the professor guiding the direction. This is an improvement - everybody levels up.
I also disagree with the premise that the primary product of astrophysics is scientists. Like any advanced science it requires a lot of scientists to make the breakthroughs that trickle down into technology that improves everyday life, but those breakthroughs would be impossible otherwise. Gauss discovered the normal distribution while trying to understand the measurement error of his telescope. Without general relativity we would not have GPS or precision timekeeping. It uncovers the rules that will allow us to travel interplanetary. Understanding the composition and behavior of stars informs nuclear physics, reactor design, and solar panel design. The computation systems used by advanced science prototyped many commercial advances in computing (HPC, cluster computing, AI itself).
So not only are we developing the tools to improve our understanding of the universe faster, we're leveling everybody up. Students will take on the role of professors (badly, at first, but are professors good at first? probably not, they need time to learn under the guidance of other faculty). professors will take on the role of directors. Everybody's scope will widen because the tiny details will be handled by AI, but the big picture will still be in the domain of humans.
after looking into it for a little while, Docling and Marker work pretty well but are very slow. I haven't found anything else that extracts math suitably. It takes 10+ minutes per pdf, so I'm going to run it on a batch of these papers overnight and create my own little gaussian splatting RAG database. It's really too bad PDF is so terrible.
Cool! I'll definitely check it out. The great thing about LLMs is I can probably have a trainer and renderer using this technology up and running for my platform in a day or two, OR I can just pick and choose parts that would work well for my implementation and merge them in.
My understanding is that those models create gaussian splats from a text prompt, kinda like a 3d version of nano banana. I'm not doing that (yet), what I'm doing is creating splats from a set of photos - aka "splat training" and then rendering the splat as a static (working on dynamism) on the Quest headset. This is pretty well-worn territory with a lot of good implementations, but I have my own implementation of a trainer in C++/CUDA (originally based in SpeedySplat, which was written in Python, but now completely rewritten and not much of SpeedySplat is left) and renderer in C++/OpenXR for the Quest (originally based on a LLM-made port of 3DGS.cpp to OpenXR, but 100% rewritten now), and I can easily integrate techniques from research.
this is actually a great question - I just extract the text with PyPDF, but did a brief search on the functionality I'd like to have (convert math equations to LaTeX, extract images, reformat in markdown, extract data from charts) and it looks like there are a couple of promising Python libs like Docling and Marker.. I should really improve this part of my workflow.
With AI, VR is even more promising. I have been working on a Gaussian splat renderer for the Quest 3, and by having Claude and ChatGPT read state-of-the-art papers, I have been able to build a training and rendering pipeline that is getting >50 fps for large indoor scenes on the Quest 3. I started with an (AI-driven) port of a desktop renderer, which got less than 1 fps, but I've integrated both training and rendering improvements from research and added a bunch of quality and performance improvements and now it's actually usable. Applying research papers to a novel product is something that used to take weeks or months of a person's time and can now be measured in minutes and hours (and tokens).
I bought a copy of Office this year to do my taxes with the excel1040.com template and Claude for Excel, and dropped my 1099s and stuff into the chat window and Claude just transferred the numbers to the correct cells and Excel did the calculations. It was super easy (also easy to check because my tax picture didn’t change much from last year). It got some things right that TurboTax always got wrong (like cost basis for ESPPs). The only part that was difficult was getting Claude to transfer the data to the IRS fillable PDFs - I probably spent longer iterating on that than it would have taken to copy-paste the data from Excel. Other than that, it worked great, highly recommend.
I write a lot of tech docs with AI so I can add some substance here. First of all, you do tell it what you’re writing about - you can give it reams of documentation on related systems, terminology, and source code. Stuff that would take you weeks to consume personally (but you probably know the gist of it from having read or skimmed it before). Let’s say you are writing a design doc for a new component. After all of this stuff describing the environment is in its context (you don’t have to type it all out, you can copy paste it, use an MCP to suck it in from a DB, read it from a file, etc), you describe at a high level what architecture choices and design constraints to make (for example, needs to be implemented with this database, this is our standard middleware, etc) and it will fill in blanks and spit out a design for you. Then you refine it - actually this component needs to work like that, unit testing strategy needs to be like this, and so on. Now give me the main modules, code stubs, suggest patterns to use, etc. continue to iterate - it will update the entire doc for you, make sure there are no dead or outdated references, etc. extremely powerful and useful.
My recollection is that this website says that a 50% score is bad when the expected value of random chance of picking the correct option among 3 is 1/3. A 50% average score means there is some signal there. If it was impossible to guess, the average score should be 33%
I think you need to look at the data before making assertions like this.
> People don't buy American cars
53% of cars sold in the US are assembled in the US versus 18% assembled in Mexico.
> things get more and more expensive. And not by a few percent, more like by 50% or more.
The total cost of manufacturing wages only account for 5-15% of the MSRP of a vehicle. So moving manufacturing from an expensive country to a cheap country only changes the price by maybe 10% due to the impact of wages.
Honestly, having spent a lot of time in Korea (which famously grew so much rice that visitors from Japan and China from the 16th century were astounded by their bounty of food), I disagree with the premise..
Queueing discipline is non-existent; people will take what they want without waiting for others who arrived first. Business standards for fair dealing are just as bad if not worse than many western societies. Family/personal connections are favored and nepotism is rampant. Driving behavior is extremely selfish and causes a lot of accidents (running red lights, default behavior at uncontrolled intersections, etc). Their problems with concentration of money and power are just as bad if not worse than the west with chaebols essentially above the law and abusing their workers to the extent that people have no time for families - so What makes Asian societies more “cooperative?” Is it just their attitude that they think they are more cooperative?
The ad campaign was super campy, there were print ads in comic books, I remember making fun of it before the movie came out - this can’t be any good, they are going to misrepresent computer geeks, it’s going to be stupid. Of course as a teen I didn’t think it was authentic enough but over time I look at it with more respect. I showed it to my 10 year old not too long ago (forgot that there was a little nudity, oh well) and I was proud of claiming the culture it represented. The thirst for knowledge, the irreverence for authority, all of the different kinds of people making a community based on shared interest and respect, all night hackathons, the adults who just don’t get it - and yeah, the music and the fashion. That’s the stuff that matters, not a hacker using a Mac or goofy technical gibberish, and that’s the stuff they got right. It was a special moment in time, and I’m glad the movie is around to encapsulate it.
people are sleeping on openai right now but codex 5.2 xhigh is at least as good as opus and you get a TON more usage out of the OpenAI $20/mo plan than Claude's $20/mo plan. I'm always hitting the 5 hour quota with Opus but never have with Codex. Codex tool itself is not quite as good but close.
There's a big difference between "I tried it and it produced crap" and "it will replace developers entirely any day now"
People who use this stuff everyday know that people who are still saying "I tried it and it produced crap" just don't know how to use it correctly. Those developers WILL get replaced - by ones who know how to use the tool.
Approximately 40-45% of _all_ US residents, natural-born or immigrant, receive more public benefits than they pay in taxes. Consider if an immigrant making a below-average wage could actually fit into both categories.
I'm not against immigration, just pointing out the flaw in your argument.
I'm very sorry, I should have phrased my original post in a kinder, less dismissive way, and kudos to you for not reacting badly to my rudeness. It is a cool repo and a great accomplishment. Implementing autograd is great as a learning exercise, but my opinion is that you're not going to get the performance or functionality of one of the large, mainstream autograd libraries. Karpathy, for example, throws away micrograd after implementing it and uses pytorch in his later exercises. So it's great that you did this, but for others to learn how autograd works, Karpathy is usually a better route, because the concepts are built up one by one and explained thoroughly.
I did it cheap - I just ran extension cables from my car (previously from a generator). I have a manual transfer switch at my furnace to safely switch between utility power and extension cable power, and just replugged appliances. I think everything drew on average about 800 watts, up to maybe 1500. I split it between 2 1.8 kW 110v outlets in the car.
my area lost power for about 3 days last week and I ran all of my house's critical systems from my EV. it was great - silent, unlike the old generator, and not counting the sunk cost of the car, extremely cheap. Cost maybe $5 in electricity to keep the furnace, refrigerator/freezer, and internet on for 3 days, contrasted with probably $50 in gasoline for a similar amount of time.
If the outage had been longer, I could have made a half-hour trip to an area that had working EV fast chargers and come back with another 5-6 days of power for the house.