Claude Opus 4.5 has been a big step up for me personally, and I used to think Sonnet 3.5 was good. It is an amazing deal at $20.
Just yesterday, it helped me parse out and understand a research paper - complete with step-by-step examples (this one: https://research.nvidia.com/sites/default/files/pubs/2016-03...). I will now go ahead and implement it myself, possibly relegating some of the more grunt-work type tasks to Claude code.
Without it, I would have been struggling through the paper for days, wading through WGSL shader code and there would be a high chance that I just give up on it since this is for a side project and not my $job.
It has been a major force multiplier just for learning things. I have had the $20 subscription for about a year now. I bump it up to the $100 plan if I happen to be working on some project that eats through the $20 allocation. This happens to be one such month. I will probably go back to the $20 plan after this month. I continue to get a lot of value out of it.
My name is on the blue plaque. I (and many others) spent countless hours on these two vehicles. Many of us are now working 12-hour shifts performing initial operations tasks.
The majority of us didn't know about the plaques until after the spacecraft were packed up and ready to be shipped to the launch-site. It was a nice surprise when we learned about it. Feels good to know that we got to sign our name on our work.
I have personally experienced this. I went to a concert in NY while living in the midwest. I was a pretty broke grad-student at the time and hence opted not to get a hotel for the night before the return flight. The concert ended late and we reached the airport by around 3:30 AM for a 7AM flight. I think I took some naps at the gate before the flight and got very little sleep on the flights.
I barely remember anything from that night. The concert itself is mostly blank except for one or two moments. I remember some moments of driving late at night afterwards (getting lost at one point) and having a very late night dinner at an IHOP. I barely remember getting on my connecting flight.
Overall a very surreal experience.
Now I make it a point to get proper sleep on such trips. What is the point of doing these things anyway if we don't get to keep the memories?
Before buying the Framework, I used a MacBook Pro for ~11 years. I am perfectly comfortable with the command-line and ssh into a Linux VM every day at work (windows laptop). I use a tmux+neovim setup that mostly works without any problems (except for random LSP issues at times).
I can just get all of the advantages of a *nix shell but with a better UI, using macOS. I have zero regrets from switching back to a Mac after my month-long experiment. My only regret is that I didn't order the M1 Max as soon as it was out and couldn't get the amount of RAM I wanted (it had a long wait by the time I decided to buy).
I bought a Framework 13 when it first came out, with exactly this hope. I gave up after about one month of madness and ordered an M1 Max Macbook Pro. I also installed Windows on the Framework with WSL. Now it gets used very rarely (like for when I want to do Windows development, or for my parents to use when they visit) and mostly sits around gathering dust.
Desktop linux has burned me one too many times for me to trust it again.
I read the same in Malayalam! That book was one of my earliest introductions to practical physics. He deconstructs pretty much all the early "perpetual machine" designs in the book.
I also particularly remember the chapter with the experiments with soap bubbles as being very interesting as a kid.
The speed at which this stuff is developing makes it very hard to keep up and contribute in a meaningful way. Especially when you have a separate full time job.
This is what happens when running inference on a neural network:
Input (list of numbers) -> (Bunch of math operations) with (other numbers) -> Output (also a list of numbers)
This applies whether you are talking about image classification, image generation, text generation etc.
The model defines what the "(Bunch of math operations)" part is. As in, do these multiplications, then add, then a tanh operation etc.
The weights define what the "(other numbers)" are. Training is the process of figuring out these weights using various methods - some of which involve example inputs/outputs (supervised learning), others don't require examples (unsupervised or self-supervised learning).
It uses "ggml", a tensor library written in C to do the math. The models weights are converted to a format that can be used by GGML. The higher level structure of the model is created in Rust be leveraging ggml data structures and the weights are passed into the C library.
PyTorch is only needed for reading the original weights while converting them.
Just yesterday, it helped me parse out and understand a research paper - complete with step-by-step examples (this one: https://research.nvidia.com/sites/default/files/pubs/2016-03...). I will now go ahead and implement it myself, possibly relegating some of the more grunt-work type tasks to Claude code.
Without it, I would have been struggling through the paper for days, wading through WGSL shader code and there would be a high chance that I just give up on it since this is for a side project and not my $job.
It has been a major force multiplier just for learning things. I have had the $20 subscription for about a year now. I bump it up to the $100 plan if I happen to be working on some project that eats through the $20 allocation. This happens to be one such month. I will probably go back to the $20 plan after this month. I continue to get a lot of value out of it.