cgreerrun
Joined 6 years ago
Karma
249
Submissions
49
Member since
Jun 22
Email: [email protected]
GitHub: https://github.com/cgreer
View on Hacker NewsRecent submissions
Comment · 1 year ago
Check out Quoridor
Comment · 1 year ago
Short explainer for how the control of the SO-100 robot works.
Comment · 1 year ago
Highly recommend LeRobot.
Comment · 1 year ago
> The code is a mixture of C++ for performance-critical parts and Python for everything else. They’re glued together using pybind11, which I’m a big fan of.
Nice, I'm a big fan of this combo! Hits the right balance of prototype speed plus performance.
Comment · 1 year ago
Not sure how you are sourcing your jobs, but fyi you can get a feed of 1M+ jobs here too:
https://xmlfeed.directemployers.org/default
Some of the players (ZipRecruiter, etc) might also give you a feed. They generally just want traffic.
I used to work on the ML system that rated resumes when the applications were submitted. The ATSes are scoring and sorting the applications that come in, so IMO it's better if everyone "applies" and their matching program runs instead of waiting for people to apply anyways.
Good luck!
Comment · 1 year ago
The SO100 arm[1] and LeRobot[2] community are great too if you're getting into robotics from the software side of things.
Demonstration of the arm: https://x.com/ChrisMGreer/status/1867278261631561996
Comment · 2 years ago
You're welcome! I highly recommend the CPU/microchip one too.
Comment · 2 years ago
The only time I really feel like I understand something is by building something with it. So actually writing a CUDA kernel to do grayscale conversion and then tweaking the code.
BUT... both the video and the article are useful before you do that. They both allow you to build a mental model of how GPUs work that you can test later on.
Comment · 2 years ago
This video is a great explainer too:
How do Graphics Cards Work? Exploring GPU Architecture (https://youtu.be/h9Z4oGN89MU?si=EPPO0kny-gN0zLeC)
Comment · 2 years ago
This is incredible. I have a biochemistry and bioinformatics background, and I've always been curious about how easy and cheap it could be to do various experiments at home. Godspeed!
Comment · 3 years ago
You're welcome! If you're interested, there is a follow up paper about a method that extends GradTree to a boosted tree as well called GRANDE[1].
Comment · 3 years ago
You can use the straight through operator!
During the forward pass you sample a discrete outcome given your NN weights to get an error for backprop. During the backward pass you directly propogate through the weights.
This GradTree paper[1] does a good job covering how to do discrete gradient-based optimization (i.e. NNs w/ discrete representations).
Another option is to use a GFlowNet[2]. Then you have a NN policy that takes discrete actions like you're playing an RL game. You're not back-propogating through something that isn't continuous, but you're utilizing a NN to make informed decisions about a problem with a discrete representation.
[1] GradTree (https://arxiv.org/pdf/2305.03515.pdf) [2] GFlowNet (https://arxiv.org/abs/2111.09266)
Comment · 3 years ago
Academics should write readable papers