Check out the our new redesigned website following all of your amazing feedback from our launch on product hunt!
The feedback is greatly appreciated and we are glad to see people enjoying it. Happy studying!
https://t.co/pUGUVjWwgm
advice to young engineers:
brother, I know you got that dog in you,
but you have to rest too.
In a world where you can do 10,000 things at the same time, you have to remember that rest is part of the work.
If you find yourself at 3:00 p.m on Thursday afternoon and you realize you've done all your work, take a breather. Go outside, stretch your arms out, and feel the sun on your face.
There will be some day in the future where you're going to have to work until 4:00am in the morning.
There will be some some day in the future where you know you need to push through the weekend.
But if you find yourself having a moment to breathe, just breathe.
We do not exist in a world that tells you that you cannot do anything anymore.
We only exist in a world where you are telling yourself to keep going.
You have to think of yourself as some kind of high-performance athlete.
You have to give yourself someone who, when they rolled their ankle in the third quarter, can push through.
We have to realize that those people have offseasons.
We have to realize those people invest in recovery.
But we live in a world where psychological capital feels more infinite than just the limitations of a biological body.
And you have to listen to your body.
Introducing paper replications for arXiv
Research codebases are notoriously hard to set up
We deploy autoresearch agents to ingest popular arXiv repos, resolve setup issues, and get the core claim running. You can now sort papers by ease of implementation 🚀
Star stories can be generated from your resume or any additional context you wish to provide. Making sessions more helpful by allowing them to relate any topic you choose to your projects and active work, giving you more in depth feedback targeted to help prepare for interviews.
Build intuition, implementation fluency, retrieval strength, conceptual understanding, and long-term mastery across topics in machine learning and software engineering.
https://t.co/pUGUVjWwgm
Get a glimpse into sessions without signing up in the Concepts tab, where you can get quick deep dives into 16 topics across classical ML, deep learning, LLMs, and optimization
Try out sessions, where you can study our 157+ topics between on machine learning and software engineering. Ask questions along the way and learn material in depth quickly.
"You need to make yourself a big target for luck, and the way to do that is to be curious. Try lots of things, meet lots of people, read lots of books, ask lots of questions."
— Paul Graham, How to Do Great Work
https://t.co/AyIk5fnKZL
Check out our Substack to view the latest deep dive into BPE from scratch and in depth view of tokenization: https://t.co/CCUEMbevZx
Also try https://t.co/pUGUVjWwgm for an in depth session on tokenization and 157+ other topics covering everything you need to know in ML and SWE.
@ionleu Deep mastery review for ML, DL, RL, and SWE concepts. 130+ topics. 6-stage sessions personalized to your resumé. Built by an MLE, for MLE candidates.
Give it a try for free: https://t.co/pUGUVjWwgm
Check out my most recent side project: https://t.co/NvBXegnWee
I built it for MLE's, Researchers, or even SWE's who want to study for interviews or just refresh their memory on a broad range of topics all in one place. It's structured, but also super flexible.
It's also free.
Most interview prep tools don't drill what matters most: your own experience.
LiminalML does.
Upload your resume. 6-stage sessions personalized to what you've built. 130+ ML and SWE topics.
Built by an MLE, for MLE's and even SWE's.
https://t.co/tGjxFlJiCj
I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then:
- the human iterates on the prompt (.md)
- the AI agent iterates on the training code (.py)
The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc.
https://t.co/YCvOwwjOzF
Part code, part sci-fi, and a pinch of psychosis :)