AVB (@neural_avb) 's Twitter Profile
AVB

@neural_avb

Creator behind Neural Breakdown on YT. Day job in MARL and LLMs.
Next vids: Multi-agent RL Navigation, Pytorch tutorial

ID: 1754194661084983296

linkhttp://youtube.com/@avb_fj calendar_today04-02-2024 17:26:41

1,1K Tweet

2,2K Followers

372 Following

Towards Data Science (@tdatascience) 's Twitter Profile Photo

.AVB explains how to “think the PyTorch way,” breaking down its role in training and shaping modern deep learning systems. towardsdatascience.com/the-basics-of-…

AVB (@neural_avb) 's Twitter Profile Photo

Very interesting use case… Without per-user fine tuning, I guess the most obvious way I can think of to reduce latency is to apply continual/lifelong prompt optimization at a per-user level. That way the system prompt has most of the useful user infos at query time. Additional

Naksh Jain (@nakshsonigara) 's Twitter Profile Photo

As I have a little reach now, I am currently looking for Research Volunteer/Research Intern Role in AI/ML/DL/DS. I have specific interests but I'd like to experiment around various topics. If you are looking for or have any opportunity in mind then do hmu. My Portfolio 👇🏻

As I have a little reach now,

I am currently looking for Research Volunteer/Research Intern Role in AI/ML/DL/DS. 

I have specific interests but I'd like to experiment around various topics.

If you are looking for or have any opportunity in mind then do hmu.

My Portfolio 👇🏻
AVB (@neural_avb) 's Twitter Profile Photo

The agentic memory project is coming along nicely! Using qdrant to host a vector db locally, doing some tool calling, some bm-25, and building the core blocks of mem0 with DSPy. I'm having a lot of fun with this one. Tutorial soon.

AVB (@neural_avb) 's Twitter Profile Photo

Shot a couple hours of footage for the upcoming from-scratch agentic memory tutorial. The final cut will probably run ~40 minutes. Covers: 1. The mem0 api (to understand what we will eventually be building from scratch) 2. DSPy basics (signatures and modules) 3. Extracting

Shot a couple hours of footage for the upcoming from-scratch agentic memory tutorial. The final cut will probably run ~40 minutes. Covers:

1. The mem0 api (to understand what we will eventually be building from scratch)
2. DSPy basics (signatures and modules)
3. Extracting
AVB (@neural_avb) 's Twitter Profile Photo

I guess the bottleneck with RL isn’t compute. More about sample efficiency. Esp with more complex tasks other than game playing. Pure RL assumes no priors or knowledge about environment dynamics. So there’s always a learning curve where the agent first must figure out the

AVB (@neural_avb) 's Twitter Profile Photo

This is devastating. I used to watch Danya's streams, esp his speedrun videos. Such a great chess player, teacher, commentator, creator. I am beyond words. This is so unfair and so random. How does one find meaning in all this. Strength and condolences to your family.

AVB (@neural_avb) 's Twitter Profile Photo

50 minute tutorial on building agentic memory systems is dropping soon on my YT. I am 90% there. We will use DSPy to create the core features of mem0 from scratch. Very excited! This project just made me happy from the inside.

50 minute tutorial on building agentic memory systems  is dropping soon on my YT. I am 90% there. We will use DSPy to create the core features of mem0 from scratch.

Very excited! This project just made me happy from the inside.