[chess engine][c]

https://youtube.com/playlist?list=PLZ1QII7yudbc-Ky058TEaOstZHVbT-2hg&feature=shared

[genAI][clone your c-lvl]
The promise of human behavioral simulation—general-purpose computational agents that replicate human behavior across domains—could enable broad applications in policymaking and social science. We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals—applying large language models to qualitative interviews about their lives, then measuring how well these agents replicate the attitudes and behaviors of the individuals that they represent. The generative agents replicate participants' responses on the General Social Survey 85% as accurately as participants replicate their own answers two weeks later, and perform comparably in predicting personality traits and outcomes in experimental
replications.
https://arxiv.org/pdf/2411.10109

[video][motivation]
While this channel supposed to be pure technical I read / listen / watch some other resources to get idea about life and choices. Some times we are not getting what we thought we could and get stressed out although we put some much into making things for the better.
I’d like to share one of the worth watching / listening videos

https://youtu.be/3iMc8uF46C0?si=suiCgH4lwmyRA60A

[ai][agent g][paper]

https://openreview.net/pdf?id=g2C947jjjQ

[ai][paper]
“Enabling effective collaboration among LLMs is a crucial step toward developing autonomous systems capable of solving complex problems. Al- though LLMs are typically used as single-model generators, where humans critique and refine their outputs, the potential for jointly trained collaborative models remains largely unexplored. Despite promising results in multi-agent communication and debate settings, little progress has been made
in training models to work together on tasks. In this paper, we present a first step towards ’Multi-agent LLM training’ (MALT) on reasoning problems. Our approach employs a sequential multi-
agent setup with heterogeneous LLMs assigned specialized roles: a generator, verifier, and refinement model iteratively solving problems. We propose a trajectory-expansion-based synthetic
data generation process and a credit assignment strategy driven by joint outcome-based rewards.
This enables our post-training setup to utilize both positive and negative trajectories to autonomously improve each model’s specialized capabilities as part of a joint sequential system. We evaluate our approach on MATH, GSM8k, and CSQA, where
MALT using Llama 3.1 8B models achieves rela tive improvements of 14.14%, 7.12%, and 9.40% respectively over the same baseline model. This demonstrates an early advance in multi-agent cooperative capabilities for performance on mathematical and common sense reasoning questions. More generally, our work provides a concrete direction for research around multi-agent LLM
training approaches.”

https://arxiv.org/abs/2412.01928

[book][Algorithms for Modern Hardware]
This is an upcoming high performance computing book noscriptd “Algorithms for Modern Hardware” by Sergey Slotin.

Its intended audience is everyone from performance engineers and practical algorithm researchers to undergraduate computer science students who have just finished an advanced algorithms course and want to learn more practical ways to speed up a program.
All book materials are hosted on GitHub, with code in a separate repository.

https://en.algorithmica.org/

[system programming][baseline]
What every system programmer should know in 12 pages:
https://assets.bitbashing.io/papers/concurrency-primer.pdf

[netflix][distributed systems]

https://netflixtechblog.com/netflixs-distributed-counter-abstraction-8d0c45eb66b2

[google][cloud][ide]

https://idx.dev/

[paper][bytedance][recommendation system]

https://arxiv.org/pdf/2209.07663

[paper][meta][large concept models]
“The current established technology of LLMs is to process input and generate output at the token level. This is in sharp contrast to humans who operate at multiple levels of abstraction, well beyond single words, to analyze information and to generate creative content. In this paper, we present an attempt at an architecture which operates on an explicit higher-level semantic representation, which we name a “concept”. Concepts are language- and modality-agnostic and represent a higher level idea or action in a flow”

https://scontent-ams4-1.xx.fbcdn.net/v/t39.2365-6/470149925_936340665123313_5359535905316748287_n.pdf?_nc_cat=103&ccb=1-7&_nc_sid=3c67a6&_nc_ohc=_Kelt2jn-pkQ7kNvgFRhMPH&_nc_zt=14&_nc_ht=scontent-ams4-1.xx&_nc_gid=AsqaBwO9TftbIqsIF6KCPA3&oh=00_AYAAj36Wbvgp4TU0V0JPoyHGs-_FesxPYaEwDvdGZcbtNw&oe=676768D2

github: https://github.com/facebookresearch/large_concept_model

[ai][genesis]
Amazing step toward with generating models. You can find multiple videos in the link:
https://genesis-embodied-ai.github.io/

[ai][llm]
Large scale multimodal agents society

“In this paper, taking e-commerce
scenarios as an example, we present LMAgent, a very large-scale and multimodal agents society based on multimodal LLMs.
In LMAgent, besides freely chatting with friends, the agents can autonomously browse, purchase, and review products, even perform live streaming e-commerce. To simulate this complex system, we introduce a self-consistency prompting mechanism to augment agents’ multimodal capabilities, resulting in significantly improved decision-making performance over the existing multi-agent system. Moreover, we propose a fast memory mechanism combined with the small-world model to enhance system efficiency, which supports more than 10,000 agent simulations in a society. Experiments on agents’ behavior show that these agents achieve comparable performance to humans in behavioral indicators. “

https://arxiv.org/pdf/2412.09237

🌟 Happy New Year, everyone! 🌟

This year has been incredible, and I’m so grateful for each of you—our channel grew to 500+ amazing members! 🎉 Thank you for your support, engagement, and encouragement along the way.

I hope the articles, videos, and books shared here added value to your journey. Let’s make this new year even more inspiring and impactful together. Here’s to learning, growing, and achieving great things in 2024!

Cheers to a fantastic year ahead! 🥂✨Love you all 💛

[neural networks]
A great video with building nerual network from scratch
https://www.youtube.com/watch?v=w8yWXqWQYmU

[video][webserver][c]

https://youtube.com/playlist?list=PLDqjvz44o_1Uj90bCt5K81NSlJhLUYG9e&si=bFTJR-YUs24JyWNz

[ai][agents]
https://huyenchip.com//2025/01/07/agents.html

[ai][long term memory][paper]

https://arxiv.org/pdf/2501.00663v1

[ai][deepseek][paper]
https://github.com/deepseek-ai/DeepSeek-V3/blob/main/DeepSeek_V3.pdf

Model Summary
Architecture: Innovative Load Balancing Strategy and Training Objective

On top of the efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free strategy for load balancing, which minimizes the performance degradation that arises from encouraging load balancing.

We investigate a Multi-Token Prediction (MTP) objective and prove it beneficial to model performance. It can also be used for speculative decoding for inference acceleration.

Pre-Training: Towards Ultimate Training Efficiency

We design an FP8 mixed precision training framework and, for the first time, validate the feasibility and effectiveness of FP8 training on an extremely large-scale model.

Through co-design of algorithms, frameworks, and hardware, we overcome the communication bottleneck in cross-node MoE training, nearly achieving full computation-communication overlap. This significantly enhances our training efficiency and reduces the training costs, enabling us to further scale up the model size without additional overhead.

At an economical cost of only 2.664M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8T tokens, producing the currently strongest open-source base model. The subsequent training stages after pre-training require only 0.1M GPU hours.

[ai][llm]

https://magazine.sebastianraschka.com/p/understanding-reasoning-llms

[llm] Super denoscriptive video from Andrej Karpathy
https://www.youtube.com/watch?v=7xTGNNLPyMI