# Feb 5, 2025

## The $6 R1 Competitor?
- The **$6 R1 competitor** demonstrates that a small model can achieve near state-of-the-art performance with minimal data, highlighting a significant breakthrough in AI efficiency.

## Ingesting PDFs and why Gemini 2.0 changes everything
- **Gemini 2.0** revolutionizes the processing of **millions of PDFs**, enhancing data extraction and analysis capabilities significantly.

## Gemini 2.0 is now available to everyone
- **Gemini 2.0** introduces three new models: **Flash**, **Flash-Lite**, and **Pro Experimental**, enhancing performance and accessibility for developers and users alike.

## DeepRAG: Thinking to retrieval step by step for large language models
- **DeepRAG** enhances **retrieval-augmented reasoning** by modeling it as a **Markov Decision Process (MDP)**, allowing for strategic and adaptive retrieval that reduces factual hallucinations in Large Language Models (LLMs).

## How to scale your model: A systems view of LLMs on TPUs
- **Scaling LLMs on TPUs** requires understanding hardware interactions and optimizing parallelism to achieve efficient training and inference, addressing common questions about costs and memory needs.

## Catgrad: A categorical deep learning compiler
- **catgrad** is a **deep learning framework** that leverages **category theory** to statically compile models, enabling training loops to operate independently of any deep learning framework, including catgrad itself.

## Prediction Games
- The **Netflix Prize** revolutionized machine learning by incentivizing improvements to its recommendation system, attracting over **5,000 teams** and demonstrating that simple algorithms could yield significant results, as evidenced by a submission that utilized **stochastic gradient descent** for matrix factorization.

## [R] On the Reasoning Capacity of AI Models and How to Quantify It
- **Recent advances in Large Language Models (LLMs)** reveal that while they excel in benchmarks like GPQA and MMLU, they struggle with complex reasoning tasks, necessitating **new evaluation methodologies** to assess their true capabilities.

## [R] Transformer-Squared: Self-adaptive LLMs
- **Transformer-Squared** enables LLMs to **dynamically adjust weights during inference**, enhancing adaptability for unseen tasks through a two-pass mechanism that utilizes task-specific 'expert' vectors trained via reinforcement learning.

## [D] How to Scale Your Model: A Systems View of LLMs on TPUs
- **Scaling LLMs** is grounded in understanding **system resources**—compute, memory, and bandwidth—allowing for precise calculations of cost, runtime, and optimal parallelism strategies.

## [P] Open-source library to generate ML models using natural language
- **`smolmodels`** is an open-source Python library that generates **ML models** from natural language descriptions, utilizing **graph search** and **LLM code generation** to optimize model training for specific tasks. [GitHub Repository](https://github.com/plexe-ai/smolmodels)

## OCR Crypto Stealers in Google Play and App Store
- **SparkCat malware** has infiltrated both Google Play and the App Store, utilizing an **OCR model** to extract sensitive crypto wallet recovery phrases from users' image galleries, marking a significant escalation in mobile malware threats.

## Gemini 2.0 is now available to everyone
- **Gemini 2.0** introduces **Flash**, **Flash-Lite**, and **Pro Experimental** models, enhancing performance and accessibility for developers and users alike.

## NVIDIA Blackwell Now Generally Available in the Cloud
- **CoreWeave has launched the first NVIDIA GB200 NVL72 instances**, enabling high-performance cloud computing for AI reasoning, which requires significant compute power and optimized software for real-time results.

## How GeForce RTX 50 Series GPUs Are Built to Supercharge Generative AI on PCs
- **NVIDIA's GeForce RTX 50 Series GPUs**, leveraging the **Blackwell architecture**, deliver up to **3,352 AI trillion operations per second (TOPS)**, significantly enhancing local AI performance for developers and enthusiasts.