⏰ Featured AI: DeepSeek AI Releases DeepEP and DeepGEMM & Convergence Releases Proxy Lite....

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Dive into the hottest AI breakthroughs of the week—handpicked just for you!

Super Important AI News 🔥 🔥 🔥

🧵🧵 Meet IntellAgent: An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System _(Promoted)

⭐  DeepSeek AI Releases DeepEP: An Open-Source EP Communication Library for MoE Model Training and Inference

📢 DeepSeek AI Releases DeepGEMM: An FP8 GEMM Library that Supports both Dense and MoE GEMMs Powering V3/R1 Training and Inference

🚨 Allen Institute for AI Released olmOCR: A High-Performance Open Source Toolkit Designed to Convert PDFs and Document Images into Clean and Structured Plain Text

💡💡 Convergence Releases Proxy Lite: A Mini, Open-Weights Version of Proxy Assistant Performing Pretty Well on UI Navigation Tasks

🧲 🧲  This AI Paper from Menlo Research Introduces AlphaMaze: A Two-Stage Training Framework for Enhancing Spatial Reasoning in Large Language Models

Featured AI Update 🛡️🛡️🛡️

🔥 DeepSeek AI Releases DeepEP: An Open-Source EP Communication Library for MoE Model Training and Inference

DeepSeek AI has recently introduced DeepEP, a communication library specifically designed for MoE models and expert parallelism (EP). DeepEP addresses the inefficiencies inherent in how tokens are dispatched and aggregated across GPUs. The library provides high-throughput, low-latency all-to-all GPU kernels—commonly referred to as MoE dispatch and combine kernels—that streamline data exchange during both training and inference. Notably, DeepEP supports low-precision operations (including FP8), aligning with techniques detailed in the DeepSeek-V3 paper. This release responds directly to the challenges of scaling MoE architectures in both intranode and internode environments...

Other AI News 🎖️🎖️🎖️

🚨 Open-Reasoner-Zero: An Open-source Implementation of Large-Scale Reasoning-Oriented Reinforcement Learning Training

🧿 How to Compare Two LLMs in Terms of Performance: A Comprehensive Web Guide for Evaluating and Benchmarking Language Models

 🧩  LongPO: Enhancing Long-Context Alignment in LLMs Through Self-Optimized Short-to-Long Preference Learning

📢   CoSyn: An AI Framework that Leverages the Coding Capabilities of Text-only Large Language Models (LLMs) to Automatically Create Synthetic Text-Rich Multimodal Data

🚨 Meet IntellAgent: An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System _(Promoted)

Coding Tutorial 🎖️🎖️🎖️

🖥️ Building an Interactive Weather Data Scraper in Google Colab: A Code Guide to Extract, Display, and Download Live Forecast Data Using Python, BeautifulSoup, Requests, Pandas, and Ipywidgets (Colab Notebook Included)

In this tutorial, we will build an interactive web scraping project in Google Colab! This guide will walk you through extracting live weather forecast data from the U.S. National Weather Service. You’ll learn to set up your environment, write a Python script using BeautifulSoup and requests, and integrate an interactive UI with ipywidgets. This tutorial provides a step-by-step approach to collecting, displaying, and saving weather data, all within a single, self-contained Colab notebook.

import requests
from bs4 import BeautifulSoup
import csv
from google.colab import files
import ipywidgets as widgets
from IPython.display import display, clear_output, FileLink
import pandas as pd