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18  novembre     06h56
Uni-MoE-2.0-Omni: An Open Qwen2.5-7B Based Omnimodal MoE for Text, Image, Audio and Video Understanding
Asif Razzaq    How do you build one open model that can reliably understand text, images, audio and video while still running efficiently? A team of researchers from Harbin Institute of Technology, Shenzhen introduced Uni-MoE-2.0-Omni, a fully open omnimodal large model that pushes Lychee’s Uni-MoE line toward...
    01h08
Focal Loss vs Binary Cross-Entropy: A Practical Guide for Imbalanced Classification
Arham Islam    Binary cross-entropy (BCE) is the default loss function for binary classification but it breaks down badly on imbalanced datasets. The reason is subtle but important: BCE weighs mistakes from both classes equally, even when one class is extremely rare. Imagine two predictions: a minority-class...
17  novembre     20h16
Google DeepMind’s WeatherNext 2 Uses Functional Generative Networks For 8x Faster Probabilistic Weather Forecasts
Michal Sutter    Google DeepMind Research have introduced WeatherNext 2, an AI based medium range global weather forecasting system that now powers upgraded forecasts in Google Search, Gemini, Pixel Weather and Google Maps Platform’s Weather API, with Google Maps integration coming next. It combines a new...
    09h17
Meta AI Introduces DreamGym: A Textual Experience Synthesizer For Reinforcement learning RL Agents
Asif Razzaq    Reinforcement learning RL for large language model LLM agents looks attractive on paper, but in practice it breaks on cost, infrastructure and reward noise. Training an agent that clicks through web pages or completes multi step tool use can easily need tens of thousands of real interactions, each...
    08h24
A Coding Guide to Implement Advanced Hyperparameter Optimization with Optuna using Pruning Multi-Objective Search, Early Stopping, and Deep Visual Analysis
Asif Razzaq    In this tutorial, we implement an advanced Optuna workflow that systematically explores pruning, multi-objective optimization, custom callbacks, and rich visualization. Through each snippet, we see how Optuna helps us shape smarter search spaces, speed up experiments, and extract insights that...
16  novembre     21h40
Google DeepMind Introduces SIMA 2, A Gemini Powered Generalist Agent For Complex 3D Virtual Worlds
Asif Razzaq    Google DeepMind has released SIMA 2 to test how far generalist embodied agents can go inside complex 3D game worlds. SIMA’s (Scalable Instructable Multiworld Agent) new version upgrades the original instruction follower into a Gemini driven system that reasons about goals, explains its plans, and...
    21h00
AI Interview Series 2: Explain Some of the Common Model Context Protocol (MCP) Security Vulnerabilities
Arham Islam    In this part of the Interview Series, we’ll look at some of the common security vulnerabilities in the Model Context Protocol (MCP) a framework designed to let LLMs safely interact with external tools and data sources. While MCP brings structure and transparency to how models access context, it...
    07h51
Comparing the Top 4 Agentic AI Browsers in 2025: Atlas vs Copilot Mode vs Dia vs Comet
Michal Sutter    Agentic AI browsers are moving the model from answering about the web’ to operating on the web. In 2025, four AI browsers define this space: OpenAI’s ChatGPT Atlas, Microsoft Edge with Copilot Mode, The Browser Company’s Dia, and Perplexity’s Comet. Each makes different design choices around...
    06h57
How to Build Memory-Powered Agentic AI That Learns Continuously Through Episodic Experiences and Semantic Patterns for Long-Term Autonomy
Asif Razzaq    In this tutorial, we explore how to build agentic systems that think beyond a single interaction by utilizing memory as a core capability. We walk through how we design episodic memory to store experiences and semantic memory to capture long-term patterns, allowing the agent to evolve its behaviour...
    02h53
Cerebras Releases MiniMax-M2-REAP-162B-A10B: A Memory Efficient Version of MiniMax-M2 for Long Context Coding Agents
Asif Razzaq    Cerebras has released MiniMax-M2-REAP-162B-A10B, a compressed Sparse Mixture-of-Experts (SMoE) Causal Language Model derived from MiniMax-M2, using the new Router weighted Expert Activation Pruning (REAP) method. The model keeps the behavior of the original 230B total, 10B active MiniMax M2, while...