Allen Institute for AI (AI2) Introduces Olmo 3: An Open Source 7B and 32B LLM Family Built on the Dolma 3 and Dolci Stack

Allen Institute for AI (AI2) is releasing Olmo 3 as a fully open model family that exposes the entire ‘model flow’, from raw data and code to intermediate checkpoints and…

OpenAI Debuts GPT-5.1-Codex-Max, a Long-Horizon Agentic Coding Model With Compaction for Multi-Window Workflows

OpenAI has introduced GPT-5.1-Codex-Max, a frontier agentic coding model designed for long running software engineering tasks that span millions of tokens and multi hour sessions. It is available today inside…

vLLM vs TensorRT-LLM vs HF TGI vs LMDeploy, A Deep Technical Comparison for Production LLM Inference

Production LLM serving is now a systems problem, not a generate() loop. For real workloads, the choice of inference stack drives your tokens per second, tail latency, and ultimately cost…

Google Antigravity Makes the IDE a Control Plane for Agentic Coding

Google has introduced Antigravity as an agentic development platform that sits on top of Gemini 3. It is not only an autocomplete layer, it is an IDE where agents plan,…

An Implementation of a Comprehensive Empirical Framework for Benchmarking Reasoning Strategies in Modern Agentic AI Systems

In this tutorial, we dive deep into how we systematically benchmark agentic components by evaluating multiple reasoning strategies across diverse tasks. We explore how different architectures, such as Direct, Chain-of-Thought,…

xAI’s Grok 4.1 Pushes Toward Higher Emotional Intelligence, Lower Hallucinations and Tighter Safety Controls

How do you build an AI assistant that feels emotionally intelligent and reliable to humans, instead of just making a bigger model? Meet Grok 4.1, xAI’s latest large language model…

How to Build an Agentic Deep Reinforcement Learning System with Curriculum Progression, Adaptive Exploration, and Meta-Level UCB Planning

In this tutorial, we build an advanced agentic Deep Reinforcement Learning system that guides an agent to learn not only actions within an environment but also how to choose its…

Google’s Gemini 3 Pro turns sparse MoE and 1M token context into a practical engine for multimodal agentic workloads

How do we move from language models that only answer prompts to systems that can reason over million token contexts, understand real world signals, and reliably act as agents on…

Focal Loss vs Binary Cross-Entropy: A Practical Guide for Imbalanced Classification

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…

Uni-MoE-2.0-Omni: An Open Qwen2.5-7B Based Omnimodal MoE for Text, Image, Audio and Video Understanding

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…