In this tutorial, we build a complete end-to-end pipeline using NVIDIA Model Optimizer to train, prune, and fine-tune a deep […]
Category: AI infrastructure
Defeating the ‘Token Tax’: How Google Gemma 4, NVIDIA, and OpenClaw are Revolutionizing Local Agentic AI: From RTX Desktops to DGX Spark
Run Google’s latest omni-capable open models faster on NVIDIA RTX AI PCs, from NVIDIA Jetson Orin Nano, GeForce RTX desktops […]
Hugging Face Releases TRL v1.0: A Unified Post-Training Stack for SFT, Reward Modeling, DPO, and GRPO Workflows
Hugging Face has officially released TRL (Transformer Reinforcement Learning) v1.0, marking a pivotal transition for the library from a research-oriented […]
Liquid AI Released LFM2.5-350M: A Compact 350M Parameter Model Trained on 28T Tokens with Scaled Reinforcement Learning
In the current landscape of generative AI, the ‘scaling laws’ have generally dictated that more parameters equal more intelligence. However, […]
Agent-Infra Releases AIO Sandbox: An All-in-One Runtime for AI Agents with Browser, Shell, Shared Filesystem, and MCP
In the development of autonomous agents, the technical bottleneck is shifting from model reasoning to the execution environment. While Large […]
NVIDIA AI Unveils ProRL Agent: A Decoupled Rollout-as-a-Service Infrastructure for Reinforcement Learning of Multi-Turn LLM Agents at Scale
NVIDIA researchers introduced ProRL AGENT, a scalable infrastructure designed for reinforcement learning (RL) training of multi-turn LLM agents. By adopting […]
Google Introduces TurboQuant: A New Compression Algorithm that Reduces LLM Key-Value Cache Memory by 6x and Delivers Up to 8x Speedup, All with Zero Accuracy Loss
The scaling of Large Language Models (LLMs) is increasingly constrained by memory communication overhead between High-Bandwidth Memory (HBM) and SRAM. […]
Yann LeCun’s New LeWorldModel (LeWM) Research Targets JEPA Collapse in Pixel-Based Predictive World Modeling
World Models (WMs) are a central framework for developing agents that reason and plan in a compact latent space. However, […]
Meta AI’s New Hyperagents Don’t Just Solve Tasks—They Rewrite the Rules of How They Learn
The dream of recursive self-improvement in AI—where a system doesn’t just get better at a task, but gets better at […]
Meet GitAgent: The Docker for AI Agents that is Finally Solving the Fragmentation between LangChain, AutoGen, and Claude Code
The current state of AI agent development is characterized by significant architectural fragmentation. Software devs building autonomous systems must generally […]