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An End-to-End Coding Guide to NVIDIA KVPress for Long-Context LLM Inference, KV Cache Compression, and Memory-Efficient Generation

In this tutorial, we take a detailed, practical approach to exploring NVIDIA’s KVPress and understanding how it can make long-context language model inference more efficient. We begin by setting up the full environment, installing the required libraries, loading a compact Instruct model, and preparing a simple workflow that runs in Colab while still demonstrating the […]

An End-to-End Coding Guide to NVIDIA KVPress for Long-Context LLM Inference, KV Cache Compression, and Memory-Efficient Generation Read More »

Meta Superintelligence Lab Releases Muse Spark: A Multimodal Reasoning Model With Thought Compression and Parallel Agents

Meta Superintelligence Labs recently made a significant move by unveiling ‘Muse Spark’ — the first model in the Muse family. Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration. https://ai.meta.com/static-resource/muse-spark-eval-methodology What ‘Natively Multimodal’ Actually Means When Meta describes Muse Spark as ‘natively multimodal,’ it means

Meta Superintelligence Lab Releases Muse Spark: A Multimodal Reasoning Model With Thought Compression and Parallel Agents Read More »

Sigmoid vs ReLU Activation Functions: The Inference Cost of Losing Geometric Context

A deep neural network can be understood as a geometric system, where each layer reshapes the input space to form increasingly complex decision boundaries. For this to work effectively, layers must preserve meaningful spatial information — particularly how far a data point lies from these boundaries — since this distance enables deeper layers to build

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Meet OSGym: A New OS Infrastructure Framework That Manages 1,000+ Replicas at $0.23/Day for Computer Use Agent Research

Training AI agents that can actually use a computer — opening apps, clicking buttons, browsing the web, writing code — is one of the hardest infrastructure problems in modern AI. It’s not a data problem. It’s not a model problem. It’s a plumbing problem. You need to spin up hundreds, potentially thousands, of full operating

Meet OSGym: A New OS Infrastructure Framework That Manages 1,000+ Replicas at $0.23/Day for Computer Use Agent Research Read More »

How to Deploy Open WebUI with Secure OpenAI API Integration, Public Tunneling, and Browser-Based Chat Access

In this tutorial, we build a complete Open WebUI setup in Colab, in a practical, hands-on way, using Python. We begin by installing the required dependencies, then securely provide our OpenAI API key through terminal-based secret input so that sensitive credentials are not exposed directly in the notebook. From there, we configure the environment variables

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Meta AI Releases EUPE: A Compact Vision Encoder Family Under 100M Parameters That Rivals Specialist Models Across Image Understanding, Dense Prediction, and VLM Tasks

Running powerful AI on your smartphone isn’t just a hardware problem — it’s a model architecture problem. Most state-of-the-art vision encoders are enormous, and when you trim them down to fit on an edge device, they lose the capabilities that made them useful in the first place. Worse, specialized models tend to excel at one

Meta AI Releases EUPE: A Compact Vision Encoder Family Under 100M Parameters That Rivals Specialist Models Across Image Understanding, Dense Prediction, and VLM Tasks Read More »

An Implementation Guide to Running NVIDIA Transformer Engine with Mixed Precision, FP8 Checks, Benchmarking, and Fallback Execution

In this tutorial, we implement an advanced, practical implementation of the NVIDIA Transformer Engine in Python, focusing on how mixed-precision acceleration can be explored in a realistic deep learning workflow. We set up the environment, verify GPU and CUDA readiness, attempt to install the required Transformer Engine components, and handle compatibility issues gracefully so that

An Implementation Guide to Running NVIDIA Transformer Engine with Mixed Precision, FP8 Checks, Benchmarking, and Fallback Execution Read More »

RightNow AI Releases AutoKernel: An Open-Source Framework that Applies an Autonomous Agent Loop to GPU Kernel Optimization for Arbitrary PyTorch Models

Writing fast GPU code is one of the most grueling specializations in machine learning engineering. Researchers from RightNow AI want to automate it entirely. The RightNow AI research team has released AutoKernel, an open-source framework that applies an autonomous LLM agent loop to GPU kernel optimization for arbitrary PyTorch models. The approach is straightforward: give

RightNow AI Releases AutoKernel: An Open-Source Framework that Applies an Autonomous Agent Loop to GPU Kernel Optimization for Arbitrary PyTorch Models Read More »

Meet MaxToki: The AI That Predicts How Your Cells Age — and What to Do About It

Most foundation models in biology have a fundamental blind spot: they see cells as frozen snapshots. Give a model a single-cell transcriptome — a readout of which genes are active in a cell at a given moment — and it can tell you a lot about what that cell is doing right now. What it

Meet MaxToki: The AI That Predicts How Your Cells Age — and What to Do About It Read More »

How to Build a Netflix VOID Video Object Removal and Inpainting Pipeline with CogVideoX, Custom Prompting, and End-to-End Sample Inference

In this tutorial, we build and run an advanced pipeline for Netflix’s VOID model. We set up the environment, install all required dependencies, clone the repository, download the official base model and VOID checkpoint, and prepare the sample inputs needed for video object removal. We also make the workflow more practical by allowing secure terminal-style

How to Build a Netflix VOID Video Object Removal and Inpainting Pipeline with CogVideoX, Custom Prompting, and End-to-End Sample Inference Read More »