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Liquid AI Releases LFM2.5-VL-450M: a 450M-Parameter Vision-Language Model with Bounding Box Prediction, Multilingual Support, and Sub-250ms Edge Inference

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Liquid AI just released LFM2.5-VL-450M, an updated version of its earlier LFM2-VL-450M vision-language model. The new release introduces bounding box prediction, improved instruction following, expanded multilingual understanding, and function calling support — all within a 450M-parameter footprint designed to run directly on edge hardware ranging from embedded AI modules like NVIDIA Jetson Orin, to mini-PC […]

Liquid AI Releases LFM2.5-VL-450M: a 450M-Parameter Vision-Language Model with Bounding Box Prediction, Multilingual Support, and Sub-250ms Edge Inference Read More »

Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput

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Long-chain reasoning is one of the most compute-intensive tasks in modern large language models. When a model like DeepSeek-R1 or Qwen3 works through a complex math problem, it can generate tens of thousands of tokens before arriving at an answer. Every one of those tokens must be stored in what is called the KV cache

Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput Read More »

How to Build a Secure Local-First Agent Runtime with OpenClaw Gateway, Skills, and Controlled Tool Execution

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In this tutorial, we build and operate a fully local, schema-valid OpenClaw runtime. We configure the OpenClaw gateway with strict loopback binding, set up authenticated model access through environment variables, and define a secure execution environment using the built-in exec tool. We then create a structured custom skill that the OpenClaw agent can discover and

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How Knowledge Distillation Compresses Ensemble Intelligence into a Single Deployable AI Model

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Complex prediction problems often lead to ensembles because combining multiple models improves accuracy by reducing variance and capturing diverse patterns. However, these ensembles are impractical in production due to latency constraints and operational complexity. Instead of discarding them, Knowledge Distillation offers a smarter approach: keep the ensemble as a teacher and train a smaller student

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A Coding Guide to Markerless 3D Human Kinematics with Pose2Sim, RTMPose, and OpenSim

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In this tutorial, we build and run a complete Pose2Sim pipeline on Colab to understand how markerless 3D kinematics works in practice. We begin with environment setup, configure the project for Colab’s headless runtime, and then walk through calibration, 2D pose estimation, synchronization, person association, triangulation, filtering, marker augmentation, and OpenSim-based kinematics. As we progress,

A Coding Guide to Markerless 3D Human Kinematics with Pose2Sim, RTMPose, and OpenSim Read More »

NVIDIA Releases AITune: An Open-Source Inference Toolkit That Automatically Finds the Fastest Inference Backend for Any PyTorch Model

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Deploying a deep learning model into production has always involved a painful gap between the model a researcher trains and the model that actually runs efficiently at scale. TensorRT exists, Torch-TensorRT exists, TorchAO exists — but wiring them together, deciding which backend to use for which layer, and validating that the tuned model still produces

NVIDIA Releases AITune: An Open-Source Inference Toolkit That Automatically Finds the Fastest Inference Backend for Any PyTorch Model Read More »

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

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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

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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

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

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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

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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 »