AI Mathematics
1. Position Loss (L_position) - Accuracy of the Mouth Shape Time Mouth opening Truth Predicted L_position = Σᵢ wᵢ × ||y_pred - y_true||² Computes the difference between predicted and ground-truth values at each time step. Important parameters (jaw opening, mouth opening) receive larger weights. jaw_open: ×2.0
IT & AI Technology
Hello! Starting with this article, we will be walking through Active Directory over the next several installments. Active Directory (AD) is a directory service developed by Microsoft. It is no exaggeration to call it the de facto standard in the IT infrastructure of today's large enterprises, and it
LLM
Hello! In the previous installments of our LLM Inference Infrastructure Provisioning Course, we covered defining inference speed, estimating request volume, calculating memory consumption, and selecting an inference engine. This time, we work through the remaining steps — GPU node sizing, load testing, and trade-off analysis — in one go, and close with
Generative AI Frontlines
Hello! Today we take a closer look at Guardian Agents, a concept Gartner recently announced. Gartner's Official Definition In June 2025, Gartner—well known for its Hype Cycle—published its perspective on Guardian Agents.Guardian agents are AI-based technologies designed to support safe and trusted interactions with AI—
LLM
Hello! In the previous installments of this course, we covered in detail how to estimate the request volume an LLM service needs to handle and how to calculate the memory a model consumes during inference. This time we dig into the fourth of the seven steps: selecting an inference engine.
AI Mathematics
Hello! Today I would like to give a comprehensive overview of lipsync technology, which we actually use in our MotionVox product. Lipsync technology, which generates natural mouth movements synchronized to speech, is an important technology for AI avatars and 3D animation production. In this article, we take a technical deep
LLM
Hello! Last time we covered how to estimate the request volume for an LLM service. This time we dig into the third of the seven steps: estimating the memory your model consumes during inference. LLM Inference Infrastructure Provisioning Course: Series Index * Part 1: Basic Concepts and Inference Speed * Part 2:
LLM
Hello! Welcome to Part 2 of the LLM Inference Infrastructure Provisioning Course. LLM Inference Infrastructure Provisioning Course: Series Index * Part 1: Basic Concepts and Inference Speed * Part 2: Estimating Request Volume for an LLM Service * Part 3: Estimating Inference-Time Memory Consumption for Your Model * Part 4: Selecting an Inference Engine
LLM
Hello! Today we begin a multi-part series on inference infrastructure provisioning — GPU provisioning in particular — for building your own LLM service in-house. Introduction As LLMs have evolved, the use of public LLMs such as ChatGPT and Claude has spread rapidly through the enterprise. Forward-looking companies, however, are moving beyond these