Sleep Foundation Model Predicts Future Diseases From One Night of PSG Data
25 days ago 高效码农SleepFM: A 585,000-Hour Foundation Model That Turns One Night of Sleep Into a Disease Crystal Ball Can a single night of polysomnography (PSG) forecast dozens of future diseases without any expert labels? Yes. SleepFM self-trains on 65 000 unlabeled recordings and beats strong supervised baselines on 1 041 phenotypes, reaching 0.84 C-Index for all-cause mortality and 0.87 for dementia. What exact problem does SleepFM solve? Core question: “Why can’t current sleep-AI generalize to new hospitals or predict non-sleep diseases?” Traditional models need (i) costly manual labels, (ii) fixed electrode montages, and (iii) a fresh training run for every new task. …
Mastering AI in 2026: 6 Skills to Outperform 90% of the Workforce
25 days ago 高效码农Mastering AI in 2026: 6 Essential Skills to Transition from Chatbots to Intelligent Systems 2025 has been a year of massive leaps in artificial intelligence. Tasks that once seemed impossible are now achievable with a few clicks. However, a quick look around reveals a surprising reality: most people are still using AI the same way they did years ago—treating it like a slightly smarter search engine or a basic Q&A machine. If you want to truly excel in 2026, you need to move beyond simple chatting. To stay ahead of 90% of the workforce, you must transition from a “tool …
Automated AI Media Software: The Future of Content Creation from Crawling to Publishing?
25 days ago 高效码农AIMedia: An In-Depth Exploration and Practical Guide to a Fully Automated AI Media Software In today’s information-saturated era, the automation of content creation and distribution has become a focal point for many media professionals and content creators. Today, we will delve into an open-source project named AIMedia, which aims to automate the entire workflow—from hot topic crawling and content generation to multi-platform publishing. Based on its official documentation, this article will dissect its architecture, features, and how to get started, while also candidly discussing its complexities and future evolution. What is AIMedia? What Problems Does It Solve? Simply put, AIMedia …
AI Agent Evaluations: The Complete 2025-2026 Guide to Bulletproof Testing
26 days ago 高效码农How to Build Reliable Evaluations for AI Agents: A Complete Practical Guide (2025–2026 Edition) If you’re building, shipping, or scaling AI agents in 2025 or 2026, you’ve probably already discovered one hard truth: The same autonomy, tool use, long-horizon reasoning, and adaptability that make powerful agents incredibly valuable… also make them extremely difficult to test and improve reliably. Without a solid evaluation system, teams usually fall into the same reactive cycle: users complain → engineers reproduce the bug manually → a fix is shipped → something else quietly regresses → repeat. Good evaluations break this loop. They turn vague feelings …
PRFL: Train 14B Video Generation Models in 67GB VRAM with 56% Smoother Motion
26 days ago 高效码农Video-Generation Models Can Also Be the Judge: How PRFL Finetunes a 14 B Model in 67 GB VRAM and Makes Motion 56 % Smoother Train on every frame (720 P × 81) without blowing memory, speed the loop 1.4×, and push motion scores from 25 → 81. All done in latent space—no VAE decoding required. 1. Why a “Judge” Is Missing in Current Video Models People type these questions into search boxes every day: “AI video motion looks fake—how to fix?” “Finetune large video model with limited GPU memory?” “Which method checks physics consistency during generation?” Classic pipelines give a …
VideoRAG: How Machines Finally Crack Extreme Long-Context Video Understanding
26 days ago 高效码农VideoRAG & Vimo: Cracking the Code of Extreme Long-Context Video Understanding Core Question: Why do existing video AI models fail when faced with hundreds of hours of footage, and how does the VideoRAG framework finally enable machines to chat with videos of any length? When we first attempted to analyze a 50-hour university lecture series on AI development, our state-of-the-art video model choked after the first three hours. It was like trying to understand an entire library by reading random pages from three books. That’s when we realized the fundamental flaw: current video understanding approaches treat long videos as isolated …
Ralph Loop: How AI Teaches Itself to Code Through Failure
26 days ago 高效码农From the Farm to the Future: How Ralph Loop Teaches AI to Code by Itself Imagine guiding a programming assistant that never gets discouraged. It writes code, runs it, fails, but instead of waiting for your instructions, it immediately examines the error message, thinks, modifies, and tries again. This cycle repeats until success. This isn’t science fiction; it’s the reality sparked by an Australian goat farmer with just five lines of code. This is the Ralph Loop—a technical paradigm that allows AI to learn through “repeated failure” and ultimately complete tasks. It’s quietly transforming how we collaborate with AI to …
UniVideo Explained: The Single Open-Source Model That Understands, Generates & Edits Videos with AI
27 days ago 高效码农UniVideo in Plain English: One Model That Understands, Generates, and Edits Videos Core question: Can a single open-source model both “see” and “remix” videos without task-specific add-ons? Short answer: Yes—UniVideo freezes a vision-language model for understanding, bolts a lightweight connector to a video diffusion transformer, and trains only the connector + diffusion net; one checkpoint runs text-to-video, image-to-video, face-swap, object removal, style transfer, multi-ID generation, and more. What problem is this article solving? Reader query: “I’m tired of chaining CLIP + Stable-Diffusion + ControlNet + RVM just to edit a clip. Is there a unified pipeline that does it all, …
Beyond Code: Building Complex AI Workflows with Claude Agent SDK
27 days ago 高效码农Beyond Code: Building Your First Non-Coding AI Workflow with Claude Agent SDK Have you ever wondered what the powerful engine behind Claude Code—one of the best coding tools available—could do besides writing code? As a developer who has long explored the boundaries of AI automation, I’ve been searching for more lightweight and direct solutions for building agents. While mainstream frameworks like CrewAI and LangChain continue to grow in complexity, I decided to turn my attention to an unexpected tool: the 「Claude Agent SDK」. My hypothesis was simple: if it can give AI exceptional coding capabilities, then applying its core principles—tool …
UniVLA Unlocked: How Hidden Language Makes Robots Finally Understand Complex Tasks
28 days ago 高效码农What is UniVLA and How It Enables Robots to Truly Understand and Execute Complex Tasks Imagine you’re teaching a robot to “put the screwdriver back in the toolbox.” Traditional approaches require writing precise motion commands for that specific robot: lift arm 15 centimeters, rotate wrist 30 degrees, apply 2 newtons of grip force. Switch to a different robotic arm, and every parameter must be recalibrated. It’s like teaching a person to do something by first explaining how to contract every muscle—inefficient and lacking universal applicability. UniVLA (Unified Vision-Language-Action) directly addresses this core challenge. It aims to enable robots to understand …
Unlock the Infinite Revenue Loop: Automate Your AI Business with Manus, Claude, and Grok
28 days ago 高效码农Unlock the Infinite Revenue Loop: An Automated AI Business Engine with Manus, Claude, and Grok By combining Manus for data analysis, Claude for content execution, and Grok for real-time trend capture, operators build a self-reinforcing info-product business loop. This system requires only 13 hours of weekly work and 56inAItoolcosts∗∗toachieveexponentialmonthlyrevenuegrowthfromzeroto∗∗80k–$150k within a year. Introduction: Why Single AI Tools Fail to Deliver High Returns In today’s digital business landscape, many people rely on a single, generic AI tool, only to find their results stagnant and their income hovering between 5,000and10,000. The root of this mediocrity lies in the singular approach to tool …
Agent Drift in Multi-Agent LLM Systems: Why Your AI Teams Fail Over Time & How to Fix It
28 days ago 高效码农Agent Drift in Multi-Agent LLM Systems: Why Performance Degrades Over Extended Interactions Core question this article answers: Why do multi-agent large language model (LLM) systems gradually lose behavioral stability as interactions accumulate, even without any changes to the underlying models, and how severe can this “agent drift” become in real-world deployments? Multi-agent LLM systems—built on frameworks like LangGraph, AutoGen, and CrewAI—are transforming enterprise workflows by breaking down complex tasks across specialized agents that collaborate seamlessly. These systems excel at code generation, research synthesis, and automation. However, a recent study highlights a critical, often overlooked issue: agent drift, the progressive degradation …
Nemotron-Speech-Streaming-En-0.6b: The Unified ASR Model for Low-Latency Streaming & Batch Transcription
28 days ago 高效码农NVIDIA Nemotron-Speech-Streaming-En-0.6b: A Powerful Model for Real-Time Speech-to-Text The Nemotron-Speech-Streaming-En-0.6b is NVIDIA’s 600M-parameter English automatic speech recognition (ASR) model, designed for high-quality transcription in both low-latency streaming and high-throughput batch scenarios. It features a native cache-aware streaming architecture, supports punctuation and capitalization out of the box, and allows runtime flexibility with chunk sizes from 80ms to 1120ms, achieving average Word Error Rates (WER) between 7.16% and 8.53%. If you’re building applications like voice assistants, live captioning, or conversational AI, you’ve probably faced a common challenge: how to achieve fast, responsive speech-to-text without sacrificing accuracy. Many traditional ASR models force a …
Context Graph: The Next-Gen Data Platform Unlocking Enterprise Agentic Automation
29 days ago 高效码农Context Graphs: Understanding Real Enterprise Processes to Unlock the Next Generation Data Platform for Agentic Automation Context is the next data platform If I asked you, “What is the actual process for signing a new contract at your company?” you might answer, “Oh, Sales submits a request, Legal reviews it, and then a leader approves it.” But that’s the “should” written in the policy manual. The reality is often this: Salesperson Zhang updates the deal stage in Salesforce, then messages Legal Specialist Li on Slack with a link to the latest Google Doc. Li leaves comments, schedules a calendar invite …
ChatGPT Health: How AI Manages Personal Health Data Securely & Transforms Healthcare
29 days ago 高效码农Introducing ChatGPT Health: A Secure AI Partner for Your Personal Health Journey Snippet/Summary: ChatGPT Health is a dedicated experience that securely integrates your personal health data, such as medical records (EHR) and app data (Apple Health, MyFitnessPal), with AI intelligence. It provides personalized insights for lab results, doctor visit preparation, and lifestyle planning within an isolated, encrypted environment where conversations are never used for model training. Why Health is Now a Core Part of the AI Experience Managing health information today is often a fragmented and overwhelming process. Vital data is scattered across patient portals, wearable devices, fitness apps, and …
NVIDIA Cosmos Reason2: Build Smarter Robots with Human-Like Physical AI Reasoning
29 days ago 高效码农Exploring NVIDIA Cosmos Reason2: A Reasoning Vision Language Model for Physical AI and Robotics Summary NVIDIA Cosmos Reason2 is an open-source, customizable reasoning vision language model (VLM) designed for physical AI and robotics. It enables robots and vision AI agents to reason like humans, leveraging prior knowledge, physics understanding, and common sense to comprehend and act in the real world. The model understands space, time, and fundamental physics, serving as a planning tool to determine the next steps for embodied agents. Available in 2B and 8B parameter versions, it requires at least 24GB GPU memory and supports Hopper and Blackwell …
NVIDIA Nemotron Streaming Speech Recognition: How 600M Parameters Redefine Real-Time ASR Deployment
29 days ago 高效码农NVIDIA Nemotron Streaming Speech Recognition: From Model Principles to Practical Deployment—How 600M Parameters Are Redefining Real-Time ASR Imagine a cross-continental video conference where your voice assistant not only transcribes everyone’s speech into text in real time but also intelligently adds punctuation and capitalization, with almost imperceptible delay. Or, when you’re conversing with your car’s voice system, its responses feel so natural and fluid, as if speaking with a person. At the heart of this experience lies the core challenge: how to make machines “understand” a continuous stream of speech and instantly convert it into accurate text. Traditional Automatic Speech Recognition …
The A.X K1 Deep Dive: A 519B MoE Model with Think-Fusion Intelligence
29 days ago 高效码农Deep Dive into A.X K1: Architecture Design and Think-Fusion Evolution of a 519B MoE Model Snippet: A.X K1 is a 519B-parameter Mixture-of-Experts (MoE) model by SK Telecom, activating only 33B parameters for efficient inference. It introduces the Think-Fusion training recipe, enabling a unified model to switch between high-speed “intuition” and deep “reasoning” modes, setting new benchmarks in Korean and multi-language AI performance. In the pursuit of Artificial General Intelligence (AGI), the industry faces a constant tug-of-war: how to maintain massive model capacity without skyrocketing inference costs. The newly released A.X K1 technical report provides a definitive answer. By leveraging a …
HyperCLOVA X 8B Omni: The Open-Source Any-to-Any Multimodal AI Unpacked
1 months ago 高效码农One Transformer, Three Modalities: Inside HyperCLOVA X 8B Omni (The Plain-English Walkthrough) “ Main keywords: HyperCLOVA X 8B Omni, any-to-any multimodal, text-image-speech model, 8-billion-parameter model, Korean-first AI, OmniServe inference, open-weight license Quick-glance answers (save you a scroll) Question Short answer What is it? An 8-billion-parameter decoder-only model that reads & writes text, images and speech in a single forward pass. Who should care? Teams that need Korean/English multimodal AI but only have 3–4 A100s, not 40. Is it really open? Weights are downloadable. Commercial use is allowed under NAVER’s custom license (credit + no illegal use). How big is the …
LTX-2 Guide: How to Generate Audio-Video Locally with Open-Source Models
1 months ago 高效码农Exploring LTX-2: How to Generate Synchronized Audio-Video with Open-Source Models Summary LTX-2 is a DiT-based audio-video foundation model that generates synchronized video and audio in a single framework, supporting high-fidelity outputs and multiple performance modes. Using its PyTorch codebase, you can run it locally to create videos with resolutions divisible by 32 and frame counts divisible by 8+1. The model features 19B-parameter dev and distilled versions, ideal for text-to-video or image-to-video tasks, with open weights and training capabilities. What Is LTX-2? Why Should You Care About This Model? Imagine wanting to create a short video where the visuals flow seamlessly …