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AI-Generated Ad Created with Google’s Veo3 Airs During NBA Finals, Slashing Production Costs by 95%
A lone AI filmmaker, a cutting-edge generative video model, and a national TV spot during one of the year’s biggest sporting events. This isn’t the plot of a sci-fi movie; it’s the new reality of advertising, and it was created in just 3 days. TLDR: First of its Kind: An AI-generated commercial for the…

OThink-R1: A Dual-Mode Reasoning Framework to Cut Redundant Computation in LLMs
The Inefficiency of Static Chain-of-Thought Reasoning in LRMs Recent LRMs achieve top performance by using detailed CoT reasoning to solve complex tasks. However, many simple tasks they handle could be solved by smaller models with fewer tokens, making such elaborate reasoning unnecessary. This echoes human thinking, where we use fast, intuitive responses for easy…

Building AI-Powered Applications Using the Plan → Files → Code Workflow in TinyDev
In this tutorial, we introduce TinyDev class implementation, a minimal yet powerful AI code generation tool that utilizes the Gemini API to transform simple app ideas into comprehensive, structured applications. Designed to run effortlessly in Notebook, TinyDev follows a clean three-phase workflow—Plan → Files → Code—to ensure consistency, functionality, and modular design. Whether building…

Microsoft AI Introduces Code Researcher: A Deep Research Agent for Large Systems Code and Commit History
Rise of Autonomous Coding Agents in System Software Debugging The use of AI in software development has gained traction with the emergence of large language models (LLMs). These models are capable of performing coding-related tasks. This shift has led to the design of autonomous coding agents that assist or even automate tasks traditionally carried…

Internal Coherence Maximization (ICM): A Label-Free, Unsupervised Training Framework for LLMs
Post-training methods for pre-trained language models (LMs) depend on human supervision through demonstrations or preference feedback to specify desired behaviors. However, this approach faces critical limitations as tasks and model behaviors become very complex. Human supervision is unreliable in these scenarios as LMs learn to mimic mistakes in demonstrations or exploit inherent flaws in…

MemOS: A Memory-Centric Operating System for Evolving and Adaptive Large Language Models
LLMs are increasingly seen as key to achieving Artificial General Intelligence (AGI), but they face major limitations in how they handle memory. Most LLMs rely on fixed knowledge stored in their weights and short-lived context during use, making it hard to retain or update information over time. Techniques like RAG attempt to incorporate external…
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