Alibaba Tongyi Lab Releases MAI-UI: A Foundation GUI Agent Family that Surpasses Gemini 2.5 Pro, Seed1.8 and UI-Tars-2 on AndroidWorld

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Alibaba Tongyi Lab have released MAI-UI—a family of foundation GUI agents. It natively integrates MCP tool use, agent user interaction, device–cloud collaboration, and online RL, establishing state-of-the-art results in general GUI grounding and mobile GUI navigation, surpassing Gemini-2.5-Pro, Seed1.8, and UI-Tars-2 on AndroidWorld. The system targets three specific gaps that early GUI agents often ignore, native agent user interaction, MCP tool integration, and a device cloud collaboration architecture that keeps privacy sensitive work on device while still using […]

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Dynamic Sorting and Filtering in C#

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If you ever wondered how you could dynamically filter and/or sort your queries without having to write a huge switch statement with all the possible properties and operations, you’ve come to the right place! Today we’ll see how we can generate these types of operations at runtime and on the fly.

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A smarter way for large language models to think about hard problems

A smarter way for large language models to think about hard problems

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To make large language models (LLMs) more accurate when answering harder questions, researchers can let the model spend more time thinking about potential solutions. But common approaches that give LLMs this capability set a fixed computational budget for every problem, regardless of how complex it is. This means the LLM might waste computational resources on simpler questions or be unable to tackle intricate problems that require more reasoning. To address this, MIT researchers developed a smarter way to allocate […]

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Understanding Multimodal LLMs

Understanding Multimodal LLMs

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It was a wild two months. There have once again been many developments in AI research, with two Nobel Prizes awarded to AI and several interesting research papers published.  Among others, Meta AI released their latest Llama 3.2 models, which include open-weight versions for the 1B and 3B large language models and two multimodal models. In this article, I aim to explain how multimodal LLMs function. Additionally, I will review and summarize roughly a dozen other recent multimodal […]

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Supervised Hebbian Learning

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Preliminaries Linear algebra Hebb Rule1 Hebb rule is one of the earliest neural network learning laws. It was published in 1949 by Donald O. Hebb, a Canadian psychologist, in his work ’ The Organization of Behavior’. In this great book, he proposed a possible mechanism for synaptic modification in the brain. And this rule then was used in training the artificial neural networks for pattern recognition. ’ The Organization of Behavior’ The main premise of the book is […]

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ML Models: Deployment and Testing in Production

ML Models: Deployment and Testing in Production

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Machine learning models are mainly developed offline but must be deployed in a production environment to process real-time data and handle the problem they were designed to solve. In this blog post, we will explore the fundamentals of deploying an ML model, discuss the challenges you may encounter, and provide steps to streamline the process for greater efficiency. What is the machine learning model deployment? Experts estimate that up to 90 percent of ML models never reach the […]

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Understanding Prompt Injection: Risks, Methods, and Defense Measures

Understanding Prompt Injection: Risks, Methods, and Defense Measures

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TL;DR Prompt injection, a security vulnerability in LLMs like ChatGPT, allows attackers to bypass ethical safeguards and generate harmful outputs. It can take forms like direct attacks (e.g., jailbreaks, adversarial suffixes) or indirect attacks (e.g., hidden prompts in external data). Defending against prompt injections involves prevention-based measures like paraphrasing, retokenization, delimiters, and instructional safeguards. However, detection-based strategies include perplexity checks, response analysis, and known answer validation. Some advanced tools also exist such as prompt hardening, regex filters, and […]

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Latent Flow Matching for Expressive Singing Voice Synthesis

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arXiv:2601.00217v1 Announce Type: new Abstract: Conditional variational autoencoder (cVAE)-based singing voice synthesis provides efficient inference and strong audio quality by learning a score-conditioned prior and a recording-conditioned posterior latent space. However, because synthesis relies on prior samples while training uses posterior latents inferred from real recordings, imperfect distribution matching can cause a prior-posterior mismatch that degrades fine-grained expressiveness such as vibrato and micro-prosody. We propose FM-Singer, which introduces conditional flow matching (CFM) in latent space to learn a […]

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The TechBeat: How to Set Goals for 2026 That Actually Stick (1/4/2026)

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## How Supercell Powers its Massive Social Network with ScyllaDB By @scylladb [ 6 Min read ] Supercell powers real-time cross-game chat, presence, and notifications for millions using ScyllaDB Cloud, enabling low-latency, scalable events. Read More. Solo Satoshi Becomes Authorized Canaan Distributor for Avalon Home […]

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