​In the evolving landscape of web development, the emergence of no-code platforms has significantly broadened access to application creation. Among these, Hostinger Horizons stands out as an AI-powered tool designed to facilitate the building, editing, and publishing of custom web applications without necessitating any coding expertise. By integrating essential services such as hosting, domain registration, and email functionalities, Hostinger Horizons offers a comprehensive solution for individuals and businesses seeking to establish a digital presence.​ Technical Overview Hostinger Horizons utilizes advanced artificial intelligence and natural language processing to interpret user inputs and generate functional web applications. The platform features a user-friendly

LLMs have shown impressive capabilities in reasoning tasks like Chain-of-Thought (CoT), enhancing accuracy and interpretability in complex problem-solving. While researchers are extending these capabilities to multi-modal domains, videos present unique challenges due to their temporal dimension. Unlike static images, videos require understanding dynamic interactions over time. Current visual CoT methods excel with static inputs but struggle with video content because they cannot explicitly localize or revisit specific moments in sequences. Humans overcome these challenges by breaking down complex problems, identifying and revisiting key moments, and synthesizing observations into coherent answers. This approach highlights the need for AI systems to manage

MIT Research Scientist Ana Trišović went from a student downloading MIT Open Learning resources in Serbia to becoming a computer scientist at CERN, Harvard, and MIT.

Supervised fine-tuning (SFT) is the standard training paradigm for large language models (LLMs) and graphic user interface (GUI) agents. However, SFT demands high-quality labeled datasets, resulting in extended training periods and high computational expenses. This dependence on extensive data creates bottlenecks in AI development workflows. Moreover, existing VLM-based GUI agents trained through SFT show performance deficiencies when confronted with out-of-domain scenarios, severely limiting their practical utility in diverse real-world applications. Rule-based reinforcement learning (RL) or reinforcement fine-tuning (RFT) is a promising alternative, requiring only dozens to thousands of samples instead of massive datasets. Various approaches have been developed to advance

Large language models (LLMs) have become vital across domains, enabling high-performance applications such as natural language generation, scientific research, and conversational agents. Underneath these advancements lies the transformer architecture, where alternating layers of attention mechanisms and feed-forward networks (FFNs) sequentially process tokenized input. However, with an increase in size and complexity, the computational burden required for inference grows substantially, creating an efficiency bottleneck. Efficient inference is now a critical concern, with many research groups focusing on strategies that can reduce latency, increase throughput, and cut computational costs while maintaining or improving model performance. At the center of this efficiency problem

Reinforcement Learning from Verifiable Rewards (RLVR) has recently emerged as a promising method for enhancing reasoning abilities in language models without direct supervision. This approach has shown notable success in mathematics and coding, where reasoning naturally aligns with structured problem-solving. While studies have demonstrated that RLVR alone can lead to self-evolved reasoning, research has largely been limited to these technical fields. Efforts to extend RLVR have explored synthetic datasets, such as those involving sequential tasks and object counting, indicating potential but also highlighting the challenges of adapting this method to different domains. Expanding RLVR to broader areas remains an open

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