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In this tutorial, we will learn how to harness the power of Dappier AI , a suite of real-time search and recommendation tools, to enhance our conversational applications. By combining Dappier’s cutting-edge RealTimeSearchTool with its AIRecommendationTool, we can query the latest information from across the web and surface personalized article suggestions from custom data models. We guide you step-by-step through setting up our Google Colab environment, installing dependencies, securely loading API keys, and initializing each Dappier module. We will then integrate these tools with an OpenAI chat model (e.g., gpt-3.5-turbo), construct a composable prompt chain, and execute end-to-end queries, all within nine concise notebook cells. Whether we need up-to-the-minute news retrieval or AI-driven content curation, this tutorial provides a flexible framework for building intelligent, data-driven chat experiences. Copy Code Copied Use a different Browser !pip install -qU langchain-d...

Multimodal foundation models have shown substantial promise in enabling systems that can reason across text, images, audio, and video. However, the practical deployment of such models is frequently hindered by hardware constraints. High memory consumption, large parameter counts, and reliance on high-end GPUs have limited the accessibility of multimodal AI to a narrow segment of institutions and enterprises. As research interest grows in deploying language and vision models at the edge or on modest computing infrastructure, there is a clear need for architectures that offer a balance between multimodal capability and efficiency. Alibaba Qwen Releases Qwen2.5-Omni-3B: Expanding Access with Efficient Model Design In response to these constraints, Alibaba has released Qwen2.5-Omni-3B , a 3-billion parameter variant of its Qwen2.5-Omni model family. Designed for use on consumer-grade GPUs—particularly those with 24GB of memory—this model introduces a practical alternative for developers ...

Google has significantly expanded the capabilities of its experimental AI tool, NotebookLM , by introducing Audio Overviews in over 50 languages . This marks a notable leap in global content accessibility, making the platform far more inclusive and versatile for a worldwide audience. Initially launched with limited support for English, NotebookLM is now rapidly evolving into a multimodal, multilingual assistant for summarizing and understanding complex documents. Solving the Comprehension Bottleneck In research, business, and education, one of the consistent challenges is information overload. While large language models (LLMs) like Gemini can generate fluent summaries, accessibility and modality gaps still limit their practical utility—especially for non-native English speakers, visually impaired users, or individuals who prefer auditory content over text. Google addresses this with Audio Overviews: human-like spoken summaries automatically generated from user-supplied source mater...

The development of agentic systems—LLMs embedded within scaffolds capable of tool use and autonomous decision-making—has made significant progress. Yet, most implementations today rely on fixed, hand-crafted orchestration strategies. These designs are inherently constrained, limiting the agent’s adaptability to new tasks and environments. As models grow in capability, the rigidity of their execution frameworks becomes a bottleneck, especially in domains such as software engineering where the task complexity and variability demand a more flexible system. In response, researchers from the University of Bristol and iGent AI have introduced SICA (Self-Improving Coding Agent)—a novel agent architecture designed to iteratively enhance its own performance by modifying its underlying code. Unlike prior methods, such as ADAS, which split responsibilities between a meta-agent and a target-agent, SICA unifies these roles. The same agent that performs the task is also responsible for evaluating ...

Reasoning with LLMs can benefit from utilizing more test compute, which depends on high-quality process reward models (PRMs) to select promising paths for search or ranking. PRMs score problem-solution pairs to indicate whether the solution is correct, and have been implemented as discriminative classifiers. However, these models require extensive resources, including human annotation, gold step-by-step solutions, or computationally intensive rollouts. LLM-as-a-judge approaches offer advantages in data efficiency and interpretability, but they perform poorly compared to specialized reward models for complex reasoning tasks, failing to recognize incorrect reasoning. This creates a challenge to maintain data-efficiency and interpretability advantages while achieving the superior performance of discriminative PRMs. Research approaches to solve process verification challenges have followed three main paths. Discriminative PRMs function as classifiers that predict numerical correctness sco...

AI models have made remarkable strides in generating speech, music, and other forms of audio content, expanding possibilities across communication, entertainment, and human-computer interaction. The ability to create human-like audio through deep generative models is no longer a futuristic ambition but a tangible reality that is impacting industries today. However, as these models grow more sophisticated, the need for rigorous, scalable, and objective evaluation systems becomes critical. Evaluating the quality of generated audio is complex because it involves not only measuring signal accuracy but also assessing perceptual aspects such as naturalness, emotion, speaker identity, and musical creativity. Traditional evaluation practices, such as human subjective assessments, are time-consuming, expensive, and prone to psychological biases, making automated audio evaluation methods a necessity for advancing research and applications. One persistent challenge in automated audio evaluation ...

Despite the remarkable progress in large language models (LLMs), critical challenges remain. Many models exhibit limitations in nuanced reasoning, multilingual proficiency, and computational efficiency. Often, models are either highly capable in complex tasks but slow and resource-intensive, or fast but prone to superficial outputs. Furthermore, scalability across diverse languages and long-context tasks continues to be a bottleneck, particularly for applications requiring flexible reasoning styles or long-horizon memory. These issues limit the practical deployment of LLMs in dynamic real-world environments. Qwen3 Just Released: A Targeted Response to Existing Gaps Qwen3 , the latest release in the Qwen family of models developed by Alibaba Group, aims to systematically address these limitations. Qwen3 introduces a new generation of models specifically optimized for hybrid reasoning, multilingual understanding, and efficient scaling across parameter sizes. The Qwen3 series expands u...

Video captioning models are typically trained on datasets consisting of short videos, usually under three minutes in length, paired with corresponding captions. While this enables them to describe basic actions like walking or talking, these models struggle with the complexity of long-form videos, such as vlogs, sports events, and movies that can last over an hour. When applied to such videos, they often generate fragmented descriptions focused on isolated actions rather than capturing the broader storyline. Efforts like MA-LMM and LaViLa have extended video captioning to 10-minute clips using LLMs, but hour-long videos remain a challenge due to a shortage of suitable datasets. Although Ego4D introduced a large dataset of hour-long videos, its first-person perspective limits its broader applicability. Video ReCap addressed this gap by training on hour-long videos with multi-granularity annotations, yet this approach is expensive and prone to annotation inconsistencies. In contrast, ann...

Devin AI recently introduced DeepWiki , a free tool that automatically generates structured, wiki-style documentation for any GitHub repository. Built using their in-house DeepResearch agent , DeepWiki aims to simplify the process of understanding unfamiliar codebases by offering a comprehensive, interactive overview directly from repository URLs. This release addresses a common pain point in software development: navigating large, often poorly documented codebases. For developers tasked with onboarding, refactoring, or auditing external projects, DeepWiki provides a practical solution by bridging the gap between raw code and accessible documentation. Overview of DeepWiki DeepWiki functions as an AI layer over GitHub repositories. When a user inputs a repository URL, the platform analyzes the project structure, source code, configuration files, and any available documentation (such as README files). Based on this analysis, DeepWiki produces an organized set of outputs, including: ...

Achieving strong, multi-step reasoning in LMs remains a major challenge, despite notable progress in general task performance. Such reasoning is crucial for complex problem-solving domains, such as scientific research and strategic planning. Traditionally, enhancing reasoning skills involves supervised fine-tuning (SFT), where models learn by imitating step-by-step reasoning demonstrations from more advanced models, such as o1. While effective, this method heavily depends on the availability of high-quality reasoning traces, which are costly and risk promoting shallow mimicry over genuine logical exploration. RL offers an alternative by enabling models to learn directly from reward signals, encouraging broader reasoning exploration. However, RL approaches are often resource-heavy and complex, raising the question of how to build reasoning-capable models cost-effectively. Following the release of strong models like o1-preview, several open-source efforts such as STILL, Sky-T1, SimpleRL...