Browsing: AI

Obvious Ventures, co-founded by Twitter’s Evan Williams, has successfully raised its fifth fund, totaling $360,360,360. This latest fund size reflects the firm’s comprehensive investment philosophy, focusing on planetary, human, and economic health, building on a history of unique fund numbers and impactful investments.

Major tech companies and emerging startups are leveraging generative AI to develop software and hardware for children. Many of these offerings are restricted to text or voice interactions, which may not fully engage young users. Three former Google employees aim to overcome this limitation with their interactive, generative AI-powered app, Sparkli.

A Code Agent built with the Transformers Agents library has achieved a top position on the challenging GAIA benchmark, demonstrating the effectiveness of code-based action generation for LLM agents. This article details the tools, multi-agent orchestration, and planning strategies employed to surpass existing solutions.

These five options make long-running jobs easier, faster, and less frustrating than Colab.

New research from Mercor, using the Apex-Agents benchmark, suggests that current AI models are not yet ready for complex white-collar tasks. Even leading models struggle with multi-domain reasoning, achieving low accuracy rates on tasks drawn from consulting, investment banking, and law, despite rapid year-over-year improvements.

In 2025, global consumer spending on mobile apps surpassed that on mobile games for the first time, reaching $85 billion, a 21% year-over-year increase. This surge was largely driven by the rapid adoption of generative AI apps, whose in-app purchase revenue tripled to over $5 billion. AI assistants like ChatGPT, Google Gemini, and DeepSeek led this growth, with users spending 48 billion hours in these applications. Major tech companies’ investments and mobile-exclusive access also played key roles in this significant market shift.

Sentiment analysis is crucial for understanding customer opinions from text and voice interactions. This article explores the technical aspects of sentiment analysis for both text and audio, comparing various machine learning models and AWS services. It discusses challenges like language ambiguity and acoustic cues, presenting experimental results and future directions for enhancing sentiment detection using generative AI.

A new era of app creation is emerging, where individuals without traditional coding backgrounds are developing “micro apps” for personal use. Driven by advancements in AI, these fleeting, highly specific applications address niche needs, offering a personalized alternative to off-the-shelf software.

The healthcare sector is experiencing a significant influx of AI investment and product development, with major tech companies making strategic moves. This rapid expansion, however, also brings concerns regarding data security and the accuracy of medical information.

Hugging Face announces the integration of fastai with its Hub, allowing fastai practitioners to easily share and load deep learning models. This collaboration aims to democratize machine learning by providing a central platform for fastai models, complete with version control and model cards. The article details how to install necessary libraries, create and share a fastai Learner, and load models from the Hub. It also highlights the Blurr library, which enables the combination of fastai and Hugging Face Transformers, further expanding model sharing capabilities.