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Google has integrated computer use capabilities into Gemini 3.5 Flash, allowing the AI model to interact with computer interfaces and perform automated tasks. This enhancement expands Gemini's practical applications beyond text-based interactions.

RubyLLM is a new Ruby framework that enables developers to seamlessly integrate multiple major AI providers into their applications. The framework simplifies the process of building AI-powered features by offering a unified interface across different LLM services.

Propane gives your product team and agents one connected, always-current view of your customers. Automatically collected from all your tools. Collaborate on a shared canvas. Commit straight to any coding or design agent. Secure, maintained, always on. You just build products people love.

Tencent EdgeOne Makers is an edge platform for modern web apps and AI agents. Build with your preferred frameworks and deploy through familiar CLI, Git, and CI/CD workflows. Get built-in agent runtime, sandboxed tools, memory, observability, model gateway support, serverless functions, and storage—without stitching together complex infrastructure. Add AI agents to existing products or launch new AI applications in minutes. Deploy AI agents like web apps.

Crewdle gives small businesses access to the AI tools they need without keeping a dozen subscriptions alive. Use chat, agents, automation, content, websites, and business tools from one platform, with usage-based billing instead of paying every month for products you barely touch. It is built for owners who want AI to help run the business without subscription sprawl, technical setup, or enterprise complexity.

Stripe Directory is a single discovery layer for developers and AI agents to find businesses on Stripe—including Stripe Apps, Projects.dev providers, and mpp.dev services—and integrate them without the need to manually search for them.

Rebel is a desktop AI workspace for agentic work. It connects your memory, meetings, files, actions, automations, and tools so AI agents can help with real work — while keeping sensitive actions behind approval checks. Built Fair Source, with portable workflows and model choice.

Agents that handle the CRM work you've been doing manually — pipeline digests, lead enrichment, data hygiene, call coaching, and much more. They run on a schedule or a signal, work across your stack, and fire without you touching them. Set them to run fully autonomously or require your approval before acting. Describe what you want to automate or start from a template. Clarify builds it from there.

you can now buy Doordash from Claude. connect Agentcard to your Openclaw, Claude or whatever you use, ask for what you want and we handle the rest. it feels magic. install from agentcard.sh and make your first order for free!

Haystack is an open-source AI framework designed to enable production-ready deployment of intelligent agents and retrieval-augmented generation (RAG) systems. The framework provides tools for building scalable AI applications with advanced capabilities.

Most people walk out of an important call thinking "I should have asked that." Ruby makes sure you don't. She listens in real time and slides you the question worth asking — through a small pill only you can see, live as the conversation unfolds. → Before: she helps you think through your goal and what to listen for. → During: she nudges you with the sharper question, at exactly the right moment. → After: she writes the recap, so you don't have to.

Swimio combines AI coaching, advanced Apple Watch swim tracking, and personalized workout generation in one platform built specifically for swimmers. Create workouts tailored to your goals, follow them directly from your Apple Watch, track pace, SWOLF, stroke efficiency and heart rate, and get actionable performance insights after every session. Built for swimmers, coaches and teams who want more than a simple lap counter.

Qwen-AgentWorld introduces language-based world models that enable AI agents to perform complex, general-purpose tasks within simulated environments. The development represents progress toward creating more versatile and capable AI agents.

Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded reasoning: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time conventions, and computing an answer. Existing benchmarks address only parts of this setting and none jointly stresses archive-groundedness, agentic exploration, and cross-domain coverage. We introduc...

Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts such as SWE-Smith, SERA, and Nemotron-Terminal typically target a single benchmark, leaving open the question of how to train models that generalize across diverse agentic tasks. The OpenThoughts-Agent (OT-Agent) project addresses this gap with a fully open data curation pipeline for training agentic models. We conduct...

Sparse voxel representation has emerged as a scalable foundation for image-to-3D Gaussian Splatting (3DGS) generation, yet current methods struggle to preserve high-frequency visual details of input images due to two structural bottlenecks. First, they adopt discriminative 2D features optimized for semantic abstraction to construct sparse voxel latents, which suppress reconstructive cues and induce a representation bottleneck. Second, in the generation stage, standard diffusion transformers lack...

Dense retrieval embedding models are a fundamental component of modern retrieval-based AI systems. Most dense retrievers are trained with contrastive objectives, which require labeled positive and negative document pairs that are often costly and difficult to obtain. In this work, we investigate whether the autoregressive next-token prediction objective of a large language model (LLM) can provide supervision for dense retrieval. The intuition is simple: if a document contains information relevan...

Generalist value models play a pivotal role in scaling robotic policy learning from large-scale, mixed-quality data. Mathematically, accurate value estimation demands deep temporal understanding, requiring models to both ground the current belief using historical context and plan over future outcomes. However, most existing robotic value models are built on Vision-Language Model (VLM) backbones that are pretrained primarily on static or temporally sparse visual observations, lacking the requisit...

Text-to-image (T2I) generation models have achieved remarkable progress in producing visually realistic images from natural language prompts. Yet it remains unclear whether their success reflects genuine causal understanding or sophisticated pattern matching over visual-textual correlations. Inspired by Russell's inductivist turkey, we introduce Counterfactual-World (CF-World), a counterfactual benchmark designed to investigate whether text-to-image models can generate images under rules that sy...

Multimodal misinformation detection is increasingly important because viral posts now combine long multilingual narratives, several images, mixed provenance, and subtle text--image framing errors. Existing benchmarks and methods remain poorly matched to this setting: they usually isolate short captions, single images, binary labels, or one manipulation source, while agentic verification remains costly under realistic evidence search. We present ReMMD, a realistic multilingual multi-image agentic...

The composition of training data, governed by the diversity of sources and their mixing strategy, is a cornerstone of Large Language Model (LLM) pre-training. Online Data Mixing (ODM), the technique of adaptively adjusting data mixtures during training, has emerged as a promising direction to improve efficiency. However, existing methods are constrained by their reliance on a singular optimization perspective, which fundamentally overlooks the need for complex LLM pre-training to consider the dy...

Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experience learning methods mostly rely on single-agent loops, where the same agent executes tasks, summarizes outcomes, and determines memory content. This setup makes agents vulnerable to the Self-Confirmation Trap: wrong-but-self-consistent trajectories are misidentified as successful experience, leading to cumulative errors during retrieval and reuse...

Generating explorable 3D scenes from a single image requires strong generative priors and accurate geometric representations suitable for downstream use. Current video diffusion models offer high-quality generation and implicitly encode multi-view geometric structure in latent space. However, existing feedforward latent scene decoders typically output volumetric 3D Gaussians that lack a well-defined surface, limiting their use in simulation or standard graphics pipelines. This motivates decoding...

We introduce NatureBench, a cross-discipline benchmark of 90 tasks distilled from peer-reviewed Nature-family publications, designed to evaluate whether AI coding agents can move beyond reproduction toward discovery on real scientific problems. NatureBench is built on NatureGym, an automated pipeline that constructs a standardized, per-task containerized environment from a source paper, addressing the environment-fragmentation problem that has limited the credibility of prior agent-on-research b...

Multimodal driving planning faces a long-standing tension between two paradigms: scoring-based methods benefit from dense reward supervision but are confined to a fixed action vocabulary, while anchor-based methods generate proposals dynamically yet suffer from sparse supervision constrained to a single ground-truth trajectory. In this work, we propose FlowR2A, which resolves this tension by reframing simulation-based rewards from discriminative targets into generative conditions. By learning th...

Diffusion transformer (DiT) research on image generation has converged to a single evaluation setup: class-conditional generation on ImageNet. While methods improve the FID and related metrics, it is increasingly unclear whether they reflect real progress in generative modeling. The natural alternative, i.e., text-to-image (T2I) generation, is perceived as too costly or inconvenient to train and evaluate and is often skipped. We argue that this perception no longer holds. We introduce NanoGen, a...

The NSA lost access to Mythos, a significant intelligence tool, amid an ongoing dispute with Anthropic over data usage and privacy concerns. The loss highlights tensions between government security agencies and AI companies over data access and operational transparency.

Qualcomm has announced its acquisition of Modular, a company specializing in AI compiler and infrastructure technology. This acquisition strengthens Qualcomm's position in the AI hardware and software optimization space.

OpenAI and Broadcom introduce Jalapeño, a custom AI chip built for LLM inference to improve performance, efficiency, and scale across AI systems.

Reid Hoffman has publicly stated that SpaceX is not an AI company and criticized xAI as a "complete train wreck," raising doubts about Elon Musk's AI ventures. The comments reflect skepticism about the viability and direction of these high-profile AI initiatives.

An analysis argues that open-source AI is the most viable path forward for most countries and regions lacking resources to develop proprietary AI systems. The perspective emphasizes the democratization of AI technology through open-source initiatives.

A researcher has challenged Microsoft's recent "quantum leap" announcement, claiming the achievement contains fundamental errors in the underlying Python code. The criticism raises questions about the validity of Microsoft's quantum computing advancement claims.