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Google's Magenta team releases RealTime 2, a collection of open and locally-runnable models for live music generation and manipulation. These models enable real-time creative applications without requiring cloud infrastructure.

Meta continues to postpone the release of its new AI model to external developers, delaying wider access to the technology.

Google introduces Gemma 4 12B, a compact multimodal AI model that combines text and image understanding without requiring separate encoders for improved efficiency. This unified architecture aims to make advanced multimodal capabilities more accessible for deployment.

Nvidia Cosmos 3 represents the latest advancement in Nvidia's autonomous AI systems for video understanding and generation. The model enables sophisticated visual intelligence capabilities for various applications.

Discover new Codex plugins, sites, and annotations that help analysts, marketers, designers, investors, and other teams get more done with AI.

Harness coding workflow for codex, claude, github copilot

Plan and autonomously build a software task end-to-end. One ready-to-paste /goal, adaptive phase count, memory preload + writeback, 3-strike self-healing recovery. Works on Claude Code and Codex.

Sem introduces a new primitive for code understanding that moves beyond Language Server Protocols by treating Git-based code entities as a first-class feature for analysis and understanding.

Dreambeans synthesises your Gmail, Calendar, Photos, YouTube, and Search overnight to deliver daily AI-generated story collections. For Google AI Ultra subscribers who want personal context surfaced proactively.

Skills that make Claude Code proactively suggest its own power tools - workflows, goals, loops, hooks - at the right moment

SciPilot Skills family - Publication-grade scientific figure copilot for Claude Code

A free, hosted job listings API with 1.8M+ listings across 60k companies. Get comprehensive active and historical job data from 30+ applicant tracking systems, with companies spanning industries and stages.

MCP server that lets AI agents drive Inkscape — interactively alongside the GUI or headlessly from the CLI

CLI, SDK, and IDE plugins for Duel Agents

Estimate whether a Hugging Face model fits and fine-tunes on your local GPU.

Anthropic releases an open-source framework designed to leverage AI capabilities for discovering and identifying software vulnerabilities. This tool aims to improve security by automating the vulnerability detection process.

The missing bridge between your ML models and your AI agents.

Open Code Review is an AI-powered CLI tool that automates code review processes and provides intelligent feedback on code quality. The tool helps developers identify issues and improve their code before submission.

Leni is the most accurate and verifiable AI for serious investment work. Built on 21,000+ decision traces and processing 100M+ rows daily, it delivers finance-grade outputs with full auditability through source links, timestamps, and grounded comps. Leni outperforms GPT, Claude, and Manus on independent benchmarks for accuracy, modeling, and valuation while giving teams the trust they need when millions are on the line. Leni is part of Google Startups and a serious machine for investors.

Dataflow-Oriented Reinforcement Learning for (Multi-)Agentic LLMs

This research paper analyzes tokenomics in agentic software engineering systems, quantifying how and where tokens are consumed in AI-assisted development workflows.

Researchers conduct a systematic study examining whether transformer models require all three projection matrices (Query, Key, Value) or if some can be eliminated. The findings could optimize transformer architecture efficiency.

This piece explains Anthropic's safety and containment strategies for deploying Claude across different products. It details the technical and operational measures implemented to ensure Claude operates within intended boundaries.

Gaussian Point Splatting is a advanced rendering technique that uses point-based representations for efficient 3D scene visualization and synthesis. This method enables faster real-time rendering while maintaining high visual quality compared to traditional approaches.

University of Toronto researchers have demonstrated a proof-of-concept AI worm capable of spreading across and compromising any internet-connected device. This security vulnerability raises critical concerns about AI-based malware and the need for improved defenses.

The article discusses recent progress toward achieving recursive self-improvement in AI systems, where AI models can autonomously enhance their own capabilities. This explores the technical challenges and implications of systems that can iteratively improve themselves.

Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form report generation, but existing benchmarks and systems remain predominantly text-centric, with limited evaluation of whether visual elements are factually reliable and well aligned with the surrounding analysis. To address this gap, we introduce TVIR (Text--Visual Interleaved Report Generation), which includes TVIR-Bench, a benchmark of 100 expert-curated multimodal deep research tasks...

Selecting the best response from multiple small-model samples using a stronger scorer is a simple inference-time strategy, but fails when the small model has already committed to incorrect reasoning paths. PRM guided search avoids this by scoring candidate continuations during generation, but requires a reward model trained with step-level labels. We propose Chunk-Level Guided Generation, a training-free alternative that uses an off-the-shelf large language model as a process scorer. At each s...

Large language models solve complex problems by generating lengthy chains of explicit reasoning tokens. While effective, this makes reasoning expensive, length-sensitive, and constrained to (discrete) natural language. While latent reasoning offers a continuous alternative, determining useful structures for intermediate latent states is an open challenge. In this paper, we formulate latent reasoning as a geometric path-approximation problem within the model's pretrained token-embedding space. We...

Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities remains highly labor-intensive. Recent end-to-end vectorized mapping methods can predict lane geometry and topology directly from sensor data, but they typically treat mapping specifications and traffic regulations as implicit, dataset-dependent supervision. Moreover, in complex scenes (e.g., worn or missing markings and ...

Reasoning models improve accuracy through extended chains of thought, but their long outputs create a memory and compute bottleneck. KV cache eviction methods reduce this cost by evicting unimportant key-value pairs from the cache, yet they often yield worse accuracy than selection-based sparse attention alternatives, which keep the full KV cache. We identify key factors crucial to KV cache eviction accuracy. First, a small fraction of value states have abnormally large magnitudes, and evicting ...

Test-time scaling improves the reasoning performance of large language models but incurs substantial cost in both total computation and latency. Existing adaptive sampling methods partially mitigate this issue by dynamically deciding when to stop sampling, yet they typically rely on heuristic rules or rely on distribution assumptions. In this work, we formulate adaptive sampling as a Markov decision process (MDP). We train a lightweight sampling controller with reinforcement learning (RL) to joi...

We present Echo Infinity, an autoregressive (AR) framework towards real-time infinite video generation that employs a learnable evolving memory to dynamically filter, abstract, and compress any-length history at constant cost. Existing methods mainly curate memory with predefined KV-cache schedules, fixed-ratio heuristic compression, or inference-time RoPE adaptation. These designs inevitably lose historical information and amplify compounding errors due to their limited cache window and ignoran...

Developing unified video generation and editing models capable of interpreting interleaved multimodal inputs is a promising yet challenging frontier field. Existing unified frameworks predominantly rely on massive models (typically 13B parameters or more) and incorporate source video conditions for editing by concatenating sequence tokens. This concatenation inevitably doubles the sequence length, quadrupling the computational complexity of the self-attention mechanism and introducing prohibitiv...

Automatic Speech Recognition (ASR) has become a key technology for human--AI interaction. However, code-switching ASR (CS-ASR) remains particularly challenging due to the severe scarcity of multilingual CS speech resources across diverse language pairs. Existing approaches primarily improve CS-ASR performance through synthetic CS speech generation or pair-specific fine-tuning on limited bilingual datasets. Nevertheless, these approaches face an inherent scalability limitation, as support for CS ...

A developer demonstrates fine-tuning an LLM to generate documentation in the style of 1995 web design and writing conventions. The project showcases creative applications of model customization for nostalgic or unconventional outputs.

CS336: Language Modeling from Scratch is a Stanford course that teaches students how to build language models from first principles. The course covers the fundamental concepts and implementations needed to create modern AI language systems.

Learn how Googlers used AI to produce Google I/O 2026.

Here are Google’s latest AI updates from May 2026

Anthropic has confidentially submitted a draft S-1 registration statement to the SEC, indicating plans for a potential public offering. The move marks a significant milestone in the AI safety company's development and growth trajectory.

Alphabet announces an $80 billion equity capital raise specifically dedicated to expanding its AI infrastructure and computational capacity to support advanced AI development.

President Trump signs a streamlined AI executive order following weeks of policy deliberation and modifications, indicating the administration's effort to establish regulatory framework for artificial intelligence development.

OpenAI outlines a blueprint for U.S. governance of frontier AI, proposing a federal framework for safety, resilience, and national security.

The S&P 500 has rejected index inclusion for SpaceX and also blocked applications from OpenAI and Anthropic.

Google has agreed to pay SpaceX $920 million per month for satellite-based compute and connectivity services.

South Korean online forums are being required to implement mandatory AI-powered image scanning and censorship tools to comply with new regulations. This policy aims to monitor and filter prohibited content automatically.

Nvidia is developing a high-performance CPU system designed for Windows PCs that aims to compete in the premium computing market.

The Pentagon has been operating an AI-powered propaganda system designed to target and influence audiences in Latin America through coordinated disinformation campaigns. This initiative raises significant concerns about the militarization of AI and its use in spreading manipulated content.

U.S. House lawmakers have introduced a draft bill that would prevent individual states from implementing their own AI regulations.

Police forces in England and Wales have been instructed to stop using AI systems to generate evidence and statements for court proceedings.

The NSA has reportedly been utilizing Anthropic's Mythos AI system to conduct cyber attacks and enhance offensive cybersecurity operations. This revelation highlights tensions between AI safety commitments and government intelligence agency applications.

A leaked document shows that Microsoft is explicitly designing its AI systems to be psychologically addictive, incorporating engagement tactics similar to social media platforms. The disclosure raises ethical questions about AI product design and user manipulation.

The article examines whether the stock market has sufficient capacity and valuation to accommodate major AI companies like Anthropic, SpaceX, and OpenAI as they continue their rapid growth.

This article explores the fundamental nature of neural networks, examining how weights form the core computational basis of AI models. The piece likely discusses how these numerical parameters encode learned representations and drive model behavior.

A software engineer expresses concerns about how LLMs are disrupting traditional software engineering career paths and discusses uncertainty about how to adapt to this changing landscape.

A designer shares that they now use Claude more frequently than Figma for their design work, highlighting the growing role of AI in creative processes.

This article examines the parallels between subscription-based creator economy platforms like OnlyFans and the emerging business models around AI services in America.

This appears to be a documentary or media piece titled 'Why Janet?' from 2023, likely exploring the story or significance of someone named Janet, though the specific context would require viewing the actual content.

A developer conducted a $1,500 experiment to assess whether large language models could successfully identify and exploit vulnerabilities in a deliberately vulnerable application. The study provides insights into the security capabilities and limitations of current LLMs.

This piece argues that AI agents need standardized protocols and frameworks similar to RSS for discoverability and interoperability. Establishing such standards could improve how AI agents are discovered, shared, and integrated across platforms.