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Claude Opus 5 is the latest version of Anthropic's Claude AI model, representing advanced improvements in reasoning, coding, and multimodal capabilities.

Flux 3 is a next-generation AI model offering improved performance and capabilities over previous versions. ### [Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models]() Echo is an open-weight AI model achieving Fable-level results at one-third the cost, demonstrating the efficiency of open-source alternatives.

The Claude Cookbook is a collection of practical examples and recipes for using Anthropic's Claude AI model in various applications.

One command connects your AI agents to your lock screen. When Claude Code, Codex, Cursor, Gemini CLI, Hermes, or Claude Cowork stops to ask, you tap yes from your phone and the run keeps moving. New in this launch: native iPhone and Android apps. Pairing is a QR code you scan from the terminal, no key to paste. Per-tool policies auto-approve the safe reads. Every decision lands in an audit trail.

Fluree AI gives every app and AI agent the same trusted context from your company data. Ask questions and get cited, verifiable answers from one live data layer, with permissions checked on every request. Instead of rebuilding prompts or relying on RAG guesses, Fluree queries structured data directly and connects to MCP-ready agents, dashboards, and apps in minutes.

Hetzner, a major hosting provider, is developing infrastructure and services for large-scale LLM inference to serve enterprises and researchers.

Describe your problem in plain English. ychasit searches 4,000+ active YC companies and finds the startups that solve it, with reasoning, pricing, and integrations. 100% Free forever. No login. No signup.

Building an AI agent backend yourself takes months: sandboxes, orchestration, retries, cost controls. HarnessRouter runs it for you. One API in, finished work out: code, files, videos, games. Trusted by top medical research institutions, leading healthcare companies, and cutting-edge startups in multiple domains.

Freesolo helps enterprise teams turn generic model capability into AI features that belong in the product. We make reinforcement learning a commodity so any team can train a small, specialized model for their task.

Most AI book services work like a black box. Your content goes in, a manuscript comes out, and you hope it sounds like you. We thought creators deserved more than hope. The Source Map color-codes every paragraph in your manuscript: your original words from newsletters, podcasts, and posts alongside the connective tissue Prosed added to bridge your ideas together. Today, over 90% of each book we assembled is the creator's own words. Now you can see that for yourself.

Researchers develop a comprehensive taxonomy of potential scenarios in which artificial intelligence could lead to omnicidal (extinction-level) outcomes. This framework helps identify and categorize the various pathways through which AI could pose existential risks to humanity.

The recent emergence of vibe-coding workflows is changing what coding agents are expected to do. Instead of merely completing code under fully specified instructions, agents are increasingly expected to transform incomplete product intent into working software by combining various abilities including planning, requirement clarification, tool use, debugging, and repository-level construction. Yet existing benchmarks have not fully caught up with this shift, evaluating agents on static, fully spec...

Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing autoregressive video diffusion pipelines carry forward observation history as conditioning context, which makes shared state difficult to maintain in multi-agent and multi-view settings. We present WorldWeaver (W^2), a streaming multi-agent video diffusion model that augments rollout with cross-agent world state registe...

Understanding motion in video is a fundamental challenge for visual learning, as frame-to-frame change entangles two sources of dynamics: camera motion and object motion. This decomposition has remained underexplored in representation learning, partly because these factors are tightly coupled in natural videos and difficult to supervise separately. Yet recovering it is important for learning robust motion representations that separate meaningful object dynamics from camera-induced variation. We ...

We introduce SANA-Video 2.0, a hybrid video diffusion transformer instantiated at 5B and 14B scales under a unified architecture. Designed to generate high-quality video up to 720p on a single GPU, SANA-Video 2.0 matches full-softmax video DiTs in quality while retaining the favorable long-sequence scaling of linear attention. To avoid quadratic attention throughout, Hybrid Linear-Softmax Attention combines gated linear attention for O(N)-dominated mixing with periodic gated-softmax anchors at a...

Controllable video generation remains challenging due to the difficulty of specifying precise multi-object interactions using text prompts or motion-control inputs that primarily constrain pixel movement. In practice, trajectory-based control often requires users to draw accurate tracks for multiple objects, which scales poorly with scene complexity and becomes ambiguous under occlusion or overlap. To enable flexible yet precise multi-subject control, we introduce GraphVid, a graph-conditioned i...

We study sinusoidal recurrence as an iterative mechanism for harmonic spectral enrichment in implicit neural representations (INRs). Our analysis reveals that sinusoidal activations induce a harmonic line spectrum, providing a spectral account of how recurrent unrolling enriches the effective spectral support. We realize this principle with a shared sinusoidal block that iteratively refines the latent representation. We empirically validate the resulting spectral behavior against feed-forward IN...

The development of generalizable robotic manipulation policies is inherently bounded by the availability of large-scale, high-fidelity scene data. While recent automated synthesis methods attempt to bridge this gap via text-to-layout hallucination or simplified procedural generation, they frequently suffer from physical implausibility and fail to capture the complex, dense clutter of actual human environments. In this paper, we introduce TableVerse, a fully automated Real2Sim pipeline that shift...

Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedback. In-context learning offers a highly sample-efficient way for agents to learn from their own interaction histories, but its gains disappear once that experience is removed from the context. Separately, context distillation provides a mechanism for internalizing contextual information into model weights. However, applying it to agents' interacti...

Spatial intelligence is essential for agents to move from static semantic understanding toward interacting with the physical world. Many spatial tasks are grounded in continuous visual scenes, where locations, regions, and paths are more naturally expressed by pointing, marking, or drawing than by reporting precise coordinates or discrete textual symbols. Yet existing spatial reasoning benchmarks usually require coordinates, options, or text, creating an answer-interface mismatch for image-gener...

Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed into tractable constraint-wise checks. This discovery--verification asymmetry suggests that a research agent should do more than simply search longer: it should recursively improve its current answer by verifying intermediate results and using the partially verified state to guide subsequent refinement. We introduce ARE...

We introduce Tencent WorkBuddy Bench, a multi-domain evaluation suite for coding agents; this report documents its construction methodology, scoring protocol, and a cross-model leaderboard. At its core is a unified evaluation framework for constructing and running distribution-informed coding-agent tasks across four work domains - Code, Web, Office, and Security. Rather than adapting public issue text, every task is reverse-engineered from a real commit, pull request, or business scenario and re...

Large language models are increasingly used in K-12 education, but existing benchmarks mainly test exam question answering rather than understanding how curriculum knowledge is structured and visually presented. We call this capability curriculum cognition. It covers prerequisite chains, concept taxonomies, experiment-concept links, pedagogical sequencing, and visual grounding. We introduce K12-KGraph, a curriculum-aligned knowledge graph extracted from official People's Education Press textbook...

On-policy self-distillation (OPSD) is promising as it removes the external teacher required by on-policy distillation (OPD), yet it still needs asymmetric information between teacher and student to ensure that the self-teacher provides a stronger learning signal than the student. Existing methods create this asymmetry either through privileged answers or visual evidence. We ask whether both can be removed, yielding a simpler form of OPSD driven purely by input conditioning. For this purpose, we ...

A developer shares their year-long journey building a real-world application using AI, highlighting the challenges and complexities of practical AI implementation.

Nvidia, Microsoft, and Meta have jointly warned against excessive regulation of open-weight AI models, arguing it could stifle innovation and competition in the AI industry.

AMD's Instinct MI455X is a high-performance AI accelerator chip designed to compete with Nvidia's offerings in data center and AI computing markets.

Oracle is laying off 21,000 employees globally and redirecting cost savings toward expanding its AI capabilities and investments to remain competitive in the AI market.

Jul 24, 2026Frontier Red TeamProject Pilot: Can AI control a drone?