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AiHUBの生成AIツール「CW Canvas」ByteDance「Seedance 2.5」に対応。 IP権利証明でフィルター解除できる「日本初」ツールのクローズドβ版ウェイトリストを公開しました🔥 ↓

The "Web app" standard for the Agentic Web/OS has arrived. It is essentially the open standardization of the third-party app/plugin ecosystem found in ChatGPT. It consists of Skills and MCP, with MCP covering standards such as MCP Apps and MCP-UI, which allow third-party apps to embed GenUI-based interactive interfaces directly into the main Agentic Web/OS interface. WebMCP is about "embedding Tools within UI," while Agent Plugins / MCP Apps are about "optionally embedding UI within Tools." The core of this kind of "app" ecosystem are Tools, rather than any specific UI representation, such as cards, icon + Activity, or windows. AppFunctions / AppIntents / AppActions on Android, iOS, and Windows, like WebMCP, also belong to the "embedding Tools within UI" model. The current CLI ecosystems around Coding Agents such as Codex and Claude Code are effectively local "native app" ecosystems: apps have to be pre-packaged, downloaded, and installed, and users cannot realistically install too many of them. By contrast, Agent Plugins / MCP Apps can be regarded as a kind of "Web app" ecosystem, because their core functionality is retrieved from the Web on demand. Skills, like manifest-style files such as mcp.json, are lightweight distributions of code with declared metadata, somewhat like HTML: they can be discovered, loaded, activated, and used on demand, in a process analogous to HTML rendering but much more intelligent. This creates an opportunity to extend existing Web standards, which are centered around the distribution of UI resources. Instead of RESTful HTTP exposing only a limited set of actions over resources, the Web could expose a vast long tail of Tools that AI can discover and understand, and that can optionally embed UI. A general-purpose Agent capable of discovering, loading, and running Agent Plugins / MCP Apps on demand would be equivalent to "the browser + Google" of the desktop Internet era. If such an Agent becomes a super app, it could easily abstract away the operating system and become the single dominant super app for most user scenarios, much like the desktop Internet era, rather than the mobile Internet era with many parallel super apps. So such an Agent will naturally tend to integrate into the entire OS and even the hardware itself, much like IE vs. Netscape. That is also why Google, OpenAI, and even ByteDance are determined to build consumer electronics hardware products.

Google's Gemini Omni Flash debuts at #1# on the Artificial Analysis Text to Video and Image to Video Leaderboards, edging out ByteDance's Seedance 2.0 on both Gemini Omni Flash is the first model in Google's Gemini Omni family, unveiled at Google I/O in May and opened to developers in public preview on June 30. Google positions Omni as a natively multimodal model that can "create anything from any input", starting with video: it accepts text, images, and video as input, generates clips with native audio, and supports conversational editing, where prompts change a video while preserving the rest of the scene. Gemini Omni Flash generates 3 to 10 second clips at 720p and 24 FPS, in 16:9 or 9:16, with longer durations coming soon. In the Artificial Analysis Video Arena, Gemini Omni Flash debuts at #1# on both the Text to Video and Image to Video Leaderboards, narrowly ahead of ByteDance's Seedance 2.0 on each. Gemini Omni Flash is priced at $0.10 per second of generated video ($6.00 per minute), matching Veo 3.1 Fast. The rate is the same for Text to Video and Image to Video. It is available now in the Gemini API, Google AI Studio, and the Gemini Enterprise Agent Platform, in the Gemini app and Google Flow for consumers, and at no cost in YouTube Shorts and the YouTube Create app. Congratulations to @GoogleDeepMind on the release! See below for comparisons between Gemini Omni Flash and other leading models in the Artificial Analysis Video Arena 🧵

Introducing GAIB Select, a new product from GAIB that opens access to some of the most sought-after pre-IPO equity in the world, onchain. First offering: ByteDance, the company behind TikTok. Now available at

Today’s been an absolutely stacked day for AI news. In case you missed it: 1. GPT 5.6 Sol and Sol Ultra dropping tomorrow. Early reviews say it’s not as smart as Fable but very positive. 2. Grok 4.5 launches from Cursor / SpaceX that claims Opus 4.7 quality at 80tps and $2/M in $6/M out price. 3. Bytedance Seedream 5 Pro launches as an image ~#2# and nearly as good as GPT Image 2 at 4x cheaper cost: $0.045-$0.09/image, specializing in edits and infographics. Easily beats Meta’s Muse Image. 4. GPT-Live launches a full duplex non-turn based voice model which allows interactions while you’re talking seamlessly, a huge upgrade in audio AI for consumer Coming soon: 1. Seedance 2.5 Pro expected to launch soon (early July) to further extend Bytedance’s lead in SOTA video gen models. Will use Seedream 5 Pro as the frame generator. 2. Gemini 3.5 Pro launching soon (July 17). Google seems to have fallen quite behind frontier and people eagerly await to see what Google can put out here amidst a string of high profile departures. 3. GPT-6 rumored to launch soon (Polymarket spikes at August 14) with a new larger retrain, taking aim at Fable.

Even Chinese companies are signing LTAs now lol RTRS: - China’s CXMT has signed a $3 billion LTA with Tencent. - CXMT is also in talks with other Chinese internet companies, including Alibaba Cloud, ByteDance, and Xiaomi. - As of Q1, CXMT’s DDR5 yields still lagged behind Western peers. - CXMT currently operates two 12-inch DRAM fabs in Hefei and one fab in Beijing, with total wafer capacity of around 300k wafers per month.

"Improved Large Language Diffusion Models" ByteDance just made bidirectional masked diffusion on-par with autoregessive LM! This paper iLLaDA trains an 8B Transformer from scratch on 12T tokens, then keeps the same denoising objective for SFT on a 25B-token instruction corpus. It improves LLaDA with GQA, tied embeddings, variable-length generation, confidence-based MCQ scoring, and packed-sequence diffusion SFT. iLLaDA-Base raises the average score from 51.1 to 63.9 and slightly exceeds Qwen2.5 7B Base at 63.3, while iLLaDA-Instruct still trails Qwen2.5 Instruct without RL alignment.

Big ideas don’t always need a big model. ByteDance's Dreamina Seedance 2.0 Mini API is now live, bringing fast, high-quality video generation to developers and builders looking to create at scale.