Image created with gemini-3.1-flash-image-preview with claude-sonnet-4-5. Image prompt: Using the provided reference image, preserve the exact crate construction with horizontal dark reddish-brown weathered slats, iron hardware, and three-panel layout with hand-painted black lettering, but replace the original address text with ‘INTERNATIONAL’ in the same loose black stencil style, and add multiple overlapping customs stamps, transit marks, and chalk notations in Cyrillic, Chinese, Arabic, and other scripts across the crate face as if it has crossed many borders, place the crate on a weathered stone customs platform or international border threshold in soft early spring light with a worn passport document leaning against its side, keep the spare documentary stillness and shallow depth of field with out-of-focus bare branches behind.
Unlocking New Creative Possibilities with Dreamina Seedance 2.0 https://www.capcut.com/newsroom/dreamina-seedance-2
Speaking of Voxtral | Mistral AI https://mistral.ai/news/voxtral-tts
Black Forest Labs, Cursor, LangChain, Mistral AI, Perplexity, Reflection AI, Sarvam AI and Thinking Machines Lab – what unites these companies? Just recently NVIDIA announced the Nemotron Coalition, gathering all of them to develop the Nemotron family of models. → The idea is
https://x.com/TheTuringPost/status/2035320446124695922
We recently worked with The Yomiuri Shimbun to analyze more than a million social media posts to map out state-sponsored information campaigns. https://t.co/rlYs43ywrE Keyword searches are fragile for modern OSINT. To fix this, our team used an ensemble of different LLMs
https://x.com/hardmaru/status/2035884310356754715
Building Intelligent Research Agents with Manus – Ivan Leo, Manus AI (now Meta Superintelligence) – YouTube https://www.youtube.com/watch?v=xz0-brt56L8
🎉 Congrats to @MistralAI on launching Voxtral 4B TTS — enterprise-grade TTS built for production voice agents. Day-0 support in vLLM Omni. 🌍 9 languages with natural prosody and emotional range 🎙️ 20 preset voices with easy adaptation to new ones ⚡ Ultra-low latency
https://x.com/vllm_project/status/2037193518519902408
Tencent’s ClawBot Links WeChat And OpenClaw In AI Agent Push https://finance.yahoo.com/markets/stocks/articles/tencent-clawbot-links-wechat-openclaw-190444135.html
This turned out to be Xiaomi’s MiMo-V2-Pro, and it is fine but not at the frontier. Most interestingly, it is not open weights? This seems to be a trend in frontier Chinese models.
https://x.com/emollick/status/2034870418813665438
🔊Introducing Voxtral TTS: our new frontier open-weight model for natural, expressive, and ultra-fast text-to-speech 🎭Realistic, emotionally expressive speech. 🌍Supports 9 languages and accurately captures diverse dialects. ⚡Very low latency for time-to-first-audio. 🔄Easily
https://x.com/MistralAI/status/2037183026539483288
Mistral AI released Voxtral TTS, a 3-billion-parameter text-to-speech model with open weights that the company says outperformed ElevenLabs Flash v2.5 in human preference tests roughly 63% of the time on standard voices and nearly 70% on voice customization. The model runs on
https://x.com/kimmonismus/status/2037149838023024753
Our first speech model, Voxtral TTS, is out. It delivers SOTA performance while significantly reducing cost compared to existing solutions, and it operates with very low latency. It uses a new architecture that combines auto-regressive generation of semantic speech tokens with
https://x.com/GuillaumeLample/status/2037274172607594609
Cursor admits its new coding model was built on top of Moonshot AI’s Kimi | TechCrunch https://techcrunch.com/2026/03/22/cursor-admits-its-new-coding-model-was-built-on-top-of-moonshot-ais-kimi/
Kimi.ai on X: “Zhilin at GTC: Introducing Attention Residuals Learning selective memory, rather than mechanically accumulating everything, is the beauty of attention. Many of you have probably read Attention Is All You Need, the 2017 Transformer paper that brought “human-like” attention into https://t.co/1coOW90s0n” / X
https://x.com/Kimi_Moonshot/status/2037010118957817988
Ksenia_TuringPost on X: “Deep transformers used to accumulate layer history. Now they are starting to retrieve from it. → @Kimi_Moonshot proposed Attention Residuals (AttnRes), driving this shift. They turn the residual stream into an attention problem. Why do we need it? Depth in Transformers mostly https://t.co/L4pMwyiRY2” / X
https://x.com/TheTuringPost/status/2037107923109953788
was messing with the OpenAI base URL in Cursor and caught this accounts/anysphere/models/kimi-k2p5-rl-0317-s515-fast so composer 2 is just Kimi K2.5 with RL at least rename the model ID
https://x.com/fynnso/status/2034706304875602030?s=20
On the Direction of RLVR Updates for LLM Reasoning | Notion https://qwen-pilot.notion.site/rlvr-direction
Qwen3.5-REAP-262B running with 200K context inside Parchi, controlling multiple browser tabs reviewing Sitegeist features against Parchi. For free 180GB VRAM TTFT is pretty slow Generation 36 tokens/s Prefill 1400 tokens/s Automate anything w Parchi https://x.com/0xSero/status/2036204079056081043
This is looking like a good prediction. Alibaba’s Qwen and Xiaomi both seem to be steering away from open weights in the 2 weeks since this post.
https://x.com/emollick/status/2034983183653986337
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