I no longer ask people what they do, I ask them what they are building

This is an important one, I think. AI progress and recommendations: openai.com/index/ai-progress…

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🚀 Step-Audio-EditX is now open source!! ✨ Zero-Shot TTS with high timbre similarity ✨ Iterative editing of dozens of audio emotion and speaking style ✨ Fine-grained control over paralinguistic features Whether for audio editing, interactive design, or personalized scenarios, it unlocks unprecedented audio expression for you! 🌟GitHub: github.com/stepfun-ai/Step-A… 📑 arXiv：arxiv.org/abs/2511.03601 🔥 Demo Page：stepaudiollm.github.io/step-… 🎮 HF playground: huggingface.co/spaces/stepfu…

If you ever wonder how Chinese frontier models like Kimi, DeepSeek, and Qwen are trained on far fewer (and nerfed) Nvidia GPUs than US models. In 1969, NASA’s Apollo mission landed people on the moon with a computer that had just 4KB of RAM. Creativity loves constraints.

this is fake

K2, K2-Thinking, and …

MoonshotAI has released Kimi K2 Thinking, a new reasoning variant of Kimi K2 that achieves #1 in the Tau2 Bench Telecom agentic benchmark and is potentially the new leading open weights model Kimi K2 Thinking is one of the largest open weights models ever, at 1T total parameters with 32B active. K2 Thinking is the first reasoning model release within @Kimi_Moonshot's Kimi K2 model family, following non-reasoning Kimi K2 Instruct models released previously in July and September 2025. Key takeaways: ➤ Strong performance on agentic tasks: Kimi K2 Thinking achieves 93% in 𝜏²-Bench Telecom, an agentic tool use benchmark where the model acts as a customer service agent. This is the highest score we have independently measured. Tool use in long horizon agentic contexts was a strength of Kimi K2 Instruct and it appears this new Thinking variant makes substantial gains ➤ Reasoning variant of Kimi K2 Instruct: The model, as per its naming, is a reasoning variant of Kimi K2 Instruct. The model has the same architecture and same number of parameters (though different precision) as Kimi K2 Instruct and like K2 Instruct only supports text as an input (and output) modality ➤ 1T parameters but INT4 instead of FP8: Unlike Moonshot’s prior Kimi K2 Instruct releases that used FP8 precision, this model has been released natively in INT4 precision. Moonshot used quantization aware training in the post-training phase to achieve this. The impact of this is that K2 Thinking is only ~594GB, compared to just over 1TB for K2 Instruct and K2 Instruct 0905 - which translates into efficiency gains for inference and training. A potential reason for INT4 is that pre-Blackwell NVIDIA GPUs do not have support for FP4, making INT4 more suitable for achieving efficiency gains on earlier hardware. Our full set of Artificial Analysis Intelligence Index benchmarks are in progress and we will provide an update as soon as they are complete.