#开源生态

Moonshot's Kimi K3 scores 156 on the Epoch Capabilities Index (ECI), setting a new open-weights record. This places it between Opus 4.6, and GPT 5.4, which released in February and March 2026 respectively, and just ahead of GPT 5.6 Luna.

Holy shit, SenseNova-Vision just dropped and it's wild. One single model that handles detection, segmentation, depth, surface normals, OCR, keypoints, 3D recon—literally everything—through plain English prompts. No more chaining 5 different specialist tools. It beats the recent unified vision attempts on key benchmarks and feels like the GPT moment for vision. Fully open source: weights, data, code, demo, paper. This is vision becoming native to foundation models. Try @SenseTime_AI now 👇🏿 GitHub: https://github.com/OpenSenseNova/ HF: https://huggingface.co/sensenova/Sens Paper: http://arxiv.org/abs/2607.06560

Yesterday, we came out of stealth at @skyfallAI in Forbes and today we're excited to announce that Skyfall AI is featured in the @WSJ's newsletter. We believe in a utopian world that will be achieved over the next decade, where humans will work in their own creative interests in space exploration, sports, arts or philosophy while machines will do all the mundane operational tasks. We want to free humanity from all the monotonous boring operational tasks that they spend their daily lives doing. The only way to achieve this vision is via creating autonomous enterprises, i.e., companies that can run by themselves with minimal human intervention. After 15 years of building in the AI space, one thing is certain. LLMs are static systems that fail at - Long horizon planning tasks - High consequential decision-making - Sample efficiency - Dynamic environment adaptation These skills are required by a business to survive and grow. Unfortunately, LLMs are not designed for the real world constraints of running an organization. Our solution is Enterprise World Models grounded in Continual Learning and World Modeling. We're taking a fundamentally different approach because we have always believed in 0 to 1 research, and not incremental n+1 RL fine tuning. We are acquiring SaaS businesses to prove our thesis for creating AI systems for the post scarcity world. This is a call to all SaaS founders to submit your company if you’re interested (🔗 in comments) Thank you Sarah @sarahklearman for covering our story on @WSJ

Jensen Huang explained how blocking China from Nvidia does not anymore means blocking China from AI. If you don’t give your competitor the best chips, they will lag behind. This is how export control stories go. China is not waiting at the door of American computer systems anymore. Huawei’s rise is an example of a ban becoming an economic boost. It creates a market and teaches domestic suppliers how to get stronger, grow and export. Now the real battle is over who controls the chips, the talent, the energy, the infrastructure, the models, the apps and the whole intelligence stack. Thinking of chip policy as a valve that can be opened and closed is wrong. Each barrier slows down one flow while speeding up another. In the long run, the threat may be to a world in which American technology is absent from the systems that the US wishes to change. --- From "Fox Business" YouTube channel, (full video link in comment)

Great explanation by Emad Mostaque, co-founder of Stability AI. "We’ll see the cost of Kimi K3 drop by 10 to 50 times, I think, over the next few months as it gets optimized. " Basically Kimi K3’s current inference cost is quite high, but that price reflects immature infrastructure, not a permanent technical limit. And that gap will not last long. US-based specialized infrastructure companies will optimize kernels, routing, quantization, batching, memory use, and serving systems around those models once the Kimi K3 weights are available. --- "Right now, it uses twice the number of tokens for the same task compared with GPT-5.6. Again, we’re going to see that cost drop because everyone and their dog is going to optimize the crap out of this. Fireworks has just raised funding at a $17 billion valuation, while others, such as Modal and Baseten, are valued at $10 billion. These are inference providers for open-source models. They’ve all raised around a billion dollars, which they’re now going to spend on optimizing the Chinese model, making it more efficient, and running it. American labs that handle the inference side of things are going to optimize the crap out of this. Therefore, we will see it catch up." ---- From "Peter H. Diamandis" YouTube channel, (full video link in comment)

Axios: Trump administration weighs restrictions while businesses move to lower-cost Chinese AI. Such a move would hand OpenAI and Anthropic near-total control of the market. American companies are turning more and more to Chinese open-weight models because they work and they’re cheap on the API side. These open-weight models can be run on private infrastructure, allowing businesses to change models and monitor private business data. US Security officials are concerned that foreign code, bad training decisions or covert behaviors could threaten users and vital systems. US Commerce had talked about putting Chinese AI labs on its Entity List, which would make it harder for Americans to get to them without a license. Officials also talked about rules on liability requirements and public pressure against US businesses using the system. But those plans were killed by officials who cared about innovation, because they believed that imposing limits on them would harm competition and slow AI development in the United States. Since then, many vocal critics of the release have left and the stronger Chinese releases have increased national security concerns.

Axios: Trump administration weighs restrictions while businesses move to lower-cost Chinese AI. Such a move would hand OpenAI and Anthropic near-total control of the market. American companies are turning more and more to Chinese open-weight models because they work and they’re cheap on the API side. These open-weight models can be run on private infrastructure, allowing businesses to change models and monitor private business data. US Security officials are concerned that foreign code, bad training decisions or covert behaviors could threaten users and vital systems. US Commerce had talked about putting Chinese AI labs on its Entity List, which would make it harder for Americans to get to them without a license. Officials also talked about rules on liability requirements and public pressure against US businesses using the system. But those plans were killed by officials who cared about innovation, because they believed that imposing limits on them would harm competition and slow AI development in the United States. Since then, many vocal critics of the release have left and the stronger Chinese releases have increased national security concerns.

The leading AI has already forked into two options. A: Closed source American that costs $26-56 per 1MM tokens. B: Open weight Chinese that costs $0.50-1 per 1MM tokens. If you force American companies to spend 50-100x more than their competitors abroad here is what will happen: 1. American companies spending 50-100x will at some point become financially impaired and the stock market will crater. This is the equivalent of the US Government saying we can only buy oil at $800/barrel even when the free market sells the same oil for $80/barrel. 2. In the short term, the revenues of the Closed American Labs may remain hi. But then their revenue will also crater because their customers in the US will be impaired and their customers abroad will have already flipped to cheaper solutions. This would be a terribly self-defeating form of intervention if it were to happen.