#开源生态

Artifacts 22: Zyphra, Cohere, Poolside, and others are expanding the breadth and diversity of the ecosystem. In this issue, a total of 30 models from may/june you should be aware of, from: NVIDIA @NVIDIAAI (3) Cohere @Cohere_Labs (2) Zhipu @Zai_org Zyphra @ZyphraAI (3) Poolside @poolsideai Moonshot AI @Kimi_Moonshot StepFun @StepFun_ai Dolphin @dphnAI Google @GoogleAI (3) Nex AGI @NexEcosystem Liquid AI @liquidai MiniMax @MiniMax_AI Swiss AI Initiative @apertusllm JetBrains @jetbrains Microsoft @Microsoft H Company @hcompany_ai Datalab @datalabto (2) Baidu @Baidu_Inc PaddlePaddle @PaddlePaddle Ideogram @ideogram_ai KREA @krea_ai Photoroom @photoroom_ML Read the issue below.

UBS says 60% of companies now watching AI budgets are moving to cheaper models and open-source Chinese models The pressure is coming from extreme bills, including users spending up to $35K/month, teams exceeding quotas by 200%, and companies cutting internal AI tools from 5 to 2. Companies are not abandoning AI, they are using model routing, which sends easy tasks to cheaper models and saves premium models for hard reasoning, code, and long-context work. Chinese open-source models such as Qwen, DeepSeek, MiniMax, GLM, and Kimi now fit the enterprise cost curve because they can be run locally or used through cloud catalogs. --- news .futunn.com/en/post/75068082/ubs-group-finds-60-have-already-started-curbing-ai-spending?level=2&data_ticket=1780870170397383

UBS says 60% of companies now watching AI budgets are moving to cheaper models and open-source Chinese models The pressure is coming from extreme bills, including users spending up to $35K/month, teams exceeding quotas by 200%, and companies cutting internal AI tools from 5 to 2. Companies are not abandoning AI, they are using model routing, which sends easy tasks to cheaper models and saves premium models for hard reasoning, code, and long-context work. Chinese open-source models such as Qwen, DeepSeek, MiniMax, GLM, and Kimi now fit the enterprise cost curve because they can be run locally or used through cloud catalogs. --- news .futunn.com/en/post/75068082/ubs-group-finds-60-have-already-started-curbing-ai-spending?level=2&data_ticket=1780870170397383

@QuantCapitalX @MiniMax_AI https://huggingface.co/nvidia/MiniMax-M3-NVFP4

@lu_sichu Ban on enterprise use of non-approved models + severe criminal penalties for using a non-approved model in the U.S. with intent to harm U.S. persons or property. This would be combined with the requirement that all models exceeding certain capabilities be approved by the USG.

something has definitely shifted in the past few weeks. seeing a huge uptick in large enterprises wanting to secure compute and post-train their own models in house, frequently on top of GLM-5.2. everyone is starting to understand how open source wins.

Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding. Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks including: ✅Terminal-Bench 2.1(77.5) ✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual) ✅NL2Repo(48.2) ✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW) ✅ClawEval(77.1) Post-trained on top of gemma4 and qwen3.5, Ornith-1.0 employs a novel self-improving training strategy in which reinforcement learning is used to generate not only solution rollouts, but also the task-specific scaffolds that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model generate higher-quality solutions in agentic coding.😎 All models are released under the MIT license, enabling full commercial and research use. 📖Tech Blog: http://deep-reinforce.com/ornith_1_0.html 🤗Huggingface: http://huggingface.co/collections/deepreinforce-ai/ornith-10

Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding. Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks including: ✅Terminal-Bench 2.1(77.5) ✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual) ✅NL2Repo(48.2) ✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW) ✅ClawEval(77.1) Post-trained on top of gemma4 and qwen3.5, Ornith-1.0 employs a novel self-improving training strategy in which reinforcement learning is used to generate not only solution rollouts, but also the task-specific scaffolds that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model generate higher-quality solutions in agentic coding.😎 All models are released under the MIT license, enabling full commercial and research use. 📖Tech Blog: http://deep-reinforce.com/ornith_1_0.html 🤗Huggingface: http://huggingface.co/collections/deepreinforce-ai/ornith-10

We're running hands-on AI workshops in Ghana using @OpenBMB's free MiniCPM-V models 🇬🇭 Submit an image (e.g. receipt,crop leaf) → AI agent reasons → takes action. All runnable locally. Facilitated by @ganzaro1 Agentic AI built for Africa's reality. #AgenticAI #AIAfrica