DeepSeek-V4-Pro-Max beats GPT-5.2 and Gemini 3.0-Pro on standard reasoning benchmarks and lands just behind GPT-5.4 and Gemini 3.1-Pro
DeepSeek V4-Pro-Max在标准推理基准测试中超越了GPT-5.2和Gemini 3.0-Pro,这表明了开源模型在性能上的巨大提升。
DeepSeek-V4-Pro-Max beats GPT-5.2 and Gemini 3.0-Pro on standard reasoning benchmarks and lands just behind GPT-5.4 and Gemini 3.1-Pro
DeepSeek V4-Pro-Max在标准推理基准测试中超越了GPT-5.2和Gemini 3.0-Pro,这表明了开源模型在性能上的巨大提升。
While our production codebase has significantly diverged, including major rewrites of core systems like authentication and data handling, we want to ensure there is still a truly open version available.
这一声明揭示了开源软件商业化的复杂现实。Cal.com选择保留开源版本但生产代码闭源,反映了开源社区面临的一个两难境地:如何在保持开放精神的同时,保护核心业务免受AI驱动的安全威胁。这种混合模式可能成为未来开源软件的发展方向。
focusing on the ~1.5K mainline open models from the likes of Alibaba's Qwen, DeepSeek, Meta's Llama
令人惊讶的是:开源语言模型生态系统已经发展出约1500个主流模型,其中包括阿里巴巴的Qwen、DeepSeek和Meta的Llama等知名模型。这一数字表明,开源AI领域已经形成了相当规模和多样性的生态系统,远超许多人的想象。
focusing on the ~1.5K mainline open models from the likes of Alibaba's Qwen, DeepSeek, Meta's Llama
令人惊讶的是:开源语言模型生态系统已经发展到约1500个主流模型的规模,这远超许多人的想象。阿里巴巴、DeepSeek等中国公司与Meta这样的科技巨头共同塑造了这个庞大而多样化的生态系统,显示了开源AI的蓬勃发展。
Donations
To add some other intermediary services:
To add a service for groups:
To add a service that enables fans to support the creators directly and anonymously via microdonations or small donations by pre-charging their Coil account to spend on content streaming or tipping the creators' wallets via a layer containing JS script following the Interledger Protocol proposed to W3C:
If you want to know more, head to Web Monetization or Community or Explainer
Disclaimer: I am a recipient of a grant from the Interledger Foundation, so there would be a Conflict of Interest if I edited directly. Plus, sharing on Hypothesis allows other users to chime in.
We’re now relaunching PRO, but instead of a paid chat and (never existing) paid documentation, your team gets access to paid gems, our visual editor for workflows, and a commercial license.
Better community building: At the moment, MDN content edits are published instantly, and then reverted if they are not suitable. This is really bad for community relations. With a PR model, we can review edits and provide feedback, actually having conversations with contributors, building relationships with them, and helping them learn.
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