我就说最近Pro怎么都秒回,@OpenAI开始忽悠了是吧
🚀OpenAI无底线坑用户!我的ChatGPT pro订阅竟然将GPT-5.6 Sol模型路由到了GPT-5.5-Mini模型!
难怪这段时间笨的要命!
本以为只有我的这样,群里的大佬们也遇到了一样的问题!
请OpenAI给我们这些Pro订阅用户一个解释!!!!
我们花着每月$200的订阅费,你就给我们用个蒸馏后的mini模型????
#ChatGPT #Codex
@thsottiaux @OpenAI @OpenAIDevs @ChatGPT @CodexReleases
Upload a regular photo.
Get something that looks like it came off a risograph press — halftone dots, paper grain, slight registration shift.
Built InkTone with Kimi K3: a browser studio for spot-ink print simulation. Not a filter. Actual print character.
Export when it feels right.
ink-tone.com
看看人家这格局
For my first post, I’m sharing a letter @NVIDIA signed on why open models matter.
AI will transform every industry, power every company, and be built by every country.
Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.
The world needs both frontier closed models and frontier open models.
images.nvidia.com/pdf/Open-W…
Giving digital photos the texture of print.
Built a tiny browser-based halftone editor with Kimi K3.
白宫称Kimi K3蒸馏Fable
We have information that Moonshot AI distilled Anthropic’s Fable for the development of its K3 model.
To do this they developed a sophisticated internal platform to conduct large scale distillation against U.S. models, allowing them to quickly switch between multiple methods of access to avoid detection. Moonshot AI has also acquired GB300-equipped servers and has accessed GB300s in Thailand, likely to train its AI models.
The United States strongly supports the free and fair development of AI, including a thriving competitive ecosystem that spans frontier models, specialized systems, open-source frameworks, and open-weight models. Legitimate AI distillation used to create smaller, more efficient models plays a vital role in this open innovation ecosystem. However, large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable.
Same README. Same skill. Two completely different homepages.
I gave Kimi K3 and GPT-5.6 Sol the same repository and prompt. The visual split was obvious. The revealing part was what each model chose as proof of the product. Here is the experiment in 8 posts.
My biased verdict: Kimi for visual direction, GPT for factual and release checks. Next time, I would let Kimi set the tone, then ask GPT to check versions, contracts, and traces from the previous model. Two passes beat betting everything on one model.
The test repo was learn-anything-skill. Its current README keeps the result of this experiment:
github.com/vesperchinn/learn…
If a homepage could foreground only one kind of proof, would you choose the coaching process or visible learning progress? I am still split.












