Is AI Getting “Dumber”? Uncovering the Game Between Parameter Accuracy and Inference Costs
Recently mingling in various programming large model communication circles, the most complained about thing is model degradation.
- Models deployed on local desktop computers are quantized models, essentially downgraded versions.
- With “vibe coding” so popular, could it be that the content output by current large models is the most valuable product – code?
Ultimately, it’s returning to domestic models.
“Previously, it was mentioned that when logging into Gemini Cli, you needed to configure the Google Cloud Project ID. This is already wrong – if it’s a personal account, there shouldn’t be this restriction. The fact that this restriction exists indicates that you’ve started triggering Google’s security system and are being identified as not being a personal account.
It’s frustrating; after using it for half a month and getting used to it, now I have to return to the embrace of cc + domestic models.
Alibaba Large Model Strategy
Alibaba (Ali) has released numerous large models, not simply a matter of “volume chasing,” but a carefully planned “Model-as-a-Service” (MaaS) ecosystem strategy. There are multiple considerations behind this, which can be summarized as “internal empowerment and external ecosystem building.”
Internal Business Driven (Inward Empowerment) Alibaba possesses an extremely vast and diverse business landscape, including e-commerce (Taobao & Tmall), finance (Ant Financial), logistics (Cainiao), cloud computing (Aliyun), and entertainment (Youku), among others.
Recent Usage Experiences of Large Models
Currently, no large model stands out as particularly superior; each company has its own strengths and preferred use cases.
Technical Documentation For feeding code or asking IT technical questions: ChatGPT and Gemini
Write Code Gather requirements and request code modifications: Claude
Blog Translation Project Musings: Historical Conversations
The initial design of the blog translation project was overly complex – first parsing Markdown format, then using placeholders to protect the content, and finally sending it to a large model for translation. This was entirely unnecessary; large models inherently possess the ability to recognize Markdown syntax and can directly process the original content while maintaining formatting during translation.
Our work shifted from debugging code to debugging the prompting of the model.