I feel that I have become more and more obsessed with studying some "primitive" CLI operations recently.
Compared to the so-called MCP and Skill, enabling AI to understand and use CLI is actually more feasible, explainable, and powerful in terms of code.
I recently deployed a website for my OpenSoul on Vercel. In the past, I might have needed to spend a lot of cognitive effort or time to understand how to operate on the Vercel page, and I would have had to spend a great deal of time reading documents (smarter people might directly feed the documents to AI and let AI summarize feasible and reliable steps).
But in fact, after ChatGPT told me that Vercel actually has a CLI, I directly asked my Copilot in VS Code to download this command line, clearly stated my needs, and it quickly solved everything else. The only thing I actually needed to do was log in to Vercel and create a key.
This suddenly reminds me of a blog post I read earlier that interviewed the father of Claude Code. The reason why Claude Code did not develop front-end pages and the like is precisely because he believes that we should focus most of our energy on the most meaningful interaction logic.
So, in an era where AI capabilities are becoming increasingly strong, perhaps what we really need is to pick up those tools that we used with the sole goal of achieving functionality when computing power was tight. What do you think?
๐ฅ UPGRADE in Kai: 30B Scaling! ๐ฅ NoesisLab/Kai-30B-Instruct NoesisLab/Kai-30B-Instruct We are incredibly excited to announce that the Kai-30B-Instruct model and its official Space are now LIVE! ๐ If you've been following the journey from Kai-0.35B to Kai-3B, you know we're rethinking how models reason. Tired of verbose, slow Chain-of-Thought (CoT) outputs that flood your screen with self-talk? So are we. Kai-30B-Instruct scales up our Adaptive Dual-Search Distillation (ADS) framework. By bridging classical A* heuristic search with continuous gradient descent , we use an information-theoretic log-barrier to physically prune high-entropy reasoning paths during training. The result? Pure implicit reasoning. The model executes structured logic, arithmetic carries, and branch selections as a reflex in a single forward passโno external scaffolding required. At 3B, we observed a phase transition where the model achieved "logical crystallization". Now, at 30B, we are giving the ADS regularizer the massive representational capacity it needs to tackle higher-order symbolic abstractions and complex reasoning tasks. ๐งช Test Kai yourself in our new Space: NoesisLab/Kai-30B-Instruct ๐ฆ Model Weights: NoesisLab/Kai-30B-Instruct Bring your hardest math, logic, and coding benchmarks. We invite the community to stress-test the limits of the penalty wall! ๐งฑ๐ฅ