DedeProGames/OpenAgent
Text Generation • 8B • Updated
• 65 • 1
DedeProGames
DedeProGames
·
AI & ML interests
Thinking and Agentic Finetuning
Recent Activity
new activity
about 12 hours ago
mradermacher/model_requests:DedeProGames/OpenAgent new activity
about 12 hours ago
huggingface/InferenceSupport:suayptalha/Qwen3-0.6B-Code-Expert liked
a model about 14 hours ago
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DedeProGames/OpenAgent
1
#1993 opened about 12 hours ago
by
DedeProGames
suayptalha/Qwen3-0.6B-Code-Expert
🚀 1
#8535 opened about 12 hours ago
by
DedeProGames
liked a model about 14 hours ago
Qwen/Qwen3.5-0.8B
🚀 13
5
#8368 opened 7 days ago
by
Atlas-0
updated a dataset 1 day ago
updated a model 4 days ago
published a model 4 days ago
reacted to NJX-njx's post with 🚀🔥 4 days ago
Post
915
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?
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?
- 1 reply
updated a model 4 days ago
stepfun-ai/Step-3.5-Flash-Base
🚀 🔥 2
#8448 opened 4 days ago
by
DedeProGames
Qwen/Qwen3.5-0.8B-Base
#8434 opened 5 days ago
by
DedeProGames
liked a dataset 5 days ago
updated a dataset 5 days ago
published a dataset 5 days ago
liked a model 5 days ago
Qwen/Qwen3.5-2B
🚀 🔥 2
1
#8367 opened 7 days ago
by
mikaelnias
liked 2 datasets 6 days ago
reacted to OzTianlu's post with 🤗 8 days ago
Post
4767
🔥 UPGRADE in Kai: 30B Scaling! 🔥
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! 🚀
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 pass—no 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.
🧪 Test Kai yourself in our new Space:
NoesisLab/Kai-30B-Instruct
📦 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! 🧱💥
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! 🚀
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 pass—no 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.
🧪 Test Kai yourself in our new Space:
NoesisLab/Kai-30B-Instruct
📦 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! 🧱💥
- 1 reply