First Principle of Programming
Remove all syntax & frameworks, what is programming at its absolute core? It's just taking an input, manipulating that input using logic, & producing output. Every program from basic calculator - massive neural network is just combination of these 3
Read 2025 survey “A Comprehensive Survey of Mixture-of-Experts”, it’s clear that MoE 2.0 is coming
Dynamic experts, continual learning & Neural OS-like architecture could turn AI into something modular and expandable not a single giant model.
Link:- https://t.co/434g46EWyD
AI doesn’t need bigger models, it needs smarter ones
That’s why Mixture-of-Experts matters
Grizz --> social tasks
Panda --> creative tasks
Ice Bear --> efficiency mode
Only needed “expert” wakes up
Compute stays low, capacity skyrockets
Click the Link:
https://t.co/CwBq0kRZ0W
AI needs smarter thinking, not just more power
Chain-of-Thought works for simple stuff, but GoT, i.e Graph-of-Thought & not Game of Thrones, tries multiple ideas at once, drops the weak, & picks the best. Faster, smarter, more human-like
Read more here
https://t.co/qe4oZB8kCc
👨🔬 To fellow ML engineers:
If you're experimenting with MoE or MLA architectures, give MUON a try.
Would love your insights, feedback, and experiments. Let's learn from each other.
In past internship, I built my own optimizer from scratch, MUON by referring to @Kimi_Moonshot research paper.
Results surprised me, & it's open source
Repo Link:- https://t.co/w9wLyyvyiS
Here’s a breakdown of why MUON works so well, especially for MoE models.
🧵👇
4. MUON Put to the Test
Model: NanoKimiK2, 8-layer MoE (SwiGLU FFNs, RoPE, MLA attention, 32 experts, top-6 routing)
Baseline: NanoGPT
With MUON:
✅ Stable expert routing
✅ Reduced valley-trapping
→ Trades a bit more compute for much better sparsity & stability
Made Kimi K2 completely from scratch as NanoKimi K2 what @karpathy did for GPT
Sparse MoE transformer (32 experts/top-6, SwiGLU FFNs, RoPE pos enc, MLA attn w/ LoRA-split) vs NanoGPT
Build Muon Optimizer completely from scratch
Link: https://t.co/w9wLyyvyiS
#LLMs#Transformers