M (∞/89)
1.8K posts



buying $pepe now is like buying bitcoin in 2010




JUST IN: ⚡ Canary Capital has filed an S-1 registration statement with the SEC for a Pepe ETF.


FIFA World Cup is still 2 months away. Patience will be rewarded. $FWC HxWrnZznqF5iYf3ckMw3FTaZQvubB53ohzpjPSNUpump







The FIFA World Cup 2026 is estimated to have 6 billion viewers worldwide and, for the first time ever, will be hosted by three nations. The next narrative is right in your face, and I'm giving you over two months to front-run it. ⏳⏳⏳ $FWC HxWrnZznqF5iYf3ckMw3FTaZQvubB53ohzpjPSNUpump



As promised. Our first paper and contribution to the amazing work going on to make open source models smaller, faster, and more accessible. So what is it, and why is it important? We discovered what appears to be a universal formula that identifies dead attention heads in any transformer, derived from physics — not fitted from data. This is wild, because up till now finding and pruning dead heads has been a manual job of trial and error. By removing unused heads, the models can get smaller and faster while still maintaining competitive quality. The core insight is geometric. LayerNorm projects every token's hidden state onto a high-dimensional sphere. Once you see that, attention heads become couplings between oscillators on that sphere — the same mathematical object physicists have studied for 50 years. And in oscillator physics, there's a precise critical point (the BKT phase transition) below which a coupling is dead. It contributes nothing. We transferred that critical point into transformer geometry and got a single formula: tau = 0.96 / sqrt(d). No parameters to tune. No model-specific calibration. You plug in the hidden dimension and it tells you which heads are dead. We validated it across six models in four architecture families — GPT-2, Qwen, Llama, Gemma — at 95-100% precision. What excites us most isn't the formula itself. It's that this same geometric understanding — treating transformers as coupled oscillator networks — has informed everything we've built since. We have a full coherence-guided compression pipeline (structured pruning, channel optimization, role-aware quantization) coming soon that uses the same single forward pass to understand a model's entire anatomy. This paper is the foundation. The repo includes a standalone scanner you can run on any Hugging Face model right now. Hopefully this work and this formula will be useful to other researchers to lead to more deterministic optimization pipelines. #project89 github.com/project-89/coh…





As the World Cup gets closer, the excitement keeps building. Here’s what I’m expecting: @realDonaldTrump will tweet about the World Cup. @elonmusk will tweet about the World Cup. @toly will tweet about the World Cup. @a1lon9 will tweet about the World Cup. $FWC is going to bring the trenches back to life!!


