alexbuiko

4 ms·

1. 1.▲
   by alexbuiko 7mo ago

   sent e-mail to you
2. 2.▲
   by alexbuiko 7mo ago

   Focusing on 'Cost per Outcome' rather than 'Cost per Token' is a vital shift for AI reliability. At SDAG [ https://github.com/alexbuiko-sketch/SDAG-Standard ], we’ve been looking at the same problem f
3. 3.▲
   by alexbuiko 7mo ago

   Exactly. What you describe as 'parsing work' is, at the architectural level, a high-entropy search across the attention heads. When a prompt is a 'wall of text,' the model's routing logic has to maintain multiple co
4. 4.▲
   by alexbuiko 7mo ago

   Those sigma numbers are incredible—dropping variance by 24x practically confirms that you’ve managed to 'trap' the model in a low-entropy state. In production, predictability (the 'anti-tangent' factor) is often worth mo
5. 5.▲
   by alexbuiko 7mo ago

   This is a brilliant breakdown of the 'Token Mix' paradox. It aligns perfectly with what we’ve been seeing while developing SDAG. When you optimize for a structured context payload (like your dependency graph), you aren't just
6. 6.▲
   by alexbuiko 7mo ago

   A decade since AlphaGo, and we’re still just scratching the surface of model alignment and efficiency. While DeepMind proved that AI can master intuition, we are now looking at the other side: the physical cost of that intuition. I’m curren