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alexbuiko

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Joined
Thu, Mar 05, 2026, 12:22 PM UTC
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Public activity
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About alexbuiko

Researcher focusing on LLM infrastructure efficiency and hyperscale observability.

Developing SDAG (Systematic Defect Awareness & Guidance) to bridge the gap between model-level routing decisions and physical hardware reliability. Interested in MoE stability, tail latency analysis, and semiconductor degradation modeling at scale.

GitHub: https://github.com/alexbuiko-sketch Open for deep technical discussions on AI infra.

Recent public activity

  1. comment
    Comment #47348359

    sent e-mail to you

  2. comment
    Comment #47339198

    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 a…

  3. comment
    Comment #47339106

    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 routin…

  4. comment
    Comment #47338849

    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 …

  5. comment
    Comment #47327028

    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 …

  6. comment
    Comment #47326958

    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…

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