• Seattle Skeptics on AI
Seattle Skeptics on AI

Tag: model performance

Scaling Laws in Practice: When to Stop Training Large Language Models
Scaling Laws in Practice: When to Stop Training Large Language Models

Tamara Weed, Apr, 30 2026

Stop wasting compute. Learn when to move past Chinchilla optimality and enter the overtraining regime to balance training costs with inference performance.

Categories:

Enterprise Technology

Tags:

scaling laws Chinchilla optimality overtraining LLMs training pipeline model performance

Recent post

  • Shadow AI Remediation: How to Bring Unapproved AI Tools into Compliance
  • Shadow AI Remediation: How to Bring Unapproved AI Tools into Compliance
  • Style Guides for Prompts: Achieving Consistent Code Across AI Sessions
  • Style Guides for Prompts: Achieving Consistent Code Across AI Sessions
  • Prompt Templates for Generative AI: Reusable Patterns for Business
  • Prompt Templates for Generative AI: Reusable Patterns for Business
  • Implementing Generative AI Responsibly: Governance, Oversight, and Compliance
  • Implementing Generative AI Responsibly: Governance, Oversight, and Compliance
  • Sparse Attention and Performer Variants: Efficient Transformer Ideas for LLMs
  • Sparse Attention and Performer Variants: Efficient Transformer Ideas for LLMs

Categories

  • Science & Research
  • Enterprise Technology

Archives

  • June 2026
  • May 2026
  • April 2026
  • March 2026
  • February 2026
  • January 2026
  • December 2025
  • November 2025
  • October 2025
  • September 2025
  • August 2025
  • July 2025

Tags

vibe coding prompt engineering large language models generative AI Large Language Models transformer architecture AI coding tools AI governance LLM security AI compliance data privacy AI development AI coding assistants responsible AI LLM optimization AI coding transformer models AI code security enterprise AI GitHub Copilot

© 2026. All rights reserved.