Caleb Writes Code

• Auto-research: Introduction to AI-driven algorithmic optimization methodology.
  • Contrasts with traditional human-led “vibe coding.”
  • Demonstrated through a restaurant inventory simulation.

Clips

• Clip title: AutoResearch explained..

  • Author / channel: Caleb Writes Code
  • URL: https://www.youtube.com/watch?v=5-ekc3eXNvs
  • Summary:
    • Initial naive algorithm fails to keep ingredients stocked, leading to over 50% failed orders.
    • Iterative learning process implemented to improve inventory management.
  • Additional Details from New Note:
    • Auto-research methodology is introduced as an AI-driven approach for optimizing algorithms.
    • Iterative learning and defined metrics are key components of the method.
    • The naive algorithm fails initially but improves through iterative learning.

• Clip title: Why Inference is hard..

  • Author / channel: Caleb Writes Code
  • URL: https://www.youtube.com/watch?v=B18zBnjZKmc
  • Summary:
    • Detailed technical overview of how LLMs are loaded and run for inference.
    • Dispels the misconception that LLMs are simple executable files.
    • Covers memory mapping and performance optimization.

Backlinks

• 2026 04 22 LLM Inference Engines Memory Mapping and Performance Optimization

Source Notes

• 2026-04-08: [[lab-notes/2026-04-08-Auto-research-AI-Driven-Algorithmic-Optimization-with-Iterative-Learni|AutoResearch explained..]]
• 2026-04-10: [[lab-notes/2026-04-10-Auto-research-AI-Driven-Algorithmic-Optimization-with-Iterative-Learni|AutoResearch explained..]]
• 2026-04-22: [[lab-notes/2026-04-22-LLM-Inference-Engines-Memory-Mapping-and-Performance-Optimization|LLM Inference: Engines, Memory Mapping, and Performance Optimization]]