629: Mythos vs World: The Model Too Good to Release, GPU Rich vs GPU Poor Leaderboard, Meta's Back With Muse Spark, Greg Brockman, Artemis II, and Lady Bird

2026-04-10 · Source: Liberty’s Highlights · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Novice, quick

Summary

The author details two personal experiments: using large language models (LLMs) for book recommendations and experiencing vivid dreams from Ashwagandha. For book tracking, the author fed a list of books read since age 16, including enjoyment grades, to Claude Opus 4.6 and GPT-5.4-Thinking. This method generated recommendations superior to Amazon's "People who read X also read Y" and allowed for exploring "BookSpace" from various angles, such as finding obscure titles. Separately, the author discusses taking 600mg of a 12:1 Ashwagandha extract (equivalent to 7,200mg) for anxiety or sleep, noting a consistent side effect of vivid dreams, despite acknowledging inconclusive scientific studies on its efficacy.

Key takeaway

For data scientists or hobbyists looking to personalize content recommendations, consider feeding your consumption history, especially with subjective ratings, to advanced LLMs like Claude Opus 4.6 or GPT-5.4-Thinking. This approach can yield more nuanced and diverse suggestions than traditional collaborative filtering, allowing you to discover content tailored to specific preferences or to fill knowledge gaps.

Key insights

LLMs can generate personalized book recommendations by analyzing reading history and enjoyment grades.

Principles

• Enjoyment is distinct from objective quality.
• Directional steering improves LLM output.

Method

Provide LLMs with a graded reading list to generate tailored book recommendations, specifying desired recommendation angles.

In practice

• Use LLMs to identify reading blind spots.
• Experiment with Ashwagandha for vivid dreams.

Topics

• AI Reading Recommendations
• Personal Data Analysis
• Large Language Models
• Ashwagandha Supplementation
• Anxiety Management

Best for: AI Engineer, Machine Learning Engineer, NLP Engineer, General Interest, AI Student, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Liberty’s Highlights.