Degrees, Drills and Algorithms: Rethinking Education in the Age of AI
AI is reshaping the value of degrees and apprenticeships, rewarding adaptable people who combine critical thinking, practical skills and human judgment.
Education
21posts
The Tool Is Not the Father
AI can either deepen self-invalidation or lower the barrier to practice. The difference lies in culture, incentives, and how we use it.
The Age of Self-Invalidation
AI’s real danger is not that we stop thinking, but that we stop believing our own imperfect thoughts are still worth having.
Review: The Welch Labs Illustrated Guide to AI
A review of a rare AI book that uses mathematics to illuminate rather than intimidate, making difficult ideas feel genuinely learnable.
The Quiet Cost of Too Many Yeses: What AI Can Learn from Good Teachers
Good teachers do not simply say yes; the post argues that AI assistants also need constructive friction to help users think better.
LLM-Guided Image Editing: Embracing Mistakes for Smarter Photo Edits
Apple's image-editing research suggests smarter creative tools may learn from failed edits instead of hiding them.
When “Errors” Speak: A Comparative Field Guide to Human and LLM Fallibility
Human and LLM errors can look similar, but their causes differ in ways that matter for trust, correction, and accountability.
From Prompts to People: What OpenAI’s Usage Study Means for Model Leaderboards
OpenAI's usage study shifts attention from benchmark scores to how ordinary people actually use ChatGPT in daily life.
Learning to Learn vs. Remembering in the Age of Ubiquitous Knowledge
In an age of ubiquitous knowledge, the post weighs adaptability against memory and asks what learning should still mean.
AI Companions in the Classroom: Parallels Between Gibson's Vision and the Educational AI Revolution
AI classroom companions echo William Gibson's fictional guides, raising questions about education, intimacy, and dependence.
GPT-5 Rollout: The Aftermath
A follow-up on GPT-5's rocky rollout, user frustration, and OpenAI's attempts to tune expectations after launch.
The Future of AI: How Self-Adapting Language Models Are Redefining Learning
SEAL points toward language models that rewrite their own training material, hinting at AI systems that learn after deployment.
Human-in-the-Loop: The Art of Teaching Machines to Ask for Help
Human-in-the-loop design is presented as the practical art of knowing when machines should stop and ask for help.
Choosing the Right OpenAI API Interface: A Developer’s Guide for 2025
A developer-focused guide to choosing between OpenAI's Chat Completions, Responses, and Assistants APIs in 2025.
The Rise of the AI Prophetes: Translating the Machine Oracle
As AI becomes an oracle, a new class of interpreters may emerge to translate machine outputs into human decisions.
Humanity’s Last Exam: The Ultimate Test for AI and the Future of Intelligence
Humanity's Last Exam is framed as a benchmark that tests not only models, but our assumptions about intelligence itself.
The Night Before Training: A Machine Learning Christmas Tale
A machine-learning Christmas poem turns training runs, GPUs, and convergence into a festive technical fable.
7 Pragmatic Principles for Responsible AI Development: Moving Beyond Feel-Good Guidelines
Seven practical principles argue for responsible AI development that moves beyond polished ethics statements and into engineering habits.
The AI Cargo Cult: Mimicry vs. True Intelligence in the Pursuit of AGI
The post warns against an AI cargo cult that confuses impressive mimicry with the harder problem of genuine intelligence.
Navigating the Unseen: The Dunning-Kruger Effect and AI Hallucinations
Human overconfidence and AI hallucination meet in a comparison of how bad certainty distorts judgment in both minds and machines.
Unlocking the Potential of Large Language Models: A Guide to Effective Prompt Engineering
A practical guide to prompt engineering techniques for getting more reliable, useful behavior from large language models.