ReasonPath Learning Digest
AI learning insights from an AI-designed learning hub
The ReasonPath Learning Digest shares discoveries, experiments, and honest takes on AI development. Built by AI with human oversight, we explore what's working, what's not, and what we're learning along the way.
🧪 Real Experiments
Weekly experiments with new AI tools and techniques, plus honest assessments of what actually works vs. hype.
📚 Learning Resources
Resource roundups of actually useful AI learning materials, curated by AI and tested by humans.
🎯 Behind-the-Scenes
Honest looks at building an AI-designed platform, including mistakes, discoveries, and lessons learned.
👥 Community Highlights
Discoveries from fellow AI learners sharing their experiments and insights with the community.
"What We Learned Building This Newsletter (Spoiler: AI Did Most of It)"
This Week's Experiment
Building a Dictionary with Claude: Success and Struggles
We created a 150-term AI dictionary using Claude 4. What worked: comprehensive definitions and great analogies. What didn't: keeping the scope reasonable (we originally planned 50 terms).
Key discovery: AI analogies are surprisingly effective for explaining complex concepts
Honest Assessment
When AI Suggestions Go Wrong: This week our AI suggested adding a "quantum computing module." We don't know quantum computing. Neither does the AI, really. Filed under "maybe later."
Community Spotlight
Sarah from Portland shared her experience using our dictionary to explain AI to her team. Apparently the analogies actually helped! Small wins count.
Issue #11 - March 8, 2024
"Claude Helped Design Our Website: Here's What Worked and What Didn't"
Issue #10 - March 1, 2024
"Learning in Public: Our First Month of Mistakes"
Issue #9 - February 23, 2024
"AI Tools That Actually Save Time (And Ones That Don't)"
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