b3126044e8
- Simplify flatten_vault API to return usize instead of MigrationResult struct - Add KEEP_FOLDERS: attachments/ and _themes/ alongside type/, config/, theme/ - Use HashSet for collision tracking in unique_filename - Update wikilinks from path-based [[folder/slug]] to title-based [[slug]] - Clean up empty directories after flattening - Flatten demo-vault-v2: move all notes from type-based subfolders to root - Update smoke tests for flat vault structure - Remove migrate_to_flat_vault from repair_vault (one-time migration only) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Topic |
AI & Machine Learning
AI and machine learning are reshaping how software is built, how content is created, and how businesses operate. This topic covers the practical side of AI — from LLMs and their applications in content workflows to the broader implications for engineering teams, developer tools, and the future of knowledge work.
Why this matters
As someone writing for engineering leaders, understanding AI is not optional — it is the single most important technology trend affecting the audience. Every newsletter edition touches AI in some form, whether directly or through its second-order effects on team structure, hiring, and product strategy. Staying sharp here means the writing stays relevant and ahead of the curve, rather than parroting surface-level takes. The note ai-wont-replace-thinking captures a core belief: AI augments judgment, it does not substitute it.
Key resources
- note-thinking-fast-and-slow — foundational mental models for reasoning about how AI interacts with human cognition
- Simon Willison's blog — consistently the best source for practical LLM insights and experiments
- Andrej Karpathy's talks and posts — deep technical intuition made accessible
- The Gradient podcast — long-form AI research conversations
- ai-wont-replace-thinking — a personal evergreen note on where AI fits in creative and analytical work
Notes
- The most interesting AI applications for a content business are in research assistance and draft generation, not in replacing the writer
- Engineering teams are adopting AI coding tools faster than leadership realizes, creating a gap between how work is actually done and how it is managed
- LLMs are surprisingly good at structured summarization but consistently bad at nuanced editorial judgment — which is exactly where human writers earn their keep
- The hype cycle around AI means there is a massive opportunity for thoughtful, skeptical-but-informed writing on the topic
- Most B2B companies are still figuring out how to talk about AI to their customers, which creates sponsorship opportunities for newsletters that do it well