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>
28 lines
2.1 KiB
Markdown
28 lines
2.1 KiB
Markdown
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aliases: ["AI & Machine Learning"]
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Is A: Topic
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---
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# AI & Machine Learning
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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.
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## Why this matters
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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.
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## Key resources
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- [[note-thinking-fast-and-slow]] — foundational mental models for reasoning about how AI interacts with human cognition
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- Simon Willison's blog — consistently the best source for practical LLM insights and experiments
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- Andrej Karpathy's talks and posts — deep technical intuition made accessible
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- The Gradient podcast — long-form AI research conversations
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- [[ai-wont-replace-thinking]] — a personal evergreen note on where AI fits in creative and analytical work
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## Notes
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- The most interesting AI applications for a content business are in research assistance and draft generation, not in replacing the writer
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- 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
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- LLMs are surprisingly good at structured summarization but consistently bad at nuanced editorial judgment — which is exactly where human writers earn their keep
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- The hype cycle around AI means there is a massive opportunity for thoughtful, skeptical-but-informed writing on the topic
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- 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
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