human-ai collaboration

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LLMs for technical editing: The good, the bad, and the ugly
24 days ago
- AI is inefficient for completely replacing human editors if brand connection and content quality matter.
- Claude performed well in identifying structural, logical, and consistency errors but poorly with typos and grammar.
- AI editing risks losing authorial voice, intent, and style, potentially making content generic.
- Both models gave contradictory advice and invented or misdiagnosed errors, requiring human adjudication.
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AI agents are not your "coworkers"
a month ago
- Marketing AI agents as digital employees can reduce human error detection by 18% and shift accountability.
- Framing AI as coworkers leads to less human responsibility and more reliance on managerial oversight, negating efficiency gains.
- AI agents should enhance human capabilities rather than replace them, as suggested by Nobel-winning economist Daron Acemoglu.
- Workers prefer AI assistance in specific areas, such as tracking case progress, not in tasks like credit verification where human judgment is valued.
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Understanding Is the New Bottleneck
a month ago
- Agents are writing more code, making it harder to keep up, but there are multiple ways to understand code beyond line-by-line diffs.
- Understanding code is not just for verification; it allows humans to participate creatively in projects, avoiding cognitive debt and enabling iterative evolution.
- Three techniques for understanding code: explanations (like literate diffs with quizzes), micro-worlds (interactive environments to intuit systems), and shared spaces (collaborative tools for team understanding).
- AI can augment human understanding by creating simulations and tools, drawing inspiration from education and historical visions like Alan Kay's interactive computing.
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human-ai collaboration

LLMs for technical editing: The good, the bad, and the ugly

AI agents are not your "coworkers"

Understanding Is the New Bottleneck