FlexiLingo
A $2 million AI workflow died with its creator because nobody else could figure out how it worked. This happens constantly in AI engineering. Someone builds an incredible automation system, gets promoted or leaves, and the team spends months trying to reverse-engineer what should have been shared knowledge. The problem isn't the complexity of the workflows, it's how we document and transfer them. Nico breaks down three proven methods that top AI teams use to package their workflows for maximum shareability. These aren't complex enterprise solutions, they're simple frameworks that turn your custom agents into teachable, replicable systems. In This Episode: >...
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B2 · 中高级
能看懂电视新闻就会觉得轻松
- 84% 的单词在 B1 或更简单的水平。
- 每分钟 154 词：自然语速。
- 整集共有 81 个较难的单词（B2 及以上）。
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来自本集开头的真实句子，每个单词都标出了难度级别。
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But when someone asks you to explain it, their eyes glaze over in about 30 seconds.
点一个单词，用你的语言查看释义。
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本集中 8 个较难的单词。点一个查看释义，然后标记一下。
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这一集适合你吗？
B2 · 中高级
能看懂电视新闻就会觉得轻松
- 84% 的单词在 B1 或更简单的水平。
- 每分钟 154 词：自然语速。
- 整集共有 81 个较难的单词（B2 及以上）。
文字稿
本集开头部分。完整文字稿在播放器中。
较难的单词（B2+）
You build an amazing AI workflow that saves you hours every week. But when someone asks you to explain it, their eyes glaze over in about 30 seconds. Sound familiar? You start with, well, first the data extraction agent pulls from these five sources,
then it passes to the classification model, which feeds into and boom, you've lost them. Here's the thing though, the teams that figure out how to share their AI workflows clearly, they're the ones scaling fastest. McKinsey found that 78% of automation projects fail, not because the tech doesn't work,
but because of poor knowledge transfer. People just can't explain what they built in a way that others can actually use. And this isn't just about being nice to your coworkers. Companies that document their AI processes see 23% faster implementation of new
以上是开头部分。 本集其余部分（13 分钟）在播放器中：每个单词都可点击，附翻译、生词收藏和单词卡。
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