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Good Morning ☀️ » Redesign underway » Multiple framework integrations inbound » Agent Token / VVAIFU LP's ? 📷 » Hyperliquid EVM Thank you for the mention Ejaaz 💕 Listen... 📷🫳

vvaifu.fun

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25,419 次观看 • 1 年前 •via X (Twitter)

游戏财经科学技术

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31,360 次观看 • 2 年前

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Wayfinder Foundation 🧭

51,650 次观看 • 1 年前

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Alex Groberman

157,952 次观看 • 4 个月前

Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon Aish and Kiriti Badam have built 50+ enterprise AI products across companies like OpenAI, Google, Amazon, and Databricks. Based on these experiences, they’ve developed a small set of best practices for building and scaling successful AI products. Our goal with this conversation is to save you and your team a lot of pain and suffering while building your AI products. We discuss: 🔸 Two key ways AI products differ from traditional software 🔸 Common patterns and anti-patterns when building successful AI products 🔸 Their framework for iteratively building AI products 🔸 Why evals aren’t a cure-all 🔸 Why obsessing about customer trust and reliability is an underrated driver of successful AI products 🔸 The skills that matter most for builders in the AI era Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Merge — The fastest way to ship 220+ integrations: 🏆 Strella — The AI-powered customer research platform: 🏆 Brex — The banking solution for startups:

Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon Aish and Kiriti Badam have built 50+ enterprise AI products across companies like OpenAI, Google, Amazon, and Databricks. Based on these experiences, they’ve developed a small set of best practices for building and scaling successful AI products. Our goal with this conversation is to save you and your team a lot of pain and suffering while building your AI products. We discuss: 🔸 Two key ways AI products differ from traditional software 🔸 Common patterns and anti-patterns when building successful AI products 🔸 Their framework for iteratively building AI products 🔸 Why evals aren’t a cure-all 🔸 Why obsessing about customer trust and reliability is an underrated driver of successful AI products 🔸 The skills that matter most for builders in the AI era Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Merge — The fastest way to ship 220+ integrations: 🏆 Strella — The AI-powered customer research platform: 🏆 Brex — The banking solution for startups:

Lenny Rachitsky

176,860 次观看 • 8 个月前