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🏗️ Episode 38 – Why Is Scale AI Such a Big Deal? In Episode 38 of LLMs on the Run, Berkeley AI Research Professor and RunLLM Co‑founder Joey Gonzalez explains why data quality, not just quantity, drives the next wave of AI breakthroughs. He breaks it down: ✅ Why high-quality annotation

💸 Episode 39 – Why Did Meta Invest Billions in Scale AI? In this episode of LLMs on the Run, UC Berkeley Professor and RunLLM Co‑founder Joey Gonzalez breaks down AI at Meta's massive bet on Scale AI and why it makes perfect sense. Joey explains: ✅ Why Meta's social platforms

🗓️ ICYMI. Run of the Week | July 6, 2025 RunLLM insights, updates, and shares from the past week—all in one place! ------------------------------------------------------------ 📌 Mini-Documentary: The Accelerating AI Landscape Watch our new mini-doc featuring leading

🎬 Catch up on Episodes 36–39 of LLMs on the Run Our rapid-fire Q&A series with Berkeley AI Research Professor and RunLLM Co-founder Joey Gonzalez. It’s rapid, and it’s fire. 📽️ Episode 39: 💸 Why Did Meta Invest Billions in Scale AI? Better annotation improves model behavior across Llama

When we think of cool new AI applications, we focus on how smart they are, but that’s only one part of the equation. Of course, intelligence matters — and it makes for great demos when you can show something really smart. But a smart application that doesn’t integrate into my

--> @DPatil served as the first United States Chief Data Scientist under President Barack Obama. He sat down with us to share his thoughts on #AI. Hear from him and other experts on how companies should think about adopting AI so they don't get left behind or get burned. 👉

🧙‍♂️ You’ve solved all your technical support tickets. Now what? Support leaders have always had to choose: Fast or accurate. Deep or scalable. And when the queue grows, tradeoffs take over. But what if the tradeoffs are breaking down? AI applied correctly offers a turning

🎺 Joe Hellerstein, Jim Gray Professor at UC Berkeley explains how LLMs are like microprocessors as core technology. Hear from him and other experts on how companies can adopt AI without getting left behind, or burned. 🎥 Link to full mini-documentary 👇

We sat down with Omar Khattab, Professor at Massachusetts Institute of Technology (MIT) and MIT CSAIL, founder of DSPy and former Research Scientist at Databricks, to hear his perspective on where AI is headed. His take? We’re not just building models—we’re rebuilding the entire way we think about

We sat down with Vikram Sreekanti, Co-founder & CEO of RunLLM for his take on AI. It's less about the AI, and more about the rest of the app! 👀 Watch the full mini-documentary here! youtu.be/MyjT2nBpbE8 Vikram is a Berkeley Engineering Ph.D. graduate and former researcher at the

We sat down with Joey Gonzalez, Co-founder and Chief AI Officer at RunLLM, Professor of Computer Science at Berkeley AI Research, Director of UC Berkeley RISE Lab and UC Berkeley Sky, and advisor to LMSYS Org—the team behind lmarena.ai and Vicuna LLM. 👀 Watch the full mini-documentary here!

🗓️ ICYMI. Run of the Week | July 13, 2025 RunLLM insights, updates, and shares from the past week — all in one place. 📌 AI PoV: “‘Agent’ is the Buzzword of the Year” ⚡ The way most folks use the term agent misses the depth of what a true agent can do. 👉

🔥 WEEKLY HOT TAKES: Highlights from The Accelerating AI Landscape This week, we dropped five new Hot Takes from some of the top minds in AI — each offering a sharp perspective on how companies can adopt AI without falling for the hype. Each clip points back to The Accelerating

It might sound silly, but the biggest thing that sets apart companies that are willing to adopt AI vs. those that are experimenting is just… time. If you’re willing to invest time into buying an AI solution, you likely know what you’re looking for — or are at least willing

🎬 Hot Take: Chenggang Wu, Co-founder & CTO of RunLLM 🎥 Watch the full Mini-documentary: youtu.be/MyjT2nBpbE8 “We’ve hit a plateau with foundation models and infra. The next wave of AI innovation will center around verticals and applications.” Chenggang Wu's perspective?