AE
AI Engineer
Episodes
- Teaching Gemini to Speak YouTube: Adapting LLMs for Video Recommendations to 2B+DAU - Devansh Tandon
- Transforming search and discovery using LLMs — Tejaswi & Vinesh, Instacart
- Netflix's Big Bet: One model to rule recommendations: Yesu Feng, Netflix
- 360Brew: LLM-based Personalized Ranking and Recommendation - Hamed and Maziar, LinkedIn AI
- What We Learned from Using LLMs in Pinterest — Mukuntha Narayanan, Han Wang, Pinterest
- Measuring AGI: Interactive Reasoning Benchmarks for ARC-AGI-3 — Greg Kamradt, ARC Prize Foundation
- RL for Autonomous Coding — Aakanksha Chowdhery, Reflection.ai
- Recsys Keynote: Improving Recommendation Systems & Search in the Age of LLMs - Eugene Yan, Amazon
- OpenAI's Sean Grove: Code is NOT all you do
- OpenAI's Sean Grove: Everything is a Spec: The Universal Language of Intent
- Benchmarks Are Memes: How What We Measure Shapes AI—and Us - Alex Duffy, Every.to
- Small AI Teams with Huge Impact — Vik Paruchuri, Datalab
- Rethinking Team Building: how a 30-person Startup serves 50 Million Users — Grant Lee, Gamma
- Building a 10 person unicorn - Max Brodeur-Urbas, Gumloop
- Using OSS models to build AI apps with millions of users — Hassan El Mghari
- Bolt.new: How we scaled $0-20m ARR in 60 days, with 15 people — Eric Simons, Bolt
- Prompt Engineering and AI Red Teaming — Sander Schulhoff, HackAPrompt/LearnPrompting
- Survive the AI Knife Fight: Building Products That Win — Brian Balfour, Reforge
- Automating Escrow with USDC and AI - Corey Cooper, Circle
- How LLMs work for Web Devs: GPT in 600 lines of Vanilla JS - Ishan Anand