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DeepLearningAI

AI Agents for Image and Video Generation. #GoogleCloud #DeepLearningAI #AIAgents Local AI is just getting started, and there’s room for you to shape it. AI Dev | San Francisco 2026 | 2,599 AI Developers Spec-driven development and local AI are a natural pair. Your coding agent keeps solving the same problem Frontier models for the big problems. #aiagents #jetbrains #deeplearning The big brain for the big work... #aiagents #buildinpublic #deeplearning #jetbrains @JetBrainsTV Take back control of your AI coding workflow 3rd Place Winner: Voice AI Prevents Data Loss. Coding Agent Calls Developer Before Deleting Records 2nd Place Winner: Coding Agent Calls Developer to Pitch Launch Strategy 1st Place Winner: Coding Agent Calls Developer to Resolve Code Block AI writes your code. Who reviews it? Fast inference changes what you can build 7-day Voice AI Build Challenge Voice for AI Agents and Applications Optimize, deploy, and benchmark an open-source LLM with vLLM Build Your Own App In Just 30 Minutes! Full Course with Andrew Ng How good is AI memory? AI Dev 26 x SF | Ara Khan: Evals Are Broken Use Them Anyway Semantic Search Starts With Embeddings AI Dev 26 x SF | Andi Partovi: Why Every Agent Needs a Simulation Sandbox AI Dev 26 x SF | João Moura: Building Recurring, Governed, and Embedded Enterprise Workflows AI Dev 26 x SF | Luke Kim: The Agent Data Stack—Why Every AI Agent Needs Its Own Data Stack AI Dev 26 x SF | Manos Koukoumidis & Stefan Webb: VibeML: Build your AI model in hours, not months AI Dev 26 x SF | Daniel Beutel: Flower SuperGrid Agents AI Dev 26 x SF | Andrew Filev: Multi Model Pipelines—How to Get Better AI Results for Less AI Dev 26 x SF | Diamond Bishop: The Next 100 Agents. Building the Agent Native Office AI Dev 26 x SF | Paul Everitt: The Shift to Agentic Engineering AI Dev 26 x SF | Andrew K. Davies: Deterministic Memory: How to Build an AI That Cannot Lie - YouTube
AI Dev 26 x SF | Or Dagan: Optimizing Accuracy, Cost, and...
DeepLearningAI · 2026-05-23 · via DeepLearningAI
Most agentic systems rely on hardcoded heuristics to navigate execution decisions (e.g. which models, tools, …

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