Quick Overview: More code, fewer staff — the industry is on a bender. But what about quality? At As MCP systems scale from local setups to shared infrastructure, At this workshop, Eda Zhou & Mahdi Ghodsi from AMD explored building personal

Ai Dev 26 X Sf - Detailed Overview & Context

More code, fewer staff — the industry is on a bender. But what about quality? At As MCP systems scale from local setups to shared infrastructure, At this workshop, Eda Zhou & Mahdi Ghodsi from AMD explored building personal Most agentic systems rely on hardcoded heuristics to navigate execution decisions (e.g. which models, tools, and test-time ... Building your first agent is exciting. Building a platform that can evolve into an office where dozens of teams can safely deploy ... Documents are often the true starting point for real-world

In this talk, Ankit Mathur from Databricks, discussed the governance and security challenges of rolling out coding tools at ... Paige Bailey gave us an overview of the latest model releases from Google DeepMind, especially in the multimodal space ... In this talk by Zencoder's Andrew Filev, attendees learned how decomposing tasks into pipelines and dynamically routing them ... This talk by Cline's Ara Khan explains why they went from "evals are useless" to using them as a core part of my agent ... This talk by Qdrant's Thierry Damiba shows how to build a real-time video anomaly detection system that works in open-world ...

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AI Dev 26 x SF: Andrew Ng: The Future of Software Engineering
AI Dev 26 x SF | Paul Everitt: The Shift to Agentic Engineering
AI Dev 26 x SF | Matthew Xu: The 4-Legged Identity Challenge
AI Dev 26 x SF | Adit Abraham: Better Agents with Better Data
AI Dev 26 x SF | Brandon Waselnuk: Building the Context Engine AI Agents Need
AI Dev 26 x SF | Eli Schilling: Hands On Agent Context & Memory Engineering with Oracle AI Database
AI Dev 26 x SF | Carter Rabasa: File Systems Are the New Primitive for AI Agents
AI Dev 26 x SF | Eda Zhou & Mahdi Ghodsi: Building Personal AI Agents with Open Source Models
AI Dev 26 x SF | Or Dagan: Optimizing Accuracy, Cost, and Latency in Real-World Agents
AI Dev 26 x SF | Diamond Bishop: The Next 100 Agents. Building the Agent Native Office
AI Dev 26 x SF | David Park: Building Production Grade Agentic Systems with ADE
AI Dev 26 x SF | Melissa Herrera: Your Agents Should Be Durable
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