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UID:pretalx-devconf-us-2026-RHC8JN@pretalx.devconf.info
DTSTART;TZID=EST:20260924T152000
DTEND;TZID=EST:20260924T155500
DESCRIPTION:Agentic AI systems are transforming how developers approach the
  Software Development Lifecycle\, enabling a shift from "coder of every li
 ne" to "architect of outcomes." These autonomous agents understand context
 \, reason about complex problems\, and execute multi-step workflows across
  the entire SDLC. We will demonstrate how agentic AI tools accelerate open
  source development while maintaining code quality and security.\n\n### Th
 e session will include:\n- Overview of agentic AI capabilities: multi-file
  refactoring\, legacy modernization\, security-first development\, and kno
 wledge transfer.\n- Architectural patterns for AI integration: Model Conte
 xt Protocol (MCP) for connecting to external systems and Agent Skills for 
 domain-specific guidance.\n- Demonstrations of context management\, spec-d
 riven development\, MCP-based integrations\, and agent skill implementatio
 n.\n- Best practices for balancing automation with human oversight in open
  source projects.\n\nAttendees will leave with practical understanding of 
 how to leverage agentic AI to accelerate development\, while simultaneousl
 y maintaining control using intent-based specifications in their open sour
 ce projects.\n\n### Target Audience\nOpen source maintainers\, software ar
 chitects\, DevOps engineers\, and developers interested in AI-assisted dev
 elopment
DTSTAMP:20260727T165139Z
LOCATION:Ladd Room (Capacity 170)
SUMMARY:Agentic AI in Open Source Development: From Autopilot to Co-Pilot -
  James Busche\, Rafael Vasquez
URL:https://pretalx.devconf.info/devconf-us-2026/talk/RHC8JN/
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UID:pretalx-devconf-us-2026-ZZBY9J@pretalx.devconf.info
DTSTART;TZID=EST:20260925T131000
DTEND;TZID=EST:20260925T134500
DESCRIPTION:In this talk\, we will introduce vLLM and explore why monitorin
 g matters for LLM serving. We'll start from why serving LLMs is hard in th
 e first place and build up to what that complexity looks like as real\, ob
 servable metrics.\n\nvLLM is a high-performance library designed for LLM i
 nference and serving. Its core optimizations\, including PagedAttention\, 
 continuous batching\, and memory management\, directly shape the behavior 
 you'll see in production. Understanding what those techniques are doing un
 der the hood makes it easier to understand what your metrics are actually 
 telling you.\n\nThe session will include a demo showing how to instrument 
 vLLM with observability tooling and interpret the signals that matter: KV 
 cache usage\, request queue depth\, and end-to-end latency. No GPU or prod
 uction cluster required. Just a small model running locally and some pract
 ical monitoring tools.\n\nBy the end of this session\, you'll have a clear
 er mental model of LLM serving\, a working understanding of vLLM's built-i
 n observability\, and the monitoring foundation to understand what's happe
 ning inside your server before something goes wrong.
DTSTAMP:20260727T165139Z
LOCATION:Ladd Room (Capacity 170)
SUMMARY:Portrait of a Server on Fire - Rafael Vasquez
URL:https://pretalx.devconf.info/devconf-us-2026/talk/ZZBY9J/
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