Oct 07, 2026

Stack Overflow - From Idea to Product | DevDay 2026

A VP of Product at Stack Overflow explains how a small team used OpenAI Codex to build two very different products: the enterprise knowledge intelligence layer Stack Internal and the experimental agent-first platform Stack Overflow for Agents. The discussion covers how to tune guardrails to product risk, how archetypes simplify multi-source enterprise ingestion, and how a community built for humans can be extended so agents both consume and contribute knowledge.

Oct 07, 2026

The Software Factory: From Bug Report to Production Code — Davis Palmie, Factory

Davis Palmie of Factory explains the software factory, a system of agents that turns bug reports and user feedback into production code. He covers leadership concerns about cost, model choice, engineering roles, and org structure, argues that coding was never the bottleneck, and lays out principles for a factory: model agnosticism, sovereign deployment, and SDLC-wide integration. He also describes eight pillars of agent readiness, starting with narrow use cases like incident triage, and closes with why outcome metrics should replace token counts.

Oct 07, 2026

Why AI Agents Should Have Their Own Sandbox — Philipp Schmid, Google DeepMind

Philipp Schmid of Google DeepMind introduces the Gemini Interactions API, a unified interface for models and agents with server-side state and long-running jobs. He then shows managed cloud sandboxes for Antigravity agents that persist, can be shared, and automatically pick up skills and AGENTS.md files, closing with a live AI talk radio demo built from a single prompt.

Oct 07, 2026

Why Reflection AI Is Making the Open Intelligence Bet - and What It Means for Deploying Models

Joe Spisak of Reflection discusses why open models and open intelligence matter, covering RL-driven customization, environments as the new dataset, the OpenEnv project, PyTorch and Llama lessons, inference scaling, and the case for ecosystem control and open safety.

Oct 07, 2026

From 36% to 100%: How Self-Improving Agents Write Their Own Skills — Rafal Wilinski, Runlayer

Rafal Wilinski of Runlayer argues that when one agent solves a hard problem, that knowledge should become a skill shared across the whole company. He covers why skills matter more as agents run longer, three problems with current skills, using MCP as a governed distribution layer, distilling skills from successful and failed agent traces inspired by the Voyager paper, and the organizational flywheel as a durable competitive advantage.

Oct 07, 2026

Grep or Embeddings? Agentic Search Over Company Documents — George He, LlamaIndex

George He, head of engineering at LlamaIndex, explores when agents need pre-indexed vector search and when plain file traversal with grep is enough. He contrasts Claude Code's embedding-free approach on code with the harder reality of large, messy, multimodal, permissioned company documents, and proposes a harness of hybrid retrieval, metadata filtering, grep, and reading tools backed by LlamaParse and LiteParse. The talk closes with a live demo of an agent building a cash-flow table from 135 Alphabet filings, grounded in source files.