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AI AT WORK WATCH TECHNICAL

AIE CODE adds Nov 10 talks on agents and software factories in San Francisco

According to a post on X by @aiDotEngineer, the Nov 10 day of AIE CODE in San Francisco has added talks to its "Agents and Software Factories" lineup. The new speakers include Composio's Karan Vaidya on agents beyond coding, Fivetran's Fraser George on agent context, Netflix's Utkarsh Shrivastava on a shared AI assistant beyond MCP, and Goodfire's Dan Balsam on detecting reward hacking inside models. The post also states that Nov 10 requires the workshop add-on.

Source: Scobleizer News Pulse (X) · x.com Found via Robert Scoble’s Scobleizer News Pulse · a post on X, not independently verified Published 2026-11-04T14:40:00+00:00 Detected 2026-11-04T22:19:47+00:00
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According to a post on X by @aiDotEngineer, the Nov 10 day of AIE CODE in San Francisco has added talks to its "Agents and Software Factories" lineup. The new speakers include Composio's Karan Vaidya on agents beyond coding, Fivetran's Fraser George on agent context, Netflix's Utkarsh Shrivastava on a shared AI assistant beyond MCP, and Goodfire's Dan Balsam on detecting reward hacking inside models. The post also states that Nov 10 requires the workshop add-on.

AI-assisted summary based on the listed source.

The Nov 10 day in San Francisco adds talks from Composio's Karan Vaidya (agents beyond coding), Fivetran's Fraser George (agent context), Netflix's Utkarsh Shrivastava (a shared AI assistant beyond MCP) and Goodfire's Dan Balsam (detecting reward hacking inside models). Nov 10 requires the workshop add-on. Why it...

The post describes the talks as a look at how large companies are running coding agents, which may be useful for engineering teams building similar workflows. Anyone who wants the Nov 10 sessions will need to add the workshop add-on, so it is a planning and cost step to factor in. Watch for the full session descriptions to judge how practical the talks will be.

Teams using AI at work may want to compare this against current productivity and review workflows.

Signal Strength 81% Technical label WATCH Public Interest 33 Category AI AT WORK Reader Depth TECHNICAL

Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.

Public Interest components
Recognizable Entity Score 0 Practical Impact Score 46 Novelty Interest Score 70 Consequence Score 22 Curiosity Score 16 Shareability Score 49

VQV surfaced this signal because it is recent, relevant to AI Coding Tools, connected to Scobleizer News Pulse (X).