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OpenAI details Codex Windows sandbox architecture, showing how SIDs, ACLs, restricted tokens, and dedicated sandbox accounts enable safe execution of autonomous coding tasks. The design balances isolation with real developer workflows and shows how OS security primitives must be composed for AI agents on local development environments.
LinkedIn’s Karthik Ramgopal and Prince Valluri discuss leveraging AI as a new execution model for large-scale engineering. They explain how to move beyond fragmented implementations by building platform abstractions for orchestration, structured context, and safe tooling like MCP. They share architectural insights from real-world coding, observation, and UI testing agents built at LinkedIn.
Netflix has shared details about Service Topology. This internal system creates and updates a live dependency graph for thousands of microservices. It helps engineers see how services connect and resolve issues more quickly. The system merges three separate data sources into a single, queryable graph. It updates almost in real-time as traffic patterns shift.
Dropbox has unveiled Nova, an internal platform designed to orchestrate and operationalize AI coding agents across the company's engineering workflows.
LiteRT-LM brings native support for Gemma 4 Multi-Token Prediction (MTP) drafters, enabling up to 2.2x faster inference. The framework is expanding beyond Kotlin and C++ adding support for new Swift and a JavaScript APIs.
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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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How OpenAI Built a Secure Windows Sandbox for Codex Agents
OpenAI details Codex Windows sandbox architecture, showing how SIDs, ACLs, restricted tokens, and dedicated sandbox accounts enable safe execution of autonomous coding tasks. The design balances isolation with real developer workflows and shows how OS security primitives must be composed for AI agents on local development environments.
By Leela KumiliPresentation: Platform Teams Enabling AI - MCP/Multi-Agentic Tools Across Linkedin
LinkedIn’s Karthik Ramgopal and Prince Valluri discuss leveraging AI as a new execution model for large-scale engineering. They explain how to move beyond fragmented implementations by building platform abstractions for orchestration, structured context, and safe tooling like MCP. They share architectural insights from real-world coding, observation, and UI testing agents built at LinkedIn.
By Karthik Ramgopal, Prince ValluriHow Netflix Maps Thousands of Microservices in Real-Time
Netflix has shared details about Service Topology. This internal system creates and updates a live dependency graph for thousands of microservices. It helps engineers see how services connect and resolve issues more quickly. The system merges three separate data sources into a single, queryable graph. It updates almost in real-time as traffic patterns shift.
By Claudio MasoloDropbox Introduces Nova, an Internal Platform for Running AI Coding Agents at Scale
Dropbox has unveiled Nova, an internal platform designed to orchestrate and operationalize AI coding agents across the company's engineering workflows.
By Craig RisiGoogle LiteRT-LM Speeds Up Local Inference Up to 2.2x With Gemma 4 Multi-Token Prediction
LiteRT-LM brings native support for Gemma 4 Multi-Token Prediction (MTP) drafters, enabling up to 2.2x faster inference. The framework is expanding beyond Kotlin and C++ adding support for new Swift and a JavaScript APIs.
By Sergio De Simone