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Topics

Development

Featured in Development

Accelerating Performance by Incrementally Integrating Rust into Existing Codebase

Lily Mara explains how to avoid high-risk software rewrites through incremental FFI refactoring. She shares how engineering teams can replace Python bottlenecks with Rust via PyO3, demonstrating how to achieve dramatic function-level speedups, seamless integration testing, and meaningful infrastructure cost savings without microservice overhead.

All in development

Architecture & Design

Featured in Architecture & Design

Five Ways To Use AI Coding Agents to Improve Your Software Architecture

Modern architectures often use legacy services for specific tasks, but these services may lack accurate documentation and using them can be risky if you don't understand them well. AI coding agents can help close that knowledge gap.

All in architecture-design

AI Infrastructure

Featured in AI, ML & Data Engineering

From Consumers to Builders: Turning 200 of our Team into Agent Creators in 2 Weeks

Ben Maraney shares how Forter demystified AI agent creation for technical and non-technical staff. He discusses leveraging custom MCP servers, combining no-code and code-based platforms, sidestepping complex RAG setups, and aligning security and legal teams to accelerate internal agent adoption across R&D.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

From Staff Platform Engineer to a 16z Founder: What I Wish I'd Known

Gonzalo Maldonado explains how technical leaders can translate engineering skills into startup success using his "VC Abstraction Layer Knowledge" (VALK). He shares hard-won lessons on finding product-market fit, structuring pitches like technical docs, avoiding co-founder drama, and evaluating internal platforms as commercial ventures in a post-ZIRP environment.

All in culture-methods

DevOps

Featured in DevOps

Securing AI Agents: Identity, Authorization, and the DPACT Framework

In this episode, Sahil Agarwal talks about the critical challenges of identity, authorisation, and security in the age of AI agents. Agarwal introduces the DPACT framework (Delegation, Policy, Auditability, Context, and Time) as a blueprint for building responsible, guardrailed agentic systems, moving away from simple token-based access toward bounded, delegated authority.

All in devops

Events

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Architect Certification

Distributed systems, decentralized decisions, platform engineering, and AI architecture.
Online. Register Now.

AI Security & Privacy Engineering Certification

Secure and govern production AI systems, from sensitive data to guardrails, evals, and audits.
Online. Register now.

AI-Assisted Engineering Certification

Your coding agent moves fast. Who checks whether it's making the codebase better?
Online. Register Now.

High-Performing Teams Certification

Compare team design, delivery flow, AI roles, and metrics with experienced practitioners.
Online. Register Now.

QCon San Francisco

What's working across AI, architecture, and leadership, from the teams doing it.
Register. Early bird ends Oct 13.

QCon AI New York

Production AI across agents, context, evals, security, and infrastructure, from the senior engineers building it.
Register. Early bird ends Oct 13.

QCon London

What early-adopter teams have proven in production, across 15 engineering tracks.
Register. Early bird ends Oct 13.

InfoQ Homepage Interviews John Lam on IronRuby, Microsoft and Open Source

John Lam on IronRuby, Microsoft and Open Source

29:14

Bio

John Lam is Program Manager on the Dynamic Language Runtime team at Microsoft. He created RubyCLR, and is now working on the team that is creating IronRuby, Microsoft's implementation of Ruby.

About the conference

Oct 30, 2008

Interview with

John Lam

by

Rob Bazinet

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