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Article: Architecture in a Flow of AI-Augmented Change

While AI adoption is surging, most organizations fail to scale past pilots. The solution lies in organizational structure, not just technology. This article details how architects can enable "fast flow" by defining clear domains and guardrails. Learn how to shift from controlling outcomes to curating context, allowing AI to drive continuous, valuable business change. By Jonathan McPhail, Juan Medina, Jake DeCrane, Isuru Wijesundara

Meta's Optimization Platform Ax 1.0 Streamlines LLM and System Optimization

Now stable, Ax is an open-source platform from Meta designed to help researchers and engineers apply machine learning to complex, resource-intensive experimentation. Over the past several years, Meta has used Ax to improve AI models, accelerate machine learning research, tune production infrastructure, and more. By Sergio De Simone

Article: Trustworthy Productivity: Securing AI Accelerated Development

Autonomous AI agents amplify productivity but can cause severe damage without safeguards. Defend the ReAct loop—context, reasoning, and tools—through provenance gates, planner-critic separation, scoped credentials, sandboxed code, and STRIDE/MAESTRO threat modeling. With robust logging, bounded autonomy, and red-teaming, agents can deliver trustworthy productivity while minimizing risk. By Sriram Madapusi Vasudevan

Google Unveils New Tools and Libraries for Building AI Glasses Apps

With the release of the Android XR SDK Developer Preview 3, Google has introduced two new libraries to help developers create AI Glasses experiences, Jetpack Projected and Jetpack Compose Glimmer. ARCore for Jetpack XR has also been expanded to work with AI Glasses, adding motion tracking and geospatial capabilities. By Sergio De Simone

Podcast: Building a More Appealing CLI for Agentic LLMs Based on Learnings from the Textual Framework

Will McGugan, the maker of Textual and Rich frameworks, speaks about the reasoning of developing the two two libraries and the lesson learned. Also, he shares light on Toad, his current project, which he envisions being a more visually appealing way of interacting with agentic LLMs through command line. By Will McGugan

Five AI Security Myths Debunked at InfoQ Dev Summit Munich

Katharine Jarmul challenged five common AI security and privacy myths in her InfoQ Dev Summit Munich 2025 keynote: that guardrails will protect us, better model performance improves security, risk taxonomies solve problems, one-time red teaming suffices, and the next model version will fix current issues. She said that current approaches to AI safety rely too heavily on technical solutions. By Karsten Silz

Presentation: Securing AI Assistants: Strategies and Practices for Protecting Data

Andra Lezza explains the criticality of data security for AI copilots, detailing the OWASP AI Exchange threat model and the OWASP Top 10 LLM risks. She reviews two copilot architectures - independent (single domain) and integrated (multi-tenant) - listing specific threats, controls, and best practices like granular authorization, templates, and DevSecOps to secure the entire AI data supply chain. By Andra Lezza

Podcast: Platform Engineering for AI: Scaling Agents and MCP at LinkedIn

QCon AI New York Chair Wes Reisz talks with LinkedIn’s Karthik Ramgopal and Prince Valluri about enabling AI agents at enterprise scale. They discuss how platform teams orchestrate secure, multi-agentic systems, the role of MCP, the use of foreground and background agents, improving developer experience, and reducing toil. By Karthik Ramgopal, Prince Valluri

Agentic Postgres: Postgres for Agentic Apps with Fast Forking and AI-Ready Features

Tiger Data, the company behind TimescaleDB, has launched Agentic Postgres, a Postgres-based database designed for both AI agents and developers. It extends Postgres with fast forking, an MCP server, native BM25 and vector search, and includes a CLI for terminal access. By Sergio De Simone

LG Will Let TV Owners Delete Microsoft Copilot After Customer Outcry

How Apollo 11’s onboard software handled overloads in real time lessons from Margaret Hamilton’s work

"Trucker wrongly detained through casino’s AI identification software now suing officer after settling suit with casino"

so many ai agent tools out there… these ones actually helped me as a beginner

Bipartisan group of senators, led by Graham, push to sunset Section 230

Gamers Are Extremely Mad About AI: In-game slop was bad enough. Now AI is driving up prices, too.

What is something AI still struggles with, in your experience?

Just another rant about AI Agents

WSJ let an Anthropic “agent” run a vending machine. Humans bullied it into bankruptcy

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