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Magika 1.0: Smarter, Faster File Detection with Rust and AI
Google has just released version 1.0 of Magika, a substantial rewrite of its open-source file type detection system. The new version leverages AI to support a broader range of file types and is built in Rust for maximum speed and security. By Sergio De Simone
Learnings from Cultivating Machine Learning Engineers as a Team Manager
As an AI team manager, Vivek Gupta stays broadly informed to guide AI experts effectively and drive the team. Engineers need feedback on both technical and interpersonal skills, Gupta mentioned at Dev Summit Boston. He stresses learning time, asking for help, and cross-team collaboration. Mentorship, data handling, and human-in-the-loop validation are key to success for machine learning engineers. By Ben Linders
Article: InfoQ Java Trends Report 2025
This report summarizes how the InfoQ Java editorial team and several Java Champions currently see the adoption of technology and emerging trends within the Java and JVM space in 2025. We focus on Java the language, as well as related languages like Kotlin and Scala, the Java Virtual Machine (JVM), and Java-based frameworks and utilities. By Michael Redlich, Erik Costlow, Karsten Silz, Trisha Gee, Marit van Dijk, Richard Fichtner, Bert Jan Schrijver
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
OpenAI's New GPT-5.1 Models are Faster and More Conversational
OpenAI recently released upgrades to their GPT-5 model. GPT‑5.1 Instant, the default chat model, has improvements to instruction following. GPT‑5.1 Thinking, the reasoning model, is faster and gives more understandable responses. GPT‑5.1-Codex-Max, the coding model, is trained to use compaction to perform long-running tasks. By Anthony Alford
Replit Introduces New AI Integrations for Multi-Model Development
Replit has introduced Replit AI Integrations, a feature that lets users select third-party models directly inside the IDE and automatically generate the code needed to run inference. By Daniel Dominguez
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qlib
Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
onnxruntime
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
cleanrl
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
inngest
The leading workflow orchestration platform. Run stateful step functions and AI workflows on serverless, servers, or the edge.
qdrant
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/