InfoQ Homepage Generative AI Content on InfoQ
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Celebrating 20 Years of InfoQ
InfoQ celebrates its 20th anniversary. To mark the occasion, we have published a walk-through of the trends InfoQ called early, where they sit on the adoption curve today, and how that curve may evolve over the next decade.
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Sarang Kulkarni on Lessons from Building Deep Research Agents in Production
Deep Research Agentic Systems are AI Agents designed to conduct multi-step research for complex tasks using dynamic reasoning, multi-hop information retrieval, and generate structured analytical reports. Sarang Kulkarni from Thoughtworks spoke at Arc of AI Conference 2026 on how to deploy multi-agent research systems for deep reasoning, and the lessons learned from developing Deep Research Agents.
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Uber Improves Restaurant Recommendations Using Real-Time Signals and Listwise Ranking
Uber updates its Uber Eats Home Feed recommendation system using near real-time user sequence features and a Generative Recommender model. The system evolves from hand-crafted features to transformer-based sequence modeling, reduces feature freshness from 24 hours to seconds, and shifts from pointwise scoring to listwise GenRec for improved contextual ranking and real-time personalization.
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Anthropic Traces Six Weeks of Claude Code Quality Complaints to Three Overlapping Product Changes
Anthropic published a postmortem tracing six weeks of Claude Code quality complaints to three overlapping product-layer changes: a reasoning effort downgrade, a caching bug that progressively erased the model's own thinking, and a system prompt verbosity limit that caused a 3% quality drop. The API and model weights were unaffected. All issues were resolved April 20.
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Cloudflare Announces Agent Memory, a Managed Persistent Memory Service for AI Agents
Cloudflare announced Agent Memory in private beta, a managed service that extracts structured memories from AI agent conversations and retrieves them on demand using five-channel parallel retrieval with Reciprocal Rank Fusion. Shared memory profiles let teams of agents access common knowledge. Competitors include Mem0, Zep, LangMem, and Letta.
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DBmaestro MCP Server Puts Natural Language in Control of Database Pipelines
DBmaestro has launched an MCP server that connects AI agents and enterprise copilots to its database DevOps platform, allowing teams to issue natural language commands that trigger real, governed platform workflows. The MCP server, announced on 7 April 2026, allows DBAs to expose DBmaestro's release automation, source control, CI/CD orchestration, and compliance capabilities through MCP.
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AWS Announces General Availability of DevOps Agent for Automated Incident Investigation
AWS has announced the general availability of DevOps Agent, a generative AI–powered assistant designed to help developers and operators troubleshoot issues, analyze deployments, and automate operational tasks across AWS environments.
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Google Opens Gemma 4 Under Apache 2.0 with Multimodal and Agentic Capabilities
Google has announced the release of Gemma 4, a series of open-weight AI models, including variants with 2B, 4B, 26B, and 31B parameters, under the Apache 2.0 license. Key features include enhanced video and image processing, audio input on smaller models, and extended context windows up to 256K tokens.
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Zendesk Says AI Makes Code Abundant, Shifting the Bottleneck to “Absorption Capacity”
Zendesk argues that GenAI shifts the bottleneck in software delivery from writing code to “absorption capacity”, which is the organisation’s ability to define problems clearly, integrate changes into the wider system, and turn implementation into reliable value. As code becomes abundant, architectural coherence, review capacity, and delivery flow become the main constraints.
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Teleport Report Finds Over-Privileged AI Systems Linked to Fourfold Rise in Security Incidents
Enterprises that grant excessive access permissions to AI systems experience 4.5 times as many security incidents as those that do not, according to The 2026 State of AI in Enterprise Infrastructure Security, a report published by infrastructure identity company Teleport. The study found that identity management hasn't kept up with AI adoption in production systems.
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QCon London 2026: AI Agents Write Your Code. What’s Left for Humans?
Hannah Foxwell began her QCon London 2026 talk by noting that the long-sought velocity in development has arrived, but the industry is unsure how to use it. She set aside the technical details of agentic coding, focusing instead on its implications for the people working with these systems.
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Vercel Releases JSON-Render: a Generative UI Framework for AI-Driven Interface Composition
Vercel has open-sourced json-render, a framework that enables AI models to create structured user interfaces from natural language prompts. Released under the Apache 2.0 license, it supports multiple frontend frameworks and features a catalog of components defined by developers. Community feedback includes both support and skepticism, highlighting its differences from existing standards.
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QCon London 2026: Refreshing Stale Code Intelligence
At QCon London 2026, Jeff Smith discussed the growing mismatch between AI coding models and real-world software development. While AI tools are enabling developers to generate code faster than ever, Smith argued that the models themselves are increasingly “stale” because they lack the repository-specific knowledge required to produce production-ready contributions.
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Where Do Humans Fit in AI-Assisted Software Development?
An article on Martin Fowler’s blog by Kief Morris examines the role of humans in AI-assisted software engineering, arguing developers are unlikely to move fully “out of the loop.” Instead, teams may work “on the loop,” designing tests, specifications, and feedback mechanisms to guide AI agents, as industry discussions focus on how such systems should be verified and governed.
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QCon London 2026: Reliable Retrieval for Production AI Systems
At QCon London 2026, Lan Chu, AI tech lead at Rabobank, shared lessons from deploying a production AI search system used internally by more than 300 users across 10,000 documents. Her experience shows that most failures in RAG systems stem from indexing and retrieval, rather than the language model itself.