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Topics

Featured in Development

Go Channels: Understanding Happens-Before for Safe Concurrency

This article dives into the happens-before semantics of Go channels, explaining how they relate to memory visibility, synchronization, and concurrency correctness. We'll examine subtle pitfalls, illustrate them with examples, and explore the architectural implications for system designers.

Go Channels: Understanding Happens-Before for Safe Concurrency

All in development

Architecture & Design

Featured in Architecture & Design

If Architectures Could Talk, They’d Quote Your Boss

Software architecture reflects how organizations communicate and make decisions. Failures stem from misaligned incentives, unclear ownership, and structural gaps—not technical flaws. Architects must design not just systems, but the conditions for systems to thrive, using platform thinking to reduce friction and foster autonomy.

If Architectures Could Talk, They’d Quote Your Boss

All in architecture-design

AI Infrastructure

Featured in AI, ML & Data Engineering

Deploy MultiModal RAG Systems with vLLM

Stephen Batifol discusses building and optimizing self-hosted, multimodal RAG systems. He breaks down vector search, nearest neighbor indexes (FLAT, IVF, HNSW), and the critical role of choosing the right embedding model. He then explains vLLM inference optimization (paged attention, quantization) and uses Mistral's Pixtral to detail multimodal large language model architecture.

Deploy MultiModal RAG Systems with vLLM

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

Systems Thinking for Scaling Responsible Multi-Agent Architectures

Nimisha Asthagiri explains the critical need for responsible AI in complex multi-agent systems. She shares practical techniques for engineering leaders and architects, applying systems thinking and Causal Flow Diagrams. She shows how these methods help predict and mitigate the unintended consequences and structural risks inherent in autonomous, learning agents, using a scheduler agent example.

Systems Thinking for Scaling Responsible Multi-Agent Architectures

All in culture-methods

Featured in DevOps

From Grassroots to Enterprise: Vanguard's Journey in SRE Transformation

Christina Yakomin shares Vanguard's SRE transformation: from quarterly testing of monoliths to a mature DevOps model with continuous delivery. She explains the SRE coaching hub, self-service tools, and advanced techniques like request-rate autoscaling. She details modern challenges, including region failure game days and testing AI-backed contact centers.

From Grassroots to Enterprise: Vanguard's Journey in SRE Transformation

Events

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QCon San Francisco 2025

Get proven patterns to de-risk modern architectures. See how engineers scale cloud-native systems, improve observability, and evolve reliable platforms at pace.

Early Bird ends Oct 14.

QCon AI New York 2025

Move beyond AI demos to real engineering impact. Discover how teams embed LLMs, govern models, and scale inference pipelines to accelerate development securely.

Early Bird ends Oct 14.

QCon London 2025

Benchmark your systems against leading engineering teams. See what really works in FinOps, modern Java, and distributed data architectures to balance cost, scale, and reliability.

Early Bird ends Oct 14.

InfoQ Homepage Presentations Sneek Peek at Spring Data Cassandra

AI, ML & Data Engineering

Sneek Peek at Spring Data Cassandra

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25:21

Summary

Mark Paluch and John Blum present the changes in Spring Data Cassandra and what to expect from the upcoming version.

Bio

Mark Paluch is a Software Craftsman, Spring Data Engineer at Pivotal, a member of the CDI 2.0 expert group, and Lead of the lettuce Redis driver. John Blum is a member of Spring Data Team at Pivotal.

About the conference

SpringOne Platform brings together the people, process and tools for delivering and operating software services. Learn and share with the startups and enterprises leveraging modern Java with Spring connecting all the pieces of the modern software puzzle from developer, operator, architect, data scientist to executive.

Recorded at:

Dec 24, 2016

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This content is in the AI, ML & Data Engineering topic

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