InfoQ Homepage Programming Content on InfoQ
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Kubernetes without YAML
David Flanagan discusses using programming languages to describe Kubernetes resources, sharing constructs to deploy Kubernetes resources, and making Kubernetes resources testable and policy-driven.
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Building a Successful Platform: Acceleration, Autonomy & Accountability
Smruti Patel discusses successful platform adoption. She explores topics including failed platform-building efforts, the three pillars of a successful platform, and more.
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Lessons Learned from Building LinkedIn’s AI Data Platform
Felix GV provides an overview of LinkedIn’s AI ecosystem, then discusses the data platform underneath it: an open source database called Venice.
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The AI Revolution Will Not Be Monopolized: How Open-Source Beats Economies of Scale, Even for LLMs
Ines Montani discusses why the AI space won’t be monopolized, covering the open-source model, common misconceptions about use cases for LLMs in industry, and principles of software development.
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Building a Rack-Scale Computer with P4 at the Core: Challenges, Solutions, and Practices in Engineering Systems on Programmable Network Processors
Ryan Goodfellow discusses lessons learned and open source tooling developed while delivering a product on top of the Tofino 2 switch processor.
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Retrieval-Augmented Generation (RAG) Patterns and Best Practices
Jay Alammar discusses the common schematics of RAG systems and tips on how to improve them.
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Large Language Models for Code: Exploring the Landscape, Opportunities, and Challenges
Loubna Ben Allal discusses Large Language Models (LLMs), exploring the current developments of these models, how they are trained, and how they can be leveraged with custom codebases.
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Optimizing JVM for the Cloud: Strategies for Success
Tobi Ajila discusses the challenges and innovations in JVM performance for cloud deployments, highlighting the integration of these JVM features with container technologies.
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Modern Compute Stack for Scaling Large AI/ML/LLM Workloads
Jules Damji discusses which infrastructure should be used for distributed fine-tuning and training, how to scale ML workloads, how to accommodate large models, and how CPUs and GPUs can be utilized.
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Building Guardrails for Enterprise AI Applications W/ LLMs
Shreya Rajpal introduces Guardrails AI, an open-source platform designed to mitigate risks and enhance the safety and efficiency of LLMs.
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Global Capacity Management through Strategic Demand Allocation
Ranjith Kumar discusses abstractions and guarantees, the design and implementation for managing workloads across 10s of regions, categorizing & modeling, and achieving global capacity management.
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Sleeping at Scale - Delivering 10k Timers per Second per Node with Rust, Tokio, Kafka, and Scylla
Lily Mara and Hunter Laine walk through the design of a system, its performance characteristics, and how they scaled it.
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