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Happy 10th year, JCertif!

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Welcome to Codetown!

Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.

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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.

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InfoQ Reading List

Microsoft Ships OData .NET (ODL) 9.0.0 Preview 3: Safety, Modern APIs, and Spec Compliance

Microsoft released OData .NET (ODL) 9.0.0 Preview 3, the latest preview iteration of the OData .NET client and core libraries, continuing the modernisation effort of the library. This preview focuses on safer default behaviours, runtime API cleanup, and closer conformance with the OData specification as the team works toward a stable 9.x release.

By Edin Kapić

Uber Gets Ready for AI in Network Observability with Cloud Native Overhaul

Transportation company Uber has publishing a detailed account of its new observability platform on it's blog, highlighting that for them, network visibility is now a strategic capability rather than a set of discrete monitoring tools.

By Matt Saunders

Rspack Releases Version 1.7: Final 1.x Update Before 2.0 Transition

Rspack 1.7 has launched, enhancing performance and plugin compatibility as it prepares for a major version transition. Key features include improved SWC plugin compatibility, native asset importing as bytes, and default lazy compilation for dynamic modules. With performance gains reported up to 80%, Rspack offers a faster, Rust-based alternative to webpack while maintaining API compatibility.

By Daniel Curtis

Presentation: WASM Components are a FaaS' Best Friend

Laurent Doguin shares why Wasm’s cold-start performance and security model make it the ideal FaaS runtime. He discusses the WebAssembly Component Model for polyglot interoperability and explains how to build distributed, provider-based architectures using CNCF wasmCloud and NATS. Ideal for architects looking to scale "scale-to-zero" infrastructure without the overhead of heavy containers.

By Laurent Doguin

Article: Autonomous Big Data Optimization: Multi-Agent Reinforcement Learning to Achieve Self-Tuning Apache Spark

This article introduces a reinforcement learning (RL) approach grounded in Apache Spark that enables distributed computing systems to learn optimal configurations autonomously, much like an apprentice engineer who learns by doing. The author also implements a lightweight agent as a driver-side component that uses RL to choose configuration settings before a job runs.

By Hina Gandhi

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