Carol McDonald
  • Female
  • Jacksonville, Florida
  • United States
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What are your main interests in software development?
Java, Web, Ajax, Java EE, Spring, Seam, JPA, Web Services
Do you have a website?
http://weblogs.java.net/blog/caroljmcdonald/
Anything else you'd like to add? Where do you live? (optional!)
Je parle Francais. Ich kann Deutsch.

Carol McDonald's Blog

movie recommendations with Spark machine learning

Posted on August 4, 2015 at 11:15am 0 Comments

Tutorial on Apache Spark, movie recommendations with machine learning 

This post discusses building a recommendation model from movie ratings using an iterative algorithm and parallel processing with Apache Spark MLlib.

https://dzone.com/links/parallel-and-iterative-processing-for-machine-lear.html

An Inside Look at the Components of a Recommendation Engine

Posted on April 13, 2015 at 9:14am 1 Comment

Recommendation engines help narrow your choices to those that best meet your particular needs.  In this post, we’re going to take a closer look at how all the different components of a recommendation engine work together. We’re going to use collaborative filtering on movie ratings data to recommend movies. The key components are a collaborative filtering algorithm in Apache Mahout to build and train a machine learning model,…

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screencast about MySQL for Developers

Posted on March 30, 2009 at 10:30am 0 Comments

Here is a screencast about MySQL for Developers



If you are a developer using MySQL, you should learn enough to take advantage of its strengths, because having an understanding of the database can help you develop better-performing applications. This session will talk about MySQL database design and SQL tuning for developers. Some topics include:



* MySQL Storage Engine Architecture

* Schema, the basic foundation of performance

* Think about performance when… Continue

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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

Designing Memory for AI Agents: Inside Linkedin’s Cognitive Memory Agent

LinkedIn introduces Cognitive Memory Agent (CMA), generative AI infrastructure layer enabling stateful, context-aware systems. It provides persistent memory across episodic, semantic, and procedural layers, supporting multi-agent coordination, retrieval, and lifecycle management. CMA addresses LLM statelessness and enables production-grade personalization and long-term context in AI applications.

By Leela Kumili

Pretext.js Bypasses DOM Layout Reflow, Enabling Advanced UX Patterns at 120 FPS

Cheng Lou, a Midjourney engineer, recently released Pretext, a 15KB open-source TypeScript library that measures and lays out text without browser layout reflows, enabling advanced UX/UI patterns like infinite lists, masonry layouts, and scroll position anchoring to run at 60-120 fps. Pretext was built using an AI loop that reverse-engineered the DOM’s layout calculations.

By Bruno Couriol

Subagents in Gemini CLI Enable Task Delegation and Parallel Agent Workflows

Google has introduced subagents in Gemini CLI, a new capability designed to help developers delegate complex or repetitive tasks to specialized AI agents operating alongside a primary session.

By Robert Krzaczyński

Presentation: Event-Driven Patterns for Cloud-Native Banking - What Works, What Hurts?

Chris Tacey-Green discusses the shift from synchronous commands to asynchronous events within highly regulated environments. He explains the critical role of Inbox and Outbox patterns in preventing data loss, the nuances of event versioning, and how to maintain decoupling between domains. He shares "battle-tested" principles for implementing fault tolerance and managing eventual consistency.

By Chris Tacey-Green

Article: Building Production-Ready tRPC APIs: The TypeScript Alternative to Apollo Federation

This article details our migration from Apollo Federation to a TypeScript-based tRPC stack, which resulted in an 89% reduction in bugs and 67% faster response times. It also covers the mistakes we made, the unexpected performance gains, and an overview of the production architecture we use today to handle 2.4 million daily requests with 99.97% uptime.

By Dinesh Kumar Elumalai

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