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

Sixteen Claude Agents Built a C Compiler Without Human Intervention... Almost

In an effort to probe the limits of autonomous software development Anthropic used sixteen Claude Opus 4.6 AI agents to build a Rust-based C compiler from scratch. Working in parallel on a shared repository, the agents coordinated their changes and ultimately produced a compiler capable of building the Linux 6.9 kernel across x86, ARM, and RISC-V, as well as many other open-source projects.

By Sergio De Simone

From Paging to Postmortem: Google Cloud SREs on Using Gemini CLI for Outage Response

A recent article by Google Cloud SREs describes how they use the AI-powered Gemini CLI internally to resolve real-world outages. This approach improves reliability in critical infrastructure operations and reduces incident response time by integrating intelligent reasoning directly into the terminal-based operational tools.

By Renato Losio

Firestore Adds Pipeline Operations with Over 100 New Query Features

Google has overhauled Firestore’s query engine, introducing "Pipeline operations" that enable complex server-side aggregations and array unnesting. The update shifts Firestore Enterprise toward an optional indexing model, allowing architects to prioritize write speed and lower costs. While it brings parity with MongoDB-style aggregations, the preview currently lacks real-time and emulator support.

By Steef-Jan Wiggers

Nuxt Studio Released: Open Source CMS for Content Editing in Production

Introducing Nuxt Studio: the ultimate open-source content management solution for Nuxt websites, offering a powerful self-hosted module for complete control over your content. With an intuitive visual editor, real-time previews, and seamless Git integration, elevate your development workflow while bridging the gap for content creators. Unleash your team's potential today!

By Daniel Curtis

Presentation: Building Embedding Models for Large-Scale Real-World Applications

Sahil Dua discusses the critical role of embedding models in powering search and RAG applications at scale. He explains the transformer-based architecture, contrastive learning techniques, and the process of distilling large language models into production-ready student models. He shares insights on optimizing query latency, handling document indexing, and evaluating retrieval quality.

By Sahil Dua

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