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Carol McDonald
- Female
- Jacksonville, Florida
- United States
- Blog Posts (3)
- Discussions (4)
- Events
- Groups (9)
- Photos
- Photo Albums
- Videos
Carol McDonald's Groups
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Community Corral
23 members
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iPhone Development
40 members
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Database SIG
13 members
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JEE Town
28 members
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Spring Town
47 members
Carol McDonald's Discussions
Some Java Concurrency Tips
Started Sep 18, 2009
Java Enterprise Tech Tips: Locking and Concurrency in Java Persistence 2.0
Started Sep 14, 2009
Comparing Tomcat and Glassfish
Started Mar 6, 2009
Gifts Received
Carol McDonald has not received any gifts yet
Carol McDonald's Page
Profile Information
- 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 1 Like
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 0 Likes
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,…
Continuescreencast about MySQL for Developers
Posted on March 30, 2009 at 10:30am 0 Comments 0 Likes
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
Happy 10th year, JCertif!
Notes
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.
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
JobRunr Introduces ClawRunr, an Open-Source Java AI Agent
JobRunr has introduced ClawRunr, an open-source Java AI agent for scheduled, recurring, and one-off background tasks. Formerly JavaClaw, it runs on users' hardware and combines conversational interaction with persistent task execution, MCP tools, browser automation, and web, Telegram, and Discord channels, while using JobRunr for scheduling, retries, and monitoring.
By Diogo CarletoConfluent Moves Schema IDs to Kafka Headers to Simplify Schema Governance
Confluent introduces a new approach in Apache Kafka that moves schema IDs from message payloads to record headers, aiming to simplify schema governance and evolution. The update integrates with Schema Registry, improves compatibility across serialization formats, and reduces coupling between data and metadata in event-driven architectures.
By Leela KumiliMeta Deploys Unified AI Agents to Automate Performance Optimization at Hyperscale
Meta has unveiled a new AI-driven capacity efficiency platform that uses unified AI agents to automatically detect and resolve performance issues across its global infrastructure, marking a significant step toward self-optimizing systems at hyperscale.
By Craig RisiPresentation: The Next Generation of AI Products
Hilary Mason shares her journey from academia to building AI products at scale. She discusses the shift from discrete engineering to probabilistic mindsets, explaining why managing "human considerations" is the hardest part of the stack. She explains the "existential crisis" for engineers, arguing that great architecture today is about context management, systems thinking, and good taste.
By Hilary MasonVitest 4.1: Test Tags, Native Node.js Execution and AI Agent Reporter
Vitest 4.1, developed by VoidZero, enhances JavaScript testing with features like test tags for filtering and configuring tests, an experimental mode to bypass Vite's module runner, and new lifecycle hooks. It supports Vite 8 from the start. Notably, it reports improvements in performance compared to Jest. The release addresses issues and provides guides for migration.
By Daniel Curtis
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