Codetown
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April 2015 Blog Posts (2)
JavaOne
Folks,
Perhaps this is old news for you, but JavaOne San Francisco registration is live. Various saving options are available leading up to the conference, and to take advantage of the current US$600 in savings registration needs to be completed by May 31st, 2015 (11:59pm PT).
I encourage you to read through the registration options by visiting the JavaOne registration site:…
Added by Michael Levin on April 24, 2015 at 11:00am — No Comments
An Inside Look at the Components of a Recommendation Engine
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,…
ContinueAdded by Carol McDonald on April 13, 2015 at 9:14am — 1 Comment
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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
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OpenAI described how it scaled PostgreSQL to support ChatGPT and its API platform, handling millions of queries per second for hundreds of millions of users. By running a single-primary PostgreSQL deployment on Azure with nearly 50 read replicas, optimizing query patterns, and offloading write-heavy workloads to sharded systems, OpenAI maintained low-latency reads while managing write pressure.
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By Steef-Jan WiggersArticle: You’ve Generated Your MVP Using AI. What Does That Mean for Your Software Architecture?
AI‑generated code creates implicit architectural decisions, forcing teams to rely on experimentation to validate quality attributes. To get useful results from AI, teams must clearly express trade‑offs and reasoning so the model can generate solutions aligned with desired QARs.
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By Thiago Ghisi
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