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Vlad Sanchez's Groups
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iPhone Development
40 members
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Python
15 members
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Other Languages on the J…
9 members
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Database SIG
13 members
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Vlad Sanchez's Page
Profile Information
- What are your main interests in software development?
- Python, Databases, ORM, XP, Agile, SOA, ETL
- Anything else you'd like to add? Where do you live? (optional!)
- =D
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
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By Thiago GhisiAirbnb Expands Global Checkout with “Pay as a Local,” Scaling to 220 Markets in 14 Months
Airbnb expands its global checkout with the “Pay as a Local” initiative, supporting over 20 locally preferred payment methods across 220 markets. The company replatformed its payments system with domain-oriented services, reusable flow archetypes, and a centralized configuration, enhancing integration speed, reliability, testing, and observability for diverse payment methods worldwide.
By Leela KumiliMyTerms: A New IEEE Standard Enabling Online Privacy and Aiming to Replace Cookies
Nicknamed MyTerms, the new 7012-2025 IEEE standard defines mechanisms for exchanging personal information between individuals and online service providers, and specifies how individuals can enforce their privacy requirements during transactions.
By Sergio De SimoneDaggr Introduced as an Open-Source Python Library for Inspectable AI Workflows
The Gradio team has released Daggr, a new open-source Python library designed to simplify the construction and debugging of multi-step AI workflows. Daggr allows developers to define workflows programmatically in Python while automatically generating a visual canvas that exposes intermediate states, inputs, and outputs for each step in the pipeline.
By Robert KrzaczyńskiArticle: Why Most Machine Learning Projects Fail to Reach Production
In this article, the author diagnoses common failures in ML initiatives, including weak problem framing and the persistent prototype-to-production gap. The piece provides practical, experience-based guidance on setting clear business goals, treating data as a product, and aligning cross-functional teams for reliable, production-ready ML delivery.
By Wenjie Zi
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I am a newbie at the club, been working as a J2EE developer in Orlando for three years and didn't know the club exists.