Codetown ::: a software developer's community
We have a chance for a special Ceylon talk on either Mon 10/13, Fri 10/17, Mon 10/20, or Fri 10/24 in Orlando at OrlandoJUG and/or Gainesville at GatorJUG.
As you all know, OJUG meets 4th Th and GatorJUG meets 2nd Wed and these are Mondays and Fridays, so I need your…
ContinueAdded by Michael Levin on June 27, 2014 at 11:15am — 4 Comments
If you're a JUG member, the JavaOne event team wants you to know there's a special discount for you:
"We are offering our JUG’s a special discount for the month of June to
register for JavaOne 2014. The discount will provide an additional $200
savings off the current Early Bird price of $1,650. This…
Added by Michael Levin on June 5, 2014 at 9:02am — No Comments
These days, it's hard to stick with just one language. Sure, you may be a Java developer or a Rubyist and "not interested" in learning another language. We tend to get comfortable in our comfort zone. But, being a polyglot has its advantages.
Tim Crowley just presented at SunJUG and…
ContinueAdded by Michael Levin on June 4, 2014 at 11:00am — No Comments
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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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Kyra Mozley discusses the evolution of autonomous vehicle perception, moving beyond expensive manual labeling to an embedding-first architecture. She explains how to leverage foundation models like CLIP and SAM for auto-labeling, RAG-inspired search, and few-shot adapters. This talk provides engineering leaders a blueprint for building modular, scalable vision systems that thrive on edge cases.
By Kyra Mozley
In this series, we examine what happens after the proof of concept and how AI becomes part of the software delivery pipeline. As AI transitions from proof of concept to production, teams are discovering that the challenge extends beyond model performance to include architecture, process, and accountability. This transition is redefining what constitutes good software engineering.
By Arthur Casals
To prevent agents from obeying malicious instructions hidden in external data, all text entering an agent's context must be treated as untrusted, says Niv Rabin, principal software architect at AI-security firm CyberArk. His team developed an approach based on instruction detection and history-aware validation to protect against both malicious input data and context-history poisoning.
By Sergio De Simone
Introducing Claude Cowork: Anthropic's groundbreaking AI agent revolutionizing file management on macOS. With advanced automation capabilities, it enhances document processing, organizes files, and executes multi-step workflows. Users must be cautious of backup needs due to recent issues. Explore its potential for efficient office solutions while ensuring data integrity.
By Andrew Hoblitzell
Meta has revealed how it scales its Privacy-Aware Infrastructure (PAI) to support generative AI development while enforcing privacy across complex data flows. Using large-scale lineage tracking, PrivacyLib instrumentation, and runtime policy controls, the system enables consistent privacy enforcement for AI workloads like Meta AI glasses without introducing manual bottlenecks.
By Leela Kumili
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