Codetown ::: a software developer's community
Hi! Is anyone interested in one of these books? If so please follow/retweet and tell me which book. Thanx!…
Added by Adam Davis on November 5, 2018 at 9:30pm — No Comments
What is Groovy and why should I care?
Hello again, it's me, Adam. Earlier this year, I finished my self-published book, Learning Groovy,…
ContinueAdded by Adam Davis on May 25, 2016 at 3:00pm — No Comments
Just a reminder: Oracle plans to stop updating Java 7 in April of this year (next month).
As outlined in the Oracle JDK Support Roadmap, after April 2015, Oracle will not post further updates of Java SE 7 to its public download sites.
This might be a good time to read …
ContinueAdded by Adam Davis on March 4, 2015 at 10:22pm — No Comments
Slightly modified from original post: http://adamldavis.com/
There’s a hot new programming language that I’m excited about. It can be used dynamically or statically-typed, your choice. It supports functional programming constructs, including first-class functions, currying, and more. It has multiple-inheritance, type inference, and meta-programming. It also integrates really well…
ContinueAdded by Adam Davis on February 28, 2015 at 3:00pm — 3 Comments
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.
Check out the Codetown Jobs group.

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
© 2026 Created by Michael Levin.
Powered by