Codetown ::: a software developer's community
There's theory, fiction, and fantasy. . .and then there's real life. Life's lessons—successes and failures alike—have much to teach us. Since there is only so much that one person can experience in a…Continue
Started by Michael Levin Jun 10, 2016.
Brad wrote: I noticed that in the Florida Mall, there are a lot of kiosks and the sellers there want to sell their stuff.…Continue
Started by Michael Levin. Last reply by Michael Levin Oct 31, 2011.
Coincidently, I was reading an article about "Startup Weekend" when I received the email from Mike about demo city. http://startupweekend.org/ I would…Continue
Tags: startup, jobs, ideas, web2.0, web
Started by Tom Duerr. Last reply by Mario Talavera Mar 6, 2011.
Does your startup need an elegant web presence? It's amazing that some very simple websites…Continue
Tags: entrepreneurship, startups, startup
Started by Michael Levin. Last reply by Michael Levin Mar 1, 2011.
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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.
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
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