Codetown ::: a software developer's community
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Getting started with Arduino(photo by Nicholas Zambetti) I just went to the Maker Faire in the Bay Area with my friend Michael Hauser. The expo was filled with Ardui… Started by Michael Levin |
0 | Jun 14, 2009 |
Arduino sensorsI was talking with my friend Daniel at the McRorie Community Garden in Gainesville yesterday. He said that gardening sensors were all the… Started by Michael Levin |
0 | May 8, 2009 |
ProSenseMarko Stankovic is involved with an EC-funded project called ProSense. He describes it here, on his Codetown blog. Marko tells me that he a… Started by Michael Levin |
0 | Mar 28, 2009 |
What are you doing with SunSPOT?Please share your projects (or ideas) here. Started by Michael Levin |
0 | Mar 18, 2009 |
Roger Meike Discusses SunSPOTs at GoogleStarted by Michael Levin |
0 | Mar 18, 2009 |
What is a SunSPOT?"What is a SPOT? It’s a piece of hardware that has an array of sensors, an IO port, a radio for wireless communications, a set of LEDs, so… Started by Michael Levin |
0 | Mar 18, 2009 |
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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