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Sensor Village
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Sensors open up worlds of possibilities. SunSPOT is Java compatible sensor technology. Small Programmable Object Technology = SPOT. Working with SunSPOT or other sensors? Experimenting or thinking about it? Come on in! We want to hear all about it!
Members: 5
Latest Activity: Sep 6, 2011
SunSPOT Overview
Discussion Forum
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 Arduino!…Continue
Tags: maker, faire, gainesville, hackerspace, banzi
Started by Michael Levin Jun 14, 2009.
Arduino sensors
I was talking with my friend Daniel at the McRorie Community Garden in Gainesville yesterday. He said that gardening sensors were all the…Continue
Tags: instructables, arduino, sensors
Started by Michael Levin May 8, 2009.
What are you doing with SunSPOT?
Please share your projects (or ideas) here.
Tags: sunspot
Started by Michael Levin Mar 18, 2009.
Roger Meike Discusses SunSPOTs at Google
Started by Michael Levin 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, some switches, a rechargeable battery and a USB port for…Continue
Started by Michael Levin Mar 18, 2009.
SunSPOT Reading List
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Comment Wall
Comment
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Comment by Michael Levin on May 27, 2009 at 9:16am
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Vlad, I have been looking at Arduino and will go to the http://makerfaire.com/ in a few days! I'll let you know what I learn...
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Comment by Vladimir Vivien on May 27, 2009 at 7:46am
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Mike, great group. I have a SunSpot and it's a great toy (platform). Also, couple of other great Java-based embedded computing platforms 1) http://www.sentilla.com/developer.html Sentilla Perks and the http://BugLabs.com. Check them out.
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Happy 10th year, JCertif!
Notes
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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
Presentation: How to Unlock Insights and Enable Discovery Within Petabytes of Autonomous Driving Data
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 MozleyArticle Series - AI Assisted Development: Real World Patterns, Pitfalls, and Production Readiness
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 CasalsHow CyberArk Protects AI Agents with Instruction Detectors and History-Aware Validation
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 SimoneAnthropic announces Claude CoWork
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 HoblitzellTracking and Controlling Data Flows at Scale in GenAI: Meta’s Privacy-Aware Infrastructure
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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