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Kotlin Thursdays - Networking in Android and KotlinWe have an existing Android Avocado Facts application created in an older Kotlin Thursdays post, but for any Android application to be s… Started by Amanda Hinchman-Dominguez |
0 | Apr 11, 2019 |
Kotlin Thursdays - Introduction to Functional Programming Part 2Resources Higher-Order Functions and Lambdas:https://kotlinlang.org/docs/reference/lambdas.html FP in Kotlin Part 1: https://medium.com… Started by Amanda Hinchman-Dominguez |
0 | Apr 11, 2019 |
Kotlin Thursdays - Introduction to Functional Programming in Kotlin Part 1Resources Higher-Order Functions and Lambdas:https://kotlinlang.org/docs/reference/lambdas.html Introduction Welcome to Kotlin Thursda… Started by Amanda Hinchman-Dominguez |
0 | Apr 11, 2019 |
Kotlin Thursdays - Introduction to Kotlin Generics, Part 2Hi folks! Welcome to Kotlin Thursdays. Last week, we examined classes, types, generic functions & parameters as well as covariance &… Started by Amanda Hinchman-Dominguez |
0 | Apr 9, 2019 |
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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