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Clojure Discussions (4)
| Discussions | Replies | Latest Activity |
|---|---|---|
Howard Lewis Ship on Clojure at GatorJUGHoward gave a talk about Clojure last night at the GatorJUG. Wow! We all learned so much. Howard used IntelliJ Idea to code on the fly from… Started by Michael Levin |
0 | Feb 13, 2014 |
EasyBThis just in from Luis Espinal of MJUG: http://www.easyb.org/ The EasyB syntax for writing stories and specifications is a lot more succ… Started by Michael Levin |
0 | Jul 27, 2011 |
Why Clojure?My bud Matt Raible blogged about reading a Scala book and I mentioned Stuart Holloway's "Programming Clojure". Matt replied "I like Scala… Started by Michael Levin |
0 | Jan 16, 2010 |
A First Web Project with ClojureLast nights GatorJUG prestation on Clojure with Eric Lavigne introduced us to Clojure's language elements. Say your customer, a timeshare c… Started by Michael Levin |
3 |
Jan 16, 2010 Reply by Eric Lavigne |
Happy 10th year, JCertif!
Notes
Welcome to Codetown!
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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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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