Codetown
Codetown ::: a software developer's community
Discussion Forum (6)
| Categories | Discussions | Latest Activity |
|---|---|---|
TestingThis category is all about testing. TDD, JUnit, etc... |
12 | No activity yet |
AI, ML & Big DataArtificial Intelligence, Machine Learning, Big Data. Let’s chat! |
0 | No activity yet |
Dynamic LanguagesLove Jython. JRuby? It rocks. This category is all about dynamic languages. Especially dynamic languages that run on the JVM. |
8 |
Feb 15, 2016 Reply by Michael Levin |
JavaJava in general. |
36 |
Feb 8, 2019 Reply by sneha gulati |
Functional ProgrammingClojure, Lambda expressions, all the ins and outs of functional programming from languages to use cases. If you've got functional programming on your mind, this is the place to discuss it! |
2 | No activity yet |
TrendsThis category is about what's hot and what's not. |
2 | No activity yet |
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.
Looking for Jobs or Staff?
Check out the Codetown Jobs group.
InfoQ Reading List
C++26: Reflection, Memory Safety, Contracts, and a New Async Model
The C++26 standard draft is now complete, reports Herb Sutter, long-time C++ expert and former chair of the ISO C++ standards committee. The finalized draft introduces reflection, enhances memory safety without requiring code rewrites, adds contracts with preconditions and postconditions alongside a new assertion statement, and establishes a unified framework for concurrency and parallelism.
By Sergio De SimoneMeta Reports 4x Higher Bug Detection with Just-in-Time Testing
Meta introduces Just-in-Time (JiT) testing, a dynamic approach that generates tests during code review instead of relying on static test suites. The system improves bug detection by ~4x in AI-assisted development using LLMs, mutation testing, and intent-aware workflows like Dodgy Diff. It reflects a shift toward change-aware, AI-driven software testing in agentic development environments
By Leela KumiliCNCF Warns Kubernetes Alone Is Not Enough to Secure LLM Workloads
A new blog from the Cloud Native Computing Foundation highlights a critical gap in how organizations are deploying large language models (LLMs) on Kubernetes: while Kubernetes excels at orchestrating and isolating workloads, it does not inherently understand or control the behavior of AI systems, creating a fundamentally different and more complex threat model.
By Craig RisiAnthropic Introduces Agent-Based Code Review for Claude Code
Anthropic has introduced a new Code Review feature for Claude Code, adding an agent-based pull request review system that analyzes code changes using multiple AI reviewers.
By Daniel DominguezPresentation: Speed at Scale: Optimizing the Largest CX Platform Out There
Matheus Albuquerque shares strategies for optimizing a massive CX platform, moving from React 15 and Webpack 1 to modern standards. He discusses using AST-based codemods for large-scale migrations, implementing differential serving with module/nomodule, and leveraging Preact to shrink footprints. He explains how to balance cutting-edge performance with strict legacy browser constraints.
By Matheus Albuquerque
© 2026 Created by Michael Levin.
Powered by
