Codetown
Codetown ::: a software developer's community
JavaScript Village Discussions (3)
| Discussions | Replies | Latest Activity |
|---|---|---|
Uber Case Study - Using Node.jsI was looking around for case studies about companies using Node and ran across this one. Uber's architecture involves Node. This discussio… Started by Michael Levin |
0 | Jun 22, 2017 |
Know Node?If you're interested in Node.js, this is a great starting point. Ryan Dahl is the author. This is a video of his intro presentation. http:/… Started by Michael Levin |
0 | Feb 18, 2017 |
Welcome to JavaScript Village!You're going to love it here! Like minded people and a mind boggling assortment of architectures to choose from and learn to master. Someon… Started by Michael Levin |
0 | Feb 18, 2016 |
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: The Time is Now: Delight Your Developers with User-Centric Platforms & Practices
Ana Petkovska discusses creating platform teams, establishing the team API, engagement of early adopters, easing adoption and providing a high quality product.
By Ana PetkovskaDeepSeek Open-Sources DeepSeek-V3, a 671B Parameter Mixture of Experts LLM
DeepSeek open-sourced DeepSeek-V3, a Mixture-of-Experts (MoE) LLM containing 671B parameters. It was pre-trained on 14.8T tokens using 2.788M GPU hours and outperforms other open-source models on a range of LLM benchmarks, including MMLU, MMLU-Pro, and GPQA.
By Anthony AlfordArticle: A Framework for Building Micro Metrics for LLM System Evaluation
LLM accuracy is a challenging topic to address and is much more multi-dimensional than a simple accuracy score. Denys Linkov introduces a framework for creating micro metrics to evaluate LLM systems, focusing on goal-aligned metrics that improve performance and reliability. By adopting an iterative "crawl, walk, run" methodology, teams can incrementally develop observability.
By Denys LinkovGoogle Releases Experimental AI Reasoning Model
Google has introduced Gemini 2.0 Flash Thinking Experimental, an AI reasoning model available in its AI Studio platform.
By Daniel DominguezGoogle Vertex AI Provides RAG Engine for Large Language Model Grounding
Vertex AI RAG Engine is a managed orchestration service aimed to make it easier to connect large language models (LLMs) to external data sources to be more up-to-date, generate more relevant responses, and hallucinate less.
By Sergio De Simone
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