Codetown ::: a software developer's community
Time: July 1, 2009 from 6pm to 9pm
Event Type: meeting
Organized By: Michael Levin
Latest Activity: Jul 1, 2009
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.
Check out the Codetown Jobs group.
Meta has officially released the first models in its new Llama 4 family—Scout and Maverick—marking a step forward in its open-weight large language model ecosystem. Designed with a native multimodal architecture and a mixture-of-experts (MoE) framework, these models aim to support a broader range of applications, from image understanding to long-context reasoning.
By Robert KrzaczyńskiFabien Deshayes spoke on how Monzo has created and optimised their developer experience teams in a talk at QCon London 2025. Deshayes outlined some techniques for building an effective Developer Experience platform, focusing on three key aspects: assembling effective teams, building impactful products, and communicating value across the organisation.
By Matt SaundersDiff Authoring Time (DAT). DAT is a new metric developed by engineers at Meta to measure the duration required for developers to submit changes, known as "diffs," to the codebase. By tracking the time from the initiation of a code change to its submission, DAT offers insights into the efficiency of the development process and helps identify areas for improvement.
By Craig RisiAnil Rajput and Rema Hariharan discuss the crucial role of CPU architecture in optimizing Large Language Model (LLM), specifically Llama, performance. They explain hardware-software synchronization for TCO reduction and latency improvements. Learn about core utilization, cache impact, memory bandwidth considerations, and the benefits of chiplet architecture for LLM deployments on CPUs.
By Anil Rajput, Rema HariharanIn this article, authors discuss how multi-model retrieval augmented generation (RAG) techniques can enhance AI by integrating multiple modalities like text, images, and audio for deeper contextual understanding, with help of a practical example of a healthcare application.
By Suruchi Shah, Suraj Dharmapuram
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