Codetown ::: a software developer's community
What's Craig Newmark, founder of craigslist, up to these days? Tune into Swampcast, a podcast from the Swamp, and find out.
Added by Michael Levin on December 25, 2011 at 10:39am — No Comments
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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.
In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Satish Kothapalli about the transformative impact of AI and vibe coding in life sciences software development, the acceleration of drug development timelines, and the evolving roles of developers in an AI-augmented environment.
By Satish Kothapalli
Unlock the power of AWS Lambda Managed Instances, seamlessly combining serverless functions with Amazon EC2 for optimal performance and cost efficiency. Designed for steady-state workloads, this solution automates instance management, reduces cold starts, and enables multi-concurrency.
By Steef-Jan Wiggers
Louis Ryan shares a compelling vision for modern cloud native hybrid networking. He critiques primitive network abstractions (the "Big IP" problem) and rigid security policies that rot and cause SPOFs. Discover how architects can elevate network functionality, bake in identity (mTLS/PKI), and leverage composability to achieve repeatable policy enforcement everywhere their applications run.
By Louis Ryan
Grab updated its internal platform to monitor Apache Kafka data quality in real time. The system uses FlinkSQL and an LLM to detect syntactic and semantic errors. It currently tracks 100+ topics, preventing invalid data from reaching downstream users. This proactive strategy aligns with industry trends to treat data streams as reliable products.
By Patrick Farry
Serving Large Language Models (LLMs) at scale is complex. Modern LLMs now exceed the memory and compute capacity of a single GPU or even a single multi-GPU node. As a result, inference workloads for 70B+, 120B+ parameter models, or pipelines with large context windows, require multi-node, distributed GPU deployments.
By Claudio Masolo
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