Codetown ::: a software developer's community
Ensemble: service orchestration for the cloud (my work for Ubuntu Server) http://fewbar.com/2011/06/so-what-is-ensemble-anyway - via Jim Baker
Jim and I spent some time…
ContinueAdded by Michael Levin on October 30, 2011 at 5:30pm — 2 Comments
We had a great time at the OrlandoJUG meeting tonight. It was an Open Spaces style meeting and a potluck dinner. We had some new faces and certainly covered interesting topics. Here are a few:
1. Cloud Computing
2. JavaOne 2011
3. Managing dev, test and production environments
4. OSGi - what it is and how…
ContinueAdded by Michael Levin on October 27, 2011 at 11:11pm — No Comments
Added by Bulama Yusuf on October 26, 2011 at 11:24am — No Comments
Added by Michael Levin on October 25, 2011 at 8:00pm — No Comments
Added by Michael Levin on October 25, 2011 at 10:30am — No Comments
Here's a website with open source code:
ching(6), the old amusement found in BSD 4.[234], has disappeared from the face of the net. I wanted it back. Fortunately finding the full text of the Wilhelm translation of the I Ching was easy. So was writing a program to read it.…
ContinueAdded by Michael Levin on October 9, 2011 at 2:00pm — 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.
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In an effort to probe the limits of autonomous software development Anthropic used sixteen Claude Opus 4.6 AI agents to build a Rust-based C compiler from scratch. Working in parallel on a shared repository, the agents coordinated their changes and ultimately produced a compiler capable of building the Linux 6.9 kernel across x86, ARM, and RISC-V, as well as many other open-source projects.
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Sahil Dua discusses the critical role of embedding models in powering search and RAG applications at scale. He explains the transformer-based architecture, contrastive learning techniques, and the process of distilling large language models into production-ready student models. He shares insights on optimizing query latency, handling document indexing, and evaluating retrieval quality.
By Sahil Dua
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