Codetown
Codetown ::: a software developer's community
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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
University Researchers Publish Analysis of Chain-of-Thought Reasoning in LLMs
Researchers from Princeton University and Yale University published a case study of Chain-of-Thought (CoT) reasoning in LLMs which shows evidence of both memorization and true reasoning. They also found that CoT can work even when examples given in the prompt are incorrect.
By Anthony AlfordPresentation: Evolving Trainline Architecture for Scale, Reliability and Productivity
Milena Nikolic discusses how Trainline's systems architecture has evolved over the past 5 years to cater changes, as well as what's coming next.
By Milena NikolicArticle: Taking Advantage of Cell-Based Architectures to Build Resilient and Fault-Tolerant Systems
Cell-based architectures offer a robust approach to building resilient systems. They achieve this through the core principles of isolation, autonomy, and replication. Each cell manages its resources and makes decisions autonomously. Observability for cell-based architecture requires a tailored approach to address the unique challenges and opportunities presented by this distributed system design.
By Yury Niño RoaJava News Roundup: WildFly 34, Stream Gatherers, Oracle CPU, Quarkiverse Release Process
This week's Java roundup for October 14th, 2024 features news highlighting: the release of WildFly 34; JEP 485, Stream Gatherers, proposed to target for JDK 24; Oracle Critical Patch Update for October 2024; and a potential leak in the SmallRye and Quarkiverse release processes.
By Michael RedlichMicrosoft and Tsinghua University Present DIFF Transformer for LLMs
Researchers from Microsoft AI and Tsinghua University have introduced a new architecture called the Differential Transformer (DIFF Transformer), aimed at improving the performance of large language models. This model enhances attention mechanisms by refining how models handle context and minimizing distractions from irrelevant information.
By Daniel Dominguez
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