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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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In this article, author Abhishek Goswami shares a practitioner's playbook with development practices, that describes building agentic AI applications and scaling them in production. He also presents core architecture patterns for agentic application development.
By Abhishek Goswami
The pandas team has released pandas 3.0.0, a major update that changes core behaviors around string handling, memory semantics, and datetime resolution, while removing a substantial amount of deprecated functionality. The release introduces several changes to core behaviors in the library’s API.
By Robert Krzaczyński
A new CNCF report identifies Kubernetes as the primary engine for AI growth, with 82% production adoption. However, technical maturity has outpaced organisational change. Human factors, such as siloed team structures and a lack of cross-functional collaboration, now serve as the leading barriers to successful deployment, making cultural transformation the decisive factor for AI scaling.
By Mark Silvester
Now available in technical preview on GitHub, the GitHub Copilot SDK lets developers embed the same engine that powers GitHub Copilot CLI into their own apps, making it easier to build agentic workflows.
By Sergio De Simone
Celebrating its 20th anniversary, QCon’s 2026 conferences in London and San Francisco will focus on the engineering realities of agentic AI, resilient architectures, and platform ROI. The programs continue the series' two-decade tradition of practitioner-led content, curated by senior engineers from companies like Zoox, UBS, and LinkedIn.
By Artenisa Chatziou
© 2026 Created by Michael Levin.
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Hi Wei and welcome to Codetown. All the best, Mike