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Mathematical Animation Engine

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I recently discovered an interesting Python library called Manim . It’s a powerful tool that allows us to create beautiful animations of mathematical concepts and geometric shapes such as polygons, graphs, and functions. What makes Manim even more exciting is that it can be used directly in Jupyter Notebook. This means I can combine code, text, and animation all in one place — perfect for explaining mathematical ideas in a more dynamic and visual way. To try it out, I created a simple example using Manim to illustrate the Fourier Transform , one of my favourite topics from university. With just a few lines of code, Manim can generate smooth, professional-looking animations that show how complex waveforms are built from simple sine components. I’m excited to explore more possibilities with Manim and use it to present other mathematical concepts in a creative and engaging way. 1. Installation !sudo apt update !sudo apt install libcairo2-dev \    texlive texlive-la...
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[Event] JManc 2025

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I had the opportunity to attend JManc again this year—and once more, it was fabulous. The sessions were organic, deeply technical, and full of insights shared by developers across different domains. Here's my wrap-up from the day, highlighting a few memorable sessions and conversations. 1 Tony Wilson, Manchester 30 Years of Java — Jim Gough’s Retrospective Jim Gough delivered a fantastic keynote on the evolution of Java over three decades. As a Java Champion, his perspective mixed historical insight with a touch of humor and lived experience. Here are some of the milestones he covered: Applets & Early UI: Java’s original fame came from applets—tiny applications that ran inside web browsers.  AWT & Swing: Early GUI development in Java involved the AWT toolkit, later replaced by the more versatile Swing. JavaFX: The modern Java UI framework that succeeded Swing. It was eventually removed from the JDK and spun out as an open-source project. While less commonly used in modern ...
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[Event] Accelerate AI with Cloud Run

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I was excited to attend the event Accelerate AI with Cloud Run , organised by Google Developer Group (GDG) North West. Unlike the typical seminar-style tech meetups, this event was so different. It wasn't just about listening, it was about building. The combination of an expert talk and hands-on labs felt perfect for a developer audience. The workshop started with a session delivered by Richard Bannister from Google. His talk focused on building AI agents on Google Cloud using the Agent Developer Kit (ADK), which is a framework designed to help developers create, deploy, and scale AI-driven agents efficiently. Key takeaways from the session: A structured architecture for agent development includes: Agent Build:  Capabilities to design, build and test individual AI agents and multi-agent systems Agent Ops: Capabilities to deploy, manage, evolve and optimise AI agents Data Platforms for Agents: Capabilities to acquire, prepare, store and access data for AI agents Security for Agent...
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