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From our blog

Here, we are sharing our experience and best practice of using DataFibers as well as other big data technology.

Under the Hood: Deconstructing Robotics with ROS 2 Architectures

on August 16, 2026

Under the Hood: Deconstructing Robotics with ROS 2 Architectures Robotics is no longer a futuristic fantasy; it’s a rapidly evolving field pushing the boundaries of automation, AI, and embedded systems. From industrial manipulators in factories to autonomous delivery drones and sophisticated surgical assistants, robots are becoming indispensable. But what truly makes these complex machines tick? How do their diverse components – sensors, actuators, and decision-making algorithms – communicate and cooperate seamlessly?

Under the Hood: Deconstructing Godot's Engine Architecture

on August 12, 2026

The Godot Engine has rapidly grown in popularity, admired for its open-source nature, intuitive editor, and efficient workflow. While many appreciate its user-friendly interface, the true power and flexibility of Godot lie in its elegant, component-based architecture and robust extensibility mechanisms. This deep dive aims to pull back the curtain, exploring the core engine design, GDScript’s internals, and how Godot enables high-performance extensions. The Recursive Heartbeat: Godot’s Scene Tree and Nodes At the very foundation of every Godot project is the Scene Tree.

RAG Under the Hood: Deconstructing Advanced Retrieval Architectures for LLMs

on August 9, 2026

RAG Under the Hood: Deconstructing Advanced Retrieval Architectures for LLMs Retrieval-Augmented Generation (RAG) has rapidly become an indispensable pattern for grounding Large Language Models (LLMs) with external, up-to-date, and domain-specific knowledge. While the core concept of “retrieve-then-generate” seems straightforward, building a robust, high-performance RAG system that reliably delivers accurate and relevant answers requires a deep understanding of its intricate components and advanced architectural patterns. This isn’t just about plugging an LLM into a vector database; it’s about engineering a sophisticated information retrieval pipeline.

Gemini's Inner Workings: A Deep Dive into Tensor Processing and Model Parallelism

on August 2, 2026

The advent of large language models (LLMs) like Google’s Gemini has revolutionized what’s possible in AI. While much attention is paid to their impressive capabilities, the underlying infrastructure and architectural patterns that enable them are a testament to cutting-edge engineering. This post dives deep into the “under-the-hood” aspects of Gemini, focusing on the specialized hardware for tensor processing and the intricate strategies employed for model parallelism. The Tensor Processing Engine: Beyond the CPU At the heart of any LLM’s ability to perform complex calculations lies its proficiency in handling tensors.

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Know more about DataFibers?

From our blog

Under the Hood: Deconstructing Robotics with ROS 2 Architectures

Under the Hood: Deconstructing Godot's Engine Architecture

RAG Under the Hood: Deconstructing Advanced Retrieval Architectures for LLMs

Gemini's Inner Workings: A Deep Dive into Tensor Processing and Model Parallelism

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