Bitu Technology is the registered company of Tubi's China team. We are engineers passionate about leveraging advanced technology to improve lives, and we hope to use this channel to connect and advance together.
The article describes Tubi's three‑month experiment to replace traditional waterfall and rigid sprint models with a flexible Release Train system, outlining the motivations, principles, team roles, tooling choices, successes, and areas needing improvement for a fast‑growing startup.
This article explains how Android's fragment system introduced in API Level 11 can be enhanced using the open‑source FragmentOperator library to tag, group, skip, and enforce single‑instance fragments, providing Kotlin code examples and practical scenarios for mobile app navigation.
This article explains how to propagate request‑level context in a Node.js HTTP service by building a call‑tree with async_hooks, avoiding monkey‑patching, handling lifecycle quirks, preventing memory leaks, and using reference‑counted cleanup to ensure reliable context queries.
At Tubi, the Hubble release‑management platform was created to tackle the complexities of synchronizing and deploying code across more than a hundred devices, automating changelog generation, Slack notifications, test‑environment tracking, and CI performance reporting, thereby reducing manual overhead and accelerating multi‑platform iteration.
This article explains why Tubi adopted Node.js’s async_hooks API, describes the challenges of context propagation in asynchronous calls, demonstrates a monkey‑patching solution for HTTP servers, and shares production‑grade practices for tracing, retries, and memory management in their micro‑framework.
This article describes Tubi's experiment with a Long Short‑Term Memory (LSTM) based recommender, covering the model architecture, training setup, performance metrics, and observed recommendation improvements on their video streaming platform.
This article provides a comprehensive overview of deep learning, covering its definition, fundamental machine‑learning components, illustrative examples such as hot‑dog classification and house‑price prediction, the mathematics of cost functions and gradient descent, back‑propagation via the chain rule, and practical resources and code snippets using Torch.
