October 2025 Summaries

Fifteen years after its inception, Video.js, the popular open-source HTML5 video player, is undergoing a comprehensive rebuild for the modern web under the stewardship of Mux, aiming to address outdated architecture and enhance performance. The new Video.js v10 features a groundbreaking modular design that separates the UI from the media renderer, allowing developers to create customized, lightweight players to fit specific needs while reducing bundle sizes significantly. Inspired by modern JavaScript frameworks, it integrates seamlessly into various development stacks, such as React, Svelte, and Vue, by utilizing a custom compiler that adapts skins across JavaScript and CSS frameworks. The project remains community-driven, with contributions from industry leaders like NBC and Netflix, and continues its commitment to being free and open source. The updated player will offer a beautifully designed default skin and is currently in a phased release, starting with a technology preview and aiming for a full-feature parity release by the end of 2026.

The text discusses the development of a semantic video search tool designed to enhance the retrieval of specific video moments using a combination of Mux and Supabase. The project aims to address the challenge of recalling memorable video moments by allowing users to input queries and receive precise video clips as results, directly linked to their search terms. This initiative utilizes a tech stack comprising Mux for video infrastructure, which provides storage, auto-generated captions, and player functionality, alongside Supabase for application infrastructure and Next.js for the frontend. Videos are broken into manageable chunks, each embedded with OpenAI's model, to facilitate efficient retrieval through a Retrieval-Augmented-Generation approach. The project is particularly timely for the Demuxed conference, offering a rich archive of video content to test the tool. The text also explores potential improvements, such as enhancing query rewriting, increasing processing speed, and incorporating visual data into the search process, and invites feedback and collaboration from those who have developed similar tools.

HEVC, also known as H.265 or High Efficiency Video Coding, is a highly efficient video encoding format that reduces bitrate and file size without compromising visual quality, making it popular for smartphone recordings. Mux has now included HEVC as a standard input codec, eliminating the need for an initial transcoding step to H.264, which previously caused delays proportional to the video's length. This change allows HEVC videos to enter the processing pipeline immediately, significantly speeding up the availability of content for playback, which is particularly advantageous for mobile content intended for quick social sharing. Existing Mux users can benefit from this faster processing automatically when uploading HEVC videos that meet the standard input requirements, without requiring any code changes, and Mux provides documentation for optimizing processing times and offers support for new users interested in leveraging HEVC capabilities.

Taiv, an AI-first technology company, transforms bars and restaurants' TV screens into revenue-generating tools by replacing generic ads with relevant content, using a proprietary AI-driven TV box. Facing challenges with inconsistent video formats from multiple sources, Taiv partnered with Mux's Video API to standardize video delivery, allowing them to scale their device network by six times without added maintenance, thereby freeing their engineering team to focus on AI innovations. Mux's platform proved to be a developer-friendly, scalable solution that offered seamless integration and excellent support, helping Taiv maintain smooth video playback across thousands of locations. With Mux handling video infrastructure, Taiv can now explore dynamic video generation for personalized advertising based on real-time data, enhancing the customer experience and focusing on their core AI and algorithm development.

Integrating Mux, a video API, with Supabase, an open-source Postgres development platform, can streamline the management and processing of video data for applications. While some simple Mux integrations might not require a database, more complex applications benefit from using Supabase to handle additional functionalities like user permissions, video categorization, and viewership analytics. The integration process involves setting up a mux schema in Supabase, backfilling existing assets, and creating Edge Functions to handle webhooks for automated workflows, such as AI-driven content moderation using OpenAI's models. The integration allows for the creation of sophisticated workflows that can automatically generate content tags or perform moderation tasks based on video readiness, enhancing the capability to manage video assets efficiently. Supabase's Edge Functions, though limited by execution time based on the plan, enable developers to run local models, update databases, and interact with third-party APIs, making it a versatile tool for building advanced video applications.

Mux Data has introduced filter sets, a new feature designed to streamline data monitoring and analysis by allowing users to create, save, and share specific combinations of filters for consistent data views across various dashboards. Teams can use these named, reusable collections of filters to ensure everyone is focused on the correct metrics, thus reducing errors caused by misaligned data views. Filter sets can be public or private and are applicable across different dimensions such as geography, platform, and device type, as well as metric-based filters like rebuffering percentages and playing time. They also offer the ability to pre-stage filters for events that have yet to occur, ensuring operational readiness for major live events. The feature enhances productivity by eliminating repetitive filter configurations and maintaining consistent data analysis standards, making it an especially useful tool for organizations with complex data monitoring needs.

Reelay, an AI meeting note-taker designed around video from inception, relies heavily on Mux's video infrastructure for its operations, transforming recorded meetings into actionable insights using AI-generated transcripts and summaries. Mux's proactive approach to cost management, including a surprising 23% price reduction and the implementation of Instant Cold Storage, has helped Reelay manage scaling challenges associated with increased video processing and storage costs. This cost-effective storage solution automatically moves videos to cheaper cold storage until first accessed, allowing Reelay to control costs while maintaining service quality. Deric Frost, Reelay's Founder and CEO, expresses unwavering loyalty to Mux due to their collaborative problem-solving, zero downtime, and the strong vendor relationship that has evolved into a trusted partnership, recommending Mux to others for its reliable video processing and storage capabilities.

Smartzer, an interactive shopping platform founded in London in 2012, faced significant challenges in manual video encoding and captioning processes, which consumed considerable engineering resources and hindered growth. The adoption of Mux's automated video processing and live captioning features transformed Smartzer's operations, allowing them to scale effectively and focus on enhancing their core shopping technology. Mux's capabilities enabled Smartzer to handle large live shopping events with up to 175,000 viewers, achieving a 97% playback success rate and ensuring robust infrastructure to support high traffic. This technological advancement not only facilitated seamless video experiences but also opened new revenue streams, including a partnership with a major advertising company to develop a new shoppable video ad format. Smartzer's enhanced video infrastructure and Mux's reliable support have driven significant business impact, with high conversion rates and substantial revenue generation from both large and smaller events.

Mux has introduced a new feature called Billing Breakdown in its dashboard to provide users with clear insights into their video infrastructure costs without the hassle of analyzing individual invoices and spreadsheets. This feature offers a visual representation of spending trends over the last six billing cycles, highlighting committed charges, overages, and applied credits. Users can easily understand cost fluctuations, such as why a particular month's bill was higher or lower, by examining the detailed breakdown of expenses across major spending areas like video or data usage, committed amounts, and other charges. The tool also allows users to track monthly credits under their subscription plans, ensuring comprehensive billing transparency for all users, from free plans to contract holders. The Billing Breakdown aims to save users time and effort, enabling them to focus more on building their products rather than deciphering billing data.

Mastra, a TypeScript agent framework, simplifies the creation of AI-powered chatbots and agents by enabling meaningful interactions with systems through a user-friendly interface. This guide demonstrates how to use Mastra to connect with a local MCP server—specifically Mux's Local MCP server—to scan video catalogs for anomalies such as duplicate or broken files. The process involves creating a Mastra instance, updating the Mastra project to connect with the MCP server, and customizing prompts for specific functionalities. The guide further details the integration of Anthropic's Claude model for improved interaction and anomaly detection, replacing the default OpenAI models. Through the setup, users can dynamically update tools, ensuring the framework remains adaptable, and test their configurations by running the agent in a local environment. This approach reduces the time and effort needed to identify and address anomalies in video uploads while providing flexibility in modifying agent behavior to suit evolving needs. Additionally, Mux offers a remote MCP server for easier integration into existing AI tools, streamlining the process for users seeking efficient interaction with Mux’s APIs.