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Ryan Orban

Ryan Orban

Subject
12 entries

Real Time

Bookmarks

  1. Bluerain

    Bluerain visualizes the Bluesky firehose as Matrix-style falling text rain — each character stream is a live post flowing through the AT Protocol. A beautiful way to sense the scale and flow of social network activity.

  2. WhisperFusion: real-time voice conversations with AI

    WhisperFusion is Collabora's pipeline for real-time voice conversations with AI — combining WhisperLive (real-time STT) with WhisperSpeech (TTS) and an LLM to create a fully local end-to-end spoken AI assistant.

  3. Adding Elixir to a Next.js Chat App

    Linen's engineering post on adding Elixir to their Next.js chat app for real-time features — why they chose Elixir/Phoenix over Node.js for WebSockets, the architecture trade-offs, and how they run both stacks in production. A candid account of a polyglot architecture decision.

  4. Querying Live Crypto Trades with SQL in Real-Time

    QuestDB tutorial showing how to query live cryptocurrency trade data in real-time with SQL — ingesting from Coinbase's WebSocket feed into QuestDB and running time-series queries. A practical demo of time-series SQL for financial market data.

  5. Real-Time Machine Learning: Challenges and Solutions

    Chip Huyen's definitive survey of real-time ML challenges — the engineering gap between batch ML pipelines and systems that must make predictions on live data with low latency. Covers online prediction, continual learning, and the infrastructure needed to bridge training and serving.

  6. Spark, Storm and Real-Time Analytics

    An early comparison of Apache Storm and Spark Streaming for real-time analytics — covering their different processing models, latency characteristics, and use cases. A snapshot of the stream processing options available before Kafka Streams, Flink, and other tools matured.

  7. Python and Real-time Web

    Technical overview of real-time web patterns in Python circa 2013 — comparing Tornado, Twisted, gevent, and the emerging WebSocket ecosystem. A snapshot of Python's async web story before asyncio standardized the concurrency model.

  8. 5 Reasons Why the Future of Hadoop Is Real-Time

    GigaOM's 2013 argument for why Hadoop was evolving toward real-time processing — covering YARN, Storm, and in-memory frameworks as the drivers. A snapshot of the moment when batch-only Hadoop started feeling inadequate.

  9. Tweetping — Real-Time Twitter Visualization

    Tweetping visualized the Twitter streaming API as a real-time pulsing global map — each tweet a dot appearing at its geolocation. A landmark example of combining streaming data APIs with real-time browser visualization.

  10. 5 Trends That Are Changing How We Do Big Data

    GigaOM's 2012 survey of five trends reshaping big data: real-time processing rising against batch, NoSQL maturity, cloud-based data platforms, open-source ecosystem growth, and the shift from data collection to data monetization. A useful snapshot of where the field was heading at Hadoop's peak.

  11. Socket.IO

    Socket.IO homepage bookmarked in 2012, when real-time web apps required elaborate workarounds. Socket.IO abstracted WebSockets with cross-browser fallbacks (long-polling, Comet), making bidirectional communication practical before WebSockets were universally supported.

  12. Firebase: Real-Time Backend as a Service

    Firebase (founded 2011, acquired by Google in 2014) was a real-time backend-as-a-service that synced data across clients via WebSockets — eliminating the need to write server-side code for data storage and live updates. It became the template for the 'serverless' and BaaS movement.

All bookmarks