# Streams Published articles for Streams. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more DevFeed: [Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more]() Original publisher: [Read original article]() Author: Bosmat Tuvel Published: 2026-09-14T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Redis](), [Data structures](), [Data Management](), [Open Source]() Tags: [data-management](), [improvements](), [memory](), [new-features](), [operations](), [performance](), [redis](), [streams](), [tech](), [time-series]() ### AI overview Redis 8.10 in Redis Open Source introduces compact hashes, incremental backup and restore, JSONPath syntax extensions, more flexible Stream consumption, new Set cardinality operations, atomic movement of multiple List elements, and enhanced Time Series capabilities. The release also improves memory efficiency, throughput, and operational reliability at scale. ### Source excerpt Redis 8.10 in Redis Open Source is now available, delivering improvements that make Redis more memory efficient, expressive, and easier to operate at scale. Highlights include compact hashes with up to 50% lower memory usage and 2x higher hash loadin... ## Quiz: The subprocess Module: Wrapping Programs With Python DevFeed: [Quiz: The subprocess Module: Wrapping Programs With Python]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-11T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Python](), [Processes](), [Streams](), [Bash](), [Exception]() Tags: [code](), [exception](), [processes](), [python](), [streams](), [tutorial]() ### AI overview An interactive 13-question quiz testing understanding of Python's subprocess module, including launching external programs, handling exit codes and failures, preventing hangs with timeouts, and working with standard streams. ### Source excerpt Test your understanding of Python's subprocess module. Run shell commands, handle exit codes and timeouts, and work with standard streams. ## Build an AI Voice Assistant with Twilio Voice and Media Streams, OpenAI's GPT-Live API, and Node.js DevFeed: [Build an AI Voice Assistant with Twilio Voice and Media Streams, OpenAI's GPT-Live API, and Node.js]() Original publisher: [Read original article]() Author: Paul Kamp Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Artificial Intelligence](), [Node.js](), [OpenAI](), [Streams](), [API](), [WebSocket](), [Fastify](), [API keys](), [.env]() Tags: [ai](), [api](), [audio](), [developer-insights](), [env-file-security](), [js](), [ngrok](), [node-js](), [openai](), [sign-up](), [streams]() ### AI overview This tutorial explains how to build an AI voice assistant that answers phone calls using Twilio Programmable Voice and Media Streams, OpenAI's GPT-Live-1 API, and a Node.js server. It also covers web search and custom function tool calls. ### Source excerpt Build an AI voice assistant that answers a phone call with Twilio Programmable Voice and Media Streams, powered by OpenAI's GPT-Live-1. ## Build a real-time market data app with ClickHouse and Massive DevFeed: [Build a real-time market data app with ClickHouse and Massive]() Original publisher: [Read original article]() Author: Lionel Palacin Published: 2026-09-04T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [App](), [data](), [Node.js](), [Back end](), [API keys]() Tags: [analytics](), [api](), [app](), [backend](), [clickhouse](), [compression](), [data](), [demo](), [events](), [how-to](), [latency](), [low-latency](), [node](), [node-js](), [react](), [real-time](), [rest-api](), [storage](), [streams](), [visualization]() ### AI overview A tutorial for building a real-time market-data application that ingests, stores, and queries trade and quote ticks with Massive and ClickHouse, using Node.js for backend work and React for live visualization. ### Source excerpt Learn how to build a real-time financial analytics application with Massive and ClickHouse that scales to thousands of events per second. ## Build a unified AI agent architecture with DynamoDB and Bedrock DevFeed: [Build a unified AI agent architecture with DynamoDB and Bedrock]() Original publisher: [Read original article]() Author: Dhananjay Karanjkar Published: 2026-08-21T18:19:23Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots]() Tags: [advanced-300](), [agent](), [agents](), [ai](), [ai-agent](), [amazon-bedrock](), [amazon-dynamodb](), [amazon-opensearch-service](), [ann](), [api](), [architecture](), [aws-lambda](), [data-architecture](), [database](), [embeddings](), [how-to](), [retrieval](), [search](), [streams](), [sync](), [vector]() ### AI overview A tutorial for building an Amazon Bedrock agent that uses one DynamoDB table for structured data and semantic vector search. DynamoDB Streams generates embeddings when content changes, keeping the vector index synchronized. ### Source excerpt With native vector search in Amazon DynamoDB, you can store vector embeddings alongside your operational data in a single table. This post shows how to build a unified AI agent architecture where an Amazon Bedrock agent uses one DynamoDB table for both structured lookups and semantic search, with a DynamoDB Streams pipeline that keeps embeddings in sync. ## How Fly.io logging works and how to collect application logs DevFeed: [How Fly.io logging works and how to collect application logs]() Original publisher: [Read original article]() Author: Muhammed Ali Published: 2026-08-13T07:00:00Z Content type: tutorial Language: en Sources: [Honeybadger]() Topics: [fly.io](), [Logging](), [App](), [FastAPI](), [Streams](), [Deployment]() Tags: [app](), [deployment](), [devops-articles](), [fastapi](), [fly-io](), [guide](), [how-to](), [logging](), [streams](), [vector]() ### AI overview This tutorial explains how Fly.io collects and routes application logs, including output from standard output and standard error. It also demonstrates a FastAPI application that generates different log messages and describes handling logs with Honeybadger. ### Source excerpt Logging is an import part of debugging an app. Without a good method of catching errors or logs in general, you would end up with uncaught issues and could also lose valuable customers in the process. Read this article to learn how to effectively handle logs on Fly.io. ## CRLF-Powered Desync Attacks: Beheading HTTP Streams DevFeed: [CRLF-Powered Desync Attacks: Beheading HTTP Streams]() Original publisher: [Read original article]() Author: Tom Stacey Published: 2026-08-05T23:30:00Z Content type: article Language: en Sources: [PortSwigger Research]() Topics: [Vulnerabilities](), [web applications]() Tags: [attacks](), [browser](), [http](), [research](), [streams](), [vulnerabilities]() ### AI overview A security research paper on turning HTTP header injection into request-smuggling desynchronization attacks, including CRLF-powered desync worms and browser-assisted exploitation techniques. ### Source excerpt Abstract In this paper we'll show that HTTP Header Injection is severely underestimated. Forget open redirects or Cross-Site Scripting and instead, embrace the catastrophic potential of the CRLF-Power ## AI Gateway now supports streaming transcription DevFeed: [AI Gateway now supports streaming transcription]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-22T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Streaming](), [asr](), [SDKs](), [vercel ai sdk](), [Latency](), [Whisper](), [Artificial Intelligence]() Tags: [agent](), [agents](), [gpt](), [latency](), [openai](), [sdk](), [speech](), [streaming](), [streams](), [transcription](), [transcripts](), [voice](), [whisper]() ### AI overview AI Gateway now supports beta streaming transcription through the AI SDK's streamTranscribe function. Applications can stream audio as it is captured and receive partial and final transcript updates with low latency, enabling use cases such as live captioning, voice input, and voice-enabled agents. ### Source excerpt AI Gateway now supports streaming transcription. Previously, transcription required a complete audio file and returned the full transcript in a single response. Now you can stream audio in as it's captured and get transcript updates back as the model produces them, keeping latency low for uses like live captioning and voice input. Streaming transcription is in beta and available through the AI SDK's streamTranscribe function with any streaming-capable transcription model. The example below streams raw PCM audio to openai/gpt-realtime-whisper and prints each transcript delta as it arrives. The result stream also carries partial and final transcripts: The same code works across providers: swap the model string to use xai/grok-stt or any other streaming-capable transcription model. Streaming transcription also makes it easy to add a voice mode to an agent. Stream the user's speech to a transcription model and pass the live text to your agent. The agent itself does not change: it still receives text, so this works with any text-based agent. For agents that speak back, pair it with speech generation, or use realtime voice for full two-way conversation. For more information on streaming transcription on AI Gateway, see the documentation. Read more ## Full CDC semantics land in the Iceberg output for Redpanda Connect DevFeed: [Full CDC semantics land in the Iceberg output for Redpanda Connect]() Original publisher: [Read original article]() Author: Prakhar Garg Published: 2026-07-14T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Apache Iceberg](), [clickhouse](), [Database](), [Kafka]() Tags: [analytics](), [clickhouse](), [data](), [database](), [engineering](), [product](), [real-time](), [redpanda-connect](), [streams](), [sync]() ### AI overview This hands-on article explains how Redpanda Connect v4.99.0 applies full change data capture semantics to Apache Iceberg tables. Its output supports row-level inserts, upserts, and deletes, allowing a lakehouse table to stay synchronized with a source database in real time through a single pipeline definition. ### Source excerpt Full CDC semantics in Iceberg mean the lakehouse reflects what the source database looks like right now, not what it looked like during last night's batch window. Get to know the latest addition to Redpanda Connect. ## ScyllaDB 2026.2: DynamoDB Streams and Vector Search, Trie Indexes, and Strongly Consistent Tables DevFeed: [ScyllaDB 2026.2: DynamoDB Streams and Vector Search, Trie Indexes, and Strongly Consistent Tables]() Original publisher: [Read original article]() Author: Tzach Livyatan Published: 2026-06-29T19:18:20Z Content type: release Language: en Sources: [ScyllaDB]() Topics: [2026.2](), [Alternator (for DynamoDB)](), [API](), [Amazon DynamoDB](), [ScyllaDB Cloud](), [AI search](), [Streams](), [Replication](), [event driven](), [Latency]() Tags: [2026-2](), [amazon-dynamodb](), [api](), [event](), [event-driven](), [features](), [latency](), [performance](), [product](), [replication](), [scylladb](), [scylladb-cloud](), [search](), [streams](), [updates](), [vector]() ### AI overview ScyllaDB 2026.2 adds generally available DynamoDB Streams compatibility and a Vector Search extension to Alternator, alongside ScyllaDB Cloud integrations, Trie index performance improvements, stability updates, and experimental strongly consistent tables and online migration. ### Source excerpt ScyllaDB 2026.2 brings a combination of GA new features, exciting experimental features, and multiple stability and external use case improvements. ## We put a Redis server inside our runtime DevFeed: [We put a Redis server inside our runtime]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-06-25T00:00:00Z Content type: article Language: en Sources: [Encore Updates]() Topics: [Redis](), [Rust](), [Development](), [Cache]() Tags: [cache](), [development](), [protocol](), [redis](), [rust](), [scripting](), [streams](), [transactions]() ### AI overview Encore describes how its runtime provides an in-memory Redis server for local development and tests, aiming to keep behavior consistent with Redis in production. The Rust runtime embeds a port of miniredis as a library that supports commonly used Redis data types, streams, pub/sub, transactions, Lua scripting, TCP, and the Redis wire protocol. ### Source excerpt How we run an in-memory Redis inside the runtime for local development and tests, and how we keep it behaving the same as the Redis you run in production. ## Appcues delivers personalized customer engagement with ClickHouse Cloud DevFeed: [Appcues delivers personalized customer engagement with ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-06-18T16:21:05Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [data](), [dashboards](), [SDKs]() Tags: [airflow](), [analytics](), [apis](), [aws](), [batch](), [clickhouse](), [cloud](), [dashboards](), [data](), [databases](), [latency](), [observability](), [product-analytics](), [real-time](), [saas](), [sdks](), [streams]() ### AI overview Appcues moved its real-time customer-facing analytics and segmentation workload to ClickHouse Cloud on AWS. The article reports lower query and ingestion latency alongside reduced analytics spend at a scale of 1.31 PB and 410 billion events. ### Source excerpt Appcues cut P95 query times 90%, ingestion latency 99%, and analytics spend 23% by migrating from Snowflake and Airflow to ClickHouse Cloud for real-time segmentation across 1.31 PB of data. ## Workflow Streams: Live interactivity for agents and other applications DevFeed: [Workflow Streams: Live interactivity for agents and other applications]() Original publisher: [Read original article]() Author: Johann Schleier-Smith Published: 2026-06-17T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Streaming](), [User Interfaces](), [Library](), [clients](), [Artificial Intelligence]() Tags: [agents](), [product-news](), [sdk](), [streaming](), [streams](), [typescript](), [ui]() ### AI overview Temporal introduces Workflow Streams, a durable streams abstraction for building interactive UIs with incremental updates. The feature supports AI-agent status, reasoning, tool-call, and generated-text updates, and is in Public Preview for Python and TypeScript. ### Source excerpt Workflow Streams brings durable interactivity to Temporal. You get stream agent reasoning, tool calls, and status updates to any UI. ## Powering self-driving vehicle analytics at Avride with ClickHouse Cloud DevFeed: [Powering self-driving vehicle analytics at Avride with ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-05-11T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [data](), [Apache Iceberg](), [telemetry](), [Grafana]() Tags: [analytics](), [apache-iceberg](), [autonomous](), [aws](), [clickhouse](), [cloud](), [data-infrastructure](), [driving](), [grafana](), [latency](), [lidar](), [metrics](), [robots](), [storage](), [streams](), [telemetry](), [tooling](), [visualization]() ### AI overview Avride uses ClickHouse Cloud as the data backbone for its autonomous vehicles and delivery robots, supporting ride-data indexing, metrics, analytics, and internal tooling. Its migration from Apache Iceberg reduced index lookup and ingestion latency. ### Source excerpt Avride replaced Apache Iceberg with ClickHouse Cloud, cutting index lookup latency from 20 seconds to under 100ms and ingestion from hours to seconds. ## postmarketOS funds work to upstream q6voice call-audio support for Qualcomm devices DevFeed: [postmarketOS funds work to upstream q6voice call-audio support for Qualcomm devices]() Original publisher: [Read original article]() Published: 2026-05-08T00:00:00Z Content type: article Language: en Sources: [postmarketOS Blog]() Topics: [Linux](), [Kernel](), [Qualcomm](), [Mobile](), [Software](), [Streams]() Tags: [android](), [audio](), [cellular](), [devices](), [driver](), [kernel](), [linux](), [maintenance](), [mobile](), [modem](), [patches](), [phones](), [postmarketos](), [qualcomm](), [software](), [streams]() ### AI overview postmarketOS is funding work to upstream q6voice support for Qualcomm devices. The project targets kernel patches and the q6voiced userspace daemon used to enable call audio on Linux mobile devices, reducing reliance on kernel forks and lowering maintenance burden. ### Source excerpt As we have mentioned in the 2026 financial budget, we are able to follow up last year's success of financing important audio improvements in postmarketOS and the wider Linux Mobile ecosystem with another project this year! This time we will tackle the currently unupstreamable q6voice patches that every kernel fork for Qualcomm devices has to carry in order to be able to do audio calls. This is one of the last pieces of technical debt that hold back most devices from using a pure mainline kernel without additional patches, which would in turn lower the maintenance burden significantly, make call audio more reliable and would make it easier for other Linux distributions to run on these devices as well. The proof of concept It was early 2020, not even three years had passed after postmarketOS was published, and most people reading this probably had not heard of the project yet. At that time, Minecrell and Nikita were very busy maintaining and bringing up the MSM8916 family of devices. In the process, they achieved a considerable milestone: they managed to get calls (!907) and call audio (!1228, !1233) working on mainline for devices released with Android. This, for the first time, allowed those devices to be used as actual phones! Specifically for enabling call audio, Minecrell wrote two distinct pieces of software: The q6voice kernel driver: first included in a release of the msm8916 close-to-mainline kernel, it talks to the modem, and configures it so the audio that is being received from the cellular tower can be consumed in software. The q6voiced user-space daemon talks to the kernel to start or stop the audio streams so one can hear the other person on the other side of the call. Although an extraordinary achievement, the implementation was considered a workaround by the authors. From the initial commit: Eventually this should go away by implementing codec2codec support for q6voice in the kernel. In that case, the stream would be started once the audio routes are ## Trip report: Node.js collaboration summit (2026 London) DevFeed: [Trip report: Node.js collaboration summit (2026 London)]() Original publisher: [Read original article]() Published: 2026-04-24T00:00:00Z Content type: news Language: en Sources: [Node.js Blog]() Topics: [Node.js](), [releases](), [Vulnerabilities]() Tags: [api](), [collaboration](), [javascript](), [node-js](), [release-schedule](), [security](), [streams](), [vulnerabilities]() ### AI overview A trip report from the Node.js Collaboration Summit covers survey discussions, a revised release schedule beginning with Node.js v27, and a proposed unified Streams API for Web and Node.js. ### Source excerpt Node.js® is a free, open-source, cross-platform JavaScript runtime environment that lets developers create servers, web apps, command line tools and scripts. ## March 2026 Baseline monthly digest DevFeed: [March 2026 Baseline monthly digest]() Original publisher: [Read original article]() Author: Jeremy Wagner GitHub LinkedIn Homepage Published: 2026-04-14T07:00:00Z Content type: news Language: en Sources: [web.dev: Blog]() Topics: [web applications](), [networking]() Tags: [api](), [browser](), [interoperability](), [latency](), [networking](), [streaming](), [streams](), [web-platform]() ### AI overview A March 2026 Baseline digest covering newly interoperable web-platform features, including math font support, iterator combination, readable byte streams, the Reporting API, and WebTransport. ### Source excerpt Read about various happenings with Baseline during March 2026. ## Optimizing Vercel Sandbox snapshots DevFeed: [Optimizing Vercel Sandbox snapshots]() Original publisher: [Read original article]() Author: Guðmundur Bjarni Ólafsson Published: 2026-04-02T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Amazon S3](), [Go Language]() Tags: [aws](), [benchmarking](), [cache](), [compression](), [concurrency](), [firecracker](), [go](), [http](), [infrastructure](), [performance](), [s3](), [sandbox](), [streams](), [vercel]() ### AI overview Vercel describes optimizing Sandbox filesystem snapshot restores from over 40 seconds at p75 to under one second through parallel S3 range downloads, parallel decompression, streamed decompression, and local caching. ### Source excerpt When we recently shipped filesystem snapshots in Vercel Sandbox to let teams capture and restore a sandbox's entire filesystem state, our initial engineering focus was entirely on reliability, making sure the system would never fail to snapshot or lose data. Once that foundation was stable, our attention turned to performance. p75 snapshot restores were taking over 40 seconds, and through parallelization and local caching, we brought that under one second. What a snapshot looks like on disk Vercel Sandbox runs on the same infrastructure as our internal builds product, Hive. Each sandbox is an isolated container inside a Firecracker microVM. A snapshot is a compressed copy of the sandbox's disk. We're working with two different files: The raw disk image (.img), which can be several GBs A compressed version in our custom VHS format (Vercel Hive Snapshot), which is what gets uploaded to and downloaded from S3 When you call sandbox.snapshot(), we compress the .img into a .vhs and upload it to S3. When you call Sandbox.create() with a snapshot, we download the .vhs and decompress it back. Without compression, every snapshot operation transfers hundreds of MBs to low GBs over the network, adding seconds to tens of seconds to every restore. Parallelize you shall With reliability in place, we turned to the restore path, which was painfully sequential. We'd download the entire .vhs file from S3 in a single request, wait for it to finish, then decompress it in a single thread. Snapshots range from 200MB to a few GBs, so that single S3 download alone could take several seconds to tens of seconds. We used the Range HTTP header to download chunks in parallel instead, with the AWS Go SDK's transfermanager API handling the orchestration. After benchmarking different concurrency levels and chunk sizes, we ended up with 2-5x faster downloads. We applied the same thinking to decompression. Our .vhs format stores a header and a frame for each allocated region of the disk image, so ins ## ESP-IDF v6.0: Default libc Switches from Newlib to PicolibC DevFeed: [ESP-IDF v6.0: Default libc Switches from Newlib to PicolibC]() Original publisher: [Read original article]() Author: John Lee Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Blog on Developer Portal]() Topics: [ESP-IDF](), [C](), [Embedded Systems](), [migration](), [Streams](), [Library](), [POSIX]() Tags: [apis](), [architectures](), [blog](), [c](), [compatibility](), [embedded](), [embedded-systems](), [esp-idf](), [firmware](), [libc](), [libraries](), [memory](), [migration](), [newlib](), [performance](), [picolibc](), [posix](), [streams](), [systems](), [toolchains]() ### AI overview ESP-IDF v6.0 changes its default C library from Newlib to PicolibC. The article compares the libraries' memory usage, standard I/O behavior, compatibility, and migration considerations, including measurements on ESP32-C3. ### Source excerpt ESP-IDF v6.0 switches the default C library from Newlib to PicolibC. This article compares both libraries in terms of memory usage, stdio behavior, compatibility, and migration tradeoffs, and explains when keeping Newlib still makes sense. ## ClickHouse Release 26.2 DevFeed: [ClickHouse Release 26.2]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-03-16T10:47:57Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data](), [datasets](), [JSON](), [cURL](), [real-time](), [Command-line interface](), [Terminal](), [servers]() Tags: [bug](), [clickhouse](), [community](), [contributors](), [data](), [datasets](), [features](), [json](), [production](), [real-time](), [release](), [server](), [streams]() ### AI overview ClickHouse 26.2 is a winter release with 25 new features, 43 performance optimizations, and 183 bug fixes. It makes the text index and QBit data type production-ready, adds time-based batching for infinite inserts, and improves joins, JSON parsing, and inserts using min-max indices. The article also demonstrates loading a real-time Wikimedia changes feed into ClickHouse. ### Source excerpt ClickHouse 26.2 is here! In this post, text-index and QBit data type become production-ready. ## pg\_stat\_ch: a PostgreSQL extension that exports every metric to ClickHouse DevFeed: [pg\_stat\_ch: a PostgreSQL extension that exports every metric to ClickHouse]() Original publisher: [Read original article]() Author: Kaushik Iska Published: 2026-02-13T13:22:22Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [Database](), [data]() Tags: [analytics](), [apm](), [clickhouse](), [extension](), [open-source](), [postgresql](), [streams]() ### AI overview ClickHouse announces pg_stat_ch, an open-source PostgreSQL extension that records each query execution as an event and streams it to ClickHouse for query-performance analysis. ### Source excerpt We're open sourcing pg_stat_ch: a PostgreSQL extension that turns every query execution into a fixed-size ~4.6KB event and streams them into ClickHouse. ## Accelerating Mathematical and Scientific Discovery with Gemini Deep Think DevFeed: [Accelerating Mathematical and Scientific Discovery with Gemini Deep Think]() Original publisher: [Read original article]() Author: Thang Luong; Vahab Mirrokni Published: 2026-02-09T16:12:06Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Machine Learning & Artificial Intelligence](), [Combinatorial optimization](), [Algorithms](), [Mathematics](), [Optimization](), [Computer science](), [Training AI Models](), [Streams](), [data](), [Network]() Tags: [ai](), [algorithms](), [combinatorial-optimization](), [data](), [gemini](), [optimization](), [physics](), [research](), [science](), [streams](), [training]() ### AI overview An advanced version of Gemini Deep Think helped researchers resolve long-standing problems across algorithms, machine learning optimization, combinatorial optimization, economics, and physics. The article highlights new counterexamples, mathematical explanations for AI training techniques, an extension of an auction theorem to real-valued bids, and a closed-form solution for integrals involving cosmic-string singularities. ### Source excerpt Research papers point to the growing impact of Deep Think across fields ## PostgreSQL + ClickHouse as the Open Source unified data stack DevFeed: [PostgreSQL + ClickHouse as the Open Source unified data stack]() Original publisher: [Read original article]() Author: Lionel Palacin Published: 2026-01-27T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [PostgreSQL](), [Open Source](), [data](), [real-time](), [Streams](), [SQL](), [GitHub]() Tags: [clickhouse](), [data](), [github](), [open-source](), [postgresql](), [real-time](), [sql](), [streams](), [systems]() ### AI overview This article presents an open source unified data stack that combines PostgreSQL for transactional workloads with ClickHouse for analytics at scale. It explains how components such as PeerDB and pg_clickhouse enable data replication and direct analytical queries without rewriting applications or SQL. ### Source excerpt A ready-to-use open source data stack that combines PostgreSQL and ClickHouse to handle transactional and analytical workloads. ## From VHS to Interactive Streams: Customization at Scale with the NFL Sunday Ticket on YouTube DevFeed: [From VHS to Interactive Streams: Customization at Scale with the NFL Sunday Ticket on YouTube]() Original publisher: [Read original article]() Author: Shawn DeVries Published: 2026-01-23T23:05:00Z Content type: article Language: en Sources: [8th Light Insights]() Topics: [Streaming](), [personalization](), [Sports](), [real-time](), [Playback](), [ui](), [parallel](), [Mobile]() Tags: [customization](), [interactive](), [mobile](), [parallel](), [personalization](), [platform-innovation](), [playback](), [real-time](), [scale](), [sports](), [streams](), [ui]() ### AI overview This article examines how NFL Sunday Ticket on YouTube moved live sports viewing from a fixed broadcast toward personalized, interactive streams. It describes custom Multiview layouts, fantasy integrations, spoiler controls, playback options, and interfaces that work across TVs, mobile devices, and tablets. ### Source excerpt It's the mid-90s, it's Sunday morning, and you're about to set your VCRs to record on your NFL game of choice You'd capture everything, commercials, dead time, halftime filler, and hope the tape didn't cut out in overtime. It was static. One-size-fits-all. Then TiVo arrived. That pause button changed the game. Suddenly, fans could freeze a field goal, run to grab game day food, and unpause just in time for the kick. Control entered the living room. Fast forward to NFL Sunday Ticket on YouTube. Fans are now building their own broadcast in real time. The cultural shift is clear: live sports have moved from passive consumption to personalized control over the gameday experience. Raising the Stakes For nearly three decades, NFL Sunday Ticket lived only on DirecTV. It was a premium cable-era product; expensive, exclusive, and tied to satellite hardware. Fans outside their home market had little choice but to buy into the bundle if they wanted every game. That era is over. When YouTube secured the Sunday Ticket package, it leapt from the satellite dish into the streaming stack. And with that leap came a redefinition of expectations: reliability is still required, but now the baseline includes interactivity and personalization at a massive scale. Inside Sunday Ticket's interactive layer: Custom Multiview Fans can build their own layout with two, three, or four games running in parallel. Local broadcasts integrate seamlessly for YouTube TV base subscribers. Multiview works across TVs, mobile, and tablets, keeping the experience consistent. Fantasy View Integration Users can link NFL Fantasy and Yahoo Fantasy accounts. This allows fantasy stats and team updates surface directly in the stream. Spoiler Mode & Playback Features Hide scores until you're ready to see them. Advanced playback controls such as broadcast delay toggle All the features create more ways to personalize the live feed. Improved UI & Side-by-Side Experience Dual-column layouts show live action next to stats [Next page]()