add https://www.uber.com/en-US/blog/engineering/rss - sfeed_tests - sfeed tests and RSS and Atom files
(HTM) git clone git://git.codemadness.org/sfeed_tests
(DIR) Log
(DIR) Files
(DIR) Refs
(DIR) README
(DIR) LICENSE
---
(DIR) commit 7e4d0985d3e5f0e28b89bd2e8fdc80083c575b20
(DIR) parent 26ea2fc71d836f8cca823b3a69d79211e2efcd20
(HTM) Author: Hiltjo Posthuma <hiltjo@codemadness.org>
Date: Fri, 31 Jan 2025 11:09:11 +0100
add https://www.uber.com/en-US/blog/engineering/rss
quirks: requires the HTTP request header "Accept: text/html" to be set.
Otherwise there would be a "406 Not Acceptable" status (even Accept: */* does
not work).
The XML data contains many unnecesary CDATA sections.
Timestamp format is incomplete, but should be parsable by sfeed.
Either way a good quirky test to add.
Diffstat:
A input/sfeed/realworld/uber.com_eng… | 321 +++++++++++++++++++++++++++++++
1 file changed, 321 insertions(+), 0 deletions(-)
---
(DIR) diff --git a/input/sfeed/realworld/uber.com_engineering.rss.xml b/input/sfeed/realworld/uber.com_engineering.rss.xml
@@ -0,0 +1,320 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<rss version="2.0">
+ <channel>
+ <title>
+ <![CDATA[Engineering | Uber Blog]]>
+ </title>
+ <link>https://www.uber.com</link>
+ <description>
+ <![CDATA[]]>
+ </description>
+ <lastBuildDate>Fri Jan 31 2025 10:04:42 GMT+0000 (Coordinated Universal Time)</lastBuildDate>
+ <language>en</language>
+ <item>
+ <title>
+ <![CDATA[MySQL At Uber]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/mysql-at-uber/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2025-01-30 14:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Backend]]>
+ </category>
+ <description>
+ <![CDATA[<p>How does Uber achieve 99.99% availability across 2,000+ MySQL® clusters? Learn how we manage our MySQL fleet at scale, from architecture to control plane optimizations.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[How Uber Uses Ray® to Optimize the Rides Business]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/how-uber-uses-ray-to-optimize-the-rides-business/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2025-01-09 14:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Backend]]>
+ </category>
+ <description>
+ <![CDATA[<p>Large-scale computation is a major back end and infrastructure challenge for Uber to solve as we scale. We applied a compute engine called Ray® in Uber’s marketplace to improve computation efficiency and engineering productivity.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[Serving Millions of Apache Pinot™ Queries with Neutrino]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/serving-millions-of-apache-pinot-queries-with-neutrino/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-12-11 14:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Data / ML]]>
+ </category>
+ <description>
+ <![CDATA[<p>At Uber, we serve 500 million Pinot queries every day. Learn how we optimized and built an internal fork of Presto to support query features like window functions and sub-queries, all while supporting sub-second latencies at thousands of QPS.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[Introducing the Prompt Engineering Toolkit]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/introducing-the-prompt-engineering-toolkit/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-11-26 14:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Data / ML]]>
+ </category>
+ <description>
+ <![CDATA[<p>LLM iteration can happen with speed and safety! Explore how Uber launched a prompt toolkit for LLMs that helps engineers create, manage, and evaluate prompts with dynamic contextualization, batch generation, and robust safety checks.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[The Accounter: Scaling Operational Throughput on Uber’s Stateful Platform]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/the-accounter/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-11-21 14:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Backend]]>
+ </category>
+ <description>
+ <![CDATA[<p>Uber slashed operational costs using The Accounter—an intelligent state manager that orchestrates large-scale tasks across our entire infrastructure, optimized for time and resources.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[Unified Checkout: Streamlining Uber’s Payment Ecosystem]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/unified-checkout/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-11-14 14:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Backend]]>
+ </category>
+ <description>
+ <![CDATA[<p>From payment chaos to calm: Discover how Uber’s game-changing Unified Checkout System powers payment methods across every product line, worldwide.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[Presto® Express: Speeding up Query Processing with Minimal Resources]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/presto-express/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-11-07 14:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Data / ML]]>
+ </category>
+ <description>
+ <![CDATA[<p>Slow Presto® queries can hinder data-driven operations. At Uber, we designed Presto express to achieve a 50% improvement in the end-to-end SLA for 70% of queries using query analysis, real-time insights, and resource isolation.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[Enabling Infinite Retention for Upsert Tables in Apache Pinot]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/enabling-infinite-retention-for-upsert-tables/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-10-31 13:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Data / ML]]>
+ </category>
+ <description>
+ <![CDATA[<p>With contributions from Uber and others, Apache Pinot™ now supports deletion with upsert tables! Learn how Uber drove these advancements and how you can benefit from cost-efficient infinite retention.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[Streamlining Financial Precision: Uber’s Advanced Settlement Accounting System]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/ubers-advanced-settlement-accounting-system/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-10-24 13:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Backend]]>
+ </category>
+ <description>
+ <![CDATA[<p>Discover how Uber’s cutting-edge settlement accounting system processes over 1.2 billion transactions monthly, ensuring precise financial tracking, preventing fraud, and managing regulatory compliance with unmatched efficiency.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[Open Source and In-House: How Uber Optimizes LLM Training]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/open-source-and-in-house-how-uber-optimizes-llm-training/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-10-17 15:30:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Engineering]]>
+ </category>
+ <description>
+ <![CDATA[<p>Exploring beyond third-party LLMs, Uber leverages in-house LLM training to embed domain-specific knowledge and support GenAI applications. Embracing open-source solutions unlocks top-tier training throughput and GPU utilization.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[Genie: Uber’s Gen AI On-Call Copilot]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/genie-ubers-gen-ai-on-call-copilot/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-10-10 13:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Data / ML]]>
+ </category>
+ <description>
+ <![CDATA[<p>Explore how Uber is leveraging Genie, its Generative AI-powered On-Call CoPilot, to transform on-call operations and empower engineering teams.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[Making Uber’s ExperimentEvaluation Engine 100x Faster]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/making-ubers-experiment-evaluation-engine-100x-faster/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-10-03 13:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Backend]]>
+ </category>
+ <description>
+ <![CDATA[<p>Learn how Uber was able to reduce evaluation latencies by a factor of 100x in their Experimentation platform, which is used to empower decision making across the company by processing over 10 million evaluations per second.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[Preon: Presto Query Analysis for Intelligent and Efficient Analytics]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/preon/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-09-26 13:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Data / ML]]>
+ </category>
+ <description>
+ <![CDATA[<p>Discover how to enable intelligent and efficient data analytics at Uber scale with Preon, a Presto Query Analysis service that unlocks insights for deduplicating queries, creating efficient table layouts, and more.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[How to Measure Design System at Scale]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/design-system-at-scale/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-09-24 13:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Engineering]]>
+ </category>
+ <description>
+ <![CDATA[<p>Learn how Uber made a breakthrough in tracking design metrics across Figma, Android, and iOS with Design System Observability.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[QueryGPT – Natural Language to SQL Using Generative AI]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/query-gpt/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-09-19 13:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Backend]]>
+ </category>
+ <description>
+ <![CDATA[<p>Discover how QueryGPT revolutionizes SQL query generation at Uber! Learn about the cutting-edge AI that turns natural language prompts into efficient SQL queries, boosting productivity at Uber. Dive into our journey of innovation and transformation. </p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[Transforming Executive Travel: Delegate Booking with Uber]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/executive-travel/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-09-12 13:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Backend]]>
+ </category>
+ <description>
+ <![CDATA[<p>Find out how Uber for Business launched delegate profiles on Administrative Professionals Day, empowering executive assistants to manage executive travel, streamlining processes, and optimizing efficiency.</p>
+]]>
+ </description>
+ </item>
+ <item>
+ <title>
+ <![CDATA[DataMesh: How Uber laid the foundations for the data lake cloud migration]]>
+ </title>
+ <link>
+ <![CDATA[https://www.uber.com/blog/datamesh/]]>
+ </link>
+ <pubDate>
+ <![CDATA[2024-09-10 13:00:00]]>
+ </pubDate>
+ <category>
+ <![CDATA[Data / ML]]>
+ </category>
+ <description>
+ <![CDATA[<p>Learn how Uber is streamlining the Cloud migration of its massive Data Lake by incorporating key Data Mesh principles.</p>
+]]>
+ </description>
+ </item>
+ </channel>
+</rss>
+\ No newline at end of file