# data Data are representations of facts, concepts, or instructions suitable for communication, interpretation, or processing by people or automated means. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## GNOME 51 released DevFeed: [GNOME 51 released]() Original publisher: [Read original article]() Author: corbet Published: 2026-09-17T13:08:12Z Content type: release Language: en Sources: [LWN.net]() Topics: [version](), [App](), [data](), [interface](), [file]() Tags: [application](), [changes](), [data](), [desktop](), [file](), [gnome](), [gnome-51](), [improvements](), [interface](), [maps](), [offline](), [performance](), [transit](), [version]() ### AI overview GNOME 51 has been released with performance improvements, offline data and improved transit information in Maps, a new file previewer interface, and other changes. ### Source excerpt Version 51 of the GNOME desktop environment has been released. The list of changes includes a number of performance improvements, offline data and better transit information in the Maps application, a new interface for the file previewer, and more. ## Australia's AI opportunity starts with data DevFeed: [Australia's AI opportunity starts with data]() Original publisher: [Read original article]() Author: Sean MacKirdy Published: 2026-09-17T01:00:00Z Content type: opinion Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [data](), [Artificial Intelligence](), [AI Strategy]() Tags: [agentic-ai-government](), [ai](), [ai-adoption](), [australia](), [data](), [platform](), [public-sector-media-entertainment]() ### AI overview Elastic's participation in Australia's first Parliamentary AI Industry Showcase emphasized that data is a central constraint on AI performance and a prerequisite for deploying AI that is effective, safe, and trusted. ### Source excerpt Elastic, a Founding Partner at Australia's first Parliamentary AI Industry Showcase, highlights that data, not just models, is the key to deploying safe, effective, and trusted AI. ## Local and distributed cache coherence: stale data caused by missed invalidation messages DevFeed: [Local and distributed cache coherence: stale data caused by missed invalidation messages]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-17T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Caching](), [Redis](), [data](), [Network]() Tags: [cache](), [cache-coherence](), [caching](), [consistency](), [data](), [distributed-cache](), [distributed-systems](), [jvm](), [network](), [redis](), [ttl]() ### AI overview The article explains that adding an in-process cache in front of a distributed cache can improve latency but creates independently stale copies across service instances. It focuses on Redis pub/sub invalidation, which provides no persistence, acknowledgement, retry, or replay, allowing instances to miss invalidation messages and serve stale data until their TTL expires. ### Source excerpt An in-process cache in front of a distributed cache removes a network hop and adds one independent copy of the data per instance, each of which can be stale on its own schedule. Invalidation is normally broadcast over pub/sub, which is fire and forget, so any instance that is restarting, garbage collecting or briefly disconnected simply misses the message and serves stale data until its TTL expires. That TTL is not a performance setting, it is the maximum duration of incorrectness. ## Enterprises are sweating legacy IT assets as AI investment grows DevFeed: [Enterprises are sweating legacy IT assets as AI investment grows]() Original publisher: [Read original article]() Author: Dan Robinson Published: 2026-09-16T16:15:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [legacy](), [Hardware](), [mainframes](), [data](), [business logic](), [Artificial Intelligence]() Tags: [ai](), [artificial-intelligence](), [business-logic](), [data](), [ensono](), [hardware](), [it-modernization](), [legacy](), [legacy-systems](), [mainframes](), [systems]() ### AI overview The article examines how enterprises are dealing with legacy IT assets as AI investment grows. It reports that mainframes and other hardware may contain the data and business logic needed to build AI services. ### Source excerpt Hardware such as mainframes found to hold the data and business logic needed to build those AI services ## How to connect AI usage to business value DevFeed: [How to connect AI usage to business value]() Original publisher: [Read original article]() Published: 2026-09-16T12:00:00Z Content type: tutorial Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ChatGPT](), [codex](), [data](), [Software Engineering]() Tags: [ai](), [analytics](), [business-value](), [chatgpt](), [codex](), [product](), [training]() ### AI overview This tutorial explains how ChatGPT Work and Codex analytics help administrators connect AI adoption, usage, spending, supported tasks, and outcomes to business decisions. It covers views for identifying training needs, evaluating workflows, reviewing model and tool usage, and monitoring Codex contributions and code-review activity. ### Source excerpt Learn how ChatGPT Work and Codex analytics help teams understand AI usage and spend, identify training needs, and connect adoption to business outcomes. ## Ministry of Justice apologizes after court staff accessed Southport victims' files DevFeed: [Ministry of Justice apologizes after court staff accessed Southport victims' files]() Original publisher: [Read original article]() Author: Connor Jones Published: 2026-09-16T10:42:28Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [data](), [Security & Privacy]() Tags: [data-protection](), [information](), [security](), [united-kingdom]() ### AI overview The UK Ministry of Justice apologized after court staff accessed files belonging to Southport victims. The files contained sensitive personal data, but there is no evidence that the information was shared with third parties. ### Source excerpt Sensitive personal data was involved, but there is no evidence it was shared with third parties ## A Case for Time Tracking: Data Driven Time-Management DevFeed: [A Case for Time Tracking: Data Driven Time-Management]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-08-08T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Google Calendar](), [data](), [Development](), [Tech Lead]() Tags: [data](), [developer](), [engineering](), [google](), [news](), [tech-lead](), [time-management](), [time-tracking](), [web-frontend]() ### AI overview The author describes tracking all work activities as personal Google Calendar events. They argue that the resulting data provides a clearer view of how time is spent and helps inform decisions, productivity, and confidence about work. ### Source excerpt By Oliver Northwood Tl;dr: Time-tracking everything I do in Google Calendar has made me happier, more productive, and ... Read more ## Memcached-Backed Content Infrastructure DevFeed: [Memcached-Backed Content Infrastructure]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-05-15T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Caching](), [Back end](), [data](), [Front end]() Tags: [backend](), [cache](), [engineering](), [infrastructure](), [memcached](), [news](), [profiling]() ### AI overview Khan Academy describes testing and rolling out a Memcached-backed content-serving backend. The article explains the existing content versioning and storage model, the performance limitations of fetching many individual items, and the prior approach of loading a compressed site-wide content bundle at server startup. ### Source excerpt By Ben Kraft Last post, I wrote about how we did profiling on App Engine's Memcached service to ... Read more ## New data pipeline management platform at Khan Academy DevFeed: [New data pipeline management platform at Khan Academy]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-04-30T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [data](), [DataOps](), [systems]() Tags: [batch](), [bigquery](), [cloud-dataflow](), [data](), [data-pipelines](), [debugging](), [engineering](), [infrastructure](), [news](), [pipeline](), [scheduling]() ### AI overview Khan Academy developed Khanalytics to manage its growing collection of data pipelines. The platform provides a sandboxed environment for batch jobs, a web interface, automatic parallelization, centralized logs, and pipeline scheduling with dependencies. ### Source excerpt By Ragini Gupta Data is very crucial to Khan Academy and is itself an internal product for the ... Read more ## University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK DevFeed: [University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-16T05:00:42Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [AI Models](), [Supercomputing](), [DGX Spark](), [Simulation](), [Training AI Models](), [data](), [Inference](), [real-time]() Tags: [ai](), [ai-for-good](), [ai-models](), [climate](), [compute](), [dgx-spark](), [government](), [healthcare](), [inference](), [nvidia](), [open-source](), [real-time](), [science](), [supercomputing](), [training](), [uk]() ### AI overview The University of Manchester is working with NVIDIA to use Earth-2 generative AI models to forecast air pollution across the U.K. The team trained Earth-2 CorrDiff on chemistry-climate simulation data using Isambard-AI, added StormCast for time-dependent forecasts using air-quality observations, and demonstrated workflows on DGX Spark. ### Source excerpt Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U.K. alone last year. Data-driven insights can help -- but computing air quality with traditional chemistry-based models is expensive, which limits how detailed they can be and how regularly they can be run. David Topping, a professor in the [...] ## Java 27 adds improvements to garbage collection, headers, data security, and quantum key support DevFeed: [Java 27 adds improvements to garbage collection, headers, data security, and quantum key support]() Original publisher: [Read original article]() Author: Joab Jackson Published: 2026-09-16T04:50:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [java-27](), [Java](), [Security](), [data]() Tags: [devops](), [garbage-collection](), [improvements](), [java](), [java-27](), [jdk](), [jdk-27](), [openjdk](), [post-quantum-cryptography]() ### AI overview Java 27 introduces improvements to JDK garbage collection, header sizes, data security, and quantum key support, with the stated aim of reducing developer friction. ### Source excerpt Improvements in JDK garbage collection, header sizes, data security and quantum key support all should minimize developer friction ## DuckDB Skills for Claude Code DevFeed: [DuckDB Skills for Claude Code]() Original publisher: [Read original article]() Author: The DuckDB team Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [DuckDB]() Topics: [DuckDB](), [Claude Code](), [Command-line interface](), [data](), [CSV](), [parquet]() Tags: [claude-code](), [cli](), [csv](), [data](), [duckdb](), [parquet](), [using-duckdb]() ### AI overview This post introduces the duckdb-skills plugin for Claude Code. The plugin uses the DuckDB CLI to inspect data files, run SQL queries, convert formats, explore object storage and spatial data, search documentation, and recall earlier sessions. It explains installation, how the skills select appropriate commands, and how Claude uses DuckDB for exact query results. ### Source excerpt The duckdb-skills plugin gives Claude Code a growing number of skills that use the DuckDB CLI to read data files, run queries, convert formats, explore object storage, work with spatial data, search the documentation and recall earlier sessions. ## Google tag gateway: Now on Webflow DevFeed: [Google tag gateway: Now on Webflow]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Webflow Blog]() Topics: [gateway](), [webflow](), [Google](), [Confidential Computing](), [data](), [configuration](), [Deployment]() Tags: [confidential-computing](), [configuration](), [conversion](), [data](), [deployment](), [development](), [gateway](), [google](), [measurement](), [webflow]() ### AI overview Webflow announces Google tag gateway, which routes conversion data through a site's first-party infrastructure. Existing native Google tag integration users can activate it by republishing their site, while new users can follow a code-free setup workflow. ### Source excerpt When you build on Webflow, you're creating experiences designed to help your business stand out and grow. Underpinning those experiences with a strong measurement foundation can help you understand what's driving results and make more informed marketing decisions. Now, Webflow makes it easier to strengthen your measurement foundation. ## Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs DevFeed: [Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [data](), [Large Language Model](), [Code](), [Retrieval-Augmented Generation](), [Access Control](), [Graphs](), [GitHub]() Tags: [agents](), [classification](), [code](), [data](), [documentation](), [governance](), [production](), [provenance](), [retrieval](), [vector-database]() ### AI overview Glyph is a production system for generating column descriptions and assigning sensitivity-ontology labels in enterprise data catalogs. It uses cooperating LLM agents, source-code-grounded retrieval, parallel tagging strategies, vector-based metadata matching, and ranked-output fusion to support auditable cataloging. ### Source excerpt Enterprise data lakes accumulate tables faster than human stewards can document or classify them, leaving columns with missing descriptions and unassigned governance labels. This documentation debt undermines data discovery, access control, and regulatory compliance. We present Glyph, a production system that frames two coupled problems, column description generation and column type annotation for data classification, as cooperating LLM agents orchestrated as stateful graphs. The Descriptor grounds generation in the pipeline source code that produces each column, retrieved on demand from an... ## Building an AI-native data & insights operating system at Webflow DevFeed: [Building an AI-native data & insights operating system at Webflow]() Original publisher: [Read original article]() Author: Ashwini Chaube Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [data](), [Artificial Intelligence](), [Platform Engineering](), [Data Science](), [decision-making](), [Authorization]() Tags: [agentic](), [ai](), [data](), [data-insights](), [inside-webflow](), [permissions](), [review](), [self-service](), [skills]() ### AI overview Webflow describes how its Data & Insights team built an AI-native operating system for trusted self-service analytics. The approach combines governed data, encoded business context, reusable skills and agents, permissions, architectural controls, review practices, and human judgment, while also changing how the team works through agent-first workflows, learning, and experimentation. ### Source excerpt How we built the governed foundations for trusted self-service analytics while transforming the way our own team works. ## How to Build an Endpoint Data Loss Prevention Strategy for Your Development Team DevFeed: [How to Build an Endpoint Data Loss Prevention Strategy for Your Development Team]() Original publisher: [Read original article]() Author: Alex Tray Published: 2026-09-15T21:03:09Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [data loss prevention](), [Development](), [Access Control](), [data]() Tags: [access-control](), [cybersecurity](), [cybersecurityawareness](), [data-loss-prevention](), [data-protection](), [database](), [development](), [encryption](), [endpoint-security](), [hardcoded-credentials](), [it-security](), [net-conf](), [remote-access](), [secrets](), [sensitive-data](), [web-development]() ### AI overview A tutorial for development teams on building endpoint data loss prevention strategies. It covers auditing laptops for secrets and sensitive data, removing unnecessary copies, rotating exposed credentials, and establishing access controls. ### Source excerpt A developer's laptop holds more sensitive data than most people realize: API keys, database credentials, staging environment secrets, and sometimes entire copies of production data pulled down "just f ## Beyond the model: Engineering AI infra with scientific judgement DevFeed: [Beyond the model: Engineering AI infra with scientific judgement]() Original publisher: [Read original article]() Author: AirbnbEng Published: 2026-09-15T17:06:18Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Agent Harness](), [AI Agent](), [Data Science](), [data](), [datasets](), [Large Language Model]() Tags: [agent-harness](), [ai](), [ai-agent](), [data](), [data-science](), [datasets](), [engineering](), [llms](), [quality](), [science](), [trust]() ### AI overview Airbnb describes an agent harness for data science that embeds scientific methodology around an AI model. The system guides agents through framing questions, selecting evidence, and recording decisions so unstructured-data investigations can be reproduced, audited, challenged, and extended across languages, geographies, and LLM-based products. ### Source excerpt How Airbnb's agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure. By: Wren Dougherty Ask a coding agent to analyze 100,000 customer support conversations and within minutes you'll have a polished taxonomy, precise prevalence numbers, and an executive-ready summary. What you can't see is the investigation that produced them: the methods it chose, the evidence it weighed, how much to trust it, or whether a second request would agree. All that reaches you is the polish. The model is undeniably intelligent, but intelligence without methodology is not science. LLMs certainly make for confident scientists, but we need them to be responsible ones. Smarter models help, but intelligence has never been the whole of science, in people or in machines. The method is as much the product as the answer. That is the idea behind the agent harness we built for data science: the methodology itself, built as infrastructure around the model. It governs how an AI agent operates, from framing a question to selecting evidence to recording decisions, so results can be reproduced, audited, and challenged, and the method shared, inspected, and built on. The challenge of unstructured data exploration In 2025, Airbnb was preparing to launch an AI customer service assistant. Before it could ship, we needed to understand exactly what kinds of situations it would face in the real world. That included rare events that could be risky for AI to interact with, and involved examining their taxonomy and prevalence to create the datasets that would help us build a more responsible product. The investigative work to do this was rigorous, but the process was deeply artisanal. Months of high-touch iteration went into each investigation, from finding the right data, reviewing samples with experts, and generating representative datasets, and the method was manually curated across notebooks, tables, docs, and indiv ## What I learned organizing KCD Lima 2026 DevFeed: [What I learned organizing KCD Lima 2026]() Original publisher: [Read original article]() Author: Ronald Requena | CNCF Ambassador Published: 2026-09-15T16:24:05Z Content type: opinion Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Kubernetes](), [data]() Tags: [2026](), [blog](), [community](), [event](), [kubernetes](), [report](), [survey]() ### AI overview Ronald Requena reflects on organizing the third Kubernetes Community Days Lima, held on July 18, 2026. He describes the event's growth, operational challenges, program, sponsorship, and attendee feedback, including 2,244 registrations, more than 900 attendees, 54 sessions, and a 4.7 out of 5 satisfaction rating. ### Source excerpt On July 18, 2026, we held the third edition of Kubernetes Community Days Lima at UTEC in Barranco. By now, we have already sent the Transparency Report to the CNCF, thanked our sponsors, and processed the... ## DigiCert's AI Trust framework for governing enterprise AI agents and models DevFeed: [DigiCert's AI Trust framework for governing enterprise AI agents and models]() Original publisher: [Read original article]() Author: Robin Birtstone Published: 2026-09-15T15:00:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [ai-governance](), [AI Agent](), [Authorization](), [Security](), [data]() Tags: [agents](), [ai](), [ai-governance](), [authorization](), [data](), [security](), [sponsored]() ### AI overview This sponsored feature presents DigiCert's AI Trust framework for governing enterprise AI agents. The framework uses public key infrastructure, DNS, and attestation to help organizations identify agents, track data and credentials, stop compromised agents, and reconstruct incidents. ### Source excerpt SPONSORED FEATURE: DigiCert wants to hand every agent a passport, complete with an expiry date and a named human owner ## CenterPoint Energy confirms intruder helped themselves to customer information DevFeed: [CenterPoint Energy confirms intruder helped themselves to customer information]() Original publisher: [Read original article]() Author: Connor Jones Published: 2026-09-15T14:14:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Cybersecurity](), [data]() Tags: [breach](), [cyber-crime](), [data]() ### AI overview CenterPoint Energy confirmed that an intruder accessed customer information while the Texas utility investigates a breach. A forum post claims that 7.49 million files may be available. ### Source excerpt Forum post claims 7.49 million files up for grabs as Texas utility investigates breach ## Just 4.4% of Enterprise NAS Capacity Is Active, CTERA Cold Data Storage Report Finds DevFeed: [Just 4.4% of Enterprise NAS Capacity Is Active, CTERA Cold Data Storage Report Finds]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-15T12:00:00Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [data](), [Filesystems](), [Security](), [Replication](), [Artificial Intelligence]() Tags: [data](), [enterprise](), [enterprise-nas](), [enterprise-storage](), [nas](), [report](), [security](), [storage]() ### AI overview StorageReview reports on CTERA's Cold Data Storage Report, which analyzed 16 petabytes of production data from 856 enterprise NAS file-share discovery scans. The report found that 4.4% of stored capacity was actively used within 90 days, while most files were cold or inactive. The article discusses the resulting storage, data-protection, security, and AI-retrieval implications. ### Source excerpt CTERA has published The Cold Data Storage Report, an analysis of 16 petabytes of live production data across 856 file-share discovery scans in enterprise NAS environments, including regulated industries, and the headline number is that just 4.4% of stored capacity is actively used, which the report page defines as modified within a 90-day window. The The post Just 4.4% of Enterprise NAS Capacity Is Active, CTERA Cold Data Storage Report Finds appeared first on StorageReview.com. ## How Everpure proposes reducing GPU idle time by improving AI data access DevFeed: [How Everpure proposes reducing GPU idle time by improving AI data access]() Original publisher: [Read original article]() Author: Chris Mellor Published: 2026-09-15T08:00:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [AI Infrastructure](), [GPU](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [AI Agent](), [data-processing](), [data]() Tags: [agents](), [ai](), [ai-infrastructure](), [ai-ml](), [data-processing](), [gpu](), [hardware](), [rag](), [sponsored]() ### AI overview This sponsored feature describes Everpure's approach to reducing GPU idle time in AI systems by improving access to large-scale insurance data. It discusses central metadata indexing, storage performance, self-describing data, and integration with Nvidia GPU infrastructure for AI agents and retrieval-augmented generation. ### Source excerpt SPONSORED FEATURE: The vendor's AI solutions are dedicated to increasing GPU utilization and avoiding costly GPUs doing nothing while waiting for data ## Aiven, Confluent, Redpanda, StreamNative and Ververica Form Streamhouse Working Group DevFeed: [Aiven, Confluent, Redpanda, StreamNative and Ververica Form Streamhouse Working Group]() Original publisher: [Read original article]() Author: Streamhouse Working Group Published: 2026-09-15T07:00:00Z Content type: release Language: en Sources: [Confluent: Data in motion]() Topics: [data-architecture](), [real-time](), [data](), [Streaming](), [Artificial Intelligence]() Tags: [agents](), [ai](), [architectures](), [confluent](), [data](), [distributed](), [news](), [real-time](), [streaming]() ### AI overview Aiven, Confluent, Redpanda, StreamNative, and Ververica announced the Streamhouse Working Group and published a vendor-neutral definition of Streamhouse. The proposed data architecture is designed to keep business context continuously available to production applications, analytics, and AI agents, with real-time, production-native, and decentralized attributes. ### Source excerpt New industry initiative establishes an open category for data architectures that power real-time applications and AI agents ## How to Build a Fraud Signal-Sharing Strategy Across Teams and Vendors DevFeed: [How to Build a Fraud Signal-Sharing Strategy Across Teams and Vendors]() Original publisher: [Read original article]() Author: Jacob Sanchez Published: 2026-09-04T16:37:55Z Content type: tutorial Language: en Sources: [Sift Science]() Topics: [data](), [Security](), [dashboards](), [Slack]() Tags: [cross-team-fraud-collaboration](), [dashboards](), [data](), [fraud](), [fraud-kpis](), [fraud-prevention-strategy](), [fraud-signal-sharing](), [signal](), [signal-sharing-strategy](), [slack](), [strategy](), [teams](), [trust-and-safety](), [vendor-data-sharing]() ### AI overview This how-to article discusses building fraud signal-sharing programs across internal teams and vendors. It explains how shared signals such as PII, IP addresses, device data, and activity patterns can support fraud prevention, security, legal, compliance, growth, marketing, and customer support. It also compares informal sharing through Slack and email with shared dashboards and reports. ### Source excerpt I recently joined Jerry Hoff, CEO of AppSec Training, for a Blueprint Series session on fraud signal sharing, and it's a topic I keep coming back to. Fraud, trust and safety, and security teams often work from separate systems with no shared view of the same bad actor. That gap slows response time and lets [...] The post How to Build a Fraud Signal-Sharing Strategy Across Teams and Vendors appeared first on Sift. [Next page]()