# Data Published articles for Data. 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. ## Write End-to-End Tests in Your Backend's Language DevFeed: [Write End-to-End Tests in Your Backend's Language]() Original publisher: [Read original article]() Author: James McConkey Published: 2026-09-17T12:00:42Z Content type: tutorial Language: en Sources: [Atomic Object]() Topics: [Testing](), [Playwright](), [FastAPI](), [Python](), [test data](), [Pydantic](), [SQLAlchemy](), [ASP.NET Core](), [PostgreSQL](), [Docker Compose]() Tags: [asp-net-core](), [code](), [data](), [docker-compose](), [fastapi](), [playwright](), [postgresql](), [project-team-management](), [python](), [sqlalchemy](), [tests](), [the-software-life]() ### AI overview This article argues that end-to-end test-data setup is often the main design challenge, because tests must create consistent domain records while running alongside other tests. It recommends using browser-testing tools in the backend's language when possible, keeping meaningful relationships inline, and extracting small creation helpers without hiding scenario intent. ### Source excerpt The browser is often the easiest part of an end-to-end test. Consider a test that verifies a user can complete an overdue task. The visible interaction is small: sign in, find the task, click Complete, and observe the new status. Before any of that can happen, the test needs a workspace, a user, a project, and [...] The post Write End-to-End Tests in Your Backend's Language appeared first on Atomic Spin. ## 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. ## Mir/Wayland-Powered Miracle-WM 0.11 Released With New Overview Mode, Window Urgency DevFeed: [Mir/Wayland-Powered Miracle-WM 0.11 Released With New Overview Mode, Window Urgency]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-17T00:58:42Z Content type: release Language: en Sources: [Phoronix]() Topics: [Wayland](), [Library](), [canonical]() Tags: [commands](), [data](), [desktop-linux](), [events](), [github](), [image](), [input](), [ipc](), [library](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [overview](), [phoronix](), [phoronix-test-suite](), [plugins](), [release](), [ubuntu-benchmarks](), [ubuntu-hardware](), [update](), [wayland](), [window](), [windows]() ### AI overview Miracle-WM 0.11, a Wayland compositor built on the Mir library, adds an overview mode, window urgency support, plugin IPC events and commands, and support for newer Mir protocols. The release is built on Mir 2.29. ### Source excerpt Canonical engineer Matthew Kosarek rolled out Miracle-WM 0.11 today as an end-of-summer update to this Wayland compositor built atop the Mir library... ## 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 ## OpenVDB Introduces SIMD Framework With Some 2~4x Performance Improvements DevFeed: [OpenVDB Introduces SIMD Framework With Some 2~4x Performance Improvements]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-16T10:05:23Z Content type: news Language: en Sources: [Phoronix]() Topics: [Data structures](), [Tooling](), [x86](), [releases](), [cudnn](), [Open Source]() Tags: [avx](), [command-line](), [cuda](), [data](), [data-structure](), [desktop-linux](), [github](), [kernels](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [release](), [ubuntu-benchmarks](), [ubuntu-hardware](), [x86]() ### AI overview OpenVDB 13.1 introduces a SIMD framework using Agner Fog's VectorClass Library for explicit x86 vectorization up to AVX-512. Adapted point transfer algorithms reportedly achieve 2x to 4x performance improvements on modern AVX-512 x86_64 CPUs. The release also includes NanoVDB CUDA resource-management and kernel improvements, plus updates to vdb_tool. ### Source excerpt OpenVDB is the sparse volume data structure and tooling library maintained by the Academy Software Foundation. OpenVDB in turn is used by various CGI software for dealing with sparse volumetric data such as Houdini, RenderMan, and Cinema 4D to the open-source Blender. It's even won an Academy Award for technical achievement while now in 2026 it's finally establishing a SIMD framework for better leveraging modern x86 ISA capabilities... ## 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 ## 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 ## Build a WhatsApp AI agent with Appwrite Functions and TablesDB DevFeed: [Build a WhatsApp AI agent with Appwrite Functions and TablesDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI Agent](), [Messaging](), [Serverless](), [API](), [Database]() Tags: [ai](), [ai-agent](), [api](), [app](), [build](), [data](), [database](), [messages](), [messaging](), [meta](), [serverless](), [support](), [tutorial](), [tutorials](), [whatsapp]() ### AI overview This tutorial shows how to build a WhatsApp AI support agent with Appwrite Functions and TablesDB. One function receives and stores incoming messages, while a second function retrieves conversation history, uses an order-lookup tool, sends replies through Meta's WhatsApp Cloud API, and stores the responses. Conversation summaries help keep long threads within the model's token budget. ### Source excerpt Turn a WhatsApp number into an AI support agent. Two Appwrite Functions receive messages and reply, TablesDB keeps the conversation history, and a compaction step keeps the context small. ## 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. ## Bolt.new tests Forge, offering more coding-model usage in exchange for anonymized developer sessions DevFeed: [Bolt.new tests Forge, offering more coding-model usage in exchange for anonymized developer sessions]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-15T18:47:23Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI-assisted coding](), [Training AI Models](), [Open Source Models & Datasets](), [Agent Harness]() Tags: [agent](), [agent-harness](), [ai](), [ai-development](), [ai-engineering](), [ai-models](), [coding-agents](), [data](), [developer-tools](), [developers](), [training]() ### AI overview Bolt.new is testing Forge, a research preview for individual Pro subscribers that offers up to 50 times more usage of open-weight coding models in exchange for opting in to share anonymized coding sessions. The sessions may include prompts, source code, fix traces, and conversations with the coding agent, and will support an Arcee AI project to train a trillion-parameter-class open-weight model. ### Source excerpt Bolt.new, StackBlitz's browser-based AI development platform, is testing a new trade with developers: more coding-model usage in exchange for training The post Bolt is giving developers 50x more compute. But there's a catch. appeared first on The New Stack. ## 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 ## 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 ## Safeguarding LLM-Assisted Dev at Guardsquare | Guardsquare DevFeed: [Safeguarding LLM-Assisted Dev at Guardsquare | Guardsquare]() Original publisher: [Read original article]() Author: Noah Fraiture - Backend Engineer Published: 2026-09-15T13:03:38Z Content type: article Language: en Sources: [Guardsquare Blog]() Topics: [Development](), [Large Language Model](), [Cybersecurity](), [Security & Privacy](), [agent observability]() Tags: [ai-agents](), [android](), [containers](), [cybersecurity](), [data](), [dev](), [developer](), [development](), [ios](), [llm](), [llm-gateway](), [thought-leadership]() ### AI overview Guardsquare explains why it adopted LLM-assisted software development despite risks involving sensitive intellectual property, personally identifiable information, and agent access to developer infrastructure. The post describes safeguards including separating sensitive code, isolating agent execution, and controlling model access and outbound data through an LLM gateway and guardrail service. ### Source excerpt This post is not meant to tell you how to use large language models (LLMs) or to claim we've found the right approach. As a cybersecurity company working with particularly sensitive IP, our decision to use LLMs for development was never just about productivity. The broader enthusiasm around LLMs was not itself a reason for us to adopt them quickly. For some time, our position was that the risks outweighed the productivity gains, and incidents involving AI agents elsewhere in the industry reinforced that assessment. ## Beyond the data: what a Business Analyst does at Nubank DevFeed: [Beyond the data: what a Business Analyst does at Nubank]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-15T12:14:41Z Content type: opinion Language: en Sources: [Nubank]() Topics: [Data analysis](), [decision-making](), [context](), [Tech Lead]() Tags: [adoption](), [business-analyst](), [data](), [data-analysis](), [data-analytics](), [data-science-machine-learning](), [decision-making](), [manager](), [tech-lead](), [user-experience]() ### AI overview A first-person account of the Business Analyst role at Nubank, describing how BAs connect data analysis, context, and experimentation to product and business decisions. The article emphasizes clarifying trade-offs, investigating metrics, and collaborating with product and technical roles. ### Source excerpt Understand how Business Analysts at Nubank use data, context, and experimentation to guide product decisions. The post Beyond the data: what a Business Analyst does at Nubank appeared first on Building Nubank. ## 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. ## 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. ## How Databricks' marketers use data 3x more with Genie, an AI analytics assistant DevFeed: [How Databricks' marketers use data 3x more with Genie, an AI analytics assistant]() Original publisher: [Read original article]() Author: Elizabeth Dobbs; Thomas Russell; Katy Yuan; Sydney Sundell Published: 2026-09-15T00:36:33Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Artificial Intelligence](), [data](), [dashboards]() Tags: [ai](), [best-practices](), [dashboards](), [data](), [databricks](), [enterprise](), [governance](), [marketing](), [metrics]() ### AI overview Databricks describes how its marketing organization unified campaign, sales, CRM, web analytics, advertising, and other data in a governed lakehouse. It built Marge, a Genie Agents-based conversational analytics assistant that answers marketers' natural-language questions using governed enterprise data. The article says this approach helped the marketing department use data three times more often in decisions. ### Source excerpt Most marketing teams aspire to be data-driven. In practice, getting a trusted answer,... [Next page]()