# Use Cases Published articles for Use Cases. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 8 Redis Use Cases Engineers Should Know DevFeed: [8 Redis Use Cases Engineers Should Know]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-01T12:54:52Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Redis](), [Data structures](), [Caching](), [real-time](), [Server](), [systems]() Tags: [caching](), [data-structure](), [real-time](), [redis](), [server](), [use-cases]() ### AI overview This article explains that Redis is more than a caching system. It describes how Redis data structures support real-time application patterns such as counters, sessions, queues, leaderboards, rate limiting, coordination, event logs, notifications, and expiring state. ### Source excerpt Most engineers know Redis for caching. But that's only the beginning... ## Building your golden path: Lessons from the trenches DevFeed: [Building your golden path: Lessons from the trenches]() Original publisher: [Read original article]() Author: Pablo Castelo Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [internal developer portal](), [Backstage](), [data]() Tags: [article](), [backstage](), [catalog](), [complexity](), [integrations](), [internal-developer-portal](), [use-cases]() ### AI overview This article shares practical lessons from implementing a golden path through an internal developer portal. It emphasizes that adoption depends more on user experience and accurate catalog data than on technical sophistication, recommending a minimal starting point with a service catalog and a few common-use-case templates. ### Source excerpt Lessons and a "what not to do" compilation for organizations implementing a golden path. It moves beyond the theoretical concept to cover the difficult realities of implementation ## How Expedia Group Builds AI That Lasts at Scale DevFeed: [How Expedia Group Builds AI That Lasts at Scale]() Original publisher: [Read original article]() Author: Xavier Amatriain Published: 2026-07-14T11:01:01Z Content type: opinion Language: en Sources: [Expedia]() Topics: [Artificial Intelligence](), [AI Strategy](), [AI Development](), [Development](), [sdlc](), [Monitoring]() Tags: [agentic-ai](), [ai](), [data](), [evaluation](), [generative](), [governance](), [innovation](), [machine-learning](), [monitoring](), [recommendations](), [sdlc](), [software-engineering](), [tooling](), [use-cases]() ### AI overview Expedia Group describes a framework for building, deploying, and evolving AI systems that remain reliable and scalable over time. The article emphasizes principles covering business value, ownership, governance, evaluation, safe rollout, and monitoring, and describes Agentic Release tollgates that translate those principles into launch checks integrated with the SDLC. ### Source excerpt Expedia Group Technology -- InnovationA framework for how we build, deploy, and evolve AI systems for impact and scalePhoto by Florian Wehde on Unsplash There's an important distinction between Artificial Intelligence (AI) that just works today and AI that lasts at scale. Many companies optimize hard for the first one without ever asking whether they're building the second. Velocity without discipline and strategic direction is a liability, not an asset. The hardest part of building AI at scale isn't getting a model to work once. It's building systems that continue to work, scale beyond individual teams and use cases, and improve consistently over time. Today's AI systems do more than just predict and optimize. They converse, reason, and increasingly take action. An autonomous system making decisions on a traveler's behalf creates a very different set of expectations around reliability, governance, and accountability. As AI takes on more of those roles, the principles behind how these systems operate matter more than ever. At Expedia Group™, we have spent years applying AI and machine learning across the traveler journey from personalization, ranking, and recommendations, to fraud prevention, customer support, and, more recently, generative and agentic AI experiences. That depth of experience is what led us to develop a set of machine learning and AI principles to guide how we build, deploy, and evolve AI systems across the company. The goal is simple: make sure the systems we build create real business value, scale across the company, and operate safely. These principles define how we measure, design, govern, and operate the systems we use. From principles to practice Publishing principles is the easy part. The harder and more important work is turning them into operating mechanisms: recommendations, requirements, tooling, and release processes that teams actually use. At Expedia Group, we have started doing this through Agentic Release tollgates: a set of recommend ## What Is NBASE-T? More Bandwidth Without Replacing Your Cabling DevFeed: [What Is NBASE-T? More Bandwidth Without Replacing Your Cabling]() Original publisher: [Read original article]() Author: j2sw Published: 2026-06-25T03:44:25Z Content type: tutorial Language: en Sources: [Justin Wilson (j2sw)]() Topics: [Ethernet](), [Network](), [Hardware](), [Wi-Fi 7]() Tags: [2-5g-ethernet](), [cabling](), [cat5e](), [cat6](), [ethernet](), [hardware](), [interface](), [nbase-t](), [network](), [network-operations](), [speed](), [structured-cabling](), [use-cases](), [wi-fi-7](), [wifi-7]() ### AI overview This tutorial explains how NBASE-T provides 2.5 Gbps and 5 Gbps Ethernet speeds between traditional Gigabit and 10 Gigabit Ethernet over existing copper cabling. It covers auto-negotiation, RJ-45 compatibility, and use cases such as Wi-Fi access points and security cameras. ### Source excerpt NBASE-T bridges the gap between 1 Gigabit and 10 Gigabit Ethernet by supporting 2.5 Gbps and 5 Gbps over existing copper cabling. Learn how it works, where it makes sense, and what network engineers should look for when deploying and troubleshooting multi-gigabit links. The post What Is NBASE-T? More Bandwidth Without Replacing Your Cabling appeared first on Justin Wilson (j2sw). ## Increased limit for projects per Git repo DevFeed: [Increased limit for projects per Git repo]() Original publisher: [Read original article]() Author: Anthony Shew Published: 2026-06-22T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Git]() Tags: [git](), [hobby](), [projects](), [use-cases]() ### AI overview Vercel has increased the number of projects that Hobby users can connect to a single Git repository from 10 to 25, supporting monorepos and codebases mapped to multiple deployable applications. ### Source excerpt Hobby users can now connect up to 25 projects per repository, up from 10. This makes it easier for users to onboard monorepos and use cases where one codebase maps to many deployable apps. Learn more about repository connection limits. Read more ## Trust Is the Currency, Knowledge Is the Engine DevFeed: [Trust Is the Currency, Knowledge Is the Engine]() Original publisher: [Read original article]() Author: UX Magazine Team Published: 2026-05-26T09:18:36Z Content type: opinion Language: en Sources: [UX Magazine]() Topics: [Artificial Intelligence](), [data]() Tags: [agent](), [ai](), [chatbots](), [enterprise](), [mental-models](), [training](), [use-cases](), [workflow]() ### AI overview Mastercard's central AI and data team receives roughly a thousand AI requests each year, with growing interest in agents rather than chatbots. The article argues that organizations should build AI knowledge and fluency before deployment, because polished demonstrations may conceal serious engineering weaknesses. It also describes a simple framework for prioritizing AI use cases around more secure, smarter, and more personal commerce and a stronger Mastercard. ### Source excerpt What Mastercard's AI lead understands about enterprise transformation that most organizations are still missing. Federico Cohen Freue fields roughly a thousand AI requests a year. That's the incoming volume to Mastercard's central AI and data team -- proposals, ideas, and asks from across a global enterprise trying to figure out where and how to deploy The post Trust Is the Currency, Knowledge Is the Engine appeared first on UX Magazine. ## Top 10 API Gateway Use Cases in System Design DevFeed: [Top 10 API Gateway Use Cases in System Design]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-04-12T12:00:32Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Amazon API Gateway](), [Architecture & Design](), [Microservices](), [API]() Tags: [api-gateway](), [microservices](), [monolith](), [system-design](), [use-cases]() ### AI overview An article about ten API gateway use cases in system design, particularly as systems evolve from monoliths to microservices. The supplied evidence does not detail the individual use cases. ### Source excerpt As your system evolves from a monolith to microservices, a pattern quickly emerges: every service starts rebuilding the same things. ## Presentation: netlab Overview and Use Cases (NetBCN) DevFeed: [Presentation: netlab Overview and Use Cases (NetBCN)]() Original publisher: [Read original article]() Published: 2026-03-05T08:24:00Z Content type: article Language: en Sources: [ipSpace.net blog]() Topics: [Network](), [Automation](), [Testing]() Tags: [automation](), [event](), [netlab](), [network-automation](), [presentation](), [prototyping](), [use-cases]() ### AI overview The article announces a presentation on netlab use cases delivered at the NetBCN event. The presentation covers examples involving rapid prototyping, testing network automation software, and working with vendor technical assistance. ### Source excerpt Yesterday, I had a short presentation on netlab use cases during the NetBCN event. It covered a dozen examples, from rapid prototyping to testing network automation software and arguing with vendor TAC. I added the "use cases" part of the presentation to the standard netlab presentation; you can view the results on ipSpace.net (no account or registration required). Explore ## How Gremlin makes disaster recovery testing easier and faster DevFeed: [How Gremlin makes disaster recovery testing easier and faster]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2026-03-04T00:00:00Z Content type: tutorial Language: en Sources: [Gremlin Blog]() Topics: [Disaster Recovery](), [Resilience](), [Testing](), [Cloud]() Tags: [backup](), [cloud](), [disaster-recovery](), [failover](), [gremlin](), [resilience](), [testing](), [use-cases]() ### AI overview The article explains how Gremlin's Disaster Recovery Testing helps teams test disaster recovery plans by simulating failures such as zone evacuations, region failovers, and cloud-provider outages. It recommends establishing service baselines with test suites, running tests regularly, and repeating them to verify fixes. ### Source excerpt Gremlin's Disaster Recovery Testing makes it easy to run zone evacuations, region failovers, and more for a fraction of the lift of traditional disaster recovery testing. ## SCIM Support Survey DevFeed: [SCIM Support Survey]() Original publisher: [Read original article]() Author: Keycloak Core IAM Team Published: 2026-01-19T00:00:00Z Content type: article Language: en Sources: [Keycloak Blog]() Topics: [Keycloak](), [IAM]() Tags: [community](), [iam](), [idm](), [kerberos](), [keycloak](), [ldap](), [openid-connect](), [saml](), [sso](), [survey](), [use-cases]() ### AI overview Keycloak is targeting version 26.6 to begin supporting System for Cross-domain Identity Management (SCIM). The project is conducting a survey to understand community use cases and prioritize capabilities for the initial scope. ### Source excerpt We are targeting Keycloak 26.6 to start supporting System for Cross-domain Identity Management (SCIM). The initial scope have been defined in this issue, but we want to ensure that we are addressing the most important use cases for our community. In order to better understand your needs and use cases around SCIM, we would greatly appreciate your participation in a brief survey. Your feedback will be invaluable in helping us to prioritize capabilities and ensure we are addressing the use cases that matter most to you. You can find the survey here. ## Introducing OpenAI Academy for News Organizations DevFeed: [Introducing OpenAI Academy for News Organizations]() Original publisher: [Read original article]() Published: 2025-12-17T06:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [OpenAI Academy](), [Artificial Intelligence](), [Learning]() Tags: [ai](), [collaboration](), [global-affairs](), [governance](), [learning](), [openai](), [openai-academy](), [partnership](), [reporting](), [training](), [use-cases]() ### AI overview OpenAI is launching the OpenAI Academy for News Organizations with the American Journalism Project and The Lenfest Institute. The learning hub provides AI training, practical newsroom use cases, open-source resources, and guidance on responsible use for journalists, editors, and publishers. ### Source excerpt OpenAI is launching the OpenAI Academy for News Organizations, a new learning hub built with the American Journalism Project and The Lenfest Institute to help newsrooms use AI effectively. The Academy offers training, practical use cases, and responsible-use guidance to support journalists, editors, and publishers as they adopt AI in their reporting and operations. ## Helping 1,000 small businesses build with AI DevFeed: [Helping 1,000 small businesses build with AI]() Original publisher: [Read original article]() Published: 2025-11-20T06:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [OpenAI Academy](), [AI Strategy]() Tags: [ai](), [ai-adoption](), [communication](), [company](), [course](), [customer](), [marketing](), [openai](), [productivity](), [skills](), [small-business](), [use-cases]() ### AI overview OpenAI Academy is partnering with DoorDash, SCORE, and local business support organizations to host the Small Business AI Jam. The nationwide workshop will give more than 1,000 small business owners hands-on training and help them build AI tools for marketing, customer communication, and everyday business tasks. ### Source excerpt OpenAI is partnering with DoorDash, SCORE, and local organizations to help 1,000 small businesses build with AI. The Small Business AI Jam gives Main Street business owners hands-on tools and training to compete and grow. ## How AI is giving Northern Ireland teachers time back DevFeed: [How AI is giving Northern Ireland teachers time back]() Original publisher: [Read original article]() Author: Lila Ibrahim Published: 2025-11-10T16:50:39Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Generative AI](), [Google]() Tags: [ai](), [education](), [gemini](), [generative-ai](), [google](), [none](), [professional-development](), [use-cases]() ### AI overview A six-month pilot involving 100 Northern Ireland teachers integrated Gemini and Google Workspace tools into classroom work. Participating teachers reported saving an average of 10 hours per week, which they redirected toward student engagement and professional development. The article describes administrative, lesson-planning, content-creation, and accessibility use cases, including using NotebookLM to create podcasts and visual source representations. ### Source excerpt A six-month long pilot program with the Northern Ireland Education Authority's C2k initiative found that integrating Gemini and other generative AI tools saved participating teachers an average of 10 hours per week. ## Cyborg and Redpanda: Secure streaming pipelines for enterprise AI DevFeed: [Cyborg and Redpanda: Secure streaming pipelines for enterprise AI]() Original publisher: [Read original article]() Author: Nicolas Dupont Published: 2025-10-14T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [Streaming](), [Retrieval Augmented Generation (RAG)](), [Encryption](), [Security](), [Embeddings](), [data]() Tags: [ai](), [embeddings](), [encryption](), [enterprise](), [pipelines](), [product](), [rag](), [real-time](), [security](), [security-best-practices](), [streaming](), [use-cases]() ### AI overview This article explains how CyborgDB and Redpanda Connect can secure enterprise AI streaming pipelines. The approach encrypts vector embeddings before storage while supporting semantic search and RAG over sensitive data, helping organizations address security and compliance concerns. ### Source excerpt Stream events from Redpanda Connect into CyborgDB for confidential, real-time Enterprise AI workflows. ## RAG Demystified: From Math to Self-Hosted Code DevFeed: [RAG Demystified: From Math to Self-Hosted Code]() Original publisher: [Read original article]() Author: RisingStack Engineering Published: 2025-09-18T03:02:08Z Content type: tutorial Language: en Sources: [RisingStack]() Topics: [Retrieval Augmented Generation (RAG)](), [Large Language Model](), [foundation-models](), [Self-hosted](), [Artificial Intelligence]() Tags: [ai](), [code](), [large-language-model](), [open-source](), [rag](), [retrieval-augmented-generation](), [self-hosted](), [use-cases]() ### AI overview This introductory article explains Retrieval-Augmented Generation (RAG), foundation models, and why adding specialized or up-to-date context can extend large language model capabilities. It introduces use cases such as support chatbots and tailored summaries and indicates that later chapters will cover RAG-related code, including self-hosted open-source models. ### Source excerpt In today's AI hype you cannot miss the term "RAG," which stands for Retrieval Augmented Generation. In plain English, it stands for customizing large language model reasoning with your own context and knowledge. I searched a lot of resources and AI-generated content for this fairly simple technique to be explained well. I'm still looking for [...] The post RAG Demystified: From Math to Self-Hosted Code appeared first on RisingStack Engineering. ## Lessons from Alaska's outage: Redundant ≠ resilient DevFeed: [Lessons from Alaska's outage: Redundant ≠ resilient]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2025-07-24T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Resilience](), [Availability](), [Cache](), [Database](), [Network]() Tags: [architectures](), [cache](), [cost](), [data](), [network](), [outage](), [resilience](), [testing](), [use-cases]() ### AI overview The article examines Alaska Airlines' outage to explain why redundancy does not automatically provide resilience. It argues that resilience testing, including blackhole experiments that simulate unavailable resources, provides data for balancing redundancy costs against outage risk. ### Source excerpt Redundancy is a core part of designing resilient architectures, but only if you build the right amount of redundancy. Find that sweet spot of redundancy with resilience testing. ## 3 VNet Use Cases to Simplify Internal Access (Without VPNs) DevFeed: [3 VNet Use Cases to Simplify Internal Access (Without VPNs)]() Original publisher: [Read original article]() Author: jackson.pitts@goteleport.com (Jack Pitts) Published: 2025-07-23T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Network](), [Command-line interface](), [Access Control](), [migration](), [cURL](), [container]() Tags: [access-control](), [cli](), [container](), [curl](), [developer-tools](), [migration](), [permissions](), [policies](), [security](), [use-cases](), [vpn]() ### AI overview This article explains how Teleport VNet provides secure access to internal TCP applications and services without traditional VPNs or manual tunnels. It presents use cases involving internal APIs, private registries, developer tools, and custom DNS, and describes identity-based access controls and role-based permissions. ### Source excerpt Discover how to use Teleport VNet for secure internal access without VPN friction. Explore three real-world use cases: CLI access, internal APIs, and VPN migration. ## Navigating Enterprise AI Implementation: Risks, Rewards, and Where to Start DevFeed: [Navigating Enterprise AI Implementation: Risks, Rewards, and Where to Start]() Original publisher: [Read original article]() Author: Matt Jarvis Published: 2025-07-21T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [AI Strategy](), [Artificial Intelligence](), [Security](), [Data Quality](), [Retrieval Augmented Generation (RAG)](), [Large Language Model](), [Structured-data](), [Documentation]() Tags: [ai](), [ai-adoption](), [apis](), [application-security](), [best-practices](), [blog](), [compliance](), [data-quality](), [devops](), [devsecops](), [enterprise](), [governance](), [interest](), [metrics](), [rag](), [security](), [snyk-platform](), [trust](), [use-cases]() ### AI overview This article provides guidance for implementing enterprise AI responsibly, emphasizing trust, governance, security, data quality, and measurable value. It highlights RAG as a practical use case and recommends improving structured data, protecting sensitive information, setting realistic metrics, continuously evaluating results, and addressing employee concerns about automation and changing roles. ### Source excerpt Learn how to navigate enterprise AI implementation with a focus on trust, security, and value. Discover key considerations, high-impact use cases like RAG and content generation, and best practices for building a secure AI foundation. ## Track real-time ad analytics with Snowflake (the easy way) DevFeed: [Track real-time ad analytics with Snowflake (the easy way)]() Original publisher: [Read original article]() Author: Gaurav Thalpati Published: 2025-07-08T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [real-time](), [data](), [Streaming](), [Learning](), [Docker](), [Containers](), [GitHub](), [Command-line interface](), [Artificial Intelligence]() Tags: [ai](), [analysis](), [analytics](), [build](), [confluent](), [containers](), [developer](), [digital-advertising-analytics-platform](), [docker](), [github](), [implement-ad-analytics-with-snowflake](), [kafka](), [metrics](), [pipelines](), [platform](), [real-time](), [real-time-ad-analytics-snowflake](), [real-time-advertising-data-analysis](), [redpanda-snowflake-integration](), [snowflake-ad-analytics-tutorial](), [snowflake-and-redpanda-for-ad-analytics](), [snowflake-connector-for-ad-analytics](), [snowflake-digital-ad-tracking](), [snowflake-real-time-data-pipeline](), [streaming](), [tech](), [track-digital-ad-engagement-metrics](), [tutorial](), [use-cases]() ### AI overview This tutorial explains how to build a real-time digital advertising analytics pipeline with Redpanda and Snowflake. It uses impression and click data to calculate engagement metrics such as click-through rate, with a no-code, configuration-driven Snowflake connector and Snowpipe Streaming. The tutorial also covers running a self-managed Redpanda cluster with Docker for learning and testing. ### Source excerpt Learn how to build scalable, high-performance pipelines for digital advertising use cases with Snowflake and Redpanda -- a developer-first real-time data platform. ## Welcome the NVIDIA Llama Nemotron Nano VLM to Hugging Face Hub DevFeed: [Welcome the NVIDIA Llama Nemotron Nano VLM to Hugging Face Hub]() Original publisher: [Read original article]() Author: Amanda Saunders; Amala Sanjay Deshmukh; Kateryna Chumachenko; Annie Surla; Karan; Tuomas Rintamaki; Matthieu Le; Yu Yao; Chen Cui; Timo Roman; Zhiding Yu; Mike Ranzinger Published: 2025-06-27T21:09:27Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [vlm](), [llama](), [Nemotron](), [Nvidia](), [hugging face](), [multimodal](), [Benchmark](), [NeMo](), [datasets]() Tags: [benchmark](), [data](), [datasets](), [hugging-face](), [idp](), [llama](), [multimodal](), [nemo](), [nemotron](), [nvidia](), [ocr](), [performance](), [recognition](), [train](), [use-cases](), [vision](), [vlm](), [workflows]() ### AI overview NVIDIA Llama Nemotron Nano VL is an 8B vision-language model for intelligent document processing. Available on Hugging Face, it extracts and interprets text, tables, charts, diagrams, and other information from complex documents. ### Source excerpt NVIDIA Llama Nemotron Nano VL is a state-of-the-art 8B Vision Language Model (VLM) designed for intelligent document processing, offering high accuracy and multimodal understanding. Available on Hugging Face, it excels in extracting and understanding information from complex documents like invoices, receipts, contracts, and more. ## Edge Functions: Deploy from the Dashboard + Deno 2.1 DevFeed: [Edge Functions: Deploy from the Dashboard + Deno 2.1]() Original publisher: [Read original article]() Author: Saxon Fletcher; Nyannyacha; Lakshan Perera Published: 2025-04-01T07:00:00Z Content type: article Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [dashboards](), [Deno](), [API](), [Command-line interface](), [Code](), [Docker](), [Syntax Highlighting](), [Artificial Intelligence](), [ci](), [Git]() Tags: [ai](), [api](), [apis](), [ci](), [cli](), [code](), [docker](), [edge](), [git](), [refactoring](), [testing](), [update](), [use-cases]() ### AI overview Supabase now lets developers create, edit, test, and deploy Edge Functions directly from the Supabase Dashboard. The update adds Deno support, templates, an inline AI Assistant, a built-in tester, Docker-free deployment through the Supabase CLI, and public APIs for custom integrations and workflows. ### Source excerpt You can create, test, and deploy Edge Functions directly from the Supabase Dashboard. ## Finding unused targets with bazel DevFeed: [Finding unused targets with bazel]() Original publisher: [Read original article]() Author: Keith Smiley Published: 2025-03-24T18:00:00Z Content type: tutorial Language: en Sources: [Keith Smiley]() Topics: [Graphs](), [coding](), [Code](), [toolchains]() Tags: [bazel](), [build](), [code](), [coding](), [dependencies](), [graph](), [toolchains](), [use-cases]() ### AI overview This article explains how to use bazel query to inspect a build graph and identify unused targets. It presents a query that subtracts dependencies of top-level targets from all first-party targets, then discusses handling edge cases with kind filters and special tags such as allow-unused. It also describes tagging toolchains and other targets whose dependencies should be treated as used. ### Source excerpt Once you've fully migrated a codebase to bazel, one of the many advantages is that you can easily inspect your build graph using bazel query. One of the many things you can do with queries is write scripts to enforce coding standards, or in today's example, find unused targets that can lead to discovering unused code. The simplest version of this query starts like this: let all_targets = //... in let top_level_targets = tests($all_targets) union kind("(.*_binary) rule", $all_targets) in $all_targets - deps($top_level_targets) This initial version discovers all your first party targets, and then subtracts the dependencies of all "top level" targets, so that any remaining targets are considered unused. The idea of "top level" targets is better defined as: anything that you consider to be important enough that its dependencies are used. Depending on your codebase you might want to exclude test targets from this so that targets only in the dependencies of test targets are diagnosed as unused (this won't work if you have intentional testonly dependencies, although you could special case those as shown below). Once you have this initial query, you can start iterating in order to handle in edge cases. For example it's likely that you have some targets that aren't binaries or tests, but are considered used. There are 2 approaches I would recommend to handle this. First you can continue to build out the kind filter: kind("(.*_binary|platform|test_suite) rule", $all_targets) The downside with this approach is it can get unwieldy quickly. I like adding rules here that have many uses, but for other one off cases another approach you can use is to expand the query to look for special tags: let all_targets = //... in let top_level_targets = tests($all_targets) union kind("(.*_binary|platform|test_suite) rule", $all_targets) in let allowed_unused = attr(tags, allow-unused, $all_targets) in $all_targets - deps($top_level_targets) - $allowed_unused Then you can add tags = ["allow-un ## Maximizing your reliability on AWS DevFeed: [Maximizing your reliability on AWS]() Original publisher: [Read original article]() Author: Andre Newman Published: 2025-01-13T00:00:00Z Content type: tutorial Language: en Sources: [Gremlin Blog]() Topics: [Amazon Web Services](), [Shared Responsibility Model](), [Amazon EC2](), [Availability](), [cloud-infrastructure](), [Amazon Elastic Kubernetes Service](), [App]() Tags: [amazon](), [amazon-ec2](), [availability](), [aws](), [cloud](), [compute](), [how-to](), [infrastructure](), [learn](), [shared-responsibility-model](), [use-cases]() ### AI overview This article explains how to improve workload reliability on AWS despite the cloud platform's limited visibility and shared responsibility model. It examines failure modes and mitigation options for services including Amazon EC2, Amazon EKS, and ECS, with emphasis on resilience, recoverability, fault tolerance, and EC2 auto-scaling groups. ### Source excerpt Learn how to maximize reliability when running workloads on Amazon EC2, EKS, ECS, and other services. ## Calendars in Postgres using Foreign Data Wrappers DevFeed: [Calendars in Postgres using Foreign Data Wrappers]() Original publisher: [Read original article]() Author: Bo Lu Published: 2024-12-20T07:00:00Z Content type: tutorial Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [API keys](), [wasm](), [API](), [JSON](), [Databases](), [SQL]() Tags: [api](), [api-keys](), [data](), [database](), [developer](), [integration](), [json](), [postgres](), [sql](), [use-cases](), [wasm]() ### AI overview This tutorial shows how to connect Cal.com with Supabase using Postgres Foreign Data Wrappers. It explains how to configure a Wasm wrapper and foreign server, define foreign tables, query calendar data, and create Cal.com bookings directly from Postgres transactions. ### Source excerpt Calendar data integration with Cal.com using Wasm foreign data wrapper on Supabase [Next page]()