# Push Notifications Published articles for Push Notifications. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Inside the Geofence: Faster Pickups Start Here DevFeed: [Inside the Geofence: Faster Pickups Start Here]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-05-12T13:14:04Z Content type: article Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [Testing](), [Mobile](), [real-time](), [Wi-Fi](), [data](), [App]() Tags: [app](), [automation](), [geofencing](), [gps](), [location](), [location-based](), [mobile](), [notifications](), [push-notifications](), [quality-assurance](), [real-time](), [technology](), [testing](), [wi-fi](), [workflows]() ### AI overview The article explains how McDonald's integrates geofencing into mobile field testing to evaluate digital features under real-world conditions such as drive-thru lanes, parking areas, and restaurant environments. It reports that this approach improved accuracy, timing, and reliability for experiences including Ready on Arrival, curbside pickup, loyalty, notifications, and order-preparation workflows. ### Source excerpt Geofencing field testing turns noisy GPS signals into reliable arrivals for drive-thru, curbside, and beyond. By Chandrani Gupta Chowdhury, Sr. Manager, Quality Assurance Engineering Quick Bytes: Lab testing alone can't fully capture the real-world conditions that shape mobile experiences inside McDonald's restaurants As customers drive toward a restaurant, pull into a parking spot, or move through a drive-thru lane, geofencing-enabled field testing shows how digital features respond in real time This approach improved accuracy, timing, and reliability -- strengthening customer experiences like Ready on Arrival (ROA), curbside, and loyalty Mobile experiences don't happen in perfect conditions -- they happen in cars, parking lots, drive-thru lanes, and busy restaurants where signals fluctuate and customer behavior is unpredictable. For McDonald's, ensuring digital features work reliably in these real-world environments is critical to delivering fast, seamless experiences at scale. To meet that challenge, teams began integrating geofencing into field testing, allowing them to observe how mobile features perform as customers approach, arrive, and move through restaurant spaces. Inside the digital boundary of the restaurant Geofencing is a location-based technology that leverages GPS, Wi-Fi, and cellular data to create virtual boundaries around physical locations, enabling applications to trigger predefined actions when users enter or exit those zones. The benefits of incorporating geofencing into field testing are significant. From an operational standpoint, it helps ensure restaurants are prepared to receive and fulfill digital orders efficiently, reducing customer wait times and improving order accuracy. From a customer experience perspective, geofencing enables timely, relevant, and seamless interactions, reinforcing trust in the digital journey. Within the McDonald's ecosystem, geofencing can be applied across restaurant footprints -- including parking areas and drive ## Introducing Action Push Native DevFeed: [Introducing Action Push Native]() Original publisher: [Read original article]() Author: Jacopo Beschi Published: 2025-08-18T17:00:00Z Content type: release Language: en Sources: [37signals Dev]() Topics: [Rails](), [notifications](), [push-notification](), [Mobile](), [rate-limiting](), [HTTP](), [Amazon Web Services]() Tags: [aws](), [http](), [mobile](), [notifications](), [push-notification](), [push-notifications](), [rails](), [rate-limiting]() ### AI overview 37signals introduces Action Push Native, an open-source Rails gem for sending push notifications directly through Apple Push Notification service and Google Firebase Cloud Messaging. The gem supports retries, rate limiting, and automatic removal of inactive devices. ### Source excerpt A Rails gem for sending push notifications to mobile platforms. ## Replacing AWS Step Functions with SQS FIFO queues and cutting the cost in half DevFeed: [Replacing AWS Step Functions with SQS FIFO queues and cutting the cost in half]() Original publisher: [Read original article]() Author: Alexei Liulin Published: 2025-03-14T14:47:27Z Content type: article Language: en Sources: [Homegate Engineering Blog - Medium]() Topics: [Amazon Simple Queue Service (SQS)](), [AWS Step Functions](), [Amazon Web Services]() Tags: [aws](), [aws-step-functions](), [batching](), [cloud-computing](), [cost](), [cost-optimization](), [email](), [fifo](), [lambda](), [notifications](), [push-notifications](), [queue](), [sqs](), [sqs-queue](), [workflow]() ### AI overview The article describes SMG Real Estate's Search Alerts system, which batches property-match notifications for delivery by email or mobile push notification. It explains the original AWS Step Functions implementation and its transition-cost problem, motivating a more cost-efficient approach using SQS FIFO queues. ### Source excerpt One of the core services for SMG Real Estate is Search Alerts -- a service that allows users to be notified about new properties published on ImmoScout24 and Homegate. For example, if a user is looking for a new apartment having N rooms and costing less than X, and not finding any matches now, they can create a search alert with those search criteria. When a new property matching those criteria is published, the user will be notified either via email or a mobile push notification. About the Search Alerts system Users can choose the frequency of notifications -- either every 5 minutes or every 4 hours (assuming there are any to be delivered). The vast majority of search alerts are configured with the 5-minute frequency. Just to get an idea of the scale, we have: Millions of published properties match saved search alerts every day More than a million emails and push notifications sent daily Check out our "Homegate's fast and modern search experience helps users find their dream home" blog post for an overview of how Search Alerts work. The Original Implementation In the original implementation we used an AWS Step Function to achieve the 5- minute batching of notifications: When a new property matching a search alert arrived, the StartSendNotificationProcess lambda would start a Step Function execution specific to that search alert. All it did was waiting for 5 minutes while the matches accumulated in the matches-{searchAlertId} SQS queue, which was programmatically created for that specific search alert If more matching listings arrived during the 5-minute waiting period, the StartSendNotificationProcess lambda attempted to start the step function execution with the same name. When it failed with the ExecutionAlreadyExists error, we knew there was already a SF for waiting. That way the deduplication of notification processes was guaranteed. After 5 minutes of waiting time, the Step Function execution proceeded with triggering the LoadMatches lambda that received the acc ## Windmill for AI Workflows - Investing.com Case Study DevFeed: [Windmill for AI Workflows - Investing.com Case Study]() Original publisher: [Read original article]() Author: Yonathan Adest Published: 2025-02-24T00:00:00Z Content type: article Language: en Sources: [Windmill Blog]() Topics: [Automation](), [data-processing](), [Artificial Intelligence](), [etl](), [tracing](), [Logging](), [PostgreSQL](), [airflow]() Tags: [ai](), [airflow](), [automation](), [case-study](), [case-study-testimonial-fintech-investing](), [data-processing](), [docker-compose](), [embeddings](), [etl](), [fintech](), [human-review](), [investing](), [pdf](), [postgresql](), [push-notifications](), [recommendations](), [testimonial](), [windmill](), [workflows]() ### AI overview This Investing.com case study describes how its AI team uses Windmill to orchestrate content processing and distribution, stock-analysis report generation, and ETL workflows. The workflows use webhooks, AI models, vector embeddings, human review, and PostgreSQL, while Windmill provides tracing, logging, and Docker Compose deployment. ### Source excerpt This is a testimonial from Yonathan Adest, CTO at Investing.com, about how Windmill has helped them to automate their workflows and improve their data processing capabilities. ## Droidcon NYC 2024 App Update - Tadeas Kriz DevFeed: [Droidcon NYC 2024 App Update - Tadeas Kriz]() Original publisher: [Read original article]() Published: 2024-09-13T00:00:00Z Content type: release Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [compose-multiplatform](), [Android](), [iOS](), [App](), [multiplatform]() Tags: [android](), [compose](), [compose-multiplatform](), [design](), [droidcon](), [features](), [google-play](), [ios](), [kotlin](), [kotlin-multiplatform](), [push-notifications](), [swiftui](), [update]() ### AI overview Touchlab describes updates to the Droidcon NYC 2024 Android and iOS apps, including a Compose Multiplatform venue map, push notifications for attendee messaging, background schedule updates, and Material 3 theming. It also previews a universal Droidcon app for future events. ### Source excerpt We've updated the Droidcon NYC 2024 app with a new design and features. The app now uses Material 3 theming, and includes push notifications for in-conference messaging. ## Apple's iOS 17.4 Changes Threaten Progressive Web App Features in the EU DevFeed: [Apple's iOS 17.4 Changes Threaten Progressive Web App Features in the EU]() Original publisher: [Read original article]() Author: Alex Russell Published: 2024-02-25T00:00:00Z Content type: opinion Language: en Sources: [Alex Russell]() Topics: [PWA](), [iOS](), [Web](), [Mobile]() Tags: [browsers](), [competition](), [compliance](), [device-apis](), [dma](), [geofencing](), [ios](), [push-notifications](), [pwa](), [shortcuts](), [web](), [webdev]() ### AI overview The article argues that Apple's iOS 17.4 changes in the EU will restrict progressive web app capabilities, including storage, push notifications, app-like interfaces, and device APIs. It contrasts the threatened changes with continued PWA support on Windows and Android and frames them as a response to the EU Digital Markets Act. ### Source excerpt Update: OWA is out with an open letter appealing to Apple to do better. If you care about the future of the web, I encourage you to sign it, particularly if you live in the EU or build products for the common market. After weeks of confusion and chaos, Apple's plan to kneecap the web has crept into view, menacing a PWApocalypse as the March 6th deadline approaches for compliance with the EU's Digital Markets Act (DMA). The view from Cupertino. The DMA requires Apple to open the iPhone to competing app stores, and its lopsided proposal for "enabling" them is getting most of the press. But Apple knows it has native stores right where it wants them. Cupertino's noxious requirements will take years to litigate. Meanwhile, potential competitors are only that. But Cupertino can't delay the DMA's other mandate: real browsers, downloaded from Apple's own app store. Since it can't bar them outright, it's trying to raise costs on competitors and lower their potential to disrupt Apple's cozy monopoly. How? By geofencing browser choice and kneecapping web apps, all while gaslighting users about who is breaking their web apps. The immediate impact of iOS 17.4 in the EU will be broken apps and lost data, affecting schools, governments, startups, gamers, and anyone else with the temerity to look outside the one true app store for even a second. None of this is required by the DMA, as demonstrated by continuing presence of PWAs and the important features they enable on Windows and Android, both of which are in the same regulatory boat. The data loss will be catastrophic for many, as will the removal of foundational features. Here's what the landscape looks like today vs. what Apple is threatening: PWA Capability Windows Android iOS 17.3 iOS 17.4 App-like UI ✅ ✅ ✅ ❌ Settings Integration ✅ ✅ ✅ ❌ Reliable Storage ✅ ✅ ✅ ❌ Push Notifications ✅ ✅ ✅ ❌ Icon Badging ✅ ✅ ✅ ❌ Share-to PWA ✅ ✅ ❌ ❌ App Shortcuts ✅ ✅ ❌ ❌ Device APIs ✅ ✅ ❌ ❌ Apple's support for powerful web apps wasn't stellar, b ## Explainable and Accessible AI: Using Push Notifications to Broaden the Reach of ML at Headspace DevFeed: [Explainable and Accessible AI: Using Push Notifications to Broaden the Reach of ML at Headspace]() Original publisher: [Read original article]() Author: Headspace Published: 2021-10-18T19:08:11Z Content type: article Language: en Sources: [Stories by Headspace on Medium]() Topics: [Machine learning](), [recommendation systems](), [Cloud](), [Back end](), [Android](), [iOS](), [Web]() Tags: [ai](), [android](), [backend](), [cloud](), [headspace](), [ios](), [machine-learning](), [ml](), [notifications](), [push-notification](), [push-notifications](), [recommendations](), [web]() ### AI overview Headspace describes infrastructure that combines machine-learning recommendations, cloud services, and Braze to deliver personalized content through push notifications to members who have not recently opened the app. The article explains the limitations of relying on in-app access and says the initial experiment was implemented as an evergreen production model. ### Source excerpt Author: Matt Linder / Co-Author: Koyuki Nakamori TL;DR Headspace wanted to serve personalized content to members who had not recently opened the app We built out infrastructure that allowed us to leverage our Cloud services, plus the 3rd party Braze Customer Engagement platform, to serve Machine Learning-recommended content to members via push notifications Our first experiment doing so got GREAT results We implemented that experiment as an evergreen model in production You should, too Introduction and Problem Statement Headspace's core products are iOS, Android, and web-based apps that focus on improving the health and happiness of their users through mindfulness, meditation, sleep, exercise, and focus content. Machine learning models are core to our user experiences by offering recommendations that engage our users with relevant, personalized content that builds consistent habits in their lifelong journey. Headspace also has an amazing Lifecycle marketing team, who do incredible work in providing regular communications -- through email, push notifications, in-app modals, and other surfaces -- that further our users' engagement, helping them on their journey from prospects to members to advocates. Until recently, those two aspects -- Machine Learning and the out-of-app communication channels served by Lifecycle- were totally separate. Traditionally, users consume our ML models' personalized recommendations by Navigating to one of the app's tabs / views that contains content (for example, our Today tab). Sending a request from the app client to ask our backend content services to load content to serve users. Our content services then forward the request to our Prediction Service, which supplies a content recommendation for that user (or, if none is available, generates a default fallback content to serve). The Problem This approach has three primary limitations: It isn't able to directly respond to the immediate user's context. For instance, if a user has recently sear ## How To Stay On Top Of Today's Information Overload DevFeed: [How To Stay On Top Of Today's Information Overload]() Original publisher: [Read original article]() Published: 2020-06-22T00:00:00Z Content type: tutorial Language: en Sources: [Markus Hatvan]() Topics: [email](), [notifications](), [context]() Tags: [email](), [notifications](), [productivity](), [push-notifications]() ### AI overview The article offers personal strategies for managing information overload, including organizing email into folders, periodically cleaning them, unsubscribing from unwanted newsletters, and disabling push notifications to reduce distractions. ### Source excerpt As a naturally curious person with a thirst for knowledge, it is easy to get swamped by the sheer amount of information that is available and directed at you. ## Firebase Predictions: User Segmentation and Monetization Strategies DevFeed: [Firebase Predictions: User Segmentation and Monetization Strategies]() Original publisher: [Read original article]() Author: Vladimir Jovanović Published: 2020-05-22T09:44:00Z Content type: tutorial Language: en Sources: [Vladimir Jovanović]() Topics: [Firebase](), [Machine learning](), [Users]() Tags: [android](), [firebase](), [firebase-predictions](), [machine-learning](), [programming](), [push-notifications](), [remote-config]() ### AI overview This introduction explains how Firebase Predictions applies machine learning to Firebase Analytics data to segment users by their likelihood of taking specific actions within the next 7 days. It presents retention and monetization strategies using Remote Config, ads, push notifications, and in-app messages. ### Source excerpt Firebase provides a wide variety of tools, something for every developer and something for every problem. But did you know that it has a tool that enables you to see into the future?! ## ⏰Scheduling FCM Push Notifications🔔 On Device -- Android📱 DevFeed: [⏰Scheduling FCM Push Notifications🔔 On Device -- Android📱]() Original publisher: [Read original article]() Author: Shreyas Patil Published: 2019-12-19T02:44:55Z Content type: tutorial Language: en Sources: [Shreyas Patil's Blog]() Topics: [Android](), [Firebase]() Tags: [android](), [firebase](), [how-to](), [on-device](), [others](), [push-notifications](), [scheduling]() ### AI overview A tutorial on scheduling Firebase Cloud Messaging push notifications locally on Android. It describes sending a data payload to an FCM topic in advance, processing it on the device, and scheduling the notification so it can appear even if the user is offline at the scheduled time. ### Source excerpt Learn how to schedule FCM push notifications on-device in Android without expensive cloud cron jobs, using AlarmManager and WorkManager. ## FIFA and IPL 2018: Recalibrating Our Architecture for Scalability DevFeed: [FIFA and IPL 2018: Recalibrating Our Architecture for Scalability]() Original publisher: [Read original article]() Author: Pranay Warke Published: 2018-07-18T07:00:34Z Content type: article Language: en Sources: [CleverTap]() Topics: [Architecture & Design](), [Scalability](), [data-processing](), [Amazon Web Services]() Tags: [api](), [architecture](), [aws](), [clevertap](), [clevertap-technology-engineering](), [data-processing](), [engineering](), [push-notifications](), [real-time](), [rest-api](), [scalability](), [technology]() ### AI overview CleverTap describes how it prepared its architecture to handle large traffic spikes during the 2018 FIFA World Cup and Indian Premier League. The article outlines data collection, processing, storage, and message delivery layers, with a focus on real-time ingestion and notifications. ### Source excerpt What a fantastic summer for sports fans! Between the Indian Premier League (IPL) cricket season and the FIFA World Cup, The post FIFA and IPL 2018: Recalibrating Our Architecture for Scalability first appeared on CleverTap. ## How to remove duplicates in large datasets DevFeed: [How to remove duplicates in large datasets]() Original publisher: [Read original article]() Author: Suresh Kondamudi Published: 2016-04-19T17:17:05Z Content type: tutorial Language: en Sources: [CleverTap]() Topics: [datasets](), [Data structures](), [hash]() Tags: [bloom-filter](), [data-and-analytics](), [data-and-analytics-data-insights-engineering-data-science](), [data-insights](), [data-science](), [data-structures](), [datasets](), [engineering](), [hash](), [how-to](), [push-notifications]() ### AI overview This tutorial explains how probabilistic data structures, especially Bloom filters, can reduce memory requirements when identifying duplicate push notification tokens in datasets ranging from hundreds of millions to billions of records. ### Source excerpt Dealing with large datasets is often daunting. With limited computing resources, particularly memory, it can be challenging to perform even The post How to remove duplicates in large datasets first appeared on CleverTap. ## Fixing notification icon for Android Lollipop and above DevFeed: [Fixing notification icon for Android Lollipop and above]() Original publisher: [Read original article]() Author: Mrinal Parekh Published: 2016-04-06T14:51:40Z Content type: tutorial Language: en Sources: [CleverTap]() Topics: [Android](), [Image]() Tags: [android](), [app](), [app-development](), [engineering](), [push-notifications](), [push-notifications-technology-app-development-engineering-android](), [technology]() ### AI overview A tutorial on fixing Android notification icons that appear as solid white squares or render incorrectly after Android Lollipop. It explains Android's alpha-only icon requirements and recommends using a PNG with a transparent background. ### Source excerpt Have you noticed a notification bar with an icon that is just a solid white square? Or are you having The post Fixing notification icon for Android Lollipop and above first appeared on CleverTap. ## 2015: a year in review DevFeed: [2015: a year in review]() Original publisher: [Read original article]() Author: Monica Dinculescu Published: 2015-12-22T00:00:00Z Content type: opinion Language: en Sources: [Monica Dinculescu]() Topics: [Chromium](), [JavaScript](), [Internet](), [Web platform](), [C++](), [browser](), [GitHub]() Tags: [blog-posts](), [c-plus-plus](), [chromium](), [github](), [javascript](), [push-notifications](), [web-platform](), [year-in-review]() ### AI overview A personal year-in-review describing burnout after the launch of Chrome's Profiles UI, the author's departure from Chromium, and joining Polymer to work on JavaScript and the web platform. The author also reflects on projects, talks, and lessons from the year. ### Source excerpt I've never really done a year in review. One day, I'd like to open source my goals, but since I'm still a chicken, this is a baby step towards that. Plus, this is one of the first years I'm really proud of, and things that you're proud of tend to live on the Internet, for posterity. Here's what my GitHub contributions say about it: Burning out The year started off really poorly. My team had just shipped the new Profiles UI in Chrome, after a year and a half of hard work, and it was met with a looooot of Internet anger. On one side we had data to prove that the change we did was right, which made the powers that be want to stick by it; on the other side I had Twitter, who was calling me names and wanting me fired. Kind of ironic, since I was just the person who implemented the feature and had no power to change it. I think what burnt me out wasn't waking up to a stream of negative emails and tweets, it was knowing that there was absolutely nothing I could do about it other than wait. So I started working on dumb side projects to feel better. I made a link aliaser. I bought dumb domains. I wrote blog posts about the only thing that I knew, which was working on Chromium. I noticed that not working on Chromium made me happy. So I bit the bullet, left Chromium, and joined Polymer. Joining Polymer Looking back, I picked Polymer for a bunch of silly reasons that ended up working out spectacularly well. I wanted to leave Montreal. I wanted to work on JavaScript, since it was the only thing keeping me going. I didn't want to commute to Mountain View, which reduced my options by like a billion percent, and I wanted to ship things. Polymer had all of that. So on April 15, I packed my cat and my books and moved to San Francisco. Polymer is my dream job. I get to write code that I'm genuinely passionate about. I get to try to change the web platform, and talk about why I think we're doing the right thing. Most importantly, I get to ship something everyday. It turns out that's a ## W3C Push API Crash Course DevFeed: [W3C Push API Crash Course]() Original publisher: [Read original article]() Author: Nick Comer (nicholas@zumba.com) Published: 2015-05-29T00:00:00Z Content type: tutorial Language: en Sources: [Zumba]() Topics: [Chrome](), [notifications](), [W3C](), [Google Chrome](), [API](), [Google]() Tags: [api](), [chrome](), [google-chrome](), [notifications](), [push-notifications](), [w3c]() ### AI overview A tutorial introducing push notifications in Google Chrome through the W3C Push API. ### Source excerpt Getting started with push notifications in Google Chrome ## Signal 2.0: Private messaging comes to the iPhone DevFeed: [Signal 2.0: Private messaging comes to the iPhone]() Original publisher: [Read original article]() Published: 2015-03-02T00:00:00Z Content type: release Language: en Sources: [Signal Blog]() Topics: [iphone](), [Messaging](), [Android](), [Cryptography](), [Mobile](), [Open Source]() Tags: [android](), [cryptography](), [development](), [github](), [iphone](), [messaging](), [mobile](), [notifications](), [open-source](), [pull-requests](), [push-notifications](), [signal]() ### AI overview Signal 2.0 brings end-to-end encrypted group, text, picture, and video messaging between Signal on iPhone and TextSecure on Android, without SMS or MMS fees. It also combines private calling and messaging in one app, with asynchronous delivery and push notifications. ### Source excerpt Today we're releasing Signal 2.0, with support for TextSecure private messaging. It is now possible to send end-to-end encrypted group, text, picture, and video messages between Signal on iPhone and TextSecure on Android, all without SMS and MMS fees. Signal 2.0 blends private phone calls and private messaging into a single frictionless interface, allowing you to focus on what's important by quickly organizing your conversations using an inbox/archive work flow. Read more...