# daily Published articles for daily. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Automatically Archive Old NetBox Branches DevFeed: [Automatically Archive Old NetBox Branches]() Original publisher: [Read original article]() Author: Rick Donato Published: 2026-08-14T08:00:15Z Content type: release Language: en Sources: [Packet Coders - Learn Network Automation]() Topics: [NetBox](), [Job](), [Database]() Tags: [automatically](), [daily](), [job](), [netbox](), [reduce](), [release](), [tips]() ### AI overview NetBox Branching v1.1.2 introduces the auto_archive_days setting, which runs a daily background job to archive merged branches older than a specified number of days. Automatic archival is disabled by default, and archived branches cannot be reverted. ### Source excerpt A common question we get from students using NetBox Branching is: How can I automatically remove stale branches from NetBox? Therefore, I thought I'd share the latest addition to NetBox Branching, introduced in v1.1.2. This release adds the auto_archive_days setting, allowing you to automatically ## Engineering stories behind the Medium Daily Digest Algorithm: Part 4 DevFeed: [Engineering stories behind the Medium Daily Digest Algorithm: Part 4]() Original publisher: [Read original article]() Author: Raphael Montaud Published: 2025-08-25T18:31:30Z Content type: article Language: en Sources: [Medium]() Topics: [Algorithm](), [data]() Tags: [algorithm](), [daily](), [database](), [improvements](), [issue](), [machine-learning](), [programming](), [recommendation-system](), [software-engineering](), [technical]() ### AI overview This engineering article examines how Medium investigated repetitive Daily Digest recommendations reported by power users and made incremental improvements to the underlying algorithm. The series reports that the work improved conversion to paying memberships by 10%, reduced operating costs, and made the system more flexible and maintainable. ### Source excerpt Cross-Digest diversification In this part 4, we'll see how we went from investigating a few complaints from digest power users to improving our digest recommendations across the board. Intro: This is a 4-part series breaking down improvements to the algorithm behind the Medium's Daily Digest over the past year. When we started this work, the Digest was suboptimal -- and since it's a huge distribution surface, reaching millions of readers every day, we started working on incremental improvements.By the end of these projects, the digest was 10% more likely to convert users to paying members, less expensive to run, more flexible and easier to maintain and it's now providing higher quality recommendations for all our users, including our "power readers".This is told through the lens of our engineering team tackling a series of challenges one by one. Medium has a small team but we operate on a big scale. We're working our way through some technical debt and at the same time, striving to provide the best experience for our readers. This is the source of many interesting challenges.I hope this series helps you understand how the recommendations algorithm work and can help others who are facing similar technical challenges.Some Concepts Here's a little cheat sheet with some concepts you may need to follow along this story You may need this to understand the rest of this postUser Complaints After we shipped all the changes mentioned in the previous installments of this series, we started seeing some support tickets coming in related to the digest: User complaints that started off our investigation I think we should appreciate the level of thoughtfulness our users put in those support tickets. We review those carefully and we take pride in reading and answering every support tickets. Those were forwarded to the recommendation team and we immediately thought that maybe we oversteered to much when we removed some of the filtering rules for the digest. User digests were too repet ## Why We Believe in In-Person Onboarding at Teleport DevFeed: [Why We Believe in In-Person Onboarding at Teleport]() Original publisher: [Read original article]() Author: info@goteleport.com (Amanda Pessica) Published: 2025-07-10T00:00:00Z Content type: opinion Language: en Sources: [Teleport]() Topics: [trust](), [Job]() Tags: [3](), [behavior](), [ceo](), [culture](), [daily](), [distributed](), [money](), [new-job](), [onboarding](), [people](), [remote](), [screen](), [slack](), [team](), [travel](), [trust](), [velocity](), [zoom]() ### AI overview Teleport explains why it brings new hires together in person for onboarding despite the costs and logistics. The company says the three-day program helps employees connect, build trust, understand its culture, and ramp up faster. ### Source excerpt In a remote-first world, in-person onboarding is still invaluable. At Teleport, we see it as a strategic investment in connection, trust, and team velocity. ## Migrating from Logseq to Obsidian DevFeed: [Migrating from Logseq to Obsidian]() Original publisher: [Read original article]() Author: Harsh Shandilya Published: 2025-03-09T10:29:00Z Content type: tutorial Language: en Sources: [Posts on Harsh Shandilya]() Topics: [Obsidian](), [migration](), [task management](), [configuration]() Tags: [configuration](), [daily](), [git](), [logseq](), [migrate](), [notes](), [obsidian](), [plugins]() ### AI overview The author describes migrating a daily journaling workflow from Logseq to Obsidian after Logseq's Electron version became end-of-life and was removed from Nixpkgs on security grounds. The article covers file changes, enabled plugins, configuration settings, and unresolved migration issues. ### Source excerpt Logseq's clunky apps and glacial development pace finally motivated me to migrate my journaling to Obsidian, here's how I did it. ## Running Effective Agile Standup Meetings DevFeed: [Running Effective Agile Standup Meetings]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2019-11-06T00:00:00Z Content type: opinion Language: en Sources: [Scott Johnson]() Topics: [Agile](), [Self-organizing Team]() Tags: [agile](), [daily](), [effective](), [google](), [management](), [meetings](), [offsite](), [scrum](), [startup](), [team](), [zoom]() ### AI overview A personal account of effective daily Agile stand-up meetings for a team of eight. The recommended practices include starting on time regardless of attendance, scheduling meetings five minutes after the hour, using a consistent meeting URL, and following a simple three-question format. ### Source excerpt So my new gig has moved to doing daily agile standup meetings and while I've done these before, I don't think I've ever seen them done quite so consistently well. And the credit for this goes to my boss, Dave Sifry, who is our lead facilitator. We are a team of 8 people and, so far, we have brought them in at under the scheduled 15 minutes per meeting every time. Here are the techniques that we are using: Start on time. If everyone isn't there, well, the meeting happens anyway. Ideally, if the team lead isn't on time, the meeting happens anyway. Scheduled for 5 minutes after the hour. This allows people in other meetings to make it. A consistent Url. This is the one down side to Zoom - the meeting can't start if the leader isn't present. If we used Google Meet then that wouldn't be an issue. A simple format. A ridiculously simple, fast format: Here's what I'm up to today Here's what I'm stuck on. How can I help anyone else? While I'm generally not a fan of meetings, agile stand ups, done this way, tend to bring developers into line. Even if a developer isn't good with email - or good with text messages - normally they can handle "show up at time X for 15 minutes every day". In closing, while I'm not a huge proponent of heavy weight agile processes (think scrum / agile velocity), I am really enjoying daily stand ups again. Recommended. ## Running a Daily Agile Standup Meeting DevFeed: [Running a Daily Agile Standup Meeting]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2019-11-01T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Agile](), [Standup Meeting]() Tags: [agile](), [daily](), [standup-meeting]() ### AI overview A concise guide to running a daily Agile standup meeting, organized around what participants are doing, what is blocking them, and how they can help one another. ### Source excerpt Simple agile stand ups Here's what I'm up to today Here's what I'm stuck on How can I help anyone else ## How to Setup a Scheduled Scala Spark Job DevFeed: [How to Setup a Scheduled Scala Spark Job]() Original publisher: [Read original article]() Published: 2019-03-27T00:00:00Z Content type: tutorial Language: en Sources: [Curalate]() Topics: [Apache Spark](), [Scala](), [Amazon Web Services](), [Amazon S3](), [Hadoop](), [SDKs](), [Command-line interface]() Tags: [aws](), [cli](), [daily](), [data](), [ec2](), [emr](), [github](), [hadoop](), [how-to](), [job](), [pipeline](), [s3](), [scala](), [scheduled](), [sdk](), [spark](), [weekly]() ### AI overview A tutorial for packaging and deploying a Scala Spark job as a fat JAR, uploading it to Amazon S3, and configuring it to run on a schedule through AWS Data Pipeline and EMR. ### Source excerpt Have you written a Scala Spark job that processes a massive amount of data on an intimidating amount of RAM and you want to run it daily/weekly/monthly on a schedule on AWS? I had to do this recently, and couldn't find a good tutorial on the full process to get the spark job running. Included in this article and accompanying repository is everything you need to get your Scala Spark job running on AWS Data Pipeline and EMR. Code Repo This tutorial is not going to walk you through the process of actually writing your specific Scala Spark job to do whatever number crunching you need. There are already plenty of resources available (1, 2, 3) to get you started on that. The code template for setting up a Spark Scala job is available in this GitHub repo. Assuming that you have already written your Spark Job and are only using the AWS Java SDK to connect to your AWS data stores, drop your code in the Main function of SparkJob.scala and run the deploy.sh script to upload the fat jar to your S3 bucket. If you do take other dependencies, then it may take some extra work on your part. To run a Scala Spark job on AWS you need to compile a fat jar that contains the byte code for your job and all of the libraries it needs to run. This project already has the sbt-assembly plugin setup and a assemblyMergeStrategy set up to package the Spark, Hadoop, and AWS SDK together in the fat jar. If you need to add in other libraries that do not play well with each other, or are using a noncompatible version of Spark for this current repo, there are a few good resources available to help you through the needed build.sbt modifications. Outside of the previously mentioned needed changes you need to set a few parameters in the deploy.sh script. Mainly the deploymentPath to your specific S3 bucket, adding a profile to the AWS CLI command to upload to your specific S3 bucket if it's private, and changing the resulting fat jar name if you please. The deploy script uses the AWS CLI to upload the fat ## Announcing Automated Daily Backups for the Firebase Database DevFeed: [Announcing Automated Daily Backups for the Firebase Database]() Original publisher: [Read original article]() Author: Matthew Tse Published: 2016-10-19T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Databases](), [Google Cloud Platform (GCP)](), [JSON](), [Compression]() Tags: [automated](), [backups](), [cloud](), [cloud-storage](), [compression](), [daily](), [firebase](), [firebase-database](), [json](), [launch](), [realtime-database](), [recovery](), [release](), [storage](), [tutorials](), [updates]() ### AI overview Firebase announces self-service automated daily backups for Firebase Database data and rules. Blaze customers can export backups to Google Cloud Storage, use Gzip compression, apply an optional 30-day lifecycle policy, view backup history, and manually queue snapshots. ### Source excerpt News, tutorials, and updates from the Firebase team. ## ReactOS Printing Stack Development DevFeed: [ReactOS Printing Stack Development]() Original publisher: [Read original article]() Published: 2015-07-25T00:00:00Z Content type: article Language: en Sources: [Front Page on ReactOS Website]() Topics: [ReactOS](), [Windows]() Tags: [compatibility](), [daily](), [free](), [open-source](), [opensource](), [os](), [react](), [reactos](), [win32](), [winapi](), [windows]() ### AI overview A bachelor thesis project is developing a previously nonexistent printing stack for ReactOS. The work tracks daily progress in the colins-printing-for-freedom branch and considers compatibility with Windows printing APIs. ### Source excerpt As some of you may already know, my university gave me the unique opportunity to choose a ReactOS topic for my bachelor thesis. Three months ago, my decision fell on the Printing Stack, which was totally non-existing in ReactOS at that time. Since then, you can watch my daily progress in the colins-printing-for-freedom branch. The Printing Stack as found in modern Windows® versions is a beast of its own. Microsoft® never wanted to sacrifice backwards compatibility with each new Windows version, so the widely used higher level GDI Printing APIs (like StartDoc or EndDoc found in gdi32. ## Automatically compact CouchDB databases in version 0.11.x DevFeed: [Automatically compact CouchDB databases in version 0.11.x]() Original publisher: [Read original article]() Author: Tom Published: 2012-11-08T00:00:00Z Content type: tutorial Language: en Sources: [Tomasz Gągor]() Topics: [couchdb](), [Databases](), [Script](), [Bash](), [cURL](), [HTTP](), [JSON]() Tags: [bash](), [couchdb](), [curl](), [daily](), [databases](), [http](), [json](), [linux](), [performance](), [post](), [script]() ### AI overview This tutorial presents a Bash script that can run from cron to compact all CouchDB databases on version 0.11.x. It states that regular compaction helps manage database growth, performance, and space usage. ### Source excerpt CouchDB databases on version 0.11.x swell very fast. They should be compacted daily for best performance and space usage. Here is my script that could be run in cron and will compact all databases: #!/bin/bash IP="10.0.0.121" DBS=`curl -sS -X GET http://$IP:5984/_all_dbs | sed -r "s/([,\"[])|(\])+/ /g"` for d in $DBS; do curl -H "Content-Type: application/json" -X POST http://$IP:5984/$d/_compact done More informations about compacting could be found here external link (also for version 1.2.x).