# DRIVE NVIDIA DRIVE is an automotive autonomous-vehicle development platform providing SDKs, developer kits, operating-system components, and compute hardware for building and testing AV software. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## From Livestream To Library: Publishing Flock To Fedora's Session Recordings DevFeed: [From Livestream To Library: Publishing Flock To Fedora's Session Recordings]() Original publisher: [Read original article]() Author: Akashdeep Dhar Published: 2026-09-09T08:00:00Z Content type: article Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [Tooling](), [DRIVE](), [Google](), [VLC](), [Quartz]() Tags: [2025](), [2026](), [article](), [community](), [drive](), [events](), [fedora-project-community](), [ffmpeg](), [flock-2026](), [google](), [livestream](), [livestreams](), [marketing](), [media](), [tooling](), [youtube]() ### AI overview This article documents the process of turning raw Flock To Fedora livestream footage from 2025 and 2026 into published session recordings. It covers vendor and platform use, footage organization, metadata and thumbnail preparation, timestamp annotation with VLC Media Player, and upload workflows for YouTube and PeerTube. ### Source excerpt Intro After months of good old fashioned struggle with Google Drive and YouTube Studio, we were finally able to prepare the video sessions from the 2025 and 2026 Flock To Fedora event livestreams. These are now released to our YouTube and PeerTube communities. This article describes the details of the entire process that got us from the raw footage to the [...] ## Compute that takes any shape DevFeed: [Compute that takes any shape]() Original publisher: [Read original article]() Author: Luke Phillips-Sheard Published: 2026-09-01T07:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Cloud](), [Provisioning](), [cloud-infrastructure](), [Vercel](), [cpu](), [IO](), [DRIVE]() Tags: [agent](), [cloud](), [code](), [compute](), [cpu](), [image](), [infrastructure](), [io](), [memory](), [pricing](), [real-time](), [sandbox](), [storage](), [vercel]() ### AI overview Vercel presents Fluid as a unified compute system that assembles machines for different workloads, changes configuration dynamically, and absorbs burst capacity in real time. It supports builds, sandboxes, and functions, using isolated virtual machines, custom images, and separate storage connections. ### Source excerpt Workloads differ in how long they run, how much memory they take, and how much of the environment they control. That previously meant a different compute primitive for each job, so product iterations required provisioning infrastructure, not just working on a feature. We built Vercel so that none of this has to be your problem. The compute layer is a single system with a simple job. Take any workload, assemble the machine it needs, swap configuration on the fly, and absorb burst capacity in real time. We call this Fluid. Builds ran on it first, then sandboxes, and now functions. If you've shipped on Vercel, you've been on Fluid without knowing it. Fluid compute now runs over 15 million builds a day, 25 million sandboxes a week, and a trillion requests a month. A history of compute Changing a machine used to mean changing it by hand. You went down to the computer store, bought a hard drive or a stick of RAM, and swapped it in yourself. Then you could rent bare metal when you needed it, and use it to serve a website, host a database, or send email. Then the cloud let you request a machine in any configuration you wanted, choose its operating system, use it, and throw it away when you were done. For agents, even the cloud is too slow. A standard VM can't provision fast enough to keep up with how they work, but this is where Fluid excels. How a workload runs on Fluid When a workload comes in, Fluid assembles a machine to fit its shape. If an agent needs to run code, Hive provides an isolated VM, usually one already warm, so it's ready instantly. Your own image boots on top as the environment. A Drive connects, and your files are right where you left them, because storage was never tied to the machine. Each workload needs something different. A build is compute-bound, so it wants a beefy machine, heavy on CPU and memory. A function is IO-bound, loading specific code to run the instant a request lands, usually on a small VM. A sandbox is flexibility-bound, taking whatever ## Unifi UNAS 2 and UNAS 4. - One Year Later DevFeed: [Unifi UNAS 2 and UNAS 4. - One Year Later]() Original publisher: [Read original article]() Author: Rob Andrews Published: 2026-08-26T11:01:27Z Content type: comparison Language: en Sources: [NAS Compares]() Topics: [UNAS 2](), [UNAS 4](), [DRIVE](), [Ubiquiti](), [systems]() Tags: [drive](), [hdd](), [network-attached-storage-nas](), [review](), [storage](), [ubiquiti](), [unas](), [unas-2](), [unas-2-1-year-later](), [unas-2-bad](), [unas-2-good](), [unas-2-nas](), [unas-2-vs-4](), [unas-2026](), [unas-4](), [unas-4-1-year-later](), [unas-4-nas](), [unas-4-ne-year-later](), [unas-4-review](), [unas-review](), [unas-vs-qnap](), [unas-vs-synology](), [unas-vs-ugreen](), [uncategorised](), [unifi-2026](), [unifi-2027](), [unifi-enas](), [unifi-nas](), [unifi-nas-2026](), [unifi-nas-review](), [unifi-unas-2](), [unifi-unas-4](), [unifi-unas-6](), [unifi-unas-pro]() ### AI overview A one-year-later review of Ubiquiti's UniFi UNAS 2 and UNAS 4 desktop storage systems, including their positioning within the UniFi Drive platform and the UNAS 2's two-HDD-bay design for smaller deployments. ### Source excerpt UniFi UNAS 2 and UNAS 4 1 Year Later Review When Ubiquiti introduced the UNAS 2 and UNAS 4 in September 2025, the company was attempting to bring its comparatively simple UniFi Drive storage platform into a pair of compact desktop systems. The UNAS 2 targeted smaller deployments with 2 HDD bays, while the UNAS [...] ## Backblaze Drive Stats: How an Open Dataset Powers Academic and AI/ML Research DevFeed: [Backblaze Drive Stats: How an Open Dataset Powers Academic and AI/ML Research]() Original publisher: [Read original article]() Author: Stephanie Doyle Published: 2026-08-13T15:08:38Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [dataset](), [DRIVE](), [Machine learning](), [Transformer](), [Disk image]() Tags: [ai-ml](), [articles](), [b2cloud](), [cloud-storage](), [data](), [drive](), [featured](), [featured-cloud-storage](), [hard-drive-stats](), [ml](), [research](), [source](), [storage]() ### AI overview The article explains how Backblaze Drive Stats evolved from an internal hard-drive reliability tool into an open dataset used in academic and AI/ML research. It describes the dataset's real-world scale, quarterly publication, SMART attributes, labeled failures, broad manufacturer coverage, and use in hard-drive failure prediction research. ### Source excerpt Backblaze Drive Stats has been cited in more than 105 academic papers and AI/ML projects since 2018. Explore the research it powers and download the dataset. The post Backblaze Drive Stats: How an Open Dataset Powers Academic and AI/ML Research appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Drives for Vercel Sandbox in Private Beta DevFeed: [Drives for Vercel Sandbox in Private Beta]() Original publisher: [Read original article]() Author: Tom Lienard Published: 2026-06-05T00:01:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [DRIVE](), [Vercel](), [mount](), [SDKs](), [Command-line interface]() Tags: [cli](), [dependencies](), [drive](), [mount](), [production](), [sandbox](), [sandboxes](), [sdk](), [storage](), [vercel]() ### AI overview Vercel Sandbox introduces Drives in private beta, providing persistent attachable storage that remains available across disposable sandbox lifecycles. Drives can preserve agent workspaces, repositories, dependencies, and build outputs, and can be managed through the beta SDK or CLI. ### Source excerpt Vercel Sandbox now supports drives in private beta. Drives are persistent, attachable storage with a lifecycle independent from any sandbox. Create a drive once, then mount it at a configurable path when starting a sandbox. When the sandbox stops, the drive remains available to attach to a later sandbox. Install the beta SDK (@vercel/sandbox@beta) or beta CLI (sandbox@beta), then create and mount a drive: Sandbox Drives are useful for: Keeping agent workspaces across disposable sandboxes Retaining cloned repositories, dependencies, and build outputs Managing data independently from the sandbox lifecycle During the private beta, a drive can be mounted read-write by one sandbox at a time. Sandbox drives should not be used for production data while in private beta. Sign up here to join the waitlist, learn more in the docs, or read the complete guide. Read more ## Learn ChatGPT workflows for finance teams DevFeed: [Learn ChatGPT workflows for finance teams]() Original publisher: [Read original article]() Published: 2026-04-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Data analysis](), [DRIVE](), [CSV]() Tags: [analysis](), [business](), [chatgpt](), [communication](), [data](), [drive](), [finance](), [learn](), [openai-academy](), [tools](), [workflows]() ### AI overview This article explains how finance teams can use ChatGPT to structure messy inputs, analyze spreadsheets and CSV files, draft clear financial communication, and standardize recurring work such as variance commentary, forecasts, and close updates. It emphasizes that ChatGPT supports finance judgment rather than replacing it. ### Source excerpt Learn practical ChatGPT workflows for financial analysis, reporting, planning, and decision-ready communication. ## Why traditional motivation techniques can undermine intrinsic motivation in tech teams DevFeed: [Why traditional motivation techniques can undermine intrinsic motivation in tech teams]() Original publisher: [Read original article]() Author: Tom Published: 2025-09-26T00:00:00Z Content type: opinion Language: en Sources: [Tomasz Gągor]() Topics: [DRIVE]() Tags: [autonomy-mastery-purpose](), [best-practices](), [daniel-pink-drive](), [effective-leadership-strategies](), [intrinsic-motivation](), [leadership](), [leadership-best-practices](), [motivation-in-tech-teams](), [people](), [practical](), [strategies](), [team](), [team-engagement](), [tech]() ### AI overview This workshop-style article examines how traditional rewards, pressure, and micromanagement can undermine intrinsic motivation, especially in complex and creative tech work. Drawing on Daniel Pink's Drive, it encourages team leaders to support autonomy, mastery, and purpose. ### Source excerpt Discover why traditional motivation techniques can backfire in tech teams and how leaders can foster intrinsic motivation through autonomy, mastery, and purpose. Learn actionable strategies to empower your team and boost engagement, based on insights from Daniel Pink's "Drive." ## Removing Google as a Single Point of Failure Part 2: Gmail DevFeed: [Removing Google as a Single Point of Failure Part 2: Gmail]() Original publisher: [Read original article]() Published: 2020-03-18T00:00:00Z Content type: article Language: en Sources: [Jake Wharton]() Topics: [Google](), [data](), [DRIVE](), [Homelab]() Tags: [backing-up](), [data](), [google](), [home-server](), [rsync](), [security]() ### AI overview The article explains how to reduce dependence on Google as a single point of failure while continuing to use Google services as the source of truth. It covers backing up Google Photos and Google Drive to a home server and rsync.net, then describes using a custom domain and Fastmail to preserve email access and support catch-all addresses. ### Source excerpt I want to remove Google as a single point of failure in my life. In the first blog post on this subject I detailed my setup for backing up Google Photos and Google Drive contents onto my home server and remotely to rsync.net. Left out of that post was a solution for Gmail because I hadn't found one yet. Now I have. Source of truth That first post started with an important qualification: This does not mean that I'm going to stop using Google products. Quite the opposite. Gmail, Google Photos, and Google Drive will remain the source-of-truth for all of the things I listed above. What's different is that should Google disappear tomorrow (or just my account) I would lose no data. This was easy to achieve with Photos and Drive because the data is all there is. With email that's unfortunately not true. Incrementally backing up the email data is pretty straightforward-we'll get into that shortly. But with Gmail your email address is still tied to the @gmail.com domain. So if my account or all of Google disappears, I won't be able to receive any more email. Of course the "easy" fix here is to just use a domain that I control. Obviously I own jakewharton.com, and I intend to set that up, but I wanted something shorter. I've owned cob.io for many years with the intention of setting up j@cob.io, but I go by "Jake". Luckily the last few years have seen an influx of new TLDs so I managed to grab ke.fyi. Say hello to j@ke.fyi! Having an email on my own domain doesn't address the problem that there's still hundreds or thousands of services that I've given the Gmail address to. While I can migrate many, there are inevitably those which I can't or that I simply don't know exist. The old address needs to remain working. Fastmail After browsing a few hosted email solutions, I settled on Fastmail (Note: referral link). In addition to a positive recommendation from a friend, there were a few key motivating factors. Domain catch-all A popular feature of Gmail is the ability to append a + ## Removing Google as a Single Point of Failure DevFeed: [Removing Google as a Single Point of Failure]() Original publisher: [Read original article]() Published: 2020-02-19T00:00:00Z Content type: article Language: en Sources: [Jake Wharton]() Topics: [Google](), [data](), [DRIVE](), [Persistence](), [Docker](), [Docker Container](), [API]() Tags: [api](), [backup](), [data](), [docker](), [docker-container](), [drive](), [google](), [persistence](), [sync]() ### AI overview The article describes a plan to reduce dependence on Google as a single point of failure while continuing to use Gmail, Google Photos, and Google Drive as sources of truth. It recommends exporting data with Google Takeout and using recurring exports plus rclone to incrementally synchronize Drive contents into redundant storage, with Docker-based monitoring for sync health. ### Source excerpt I want to remove Google as a single point of failure in my life. They have two decades of my email. They have two decades of my photos. They have the only copy of thousands of documents, projects, and other random files from the last two decades. Now I trust Google completely in their ability to correctly retain my data. But I think it's clear that over the last 5 years the company has lost something intrinsically important in the way it operates. I no longer trust them not to permanently lock me out of my account. And I say this as a current Google employee. This year I've embarked on a mission to reclaim ownership of my data. This does not mean that I'm going to stop using Google products. Quite the opposite. Gmail, Google Photos, and Google Drive will remain the source-of-truth for all of the things I listed above. What's different is that should Google disappear tomorrow (or just my account) I would lose no data. Get Your Data Step 1: Takeout The first thing you need to do today is visit takeout.google.com and export your Gmail, Photos, and Drive data (and anything else you want). This will send you links to a set of 50GB .tar.gz files of your data that you can download. That is, provided it works. It took me 5 attempts of exporting just my Photos data to have one succeed. Persistence pays off, though, so don't give up even though this is a slow process. Get. Your. Data. Google providing the Takeout service is amazing, but as far as a backup solutions go it is woefully inadequate. It's an extremely manual, slow, and non-incremental process. However, it's also comprehensive in ways that no other solution can match. Because of that, I have a monthly recurring task to perform a Takeout. Do it during a boring meeting so it feels less of a chore and more of a welcome distraction. Seriously, do this today! Step 2: Drive Sync The rclone tool can incrementally sync your Google Drive contents. It will also take Google's proprietary document formats and convert them into ## Devcon4 Videos and Pictures Released! DevFeed: [Devcon4 Videos and Pictures Released!]() Original publisher: [Read original article]() Author: Deva the Devcon Unicorn Published: 2018-12-10T00:00:00Z Content type: release Language: en Sources: [Ethereum Foundation Blog]() Topics: [Ethereum](), [Google](), [DRIVE]() Tags: [announce](), [devcon](), [drive](), [ethereum](), [events](), [google](), [photos](), [presentation](), [videos]() ### AI overview The article announces that presentation videos and photos from Ethereum's Devcon4 are available. It identifies SlidesLive and YouTube as viewing options and provides access to event photos through Google Drive. ### Source excerpt We're happy to announce that the presentation videos from Devcon4 are now available for viewing! As promised, we recorded sessions from the following rooms: Spectrum (Main Stage), Prism (Side Stage), Radiant Orchid (Breakout Room), and Ultra Violet (Breakout Room). This year you have two viewing options: SlidesLive and... ## Re-adding failed drive in mdadm DevFeed: [Re-adding failed drive in mdadm]() Original publisher: [Read original article]() Author: Tom Published: 2013-12-12T00:00:00Z Content type: tutorial Language: en Sources: [Tomasz Gągor]() Topics: [Mdadm](), [DRIVE]() Tags: [array](), [automatically](), [failed](), [i](), [issue](), [linux](), [mdadm](), [raid]() ### AI overview A practical account of re-adding a previously failed drive to an mdadm RAID5 array after a hot spare had rebuilt the array. The author reports testing the drive, adding it as a spare, failing the slower replacement drive, removing it, and re-adding the tested drive. ### Source excerpt Yesterday I have problem with fglrx witch cause ugly system reset. After that, one of my drives was marked as failed in RAID5 array. Hotspare was automatically used to rebuild array. But this hotspare is the oldest and slowest drive I've got... After rebuild I've tested failed drive and it was fine - no bad block, no any other issue - so I wanted it running back in array. What I do: ## The benefits of having data DevFeed: [The benefits of having data]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2012-01-10T00:00:00Z Content type: opinion Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [data](), [DRIVE](), [Google](), [systems]() Tags: [analysis](), [articles](), [data](), [drive](), [engineering](), [google](), [measurement](), [models](), [testing]() ### AI overview The article compares Google and Seagate studies of hard-drive failure rates and temperature. It argues that Google's analysis of failure data from more than 100,000 drives provides a better basis for conclusions than Seagate's accelerated-aging tests and statistical models, whose assumptions are not sufficiently justified. ### Source excerpt The benefits of having data Two ways to look at drive failures and temperature. Almost all recent articles and papers I have read on hard drive failure rates refer to either Failure Trends in a Large Disk Drive Population from Google, or Estimating Drive Reliability in Desktop Computers and Consumer Electronics Systems from Seagate. Despite both sounding and looking authoritative, these papers come to some wildly different conclusions, and couldn't be more different in their approach. How does temperature affect drive failure rate? The Seagate paper says an increase from 25C to 30C increases it by 27%. The Google paper suggests a decrease of around 10%. How can the two most widely used studies differ by so much? It's really because these papers use completely different approaches: the Google study uses simple analysis, while the Seagate paper uses powerful and sophisticated models, accelerated aging, and complex statistical tools. Despite sounding less authoritative, the Google paper is much better. The Seagate paper doesn't actually present the results of testing drives at different temperatures. Instead, all the drives were tested using a standard accelerated aging approach, in an oven heated to 42C. Another standard accelerated aging technique, the Arrhenius Model, was used to estimate the effect of temperature on failure rates. The Seagate paper goes on to use Weibull modeling, and a fairly sophisticated Bayesian approach to estimating the Weibull parameters. The underlying, and unmentioned, assumption is that the failure rate of drives is proportional to the reaction rate constant, or the speed that an unlimited chemical reaction would proceed at a given temperature. No attempt is made to justify this choice, other than appealing to standard textbooks describing the approach. The Google paper, on the other hand, doesn't use any statistical concepts that would be unfamiliar to an undergraduate engineering student. Instead, they use the failure data from over a h ## ReactOS Newsletter 65: Cross-Platform Storage API and Sound Mixer Progress DevFeed: [ReactOS Newsletter 65: Cross-Platform Storage API and Sound Mixer Progress]() Original publisher: [Read original article]() Published: 2009-10-11T00:00:00Z Content type: article Language: en Sources: [Front Page on ReactOS Website]() Topics: [ReactOS](), [cross-platform](), [x86](), [DRIVE](), [systems]() Tags: [api](), [audio](), [boot](), [compatibility](), [cross-platform](), [filesystem](), [free](), [open-source](), [os](), [partitions](), [react](), [reactos](), [storage](), [win32](), [winapi](), [x86]() ### AI overview ReactOS Newsletter 65 reports progress on a new name-based storage and bootloader API intended to improve cross-platform support, along with ongoing filesystem-driver updates, a compatibility layer, and sound mixer enumeration. ### Source excerpt Disks and Partitions One of the non-cross platform aspects of ReactOS was in how the bootloader dealt with disks and partitions. The naming scheme used was very x86 centric and also was limited in which partitions it could access. Hervé Poussineau started working on a new API to remove the limitations, one based on names instead of drive numbers. Previously on platforms like PPC and ARM, developers would have needed to give numbers to each storage device they wanted to use.