# DRIVE Published articles for DRIVE. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin DevFeed: [How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-15T16:55:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Groq 3 LPX](), [LPX](), [NVIDIA Vera Rubin](), [Vera Rubin NVL72](), [groq](), [Inference](), [Low Latency](), [long-context]() Tags: [agentic-ai-generative-ai](), [ai-factory](), [ai-inference](), [ai-platform](), [drive](), [dsx](), [groq](), [groq-3-lpx](), [inference](), [inference-performance](), [latency](), [low-latency](), [lpx](), [nvidia-vera-rubin](), [nvl72](), [performance](), [power-management](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview This NVIDIA developer article explains how Groq 3 LPX uses deterministic execution across 256 LPU chips to support low-latency inference on NVIDIA Vera Rubin. It describes compiler-scheduled execution and power-management techniques including Preemptive Power and Clock Period Synthesis. ### Source excerpt Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize... ## Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX DevFeed: [Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX]() Original publisher: [Read original article]() Author: Gerardo Delgado Published: 2026-09-14T15:00:52Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [Local AI](), [Artificial Intelligence](), [NVIDIA RTX](), [Windows](), [GeForce](), [qwen](), [NVIDIA DGX](), [Cloud](), [GitHub](), [Slack](), [Google](), [Microsoft]() Tags: [agentic-ai](), [ai](), [ai-agents](), [cloud](), [dgx-spark](), [drive](), [geforce](), [github](), [google](), [local-ai](), [microsoft](), [nvidia-dgx](), [nvidia-rtx](), [open-source](), [qwen](), [rtx-pro](), [rtx-spark](), [slack](), [windows]() ### AI overview Perplexity is adding Portable Computer to its Windows app for compatible NVIDIA GeForce RTX PCs and NVIDIA RTX PRO Workstations. The local agent uses NVIDIA-accelerated models to plan multistep tasks, analyze files, and keep sensitive information on the device, while users can authorize cloud support for more advanced research and reasoning. ### Source excerpt As local models become more capable, AI agents can handle more work directly on a PC while keeping sensitive information on the device. Portable Computer is a local version of the agent Perplexity Computer that plans and carries out multistep tasks. Accelerated by NVIDIA GPUs, it uses local models to analyze data, bring together information [...] ## 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 [...] ## 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 [...] ## How to use Gemini Notebook to learn faster DevFeed: [How to use Gemini Notebook to learn faster]() Original publisher: [Read original article]() Author: Jeff Morhous Published: 2026-08-26T10:06:43Z Content type: tutorial Language: en Sources: [The AI-Augmented Engineer]() Topics: [Learning](), [Artificial Intelligence](), [App](), [Google](), [Obsidian](), [Markdown]() Tags: [ai](), [app](), [drive](), [gemini](), [how-to](), [learn](), [learning](), [markdown](), [notes]() ### AI overview This tutorial explains how to use Gemini Notebook as a source-grounded learning tool. It covers adding sources such as pasted text, uploaded files, links, and Google Drive content, then generating study materials including briefing documents, quizzes, flashcards, and podcasts. ### Source excerpt Gemini Notebook can help you learn anything twice as fast. I'll show you how. ## 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 ## Cloudflare DDoS Threat Report H1 2026: 1 Tbps attacks soar as DNS floods and geopolitical tensions drive a new wave DevFeed: [Cloudflare DDoS Threat Report H1 2026: 1 Tbps attacks soar as DNS floods and geopolitical tensions drive a new wave]() Original publisher: [Read original article]() Author: Cloudforce One Published: 2026-08-11T13:00:00Z Content type: article Language: en Sources: [Cloudflare Blog]() Topics: [DDoS](), [Cloudflare](), [Threat Research](), [Cloudforce One](), [Network](), [data](), [Cybercrime](), [High Profile Threats]() Tags: [analysis](), [attacks](), [cloudflare](), [cloudforce-one](), [data](), [ddos](), [dns](), [drive](), [global](), [government](), [industry](), [iran](), [media](), [network](), [radar](), [streaming](), [threat-report]() ### AI overview Cloudflare's H1 2026 DDoS Threat Report analyzes attacks from January through June 2026. It highlights a 519% quarter-over-quarter increase in attacks exceeding 1 Tbps, a shift toward DNS and CLDAP reflection and amplification vectors, and the influence of geopolitical events on attack patterns. The report also covers attack volumes, an April peak, and the possible impact of Operation PowerOFF. ### Source excerpt In the first half of 2026, Cloudflare detected a 519% surge in hyper-volumetric DDos attacks across its network. These attacks were driven heavily by DNS and CLDAP reflection vectors. This report breaks down how major geopolitical conflicts reshaped the global cyber threat landscape. ## How to Add the 007revad Community Package Source to Synology DSM DevFeed: [How to Add the 007revad Community Package Source to Synology DSM]() Original publisher: [Read original article]() Author: Rob Andrews Published: 2026-08-10T16:00:01Z Content type: tutorial Language: en Sources: [NAS Compares]() Topics: [Synology](), [synology nas](), [Package Management](), [Script](), [Homelab](), [Open Source](), [Hardware](), [Transcodings](), [GitHub](), [Network]() Tags: [007revad](), [007revad-mod](), [007revad-synology](), [airconnect](), [aqc-unlock](), [command-line](), [compatibility](), [configuration](), [data-recovery](), [dave-russell](), [documentation](), [drive](), [dsm-7](), [github](), [hardware](), [homebridge](), [homelab](), [librespeed](), [nas](), [nas-apps](), [nas-mods](), [nas-software](), [nascompares](), [network](), [network-attached-storage-nas](), [ookla-speedtest](), [open-source](), [openspeedtest](), [package-management](), [plex](), [projects](), [ssh](), [storage](), [synocr](), [synology](), [synology-007revad](), [synology-10gbe](), [synology-2-5g-driver](), [synology-2026](), [synology-2027](), [synology-5gbe-driver](), [synology-add-ons](), [synology-apps](), [synology-community](), [synology-community-packages](), [synology-container-manager](), [synology-nas](), [uncategorised]() ### AI overview This tutorial explains how to add 007revad's unofficial community package source to Synology DSM Package Center. It describes how the source makes selected community packages easier to discover, install, and update, while noting that package availability depends on the NAS model, CPU architecture, and DSM version. ### Source excerpt A New and Easier Way to Mod Your Synology NAS Synology's gradual shift towards business and enterprise users has changed what some home, enthusiast and homelab users can do with DSM, particularly when it comes to 3rd-party drives, SSDs, network adapters, hardware transcoding and other unofficial modifications. Community-developed scripts have helped restore or extend many [...] ## Monitor Your Drive Health with Performance Co-Pilot on Fedora DevFeed: [Monitor Your Drive Health with Performance Co-Pilot on Fedora]() Original publisher: [Read original article]() Author: Paul Evans Published: 2026-08-10T08:00:00Z Content type: article Language: en Sources: [Fedora Magazine]() Topics: [Fedora](), [Monitoring](), [Grafana](), [telemetry]() Tags: [data](), [diagnostics](), [drive](), [farm](), [fedora-project-community](), [grafana](), [hdd](), [health](), [home-server](), [metrics](), [monitor](), [monitoring](), [nvme](), [open-source](), [performance](), [performance-metrics](), [self-hosted](), [server](), [smart](), [ssd](), [telemetry](), [wwid]() ### AI overview This article explains how to monitor drive health on Fedora using Performance Co-Pilot (PCP). It covers SMART metric collection for HDDs, SSDs, and NVMe drives, NVMe error-log decoding, WWID-based tracking, Seagate FARM telemetry, continuous metric collection, and Grafana dashboards for viewing drive-health trends. ### Source excerpt You wake up one morning to find your home server unresponsive, after some investigation you discover a failed NVMe drive taking your self-hosted services and data with it. Perhaps you're a system administrator and a workstation's SSD has been silently accumulating errors for months, and now a user is reporting corrupted files. Drive failures are [...] ## Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super DevFeed: [Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-04T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [foundation-models](), [Cosmos](), [model-deployment](), [Reinforcement learning](), [Inference](), [GitHub](), [hugging face](), [Open Source Models & Datasets]() Tags: [accelerate](), [automotive-transportation](), [autonomous](), [benchmarks](), [cosmos](), [customization](), [data](), [developer-tools-techniques](), [developers](), [development](), [diffusion](), [drive](), [driving](), [evaluation](), [featured](), [fine-tuning](), [generate](), [generation](), [github](), [hugging-face](), [inference](), [learning](), [models](), [nvidia](), [reasoning](), [robot-navigation](), [robotics](), [simulation-modeling-design](), [top-stories]() ### AI overview NVIDIA Alpamayo 2 Super is an open 34-billion-parameter reasoning vision-language-action model for autonomous vehicle development. It combines NVIDIA Cosmos 3 Super Reasoner with a diffusion-based Action Expert to generate trajectories, reasoning traces, meta-actions, scene answers, and auto-labels across development workflows. ### Source excerpt Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding, and data... ## 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 ## Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery DevFeed: [Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery]() Original publisher: [Read original article]() Published: 2026-05-19T17:52:00Z Content type: news Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [coding](), [Google](), [Benchmark](), [Data Science](), [Software](), [Time Series](), [Mathematics]() Tags: [ai](), [benchmark](), [benchmarks](), [code](), [coding](), [drive](), [general-science](), [google](), [machine-intelligence](), [natural-language-processing](), [open-source-models-datasets](), [time-series]() ### AI overview Google Research introduces Empirical Research Assistance (ERA), an AI tool that uses Gemini to write and optimize scientific code. ERA searches literature, explores solutions, evaluates results, and uses tree search to optimize code for computational experiments. Tests across genomics, public health, satellite imagery, neuroscience, time-series forecasting, and mathematics show expert-level benchmark performance. ERA also helped build the Computational Discovery prototype, which is being made available through Google Labs and Gemini for Science. ### Source excerpt General Science ## 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. ## UniFi Dream Machine Pro Max: Features, Shadow Mode, and Network Throughput Testing DevFeed: [UniFi Dream Machine Pro Max: Features, Shadow Mode, and Network Throughput Testing]() Original publisher: [Read original article]() Author: Techno Tim Published: 2024-04-23T13:00:00Z Content type: comparison Language: en Sources: [Techno Tim]() Topics: [Network](), [Hardware]() Tags: [cpu](), [drive](), [hardware](), [homelab](), [network](), [ssd](), [testing](), [unifi](), [upgrades](), [video]() ### AI overview A video review of the UniFi Dream Machine Pro Max examines its hardware upgrades, configures and tests Shadow Mode, and measures network throughput while comparing it with predecessor devices. ### Source excerpt The UDM Pro Max is here and it's packed with upgrades like a faster CPU, more RAM, an internal SSD, more eMMC, Dual Drive bays and more! Today we check on the new UniFi Dream Machine Pro Max, configure and test Shadow Mode, and test network throughput to see if this really is the fastest UniFi Dram Machine yet. 📺 Watch Video Get your UDM Pro Max here: https://l.technotim.com/udm-pro-max... ## 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... ## 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