标签：#ux

Seri: Proxmox VE Cluster ve Corosync | Hafta 5 Serinin adı "Cluster ve Corosync"; ama dört modüldür ağırlık HA Manager, resource affinity ve CRS'teydi, Corosync'in kendisine (redundant link'ler, izleme araçları) hiç dönmemiştim. Bu modülde iki konuyu birleştirip derinlemesine işledim: birden fazla corosync link'i tanımlayıp gerçekten birini kesip diğerinin devralmasını kanıtlamak, ve günlük operasyonda kullanılacak izleme araçlarını tek tek denemek. İkisi de planladığımdan çok daha fazla soru açtı; biri yanlış bir config anahtarı yüzünden saatler süren bir araştırmaya dönüştü, diğeri ise hiç beklemediğim bir kilitlenme keşfiyle bitti. Bölüm 1: Redundant Corosync Links Kurulum: İkinci Link'i Eklemek Şu ana kadar cluster'ımızda tek bir corosync link'i vardı ( link1 , izole corosync-net ağı). Management ağını ( 192.168.122.x ) link0 olarak ekleyip gerçek bir yedeklilik kurdum; /etc/pve/corosync.conf 'u kopyalayıp düzenleyip atomik olarak yerine taşıdım: cp /etc/pve/corosync.conf /etc/pve/corosync.conf.new # nodelist'teki her node'a ring0_addr ekledim, totem'e ikinci bir interface bloğu ekledim mv /etc/pve/corosync.conf.new /etc/pve/corosync.conf Doğrulama: corosync-cfgtool -s LINK ID 0 udp addr = 192.168.122.11 status: ... connected ... connected LINK ID 1 udp addr = 10.10.10.11 status: ... connected ... connected Teknik olarak başarılı; iki link de bağlı. Ama log'a dikkatlice bakınca, mimarimizin niyetini tersine çeviren bir şey oldu: [KNET ] rx: host: 3 link: 0 is up [KNET ] host: host: 3 (passive) best link: 0 (pri: 1) link_mode: passive modunda, öncelik eşitken düşük numaralı link kazanıyor . link0 'ı sonradan eklediğim için, o Corosync'in asıl trafiğini üstlenmiş; Modül 0'da özellikle izole ettiğimiz corosync-net ( link1 ) sessizce yedek konuma düşmüştü. Yanlış Anahtar, Saatler Süren Bir Araştırma Bunu düzeltmek için link1 'e daha yüksek öncelik vermeye çalıştım: interface { linknumber : 0 priority : 5 } interface { linknumber : 1 priority : 10 } İşe yaramadı. cor

I just started a blog and published my first blog post about Logical Volume Management. I'm new to documenting my work, so I'd really appreciate any feedback on the content, clarity, or writing style in general. This site is a mix of a blog and a portfolio. Since I'm new to all of this, it would be great to get some feedback on whether this post works well just as a blog post, or if it actually holds up as a portfolio project too, before I keep writing more. www.mvtechblog.com Thanks in advance.

Have you ever been coding and then gotten into that flow state? You know where hours pass by , and it feels to you it's only ben a few minutes? Me too. One thing that really helps me get into that state is music. So I create my own music Lo-Fi server called compile and chill. As a part of this project, I created three radio stations. Each station can generate a 16:9 scene with Amazon Bedrock , compose an instrumental loop with ElevenLabs, and turn an illustration into a six-second video through OpenRouter. Generated files live in private Amazon S3 storage and return to the browser through the Nuxt server. I also added a Stream Deck API interface! This tutorial shows how to build this radio station from start to finish. The complete source code is available in the Compile & Chill repository . Watch the full video on YouTube . Prerequisites You need the following tools for the complete build: Node.js 22.19 or newer. The locked Nuxt 4.5.2 release requires Node 22.19+, 24.11+, or 26+. npm 10 or newer. An AWS account and a configured AWS Command Line Interface (AWS CLI) profile. The AWS Serverless Application Model (AWS SAM) CLI for the private storage stack. Access to Stability AI Stable Image Ultra through Amazon Bedrock in us-west-2 . An ElevenLabs API key for music generation. An OpenRouter API key for animated scenes. The provider credentials are optional. Without them, the UI, bundled scene, station switching, player, and Focus Block timer still work. The identity running the app needs bedrock:InvokeModel plus bucket-scoped permissions for s3:GetObject , s3:PutObject , s3:DeleteObject , s3:DeleteObjectVersion , and s3:ListBucketVersions . Use a role or profile scoped to the station bucket rather than an administrator identity. For this project I included infrastructure as code with SAM to help setup the AWS parts. It's also included in the repo. Steps 1. Run the station without credentials Pull down the repo and get started! git clone https://github.com/ErikCH/comp

I'm not sure if you've noticed, but there's a lot of software that just isn't very good. This isn't to say that I make the best software or am somehow better than the people who created all of this software. It's just an observation that the standards of what is deemed "shippable" seems to be at an all time low. We are so used to errors and interactions not working the first time that we all have a personal troubleshooting loop. Bugs and broken interactions are just becoming the norm because, as users, we've learned to accept them. One experience yesterday made this point especially poignant to me. Since my wife and I just moved, we're approaching our first bill cycle. I have six or seven different services that I needed to set up my online accounts with. This was an onslaught. Misconfigured form inputs. Forms getting cleared on errors. Logins not persisting. Disabled paste for bank account numbers (I think this one might be more standard, but paste will always be more accurate than me typing it manually, and I will die on this hill). Pages straight up crashing. Some companies already had an online account for us at our old house, and trying to get that user account to point to a new service account number was a trial out of the fifth circle of hell. I did eventually get everything set up the way that I needed to. But, it was a harrowing experience. Amidst the muck, our trash company of all services had an excellent user experience. The website looked like something from the early 2000s. It had un-styled form inputs and full page loads when navigating (*gasp*). But, I created a new account using my account number and paid our first bill, with some helpful error messages that didn't wipe out all of the inputs that I'd already entered, all in about three minutes. I don't have a bow to put on this rant, but I do think that it's ironic that in the name of UX, we've adopted systems that are so complex and abstract that it's perfectly acceptable for a form submission to w

For a long time, whenever we hear 'User Experience', we instinctively think of UI/UX designers, product designers, or maybe frontend engineers. Why? Because we tend to think users interact first with a graphical or command-line interface, while the backend engine plays little to no role in how they experience the product. The first half is correct. The second half, incorrect. A user doesn't experience your frontend in isolation. They experience the entire system. As I continue to compound my experience building products as a backend-leaning engineer, I've found it increasingly necessary to think beyond whether an endpoint works or whether an architecture is technically sound. I have to ask: How does this technical decision affect the user's experience? Here's how. 1. API Response Times Become UX A user doesn't care that your endpoint executes 17 database queries, that your service is making five downstream requests, or that your server is experiencing a cold start. They care that they clicked “Pay” three seconds ago and nothing has happened. Eventually, they may refresh the page, click the button again, or abandon the application altogether. The frontend can add a beautiful loading animation, but it cannot completely hide a system that is fundamentally slow. 2. Error Messages Become UX One of the easiest ways to see the relationship between backend engineering and UX is through errors. Imagine trying to make a payment and receiving: 400 Bad Request Technically, something has gone wrong. But the user has learned almost nothing. Compare that with: “Your payment could not be completed because your card was declined. Please try another payment method.” Good backend error handling should therefore answer three questions: What happened? Why did it happen? What can the user do about it? 3. API Design Becomes UX API design can feel very far removed from UX. After all, users don't see JSON responses. But, developers build products using those responses. The decisions we make

Say hello to a revamped collection of perfectly proportioned 38-mm Speedmasters.

The blunt tool is still in a lot of GRUB files: GRUB_CMDLINE_LINUX_DEFAULT = "isolcpus=0,1" Then update-grub (or grub2-mkconfig ) and reboot. Userspace tasks stop landing on CPU0/1. That is all most people verify — they fire a few busy loops and top looks empty on those cores. IRQs do not care. isolcpus is a scheduler isolation hint. Hardware interrupts can still fire on the "isolated" CPUs. I watched seven tight loops leave 0/1 idle for processes while /proc/interrupts still ticked on those cores. If you wanted a CPU for DPDK, a user-space NIC, or a cycle-accurate loop, scheduler isolation is necessary and not sufficient . Lab notes (English original is short; this write-up is the missing IRQ half): https://sunshout.tistory.com/1620 How to see what you actually isolated After reboot: cat /proc/cmdline # isolcpus=0,1 must be there grep PREEMPT /boot/config- $( uname -r ) || true taskset -cp 1 # pick a known userspace pid; it should not be 0,1 watch -n1 'grep "^ *[0-9]" /proc/interrupts | head' If IRQs still increment on CPU0/1, isolation is incomplete. That is expected with classic isolcpus= . On newer kernels the story split: isolcpus=domain / cpusets / cgroup cpuset — userspace isolcpus=managed_irq or manual irqaffinity / /proc/irq/*/smp_affinity — interrupts nohz_full= — tick reduction, another knob, not a substitute isolcpus is also marked deprecated in some trees in favor of cpusets. The IRQ caveat did not go away when the docs changed the preferred interface. Moving IRQs by hand Find the noisy ones ( eth0 , NVMe, GPU): grep -E 'eth|nvme|enp' /proc/interrupts # smp_affinity is a hex CPU mask. CPU2 only → 4 echo 4 > /proc/irq/IRQNUM/smp_affinity Or set the default affinity so new IRQs skip 0/1: irqaffinity=2-7 in the same GRUB line (adjust to your CPU count). Some devices ignore this (managed IRQs, VFIO). Then you isolate at the driver: bind the NIC to vfio-pci and poll from a pinned thread. When this shows up next to SR-IOV Passing a VF into KVM does not pin ho

This article is to update VMware appliance ovf properties through command line. Sometimes we cannot access VC and only can access VMs through ESX UI. Check over properties exists in VM login to VM as root and execute ovfenv command root@vcf91-installer [ ~ ]# ovfenv [vm.vmname]=VCF-SDDC-Manager-Appliance-9.1.0.0300.25536191 [ROOT_PASSWORD]= [LOCAL_USER_PASSWORD]= [vami.hostname]=vcf91-installer.mylab.com [guestinfo.ntp]=172.30.20.3 [vami.ip_address_version.SDDC-Manager]=IPv4 [vami.ip0.SDDC-Manager]=172.30.20.12 [vami.netmask0.SDDC-Manager]=255.255.255.0 [vami.gateway.SDDC-Manager]=172.30.20.1 [vami.ipv6.SDDC-Manager]=null [vami.ipv6_prefix.SDDC-Manager]=null [vami.ipv6_gateway.SDDC-Manager]=null [vami.domain.SDDC-Manager]=mylab.com [vami.searchpath.SDDC-Manager]=mylab.com [vami.DNS.SDDC-Manager]=172.30.20.2,172.30.20.3 Change the directory to the VM scripts folder where all the firstboot and subsequent boot scripts are stored. cd /opt/vmware/vcf/commonsvcs/scripts/ Example to change NTP server details cd /opt/vmware/vcf/commonsvcs/scripts/ntp/ root@vcf91-installer [ /opt/vmware/vcf/commonsvcs/scripts/ntp ]# ls -ltr total 12 -r-xr-x--- 1 root vcf 853 Jun 27 02:53 update-ntp_server.sh -r-xr-x--- 1 root vcf 231 Jun 27 02:53 setup-ntp.sh -r-xr-x--- 1 root vcf 45 Jun 27 02:53 refresh-ntp.sh ./update-ntp_server.sh 172.30.20.250 reboot the VM

Two audio players do not make a fair before-and-after test. If the second player restarts from zero or takes half a second to load, the user is no longer comparing two versions of the same moment. They are comparing two memories. That is a weak way to evaluate any audio effect. It is especially weak for AI processing. A denoiser can remove a fan while softening consonants. A de-reverb model can reduce the room tail while making the voice sound less natural. The output may be cleaner without being better. The preview therefore has one job: let the listener switch quickly enough to hear both the improvement and the damage. The rule I use is deliberately boring. Both versions should contain the same edit and play from the same position. Switching should not restart playback or create a pause. The interface should not hint that one version is supposed to win. Two independent <audio> elements fail surprisingly quickly. Each owns its playback state, buffering behavior, clock, and seek operation. The user ends up finding the same position twice and comparing one sound with a memory of another. A better interface has one transport and one version control: [ Play ] [ Original | Processed ] 00:18 ━━━━━━━ 00:42 The transport decides where playback happens. The segmented control decides which signal is audible. One transport, two signals For a short preview, I decode both files into AudioBuffer s, start them at the same AudioContext time and offset, and route each through its own GainNode . Both sources run; only one gain is open. decodeAudioData() decodes complete file data and resamples it to the context's sample rate. The decoded buffers can then share the same audio clock. See the MDN documentation for format and loading details. The core is small: const context = new AudioContext (); const originalGain = context . createGain (); const processedGain = context . createGain (); originalGain . connect ( context . destination ); processedGain . connect ( context . destination )

1. O problema que o Fzf resolve Quem vive no terminal conhece a cena: Ctrl+R para buscar um comando no histórico, mas a busca é linear e só mostra um resultado por vez; cd para um diretório profundo, mas é preciso lembrar (ou digitar) o caminho inteiro; git checkout para uma branch, mas primeiro é necessário rodar git branch e copiar o nome exato. Em todos esses casos, o gargalo é o mesmo: escolher um item entre muitos, digitando cada vez mais texto até sobrar só um. O fzf (fuzzy finder) resolve isso de um jeito genérico: ele pega qualquer lista de linhas — histórico de comandos, arquivos, branches, processos, o que for — e transforma essa lista em um filtro interativo, digitado em tempo real, onde não é preciso acertar a grafia exata nem a ordem das letras. Basta digitar pedaços do que se lembra e o fzf ordena os resultados por relevância. 2. O que é o Fzf Fzf é um filtro de linha de comando escrito em Go, de código aberto, mantido por Junegunn Choi. Ele não sabe nada sobre arquivos, git ou processos — a única coisa que ele faz é ler linhas da entrada padrão ( stdin ) e devolver, na saída padrão ( stdout ), a linha (ou linhas) selecionada interativamente. Essa simplicidade é o que o torna tão versátil: qualquer comando que produza uma lista de texto pode ser "encanado" ( | ) para dentro do fzf. # a ideia básica: qualquer lista vira um menu interativo ls | fzf history | fzf git branch | fzf ps aux | fzf Na prática, o fzf raramente é usado sozinho dessa forma — o valor real aparece quando ele é integrado ao shell e a outras ferramentas, o que este artigo cobre a partir da próxima seção. 3. Instalando o Fzf O fzf está disponível nos principais gerenciadores de pacote: # Debian/Ubuntu sudo apt install fzf # Fedora sudo dnf install fzf # Arch Linux sudo pacman -S fzf # macOS (Homebrew) brew install fzf Também é possível instalar via git, o que traz um script auxiliar de configuração dos atalhos de shell (usados na próxima seção): git clone --depth 1 https://github.com/j

If you’re a designer, you’ve probably had the same problem I’ve had: there are dozens of UI/UX design...

Buildroot builds a cross-compiler, a Linux kernel and a complete root filesystem from source, driven by one Kconfig-style configuration file. Starting from the qemu_arm_vexpress_defconfig that ships with Buildroot 2026.05.1, two commands produce a bootable ARM system you can run under QEMU. The images you ship are the ones in output/images/ ; output/target/ looks like a root filesystem but must never be copied to a device. This post starts a new hands-on series on Buildroot for embedded Linux. By the end of this part you will have built a working Buildroot root filesystem for an ARM target, booted it under QEMU, and understood which generated directories are safe to ship. Later parts add your own packages, a BR2_EXTERNAL tree, kernel and bootloader integration, and reproducible image output. If the choice between build systems is still open, our earlier Yocto vs Buildroot comparison covers it; this series assumes the decision is made. What you need A Linux host, several gigabytes of free disk space, and a network connection. No development board is needed for this part; QEMU stands in for the hardware. On a Debian or Ubuntu host, this covers the mandatory packages the manual lists, plus the ncurses development files that menuconfig needs: raghu@techveda.org:~$ sudo apt install build-essential diffutils patch gzip bzip2 perl tar cpio unzip rsync file bc findutils gawk wget libncurses-dev One rule from the manual is worth stating plainly: build everything as a normal user. Buildroot never needs root, and running it as root exposes your host to any package that misbehaves during installation. The command above is the only one in this post that uses sudo . Getting Buildroot and choosing a target Download and unpack the current stable release — 2026.05.1 at the time of writing — from buildroot.org/downloads , and work from that directory. Buildroot ships ready-made configurations for many boards and emulated machines, one file each in configs/ , and make list-defconfigs

It’s been a while since I wrote a blog post. Recently, I decided to get back into writing and document something I’ve been playing around with: setting up a small homelab environment on an Oracle Cloud Free Tier VPS. As a software engineer, I’ve always been interested in what happens behind the scenes when an application moves from my laptop to an actual server. Things like networking, deployment, Linux, containers, firewalls, and DNS are all areas I’ve wanted to understand better through actual hands-on experience rather than just reading about them. The fact that I could do all of this on a free VPS made it even better. Why I Started This Experiment I initially set up an Oracle Cloud Free Tier VPS running Ubuntu with: 1 GB RAM 1 vCPU Ubuntu Linux A public IP address I wasn't planning to host anything serious on it. The main goal was simply to use it as a small playground where I could experiment with infrastructure and improve my Linux and system administration skills. Interestingly, the last time I regularly worked with a VPS was probably around seven years ago. Back then, a few friends and I used to rent servers and set up Call of Duty 4 multiplayer servers. We'd spend hours messing around with the server configuration and, of course, playing on it afterwards. Things have changed quite a bit since then. These days, I'm much more interested in software engineering, DevOps, infrastructure, and homelabbing. So I thought it would be fun to take a free VPS and see how much I could actually do with it. First Challenge: K3s on 1 GB of RAM One of the first things I wanted to try was K3s, the lightweight Kubernetes distribution. I wanted to get a basic Kubernetes environment running and use it to experiment with container orchestration. That plan didn't last very long. After installing K3s and starting the server, I noticed the memory usage climbing pretty quickly. With only 1 GB of RAM, there wasn't much room left for anything else. Once I started thinking about running

I'm use neovim in cli like in my home but im not use Ide before in my live my first try pc is arch linux and neovim So I think I'm the best person to ask what is special in neovim 1: is so lightweight use ram is just 50-20 mb ram 2: you can config anything in lua language 3: open into terminal ssh protocol edit in code into server 4: vim keybinding like Vim / Neovim Keybindings Cheat Sheet Navigation (Normal Mode) h / j / k / l : Move Left / Down / Up / Right w / b : Jump forward / backward by word e / ge : Jump to end of current / previous word 0 / ^ / $ : Go to start of line / first non-blank char / end of line gg / G : Go to first line / last line of file { / } : Jump to previous / next paragraph Ctrl + u / d : Scroll Half-page Up / Down Ctrl + b / f : Scroll Full-page Up / Down Editing & Insert Mode i / I : Insert before cursor / at start of line a / A : Append after cursor / at end of line o / O : Open new line below / above current line u : Undo Ctrl + r : Redo . : Repeat last editing command Cutting, Copying & Pasting x : Delete character under cursor dw : Delete word dd : Delete (cut) line d$ / D : Delete from cursor to end of line yy / Y : Yank (copy) line yw : Yank word p / P : Paste after / before cursor Search & Replace /pattern : Search forward for pattern ?pattern : Search backward for pattern n / N : Jump to next / previous match * / # : Search word under cursor forward / backward :%s/old/new/g : Replace all occurrences in file :%s/old/new/gc : Replace all occurrences with confirmation prompt Visual Mode v : Character-wise visual mode V : Line-wise visual mode Ctrl + v : Block-wise visual mode y : Yank selection d : Delete selection > / < : Indent / Outdent selection Text Objects (Inside / Around) ci" : Change inside quotes ( "..." ) ca" : Change around quotes (includes quotes) di( : Delete inside parentheses da( : Delete around parentheses yi{ : Yank inside curly braces Buffers, Windows & Tabs :w : Save file :q : Quit buffer :wq / :x : Save and quit

Conteúdo 1. Apresentação - Omarchy 2. Praticidade 3. Agentes de IA 4. Desuso do Mouse 5....

Dan Finneran discusses the risks of unowned AI-generated code in production and demonstrates how eBPF can intercept and control AI API traffic in Kubernetes. He explains how kernel-level socket hooks enable transparent prompt filtering, model swapping, token limits, and syscall restrictions to secure AI agents without modifying application source code or restarting containers. By Dan Finneran

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Getting your folder or file to github can be a bit of an off vibe due to the many steps especially if it's your first commit, but getting these steps right will make it easy for the other folders or files you will push afterwards.Let’s dive in CREATING A LOCAL FOLDER Depending on the OS you are using you can use Git Bash or the Terminal. For Linux which is what I am using I will use the Terminal First Step Start by creating a folder in the terminal: mkdir your project folder name . then change directory: cd ~/to the folder you have just created Now we need to format our folder by creating a few files inside it Data file README.md code file if you will be using code. To check if you have created these files inside your folder: run:, ls This calls out all the files that are inside your folder. Let's tackle the files we have just added. Data Folder Run command: mkdir data This creates a data folder. This is where you will add your data e.g Excel or CSV files that you will be using to run your analysis or your project. README.md Run command: touch README.md This where you will give an overview of your work, the reason you are doing the analysis,how you collected your data,the tools you used to run the analysis..Basically README.md is a file that guides anyone who goes through your analysis or project on the steps you took while doing your analysis or project.Think of it as the introduction at the start of your favourite book or novel. To write all of this you will run the command echo "#give your project a name or describe your project" >README.md README.md uses markdown language reason for the # at the beginning of the quotation.When writing the headings or subtitles use capital letters or proper style. For subtitles you need to add two ## at the beginning. If you want to write more content without overwriting what you have previously written inside the README.md file you will need to use double greater signs(>>) at the end of the quotation,run: echo #your message” >>R

A rollback plan tells you how to undo an AI change, but not what breaks first when the change stays in place. Most production incidents do not begin with a deliberate rollback; they begin with an unexpected failure mode that the author never tested. I now treat a passing fault drill as a precondition for reviewing any AI-generated server patch. The drill runs on a disposable server before a human reads a single line of the diff. Why a rollback plan is not enough A rollback plan answers a question about the past: how do we return the system to a known state? A fault drill answers a question about the future: what happens when this change meets a condition the author did not imagine? The second question decides whether you get paged at 3 a.m. A change with a perfect rollback can still fail in a way that nobody notices until the data is gone. Free model access changes the economics of this argument, because generation stops being the bottleneck and verification starts. When a draft is nearly free, the cheapest verification is the one that breaks the change on purpose. A rollback plan is documentation; a fault drill is evidence. Documentation tells you what should happen, while evidence tells you what actually happens on a real service manager. The fault drill in five steps The workflow assumes two cheap resources: a model that generates failure hypotheses from a diff, and a server that can be destroyed after the drill. MonkeyCode's free model access covers the first, and its free server option covers the second, so a drill costs almost nothing. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Any ephemeral VM or container host works if you prefer a different provider. 1. Generate failure modes before you apply anything Ask the model to enumerate failure modes for the diff, and forbid it from proposing fixes, because fixes are a distraction at this stage. The prompt below is the one I use, and it produces a catalog that the drill can test.

A surprising share of "the app is down" pages resolve to a name-resolution problem, not a broken service. The service is fine; the client can't turn a name into an address. dig is the precision tool for proving that in seconds instead of guessing. Think about it as a resolution chain, not "is DNS broken" When a name fails, work the chain: which resolver did the client ask, what did that resolver return, and does it match what authoritative DNS actually says? Most incidents live in the gap between those three. The method is boring and reliable: observe the symptom, form a hypothesis about where in the chain it breaks, test with one query, read the evidence, fix, then validate. The single most important habit: query the name from the same host and the same resolver the app uses. Running dig from your laptop proves nothing about what the pod or VM sees. The record types worth knowing You don't need all of them, but you need to recognize them: A / AAAA — name to IPv4 / IPv6 address. The usual suspect. CNAME — an alias pointing at another name. A stale or wrong CNAME sends traffic somewhere unexpected. MX — mail routing. TXT — SPF, DKIM, domain verification, and other metadata. NS — which servers are authoritative for a zone. SOA — the zone's serial and TTL defaults; the serial tells you whether a change has propagated. PTR — reverse lookup, IP back to name. The commands that actually earn their place Start with the quick answer, then get precise. dig +short api.internal.example.com +short strips everything except the answer. If it prints an IP, resolution works from this host. If it prints nothing, you have a real failure to chase. Empty output is a signal, not an error. dig api.internal.example.com A The full form. Read the status in the header: NOERROR with an ANSWER section is good; NXDOMAIN means the name genuinely doesn't exist; SERVFAIL points at a broken upstream or DNSSEC issue. Also note which SERVER answered at the bottom — that's the resolver you're actually