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The deal gives Okta identity threat detection capabilities as enterprises seek to secure AI agents and other non-human identities across cloud environments.
Netflix just signed a massive new licensing agreement worth $500 million to bring The Walking Dead Universe to international markets.
The SkyNomad N90 Max is the latest electric SUV from Xiaomi and its first extended-range EV, with a claimed range of over 1,705 km (about 1,059 miles). It will come in both five- and seven-seat configurations, as well as an N90 Max Camping Edition. One of the big features of the N90 is its highly […]
A new study estimates only 2,000 U.S. engineers have the expertise to deliver meaningful AI ROI, as enterprises race to hire forward-deployed engineers to implement AI at scale.
Federal safety regulators have given Zoox a temporary exemption that will allow the Amazon-owned autonomous vehicle technology company to charge customers for rides in its custom-built robotaxi.
Working with files in Linux isn't just about creating and editing them. Sometimes you need to: Archive multiple files into one Compress files to save disk space Share files with others Create backups Linux provides several tools for this, each with a different purpose. Let's simplify them. What is File Compression? File compression reduces the size of a file. Benefits: Saves disk space Faster file transfers Easier backups Reduces bandwidth usage Example: A 100 MB log file might become a much smaller compressed file, depending on its contents. Archive vs Compression Many beginners think they're the same. They are not. Archive Combines multiple files into a single file. Example: photos/ docs/ notes.txt ↓ backup.tar Compression Reduces the size of a file. Example: backup.tar ↓ backup.tar.gz 👉 tar archives files. gzip compresses them. 1. Create an Archive with tar tar -cvf backup.tar Documents/ #Create an archive tar -tvf backup.tar #View archive contents tar -xvf backup.tar #Extract an archive Options: c → Create v → Verbose (show progress) f → File name x → Extract Best for: Backups Bundling multiple files Moving folders 2. Compress with gzip Compress a file: gzip file.txt # Creates file.txt.gz # Result file.txt.gz gunzip file.txt.gz # decompress gzip -k file.txt # Keep original file Best for: Log files Large text files Saving disk space 3. Archive and Compress Together Most common command: # Create compressed archive tar -czvf backup.tar.gz Documents/ # Extract tar -xzvf backup.tar.gz Options: z → Use gzip compression 👉 This is one of the most common backup commands in Linux. 4. Working with ZIP Files # Create ZIP zip -r project.zip project/ # Extract unzip project.zip # List contents unzip -l project.zip Best for: Sharing files with Windows users Cross-platform compatibility 5. Compare the Tools Tool Purpose Best For tar Archive files Backups gzip Compress files Saving space tar + gzip Archive and compress Linux backups zip Archive and compress Sharing files across
Introduction It was a perfectly normal day. I opened my laptop, ready to get some work done, and then... BAM. A black screen. Not a gentle fade to black, but more like my computer shouting, "I’ve had enough of your crap!" The same operating system that had been working perfectly just five hours earlier had suddenly decided it had had enough of life. I wasn't too worried though. After all, I had ChatGPT on my side. Three hours later... Yeah... my confidence crumbled faster than my phone battery at 2%. What followed was a three-hour rabbit hole involving NVIDIA drivers, multiple Linux kernels, Secure Boot, DKMS, Xorg, GDM, journalctl , systemd , and more terminal commands than I'd like to admit. Somehow, against all odds (and probably a little divine intervention), we managed to fix it. And honestly? I enjoyed every minute of the chaos. It was like a wild adventure—except with more curse words and less danger. So I decided to document the entire debugging journey—not just because it might help someone who runs into the same issue, but also because I deserve a little sympathy after spending three hours arguing with my laptop. (And if the solution seems painfully obvious to you... please let me enjoy my victory. Don't take this away from me.😤 The Problem After rebooting my laptop, I was greeted with just a black screen. No login screen, no desktop… just nothing.** At first, I tried to enter TTY using Ctrl + Alt + F3, but that wasn’t working either. Since I wasn’t able to reach TTY directly, I had to take a different route. By editing the GRUB boot entry and booting into multi-user.target , I forced Linux to start in text-only mode, giving me access to a terminal.** For this, I edited the GRUB boot entry and appended systemd.unit=multi-user.target to the end of the kernel command line (after quiet splash ). That was the first breakthrough, though. The operating system wasn’t completely dead… only the graphical interface was failing to wake up. First Clues and Initial Ass
🇧🇷 Leia a versão em português aqui In previous parts of this series, we built the cluster from scratch: prepared the environment (Part 1), installed containerd and Kubernetes (Part 2), initialized the control-plane (Part 3), and joined the workers (Part 4). With the cluster up and all nodes in Ready state, it's time to actually put it to work: let's deploy our first application. In this article, we'll use Nginx as an example — a classic use case for validating that the cluster is working end to end, from pod creation to service exposure. Organizing the files First, create a directory to organize this deployment's manifests: mkdir nginx cd nginx Keeping Kubernetes manifests organized in per-application directories is a good practice that makes maintenance and versioning (e.g., with Git) easier as the cluster grows. Creating the Deployment A Deployment is the Kubernetes object responsible for managing pod replicas, ensuring the desired number of instances is always running — and handling things like rolling updates and automatic recovery in case of failure. Create the file nginx-deployment.yaml with the following content: apiVersion : apps/v1 kind : Deployment metadata : name : nginx-deployment labels : app : nginx spec : replicas : 2 selector : matchLabels : app : nginx template : metadata : labels : app : nginx spec : containers : - name : nginx image : nginx:1.14.0 ports : - containerPort : 80 This manifest defines: 2 replicas of the Nginx pod ( replicas: 2 ), distributed across the available workers; A selector that ties the Deployment to the pods via the app: nginx label; The nginx:1.14.0 image, exposing container port 80 . Applying the Deployment With the file saved, apply it to the cluster: kubectl apply -f nginx-deployment.yaml kubectl will create the Deployment, and from there Kubernetes takes care of scheduling the 2 pods across the available workers. Checking the Deployment To confirm the Deployment was created and has the desired number of replicas running
The company's tech chief said a fix for the error will be arriving soon.
xAI is suing Minnesota Attorney General Keith Ellison over a law passed back in May that broadly targets "nudification" apps, claiming that the statute's punitive provisions leave the company with "no practical choice but to restrict Grok Imagine's image-editing features in various ways." The law, the company argues, violates the First Amendment. Back in January, […]
Xbox is having yet another tough quarter, as revenue from content and services like its Game Pass subscription dipped 10 percent over the past few months. At the same time, Xbox hardware sales declined 13 percent, according to Microsoft's fourth-quarter earnings report released on Wednesday. The news comes just weeks after Xbox head Asha Sharma […]
Most lists that claim to help you block AI crawlers are copy-pasted and dangerously wrong about the two tokens that actually matter. Sorting them properly is not an abstract taxonomy exercise. It is the single decision that determines whether your content vanishes from AI answers while training continues — or vice versa. We maintain the crawler registry in lib/ai-crawlers.ts that powers techpotions’ free AI robots.txt generator . Every agent string and description was verified against the operator’s own crawler documentation. The registry holds 15 verified bots across four categories, and that four-way split is this article’s structure, because the categories map directly to what blocking costs you. Two tokens almost everyone gets wrong Before the list, the single most important correction to make, and almost every listicle on this query gets it wrong: Google-Extended and Applebot-Extended are not crawlers. They are robots.txt tokens — product controls that govern whether your content is used for Gemini and Apple foundation-model training. Blocking Google-Extended does not affect Google Search crawling, Google ranking, or regular Applebot search indexing. People block them believing they are opting out of AI Overviews, and are actually opting out of nothing they think they are, while leaving search indexing completely untouched. Platforms have started wiring these tokens into one-click controls. Cloudflare’s managed robots feature, released mid-2025, lets you add AI crawler rules through a dashboard toggle rather than editing a raw file — but the underlying token logic above still applies. Block AI crawlers: the four categories that decide the cost Every AI crawler we track belongs to one of four categories. The category tells you the cost of blocking it. Training crawlers scrape pages to feed a model that may never cite you. Blocking them is a defensive data decision. Assistant crawlers fetch pages to answer a live user’s question and can cite and link you. Blockin
Next.js Sitemap Not Updating? Here's the Real Fix If your Next.js sitemap is not updating after you publish new content, you're dealing with a cache-coherence bug that almost nobody writes up. It has an exact symptom, a reproducible root cause, and a one-line fix. This is the guide you'll wish you had the moment you notice /sitemap.xml serving fewer entries than your real site. The symptom: your sitemap lags behind your published content The mismatch is impossible to miss once you look. On our own site, /lab lists 11 published posts, yet /sitemap.xml shows only 7. Same database, same deploy, two different answers. If that gap sounds familiar, you're in the right place. You might have checked your afterChange hook, verified that revalidateTag('posts') fires, and even confirmed that the tagged data refreshes — only to find the sitemap still frozen. That's because the problem lives between two cache layers, not inside the data fetch. Why revalidateTag doesn't fix a stale Next.js sitemap The answer lies in what sitemap.ts actually is. According to the Next.js Metadata Files: sitemap.xml documentation, it's a special Route Handler. And like any Route Handler, Next.js caches its rendered output by default. Here's what happened in our own repository (this bug is documented in a comment at the top of app/(frontend)/sitemap.ts because it cost real indexation time): We read content from Payload using unstable_cache , tagged with the collection slug posts . An afterChange hook called revalidateTag('posts') whenever a post was published. That call did work — it invalidated the inner unstable_cache data entry. But the route's statically-rendered outer XML output was never re-run. The frozen route output kept serving the old XML built from the old data, long after the inner cache was refreshed. Two cache layers. Tag-based revalidation busted the inner one, but the outer route handler cache was never told to re-execute. That's the missing piece. The one-line fix: route-level ISR o
A few weeks ago I wrote up what agents were paying for on NetIntel , my platform of pay-per-call APIs settled in USDC over x402 — no signup, no API keys, no accounts. An agent hits an endpoint, gets a 402 Payment Required , pays a fraction of a cent, and gets structured data back. That's the whole loop. Since then the dataset has grown, I've instrumented every settled call into a proper database (payer wallet, endpoint, price, latency, transaction hash), and I've launched a second settlement rail. So this is the rewrite with real numbers instead of eyeballed ones — and the findings didn't soften. They sharpened. The setup 2,646 settled paid calls from 194 distinct paying wallets, across 101 live endpoints , over six weeks of instrumented production data. Every call in this dataset is a real on-chain payment with a transaction hash — no test traffic, no estimates. Settlement runs on Base, and as of this month on Solana too. This is still one platform's data in a young ecosystem — the caveats are at the bottom, and one of them is bigger than it looks. But the shape has now held for six weeks straight, and it's the same shape I flagged the first time. Finding 1: revenue is absurdly concentrated — and it stayed that way Five endpoints drive 69% of all revenue. One of them — a text-to-structure endpoint that takes messy input and returns strict typed JSON — is 42% by itself . The rest of the top five are all in the same family: translation, structured LLM inference, and one domain-intelligence report. The other 96 endpoints split the remaining 31%. Thirty-five of the 101 have never been paid for once. Not "underperformed" — zero settled calls, ever. When I first published this pattern I wondered if it was an artifact of a small sample. The dataset has since more than doubled and the concentration ratio barely moved. I now treat it as the market talking, not noise. Here's the part I'd want to know if I were reading this: that 42% endpoint is essentially one buyer — a wall
Waymo robotaxis are now using freeways in Phoenix with more cities to follow in the coming days.
X sued the WFA in 2024 for conducting what it called a "systematic illegal boycott" of the platform after it saw a decline in advertising revenue following Musk's $44 billion takeover of the social network
Improved efficiency in EPP mode leads to ~32% jump in "1% low" frame rates.
Polar has come out with an AI-first browser aimed at knowledge workers, and it has now raised a $5.7 million seed round led by Madrona.
Elon Musk’s platform attacked the country’s “invasive” information-gathering powers as it demands the government drop efforts to strengthen its no-children policy.
A note before the post: the mistake in the first section is genuinely mine. It cost me an evening of forking conditions in Home Assistant before I accepted the fix didn't belong in Home Assistant at all. I've left it in rather than writing around it, because it's the part I'd have wanted to read first. The first time motion-controlled lighting actually worked in my place, it didn't feel clever. It felt obvious — I walked into a dark hallway and the light was already on by the time I'd registered it was dark. That's the bar. Not smart , just attentive. Getting there with seven KNX motion sensors took me less code than I expected and one insight I wish I'd had on day one. This is Part 05 of the series. The earlier parts cover the boring-but-load-bearing groundwork: running Home Assistant in Docker and wiring up HACS . Here I'm assuming HA is up, talking KNX, and you just want the lights to behave. One sensor, two jobs, two addresses Here's the mistake I made, and it's the whole reason this post exists. KNX exposes each motion sensor to Home Assistant as a binary_sensor with device_class: motion , fed by a KNX group-address state object you configure in knx.yaml with a state_address per sensor. Simple enough. So I wired all seven sensors with one group address each and pointed both the lighting automation and the presence logic at the same signal. That works right up until you want the two to behave differently. A light should react to the smallest twitch, instantly, generously. Presence and security want the opposite: a debounce, a grace window, some scepticism before they commit. When both ride the same group address, every change you make to one quietly deforms the other. I spent an evening forking conditions in Home Assistant trying to make one signal mean two things. The fix isn't in Home Assistant at all. It's in ETS: give each physical PIR a second group address . One drives comfort lighting, the other feeds presence and the alarm path. I use a flat convention —