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I ship a lot of small products. Browser extensions, little SaaS tools, one game. Most of them live on their own subdomain and do exactly one job. For a long time the worst part of starting a new one wasn't the product. It was billing. Bank verification, ID verification, waiting for approval, recreating the same plans, wiring the same webhooks, testing the same four subscription states. Every single time. I got good at it the way you get good at anything you resent. So I stopped doing it per product and did it once for all of them. Here's the shape that fell out, including the part I had wrong for months. The thing I had wrong Paddle has a list of approved domains. My assumption was that every site taking money had to be on that list, which meant a review round per subdomain, forever. That's not what the list gates. Approved domains gate the Paddle.js checkout overlay running in a browser . That's it. The server side doesn't care: webhook signature verification: not domain gated creating a customer portal session with the API key: not domain gated your own internal endpoints receiving forwarded events: obviously not domain gated Exactly one thing in the whole flow has to happen on an approved domain, and it's the moment the overlay opens. Everything else can live wherever you want. Once I saw that, the design was basically forced. The shape One payment account. One approved domain, the apex. One webhook endpoint, on that apex, for the entire family: Paddle ──webhook──> apex.example.com/api/webhook/paddle │ ├─ verify signature ├─ read custom_data.site └─ route: own event → handle locally other site → forward raw event to that site unknown → 200 and drop it Two rules make this hold up, and both are about what the shared piece refuses to know. The dispatcher does not know a single price ID. It verifies the signature, reads one field, and forwards the raw snake_case event onward. Mapping a price to a plan, granting credits, writing to a subscription table: all of that li

THE BRICK AND THE POST-IT My chemical plant's first vulnerability wasn't a bug, a piece of malware, or a port left open to the internet. It was a brick. In the computer room — the one with a door held open by a brick — I found a sticky note with credentials on it. They weren't even the right credentials for the system I wanted to break into. But they made me think the way whoever wrote them thinks, so I tried the most obvious pair in the world: admin / admin . And I was in. A brick propping open a door that should be locked. A sticky note guarding a password. A factory-default admin/admin. Three layers of security, three layers defeated — not by a genius hacker, but by a student on day one, carrying no tools at all. If that happens in the IT office, it's a problem. When it happens on a factory floor, where that same computer commands real pumps and valves, it's a different planet. The problem: learning OT without a factory I study computer security. Lately I've been drawn to OT — operational technology, the security of factories, power plants and industrial systems. The problem is simple: you can't learn to defend a factory from a book, and nobody will lend you theirs. Then I realized the answer was already inside the question: if you don't have one, you build one. The build: three commands and a lot of patience The lab is called GRFICSv3: an open source project that simulates an entire chemical plant — the PLC, the operator interface, the network, even the server rooms — inside Docker, on a home computer. Three commands and done: curl -O https://raw.githubusercontent.com/Fortiphyd/GRFICSv3/main/docker-compose.yml docker compose pull docker compose up -d "Three commands and done" is the story version. The real version includes my first error, arriving right on schedule at command number two: permission denied while trying to connect to the docker API at unix:///var/run/docker.sock If you hit this — and you will — here's the diagnosis: the Docker daemon is running fi

The investigation was launched just a few hours after Tesla put the first production Cybercabs on the road in Austin.

We cannot forget that AI coding agents are not yet trustworthy : Researchers at a stealth startup in Israel scanned 6,214 live domains belonging to defense contractors, Fortune 500, and Big Tech companies. Of the 8,265 llms.txt and llms-full.txt files they found (many sites hosted both an llms.txt and an llms-full.txt file), 120 of them, each on a different site, pointed to one or more code packages or domain names that weren’t registered. To test what happens when an AI agent processes such files, the researchers registered a handful of the unclaimed names and hosted packages that caused any machine executing them to reach out to their server. Within an hour, the researchers received a phone-home response from a Fortune 500 company. Over time, they got a few dozen more, some from more Fortune 500 companies and others from startups. Their beacon also recorded the chain of parent processes that spawned each install, ultimately revealing that coding agents, including Claude, OpenAI’s Codex, and Nous Research’s Hermes, were involved. Anthropic, OpenAI, and Nous Research did not respond to requests for comment by the time of publication...

Microsoft opened GitHub Copilot code review for Azure Repos to all Azure DevOps customers, after acknowledging that many are not ready to migrate to GitHub. Reviews bill per use through the linked Azure subscription and appear in Cost Management 48 hours later. Budgets notify but do not stop reviews, and concurrency caps at five per organization. By Steef-Jan Wiggers

Disk full alerts at 2am? Learn the exact commands to find what's eating your Linux server's disk space and fix it fast. You get the alert: disk usage at 94%. Your app starts throwing errors, logs stop writing, and databases refuse to accept new rows. Finding the culprit fast matters — but on a server with millions of files, knowing where to look is half the battle. Here's a systematic approach to track down disk hogs in minutes, not hours. Start With the Big Picture: df Before you dig into directories, confirm which filesystem is actually full. Run: df -h — shows all mounted filesystems with human-readable sizes df -h / — focus on the root filesystem df -i — check inode usage (a filesystem can be 'full' even with free space if inodes are exhausted) Pay attention to the 'Use%' column. If you see 100% on /var or /home but not /, that tells you exactly which mount point to investigate. Inode exhaustion — df -i showing 100% — is easy to miss and causes the same symptoms as a full disk, so always check both. Drill Down With du Once you know which mount point is full, use du to find the largest directories. Start from the top of that mount point and work down: du -sh /* 2>/dev/null — sizes of every top-level directory, errors suppressed du -sh /var/* 2>/dev/null — drill into /var if that's the culprit du -ah /var | sort -rh | head -20 — list the 20 largest files and folders inside /var The pattern is always the same: run du -sh on the suspicious directory, find the largest subdirectory, repeat one level deeper. You'll usually hit the real culprit within three or four iterations. Common offenders are /var/log (runaway logs), /var/lib/docker (unused images and volumes), and /tmp (applications that don't clean up after themselves). Find Large Files Directly With find Sometimes a single enormous file is the problem — a core dump, a forgotten database export, or a log that rotated incorrectly. Use find to surface files above a size threshold: find / -xdev -size +500M -ls 2>/de

A background noise removal workflow is easy to describe and much harder to make trustworthy. The superficial version is: upload a file, run processing, download the result. The harder version is product design: what does a person need to know before committing to a result, paying for an export, or spending a limited processing allowance? A preview-first workflow answers that question by making uncertainty a first-class part of the system. Instead of asking people to trust a long-running operation, it gives them a bounded way to hear a representative outcome before they choose what happens next. This article lays out the design principles behind that approach for stored audio or video uploads. It is not a call-time or capture-time filter. The central workflow is: upload → compatibility check → preview → same segment before/after → export choice That sequence looks simple, but each boundary carries product and engineering consequences. Start with a decision, not a processing feature A preview should help a user make one specific decision: “Is this result useful enough for me to continue?” That framing prevents a common mistake: treating a preview as a small free version of the full product. A useful preview is not merely a shorter job. It needs to be comparable, understandable, and tied to the next action. For background noise removal, the most defensible comparison is a matched segment: The source and processed audio use the same time range. Playback controls make the comparison obvious. The user can choose whether to continue only after hearing that bounded example. If the before and after samples use different moments, the product is asking the user to infer too much. A quieter section in one clip can appear better even when the processing change was minor. Matching the segment removes that ambiguity and keeps the decision grounded in what the user actually heard. Put compatibility before expectation Compatibility belongs near the beginning of the workflow, before

Zack Bartel has developed Tether, an open-source project designed to integrate Apple Continuity features with Linux workstations. Tether allows users to send iMessages, sync clipboards, and view iOS notifications directly on Linux. It uses secure local network communication and a custom Bluetooth stack to ensure reliable connectivity and robust security in cross-platform interactions. By Olimpiu Pop

While restaurant owners might look to generative AI as a shortcut to sprucing up their menu, customers can viscerally sense that something is wrong with the food.

A Linux safety certification is a statement about one defined software configuration running on named hardware, assessed by a named body. It is not a statement about the Linux kernel, and it does not give your item its integrity level. The document describing how a component like the kernel enters an ISO 26262 argument at all is ISO/PAS 8926:2024, published on 29 January 2024, which moves the question away from code quality and towards classification, complexity and evidence. Red Hat's In-Vehicle Operating System is the clearest public example: certified by exida against ISO 26262 Edition 2 (2018) as a Safety Element out of Context at ASIL-B, with Renesas naming the R-Car S4 as the first platform to be certified. If you build vehicle software on Linux, a supplier will at some point hand you a claim that sounds decisive: this platform is certified. The engineer who has to integrate it then finds the claim carries almost no information on its own. A Linux safety certification is bounded by a configuration, a set of assumptions, a hardware list and an assessor. This article explains where those bounds come from, what changed in 2024, and what to ask before accepting such a claim into your own safety case. Why the kernel cannot be qualified Functional safety standards assume a particular kind of software. A classic safety component is written against a complete requirements specification, each requirement traced to a design element, to code and to a test, with structural coverage evidence at the end. That is workable for tens of thousands of lines written by one team for one purpose. The Linux kernel is not that: tens of millions of lines, contributed by thousands of people who never wrote a requirements specification for the automotive context, changing on a nine-week cadence. An earlier article here, Safety-Critical Linux: What Certifying It Actually Takes , sets out how a safety argument is assembled around Linux. This goes a level below it: by what defined mechanism

If you are an active user of AI-powered IDEs like Cursor, VS Code with Copilot, or Windsurf, you already know the sinking feeling of seeing this notification: "You have used 100% of your fast premium requests for this billing cycle." Suddenly, your snappy, context-aware coding assistant slows to a crawl or starts racking up expensive pay-as-you-go API bills. At the same time, you are likely paying $20/month for a ChatGPT Plus or Team subscription that sits underutilized in a browser tab. You use it for general questions, but it lacks direct, real-time access to your local codebase, forcing you to engage in a tedious dance of copying and pasting code blocks. What if you could bridge this gap? What if you could let ChatGPT Web do the heavy reasoning and planning using your local context, while saving your premium IDE tokens for fast auto-completions ? In this article, we’ll explore a highly novel, intermediate-level setup that does exactly this. By leveraging the Model Context Protocol (MCP) , Node.js , and secure Cloudflare Tunnels , you can route heavy code-planning tasks directly to your web-based ChatGPT Plus subscription safely and completely free of extra token charges. The Philosophy: Let ChatGPT Think, Let Your IDE Work When building complex software with AI, your workflow generally splits into two distinct phases: Reasoning & Planning (High Token Usage): This is where you ask the AI to read 10 source files, understand the architecture, design a new feature, or find a subtle bug. This consumes massive amounts of context window tokens. Execution & Autocomplete (Low Latency): This is where the AI writes single lines of code, refactors a function, or autocompletes your imports. This requires fast, inline API queries. Paying premium API rates (per token) for Phase 1 is incredibly expensive. This is where this open-source MCP bridge project shines. It exposes a read-only view of your local project as an MCP server. Your web-based ChatGPT (via custom GPTs or MCP int

Late August I kept seeing the name Dungeon Lootr in places it hadn't been before. I wanted to know whether the game was actually growing or whether I was just noticing it more. Turns out Roblox exposes enough public data to answer that without an API key, so I started pulling it every day. The endpoint is boring in the best way: curl "https://games.roblox.com/v1/games?universeIds=9656201728" You get back visits , playing , favoritedCount , updated and a few other fields. A second call to /v1/games/votes?universeIds=... gives you up and down votes. No auth, no rate-limit drama at once-a-day volume. Here is what a week of that looks like for this one game: Date Total visits Playing right now Favorites Approval Sep 2 4.83M — 25.5k 96% Sep 3 5.39M 10.8k 29.8k 96.1% Sep 4 6.85M 11.4k 38.6k 96.1% That is about 1.4 million more visits than when I checked yesterday, and favorites jumped by almost nine thousand. The updated timestamp moved twice in two days (Sep 2 23:06 UTC and Sep 4 00:56 UTC), so the developers are shipping while the curve is climbing rather than sitting on it. The part that surprised me: the game was created on January 31, 2026. It sat there for seven months doing nothing visible, then flipped in the last week of August. I do not have a clean explanation. No single creator video I can point to, no front-page placement I noticed. It just started compounding. I wrapped the two calls into a tiny CLI so I would stop retyping the universe ID: https://github.com/jackzhouqd/roblox-game-stats — plain Python, no dependencies. Point it at any universe ID and it prints the same table. A few caveats before anyone reads too much into this: visits is cumulative and not deduplicated. It counts sessions, not people. playing is a snapshot at the moment you call it. Hit it at 3am and you will get a different number than at 8pm. One day of movement means nothing on its own. A week of movement in the same direction is when I start paying attention. What I am doing with it: I

An invite-only event in Austin, Texas, kept online fans in the dark for hours.

Microsoft Execution Containers provide a cross-platform framework for isolating AI agents within secure sandboxes to protect private data and system integrity. This technology allows developers to manage the lifecycle of autonomous code while ensuring that unpredictable agentic workflows do not access sensitive local files or unauthorized network resources. The Evolution of Agent Security and Isolation Trust remains a significant hurdle for developers building modern AI agents, particularly those operating on edge systems. When agents combine local processing with cloud-based intelligence, they often require access to sensitive information to be effective. However, granting this access creates a risk that the agent might call unintended APIs or compromise private user data. Historical attempts to launch autonomous agents in the 1990s largely failed because of these security concerns. Delivering arbitrary code to local machines proved too risky for mainstream adoption. Today, hardware-assisted virtualization has changed the landscape. This technology serves as the foundation for modern security models, including isolated operating system components and cross-platform tools like the Windows Subsystem for Linux. Microsoft now utilizes these virtualization advancements to build a more reliable framework for agent operations. By running agents in secure containers or microVMs, the system separates their activities from the primary operating system. This isolation ensures that even if an agent receives a poorly constructed prompt, it cannot delete critical system files or leak sensitive information. Managing Developer Environments Developers need a way to build code in flexible environments while still planning for restricted production deployments. Microsoft Execution Containers (MXC) address this by offering a policy-based restriction model. This framework allows for the creation of managed, isolated containers that follow specific security protocols. Applying Policy-Ba

The company published a form on its website Thursday soliciting info from people who are interested in "Cybercab fleet vehicle purchasing."

A Ravenloft series is currently in development from executive producer Alfonso Cuarón, writer and executive producer John August, and Hasbro Entertainment, Deadline reports. It could bring to life one of Dungeons & Dragons' most iconic campaign settings, which got an update earlier this year with Ravenloft: The Horrors Within. Netflix's Ravenloft series will reportedly center […]

The company is about to formally launch the gold two-seater, with no steering wheel or pedals -- a move that could change Tesla forever.

Enterprise resource planning has traditionally been associated with large vendors, complex implementations, long contracts, and significant licensing investments. For decades, companies evaluating ERP systems were likely to encounter names such as SAP, Oracle, Microsoft Dynamics, and other established commercial platforms. That model is not disappearing. But the assumptions behind it are changing. In 2026, enterprises have more choices than simply selecting between competing proprietary ERP vendors. Open-source platforms such as ERPNext, Odoo, and other business application ecosystems have become credible alternatives for organizations that want greater control over their software, more flexible customization, and different approaches to total cost of ownership. At the same time, SaaS pricing, vendor lock-in, integration complexity, cloud adoption, data ownership, and increasingly capable development tools are changing how businesses think about enterprise software. This does not mean open-source ERP is automatically better. It means the ERP decision deserves to be reconsidered. The question is no longer simply: Which ERP vendor should we buy from? Increasingly, organizations are asking: How much control should we retain over the software that runs our business? What Is Open-Source ERP? Open-source ERP is enterprise resource planning software whose source code is made available under an open-source license that grants defined rights to use, inspect, modify, and distribute the software. The practical implications depend heavily on the specific project's license. For an enterprise buyer, however, the important distinction is that open-source software can provide a level of visibility and extensibility that proprietary software may not. A simplified comparison looks like this: Proprietary ERP Business │ ▼ Vendor Software │ ├── Vendor controls source ├── Vendor controls roadmap ├── Vendor controls licensing └── Vendor controls many upgrade decisions Open-Source ERP Busi

When businesses outgrow spreadsheets and disconnected SaaS tools, the next question is often whether they should buy another application, customize an existing platform, or build a system specifically around their workflows. For many organizations, building custom business software can appear expensive and technically demanding. A development team has to think about authentication, permissions, database models, APIs, user interfaces, background jobs, reporting, audit trails, and deployment. This is where open-source application frameworks can change the equation. Instead of building every foundational capability from scratch, a framework can provide the underlying architecture while developers focus their effort on the business problems that actually differentiate the organization. One example is the Frappe Framework , an open-source web application framework used to build business applications such as ERPNext. But what exactly is Frappe, and why would an organization consider using it for custom enterprise software? What Is Frappe Framework? Frappe is an open-source, Python- and JavaScript-based web application framework designed to make it easier to build database-driven business applications. Rather than being simply a collection of programming utilities, Frappe provides a broader application foundation. It includes capabilities for: Data modeling Authentication Role-based permissions REST APIs Web forms Background jobs Reporting Workflow management Notifications File attachments Activity and audit information User interfaces Database access Application configuration This means a development team can start with an application architecture that already understands many of the requirements common to business software. The important distinction is this: Frappe is a framework for building applications. ERPNext is an application built using that framework. That distinction matters when evaluating Frappe for custom software development. Frappe vs ERPNext Frappe and ERP

Anything but your console or PC can be an Xbox for 5-15 hours a month.