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Neil Fraser's disclosure highlights a regulatory change affecting the .name top-level domain. Following ICANN's approval, Verisign will eliminate third-level registrations due to declining usage. This affects about 22,000 registrants and raises security concerns, as released second-level domains could be exploited. Affected users are considering legal options to challenge the decision. By Olimpiu Pop

Hello everyone! In this article, we will take a technical look at Tencent EdgeOne Makers , a Web and Agent development and deployment platform built on Tencent EdgeOne infrastructure. Rather than following a step-by-step tutorial, we will explore how the platform works behind the scenes and how its different technologies work together to support modern web applications. We will look at the journey from source code and build processes to deployment on the EdgeOne network. We will also explore how edge computing and caching deliver content closer to users, how Edge Functions and Cloud Functions handle server-side workloads, and how EdgeOne Makers supports AI Agents, storage, deployment environments, and developer tools . By understanding how these components interact, we can get a clearer picture of what Tencent EdgeOne Makers offers and how its architecture differs from traditional web hosting. Modern web applications are built from more than a frontend. They may combine APIs, serverless computing, storage, deployment automation, and AI services. Tencent EdgeOne Makers is a Web and Agent development and deployment platform built on Tencent EdgeOne infrastructure. It brings these capabilities into one environment, connecting application development with build processes, deployment, edge delivery, serverless execution, and AI development. EdgeOne Makers evolved from EdgeOne Pages, expanding from frontend hosting to full-stack Web and Agent development. Its architecture connects source code, deployment, and EdgeOne's global edge network. 1. From Source Code to Deployment At the center of Makers is its build and deployment workflow. A project can be connected to a Git repository, allowing Makers to detect common frameworks and apply build configurations. Developers can define parameters such as the root directory, installation command, build command, and output directory. Source Code → Git Repository → Build System → Deployment → EdgeOne Infrastructure → Users A push to

Originally published on WordPress on September 27, 2025. When AI systems fail in production, the failure is often blamed on data quality, model drift, or algorithmic limitations. In practice, many of the most damaging failures originate much earlier and much lower in the stack. They come from weak security foundations that allow systems to behave in unintended ways. Reliable AI is not just about accuracy or performance. It is about whether the surrounding infrastructure enforces discipline around access, data handling, and execution paths. Infrastructure as the First Line of AI Security Every AI system depends on infrastructure that controls how compute, storage, and networking are consumed. If that infrastructure is loosely governed, the AI system inherits that weakness. A common example is a shared compute environment where multiple teams run experiments. If isolation is poorly enforced, one workload can access artifacts, logs, or intermediate data from another. The model may be mathematically sound, but the environment allows behavior that violates assumptions about separation and control. From a reliability standpoint, this creates hidden coupling. An AI job might fail or behave inconsistently because another process consumed shared resources or modified shared state. From a security standpoint, the same weakness allows unauthorized access to sensitive datasets or trained models. Strong infrastructure boundaries do not just protect against attackers. They protect teams from each other and from accidental misuse. Access Control Across the AI Lifecycle AI systems have long lifecycles that include data ingestion, preprocessing, training, evaluation, deployment, and monitoring. Each stage introduces different access needs. Problems arise when a single identity or role is allowed to operate across too many of these stages. For example, an engineer might have permission to both modify training data and deploy models. That convenience can quietly undermine trust in the

Even though networking fundamentals are often taught at the early stages of a tech career, their relevance becomes far more important when you begin working with distributed IoT and edge-driven architectures. Concepts like subnetting, routing, NAT, DNS, firewalls, and VPNs evolve from simple textbook ideas into core architectural tools that determine how devices communicate, how secure the system remains, and how reliably data moves between the edge and the cloud. This refresher looks at these fundamentals from the perspective of someone building and supporting real IoT and edge environments. The goal is not to re-teach the basics, but to reconnect them with the realities of large-scale, low-power, and cloud-connected systems. 1. Subnetting as the Backbone of IoT Network Segmentation Subnetting plays a much bigger role in IoT and edge-driven environments than most people realize. In traditional networking, subnets help organize traffic and reduce broadcast noise. In IoT, they become a core part of the system architecture. When you’re dealing with sensors, gateways, and edge compute nodes running side by side, the network must be segmented in a way that keeps each function secure and predictable. A typical LoRaWAN setup shows this clearly. The gateway LAN, the packet-forwarder network, and the edge analytics node usually sit in different subnets. This separation allows you to apply strict ACLs around what each component can communicate with, especially because IoT devices often have limited security controls of their own. Subnetting also helps manage traffic flow, ensuring that noisy sensor broadcasts don’t interfere with time-sensitive edge workloads. Beyond security, good subnet design improves fault isolation. If a node misbehaves, the impact is contained within its segment. This structure also supports multi-tenant IoT deployments, where different applications or departments share the same physical infrastructure without touching each other’s data paths. In short

Checked with a Googlebot user agent one morning: every single URL on our site returned the same 3,780-byte shell. Same <title> , zero <h1> , zero body text. The homepage, a blog post and a product page were byte-identical before JavaScript ran. Search Console agreed with the crawler rather than with us. Of 741 URLs, 116 had earned a single impression in 28 days, and a landing page that had been live for five months was still reported as "URL is unknown to Google". Here is what I actually learned fixing it, including the two things that cost us the most time. Google does render JavaScript. That is not the point. The standard reply to this problem is "Googlebot executes JS now, you are fine." It does. Several of our pages were indexed, so rendering clearly happened. But rendering is a separate, budgeted queue . A domain with little authority does not get much of that budget. So the practical question is not "can Google render our page", it is "will Google spend its budget rendering this page, today, before it decides what the page is about". There is a second problem that has nothing to do with rendering: 741 URLs that are byte-identical before render look like duplicates. You are handing a duplicate-content signal to the crawler and hoping the render queue fixes your first impression. What we built, and what we deliberately did not We wrote a post-build script that injects a real <head> into each generated HTML file: title, description, canonical, robots, Open Graph, Twitter. Head only. The body stayed exactly as the SPA served it. That was deliberate: No hydration flash. No risk of a static copy drifting out of sync with what users see. Nothing that could be read as cloaking, because the static markup is a subset of the rendered markup, not a different page. Every value is read from the same source the React page reads. Where a title is a literal inside a component, the script extracts it from that component's source rather than having anyone retype it. A number ret

Dark energy might be getting weaker. Scientists are wondering if interactions with dark matter in a “dark dimension” may be responsible.

The science curriculum the industry is offering to schools casts plastics as safe, recyclable, and a source of good jobs. Critics say it’s propaganda.

CERN engineers announced a shift from Red Hat-based distributions to Debian for its accelerator control systems. This decision stems from Red Hat's tightening compiler mandates, which threatened legacy hardware. The transition, focused on 2,200 specialized control machines, is set for completion in late 2026, while CERN's other systems will remain with Red Hat and AlmaLinux. By Olimpiu Pop

Most countries tax immovable property, and most of them argue about it badly. The argument usually skips the part that decides the answer: before any map is coloured, someone has to choose what is added up, what it is divided by, over which territory it is aggregated, and which cases are left out . Change any of those and the map can change while the underlying data stay identical. This post works through that problem with Chilean data, because Chile happens to publish the pieces needed to do it honestly: a national cadastre of every taxable property, and an official index that ranks small civic territories by socio-territorial vulnerability. The mechanics, though, are not Chilean. Any jurisdiction that assesses property for tax and then maps the result against a deprivation measure faces exactly the same four choices. The question fits in one small fraction: territorial indicator the unit you compare it against the total you want to describe ​ Adding up the assessed value inside a territory answers how much administrative value was allocated there. Dividing that same total by households, by residents or by square metres answers different questions. None of them is «the correct one» by nature; the error appears when one is presented under another's name. The arithmetic is usually innocent. The narrative is not always. Reading contract I cross two Chilean administrative registers: the real-estate cadastre of the Servicio de Impuestos Internos (SII) —Chile's tax authority, roughly the counterpart of the IRS or HMRC— and the Índice Global de Vulnerabilidad Socioterritorial (IGVUST) , a socio-territorial vulnerability index published by the Ministry of Social Development and Family. The unit of analysis is the neighbourhood unit , not the parcel, the household or the person. A word on that unit, because it has no clean equivalent elsewhere and it drives half of what follows. A Chilean unidad vecinal (UV) is a civic territory drawn for neighbourhood organisation and loca

I rent six dedicated servers from a company in Germany. Together they have more cores, more memory, and more SSD than most production clusters I worked on a decade ago. I run my own Kubernetes on them. Not managed. Not EKS. Not GKE. The whole stack, from the immutable OS up to the workloads. People who hear this ask me why, and the question usually arrives in one of two tones. The dangerous tone is "that's amazing, how do I do it" . The responsible tone is "why on earth would you do that to yourself" . This post is for the second group. Why the cloud is the right answer for almost everyone Let me get this out of the way honestly: by every conventional metric, I should be using the cloud. Managed Kubernetes has become genuinely good. EKS has dramatically improved over the last three years. GKE has always been better than people gave it credit for. The serverless options are mature. The serverless databases are mature. The observability is mature. The bill is predictable in the way a Tuesday is predictable. Self-hosting violates almost every assumption that makes a startup productive. Time is the most expensive resource you have. The cloud sells you abstractions that turn that time into product. Running your own substrate means the time goes into the substrate. If you are trying to ship a product to customers — go use the cloud. Stop reading this post. It will only confuse you. What the cloud does not sell you Here is what the cloud will not sell you, even if you are willing to pay extra: control over your own roadmap. The cloud's roadmap is the cloud's. They decide which APIs deprecate. They decide which regions get the new feature. They decide what your egress bill looks like. They decide whether your monitoring vendor — sitting on top of their infrastructure — is allowed to charge you eight times what it would cost you to host the same software yourself. They decide whether the small ML company hosting your fine-tuned model gets acquired by someone with very differ

TrueNAS CORE has been the standard for open-source storage using FreeBSD and OpenZFS. The shift to TrueNAS SCALE, based on Debian, left some users needing alternatives. FreeCORE upgrades TrueNAS CORE to FreeBSD 15.0, restoring essential features like FreeBSD Jails. While it satisfies certain administrators' needs, its long-term sustainability and maintenance by a single individual raise concerns. By Olimpiu Pop

This GIF shows what happens if you slightly stretch the data texture storing the BVH and triangle data. Child nodes storing triangles got misaligned addresses first, while AABB nodes lose them later.

"A URL shortener" sounds like a weekend project. Slug in, long URL out, 302 , done. That's what I thought too. Then real usage showed up: links opened inside Instagram's in-app browser and didn't convert, bot traffic wrecked the analytics, and one link needed to send a US visitor somewhere different from an EU visitor. Suddenly the "trivial" part was 5% of the work. I built the whole thing on FastAPI + Redis + MySQL + htmx , deliberately with no frontend framework . This post is about the parts that turned out to be interesting — the redirect hot path, geo/device routing, and escaping in-app browsers — and why htmx was the right call for a one-person team. Disclosure: I build tapurl.io , a link shortener for marketers. This is a write-up of the engineering behind it, not a pitch — everything below is patterns you can apply to any shortener. The redirect is a hot path, so treat it like one Every other page in the app can be a bit slow. The redirect cannot. It sits in front of someone's click, and it runs on every click, so it has to be a tight, predictable read. The naive version hits your database for every redirect: @app.get ( " /{slug} " ) async def redirect ( slug : str ): link = await db . fetch_link ( slug ) # DB round-trip on every click if not link : raise HTTPException ( 404 ) return RedirectResponse ( link . destination , status_code = 302 ) That's fine until you have traffic. The slug-to-link lookup is a near-perfect cache candidate — a slug maps to the same link record every time. So the real path reads from Redis first and only falls back to MySQL on a miss: async def resolve ( slug : str ) -> Link | None : cached = await redis . get ( f " link: { slug } " ) if cached : return Link . parse_raw ( cached ) link = await db . fetch_link ( slug ) if link : await redis . set ( f " link: { slug } " , link . json (), ex = 3600 ) return link Two things worth saying out loud: Cache the lookup, not the decision. You cache the link record, but the actual destination

Ev Ağı Nasıl Çalışır? Bir ev ağını en basit haliyle şöyle düşünebiliriz: Internet │ ISP ağı │ Ev Router'ı │ ┌─────────────┼─────────────┐ │ │ │ Laptop Telefon NAS Ev router'ı burada iki farklı dünyayı birbirine bağlar: ISP üzerinden ulaştığı dış ağ ve evdeki cihazların bulunduğu yerel ağ. Bu basit topolojinin arkasında LAN, WAN, subnet, DHCP, routing, NAT, firewall, bridge ve VLAN gibi kavramlar birlikte çalışır. 1. Ev ağı, ISP ve internet tarafı LAN ve WAN LAN — Local Area Network , router'ın yerel ağ tarafıdır. Evdeki laptop, telefon, NAS, televizyon gibi cihazlar genellikle bu tarafta bulunur. Örneğin router'ın LAN adresi: 192.168.1.1/24 olsun. Cihazlar da: Laptop 192.168.1.20 Telefon 192.168.1.30 NAS 192.168.1.50 adreslerini kullanabilir. Bunların tamamı aynı: 192.168.1.0/24 yerel IP ağına aittir. WAN — Wide Area Network ise router'ın kendi yerel ağı dışındaki bir upstream ağa bağlandığı taraftır. Tipik bir evde: Internet / ISP │ WAN │ Router │ LAN │ Ev cihazları şeklinde görünür. LAN ve WAN, Ethernet kablosunun fiziksel türünü tanımlamaz. Aynı standart Ethernet bağlantısı bir router için LAN, başka bir router için WAN rolünde olabilir. Örneğin: Internet │ Upstream Router LAN: 192.168.1.1 │ │ Ethernet ▼ Downstream Router WAN: 192.168.1.50 LAN: 192.168.10.1 Buradaki 192.168.1.0/24 ağı: upstream router açısından LAN, downstream router açısından WAN tarafıdır. Dolayısıyla LAN ve WAN kavramları hangi router açısından baktığımıza göre anlam kazanır . Upstream ve downstream Ağda internet veya daha üstteki ağa doğru olan yön upstream , son kullanıcı cihazlarına doğru olan yön ise downstream olarak adlandırılır. Internet │ Upstream Router │ Downstream Router │ Laptop Downstream router'ın internete doğru bağlandığı router onun upstream router'ıdır. Bu terminoloji özellikle evde bir modem/router arkasına ikinci bir router bağlandığında kullanışlı hale gelir. ISP'nin rolü ISP — Internet Service Provider , ev ağını daha büyük internet altyapısına bağlayan servis sağlayıcıdı

OpenAI announced its next big model, GPT-6 Astra, and also, by the way, that "the AGI era" is here now. Today on The Vergecast, we've got an all-star panel to break down the news of the week. First, senior AI reporter Hayden Field joins us to talk about our supposed AGI era and Nvidia's acquisition […]

This week on Uncanny Valley, we dig into the latest prediction market buzz, Flock’s AI-powered police search tool, and how tech bros don’t know how to talk about “rouge” AI agents

OpenAI claims that Astra represents "a new frontier on computer and browser use," and that it handles tasks with unmatched "speed, accuracy, and safety."

WeatherNext 3 is the latest wave of a sea change in meteorology brought out by deep learning techniques. Google says it will start feeding into weather information users see in search, Google Maps, and Gemini.

Today, I’m talking with Tom Conrad, the CEO of Sonos. Tom and I have known each other for a long time — he was the chief technology officer of Pandora, VP of product at Snap, and the chief product officer of Quibi. He was also on the board at Sonos during its disastrous 2024 app […]

From loop engineering to harnesses, squads, and open weights, the GitHub Podcast breaks down the AI terms showing up in developer conversations. The post Decoding the new AI lingo: Loops, harnesses, squads, hill climbing… oh my! appeared first on The GitHub Blog .