标签：#t

Facebook is planning some big changes to try and keep its users from jumping to rival social platforms like TikTok - changes that sound dubiously similar to becoming a TikTok clone. Facebook head Tom Alison announced today that the platform will begin testing a "reimagined experience" later this year that will put a subset of […]

Airbus selected Scaleway as its sovereign cloud provider after a tender that scored protection against non-European extraterritorial legislation alongside technical capability. Airbus frames it as complementing multi-cloud, not exiting AWS. Practitioners note the pattern is spreading past hyperscalers to small US SaaS vendors, and that sovereignty claims still require verifiable controls. By Steef-Jan Wiggers

From musical instruments and games to play pieces that encourage balance and dexterity, these toys will engage your child’s mind and body.

Analysis of satellite images of Myanmar shows dozens of alleged scam compounds have appeared in recent months, despite a purported crackdown on the criminal organizations.

“I think it should stand for artificial idiot,” one 9-year-old says. Here’s why kids of all ages are calling AI “disgusting” and “creepy.”

Ich habe einen echten Speedtest in Vanilla JS gebaut (mit Cloudflare API) Kein npm install. Kein React. Kein 200-MB-node_modules-Ordner. Nur HTML, CSS und ~250 Zeilen JavaScript, die deine echte Internetgeschwindigkeit messen. 👉 Live-Demo: dsl.nevik.de/speedtest Warum noch ein Speedtest? Es gibt Speedtest.net, FAST.com und dutzende andere. Warum also selbst bauen? Drei Gründe: Transparenz: Ich wollte genau verstehen, was gemessen wird – und was nicht. Größe: Die meisten kommerziellen Speedtests laden mehrere MB an Tracking-Scripts. Meiner ist eine einzige HTML-Datei mit eingebettetem JS. Kontrolle: Ich kann das Ergebnis direkt gegen den gebuchten Tarif des Nutzers bewerten und eine fundierte Empfehlung geben. Das Ergebnis ist ein Speedtest, der in unter 50 KB ausgeliefert wird, auf jedem Gerät läuft und echte Messwerte liefert – keine Schätzwerte. Die Architektur: Drei Phasen, drei Messungen Ein guter Speedtest misst drei Dinge: Ping (Latenz): Wie schnell kommt ein Datenpaket hin und zurück? Download: Wie schnell kommen Daten bei dir an? Upload: Wie schnell kommen Daten von dir raus? Für alle drei nutze ich die öffentliche Cloudflare-Speedtest-API , die unter speed.cloudflare.com läuft. Cloudflare betreibt eines der größten Edge-Netzwerke der Welt, hat Server in praktisch jedem Land und – ganz wichtig – erlaubt CORS für diese Endpunkte, sodass wir direkt aus dem Browser heraus messen können. Die zwei Endpunkte, die alles tragen: const CF_DOWN = ' https://speed.cloudflare.com/__down?bytes= ' ; const CF_UP = ' https://speed.cloudflare.com/__up ' ; __down?bytes=N liefert exakt N Bytes zurück. __up nimmt einen POST-Body beliebiger Größe entgegen. Das war's. Kein API-Key, keine Rate-Limits, die für unsere Zwecke relevant wären, keine Kosten. Phase 1: Ping messen (ohne WebSocket) Klassische Speedtests nutzen für den Ping oft WebSockets oder RTCPeerConnection -Tricks. Das ist komplex und fehleranfällig. Mein Ansatz: Wir laden einfach einen winzigen Datenblock (1 KB) fünfma

Building an operating system is a project I have had my eyes set on ever since I discovered free will in the realm of programming. Years ago, I did a reasonable amount of research, paying extra attention to the subject during my computer science degree and I was able to understand Operating System Theory and how it works from first principles but I never really got around to building one. I had only flimsy reasons for not embarking on it like "why build one when there are tons of working ones out there? The theoretical knowledge is enough" . More recently, I am ignoring the need to not re-invent the wheel for the joy of programming. So if you are interested in also rebuilding stuff because you can, join me on this series as I document how I am going to be building kluster. kluster is in its infancy and the direction is not clear but the one certain thing is that I will be building it entirely in Rust, save some assembly instructions and a linker script and I will be explaining every single line of code along the way. It will also be designed to target the raspberrypi 4 & 5, on qemu and on real hardware respectively. This is an opportunity for anyone who wants to see how Rust works at the lowest of levels to hop on and join the ride. Note that this series will be your biggest lesson on delayed gratification because we will write a lot of code before we even get to see anything meaningful on screen but I will foreshadow what you can get by the end of part 3 if you are patient enough: {{ image(src="/images/os-part3-result.png", alt="Part 3 Results OS Dev") }} You can also clone the source code for part 1 from Github and follow along. Project Setup First things first, let us setup the foundation of the project. I'll be straight with you, I love Rust and I enjoy using the Rust ecosystem in its entirety so I will stay true to that and use it as obsessively as any true Rustacean; I won't hold back. Without doubt, all the dependencies we need are freely available as long as

I just wanted to open a CSV file quickly. Instead, I got: Slow spreadsheet apps Online tools that upload my data Way too much friction So I defined a simple goal: Fast and frictionless Fully local (no uploads) Spreadsheet-like experience That’s what I built. What you can do with it Open CSV files instantly (drag & drop, no setup) Search and filter large datasets in seconds Edit data like a spreadsheet Export exactly what you see Why this matters Everything happens locally in your browser: Your data never leaves your device No account required No tracking, no storage Built with client-side JavaScript — no backend involved. Just open, edit, and export. How it works Built with client-side JavaScript — no backend involved. Spreadsheet-Like Editing Click a cell to select it, then press Enter or double-click to start editing. Press Enter again to save the value and move to the cell below. Use Option + Enter on macOS or Alt + Enter on Windows to insert a line break. You can also drag the small fill handle to copy a value across multiple cells. Export the Current View The exported CSV reflects the current table state, including: Search results Sorting order Visible columns Saved cell edits Try it yourself Open a CSV file, edit a few cells, and export it — all without uploading anything. 👉 https://csv-open.github.io/ No signup. No upload. Just works. Handles CSV files up to 25MB Supports multiple languages

As part of my learning journey with Django, I wanted to build a project that would challenge me beyond the basics. I decided to create RecipeHub, a web application where users can create, manage, and share recipes while exploring recipes from other users. The project started from a Django starter template, but I customized it by adding new features, redesigning the interface, and deploying it online. Features RecipeHub allows users to: Register and log in Create, edit, and delete recipes Browse recipes by category Save favourite recipes Upload recipe images Access a personal dashboard Use the application in both light and dark mode The application is fully responsive, making it easy to use on both desktop and mobile devices. Technologies Used I built the project using: Python Django Django Allauth PostgreSQL Tailwind CSS DaisyUI HTMX Vite Gunicorn Render GitHub was used for version control throughout the project. Challenges One of the biggest challenges was deployment. While everything worked locally, deploying to Render required configuring PostgreSQL, environment variables, and static files correctly. I also encountered an issue with uploaded recipe images. Since the application is hosted on Render's free tier, uploaded media is stored on an ephemeral filesystem, meaning uploaded images are lost after redeployment. Learning why this happens gave me a better understanding of the difference between development and production environments. Another challenge was redesigning the dashboards. I wanted them to feel clean and modern instead of looking like a default Django application, so I spent time improving the layout, spacing, and responsiveness. What I Learned This project helped me improve my understanding of: Django project structure Authentication and user management CRUD operations Database relationships Responsive UI design Git and GitHub workflows Deploying Django applications Debugging real-world issues More importantly, it taught me how to troubleshoot proble

If you’re tired of sponsored and AI-summarized search results, Kagi gives you a curated—and human-centric—way to find what you’re looking for.

The headline going around is that Spark 4.2 can retire your vector database. That's half true, which is the most dangerous kind of true. Spark did add real vector search, and for some workloads it genuinely removes a whole system from your stack. For others, you'd regret dropping your vector DB. Here's the honest version. The short answer If your vectors already live in your data platform and your searches are batch or analytical, Spark 4.2 can absolutely replace a separate vector database. If you're serving live, low-latency retrieval for a chatbot or search box, you probably still want a dedicated one. It's a "depends on the workload" answer, and the details matter. What Spark 4.2 actually added Spark 4.2 brought vector search into plain SQL. No bolt-on library, no separate engine. The new primitives include vector distance and similarity functions, vector normalization, vector aggregation like sum and average, and the headline one, NEAREST BY, a top-K ranking join that finds the closest matches by distance. In practice that means you can store embeddings in a Spark table and run a similarity search with SQL you already know. Those operations cover the real use cases: retrieval, recommendations, entity resolution, and candidate generation. Databricks is openly framing this as Spark becoming an AI serving layer, not just a batch engine. Why this is a big deal The value isn't that Spark invented vector search. Plenty of tools do it. The value is that you can keep your retrieval pipeline on one platform. Think about the normal setup today. Your data sits in a lakehouse or warehouse. To do vector search, you spin up a separate vector database, then build a pipeline to copy and sync embeddings into it, and keep the two in step forever. That's a second system to run, secure, pay for, and debug at 2 a.m. Spark 4.2 lets you skip that for a lot of cases. The embeddings stay where your data already is, and the search runs right there. Fewer moving parts is a real win, and i

xAI has a bold pitch for Grok 4.5: it codes about as well as Claude Opus 4.8, but does it with roughly a quarter of the tokens. That's not a "we're smarter" claim. It's a "we're just as good for far less money" claim, which in 2026 might matter more. Someone actually put it to the test, so let me walk through what the numbers say and why you should care. The claim and the pricing Grok 4.5 landed on July 8, 2026. xAI says it matches Opus 4.8 on coding while using about 4.2 times fewer output tokens to get there. The sticker price already favors Grok. It runs $2 per million input tokens and $6 per million output. Opus sits at $5 and $25. That's less than half the price on both sides before you even factor in the token efficiency. Stack the two together and the cost gap gets dramatic. What the benchmarks say The benchmarks mostly support the marketing, with a catch. On Terminal-Bench 2.1, which measures real command-line work, Grok 4.5 scored 83.3 percent to Opus 4.8's 78.9. But on SWE-Bench Pro, the harder test of fixing real open-source bugs, Opus still comes out ahead. So the honest read is not "Grok is better." It's "Grok is about as good, for a lot less." Different claim, and a more interesting one. The hands-on test Benchmarks are one thing, real work is another. The New Stack ran a head-to-head, giving both models the same three jobs in one real Rust project (the fd file-finder) inside Cursor, and tracked every token. I'm summarizing their results here, credit to them for actually measuring it. The three tasks were a bug fix, a multi-file refactor, and a feature build. The code both models produced was nearly interchangeable, so the story came down to tokens, time, and cost. On the small bug fix, Opus actually won. Both wrote an identical fix with all tests passing, but Opus did it faster and on fewer tokens. Grok's efficiency edge showed up on the bigger jobs. On the refactor, Grok used about 197K tokens versus Opus's 954K for the same result, roughly a fifth.

The US government just accused a Chinese AI company of copying an American model, and threatened sanctions over it. Whether or not the claim holds up, it's worth understanding what's actually being alleged, because the word at the center of it, distillation, is about to come up a lot. Here's the plain version. What distillation means Model distillation is training a new model using the outputs of an existing one. Instead of learning purely from raw data, the new model learns by copying how a stronger "teacher" model responds. Done legitimately, it's a normal technique for making smaller, cheaper models. Done against someone else's commercial model without permission, it's a way to clone a lot of that model's behavior on the cheap, and it usually violates the terms of service. That's the core of the accusation: that one company used another company's model as an unpaid teacher. What the White House claims Michael Kratsios, who leads the White House Office of Science and Technology Policy, accused Moonshot AI, a Beijing-based company, of distilling Anthropic's Fable model to build its Kimi K3. He alleged Moonshot built a sophisticated internal platform to run large-scale distillation against US models while switching between access methods to avoid detection. The claim didn't stop there. Kratsios also said Moonshot obtained restricted Blackwell-generation Nvidia servers through Thailand, which would sidestep US export controls. Treasury Secretary Scott Bessent followed up by putting sanctions and export-control blacklisting on the table. Why some experts are skeptical Here's the part that keeps this from being open-and-shut. Anthropic's Fable model has only been public since July 1, and Kimi K3 shipped on July 16. That's a very short window to distill a 2.8-trillion-parameter model primarily from another model's outputs. Several experts have pointed out that the timeline makes "mostly built by distilling Fable" hard to believe. So the honest framing is that this is a

Moonshot launched Kimi K3, and within 48 hours it had to stop taking new subscribers. Not because the model flopped, but because too many people wanted it. That's a strange kind of problem to have, and it's telling you something important about where AI's real bottleneck now sits. What actually happened Less than two days after Kimi K3 went live, Moonshot froze new subscriptions. The reason was blunt: demand had eaten through its available GPU capacity. In the company's own words, the model got "far more love than we expected," and in 48 hours usage pushed close to the limit of what its hardware could serve. Moonshot handled it reasonably. Existing users kept their access, and the company said it would expand capacity and reopen signups in batches. It also split its plans into two tiers, a general Kimi Membership for web and app use, and a separate Kimi Code Membership aimed at programming work. That split is a hint about which users are burning the most compute. Why the model drew that kind of demand Kimi K3 isn't a minor release. It's an open-weight model at 2.8 trillion parameters, and it reportedly beat Anthropic's Fable 5 and OpenAI's GPT-5.6 Sol on front-end coding tests. Open, cheap, and competitive on real coding is exactly the combination developers pile onto. So they did. The real story: the bottleneck moved Here's the part worth internalizing. For years the hard, expensive problem in AI was training. That's where the giant compute bills and the headlines were. Kimi K3's freeze shows the constraint shifting to inference, the cost of actually running the model for users, every request, every day. Agentic workloads are why. When an AI agent runs a coding task for minutes or hours instead of answering a single prompt, each user consumes far more compute than a chatbot ever did. Multiply that by a viral launch and you hit a GPU wall fast. Moonshot didn't run out of ideas. It ran out of chips to serve the ideas. Why this matters even if you never touch Kimi Thi

Microsoft has intervened to stop Windows 11 users with LG monitors from being bombarded with annoying McAfee trial pop-ups. In response to complaints about the LG bloatware, Microsoft's Windows chief, Pavan Davuluri, said that LG has agreed to immediately disable the McAfee pop-up from its LG Monitor App Installer, and pledged that Microsoft will "keep […]

I built Skim because I tried to find a Windows email client that doesn't suck that hard and couldn't really find one. It's not rocket science to vibecode one, right? Why has nobody done it before? Or is everyone just okay with bloated Outlook or archaic Thunderbird? Skim - free, open-source, MIT-licensed minimalistic offline-first email client with BYOK AI. Quick feature list: Installer under 5 MB Bring Your Own Key to enable AI features: Anthropic/OpenRouter, or any OpenAI-compatible server (including a local one) No menu. Minimalistic and contextual interface - buttons are shown only when you need them Keyboard-first controls: shortcuts for everything, no mouse needed Sweet warm zine design Super modest resource consumption AI features are pretty basic: Cowriter that can scan your recently sent emails, adopt your style, and then write emails and replies that sound like you "Ask this email" - chat with a particular email; it can read attachments and follow links inside it Agentic search across all connected inboxes; the "Ask your inbox" feature can scan your whole inbox One-click summarization and translation A few things I'm actually proud of under the hood The UI part is easy to vibecode. These bits were not. Getting the installer under 5 MB took real work. panic = "abort" alone dropped about 6 MB of unwind tables from the binary, that's 23% off the NSIS installer. I ship a single crypto backend (ring, with aws-lc-rs switched off) so I'm not compiling a second, unused crypto library into the thing. I even wrote a tiny Vite plugin that deletes legacy .woff fonts, since WebView2 always uses woff2 anyway. Pretty much every dependency in Cargo.toml carries a comment justifying its feature flags in kilobytes saved: rustls = { version = "0.23" , default-features = false , features = [ "ring" ] } # aws-lc-rs would compile a second, unused crypto library. ~2 MB of dead weight. No Electron, no bundled Chromium. Tauri 2 uses the system WebView2, so it installs per-user wit

Hetzner is experimenting with LLM inference. That is not a sentence I expected to write, but I think it is pretty interesting :) Before anyone moves their production AI workloads to Hetzner: this is very much an experiment . There is no billing, no SLA, no production guarantee, and currently only one model. Hetzner says it wants to learn whether people actually want this, how the system scales, which features matter, and what kind of load it can handle. So this is not a finished product launch. It is Hetzner putting something early in front of users and seeing what happens. I really like that approach. What Is Hetzner Inference? Hetzner Inference is an OpenAI-compatible API running on Hetzner's own infrastructure. You create an API token in the Experiments dashboard, point an OpenAI client at Hetzner's base URL, and use it like most other inference APIs. Right now, the only available model is Qwen/Qwen3.6-35B-A3B-FP8 . It is a 35-billion-parameter Mixture-of-Experts model with 3 billion active parameters. It accepts text and images, has a 262K context window, and uses FP8-quantized weights. That is a perfectly reasonable model for an experiment. It is small enough to serve without a ridiculous GPU cluster, but still useful enough to test the API with real workloads. Hetzner also published a short tutorial for connecting OpenCode to the API , if you want to try it without writing any code. I Tried It Because the API is OpenAI-compatible, there is almost nothing special about the integration: pip install openai from openai import OpenAI client = OpenAI ( base_url = " https://inference.hetzner.com/api/v1 " , api_key = " YOUR_TOKEN " , ) response = client . chat . completions . create ( model = " Qwen/Qwen3.6-35B-A3B-FP8 " , messages = [ { " role " : " user " , " content " : " Explain why the sky is blue in one sentence. " } ], extra_body = { " chat_template_kwargs " : { " enable_thinking " : False , } }, ) print ( response . choices [ 0 ]. message . content ) The enabl

After Trump publicly backed pillow salesman turned election denier Mike Lindell for governor of Minnesota, the local GOP pushed back strongly.

The article discusses an experiment where a code assistant had to design an agentic system using LangChain4j documentation. The assistant created a coding framework capable of writing, testing, and debugging code autonomously. Results showed that two architectural patterns—supervisor and workflow—offered different trade-offs between flexibility and execution speed during debugging tasks. By Kevin Dubois, Mario Fusco

EU law gives researchers unprecedented rights to scrutinize Big Tech. Researchers say Tik Tok, X, and Meta are still deciding who gets to look inside.