Hi HN, Gabriel here. This is a standalone binaural beat generator, combined with a two-deck local audio player built with the Web Audio API. It includes ten harmonic carrier pairs, seven brainwave-state presets, per-harmonic mixing (so the binaural tones aren't out-of-tune with the music), adjustable beat and carrier frequencies, two oscillator shapes, offline key/BPM analysis, automatic carrier matching, adaptive impulse noise, playlist support, and output recording. The main problem I've had w

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 typed a 19-character question into Claude Code: who are you? Then I opened the network logs of what my machine actually sent over the wire. I recommend you never do this. It ruins something in you. The numbers My question was 19 bytes . The request that carried it was 118,693 bytes . That's a 6,200x amplification before the model reads a single word I wrote. My actual message was 0.016% of the payload. If this were a letter, I'd be mailing a phone book with a Post-it note inside. Here's the pie, byte for byte: What Bytes Share 38 tool JSON schemas 78,935 66.8% System prompt (behavior manual, safety rules, my git status) 22,178 18.8% A list of 60 more tools it could load, plus 25 skill descriptions 16,844 14.3% My memory files, my email address, today's date 1,126 1.0% My question 19 0.016% And the tool schemas aren't even schemas, mostly. Of those 78,935 bytes, only 29,164 are machine-readable JSON schema. The other 44,392 bytes are English prose — instruction manuals for the model, shipped on every single request. One tool alone, Workflow , carries a 19,012-byte description. That's a small essay. It rides along on every message I send, forever, whether I orchestrate a multi-agent workflow or just say "hi." Sixteen of the 38 tools are for driving a browser preview I never opened. There's an iOS Simulator controller in there. I was asking a chatbot its name. The full manifest, since you asked Every tool schema in that request, largest first. "Prose" is the English description; "schema" is the actual machine-readable part. Approximate tokens = bytes ÷ 4. Tool Bytes Prose Schema ~Tokens Did I need it? Workflow (multi-agent orchestration) 21,609 19,012 1,833 ~5,400 No Artifact (publish web pages) 10,679 6,358 4,002 ~2,670 No AskUserQuestion 4,371 1,097 3,149 ~1,090 No iOS Simulator control 3,976 1,307 2,518 ~990 No ScheduleWakeup (self-pacing loops) 3,942 2,685 1,068 ~985 No visualize show_widget 3,038 648 2,259 ~760 No Agent (spawn subagents) 3,037 1,574 1,342 ~760 N

You know the feeling. You call your AI bot in Slack, hit enter, and then... nothing. Dead silence. You sit there staring at the screen. Is it actually running your code? Did the server crash? Is it just quietly judging your prompt? You have absolutely no idea. It's a black box of anxiety. The standard solution to this UX nightmare isn't much better. Most bots just spam the thread with a million separate status messages: "Claude is thinking..." "Claude is running a command..." "Claude is scratching its nose..." Congratulations, your Slack thread now looks like a chaotic WhatsApp group. We got fed up with both extremes. The "Aha!" Moment: Slack as an IDE While working on the claude-slack-bridge , we realized we didn't just want a chatbot. We wanted a local terminal experience, right inside the workspace. So, we updated the bridge with a feature that basically turns Slack into a full-blown IDE. Instead of polluting the channel with endless new messages, everything updates live on a single, dynamic message. Here is what it looks like in action: How the UX actually feels now: Real-time transparency: You literally see it run the commands and update the progress sequentially. No more guessing if it's dead. Zero thread pollution: It edits the same message block over and over. Direct delivery: When it's done, it just spits the final output (like test.pencil.png in our demo) straight into the chat. No noise. No ghosting. Just a clean, live-updating terminal. Try it out If you're building AI integrations or just want to see how we handled the Slack API rate limits to make this live-updating feel smooth, check out the code. 🔗 GitHub Repo: claude-slack-bridge I'm curious – how are you guys handling long-running AI tasks in your Slack apps? Are you Team "Spam the Thread" or Team "Silent Black Box"? Let me know in the comments.

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.

https://www.ecb.europa.eu/euro/banknotes/future_banknotes/ht...

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.