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Introduction In today's fast-paced development cycles, security can no longer be an...

I made this plugin because I felt like existing bookmark plugins were lacking some important features, including: - The ability to reorder your bookmarks - The ability to delete and move your bookmarks in bulk - The ability to view lines of code around your bookmarks - Syntax highlighting in the preview window - Resilience to the content of files changing (e.g. checking out a git commit that changes the file contents won't delete your bookmarks) Let me know what you think! If you have any feedba

This week's science news.

The Swift Observatory was launched in 2004, but recent solar storms have pushed its orbit lower, and it's in danger of burning up in Earth's atmosphere as soon as this year. To try and stave off its demise, NASA has enlisted Katalyst Space Technologies. The company's Link spacecraft launched Friday with the goal of intercepting […]

Here's what I found so far approach0.xyz : offline searchonmath.com : Gives irrelevant results such as p = m v when searching for F = m a https://search.mathweb.org/: A collection of abandoned projects and offline sites

"Forward Deployed Engineer" went from a Palantir-specific title to one of the hottest roles in AI in about eighteen months. But nobody had actually counted the market, so I did. I pulled every open FDE role I could find from public ATS job boards (Greenhouse, Lever, Ashby) across 11 companies and analyzed all 292 of them. Here is what the data says. Who is hiring Three companies account for 250 of the 292 openings: Palantir: 95 (they coined the title, and still call many of these roles "Deployment Strategist") Databricks: 85 OpenAI: 70 Then a long tail: Cohere and Scale AI (13 each), Sierra, Writer, Modal, Baseten, Ramp, and Sardine. What it pays Of the 40 roles that disclosed a US pay band, the median ran $197K to $294K , topping out at $390K plus equity at OpenAI and Sierra, with a floor around $137K. That is senior-software-engineer money for a role a lot of engineers have never heard of. International and most Palantir roles did not publish bands, so the true market is likely even broader. Three things that surprised me 1. 98% of these roles are customer-facing. This is the defining trait. It is not a backend role with occasional meetings. It is an engineer who lives in the customer's world, and if that sounds terrible to you, this is not a role you would enjoy occasionally. It is the whole job. 2. The title is chaos. The same role goes by at least four names: Forward Deployed Engineer (152), Forward Deployed Software Engineer (58), AI or Deployment Engineer (43), and Deployment Strategist (36). If you only search one term, you miss most of the market. 3. The job descriptions undersell the technical bar. JDs emphasize customer-facing work, cloud (AWS/GCP/Azure), Python, and integrations. But SQL and algorithms show up in only about a third of them, even though every FDE loop I have seen tests live coding and SQL under time pressure. The description sells the breadth. The interview tests the depth. The other details Geography: about 48% USA, but genuinely global

If you're using AI coding assistants like ChatGPT, Claude, Cursor, or Lovable, you've probably accumulated dozens of JSX components. Generating them is incredibly fast. Previewing them? Not so much. The Typical Workflow Every time I received a JSX component, I found myself repeating the same process. Create a React project (or open an existing one) Copy the JSX file Install dependencies Fix missing imports Run the development server Wait for everything to compile All of that... just to see one component. It felt like unnecessary overhead. There Had to Be a Better Way I asked myself a simple question: Why can't I just double-click a JSX file and preview it? We can instantly open images, PDFs, videos, and text files. Why should JSX files require an entire development environment? That's what inspired me to build PreviewKit . What is PreviewKit ? PreviewKit is a lightweight Windows application that lets you preview frontend components instantly. Supported file types include: ✅ JSX ✅ Vue ✅ HTML No project setup. No dependency installation. No terminal commands. Just open the file and see the result. Why I Built It AI has dramatically changed frontend development. We're no longer spending most of our time writing components—we're reviewing, comparing, and refining them. That means fast visual feedback is more important than ever. I wanted a tool that removed the repetitive setup process so I could focus on building better interfaces instead of preparing a preview environment. Who Is It For? PreviewKit is useful if you: Build React applications Work with Vue components Test standalone HTML files Generate UI with AI tools Review components from teammates Prototype interfaces quickly If opening frontend files feels slower than it should, PreviewKit was built for you. The Goal Isn't to Replace Your Framework You'll still use React. You'll still use Vue. You'll still use Vite or Next.js. PreviewKit isn't trying to replace your existing workflow. It simply removes one frustrat

▶ Try it live (in your browser): https://umbraaeternaa.github.io/loom/play.html Built solo, in the open, from Ukraine 🇺🇦. The problem nobody can scale their way out of AI now writes a large and growing share of the code that runs in the world. The uncomfortable part isn't that the code is often wrong — it's that the same model frequently writes both the code and the tests that check it. When one intelligence authors the solution and the criteria, "it passed" quietly stops meaning "it's safe." The gate becomes foolable. You can make the model bigger, but a bigger model that grades its own homework is still grading its own homework. The honest answer isn't "trust a smarter model." It's: trust only what can be independently proven — and make that proof mechanical, not a matter of hope. That is the whole idea behind LOOM. What LOOM is LOOM is a small, open-source, effect-typed language that acts as a machine-checked trust layer for AI-written code. It doesn't just run code — it proves, at a gate, exactly what the code is allowed to do, before a single line executes. If the code lies about what it does, the compiler refuses it. The slogan is: AI proposes, the compiler disposes. Today it is a research kernel with 385 self-verifying checks, all green — every feature added only with an adversarial test, so the language can only ever get greener. There's a live browser playground where a stranger can paste a program and watch the checker accept or reject it in under a minute. What it can actually do Effect honesty. Every function declares its effects — Pure, IO, Net, Alloc, FFI, Rand. Declared effects must cover what the code actually does; the lie is caught transitively through calls, branches, recursion — not just straight-line code. Capabilities, not ambient power. A foreign call has no ambient authority — un-wrapped, it's refused. A seam is the only thing that grants authority, so (seam (Pure) (ffi untrusted)) makes that code's I/O physically impossible. Reinterpreting h

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For the past two years, most of us integrated AI into our workflow using a "ping-pong" model: we write a prompt, get some code, copy-paste it, hit a bug, and paste the error back. But in 2026, the tech stack is shifting from simple chat interfaces to Autonomous AI Agents . We aren't just talking about smarter chatbots. We are talking about production-ready systems that can plan, use specialized tools, debug themselves, and interact with our local development environments. The Core Blueprint of an AI Agent Unlike a standard LLM call that finishes after a single response, an AI Agent operates in an Evaluate-Act-Learn loop. To actually build or interact with one, you need to understand its three core pillars: State & Memory: Maintaining context across complex, multi-step tasks (both short-term session state and long-term vector-based memory). Planning & Reflection: The ability to break down a high-level goal (e.g., "Scrape this e-commerce site and update our DB schema" ) into a sequence of executable tasks, and pivot if a step fails. Tools (The Game Changer): Giving the model execution capabilities via APIs, sandboxed code execution environments, and file system access. Enter MCP: The Architecture Connecting It All The biggest catalyst for this shift right now is the adoption of the Model Context Protocol (MCP) . Think of MCP as an open standard that acts like a universal adapter. Instead of writing custom, brittle glue-code for every single tool you want an AI to use, MCP provides a secure, structured way for LLMs to safely read and write to local repositories, query databases, or trigger deployment pipelines. [ AI Agent ] ──( MCP Protocol )──► [ MCP Server ] ──► [ Local Files / DB / API ] When an agent is plugged into your workspace via MCP, it doesn't just guess what your code looks like. It can scan an entire TypeScript repository, map out your Tailwind components, identify type mismatches, and apply a refactor across multiple files simultaneously. From Dev to Arch