Tinder for live SF rentals from across the web Discussion | Link

I like writing Markdown, but do not like writing it inside a plain textarea. I wanted something I could use anywhere by dropping in a single web component: ``` <writemark-editor name="body"></writemark-editor> ``` That became Writemark. It renders Markdown while you write, but Markdown remains the value you read, store, and submit. It also has source, split, and preview modes, along with slash commands, tables, task lists, code blocks, native form support, and an API for adding your own controls

submitted by /u/NXGZ [link] [留言]

awsmux fans one AWS CLI command out across hundreds of accounts and regions in parallel and merges the results into a single stream. There's an MCP server built in for agents. In a 150-session benchmark, agents using awsmux beat agents using the raw AWS CLI in every test: up to 5.4x faster, up to 2.9x cheaper, up to 7.4x fewer tokens. Single Go binary, stdlib plus cobra only. Feedback welcome.

Venture through the afterlife of the gods while playing as Faye.

Elon Musk's tunneling startup is reportedly in talks for a major new funding round.

Review-first AI cleanup for your real Gmail inbox. Discussion | Link

Make every Mac keystroke sound uniquely yours Discussion | Link

So a while back I got frustrated (classic developer origin story, I know). Every time I wanted to learn something new — a language, a CS concept — I'd end up bouncing between five different sites, half of them behind paywalls, none of them structured the way I actually think. Nothing too fancy, just... annoying. So I built my own thing: Learn by Play → https://learn-by-play-dusky.vercel.app/ Right now it has two kinds of content: Language learning — real spoken languages, and I'm currently adding voice audio so people can actually hear pronunciation instead of just reading text and guessing Programming roadmaps — clear paths for frontend, backend, algorithms, and data structures, starting with JavaScript (more languages coming, don't worry haha) The idea is simple: give people an honest, structured path to actually understand things, not just skim through content and forget it a week later. Right now it's just me, building this on my own time, nights and weekends. It works, it's live, people can use it today. But I'd love for it to grow into something bigger, and that's a lot harder to pull off solo. If any of this resonates, here's how you can help: 🛠️ Know frontend, backend, or content design? I'd genuinely love a collaborator. ☕ Want to support the project directly? The link is down below. 💬 Just curious or have ideas? DM me; I read everything, I promise. PD: If you try it out and hit a bug or something feels off, please tell me; I'll fix it as soon as I can :) This started as a personal frustration. I'd like it to become something a lot of people find genuinely useful. Resources: The platform: https://learn-by-play-dusky.vercel.app/ BuyMeACoffe: https://www.buymeacoffee.com/manuelblancodev react #sql #frontend #fullstack

Founder Journal #1 — The Beginning of NAEOS "Great software isn't built on great code alone. It's built on great foundations." The AI Revolution Is Here In just a few years, artificial intelligence has transformed the way software is built. Today, developers can ask AI to generate functions, refactor code, write tests, explain bugs, and even build entire applications. Tools like ChatGPT, Claude Code, GitHub Copilot, Cursor, Gemini CLI, and many others have fundamentally changed software development. The question is no longer: "Can AI write code?" The answer is clearly yes . The real question has become: "Can AI engineer software?" And that is a very different challenge. Writing Code Is Easy. Engineering Software Is Hard. Generating code is only one small part of software engineering. A production-ready system requires much more: Understanding business requirements Software architecture Coding standards Documentation Security policies Testing strategies Version control CI/CD Deployment Observability Team collaboration Long-term maintainability These are not isolated tasks. They form a connected engineering system. Most AI tools today excel at generating code, but they still rely heavily on humans to provide context, rules, and architectural direction. Without those, AI becomes inconsistent. The Hidden Cost of Every New Project Every time I started a new software project, I noticed the same pattern. Before writing meaningful business logic, I spent hours—or even days—recreating the engineering foundation. I had to: Decide on the architecture. Create folder structures. Define coding conventions. Write prompt libraries. Configure AI agents. Build documentation. Establish workflows. Create engineering rules. Configure quality gates. Explain the project to AI over and over again. The project changed. The technology changed. The AI model changed. But the engineering work kept repeating. Again. And again. And again. AI Can Remember Conversations. But Projects Need More Than

I've usually been commenting on threads on conference reviews. I'm now expressing my observations here. To the best of my knowledge, paper lengths have been held constant at many conferences, and some conferences have "unlimited appendices" (e.g. NeurIPS / ICML / AAAI / ....) Historically, this was probably due to cost of printing for proceedings, but now, I suspect it's also to prevent reviewer fatigue. However, I wonder if this unfairly penalizes more theoretical papers. Some background: I usually publish theoretical papers at conferences. Some get in. Those that don't, are surprisingly not because of the theory, but because of (what I feel) arbitrary reasons. This leads to this post, which contains some of my musings. In general, the amount of pre-requisite knowledge required to understand a theory paper must necessarily increase. I don't know how to quantify this, but I would expect basic linear algebra, discrete math to be a "given", and more knowledge for each subfield. To also be intellectually honest, recent work should also be cited, especially if your work builds onto it, or is inspired by it. But technical details of recent work should be left to the reviewer to look up, or be put in the appendix. What pisses me off recently is that I've seen more reviewers reject papers based on things like: "The concept is difficult", or "Certain terminology is not explained.", "While the intuition is given before the math, the math could be made easier to read." I've also seen comments like: "The paper makes comparisons to X, but X should be described in detail", and then shifting of goalposts to "The paper makes comparisons to X, but X should be described in detail in the main paper." I would say that half of the rejections I get are based on the AC echoing these points, rather on impact of work, etc. Which puzzles me a lot, given that these ACs might also be professors at universities, and they must have seen similar statements from students. For example: "The {very