标签：#product

These are the cleaning robots, water monitors, and toys actually worth buying for pool season.

WIRED test-drove a preproduction version of the Luce, the storied Italian automaker’s controversial electric car, and it was remarkably surprising. Will it convert Ferrari fanatics?

For the frequent flier who treats the Delta Sky Club like their second home.

Thanks to the surge in local AI processing and the ongoing memory shortage, it’s incredibly difficult to buy a Mac Mini.

With Apple Upgrade, the company is making it easier to pay for its products in monthly installments. Critics call it the “financialization of the affordability crisis.”

If your router is short on ports, you can add more with one of the best Ethernet switches. These are my top picks from what I’ve tested.

Local push-to-talk dictation for Mac Discussion | Link

If you've ever used an online PDF tool, you know the routine. You need to merge two files. You find a site. You upload. Then: "Sign in to download." "Free plan: 2 tasks per day." "Your result is ready — with our watermark on it." "Upgrade to remove limits." A five-second job turns into an account, a countdown, and a branded output you can't send to a client. That's the friction that made me build PDFKing — 23 PDF tools in one place, with none of the catches. No sign-up. No watermarks. No daily limits. Nothing to install. The Problem With Most "Free" PDF Tools "Free" almost always has a shape: Free tier, capped at a couple of tasks a day Sign-in wall before you can download Watermark on the output unless you pay File-size limits that push you to a premium plan None of that is about the PDF. It's about converting a person in a hurry into an account. I wanted the opposite: open the tool, do the job, close the tab. No relationship required. The Approach A few principles shaped everything: Every common PDF job in one place — no bouncing between five single-purpose sites Name tools by what they do , not by how the code works Same short flow for all of them — pick, add file, run, download Nothing gatekept — no login, no watermark, no per-day counter What's Actually In It 23 tools, grouped by what you're trying to do. Organise & optimise Merge, Split, Compress, Organise pages, Delete pages, Extract pages, Rotate, plus an Image Compressor for JPG/PNG/WEBP. Convert to & from PDF PDF to Word, Word to PDF, HTML to PDF, JPG to PDF, PDF to JPG, PDF to Text. Secure & sign Watermark, Sign, Redact, Protect (password), Unlock. Edit Crop, Add page numbers, Edit PDF (text, shapes, highlights, annotations), Edit metadata. The ones people hit first: Merge PDF , Compress PDF , PDF to Word , and Sign PDF . Privacy Isn't a Feature, It's the Default With PDFKing: there's no account, so there's nothing to log against you [confirmed on site] there's no watermark added to anything you make [con

Self updating wiki for coding agents Discussion | Link

Run AI locally and own the whole workflow Discussion | Link

I've been having the same conversation with my colleagues for months. What does it mean to create...

Every few months I hit the same wall. A client sends over a .mov file that needs to end up as an .mp4 for a web page, or someone drops a .heic photo in Slack and asks why it "won't open" on their Windows machine. My first instinct used to be brew install ffmpeg and then spend twenty minutes remembering the flags. These days I don't bother unless the job actually needs scripting or batch automation. Here's what's actually in my rotation, and when I reach for each one. When it's a one-off file and I just need it done If I'm not going to touch this format again for another six months, I'm not installing anything. Browser-based converters have gotten good enough that for a single file, they're just faster. CloudConvert is usually my first stop for anything document or spreadsheet related — it handles a wide range of formats and the interface doesn't get in the way. AhaConvert is what I use when it's image or audio work specifically; it's fully browser-based, no account needed, and it deletes uploaded files automatically after 24 hours, which matters if the file has anything client-confidential in it. Neither one requires me to think about dependencies or version conflicts, which honestly is 90% of why I use them. For quick audio grabs — pulling an MP3 out of a video file someone sent, or converting an old .wma voice memo — I've had good results with Online-Convert too. It's not pretty, but it's reliable and doesn't nag you to create an account. When I need to batch-process a folder This is where the browser tools stop being useful and FFmpeg earns its keep. If I'm converting 200 images or normalizing audio levels across a podcast archive, nothing beats a script I can rerun. for f in * .wav ; do ffmpeg -i " $f " -acodec libmp3lame " ${ f %.wav } .mp3" done I know this loop by heart at this point. If you're doing this regularly, it's worth the setup pain once and never thinking about it again. When it's part of a pipeline If file conversion is happening inside an app — sa

Local voice dictation for Mac for polished text Discussion | Link

Claude & Cursor usage in your macOS menu bar Discussion | Link

Run coding agents through the full development process Discussion | Link

Process mismatch In tools built for Scrum, a task is an input: something you file, size, and work on. In Shape Up, a task is an output — something discovered while building work that was already shaped and bet on. That's the core mismatch, and it plays out differently depending on the tool. Jira Jira does exactly what it was built to do. Its shape is Scrum's shape: a backlog, estimates, sprints. Teams bring Shape Up in anyway and try to make it fit the tool's shape. A scope becomes an epic. A task becomes a ticket. The pitch — Shape Up's document for a problem, its appetite, and a proposed solution — has no equivalent object in Jira, so it ends up living in a Confluence doc, disconnected from the work it's supposed to govern. The substitutions are each small and reasonable on their own: An estimate field is there, so it gets filled in — and the velocity report looks broken without it. Losing bets need somewhere to go, so they land in the backlog. They aren't dead, they're waiting — and now someone has to groom them. Appetite ("how much is this worth") quietly reverts to estimate ("how long will this take"). Before long, the team is running Scrum, with a backlog-refinement meeting back on the calendar. The tool's requirements pull the ceremonies back in. Linear Linear is fast and well made. It even has cycles. The mismatch here isn't a quality problem — it's an inheritance problem. Linear carries the same assumptions as Scrum, just executed better. When a cycle ends with work unfinished, Linear rolls it forward automatically into the next one. It's meant as a convenience feature. It's also the inverse of Shape Up's circuit breaker. Shape Up's bet is that the deadline is real. The whole mechanism depends on a hard stop forcing a decision — cut the scope and ship what's done, while there's still time to make that call. A tool that quietly carries unfinished work forward removes the one moment the method needs. Every six weeks, it says: the deadline was just a suggestio

A simple native macOS terminal built on Swift and libghostty Discussion | Link

So I had this idea that kept nagging at me. Every AI chat app works the same way. You type something, the model returns text or markdown, the UI renders it as a nice formatted paragraph. That is fine if you want an answer. It is genuinely boring if you want to actually build something. What if the AI could respond with a working game board you could click? What if saying "make it Barbie themed" actually transformed the whole interface while you watched? What if "add a starfield in the background" dropped an animated canvas behind your chat in real time? I spent a few weeks building exactly that. I call it FlowChat . Here is the live version: https://flowchat-public.varshithvh.workers.dev And yes, someone immediately asked it to play Tic Tac Toe and then asked it to switch to an Oppenheimer theme mid-game. I could not be prouder. The Idea Normal AI chat: model returns markdown, client renders it as text. Simple, predictable, boring. FlowChat: model returns raw HTML with CSS and JavaScript, client injects it directly into the DOM using a streaming protocol built on the browser's native template system. That one change makes the entire experience different. You are not reading about a game. You are playing one. You are not reading about a Barbie color palette. You are sitting inside one. The AI does not just answer questions. It rebuilds the UI from its responses . What You Can Actually Do With It I want to give you a feel for what this means in practice before getting into the technical bits, because the demos are more interesting than any architecture diagram. Games : Ask it to build Tic Tac Toe. You get a playable board, click-to-move, an AI opponent, win detection. Ask for Connect 4. Ask for Snake. The game renders in the chat as an agent bubble with a form inside it. Each move submits to the LLM which processes it and updates only the cells that changed. Themes : Say "change to a Barbie theme". The model injects CSS overrides and the whole interface turns pink. Me

An AI personal assistant that lives in your texts 💬 Discussion | Link

If you have ever tried to find a new physician through a search box, you already know the frustration: outdated phone numbers, doctors who left the practice two years ago, and "accepting new patients" labels that turn out to be fiction. Anyone who has read the candid breakdown in Online Doctor Directories: A User's Guide to a Very Imperfect Tool will recognize the pattern immediately, because the core problem is not laziness on anyone's part — it is a data engineering problem hiding inside a healthcare product. And for those of us who build software for a living, it is a fascinating case study in what happens when stale data meets high-stakes decisions. The Root Cause Is a Data Pipeline, Not a Design Flaw Most doctor directories aggregate information from insurance networks, state licensing boards, hospital affiliations, and self-reported provider profiles. Each of these sources updates on its own schedule, uses its own identifiers, and defines fields differently. One system records a physician under her maiden name; another lists the clinic's billing address instead of the practice location; a third still shows a specialty she stopped practicing in 2019. The result is a classic entity-resolution nightmare. Without a reliable primary key shared across sources, merge logic has to guess whether "J. Martinez, Internal Medicine, Suite 400" and "Julia Martinez-Reyes, IM" are the same human. Get it wrong in either direction and the user suffers: duplicates erode trust, while over-aggressive merging attaches one doctor's malpractice history to a stranger with a similar name. If you have ever built a CRM deduplication service or wrestled with customer identity graphs, you have fought this exact battle — just with lower stakes. Staleness compounds the problem. Physicians change practices constantly. A directory that syncs quarterly is, by definition, wrong about a meaningful slice of its records at any given moment. Harvard Health has pointed out that an ongoing physician sh