标签：#IDE

Sony will stop producing physical discs for all new PlayStation games beginning in 2028, as the company embraces an all-digital future.

With these high-tech automatic litter boxes, gone are the days of scooping and smells. Welcome to the future.

If you've ever received a .pages or .numbers file on a Windows PC, you know the pain — you can't open it. No preview, no converter built in, and Apple's iCloud web tools are slow and clunky. So I built iworkviewer.com — a free, browser-based iWork file viewer and converter. No signup, no upload to any server. Everything happens in your browser. What it does Open .pages files → view them instantly, export to PDF or .docx Open .numbers files → view spreadsheets, export to .xlsx or PDF Open .keynote files → view presentations, export to PDF or .pptx Batch convert multiple iWork files at once The tech Built with Next.js, Cloudflare Pages, and pure client-side JavaScript. All file processing happens in the browser — your files never leave your computer. Zero server costs, zero privacy concerns. Why I built it I kept seeing Reddit threads and Quora questions: "How do I open a Pages file on Windows?" The answers were always the same — use iCloud.com (slow), download some sketchy converter (risky), or ask the sender to export as PDF first (annoying). I figured: if the browser can read a file, it can convert it. And it turns out, it can. Try it 👉 iworkviewer.com Open a .pages, .numbers, or .keynote file right in your browser. Free, forever, no account needed.

3 platforms, one queue, zero editorial control. That was the state of my content automation before I sat down to spec the dashboard. LinkedIn, X, and Threads each had their own generator, their own state files, their own publishing loop. Drafts got generated, passed a quality gate, and fired into the void. If the draft was mediocre or the timing was wrong, I found out after the fact. The problem is not the automation. Automation is why I can run three platform engines without spending two hours a day managing content. The problem is that zero editorial visibility means you cannot catch the bad ones before they post. What I wanted: see every draft before it goes out. Edit inline if needed. Post immediately or schedule for the next slot. Compose something manually when I have a specific take to push. Keep the comment automation untouched because that runs high frequency, low stakes, and babysitting individual replies defeats the point. The spec came out to three core flows. Review queue. Every pregenerated draft surfaces here with full context: platform, topic, generation timestamp, quality score. One click to edit inline, one to approve for the next slot, one to post immediately. The goal is a 30 second review per draft, not a full editing session. Manual compose. Sometimes I know exactly what I want to say. A text area, platform selector, and post button. No generation, no queue, just publish. This is the escape hatch for when something is happening in real time and the pregenerated queue is irrelevant. Schedule view. A simple calendar showing what is queued for which slot across all three platforms. The generator already handles slot logic and quiet hours. The dashboard just needs to surface the state so I can see gaps and move things around without touching JSON files directly. What I deliberately left out: comment automation. That pipeline runs separately, fires frequently, and does not benefit from human review on every reply. Adding it to the dashboard would cr

Google is updating its image generator to make it faster and cheaper, making it a more useful tool for creators looking to make AI content.

Peptide drugs are popular, but FDA scientists warn they're untested, may be harmful.

Users will be able use AI to create newsletters based on their recordings.

The original Ninja Slushi has been replaced! The new best slushie machines chill faster and make soft serve.

Imagine an e-bike motor that lets you select your preferred pedaling cadence and then automatically adjusts the gears to keep your legs spinning at that exact speed, no matter how steep the hill gets - all without a fragile derailleur or heavy multi-speed cassette to maintain. Prefer manual control? No problem, you can have as […]

WIRED has tested 100-plus bed-in-a-box mattresses for a week each. Our top pick, the Helix Midnight Luxe hybrid, is the best bed you can buy online.

We’ve taken one small step towards robot police officers: a drone capable of disarming a suspect: In a June 22 video posted on the Sacramento County Sheriff’s Office’s Instagram page, an officer wearing goggles can be seen operating a drone to retrieve a knife from an armed suspect hiding inside a cluttered house. “After not responding to negotiators, a drone was deployed inside the residence,” the post says. “Drone pilots located the suspect hiding in a corner of a garage” and then used a high-powered magnet attached to the drone to grab the knife out of the suspect’s hand. In the video ­ which is soundtracked by the “Mission: Impossible” theme song—the intercepted knife can be seen spinning around in the air as the drone carries it back to the deputies...

I got laid off in March 2026. The day HR handed me the 30-day notice, I had a small panic attack, then opened my laptop and started building things. Here's the deal: I had 30 days before severance ran out, and I wanted to see how much I could ship with AI tools before the money (and motivation) ran dry. I gave myself a single rule — every project gets a 7-day deadline, otherwise I kill it. I built 6 things. One has real users. One broke in production. Two I never opened again. This is what happened, in the order I built them. 1. AI Buddy (Chrome sidebar) — shipped, 15 users A Chrome extension that puts an AI assistant in a sidebar. Select text on any page, hit a keyboard shortcut, it goes to the AI, reply shows up without you leaving the page. Works with GPT-4, Claude, Gemini, DeepSeek. No login, no credit card. Time: 11 days (April 1–11). Status: Live on Chrome Web Store. 15 real users as of June 28, 2026. Rating 4.2. What I used AI for: 90% of the code (500 lines of JavaScript, written in Cursor). The README, the Chrome Web Store description, the marketing tweets — all AI-drafted, then I rewrote the parts that sounded like AI. What went wrong: The first version had a Stripe integration. AI wrote 90% of the webhook signature verification. I had to rewrite it from scratch. Also the model-picker UI went through 5 revisions because AI kept proposing what looked right but didn't work. → Chrome Web Store 2. Weekly report generator — personal use only Every Friday at 4pm, a script grabs my git commits, Slack messages, and Linear ticket changes, throws them at GPT-4, and asks for a "manager-readable" weekly report. I review, tweak, send. Time: 2 days. ~200 lines of Python. Status: Running for 11 weeks. Has 1 user. Me. Cost is $0.12/week. What I used AI for: The prompt. It's surprisingly tricky to get GPT-4 to write a weekly report that doesn't sound like a robot. The single most useful line: "if you don't have data, write 'no progress this week' — don't make things up." T

An unadorned ebike is a blank canvas. Here, get tips for maximizing its cargo-hauling and person-carrying capabilities.

I've seen lots of so-called "smart" bike locks over the years, but none so far could justify the added cost. A newcomer that got its start securing ATMs for banks is trying to change that. There's nothing wholly unique about the TMD Chain Lock, but the combination of materials, performance, and insurance-friendly ART-2 certification makes […]

I started a gaming guide blog six weeks ago. Twenty-one articles later, it's getting traffic from Google, I have four affiliate programs set up, and I have never written a single line of code. This is not a "how to make money blogging" post. This is a practical breakdown of the tools, the workflow, and the mistakes I made so you can skip them. The blog is yxgonglue.com. It covers PC and console game guides — GTA VI pre-order comparisons, VPN setups for gaming, cloud gaming platform rankings, extraction shooter loot guides. Niche stuff. The kind of content people search for when they have a specific problem. Here is the stack that runs it. THE STACK WordPress + Kadence Theme Hosted on a standard shared hosting plan. Kadence is a free WordPress theme that loads fast and does not fight you. No page builder. No Elementor. Just the block editor and Kadence blocks for tables and formatting. The biggest lesson here: your theme does not matter as much as your content structure. Pick something lightweight. Stop theme-shopping. Start writing. Yoast SEO The free version. It gives you a red/yellow/green score for each post based on keyphrase density, subheading distribution, link count, and meta length. Is it perfect? No. Is it a useful checklist for someone who does not do SEO for a living? Absolutely. One thing Yoast taught me the hard way: Custom HTML blocks are invisible to the plugin. If you paste your article into a Custom HTML block, Yoast reads zero words, zero links, zero headings. Everything turns red. Use the regular editor. If you need a table, use a table block. Keep it simple. Google Search Console This is where you see what people actually searched before they clicked your article. The gap between what you think people search for and what they actually search for is enormous. Search Console closes that gap. Submit every new post URL manually. It takes ten seconds. Do not wait for Google to discover your site on its own. THE CONTENT WORKFLOW One Article Per Day Tw

Every developer hates writing READMEs. It's boring, repetitive, and always gets skipped. So I built ReadmeAI — describe your project, AI writes the README instantly. What it does Fill in project name, description, tech stack, features AI generates a complete professional README.md Switch between Raw and Preview tabs to see rendered markdown One click copy Tech Stack Next.js + Tailwind CSS Groq API (openai/gpt-oss-120b) Deployed on Vercel Why I built it (Write 2-3 sentences personally — mention the challenge, that you're a student builder, makes it relatable) Live link https://readmeai-three.vercel.app/ Built this in a day as part of my 30-day AI tools challenge. Would love feedback from the dev community!

TL;DR — A prototype that works for you but breaks for everyone else usually isn't bad luck. It's four repeatable culprits: you designed for one assembly, your fasteners drift, the enclosure ignores real loads, and you never wrote down why it works. Fix those, and "works on my bench" becomes "works, period." You built the thing, and it works. In your hands, on your bench, every single time. Then a friend tries it, or it sits in the garage a week, or the temperature drops one night, and it just stops. Frustrating doesn't really cover it. Here's the reassuring part: that gap between "works for me" and "works for anyone" is almost always the same small handful of culprits. You're not missing some secret skill. Once you've met them a few times, you start designing around them without even thinking about it. 1. You built it for one. Now build it for two. That first one fit because you were there — nudging, sanding, coaxing it together. The trouble is, all of that lived in your hands, not in the model. So the second copy fights you. If you can't make a second one without the fiddling, it isn't done yet. Bake the clearance into the CAD, then print one you promise not to touch up. That's the real test. 2. Your fasteners are quietly betraying you. Press-fits creep. Hot glue lets go. Jumper wires back out. Double-sided tape taps out the first warm afternoon. I know the boring fixes aren't the fun part — a screw boss, a captive nut, a bit of strain relief, a connector that actually clicks home. But boring is exactly what's still holding a year from now. 3. The enclosure is a load, not a lid. It's easy to treat the box as an afterthought. But heat, dust, and vibration are real forces working on your build. A board that runs cool in the open can slowly cook once it's sealed up. A connector that's happy on the bench can buzz itself loose in a drawer that gets opened every day. Give the heat somewhere to go, mount the board instead of letting it dangle from its wires, and clamp dow

There is a popular misconception that if you do TDD, your design also stays correct. That if the tests pass, quality is guaranteed. In AI-assisted development, this misconception is the kind that quietly accumulates — the more tests you have, the more invisible damage builds up underneath. All tests passed. The design was still broken. Here is what happened today. A function called safe_post.py had its signature changed. Two arguments — notify_sh and doctor_sh — were removed. The test suite passed in full. But the callers were still using the old signature. They were silently broken. Why did the tests pass? Because the test code itself was using the old signature. The tests had been written (by AI) at a time when the design intent was already misunderstood. The misunderstanding was baked into the tests from the start. Tests passing and the design being correct are two different things. "All tests pass" tells you only one thing: the implementation matches what the tests expect. Whether the tests express the right design intent is a separate question. TDD verifies "implementation against tests" — nothing more Let me restate the TDD definition. Red → Green → Refactor. Write a test. Write the implementation that passes the test. Refactor. In this loop, what the test verifies is whether the implementation meets the test's expectation. That is one verification — and only one. What TDD does not verify is whether the test itself correctly expresses the design intent. The structure looks like this: Design intent → Tests (← this link is not verified) ↓ Implementation (← this link is verified by tests) If the person writing the tests misunderstands the design intent, the tests will pass and the design will still be wrong. Machine learning engineer Hamel Husain calls this the "Gulf of Specification" — the gap between what you intended to measure and what your metric actually measures. Optimize hard against a flawed metric and you optimize hard in the wrong direction. The same d