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If you have ever stared at a misbehaving circuit at two in the morning, muttering about the "bug" you cannot find, you are part of a lineage that runs back to a very literal insect. On September 9, 1947, engineers working on Harvard's Mark II computer opened up the machine, found a moth wedged inside a relay, and taped it into their logbook. The note beside it reads: "First actual case of bug being found." It is one of the most charming artifacts in the history of computing, and it carries a surprisingly practical lesson for anyone who builds connected hardware today. What actually happened in 1947 The Mark II was an electromechanical monster: a room-sized calculator built from thousands of relays, switches that physically clacked open and closed to represent ones and zeros. On that September day the machine was producing errors, and the operators -- part of the U.S. Navy computing effort that Grace Hopper worked in -- traced the fault to Relay #70 in Panel F. Inside was a moth, its wings shorting across the contacts. They removed it, taped it into the logbook at 15:45, and recorded the now-famous line. That page, moth still attached, lives today in the Smithsonian's National Museum of American History. Hopper loved telling the story for the rest of her career, which is why her name is forever attached to it. She is often credited with coining "bug" and "debugging" because of that moth, but the honest history is a little different, and the difference is the interesting part. The word "bug" was already old Engineers had been calling faults "bugs" for decades before 1947. Thomas Edison used the term in an 1878 letter to describe the little glitches and "difficulties" that show up when a new invention meets the real world. By the early twentieth century "bug" was common shop-floor slang among electrical and telephone engineers. So the moth did not invent the word. What made the logbook entry so memorable is the joke buried in it: "first actual case of bug being found"

The discounts may not beat Prime Day, but I found worthwhile prices on WIRED-approved TVs, laptops, tablets, speakers, and other home gear.

Taylor Farms stirred confusion on FDA test and provided a vague recall list.

Pat Gelsinger wants to pave the way to ever more powerful artificial intelligence using tiny beams of light.

Are you tired of wrestling with raw cURL requests, parsing Server-Sent Events (SSE) by hand, and building complex polling loops just to interact with trading APIs? If you are a PHP developer or a Laravel enthusiast looking to dive into algorithmic trading, your life is about to get a whole lot easier. Meet coinquant-php , the official PHP and Laravel SDK for the CoinQuant Public API. CoinQuant is revolutionizing the way we approach algorithmic trading. It takes a trading idea described in plain English and turns it into a backtestable strategy using advanced AI. However, integrating such powerful tools into your own applications can often be a daunting task. That is where coinquant-php steps in. This robust SDK wraps the public API, providing a seamless, developer-friendly experience that lets you focus on what really matters: building profitable trading strategies. In this comprehensive guide, we will explore why coinquant-php is a game-changer for PHP developers, delve into its standout features, and show you how to get started in minutes. Why Choose CoinQuant PHP? The landscape of algorithmic trading is often dominated by Python, but PHP remains a powerhouse for web development, especially with frameworks like Laravel. coinquant-php bridges the gap, allowing PHP developers to leverage the cutting-edge AI capabilities of CoinQuant without leaving their preferred ecosystem. 1. Built for Modern PHP The library is designed for the modern era of PHP. It requires PHP 8.1+ and embraces strict typing, ensuring your code is robust and free from legacy baggage. Whether you are building a standalone script or a complex enterprise application, the SDK provides a solid foundation. 2. First-Class Laravel Integration If you are using Laravel 10, 11, 12, or 13, you are in for a treat. The package auto-registers its ServiceProvider and Facade , meaning there is absolutely zero manual wiring required. You can simply install the package and start using the CoinQuant facade anywhere

When a learner finishes a course they want a certificate that behaves like a document: something they can print at full quality, attach to a job application and that an employer can check is genuine. In this tutorial you'll build exactly that in Laravel: a Blade-designed certificate rendered as a vector PDF, with a QR code that resolves to a verification page and an email that delivers it automatically. This post originally appeared on Accreditly as How to generate verifiable PDF certificates in Laravel . Keep the design in Blade The certificate is an ordinary Blade view. Everything lives in one file, styles inline, so the markup you preview in the browser is exactly what gets rendered. If you want a designed starting point rather than a blank page, the certificate of completion template is a good base to adapt. {{-- resources/views/certificates/template.blade.php --}} <!doctype html> <html> <head> <meta charset="utf-8"> <style> body { font-family: Georgia, serif; color: #1a2233; margin: 0; } .certificate { padding: 60px; border: 6px double #b28a2f; margin: 24px; text-align: center; } .heading { font-size: 15px; letter-spacing: 4px; text-transform: uppercase; color: #b28a2f; } h1 { font-size: 44px; margin: 24px 0 8px; } .course { font-size: 22px; margin: 4px 0 28px; } .issued { font-size: 15px; color: #5a6478; } .qr { margin-top: 36px; } .verify-url { font-size: 12px; color: #5a6478; } </style> </head> <body> <div class="certificate"> <p class="heading">Certificate of Completion</p> <h1>{{ $certificate->user->name }}</h1> <p class="course">has completed {{ $certificate->course }}</p> <p class="issued">Issued {{ $certificate->issued_at->format('j F Y') }}</p> <div class="qr"> {!! QrCode::size(110)->generate(route('certificates.verify', $certificate)) !!} </div> <p class="verify-url">{{ route('certificates.verify', $certificate) }}</p> </div> </body> </html> Two things are doing quiet work here. The design flows like a document rather than being pinned to fixed pixel

The new leasing program is a big change for the hardware company, and comes as it looks to raise prices on many of its products.

NatGeo's Pompeii: Out of Time fuses historical fact and imagination to bring city's last 24 hours to life.

Everyone is arguing about AI ROI right now. Boards want a number. Consultants are selling frameworks. And the headlines look brutal: an MIT report claimed 95% of enterprise GenAI pilots showed no measurable return, and a Forbes piece this January says 56% of CEOs see zero ROI from AI. So AI is a bust, right? Here is the thing. Ask any of those same companies to turn off email for a week. Just try it. Nobody ever ran a six month ROI study on email. Nobody had to. It became the way work happens, and the return stopped being a line item because it was everywhere. I think AI is following the same path, and the data backs it up. The gap between the pilot and the person That same MIT report has a stat that got way less coverage than the 95% number: 90% of employees regularly use personal AI tools for work, while only 40% of companies have an official LLM subscription. Workers adopted it at more than twice the rate of their own employers. The report even found that 70% of workers prefer AI over a colleague for quick stuff like drafting emails and basic analysis. So the official pilot fails its KPI review while the people inside the building are quietly using AI multiple times a day. That is not a failed technology. That is a measurement problem. Worth noting: the 95% figure itself has been heavily criticized. It came from 52 interviews and a narrow definition of success. I would not build a strategy on that number in either direction. Why the ROI is invisible The value seems to be landing in the same place email's value landed: the unglamorous middle of the workday. Drafting the reply. Summarizing the thread. Turning messy notes into something you can send. None of that shows up as a new revenue line. It shows up as an hour you got back and immediately spent on something else. You cannot easily measure that, but you can feel it. Take the tools away from a developer who has been using them for a year and watch what happens. People cannot imagine working without it anymore.

Your resume tells people what you’ve done. Your GitHub shows what you’ve built. But your personal website tells people who you are. When I started learning web development, I believed that a good resume and a few GitHub repositories were enough. Like many students, I spent countless hours building projects, solving coding problems, and learning new technologies. Every new project felt like a milestone, yet all of them remained scattered across different platforms. A recruiter would have to open my resume, visit my GitHub, search for my LinkedIn profile, and perhaps never even discover the articles I had written or the experiments I had built. That made me realise something important. Developers need a place on the internet that they truly own. Not another profile. Not another social media account. A place that represents their identity, work, and journey. That’s what a personal website becomes. More Than Just a Portfolio Many people hear the words personal website and immediately think of a portfolio with a few screenshots and a contact form. A great developer website goes much further. It answers questions before anyone has to ask them. Who are you? What technologies do you enjoy working with? What problems have you solved? What kind of developer are you becoming? What have you learned recently? How can someone reach you? Instead of forcing visitors to jump across five different platforms, everything exists in one carefully designed experience. Your Name Deserves a Home Every developer works hard to build projects. Very few work equally hard to build their own identity. When someone searches your name, what should they find? Ideally, the very first result should be something you completely control. A website with your own domain isn’t just another webpage. It’s your digital home. Unlike social platforms, algorithms cannot redesign your identity overnight. You decide what visitors see first. You decide which projects matter. You decide how your story is told. Resume

Anthropic blocks authors from opting out of $1.5B settlement at last minute.

Analysis of bestseller charts shows an 80 percent dip from 2025's sales rate.

There are new 3.6 and 3.5 models today, but Google is already training Gemini 4.

It must be earnings day, because Tesla is making a Robotaxi announcement. The company said in a post on X that it was launching its Robotaxi service in Orlando and Tampa, attaching a pair of maps that show each city's service area. No word on fleet size or whether interested customers would be able to […]

With the new tools, parents and guardians will be able to view their teen's time spent on Threads, set daily time limits, adjust sleep mode, and manage their privacy settings.

Open-source LLMs stopped being the budget option in 2026. Kimi K3 sits level with Claude Opus 4.8 on the Artificial Analysis Intelligence Index (its hosted API is live; the weights themselves are expected by July 27), GLM-5.2 held the top open-model spot before it, and the field behind them is deep enough that the hard part is choosing. This ranking covers the nine best open-weight models right now — on license, context window, hardware reality, and the per-token price you actually pay. Every one of them is available through LLM Gateway with one key, at each provider's published rate, so you can A/B any two of them by changing one word in a request. 1. Kimi K3 — the open frontier Moonshot AI · 2.8T params · 1M context · $3.00 / $15.00 per M The largest open-weight model ever announced — with one caveat: the weights are not downloadable yet. Moonshot expects to release them by July 27, 2026, and the license is still unannounced; the hosted API has been live since July 16. Ranks 4th of 189 models on the Artificial Analysis Intelligence Index — tied with Claude Opus 4.8 and GPT-5.5 — and took first place in Arena's blind Frontend Code testing. Always-on reasoning, vision, tools, and output configurable up to 1M tokens. The open model to beat, priced accordingly. Full breakdown here . Best for: teams that want closed-frontier quality with open-weight freedom. 2. GLM-5.2 — the value flagship Z.ai · 744B params · 1M context · $1.40 / $4.40 per M MIT-licensed, weights on Hugging Face, and the top-ranked open model until K3 arrived. A real 1M-token context, strong agentic-coding results, and built-in web search support — with output at under a third of K3's rate and input at about half. Also the largest model on this list that fits a single 8-GPU node (or one 512 GB Mac Studio) at INT4. Best for: the best capability-per-dollar in the open field. 3. DeepSeek V4 Pro — frontier scale at commodity prices DeepSeek · 1.6T params (49B active) · 1M context · $0.435 / $0.87 per M MI

Moonshot AI released Kimi K3 on July 16, and the benchmarks put an open-weight model next to the best closed ones for the first time. The catch is access. K3 sits on Moonshot's platform, GLM-5.2 on Z.ai's, DeepSeek V4 Pro on DeepSeek's, MiniMax M3 on MiniMax's — four accounts, four billing relationships, four API dashboards, all before you have written a line of code. LLM Gateway routes every one of them through a single OpenAI-compatible endpoint. One key, one bill, and a switch between Kimi K3 and any of 200+ models is a one-word change to your request. What is Kimi K3? Kimi K3 is Moonshot AI's flagship model for long-horizon coding and agentic work. At 2.8 trillion parameters — a mixture-of-experts design that activates 16 of its 896 experts per token — it is the largest open-weight model announced to date. Moonshot has committed to publishing the full weights by July 27, 2026. The specs that matter in practice: 1M-token context window (1,048,576 tokens), with output configurable up to the same 1M — enough to hold a large repository plus its docs in a single request Always-on reasoning — K3 thinks before every answer; there is no non-thinking mode Vision, tool calls, and JSON output supported out of the box Prompt caching at a 90% discount on repeated input Early results back up the size. K3 ranks fourth of 189 models on the Artificial Analysis Intelligence Index — level with Claude Opus 4.8 and GPT-5.5 — and took first place in Arena's Frontend Code evaluation in blind developer testing. It posted 93.5% on GPQA Diamond and 88.3% on Terminal-Bench 2.1, the strongest open-weight results published on both at release. Kimi K3 pricing Through LLM Gateway you pay Moonshot's published per-token rates: Tokens Price per million Input $3.00 Cached input $0.30 Output $15.00 The cached-input rate is the number to watch. Coding agents re-send the same system prompt, file context, and conversation history on every step, so in a long agent session most of your input tokens are

I've spent my career helping people build software. This is a series of letters about what happens when the tools for building change faster than the principles behind building. Each one is a reminder that while the technology changes quickly, the questions that matter often stay the same. -- Dear past Jenna, The thing that drew you to tech in the first place, that it's always changing, is the thing that will keep you here. Tools change (sometimes for the better and sometimes not) almost weekly. People who never consider themselves technical, much less a developer, will build apps in an afternoon with this new programming language called English. Products will go from idea to deployed before you finish your first cup of coffee (you have a toddler now, so you rarely get the full cup before it goes cold anyways). "I don't know how to code" or "I'm not technical" is no longer a barrier to building. And that's exciting, given you've focused nearly your entire career helping others build software. But we can't confuse the ability to build with the wisdom to build. One of the most valuable habits you've developed over the last two decades is asking a simple question: "Should we build it?" For most of your career, "Can we build it?" was a hard question. Time, budget, complexity, maybe the tech wasn't there yet. But those constraints were usually temporary. With enough people, time, and money, just about anything is possible. But the real questions were always: Should we build this? Is this the best use of our time? Does this solve a problem our customers actually need solved, or does it create new problems? What are we choosing not to build? Those questions haven't changed, but the environment around them has. Today, almost anyone can build software. Between tools like Lovable, Bolt, Replit, Claude Code, Codex, and whatever's next, the barrier to building software is lower than it's ever been. Now the question "Can we build it?" is too easy to answer. It's almost always ye

The Cirqa Smart Band is Garmin's answer to Whoop and the Fitbit Air.

"Current economic conditions" have shifted TreeSize's business model.