Imagine you are sitting at a table in a restaurant. You have a menu in front of you with a list of delicious meals, and the kitchen is ready to cook them. However, there is a missing link. You are sitting in the dining room, and the chefs are tucked away in the back. You can't just walk into the kitchen and grab your food, and the chefs don't leave the stove to come take your order. To bridge the gap, you need a mediator. You need someone to take your order from the table, deliver it to the kitchen, and then bring the food back to you. That person is your waiter. In the digital world, an API (Application Programming Interface) is that waiter. The Plain English Definition An API stands for Application Programming Interface. Strip away the technical jargon, and an API is simply a software messenger that allows two different computer programs to talk to each other and share data. Think of it as a digital bridge. When you use an app on your phone, it doesn't contain all the data in the world inside its small download file. Instead, it uses an API to send a request over the internet to a massive server, which then sends the correct information back. The Restaurant Analogy: Side-by-Side To see how this works in real life, let’s map our restaurant experience directly to how technology works on your phone or computer: The Customer (You): This is the Client (your web browser, smartphone app, or computer). You are the one asking for information. The Menu : This is the API Documentation. It lists out exactly what items you are allowed to order and how you need to ask for them. The Waiter : This is the API. They take your request, run it over to the system, and bring you back the result. The Kitchen : This is the Server / Database. It holds all the raw data, processes the request, and prepares the final output. Where Do You See APIs in Real Life? You interact with dozens of APIs every single day without even realizing it. Here are a few common examples: "Log In with Google" or

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From strenuous hikes and serious summits to weekend rambles in the park, these boots help you make the most of your time outdoors.

The probe sent back the first pictures of the asteroid Kamo’oalewa. Next step: landing on the surface and collecting samples to send back to Earth.

The Story: Picture this: It is 10 PM. I was eating my dinner while adding one final touch to my social media app, Vlox. It should just say "Processing..." while generating a card. Simple, right? See the nightmare. 🔥 The Nightmare Scenario 📉 Suddenly, my "Download Card" button broke. htmlToImage started spitting out completely empty 0b images. The hunt was on. Failed Mission Log 🛰️ Attempt 1: Swap to html2canvas 🔄 Result: Error stating the element was not found in the cloned iframe. Verdict: The parent container was completely lost. Attempt 2: Use CoolAlertJS Toast 🍞 Result: It looked ugly and meant loading two different alert libraries for the same job? Not my game. Verdict: Total waste of bundle size. Attempt 3: Append to body + display: none 🙈 Result: The canvas process failed entirely. Verdict: Canvas snapshot engines completely ignore hidden elements. The "Aha!" Moment 💡 Why did the element vanish? Because the card generator lives entirely inside a Swal popup. When you click the download/confirm button, Swal instantly destroys that entire popup DOM tree. You cannot snapshot an element that no longer exists. 👻 The Ultimate Fix 🚀 Inside the new "Processing" popup, I appended the #card-generator-image-preview directly into the new alert container. The element stays alive in the active DOM. The snapshot succeeds perfectly. Clean code. Happy developer. Delicious dinner. Want to see the exact JavaScript code block that fixed it? Drop a comment below or check Vlox on Github .

Lately, I've been doing some deep personal reflection. I'm talking about myself, I hope no one misunderstands, on how pervasive the use of AI has become in my daily development workflow. Through a bit of self-analysis, I've discovered some interesting dynamics. Dependencies often arise from the desire to fill a void. But what kind of void does an experienced developer like me face? As a professional, I have the skills. Sure, AI helps me get things done faster, but the final product is always the translation of my vision; if I don't fully understand the solution, I discard it. I'm not looking for "magic," I'm looking for efficiency. Yet, I realize I've used AI to fill a specific void: the need for discussion. Software development is inherently solitary. The satisfaction of a successful "execution" after hours of discussions, refinements, and clashes over an architecture is an experience I miss today. The chat interface is always there, ready to respond. But there's a problem: it's a "yes-man." Even when I force it to be critical or provocative via the system's prompts, I know it's just reciting a script to please me. There's no conviction, no risk of error, none of the friction that arises when a colleague courageously defends their vision, perhaps one that conflicts with mine. We are part of a huge community, but debate often remains superficial. One might argue that posts and comments are enough, but anyone who has tried knows it doesn't work very well: a debate is truly alive only when there is no latency. In comments, the time between thinking, writing, and waiting for a response diminishes the energy of the exchange, turning it into a series of monologues rather than a dialogue. Why don't we try creating "virtual tables" where we can discuss projects, architectures, and technical choices with the natural rhythm of a conversation? Direct, real-time discussions, in person or remotely, where the exchange of ideas can spark sparks, without the filter (and delay) of

Markdown to HTML: The Fastest Way to Convert Markdown Online Markdown is one of the easiest ways to write documentation, blog posts, README files, and notes. The only problem is that many platforms require HTML instead of Markdown. Instead of installing software or using complicated editors, you can convert Markdown directly in your browser. I built MDConvertHub to make this simple. It lets you: Convert Markdown to HTML instantly Preview the output before copying Work completely in your browser No signup required Free to use I started building MDConvertHub because I wanted a collection of small Markdown tools in one place instead of visiting different websites for every task. The project now includes multiple Markdown utilities, and I'm continuously adding new tools based on real use cases. If you'd like to try it, I'd love your feedback. 👉 https://mdconverthub.com/markdown-to-html What Markdown tool do you use most often? Feedback and suggestions are always welcome. I'm building MDConvertHub one tool at a time.

Finding a co-founder is one of the hardest parts of building a startup, and most platforms weren't built for it. LinkedIn is a professional directory, not a matching network. Reddit threads are noisy and unstructured. Cold outreach is a gamble. FoundrGeeks is built specifically for this problem . It's an AI-powered co-founder and team matching platform that connects builders based on what they're building, what skills they bring, and what gaps they need to fill, not just their job title or who they already know. The problem with finding a co-founder most builders looking for a co-founder face the same wall: the people they need aren't in their network, and the platforms that exist weren't designed for this specific search. You're not just looking for someone with the right skills. You need someone at the same stage, with the same intensity, who fills exactly the gaps you have right now. And you need to know that before spending three hours on discovery calls. That's the gap FoundrGeeks fills. How FoundrGeeks works When you create a profile, you describe what you're building, what you bring to the table, and what you need. You set your stage, idea, MVP, or funded, and your weekly availability. From there, the AI takes over. It surfaces people whose strengths complement your gaps, scores each match as Strong, Good, or Potential, and generates a plain-English explanation of why each person fits what you're building right now. Three features stand out at launch: Complementary matching: the engine looks for people who fill your gaps, not mirror your background Scored matches with explanations, every match tells you exactly why, before you reach out Stage-aware feeds, as you move from idea to MVP to funded, your matches reshuffle automatically You also control your visibility, go public and let talent find you, or stay private and let the AI work quietly on your behalf. Why we built this This platform exists because of a project that never got finished. I had an idea I wa

Instead of writing long build logs or recording traditional vlogs, my co-founder and I wanted to try something different. We're documenting our startup journey by turning it into an AI-generated anime series. Not for fiction. For real startup moments. Episode 2 follows our cold outreach journey: Finding an ICP Testing different niches Sending DMs Getting ignored Learning what works (and what doesn't) We're treating this as an experiment to see whether AI-generated storytelling can make the process of building a startup more engaging than the usual "build in public" content. The goal isn't perfect animation. It's authentic documentation—with AI as the creative medium. We're still figuring it out, improving every episode, and learning as we go. Would love to hear what fellow builders and developers think about this approach. Could AI-powered anime become a new way to document products, startups, and open-source projects? Feedback is always welcome. 🚀

I built CreditMeter, a small Windows tray app that shows GitHub Copilot AI-credit usage like a taxi meter. Why I built it Agentic coding makes AI usage feel invisible until you look at the bill. Constraints no WinForms no WPF no backend no telemetry no dependency-heavy architecture Tech stack C# / .NET 9 Native AOT raw Win32 / PInvoke GitHub REST API DPAPI for local PAT storage What I learned For tiny tools, architecture is also about knowing what not to add. Repo https://github.com/cdilorenzo/CreditMeter

OpenAI just published a 3-page proof of the Cycle Double Cover Conjecture. The conjecture has been open for 50 years. The proof was generated entirely by GPT-5.6 Sol Ultra using 64 cooperating agents. There is one problem. Nobody has verified that the proof is correct. The Cycle Double Cover Conjecture is one of those problems that sounds simple enough for an undergraduate to understand but has resisted some of the best graph theorists alive. Every bridgeless graph (a graph you cannot disconnect by removing a single edge) should have a collection of cycles that covers each edge exactly twice. That is it. Paul Seymour, Paul Tutte, and others posed versions of it going back to the 1970s. Nobody proved it. Until maybe now. What OpenAI Published On July 10, 2026, OpenAI released two documents. The first is a 3-page proof. The second is the prompt they used to get the model to produce it. Both are available on their CDN. The proof itself is compact. It reduces the problem to cubic graphs (graphs where every vertex has degree 3), uses the 8-flow theorem to label edges with elements of a finite field, and then converts that labeling into a cycle double cover through a linear algebra argument. The key step is Lemma 2.2, which claims that a certain system of equations always has a solution. The proof PDF states: "The proof in this note is entirely due to GPT 5.6 Sol Ultra and the writeup with Codex (with GPT 5.6 Sol)." That is a remarkable claim. No human mathematician is credited with the proof itself. You can read it yourself here: https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98d31/cdc_proof.pdf The Prompt Is the Real Story The prompt OpenAI used is just as interesting as the proof. It is not a simple "prove this" instruction. It is a detailed engineering document for a multi-agent search system. The prompt tells the model to use "multiagent v2" with up to 64 concurrent agents. It specifies how agents should explore different mathematical approaches independen

On July 9, 2026, the European Parliament reauthorized a law that lets tech companies scan your private messages without a warrant. Here is the part that should worry you: a majority of lawmakers voted against it. 314 MEPs voted to kill the law. 276 voted to keep it. The law passed anyway. How? The rejection required an "absolute majority" of 361 votes out of 720 MEPs. Every absent MEP effectively counted as a yes. The vote was scheduled for the last day before summer recess, when many MEPs had already left Strasbourg. The European People's Party, Parliament's largest group, used a rarely invoked urgency procedure (Rule 170) to force the vote onto the floor on July 7. Two days later, the deed was done. What Chat Control 1.0 Actually Does The law is technically called the ePrivacy derogation. It allows tech companies to voluntarily scan private, unencrypted messages and emails for known child sexual abuse material (CSAM), without a warrant or prior suspicion. Platforms affected: Instagram DMs, Discord, Snapchat, Skype, Xbox messages, Gmail, and iCloud. Platforms not affected: WhatsApp and Signal, because they use end-to-end encryption. Parliament did adopt an E2EE exemption amendment with 369 votes, excluding "communications to which end-to-end encryption is, has been or will be applied" from the scanning scope. But here is the catch. Providers of E2EE services were not scanning messages anyway. The exemption preserves the status quo. It does not create new protections. The scanning is limited to "known" CSAM material, meaning previously identified photos and videos. It does not detect new or unknown material. And it remains in effect until 2028, or until a permanent regulation is agreed. The Numbers That Should Make You Doubt This Law The EU Commission's own evaluation report gives Chat Control a very poor assessment: Only 0.00000077% of messages scanned in the EU actually contained illegal material ( heise online ) False positive rates of filter technologies reach u

As the climate phenomenon sends warm water surging across the eastern Pacific, some parts of the fishing industry are suffering—but other regions are seeing a windfall.

This is a submission for Weekend Challenge: Passion Edition What I Built Loyalty Ledger —...