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One thing I've noticed after using AI for development over the past year is this: The code it generates is usually correct. The architecture slowly isn't. That doesn't happen because AI writes bad code. It happens because architecture rarely erodes all at once. Imagine a modular application with clear boundaries. The billing module talks to the orders module through its public interface. Authentication is isolated. Notifications are independent. Everything is predictable. Now imagine hundreds of AI-assisted commits over the next few months. One suggestion imports an internal class because it already exists. Another bypasses a service layer because it's shorter. A helper gets copied into another module. A database query is duplicated instead of reused. None of those changes are catastrophic. In fact, every pull request probably gets approved. The application still builds. The tests still pass. Customers never notice. Until one day, making a simple change requires touching five different modules because everything has quietly become connected. That's architecture debt. And unlike a failing test, it doesn't show up immediately. One thing I've realized is that our current tooling doesn't really watch for this. Unit tests verify behavior. Integration tests verify interactions. Linters enforce style. Static analysis finds bugs. All of those are important. But none of them are asking questions like: Should this module depend on that one? Did someone bypass a defined boundary? Are we introducing new architectural coupling? Is the overall architecture getting healthier or worse over time? Those questions usually get answered during code review. Or worse, during a production incident. The interesting part is that AI isn't really the problem. If anything, it's doing exactly what we ask it to do. It optimizes for solving the problem in front of it. Architecture, on the other hand, is about protecting the system as a whole. Those are different goals. As AI makes us write code fa

Preface Link to the repository containing all examples Back when I was at my first class of Data Structures and Algorithms, I started to solve competitive programming questions in judges like CodeForces, I didn't know why my code was slower if my solution was efficient (at least in theory), my professor explain to the class the reason why our programs were slow, because of I/O is an expensive task for computers. To start working with the examples in this article just clone the repository and play with it as long the article explains how to run the code. git clone https://github.com/MiztonCodes/OptimizeIO.git Why std::cout and std::cin are slow? In C++ by default, both std::cin and std::cout streams are synchronized with standard C scanf and printf streams and at the same time every single call to std::cin flushes the std::cout buffer, because std::cin before reading input, wants to output any pending prompt to the user, which can cause performance issues because reading and writing are expensive tasks due to the need to make calls to the operating system, the solution is to disable all of this synchronizations and manually flushing the output buffer whenever it is needed. Note: For convenience in all examples I'll use #include<bits/stdc++.h> header to simplify the imports, this header includes everything we'll need and this header only works on GCC compiler. What is input and output? When you run your program, it is like an isolated box inside your computer, ready to do some work, the input process involves moving raw data from an external source like a keyboard, a file or even the network to your program (reading) , the output process involves moving data from your program to an external destination like a monitor, the console, a file, the network or even an external device (writing) , for both processes your program does not read or write directly to the input and output sources, because each source has its own way to transfer data, C++ provides a uniform interfac

Bose headphones are already one of our favorites for comfort, sound, and noise canceling. Now they’re cheaper.

Foldables, watches and glasses could be on the way from Samsung.

Apple has warned about MacBook prices rising, making these Prime Day deals even more worthwhile to consider.

DISCLAIMER : NO AI OR LLMS WERE USED IN WRITING THIS ARTICLE . EVERY SINGLE WORD WAS TYPED MANUALLY USING A KEYBOARD I HAVE A COMPLETE UNEDITED VIDEO OF ME WRITING THE ARTICLE HERE: https://www.youtube.com/watch?v=6j1dbN64LyI submitted by /u/Inevitable_Back3319 [link] [留言]

I can understand why The Verge readers are flocking to the BirdBuddy deal we posted at the beginning of Prime Day. The BirdBuddy Pro with Solar Panels is a wholesome upgrade for your yard, but the full price of $299 is tough to stomach. After all, the BirdBuddy is essentially a video doorbell and solar […]

If you want to beef up your home security this Prime Day, Eufy’s Floodlight Camera is a great way to do it.

The White House asked OpenAI to delay the rollout of its GPT-5.6 AI models, two weeks after Anthropic had to take its most advanced AI models offline.

Assa Abloy has laid off the majority of staff at Level Home, the smart lock company known for building smart tech into traditional-looking deadbolts, and is folding the business into Kwikset, according to a source familiar with the decision. The Verge obtained exclusive details from a person familiar with the restructuring who requested anonymity as […]

Optimizing code starts with measuring it, and a measurement is only useful if it is repeatable: a 2% improvement is invisible under 5% of noise. Yet on an untuned machine the same binary can easily run several percent faster or slower between runs. In this post we take a tiny benchmark and tune the machine step by step, re-measuring after every change, until runs become deterministic. Continue reading— Tuning a Server for Benchmarking submitted by /u/david-alvarez-rosa [link] [留言]

GitHub joined the United Nations Development Programme in Ghana to explore how open source governance can support one of West Africa's most ambitious digital reform efforts. The post GitHub and UNDP team up to advance development priorities in Ghana with open source appeared first on The GitHub Blog .

We know that ICE wants to deploy eyeglasses with facial recognition that can identify people in real time. Turns out Meta is prototyping the feature with a Pentagon supplier. (Alternate news story.)

Vercel has released Eve, an open-source framework for building, deploying, and operating AI agents in production. The framework uses a filesystem-based project structure to organize agent instructions, tools, skills, subagents, communication channels, and scheduled tasks, enabling developers to define agent behavior while reducing the amount of supporting infrastructure they need to implement. By Daniel Dominguez

Join us on the livestream at 1 pm ET and ask questions about the aftermath of New Glenn.

AI models have progressed to the point where their capabilities have real political consequences. Dealing with those consequences will require collective action.

If you’ve been curious about the emerging world of smart glasses, Prime Day is your chance to try some of the best models with a rare discount from Best Buy or directly from Xreal. The high-end Xreal One Pro is $549, $50 lower than the usual price, and the more entry-level Xreal 1S is $399, […]

Peter Diamandis is the latest tech executive to argue that global surveillance will make the world a better place, following Larry Ellison's comments in 2024.

Edward Nalwamba is a 78-year-old retired pastor whose health has rapidly declined while in detention, his attorney says.

You don't have a million labeled images or a GPU farm — and you don't need them. Transfer learning lets you stand on a model someone else trained and reach high accuracy with a few examples in minutes. Here's the idea, visualized. ♻️ Race scratch vs transfer: https://dev48v.infy.uk/dl/day17-transfer-learning.html The insight The early layers of a trained network learn general features — edges, textures, shapes — that are useful for almost any vision task. Only the last layers are task-specific. So why relearn edges from scratch? Two ways to do it Feature extraction: freeze the pretrained backbone, replace the final classifier with a small new "head," and train only the head on your data. Fast, needs little data. Fine-tuning: also unfreeze the top few backbone layers and train them at a low learning rate so you adapt without wrecking what they learned. The demo races two accuracy curves: "from scratch" crawls up and plateaus low (not enough data); "transfer learning" starts high and climbs fast. Tweak the example count and freeze/fine-tune to see them respond. Why it matters now This is exactly why fine-tuning an open LLM works: a foundation model already learned language; you adapt it cheaply. Transfer learning is what makes deep learning practical for the rest of us. 🔨 Full recipe (load pretrained → freeze → new head → train → optionally fine-tune low-LR) on the page: https://dev48v.infy.uk/dl/day17-transfer-learning.html Part of DeepLearningFromZero. 🌐 https://dev48v.infy.uk