标签：#p

As a backend developer, I sometimes help companies evaluate candidates by reviewing their recorded technical interviews. However, over time, I’ve noticed a deeply ingrained misconception. When discussing memory management or data streaming, many developers explicitly state: "Iterators in Python are inherently eager. If you want true lazy loading or lazy evaluation, you have to use generators and the yield keyword." This misconception is common. Many popular bootcamps and online courses introduce lazy evaluation exclusively through generators . Custom class-based iterators are usually skipped or dismissed as boilerplate-heavy OOP theory rarely used in production Python. This confusion is further reinforced by two common educational simplifications : The List vs. Generator Expression Analogy: Beginners are taught that square brackets [...] (list comprehensions) are eager and take up memory, while parentheses (...) (generator expressions) are lazy. This often creates a false binary mental model: "generators = lazy, everything else = eager." Standard "Textbook" Examples: When courses demonstrate a custom iterator, they usually write a basic class that accepts an already fully loaded list in its __init__ and simply increments an index in __next__ . While this is valid for in-memory data, it leads developers to assume that custom iterators inherently require loading all data upfront. In reality, generators are a specialized language feature designed to implement the iterator protocol automatically . They comply with the exact same interface ( __iter__ and __next__ ). A generator is lazy not because of some magical property of the yield keyword, but simply because it adheres to this underlying contract. To show that custom iterators can be lazy without using any generators or yield keywords, I’ve put together a lightweight and reproducible benchmark. 🧪 The Experiment: Proving Lazy Loading with Custom Iterators Suppose we need to read a database export file ( test_users_db.

submitted by /u/High-Impact-2025 [link] [留言]

The Canadian pension giant will acquire an 8.2% stake in CtrlS, a tech giant that operates more than 15 data centers across India.

Must Listen! She believes the engineering bottleneck is removed with AI. submitted by /u/ShivamOujlayan [link] [留言]

We’ve trawled the depths of Amazon to find the best deals on gear we’ve tested.

Uber recently described an internal architecture for propagating identity across multi-agent AI workflows. The design aims to perserve user context, agent provenance, and scoped access as agents delegate work and call internal tools. The case study aligns with Auth0’s view that AI agents need permissions based on delegated authority, scoped credentials, and explicit human approval boundaries. By Eran Stiller

With this acquisition, DeepL is opening an office in San Francisco to expand its U.S. business.

submitted by /u/Malwarebeasts [link] [留言]

Smart mowers are an expensive alternative to old-fashioned yard work, but they’re finally good enough to consider if you’d rather sip an iced tea and watch a robot tame your lawn.

The bigger battery is standard and there are now simulated "E-Shifts."

They’re much more recyclable than the current plastic ones, and they will still probably be locked away behind that anti-theft plexiglass.

I’ve been reviewing laptops for over a decade, and this is my advice on how to find the right laptop for you.

Aditya Kumarakrishnan explains how to move past the "amnesia phase" of AI. He shares a blueprint for engineering leaders to build modular agent frameworks using CoALA, leverage decades of process science for scalable workflows, and "terraform" legacy environments into robust, event-sourced artifacts capable of handling unpredictable, cross-functional agent demands. By Aditya Kumarakrishnan

This will be the second market to have an Uber robotaxi service outfitted with Lucid EVs equipped with a self-driving system from Nuro.

Pinterest has launched 'Ask Pinterest,' an experimental AI-powered shopping app that lets users seek recommendations and inspiration through a conversational interface.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

WhatsApp is finally catching up to rivals with disappearing messages.

Infowars’ would-be creative director talks Sandy Hook, comedy’s MAGA turn, and why the future of satire may look more like a streaming startup than a late-night show.

From defections and protests to moments of national pride, the 2026 World Cup arrives amid decades of tension between identity and the state.

Hello Dev Community! 👋 It is officially Day 39 of my non-stop run toward full-stack MERN engineering! Yesterday, I mapped out basic HTTP verbs like GET and POST. Today, I advanced into Prashant Sir's (Complete Coding) backend masterclass to tackle one of the most critical core operations: Handling Data Streams and Body Parsing . When a user submits a form or uploads data, the server doesn't receive the file all at once. It arrives as an asynchronous stream of network data chunks. Today, I learned how to collect and decode those packets natively! 🧠 Key Learnings From Node.js Lecture 7 (Streams & Buffers) Node.js is designed to be non-blocking and memory-efficient. Here is the technical breakdown of how it intercepts client payloads: 1. Inbound Streams & Data Chunks I learned that incoming POST data is treated as a Readable Stream . Instead of loading a massive data file into the server memory instantly, Node transmits the payload in tiny pieces called Chunks (hexadecimal binary data). 2. Event Listeners for Requests ( req.on ) Natively, we don't have an instant req.body object. We have to listen to the network events on the incoming request stream: req.on("data", (chunk) => { ... }) : Fires every single time a fresh chunk of binary data arrives at the network interface. We push these raw chunks into a temporary array. req.on("end", () => { ... }) : Fires automatically once the stream concludes and all chunks have arrived safely. javascript if (req.url === "/submit" && req.method === "POST") { let body = []; req.on("data", (chunk) => { body.push(chunk); // Collecting raw binary chunks }); req.on("end", () => { // Concatenating and converting hexadecimal binary buffers into a readable string layout let parsedBody = Buffer.concat(body).toString(); console.log("Received Form Payload:", parsedBody); res.writeHead(200, { "Content-Type": "text/plain" }); res.end("Data received and parsed successfully!"); }); }