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Problem Link - https://leetcode.com/problems/delete-node-in-a-linked-list/ This is one of those interview questions that looks impossible at first. Normally, to delete a node from a Linked List, we need access to the previous node. But in this problem, we're only given the node that needs to be deleted. No head. No previous pointer. So how do we remove it? Let's understand the trick. Problem Statement Write a function to delete a node in a singly linked list. You are not given the head of the list. Instead, you are given only the node that needs to be deleted. Example Input: 4 -> 5 -> 1 -> 9 node = 5 Output: 4 -> 1 -> 9 Initial Thought Normally we delete a node like this: prev.next = node.next But here: We don't have prev We don't have head So the usual deletion approach is impossible. Key Observation Although we cannot delete the current node directly, we can make it look like it never existed. Consider: 4 -> 5 -> 1 -> 9 We need to delete: 5 Instead of removing node 5 , copy the value of the next node into it. 4 -> 1 -> 1 -> 9 Now remove the next node. 4 -> 1 -> 9 The original value 5 has disappeared. Mission accomplished. Intuition Copy the next node's value into the current node. Skip the next node. The current node now behaves as if it was deleted. Since the problem guarantees that the given node is not the tail node, a next node will always exist. Dry Run Input 4 -> 5 -> 1 -> 9 node = 5 Current node: 5 Next node: 1 Step 1 Copy next node value. node.val = node.next.val List becomes: 4 -> 1 -> 1 -> 9 Step 2 Skip next node. node.next = node.next.next List becomes: 4 -> 1 -> 9 Done. Optimal Java Solution class Solution { public void deleteNode ( ListNode node ) { ListNode cur = node . next ; node . val = cur . val ; node . next = cur . next ; } } Even Shorter Version class Solution { public void deleteNode ( ListNode node ) { node . val = node . next . val ; node . next = node . next . next ; } } Complexity Analysis Metric Complexity Time Complexity O(1) Space Comp

The real question isn't which framework is better. It's which one you can stop thinking about six months into the project. Key Takeaways Project Suitability — Django is built for weight. Flask is built for speed. Know which one your project actually needs before you commit. Development Flexibility — Django makes decisions so your team doesn't have to. Flask hands those decisions back. Both are features, depending on who's writing the code. Scalability & Performance — Scaling is an architecture problem first, a framework problem second. Pick the one that matches the system you're building — not the one you hope to build. Security Features — Django's protections are on by default. Flask's require you to turn them on. In a fast-moving team, that difference is more significant than it sounds. Ecosystem & Community — Both communities are active and well-documented. You won't be stuck either way. The Decision Nobody Takes Seriously Enough I've watched this play out more times than I'd like to count. A team kicks off a Python project, someone picks a framework — usually the one the most senior person knows best — and everyone moves on. Fast forward six months and the codebase is exhausting to work in. Either they're dragging a full framework through a service that should've been twenty lines of Flask, or they're rebuilding authentication from scratch on something that outgrew its lightweight origins two sprints in. The framework choice isn't irreversible. But undoing it mid-project is expensive in a way that doesn't show up in any estimate. Django and Flask are both genuinely good. What they're good for is different. That's the part worth slowing down on. What You're Actually Getting With Each One Django arrives with almost everything a web application needs already assembled — an ORM, an admin panel, authentication, form handling, CSRF protection, and more. The design assumption is that most web applications need most of these things, so it makes more sense to ship them i

Google just made it significantly cheaper to enjoy its budget AI subscription tier.

Voice translations preserve speaker's tone, pacing, pitch—with SynthID watermarks for security.

Good news for Claude devs deploying on Google Cloud. Claude Fable 5 is now in General Availability (GA) on Google Cloud. You can now access Fable 5 , as well as other Anthropic models - including Claude Opus 4.8 and Claude Sonnet 4.6 - on Agent Platform . Read more here -> [ blog ] Happy building!

This is interesting: The U.S. military has likely been quietly broadcasting codes for its global encryption network using public GPS for nearly 20 years, turning each satellite into a hidden “numbers station,” according to Steven Murdoch… That means every device that uses GPS has been receiving hidden government information for years, and nobody outside the military knew it until now. […] Murdoch discovered that this particular sentinel was transmitted by all 31 operational satellites within a window of a few hours on May 26, 2011, potentially heralding the activation of a new operational system. He confirmed that this timeline coincided with the rollout of the military’s Over-the-Air Distribution (OTAD) and the Over-the-Air Rekeying (OTAR) by cross-referencing declassified documents, including a 2015 presentation about the dates of the operation...

Individual gold atoms move around to form oxidation-proof structures.

Apple kicked off its annual developer conference with bold promises about AI. The company, CEO Tim Cook said, would be "introducing new technologies and innovations that push the limits on what's possible." But its slew of announcements - centered on a brand-new "Siri AI" - had more to do with catching up. After almost entirely […]

As expected, yesterday's WWDC keynote was mostly about AI. And also as expected, Apple tried to turn its late arrival into its sales pitch: it didn't rush into AI because it was taking its time to do things right. In this case, "right" means "with more privacy than anyone else." It's a good pitch - […]

A decade ago, commuter buses attracted big protests in San Francisco. Years later, the city is still feeling the repercussions.

Have you ever wondered how Discord keeps your channel messages available even when a server goes down? Or how Amazon DynamoDB serves petabytes of data with single-digit millisecond latency? The unsung hero powering almost all these distributed systems is hashing — a simple but powerful technique that makes even load distribution, fast lookups, and seamless scaling possible. As more applications move to distributed cloud architectures, understanding hashing for distributed systems is no longer optional for developers. Choosing the wrong hashing algorithm can lead to cascading failures, cache stampedes, and expensive downtime. This guide breaks down every core hashing technique, real-world use cases, best practices, and common pitfalls to avoid in 2026. Table of Contents What is Hashing in Distributed Systems? Core Hashing Algorithms Explained Traditional Modulo Hashing Consistent Hashing Virtual Nodes (VNodes) Rendezvous Hashing (HRW) Jump Consistent Hash Maglev Hashing Multi-Probe Consistent Hashing Consistent Hashing with Bounded Loads Real-World Applications of Distributed Hashing Head-to-Head Algorithm Comparison Best Practices for Distributed Hashing Common Pitfalls to Avoid Conclusion References What is Hashing in Distributed Systems? Hashing in distributed systems is the practice of mapping data keys (e.g., user IDs, object keys, channel IDs) to server nodes using a deterministic hash function. The core goals are: Distribute load evenly across all nodes to avoid hotspots Enable fast lookups (O(1) or O(log N)) without a central coordinator Minimize data movement when nodes are added or removed during scaling Support fault tolerance by simplifying replication across nodes The simplest implementation is modulo-based hashing , where node_id = hash(key) % N and N is the total number of nodes. While trivial to implement, it suffers from a fatal flaw: the rehashing problem. When N changes (a node is added or removed), nearly all keys are remapped to new nodes, causin

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You'll need an M1 or better to run the next release of macOS.

I hope you have a modern Apple Watch or iPad, because otherwise watchOS 27 and iPadOS 27 won't run on your device. Apple often drops support for older devices with its latest software updates, but this year it's culling even more device generations than ever before. Apple is dropping support for at least three generations […]

NotebookLM is getting a big upgrade, but it's only for AI Ultra and enterprise accounts right now.

The Trump administration released the updated version of the list four months ago and then quickly pulled it without explaining why

Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product. A few years ago, choosing a Go mocking framework was mostly a matter of personal preference. Today, things are different. Most Go developers have at least one AI coding assistant generating tests alongside them. Some teams even generate the majority of their unit tests automatically. Yet one area remains surprisingly messy: mocks. Ask an LLM to write a test for the same interface and you'll often get completely different results depending on whether your project uses GoMock, Mockery, MockIO, Minimock, Moq, or hand-written test doubles. The problem isn't that the models are bad. The problem is that mocking libraries represent very different philosophies: Strict vs flexible Generated vs runtime-created DSL-heavy vs idiomatic Go Feature-rich vs minimalist In this article we'll compare the most popular Go mocking libraries in 2026, examine their strengths and weaknesses, and discuss which one may be the best fit for your project. What Makes a Good Mocking Library? Before comparing tools, it's worth defining what matters. A good mocking library should ideally provide: Easy mock generation Clear test failures Minimal boilerplate Strong refactoring support Good IDE experience Readable tests Reliable call verification Different libraries optimize for different parts of this list. That's why there is no universally correct answer. 1. GoMock: The Enterprise Workhorse GoMock remains one of the most widely used mocking frameworks in the Go ecosystem. Originally created by Google and now actively maintained by Uber, it has become the standard choice for many large organizations. Its philosophy is straightforward: define expectations explicitly and verify them rigorously. Example func TestUserService ( t * testing . T ) { ctrl := gomock . NewController ( t ) repo := New

If you’re a user—owner?—of this cryptocurrency, this is important: On May 29, the security researcher Taylor Hornby found a critical vulnerability in Zcash Orchard privacy pool using Claude Opus 4.8. The Zcash team hired Hornby specifically to look for this kind of issue. He found one fast enough to be embarrassing. The Orchard pool is the newest and most advanced shielded transaction system in the cryptocurrency Zcash. Introduced in 2022, it allows users to send and receive ZEC while keeping transaction details private. It uses zero-knowledge proofs to validate transactions without revealing amounts or participants. The bug: a specific check that was supposed to validate transaction inputs wasn’t actually enforcing the rules it appeared to enforce. An attacker could have exploited the flaw to feed false inputs into that check and generate ZEC from nothing, with the zero-knowledge proof system blessing the fraudulent transaction as valid...

Google is making Gemini 3.5 the default model in NotebookLM

Google is rolling out "across the board" updates to NotebookLM. The AI-powered note-taking app now uses Google's upgraded Gemini 3.5 model, which will allow it to respond with "more accurate and reliable information," according to a blog post on Monday. Launched in 2023, NotebookLM allows you to interact with your notes and sources using AI, […]