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Conditional Statements Conditional statements allow JavaScript to execute different blocks of code based on whether a condition is true or false. if - The if statement executes a block of code only if the condition is true. if...else - Use if...else when you want one block of code to run if the condition is true and another block if it's false. if...else if...else - Use this when you have multiple conditions to check. switch statement - The switch statement is used when you have many possible values for one variable. Nested if statement - You can also write an if statement inside another if. Ternary Operator - An optimized one-line shorthand for standard if...else blocks ** If Statement ** let age = 20 ; if ( age >= 18 ) { console . log ( " Eligible to vote " ); } //Output: Eligible to vote ** if else Statement ** let age = 16 ; if ( age >= 18 ) { console . log ( " Eligible to vote " ); } else { console . log ( " Not eligible to vote " ); } // Output: Not eligible to vote ** if ... else if ... else ** let marks = 85 ; if ( marks >= 90 ) { console . log ( " Grade A " ); } else if ( marks >= 75 ) { console . log ( " Grade B " ); } else if ( marks >= 50 ) { console . log ( " Grade C " ); } else { console . log ( " Fail " ); } // Output: Grade B ** switch statement ** let day = 3 ; switch ( day ) { case 1 : console . log ( " Monday " ); break ; case 2 : console . log ( " Tuesday " ); break ; case 3 : console . log ( " Wednesday " ); break ; default : console . log ( " Invalid Day " ); } // Output: Wednesday // Important: The break statement stops the execution after the matching case.We must compulsory to use break statement because if you don't use break, JavaScript will continue executing the next cases even the output is correct. ** Nested if Statement ** let age = 20 ; let hasLicense = true ; if ( age >= 18 ) { if ( hasLicense ) { console . log ( " You can drive. " ); } } // Output: You can drive. ** Ternary Operator ** let isLoggedIn = true ; let systemMessage = is

Mac app with dynamic AI EQ Discussion | Link

I’m currently relearning the Core Three — HTML, CSS, and JavaScript — as I work toward becoming a full-stack JavaScript developer. Before I came back to learning software, I spent 22 years working industrial turnarounds. One lesson from that world has followed me into software engineering: Never trust a single point of failure. In industrial maintenance, there’s a safety practice called double block-and-bleed . Instead of trusting one isolation valve, you use two independent valves with a bleed point between them. If one valve leaks, you know immediately. The entire system assumes individual components can fail. Safety doesn’t come from perfect parts. It comes from independent layers of protection. That idea completely changed how I think about CI pipelines. When I first started relearning web development, my mindset was simple: Run Lighthouse. Everything green? Great. 100 across the board locally? Even better. Ship it. Different results after deployment? Uh-oh. Now I see Lighthouse as one checkpoint — not the finish line. A fast website can still have accessibility issues. An accessible site can still have broken metadata. Good SEO won’t catch rendering bugs. Passing unit tests won’t tell you if the generated HTML is malformed. Every tool has blind spots. No single tool should get the final vote. So instead of asking: “Did my tests pass?” I ask: “What kinds of failures could still slip through?” That question naturally leads to layered validation. Formatting Linting Type checking Accessibility checks Performance audits HTML validation SEO analysis Manual review None of these tools is perfect. Together, they’re much stronger than any one of them alone. The more I learn about software, the more I find myself applying lessons from heavy industry. Different environment. Different risks. The same engineering mindset. Assume components will fail. Design systems that fail safely. That’s becoming the philosophy behind every test matrix and CI pipeline I’m designing. What’s

When I first started learning Solana, one of the biggest questions I had was: "If there are no usernames or passwords, how does the blockchain know who I am?" As a Web2 developer, I was used to creating accounts with an email address, choosing a password, and relying on a company to manage my identity. After spending several days learning Solana, I realized blockchain approaches identity in a completely different way. Identity in Web2 Think about all the accounts you have today. GitHub Gmail Facebook LinkedIn Your bank Every service asks you to create another account. Each company stores your username and password in its own database. Your identity exists because they say it exists. If they suspend your account or delete it, your access disappears. In other words, your identity is owned by the platform. Identity on Solana On Solana there are no usernames. There are no passwords. There isn't even an account registration page. Instead, your identity begins with one thing: A cryptographic keypair. A keypair consists of: A Public Key A Private Key When I generated my first wallet using the Solana CLI, I immediately had a new blockchain identity. For example: Public Key: AxfVXDX7jsCw7vSnwut9hA7oX4UykE3ZeiNF6cxCKvpf This public key becomes my wallet address. Anyone can send tokens to it. Anyone can view its transactions. But nobody can spend funds from it. Why? Because only I possess the private key. Think of SSH Keys The easiest comparison for Web2 developers is SSH. When connecting to a Linux server: the server knows your public key you prove ownership using your private key Solana works almost exactly the same way. Except instead of logging into one server... you're interacting with an entire blockchain. Every transaction I make is digitally signed using my private key. Validators verify the signature before accepting the transaction. No password is ever transmitted. No administrator approves my login. The mathematics prove my identity. Why Wallets Matter One thing I f

Run a LangGraph agent long enough and the model call stops being your bottleneck. The plumbing takes over. Every step, the graph serializes its state to a checkpoint so you can resume, replay, or recover. LangGraph does that with Python's deepcopy . For a small dict that is fine. For a 250KB agent state with nested messages, tool outputs, and accumulated context, deepcopy is brutally slow, and you pay it on every single step of a long run. So I built fast-langgraph : a set of Rust accelerators for the hot paths in LangGraph, packaged as drop-in components that keep full API compatibility. Lead with the numbers, including the ones that hurt Here is what the Rust paths actually buy you, measured against the Python equivalents: Operation Speedup Where Complex checkpoint (250KB) 737x faster than deepcopy Large agent state Complex checkpoint (35KB) 178x faster Medium state Sustained state updates 13-46x Long-running graphs, many steps LLM response caching 10x at 90% hit rate Repeated prompts, RAG End-to-end graph execution 2-3x Production workloads with checkpointing And the automatic mode, the one that needs zero code changes, lands around 2.8x for a typical invocation. Now the honest part. These are not "Rust is faster at everything" numbers. The checkpoint speedup scales with state size. It is a serialization story. For a small, flat dict, Python's built-in dict is implemented in C and already fast. Rust does not win there, and the README says so plainly. The 737x is a large complex-state number, not a headline you get on a toy graph. The core idea: reimplement the critical paths, keep the API LangGraph is good. I did not want to fork it or replace it. I wanted to swap out the three operations that dominate a real workload: Checkpoint serialization. deepcopy on complex nested state is the single biggest cost in a long run. Rust does a structured serialize instead. State management at scale. High-frequency updates accumulate overhead. A Rust merge path handles append-h

If you're a music maker of a certain age, then you probably once dabbled with a pirated copy of a little app called Fruity Loops. These days it's called FL Studio, and Constantin Koehncke, is the man responsible for shepherding the pioneering digital audio workstation (DAW) through the modern age. As CEO of Image Line, […]

ChatGPT is hiring a dedicated product manager to build experiences for families, caregivers, and older adults, according to a job posting.

Google is now deleting all accounts that do not, and have never had, phone numbers associated with them if they haven't logged in within a year or so. "Urgent: Sign in to your Google Account if you want to keep it" But then it doesn't matter if you log in with the correct username and password and receive the PIN via your email. This is isn't enough. Unless a phone number is somehow added to the account one gets, "You can’t recover your account at this time because Google doesn’t have enough inf

Toni Schneider, who took over after Jay Graber stepped down, is now Bluesky's official CEO.

Donald Trump's Homeland Security regime has been at the center of two critical stories in the past two weeks. In the first, federal agents shot and killed a man and quickly got to work justifying the use of force under the flimsiest of pretenses. In the other, it made house calls to people who said […]

The Ultimate Claude Masterclass: 13 Power Features Changing the AI Game Artificial Intelligence is no longer just about asking questions and getting text answers. Anthropic’s Claude has evolved from a simple chatbot into a fully autonomous, visual, and connected AI ecosystem. If you are still using it just to draft emails, you are barely scratching the surface. Whether you are a developer, content creator, or business professional, here is your definitive guide to the 13 powerhouse features of Claude, packed with practical examples, formatted specifically for Dev.to. 🧩 Part 1: Smart Onboarding & Personalization 1. Introduction to Claude Claude stands out in the crowded AI landscape because of its advanced reasoning, high emotional intelligence, and natural, human-like writing style. From parsing complex codebases to writing creative narratives, Claude feels less like a machine and more like a brilliant colleague. 2. Import Memory From ChatGPT To Claude Switching platforms shouldn't mean losing your progress. With this feature, you can instantly migrate your entire persona, past context, and custom instructions from ChatGPT straight into Claude with a single click. Example: If ChatGPT already knows your coding style or specific brand rules, importing it means Claude hits the ground running without you having to re-explain everything. 3. Add User Preferences Tired of typing "Act as a Senior Developer" or "Keep it casual" in every single prompt? User Preferences lets you set permanent system-level instructions that Claude remembers across all new conversations. Example: You can set a preference like: I manage a technology brand. Always keep explanations direct, modular, and optimized for scalability. 🎨 Part 2: Visuals, Coding & Apps 4. Create Apps & Artifacts Using Claude For Free Claude’s Artifacts feature opens a dedicated, interactive window right next to your chat. When you ask Claude to write code, a webpage, or a game, it doesn't just show you lines of text—it re

The narrative around Artificial Intelligence and software engineering has shifted dramatically. We are no longer asking if AI will change development, but rather how we change with it. If your value as a developer is tied solely to how fast you can churn out boilerplate code, write standard API endpoints, or memorize syntax, the landscape is becoming challenging. AI can do those things in seconds. However, this isn't a death sentence for the engineering career—it is an evolution. The industry is moving away from pure "code generation" and shifting toward system architecture, integration, and governance. To remain indispensable, you need to know exactly where to direct your energy and what pitfalls to avoid. Where to Focus Your Energy To stay relevant, you must position yourself in the areas where AI struggles: high-level abstraction, complex contextual reasoning, and human leadership. 1. System Design and Enterprise Architecture AI is excellent at writing isolated functions, but it struggles with massive, interconnected systems. Focus on how components interact at scale. Understanding how to slice a monolithic application into resilient microservices, orchestrate microfrontends, or design cloud-native solutions is where the high-value work lies. 2. Code Governance and Quality Assurance With AI generating code at unprecedented speeds, codebases are expanding faster than ever. The world doesn't just need people who can create code; it needs gatekeepers who can validate it. Your role will increasingly focus on setting quality standards, establishing robust CI/CD pipelines, and ensuring that AI-generated code adheres to strict security, compliance, and performance metrics. 3. Mentorship and Team Leadership The influx of AI tools means junior engineers can produce code much earlier in their careers, but they often lack the foundational experience to spot subtle architectural flaws or security vulnerabilities. Senior developers must step up as leaders, guiding less experi

Everyone who has ever been right about something loud enough to remember it will tell you they called it. The screenshot arrives after the match, after the candle, after the election. And there is no way to know whether it was written on Monday or edited on Friday. This is the quiet rot at the center of most "track records": a prediction you cannot date is not a prediction at all. It is a memory with good lighting. The technical name for the problem is look-ahead . If a forecast can be created, tweaked, or cherry-picked after the outcome is known, then it carries zero information about skill. The only fix is to make the timing of a prediction independently checkable вАФ to prove a document existed in a specific form before a specific moment, without asking anyone to trust you, your server clock, or your database. That is precisely what OpenTimestamps does, using the Bitcoin blockchain as a shared, tamper-evident clock. Why timing is the whole game A forecast is a bet against the future. Its value comes entirely from the fact that the future was unknown when the forecast was fixed. The instant you allow post-hoc editing, every desirable property collapses: calibration becomes meaningless, Brier scores become fiction, and "I predicted this" becomes unfalsifiable. So an honest forecasting system needs one hard guarantee before anything else: this exact text existed at this exact time, and has not changed since. Note what that guarantee does not require. It does not require publishing the forecast publicly in advance (you might want it sealed). It does not require a notary, a lawyer, or a trusted timestamping company that could be subpoenaed, hacked, or simply go out of business. It requires a clock that nobody controls and nobody can wind backward. What "proof of existence" actually means The building block is a cryptographic hash вАФ typically SHA-256. Feed any file into it and you get a 64-character fingerprint. Change a single comma and the fingerprint changes compl

By the team at MailTester Ninja — a real-time email verification API that stores nothing. Everyone says "IPv6 is here." For the web, mostly true. For email , it is a mirage. We resolved the MX records of 50,000 of the most-linked domains and checked whether any of their mail servers publish an AAAA record, meaning they can actually receive over IPv6. No sending, no personal data, just DNS. 55.2% of mail-enabled domains have at least one IPv6-capable MX. That sounds healthy. It is not, because two companies carry almost the whole number: Email provider IPv6 MX Other / self-hosted ██░░░░░░░░ 18.4% Google Workspace / Gmail ██████████ 100% Microsoft 365 / Outlook █████████░ 91.3% Proofpoint ░░░░░░░░░░ 0.6% Mimecast ░░░░░░░░░░ 0% Tencent QQ ░░░░░░░░░░ 4.2% Namecheap ░░░░░░░░░░ 0.2% Cisco IronPort ░░░░░░░░░░ 4.5% Zoho ░░░░░░░░░░ 0% Barracuda ░░░░░░░░░░ 0% Google ( 100% ) and Microsoft ( 91.3% ) run IPv6 on nearly every inbox. Remove those two, the providers that already anchor most of the world's mail, and IPv6 email adoption falls from 55.2% to 12.9% . The enterprise security gateways that gate corporate mail, such as Proofpoint, Mimecast and Barracuda, are effectively not on IPv6 at all. Why it matters for deliverability. IPv6-only sending is a dead end. It reaches Gmail and Outlook and little else. Dual-stack is not optional. IPv4 is still the backbone of email, and that is where blocklists, FCrDNS and IP reputation are mature. The takeaway: IPv6 email is not adopted. Google and Microsoft adopted it for you. Plan your sending for an IPv4 world with two big IPv6 exceptions. Check any domain yourself — our free deliverability analyzer shows a domain's MX / SPF / DMARC in one click (no signup, nothing stored). Need to confirm whether a specific mailbox actually exists and is deliverable? That is exactly what MailTester Ninja's email verifier does in real time — and we store no data. Source: MailTester Ninja's open Email Infrastructure Index — a live DNS scan of 50,000 of

Hi HN, clx is an ahead-of-time compiler for standard Lua that generates C++20 and produces standalone native executables through GCC, Clang or MSVC. The project started as an experiment to see whether modern C++ could be used as a portable compiler backend instead of LLVM or direct machine code generation. The generated code is then compiled and optimized by the host toolchain. The latest release replaces the previous NaN-tagged value representation with a new shadow-types implementation, adds f

The RabbitMQ upgrade looked like a straightforward infrastructure task: move from RabbitMQ 3.X to 4.X, provision the new broker, review the client setup, confirm queues still declare correctly, restart consumers, watch the logs, and move on. But infrastructure upgrades rarely test only infrastructure. They also test the assumptions your application has been making for years. In this case, the upgrade forced a more important question: is our messaging system reliable by design, or has it simply been relying on stable conditions? That distinction matters because a message queue can appear healthy when the broker is running, the network is stable, consumers are alive, and messages are acknowledged quickly. But production systems are not judged only by how they behave when everything is fine. They are judged by how they behave during restarts, closed channels, slow handlers, bad configuration, deployment windows, and partial failure. The RabbitMQ upgrade exposed those edges. It revealed assumptions around connection lifecycle, acknowledgements, dead-letter routing, retry behavior, observability, and operational simplicity. The real lesson was not just how to upgrade RabbitMQ. The real lesson was how to build a messaging layer that is easier to operate, easier to reason about, and safer to fail. Simplicity Is an Operational Feature One of the first things the upgrade exposed was complexity. Over time, messaging code can quietly become a small internal framework. A connection helper becomes a connection manager. A consumer wrapper becomes a consumer framework. Retry helpers appear, dead-letter helpers appear, failure handlers appear, and monitoring logic gets layered on top. Each addition may have been reasonable when introduced, but during an incident, complexity has a cost. Every abstraction becomes another place to inspect. Every helper becomes another assumption to validate. Every unused file becomes a possible source of false confidence. RabbitMQ integration code doe