标签：#p

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 the type of person who could always use a little extra positive affirmation, or you have a weakness for weird gadgets, the Talking Flower might be of interest. I’m only kind of serious. The toy is based on a character from Super Mario Bros. Wonder that guides Mario through levels with quippy, whimsical […]

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, […]

Harvard astrophysicist Avi Loeb will head the UAP Science Advisory Council established by the White House, the Pentagon, the Office of the Director of National Intelligence, the FBI, and "the intelligence community." The Council will provide scientific reports and advice to the UAP Governing Board, in an effort to "resolve the nature of UAP," or […]

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

Your photos don't have to go home, but they can't stay here.

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

GitHub热门项目 | The industry-leading GraphQL client for TypeScript, JavaScript, React, Vue, Angular, and more. Apollo Client delivers powerful caching, intuitive APIs, and comprehensive developer tools to accelerate your app development. | Stars: 19,869 | 74 stars today | 语言: TypeScript

GitHub热门项目 | Focus on prompting and generating | Stars: 50,965 | 29 stars today | 语言: Python

GitHub热门项目 | Stable Diffusion web UI | Stars: 164,175 | 115 stars today | 语言: Python

GitHub热门项目 | Codex/ChatGPT multiple account load balancer & proxy with usage tracking, dashboard, and OpenCode-compatible endpoints | Stars: 2,256 | 29 stars today | 语言: Python

GitHub热门项目 | No fortress, purely open ground. OpenManus is Coming. | Stars: 57,109 | 217 stars today | 语言: Python

GitHub热门项目 | Mesh optimization library that makes meshes smaller and faster to render | Stars: 8,081 | 111 stars today | 语言: C++

GitHub热门项目 | Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. | Stars: 49,269 | 229 stars today | 语言: Go

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