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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.

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

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

Have you ever wondered how massive modern platforms like Netflix, PayPal, and LinkedIn handle millions of users simultaneously without crashing? The secret weapon behind much of the modern web is Node.js. Traditionally, JavaScript—the language that makes websites interactive—could only run inside a web browser like Chrome or Edge. Node.js changed the game by freeing JavaScript from the browser, allowing it to run directly on your computer. This means you can use it to build backend servers, automate boring computer tasks, or run powerful development tools. If you are intimidated by coding, don't worry. This guide will take you from zero to running your very first Node.js program on Windows, step-by-step. Prerequisites Before we begin, you only need two things: A computer running Windows 10 or 11. An active internet connection to download the installer. No prior coding experience or command-line knowledge is required! Step-by-Step Instructions Download the Node.js Installer First, we need to grab the official installation file. Open your web browser and go to the official website: nodejs.org. You will see two primary options to download. Always choose the LTS (Long Term Support) version. The LTS version is heavily tested, stable, and less likely to give you unexpected errors. Click the Windows Installer button to download the .msi file to your computer. Run the Setup Wizard Once the download finishes, navigate to your Downloads folder and double-click the file to open the setup wizard. Click Next on the welcome screen. Accept the license agreement and click Next. Leave the default installation folder as it is (C:\Program Files\nodejs) and click Next. On the "Custom Setup" screen, leave everything at its default and click Next. Important Step: You will see a checkbox that asks to "Automatically install the necessary tools." Leave this unchecked for now to keep your setup simple and fast. Click Next. Finally, click Install. If Windows asks for permission to make change

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

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