标签：#c

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 | No fortress, purely open ground. OpenManus is Coming. | Stars: 57,109 | 217 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 | Stable Diffusion web UI | Stars: 164,175 | 115 stars today | 语言: Python

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

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

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++

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

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

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

At re:Invent 2025, AWS announced Lambda Durable Functions . The feature introduces a checkpoint/replay mechanism that allows Lambda executions to run for up to one year, automatically recovering from interruptions by replaying from the last checkpoint. Lambda's 15-minute timeout is not a bug or a limitation to work around. It is a deliberate design choice that encourages keeping functions simple and focused, and in most cases it does its job well. When a function needs more time, the usual approach is fanout : split the work into smaller Lambdas, orchestrate them, move on. I have done it many times and it works perfectly fine. But a few days ago I was developing a new Lambda function for a pipeline orchestrated by Step Functions, and the execution time exceeded 15 minutes. I could have done the usual split, but durable functions had just come out and I wanted to try them. At first glance, durable functions can look like a replacement for Step Functions. Both services manage multi-step workflows , both offer checkpointing and automatic recovery , and both let you coordinate complex operations. For certain use cases, that might actually be the case: if your entire workflow lives inside a single Lambda, durable functions can handle everything on their own without an external orchestrator. But the AWS documentation actually suggests using them together. The "Hybrid architectures" section says it explicitly: many applications benefit from combining the two services, using durable functions for application-level logic within Lambda and Step Functions to coordinate the high-level workflow across multiple AWS services. My case fit that description, and more than a perfect architectural match, I wanted to learn how the two services actually work together and form my own opinion on when the hybrid approach makes sense. I figured integrating the two would be a small change. It was my first time working with the durable execution SDK, and since the code I write is mostly infras

Most "AI email" demos never think about deletion. The agent reads, replies, files things away, and the inbox just grows. That's fine in a demo. It is a problem the first time a real person emails your agent, because the moment that mailbox holds someone else's name, address, order history, or support complaint, you've taken on a data-protection obligation — and "we kept everything forever" is not a defensible retention policy. An Agent Account on Nylas accumulates personal data you have to be able to purge. It's a mailbox the agent owns — support@yourcompany.com answering to a model instead of a human — and every inbound message lands in it. Under GDPR that data needs two things you can prove: a retention window so it doesn't live forever, and an erasure path so you can delete a specific person's mail when they ask. This post builds both, with the curl and the CLI for each step. A quick, honest caveat before any of it: this is a docs-and-demo walkthrough, not legal advice. The Nylas primitives below cover the mail held in the mailbox . Any derived copy you made — rows in your own database, lines in your application logs, a vector store you embedded the message into — is yours to purge separately. The API can delete the message; it can't reach into your Postgres. Keep that in mind throughout. What the platform gives you Nothing new to learn on the data plane. An Agent Account is just a grant with a grant_id , so everything you already know about Messages and Threads applies directly — listing, reading, and deleting mail run against the same grant-scoped endpoints any other Nylas integration uses. Retention and erasure split cleanly into two layers: Retention is a control-plane setting. It lives on a policy — an application-scoped resource that bundles limits and spam settings — attached to the workspace your Agent Account belongs to. Two fields cap how long mail survives: limit_inbox_retention_period and limit_spam_retention_period . Set them once and Nylas deletes a

Spam traps are the failure mode nobody puts in the demo. A bounce is loud — you get a 5.x.x back, your code logs it, you move on. A complaint at least gives you a webhook. A spam trap gives you nothing . The message gets accepted, no error comes back, and somewhere a mailbox provider quietly writes your domain down as a spammer. By the time you notice, your inbox placement has already cratered and you have no single bounce to point at. That's the trap, literally. And it's the one that bites autonomous agents the hardest, because the whole appeal of an agent is that it acts without a human watching every send. Point a model at a list it scraped, let it loop, and it'll happily mail a recycled address that's been a trap for two years. The agent never sees a problem. You only see the aftermath in your deliverability dashboard a week later. I work on the Nylas CLI, so the terminal commands below are the exact ones I reach for when I'm wiring up an Agent Account to not do this. The good news is that an Agent Account is just a grant — a grant_id that works with every grant-scoped endpoint you already know — so there's nothing new to learn on the data plane. The defense is mostly discipline: validate before you send, honor every complaint, and age out the addresses that never wanted to hear from you. What a spam trap actually is, and why it's not a bounce It's worth being precise here, because the three things people lump together behave completely differently. A bounce is a rejected delivery. The receiving server tells you the address is bad, you get a message.bounced event, and you stop. Bounce handling is a solved problem — you listen, you suppress, you're done. A complaint is a recipient hitting "report spam." The mailbox provider relays that back as a feedback loop, and you get a message.complaint event. The address is real and reachable; the human just doesn't want your mail. If you keep mailing them, you're training the provider to filter you. A spam trap is neither.

This is a submission for Weekend Challenge: Passion Edition What I Built Loyalty Ledger — a fan loyalty tracker where your check-in streak, badges, and history live on Solana instead of some app's database. Live app: https://loyalty-ledger-blond.vercel.app Here's the problem I kept coming back to. Every sports app wants you to check in, engage, "prove your loyalty" — collect points, build a streak, unlock a badge. Cool. Except every single one of them throws that history away the second you stop opening the app. Switch apps and your streak resets to zero. Get banned, or the app shuts down, or they just quietly decide to wipe inactive accounts one day — and your history is just... gone. Because it was never actually yours. It was a number sitting in someone else's database, and they could reset it, inflate it, or delete it whenever they felt like it. You had zero say in it. And that bugged me way more than it probably should have. Like — we figured out how to make ownership portable for money, for domain names, for digital art. But "I've supported Argentina since 2019" 🇦🇷 still lives and dies inside one company's backend, and nobody's really questioned that. So I kept the weekend scope deliberately small: prove one fan's loyalty to one team, for real, end to end — instead of sketching ten features that are all half-fake. You connect a wallet, pick a sport and team, and check in. FIFA World Cup is the fully working path here ⚽ — that check-in sends an actual transaction that creates or updates a program-owned account, not a row in my database somewhere. Your streak count, your badge tier, the actual badge tokens — none of it exists anywhere I control. Which honestly felt a little weird to build, in a good way. Once that core loop worked, I built the rest of the identity around it: a Fan Passport that shows your streak, a derived "Fan Score," your tier (Rookie → Devoted → Veteran → Legend 🏆), a progress bar toward the next tier, an achievements grid with locked/unlocke

Hi HN! I built hidetext.sh — a way to share text, code, and files through links the server can't read. How it works: your browser generates a random key and encrypts everything locally (NaCl secretbox, XSalsa20-Poly1305). Only ciphertext is uploaded. The key goes into the URL fragment — the part after # — which browsers never send to servers. The link carries the key, my server stores the locked box, and the two only meet in a browser. A design detail I'm fairly happy with: burn-after-read doesn

The best developer tools share one quality: you forget you are using them. Think about it. Your IDE fades into the background when you are in flow. Your terminal becomes muscle memory. These tools are invisible because they match your mental model so perfectly that there is zero friction between thought and action. This is exactly what online developer utilities should aspire to. The Problem with Fragmented DevTools Most developers have a bookmarks folder full of single-purpose websites: One for JSON formatting One for timestamp conversion One for Base64 encoding One for URL decoding Every switch between these tabs is a context loss. Every tool has a slightly different UI, different copy-paste format, different quirks. The Invisible Toolkit Opennomos Json (opennomos.com/en/project/01KJ850Z7PNGXHXESBM68HE12Y) consolidates timestamp conversion, JSON formatting, and Base64 encoding into a single workspace. No install. No accounts. No pricing tiers. Just open a tab and work. This is the north star for developer tools: make them so simple that the user never has to think about the tool itself — they only think about their actual task. The Trend Is Clear We have seen this pattern across the dev ecosystem: GitHub Codespaces made local IDE setup invisible Vercel made deployment invisible Replit made runtime environment invisible The next frontier is utility tools. The sooner they become invisible, the better. Try it: opennomos.com/en/project/01KJ850Z7PNGXHXESBM68HE12Y Part of the Nomos Build-in-Public series.