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A few months ago, while interviewing for a Cloud Solutions Architect role at Microsoft, one of the interviewers asked me a question that stuck with me long after the interview ended. Not because I couldn't answer it. But because I kept thinking about whether I had answered it well. The question was: "What's the hardest part about working on mainframe technology?" At the time, I was still relatively new to the world of mainframes. And by "relatively new," I mean embarrassingly new. Before joining my current company, I didn't even know something called a "mainframe" still existed. If you'd asked me what COBOL was, I probably would've guessed it was a Pokémon. Okay that is an exaggeration but you get what I mean. I still remember early on hearing terms like KT (Knowledge Transfer) being thrown around and quietly wondering if everyone had received some secret corporate dictionary except me. The good news is that I've never been particularly afraid of looking stupid. So my strategy is simple: Ask the question. Then ask the follow-up question. Then ask the question that reveals I didn't understand the previous answer either. Surprisingly, people were usually happy to explain. Anyway, after a few KT sessions and what I'd generously describe as a "bare minimum amount of research," my brain went where most developers' brains probably would've gone. The technology The age The tooling The learning curve The fact that some of these systems were designed before I was even born All perfectly reasonable answers. But while I was sitting there in the interview, another thought appeared: "This feels too obvious." Interviewers at that level usually aren't asking for the first answer that comes to mind. They're trying to understand how you think. And the more I reflected on that question afterwards, the more I realized something interesting. The hardest part isn't the technology itself. Before I started working around large enterprise systems, my mental model of old technology was pret

Alt Carbon said the agreement followed more than a year of scientific review and due diligence, with Microsoft requiring additional verification and data-sharing measures.

SpaceX alumni Andrew Redd is betting the ocean has vast amounts of untapped geothermal energy.

GitHub热门项目 | Fast Rust bundler for JavaScript/TypeScript with Rollup-compatible API. | Stars: 13,714 | 133 stars this week | 语言: Rust

GitHub热门项目 | 🛰️ A CLI tool for tracking token usage from OpenCode, Claude Code, 🦞OpenClaw (Clawdbot/Moltbot), Pi, Codex, Gemini, Cursor, AmpCode, Factory Droid, Kimi, and more! • 🏅Global Leaderboard + 2D/3D Contributions Graph | Stars: 3,661 | 28 stars today | 语言: Rust

GitHub热门项目 | Warp is an agentic development environment, born out of the terminal. | Stars: 61,529 | 134 stars today | 语言: Rust

GitHub热门项目 | Fast Rust-based bundler for the web with a modernized webpack API 🦀 | Stars: 12,740 | 7 stars today | 语言: Rust

GitHub热门项目 | A Rust compiler front-end for IDEs | Stars: 16,534 | 10 stars today | 语言: Rust

GitHub热门项目 | Open source, composable payments platform | PCI compliant | SaaS and Self-host options | Enables connectivity to multiple payment, payout, fraud, vault and tokenization providers | Uplifts authorization with intelligent routing and revenue recovery | Reduce payment processing costs with cost observability | Reduces payment ops with reconciliation | Stars: 42,898 | 50 stars today | 语言: Rust

It’s the first estimate of how many Americans are sneaking onto Polymarket’s banned crypto-based platform.

The musician created his own line of loopers that record and layer riffs in a loop. The pricey Looper X does what it claims, but it isn’t without quirks.

It might not feel all that different from older World Cups—for better or worse.

Elon Musk is set to make hundreds of billions even as communities in Mississippi and Tennessee are fighting to stop the gas turbines powering xAI's supercomputers.

Deezer will now scan your playlists on other streaming platforms to detect AI-generated music. Deezer was the first of the big streaming services to start labeling AI-generated music. It even offered its tech to other platforms, but it doesn't seem like it had many buyers. Qobuz launched its own detection tech, while Apple and Spotify […]

Most developers never have to design a network protocol from scratch. You use HTTP, gRPC, WebSockets, or something else that already exists and has been debugged by thousands of people over many years. That is the right call for most situations. I did not take that path when building Vaylix, a key-value database engine. I designed a custom binary protocol called VTP2, and the process taught me things about networking that I would not have picked up any other way. This is not an argument that you should also build a custom protocol. For most things, you should not. This is an honest account of what I ran into. Why not HTTP The first question anyone reasonably asks is: why not just use HTTP? HTTP is everywhere. The tooling is excellent. Every language has a client. Debugging with curl is trivial. If I had used HTTP, I would have had working client libraries in a dozen languages before writing a single line of server code. The problem is that HTTP is stateless by design. Every request is independent. Every request carries headers. Every response carries headers. The model assumes that each round trip is a fresh conversation with no memory of what came before. A database session is the opposite of that. A client connects, authenticates, and then issues many commands over the same connection. The authentication should happen once. The session should carry state. Pipelining requests without waiting for each response to return should be natural, not something you fight the protocol to achieve. HTTP/2 closes some of this gap. But using HTTP/2 correctly for a stateful session model involves working against the grain of what HTTP was designed for. I would have been spending a lot of time on infrastructure that exists to make HTTP behave less like HTTP. The other issue is overhead. HTTP headers are verbose. For small key-value operations, the headers can easily exceed the payload. That felt wrong for something designed to be a tight operational data store. So I went with TCP d

My daughter Emma is 11. She's been vibe coding lately, and honestly, she's pretty good at it. The...

The company changed course after researchers spoke out against the policy, which would have covertly limited Claude’s ability to develop competing AI models.

Your health data is probably the most sensitive information you own. Yet, most "AI Health Assistants" today require you to ship your symptoms, moods, and medical history to a cloud server. In the era of Edge AI and Privacy-preserving machine learning , this is no longer a trade-off we have to make. By leveraging the MLX Framework and Apple Silicon's unified memory, we can now run on-device LLMs like Llama-3-8B directly on an iPhone. This tutorial explores how to build a 100% offline, local health journal that summarizes your daily wellness without a single byte leaving your device. If you're looking for more production-ready patterns for secure AI, definitely check out the advanced guides over at Wellally Tech Blog . Why MLX-Swift? 🍏 Apple's MLX is a NumPy-like array framework designed specifically for Apple Silicon. When brought into the Swift ecosystem via mlx-swift , it allows us to tap into the GPU and Neural Engine with incredible efficiency. The Architecture: 100% Offline Inference Unlike traditional CoreML conversions that can be rigid, MLX allows for dynamic graph execution. Here is how the data flows from your typed notes to a structured health summary: graph TD A[User Input: Health Notes] --> B[SwiftUI View] B --> C{Privacy Layer} C -->|Local Only| D[MLX-Swift Engine] D --> E[Llama-3-8B Quantized Model] E --> F[Unified Memory / GPU] F --> G[Local Inference] G --> H[Markdown Health Summary] H --> B style C fill:#f9f,stroke:#333,stroke-width:4px style E fill:#00ff0022,stroke:#333 Prerequisites 🛠️ Device : iPhone 15 Pro or later (8GB RAM is highly recommended for Llama-3-8B). Software : Xcode 15.3+, iOS 17.4+. Tech Stack : MLX Framework, SwiftUI, Llama-3-8B (4-bit quantized). Step 1: Setting Up the MLX Engine First, we need to integrate the mlx-swift package. In your Package.swift , add: . package ( url : "https://github.com/ml-explore/mlx-swift-chat" , branch : "main" ) Now, let's initialize the model. Because we are on a mobile device, we must use a quantiz

Introduction and Background The Rust ecosystem, celebrated for its memory safety and performance, relies heavily on crates —its package management system. These crates, hosted on the crates.io registry, are the building blocks of Rust projects, enabling developers to share and reuse code efficiently. However, this convenience comes with a hidden cost: a single compromised crate can cascade into a full-blown supply chain attack, as recently demonstrated by the 'onering' crate compromise . The Role of Crates in the Rust Ecosystem Crates serve as the backbone of Rust's dependency system. When a developer adds a crate to their project, Cargo , Rust's package manager, automatically resolves and includes all transitive dependencies. This mechanism, while streamlining development, amplifies the attack surface . A malicious crate, like 'onering', can propagate through multiple projects, executing harmful code during build or runtime. The attack on 'onering' exploited this very mechanism, leveraging the trust inherent in the Rust ecosystem to exfiltrate sensitive code from unsuspecting systems. The 'onering' Compromise: A Case Study in Supply Chain Vulnerability The 'onering' crate was compromised through a malicious code injection during an upload or update. This injection bypassed the insufficient security measures of the crates.io registry, which lacks robust checks for uploaded crates. Once deployed, the malicious code executed during the build process, exfiltrating sensitive data from systems that depended on it. The attack's success underscores the lack of developer vigilance regarding supply chain security and the overreliance on registry integrity . Systemic Failures and Their Mechanisms Insufficient Security Scanning: The crates.io registry failed to detect the malicious code during upload due to limited automated scanning capabilities . This allowed the compromised crate to enter the ecosystem undetected. Overreliance on Registry Security: Developers often trust cr

The CFTC is considering its first regulation for prediction markets, as arrests over "insider trading" on everything from military operations to Google Search data continue to stack up. As CoinDesk reports, a notice of proposed rulemaking says "the proposal would establish a structured framework for evaluating whether such contracts involve an activity enumerated in Section […]