标签：#us

Opus 5 will be both cheaper and less restrictive than Fable, likely making it preferable in most use cases.

GitHub热门项目 | Solution for long term memory for agent coding CLIs and to facilitate handoff between different agent vendors | Stars: 1,212 | 186 stars this week | 语言: Rust

The carmaker has said it's owed a refund in the "tens of millions of dollars."

The AI “startups” worth billions of dollars are raising alarm bells about Chinese AI. The smaller players have a totally different take.

GitHub热门项目 | A network diagnostic tool | Stars: 7,361 | 56 stars today | 语言: Rust

GitHub热门项目 | Modern server management panel built with Rust and React. Sites, databases, Docker apps, Git deploy, mail, DNS, monitoring, backups, and security — all in one panel. | Stars: 467 | 13 stars today | 语言: Rust

“I think it should stand for artificial idiot,” one 9-year-old says. Here’s why kids of all ages are calling AI “disgusting” and “creepy.”

Building an operating system is a project I have had my eyes set on ever since I discovered free will in the realm of programming. Years ago, I did a reasonable amount of research, paying extra attention to the subject during my computer science degree and I was able to understand Operating System Theory and how it works from first principles but I never really got around to building one. I had only flimsy reasons for not embarking on it like "why build one when there are tons of working ones out there? The theoretical knowledge is enough" . More recently, I am ignoring the need to not re-invent the wheel for the joy of programming. So if you are interested in also rebuilding stuff because you can, join me on this series as I document how I am going to be building kluster. kluster is in its infancy and the direction is not clear but the one certain thing is that I will be building it entirely in Rust, save some assembly instructions and a linker script and I will be explaining every single line of code along the way. It will also be designed to target the raspberrypi 4 & 5, on qemu and on real hardware respectively. This is an opportunity for anyone who wants to see how Rust works at the lowest of levels to hop on and join the ride. Note that this series will be your biggest lesson on delayed gratification because we will write a lot of code before we even get to see anything meaningful on screen but I will foreshadow what you can get by the end of part 3 if you are patient enough: {{ image(src="/images/os-part3-result.png", alt="Part 3 Results OS Dev") }} You can also clone the source code for part 1 from Github and follow along. Project Setup First things first, let us setup the foundation of the project. I'll be straight with you, I love Rust and I enjoy using the Rust ecosystem in its entirety so I will stay true to that and use it as obsessively as any true Rustacean; I won't hold back. Without doubt, all the dependencies we need are freely available as long as

I built Skim because I tried to find a Windows email client that doesn't suck that hard and couldn't really find one. It's not rocket science to vibecode one, right? Why has nobody done it before? Or is everyone just okay with bloated Outlook or archaic Thunderbird? Skim - free, open-source, MIT-licensed minimalistic offline-first email client with BYOK AI. Quick feature list: Installer under 5 MB Bring Your Own Key to enable AI features: Anthropic/OpenRouter, or any OpenAI-compatible server (including a local one) No menu. Minimalistic and contextual interface - buttons are shown only when you need them Keyboard-first controls: shortcuts for everything, no mouse needed Sweet warm zine design Super modest resource consumption AI features are pretty basic: Cowriter that can scan your recently sent emails, adopt your style, and then write emails and replies that sound like you "Ask this email" - chat with a particular email; it can read attachments and follow links inside it Agentic search across all connected inboxes; the "Ask your inbox" feature can scan your whole inbox One-click summarization and translation A few things I'm actually proud of under the hood The UI part is easy to vibecode. These bits were not. Getting the installer under 5 MB took real work. panic = "abort" alone dropped about 6 MB of unwind tables from the binary, that's 23% off the NSIS installer. I ship a single crypto backend (ring, with aws-lc-rs switched off) so I'm not compiling a second, unused crypto library into the thing. I even wrote a tiny Vite plugin that deletes legacy .woff fonts, since WebView2 always uses woff2 anyway. Pretty much every dependency in Cargo.toml carries a comment justifying its feature flags in kilobytes saved: rustls = { version = "0.23" , default-features = false , features = [ "ring" ] } # aws-lc-rs would compile a second, unused crypto library. ~2 MB of dead weight. No Electron, no bundled Chromium. Tauri 2 uses the system WebView2, so it installs per-user wit

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

This is going to be a very different article from what I usually write. No technical discussions, architecture deep dives, or engineering practices today. Instead, we're talking about something much older than software itself: music. We treat language like it's the default mode of human communication, like it's the real and only thing used to communicate, everything else is secondary, emotional, aesthetic, nice to have. But language is actually the outlier. It's the new protocol layered on top of something much older. Music is the original standard and we've basically forgotten how to read it. The Protocol Stack Think of communication like a network stack. Language is high-level. It's TCP/IP. Built on assumptions, needs learning, breaks the second you cross a boundary. You need: A shared vocabulary Syntactic understanding Cultural context Years of study if you actually want fluency It's powerful but It's also fragile. And it's recent . Written language is a few thousand years old. Spoken language is older, sure, but both are late abstractions compared to the hundreds of thousands of years humans have been syncing bodies to shared sound. Relative to that timeline? Language is yesterday's patch. Music? That's the lower-level protocol. The physical layer everything else runs on. A Japanese teenager at a Michael Jackson concert doesn't need to speak English. She doesn't need to understand what "Man in the Mirror" means as a concept. She also doesn't need a music degree. Music isn't zero -cost. Genre, culture, convention still shape how we hear it. But the entry barrier for emotional communication is way lower. A rhythm can hit urgency, celebration, sadness, or tension long before anyone understands the formal structure behind it. Her nervous system speaks that fluently. And so does everyone else in that stadium. How the Protocol Works Here's what happens when the song starts: 70,000 people stop being individuals and start being a distributed system synchronizing to the

Turning entire novels into narrated, lip-synced, motion video — locally, on a single 16GB GPU, with FLUX, Wan2.2, PuLID, MuseTalk, and ComfyUI. There's an old line: a picture is worth a thousand words. I'd extend it — a video is worth a thousand pictures. So I spent the last few months testing that idea the hard way: I built a pipeline that turns an entire novel into a narrated, lip-synced, motion video. Feed it Pride and Prejudice , or a 400-year-old tale from Strange Tales from a Chinese Studio (聊斋志异), and out comes a finished film with consistent character faces across a hundred-plus scenes. One person. One RTX 4060Ti with 16GB of VRAM. Everything local, no cloud inference. I called it iTube. And honestly — the videos came out better than I expected. Watching a novel you know become a moving, voiced, paced thing is a genuinely different experience from reading a summary of it. That's the bet: that video is a better medium for conveying a story than text is, and the finished clips have mostly convinced me the bet was right. See for yourself before reading another word about how it works: 🎬 Pride and Prejudice — a Western public-domain classic 🎬 聊斋志异 / Strange Tales from a Chinese Studio — a playlist of classical Chinese tales But this post isn't a demo reel. It's about the part nobody warns you about — the part between the models — because that's where the real work turned out to be. The models are instruments, not magic boxes Here's the single most important thing I learned, and the thing I'd want any collaborator to understand before touching a pipeline like this: Each AI package can do exactly one thing well, and your entire job is knowing precisely where each one's ability ends — then writing your script logic to route around the gaps. The stack is not exotic. Most people in generative media know these pieces: FLUX generates the still image for a scene. That's all it does — a single frame. Beautiful, controllable with the right prompt, but frozen. Wan2.2 takes

We've been testing a new project this week. The project's origin story is a mess. Upper management...

When I set out to build a provably-fair random selection system on Solana, the obvious choice for randomness was a VRF (Verifiable Random Function). Instead, I built the system around Solana's SlotHashes sysvar with a commit-reveal scheme. Here's why, and what I gave up to get there. The problem A fair-selection system needs a winner (or set of winners) chosen in a way that's fair, and just as important that participants can check for themselves without taking anyone's word for it. VRF services (Switchboard, ORAO, etc.) solve the fairness part well: they produce randomness that's unpredictable in advance and cryptographically provable after the fact. But they come with a dependency on an oracle, a fee per request, and a proof that most users will never actually verify they'll trust it because the crypto math says they can, not because they did. I wanted something a participant with no crypto background could check in a browser console. The approach: commit-reveal with slot hashes The core idea: commit to the participant list before you know the randomness, then derive the randomness from a slot hash you couldn't have predicted at commit time. rust fn derive_randomness(target_hash: &[u8; 32], participant_root: &[u8; 32]) -> [u8; 32] { let mut combined_seed = [0u8; 64]; combined_seed[..32].copy_from_slice(target_hash); // slot hash at reveal combined_seed[32..].copy_from_slice(participant_root); // Merkle root, locked at commit solana_keccak_hasher::hash(&combined_seed).to_bytes() } The flow: Commit: participant list is finalized and hashed into a Merkle root; this is written on-chain. Wait: a target slot in the future is chosen as the reveal point. Reveal: once that slot passes, its hash is pulled from SlotHashes and combined with the committed root to derive the randomness. Select: the randomness deterministically picks winners from the participant set; winners get their own Merkle root and proofs. Every draw ends up with an audit record like: rust pub struct AuditR

👋👋👋👋 Looking back on your week -- what was something you're proud of? All wins count -- big or small...

The clip features the David Bowie track “Five Years,” which includes lyrics such as “Earth was really dying (dying).”

An updated government advisory warns that Iranian hackers are exploiting systems used by water and energy providers.

In an earnings call yesterday, Tesla CEO Elon Musk did his best to paint a positive portrait of the company's robotaxi program. New cities are being added, more miles are being driven, and more people are experiencing Tesla's unsupervised vehicles. But Musk's typical bullishness seemed to be absent from the call, as the occasional trillionaire […]

The European Commission claims that Google boosted its own apps and products to the top of search rankings to the detriment of its competitors.

The number of paid robotaxi miles traveled fell 36% in the second quarter, despite expanding to new cities, according to Tesla's own figures.