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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

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.

Google continues to report big quarterly revenue, but its AI spending has skyrocketed.

Hey Techie! 🌸 Welcome to my Go series! I'll be sharing what I'm learning in ways that make sense to me, the mistakes I make and the "aha!" moments that help everything click. Whether you're learning Go too or just curious about it, I hope you'll pick up something along the way. Feel free to add any insights or experiences in the comments. Today's topic is... drumroll, please! Slices . Let's dive in! So, what exactly is a slice? When I first came across slices in Go, I thought they were another name for arrays. Turns out, they're not! A slice is internally represented by a small data structure called a slice header . Instead of storing the elements themselves, the slice header stores a pointer to the underlying array, along with its length and capacity . This realization helped me understand why modifying a slice can also modify the original array. Another interesting and convenient thing is how flexible slices are compared to arrays which are fixed size. Slices can grow using functions like append() or be resliced to work with a smaller portion of the underlying array. This flexibility is one of the reasons slices are used so frequently in Go.

If you've been in tech over the last year, you've probably noticed that almost every conversation eventually ends up talking about AI. LLMs, AI Agents, RAG, MCP, prompt engineering...there's something new every week. Like many of you, I've been spending time learning these technologies, experimenting with different tools, and trying to understand where everything is heading. But while learning AI, one question kept coming back to me. Whre does AI actually fit within Enterprise Architecture? Most conversations start with the model. I think they should start with the enterprise. Looking Back Over the last two decades, I've worked through several technology shifts. Physical infrastructure → Virtualization Virtualization → Cloud Cloud → Platform Engineering Automation → Everything Every transition introduced new tools, new platforms, and new buzzwords. But something interesting never changed. Successful enterprise systems still depended on the same fundamentals: Business goals Enterprise Architecture Reliable platforms Security Data Governance Operations Technology changed. Engineering principles didn't. That's one of the reasons I don't see AI as something completely separate. AI Is Just Another Enterprise Capability Today, AI is often treated like its own universe. Dedicated AI teams. AI platforms. AI roadmaps. AI strategies. That all makes sense. But I think there's a risk if we start treating AI as something that sits outside Enterprise Architecture. AI doesn't work in isolation. It needs good data. It needs infrastructure. It needs platforms. It needs security. It needs governance. It needs integration with business applications. And, most importantly, it needs to solve a real business problem. From an architect's point of view, AI isn't an island. It's another enterprise capability. Just like databases, APIs, messaging platforms, Kubernetes, and cloud services became part of our enterprise landscape, AI is becoming another capability that needs to be architected—n

If there's a place in the universe without GPUs, Nvidia is sending them there.

GitHub热门项目 | Offline, privacy-first grammar checker. Fast, open-source, Rust-powered | Stars: 11,873 | 590 stars today | 语言: Rust

Letting a stranger contact a car owner without giving them the number There is a small, very common problem in Indian cities that turns out to be a surprisingly good systems design exercise. A car is parked badly. It is blocking a gate, a driveway, another car. Someone needs the owner to move it, right now. The traditional fix is a phone number written on a sticker on the windscreen. That works. It also means a stranger — any stranger, forever — has the owner's personal number. Why the sticker is worse than it looks Once a number is visible on a windscreen, a few things follow: It gets scraped. Numbers on vehicles end up in marketing lists. It cannot be revoked. Change your number and every sticker you own is now wrong. It has no context. A 2am call could be a genuine emergency or someone who saw the number three months ago. It is a safety issue for some owners in a way it is not for others. A number attached to a vehicle, at a known parking spot, at predictable times, is more information than most people realise they are publishing. So the requirement is oddly specific: a stranger must be able to reach the owner, immediately, without ever learning how to reach them again. That constraint is what makes this interesting. The naive version, and why it fails First instinct: put a QR code on the vehicle that opens a page with a "call owner" button, and put the number behind the button. This solves nothing. The number is still in the page source. Anyone who wants it can get it, and now you have added a scan step for the honest majority while stopping none of the dishonest minority. Second instinct: put a contact form behind the QR. The stranger types a message, the owner gets a notification. Better on privacy, useless in practice. The person needs the car moved in the next ninety seconds. They are not filling in a form and waiting for an email. If the fast path is not there, they go back to writing an angry note. The real requirement is synchronous contact with asynchron

Cordless, handheld, robot, and traditional—we tested them all to find the vacuum that’s fantastic for fur.

The fast fix If your GitLab CI job fails with Cannot connect to the Docker daemon at unix:///var/run/docker.sock , your docker client is looking for a local socket that does not exist inside the job container, because DOCKER_HOST is not set. Point the client at the docker:dind service over TCP and the error goes away: build : image : docker:28.3 services : - name : docker:28.3-dind alias : docker variables : DOCKER_HOST : tcp://docker:2376 DOCKER_TLS_CERTDIR : " /certs" DOCKER_CERT_PATH : " /certs/client" DOCKER_TLS_VERIFY : " 1" script : - docker info - docker build -t my-app . That is the whole fix for the common case. The rest of this page explains why the socket variant of the error is different from the tcp://docker:2375 variant, and covers the two other setups (socket-mounted runners and the Kubernetes executor) where the same message shows up for a different reason. Why you get the unix socket variant specifically This error is not the same as Cannot connect to the Docker daemon at tcp://docker:2375 . The address in the message tells you exactly what the client tried: Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running? When DOCKER_HOST is empty, the Docker CLI falls back to its compiled-in default, the local unix socket at /var/run/docker.sock . Inside a GitLab CI job that uses the docker executor, that socket file simply is not there. The daemon runs in a separate docker:dind service container, not in your job container, so there is nothing listening on the local socket. The client connects, finds no socket, and prints the message above. The tcp://docker:2375 form is the opposite problem: DOCKER_HOST is set correctly but the dind service is not reachable (missing service, no privileged mode, or a TLS mismatch). If you are seeing that address instead, read the companion write-up on the tcp://docker:2375 form of this error , which walks the service and privileged-mode causes in detail. This page is about the case w

If you are an enterprise architect working on Microsoft Entra and AI agents, your first overdue job is not another policy wizard, another dashboard, or another governance steering committee. It is schema design. Specifically, it is deciding how you classify non-human identities with custom security attributes in Microsoft Entra . Not eventually. Up front. I keep seeing the same pattern across customers of every size: teams move quickly on agent experimentation, they onboard identities, they test controls, and then they realize they have no consistent attribute language for policy scope. At that point, every policy becomes a naming convention problem in disguise. That is backwards. The control plane starts with classification Custom security attributes are not decorative metadata. They are tenant-scoped key-value classifications you can assign to users, enterprise applications (service principals), and agent identities that are modeled as a service principal subtype, with dedicated role and permission boundaries for who can define and assign them ( overview , Graph model , agent identity service principal model ). That alone should change how architects think about them. This is not "nice to have taxonomy." This is policy input. Microsoft Entra Conditional Access for agents supports attribute-driven targeting with custom security attributes, and policy evaluation happens during token issuance and refresh, not just at policy authoring time ( Conditional Access for agents ). In other words: if your classification is sloppy, your runtime decisions are sloppy. Why agents raise the stakes You can say "an agent identity is still a service principal" and be technically correct. Microsoft Entra Agent ID is built on service principal infrastructure ( agent identities, service principals, and applications ). You can also miss the point. Agent identity introduces a blueprint-centered model where one blueprint can represent many agents, where blueprint-level policy decisions can

Meta's React library has integrated a Rust version of the React Compiler into its main repository, aimed at enhancing build speed and compatibility with the Rust-based JavaScript toolchain. This port, which memoizes components automatically, demonstrates significant performance improvements, boasting up to 50% faster compilation. The public API remains unchanged to facilitate easy upgrades. By Daniel Curtis

GitHub热门项目 | | Stars: 1,130 | 395 stars today | 语言: Rust