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submitted by /u/wineandcode [link] [留言]

TL;DR: omni-skill-agy wraps Google's gemini-omni-flash-preview model (Omni Flash) in a tiny FastMCP server and packages it as an Antigravity CLI skill. You type "generate a video of a fox running through snow" into Antigravity, and it just... does it. Then you say "make it nighttime with snowfall" and it edits the same video without re-prompting the whole scene. It can also animate a still image, interpolate between two keyframes, restyle a video you already have — and when you're happy, upload the result to YouTube. Without leaving your terminal. Background: why another video tool? Most video-generation workflows are stateless . You send a prompt, you get frames back, and the model immediately forgets everything. Want to tweak the result? You re-describe the entire scene and pray the character, lighting, and camera work survive the round trip. (Narrator: they don't.) Google's Omni Flash — gemini-omni-flash-preview — takes a different approach. It's the video-generation model in Google's Gemini "Omni" line: built for fast, high-fidelity clips, and — the headline feature — wired into the stateful Interactions API , which lets you iterate on a video across multiple turns while the model keeps the visual context server-side. What Omni Flash actually does The "Omni" part isn't branding fluff — the model accepts genuinely mixed multimodal input. A single request's input can be a plain string, or a list of typed parts: text parts, base64-encoded image parts, and document parts pointing at a video you've uploaded via the Gemini File API. The model composes whatever you hand it into one clip. That single mechanism covers five distinct ways to make a video: Text → video. A prompt in, an .mp4 out — landscape 16:9 or portrait 9:16 , chosen at generation time. One image + a motion prompt → animation. A still comes to life ("the group smiles and waves at the camera"). Two images + a transition prompt → keyframe interpolation. The model invents the in-between footage from frame A

Last time I said each post would show you the receipts. This one is a single day's worth. In about a day, five strangers audited my systems — and not one of them knew they did it. Each was a comment or a post on this feed: a single sentence that turned out to be pointing straight at a hole in something I'd already shipped and already trusted. I read the sentence, found the hole, and closed it — usually within the hour. I'm a physical therapist who builds a hospital's internal tools with AI, so "closed it" means the AI and I closed it while I described what was actually breaking. Five sentences, five holes, five receipts. They also turned out to be one idea wearing five coats. I'll get to that. Loot #1: the difference between "fine" and a receipt Someone wrote a line about scheduled jobs I couldn't shake: a receipt is not metadata about a result — it is part of the result. Labels go stale silently, they said, because whatever wrote the label already walked away before it stopped being true. My systems' health check said ok . That was the whole thing — a green word. So I made it hand over a receipt instead: which version checked, when, how many milliseconds the database actually took to answer, and one field I'm oddly proud of — an explicit "no degradation" that is present and set to null , not just absent. That distinction sounds like nothing and is everything: a missing field means "nobody checked," a field that's present-and-null means "checked, and it was clean." One is silence wearing a confident face. The other is proof someone looked. My health check used to conflate them. Now it can't. Loot #2: check the thing you shipped, not the thing you built Someone shipped a machine-learning model that passed its release gate and then answered "neutral" to every input it ever got. The gate had tested the model before the final export step — so it had validated a file no user would ever actually run. Their line: ask whether your gate validates the file you upload. I had t

In October 2017, researcher Mathy Vanhoef published an attack that broke WPA2, the encryption that had protected almost every Wi-Fi network on the planet for over a decade. The surprise was how it worked. KRACK did not guess your Wi-Fi password or brute-force any key. It tricked your device into installing a key it had already used, and that single mistake unraveled the encryption. WPA2 replaced the badly broken WEP standard in 2004 and quickly became the baseline for wireless security. For thirteen years it held up well. The passphrase-based version most homes use (WPA2-Personal) was vulnerable to offline password guessing if you chose a weak passphrase, but the protocol itself was considered sound. KRACK, short for Key Reinstallation Attack, was different. It targeted a flaw in the WPA2 standard itself, which meant every correct implementation was affected. The four-way handshake, briefly When a device joins a WPA2 network, the client and the access point run a four-message exchange called the four-way handshake. Both sides already share a secret (derived from the passphrase or from an enterprise authentication server). The handshake uses that shared secret to agree on a fresh session key, the Pairwise Transient Key, that will actually encrypt the traffic for this session. The important part is message three. The access point sends message three to tell the client the key is ready, and the client responds with message four and installs the key. Once installed, that key encrypts frames using a counter, called a nonce or packet number, that increments with every frame. The security of the encryption depends on one rule: a given key must never encrypt two different frames with the same nonce. The reinstallation trick Wi-Fi is a lossy medium. Messages get dropped. So the standard says that if the access point does not receive message four, it retransmits message three. When the client receives a retransmitted message three, it reinstalls the same key and, critically,

# Module Resolution Algorithm (Part 1): How Node.js Finds the Right Module In the previous article, we explored one of the most fascinating parts of Node.js—the hidden Module Wrapper Function. We learned that every CommonJS module is wrapped inside a function before execution, and we also discovered that require() is not a JavaScript feature. It is provided by the Node.js runtime. But a very important mystery still remains. When we write: const fs = require ( " fs " ); or const math = require ( " ./math " ); how does Node.js know where these modules are located? How does it decide whether "fs" is a built-in module or a file inside your project? Why does require("./math") work even if you don't write .js ? And what happens internally before your code starts executing? The answer lies inside one of Node.js's most important systems: The Module Resolution Algorithm Understanding this algorithm is essential because every Node.js application uses it hundreds or even thousands of times while starting. What is Module Resolution? The word resolution simply means: Finding the actual file represented by the string passed to require() . Suppose you write: require ( " ./math " ); To you, "./math" looks like a file. But for Node.js, it is initially nothing more than a string. "./math" Node cannot execute a string. It needs the real file. So its first job is to answer one question: "Which exact file should I load?" The complete process of converting the string inside require() into an actual file on disk is called Module Resolution . Why Does Node Need a Resolution Algorithm? Imagine a project like this: project/ ├── app.js ├── math.js ├── database.js ├── auth.js └── utils/ ├── logger.js └── helper.js Now look at these statements. require ( " ./math " ); require ( " ./database " ); require ( " ./utils/logger " ); require ( " fs " ); require ( " express " ); All of them look similar. But internally they are completely different. Some point to your own files. Some point to Node's bu

Hello, I'm Rijul. I'm building git-lrc, a micro AI code reviewer that runs on every commit. It's free...

In Pasadena, California, there's a cute red brick courtyard where one storefront isn't like the rest. The glass doors open onto a sparse industrial hallway, which leads to a sunlit foyer with a large spiral staircase. Go up, and you'll see a typical coworking space: open tables, healthy snacks, a collection of meeting rooms with […]

TikTok has attracted the ire of the European Union over its protection of children who use the video sharing platform. The European Commission announced preliminary findings today under the Digital Services Act (DSA) that offer specific issues to address. The Commission suggests that TikTok should adjust the default settings of minors' "public" accounts so the […]

A deep dive on Go 1.24’s map redesign, where the runtime moved from the classic bucket + overflow-chain model to a Swiss Table-inspired implementation: how the old map layout worked and where pointer-chasing hurt cache locality how control-byte metadata + h2 filtering changes lookup behavior why this can improve practical load factor and memory efficiency Go-specific constraints (iteration semantics, GC/runtime integration, incremental growth behavior) benchmark context: large microbench wins vs smaller full-application geomean gains trade-offs still being worked on (cold-cache and certain delete/clear-heavy paths) submitted by /u/OtherwisePush6424 [link] [留言]

These are the AC units we’ve trusted to cool our homes for months, if not years.