Can we match visual features jointly across multiple frames?... Yes! Barbara Roessle's #ICCV2023 paper proposes a differentiable pose optimization for end2end feature matching across multiple frames, thus obtaining better poses!show more

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation paper page:... Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.show more

Meta releases VGGSfM Visual Geometry Grounded Deep Structure From... Motion Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental manner by detecting and matching keypoints, registering images, triangulating 3D points, and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g., keypoint matching), but are still based on the original, non-differentiable pipeline. Instead, we propose a new deep SfM pipeline VGGSfM, where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end, we introduce new mechanisms and simplifications. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. Furthermore, we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.show more

We know a lot of you had trouble buying... robinhood:0xfe2f0fb0c00d19786a8abf98d4b1f1ac8763b167 on other platforms. Bad prices, wuotes failing, routes not found. So we fixed it: Ordo Swap is live: Built for one thing: Better execution on Robinhood Chain. Ordo searches liquidity across Uniswap V3 and V4, including hooked launchpad pools most routers miss, and can split larger swaps across multiple pools to find a better result. Every quote is also compared live against LI.FI, Kyber Network and Uniswap so you don’t have to trust us when we say the route is better, you can see it yourself before signing. No Ordo fee. Powered entirely by our own RPC and execution infrastructure. ETH, USDG, stocks, memes. One place to swap all of it.show more

Big News for the Community! We've partnered with AIDA... Ecosystem to bring you something special. 🤝 This collaboration with AIDA will unlock a new set of tools and features, all designed to make your trading experience better than ever. You can now: ✅ Access AI-powered analytics to find new opportunities. ✅ Trade seamlessly across multiple DEXs from a single terminal. ✅ Use a secure, built-in wallet for all your assets. The future of our ecosystem is now more powerful. #ICBNetwork #AIDA #AI #ICBX #Communityshow more

Depth Any Video with Scalable Synthetic Data AI physicists... and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.show more

kling 2.6 motion control is actually insane for AI... ads you can swap any person in a video with a different person and it looks completely realistic, and its way better than wan 2.2 this unlocks massive testing potential for ads you can test the same ad concept across multiple different ICPs instantly here's the workflow: >generate your new character with nano banana pro >use kling 2.6 motion control to replace the person in your existing video generate a new voice with >elevenlabs to match the character you can take one winning ad and instantly create 10 variations targeting: -different age groups -different genders -different ethnicities -different aesthetics this is the future of ad testing at scaleshow more

3D Gaussian Splatting for Real-Time Radiance Field Rendering paper... page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.show more

Today we are launching Kaito Studio beta. Starting today,... we’re moving to a new model where brands and creators can more intentionally match based on mutual fit, selection, and expectations - launching with 16 partners, with more in the pipeline as details are finalized. Since launching our waitlist in February, a rapidly growing network of creators has joined Kaito Studio, representing 80 million collective followers and $14 billion in follower net worth. These creators span 118 countries, with English-focused creators making up the largest segment, and China and Korea representing the largest country cohorts. Around 30% have also joined with TikTok, Instagram, or YouTube accounts, positioning them to bring content cross-platform. From here, Kaito Studio will focus on solving three main problems, one step at a time: - Ambassador and creator matching - helping brands find the right ambassadors or creators based on data, audience, and subject alignment - Performance attribution - measuring real impact across influence, mindshare, and conversion - End-to-end orchestration - powering a repeatable workflow from creator matching and campaign execution to measurement, evaluation, and optimization We will ramp up opportunities over the coming weeks as more brands finalize their profiles and program details. As we continue building more features for the GenAI and agentic ecosystem, stay tuned for more opportunities ahead.show more

Introducing Zest Protocol Stacks Swap, The ultimate swap experience... for the stacks.btc ecosystem. Live now. Stacks swap is built with more routes, faster backend and UX than any DEx product previously built on Stacks. Stacks Swap compares hundreds of liquidity pools across every major DEX on Stacks and returns the route with the best output for every trade. How it works: 🟠 Smart order routing. Large swaps split across multiple pools and multiple DEXes at once. When no direct pool exists, the router chains hops through intermediate tokens. The full route executes as a single transaction. 🟠 All or nothing. Every swap is one atomic Stacks transaction. Either the full route completes and you receive at least the guaranteed minimum, or the entire transaction reverts and no tokens leave your wallet. 🟠 Price protection enforced on-chain. Every swap carries a minimum-received amount written into the transaction itself. If the market moves before confirmation, the swap reverts instead of filling at a worse price. No app, server, or operator can override this check. 🟠 Non-custodial by design. Tokens move from your wallet, through the pools, and back to your wallet within a single transaction. No deposits, no balances, no accounts. 🟠 Zero additional fees. Pool fees are set by the DEXes and already priced into every quote. Zest Protocol charges nothing on top. 🟠 Supported liquidity sources: Bitflow , Velar, ALEX 🟧 No. 1 Bitcoin DeFi, and Arkadiko Protocol 🟧🗿, plus direct protocol conversion through Stacking DAO for stSTXbtc swaps. The quote you approve is the worst case you can receive. The swap fills at that amount or better, or it does not happen at all. We built Stacks Swap using Zest Protocol's existing internal infrastructure. When we realised that it performed much better than existing DEx products, we decided it made sense to release it for the benefit of the ecosystem. Happy swapping.show more

over the weekend, i built an app that i... sincerely hope you will never have a need for, but if you do happen to need a friendly, free, private mri viewer designed to make it easy for you to track tumor progression, you can try it here: here's the story: as some of you may know, last last september, my six year old daughter mira was diagnosed with an extremely rare brain tumor called an adamatinomatous craniopharyngioma, and since then our family has been doing everything we can possibly do to find a cure for her. we tortured chatgpt deep research, put together our own private research team, raised $1.4M and donated it all to Hankinson-Mitra Lab research thanks to $MIRA, explored every remotely applicable drug whether on the market or not, and even began working with md anderson to develop a personalized vaccine that we hope can lead to a more permanent cure unfortunately, we received the devastating news last march that the tumor has continued to grow since her initial surgery, and we had to start to consider more drastic options which would have seriously impacted mira’s quality of life. thankfully, with the help of dr. sabine mueller UCSF Benioff SF and the Hankinson-Mitra Lab at the university of colorado, in april, we started her on an alternative but extremely experimental treatment for this disease. to our unimaginable relief, her tumor has responded extraordinarily well to this treatment which combines tocilizumab (an arthritis drug that blocks IL6 receptors) with avastin (a colon cancer drug that inhibhits VEGF proteins). we know this, because mira gets an MRI scan of her brain every few months. and every time we get a new scan, the first thing we do is compare it against her last scan. so we have to find the matching weight of the scan, and then find the same plane, and then carefully find the slices of the scan where the tumor is visible, and then find the closest match to last month’s scan, then adjust the zoom, rotation, and brightness / contrast so they all look the same. we got pretty good at this. but it shouldn't be this hard. so i built last weekend using gemini 3 with some gpt 5.2 xhigh. you just import your DICOM MRI files (either zip, files or a folder), and you can align all of your scans across multiple dates instantly, just click and drag a rectangle around the tumor on any image, and it will use some very clever algorithms to automatically align up and find the closest matching slice from all your other scans, match the brightness / contrast, rotation, pan, zoom, and even shear to make sure the registration is as close as possible, and make it as easy to possible to compare tumor progression. it has a grid view so you can see all your scans for the same location all at once, and an overlay view so you can quickly compare two scans visually (by holding down the space bar to toggle quickly between two scans), along with tools to animate your scans both within the same sequence as well as over multiple scans to show progression. there is no server, it runs entirely locally on your browser - nothing ever gets uploaded and it's all open source: if you've found this useful, please consider a donation to the UCSF Benioff SF hospital foundation, who has given us extraordinary care over the past year or so:show more

Over the past few weeks, I’ve been looking for... different ways to access Seedance 2.0. Then TopviewAI told me that their platform supports Seedance 2.0 — and it can also seamlessly combine multiple clips together. I used the prompt below to create this video, and the result is absolutely amazing! Use 🩵Image 1 as the first frame, referencing the character design, outfit color palette, and overall visual style of 🩵Image 1. The girl is performing a high-speed downhill skateboard ride on a winding suburban mountain road. The shot uses a Steadicam follow perspective, with an intense sense of speed throughout. The powerful wind generated by the fast ride makes her hair and clothing whip violently in the air. At the beginning, the girl pushes off with one foot to gain speed, then lowers her body to reduce wind resistance and continues accelerating. The scene features heavy motion blur to emphasize the extreme speed of the skateboard. While riding, she repeatedly shifts her center of gravity downward and leans left and right through multiple turns on the road. As she carves into the corners, the arm on the inside of the turn lowers as if lightly trying to touch the ground. On straight sections, she bends forward, keeps her knees low, and places both hands behind her back to minimize drag. In the distance, fireworks are going off above a seaside town, while a passenger airplane flies across the sky. The overall visual style should be ultra-realistic, with highly lifelike image quality and realistic photographic cinematography. No background music, only environmental sound design.show more

🎮 𝐅𝐞𝐛𝐫𝐮𝐚𝐫𝐲 𝟐𝟎𝟐𝟓 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐔𝐩𝐝𝐚𝐭𝐞 🚨 Big updates across... multiple areas of Varsity this month! • Coach Schemes in Dynasty Mode – Every coach now has a unique scheme and playstyle, influencing how your team plays. Do you stick with your staff and try new strategies, or hire a coach who specializes in your preferred system? • Audio & Settings Enhancements – A custom Varsity marching band drumline is being composed to add more energy to the game. Plus, menus now have sound effects, and new resolution options (16:9 and 16:10 ultrawide) have been added. • 3D Player & Stadium Models – The new player models are fully integrated, showcasing enhanced customization and animations. Pro-style stadiums for playoffs and championships are also in development! • Motion Capture Upgrades – Defensive back (DB) movements have been refined using motion capture for better realism. We’ve also improved base locomotion (running, turning, and movement) and are hiring a full-time AAA animation specialist. This month was all about refining core gameplay features and bringing more realism to Varsity. Stay tuned for more updates as we keep pushing towards launch! Support us on Patreon for Steam Pre-Alpha PlayTest access, and don’t forget to wish list on Steam! #Varsity #HSFootballVideoGame #DynastyMode #FootballGaming #HighSchoolFootball #CoachSchemes #3DModels #MotionCaptureshow more

The Visual Studio Code insiders version that just shipped... and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.show more

Dear Hunters, It has come to the development team's... notice that some illegally gained material "leaks" are spreading on multiple platforms. We are sorry for this situation. We are sorry if this illegal leak undermines the ceremonial first meeting between you and Sylus. Every love interest's first meeting with the hunter is a precious memory that cannot be repeated. For a long time in the past, planning content, promoting development, optimization and modification... We have been trying our best to prepare for this surprising "first meet." However, this incident disrupted the plan and undermined the experience for all players. Meanwhile, we also believe that the first glimpse may not be everything, because you and Sylus will have countless days of surprise that are worth looking forward to. Rather than let malicious leaks disrupt the pace, it is better to go forward bravely—debut of Sylus's Preview. The development team will continue to prepare for your formal first meeting. We sincerely hope that after he comes to your side, you two will gradually get to know each other and get along well. If the future ahead is good enough, the regret caused by this incident will be soothed. To whom behind this illegal and vicious incident, Obtaining and maliciously leaking business secrets through illegal means may give you a certain sense of accomplishment, but these acts will not escape the law. The investigation of this incident has been fully launched, and we will take all necessary rights protection measures, including but not limited to civil and criminal rights protection, according to the results of the investigation, to pursue to the end, and severely investigate the legal responsibility of the infringer. We sincerely call on Deepspace Hunters to join us in stopping the spread of this leak. The special mailbox for collecting information and clues about vicious access and leaks related to confidential content has been opened. If you encounter related behaviors or clues that are suspected of malicious leaking, stealing, and spreading undisclosed game content, you are welcome to send relevant information to the following email address: [[email protected]], the relevant departments will follow up the first time. Thank you again for your understanding and support through all this time. I hope we can work together to maintain a healthy and harmonious community environment and protect Linkon City for every player. Love and Deepspace Global Teamshow more

AI VIDEO CONQUERS ANATOMICAL STABILITY AND TEXTILE DYNAMICS FROM... AN EXTREME LOW ANGLE PERSPECTIVE A breathtaking young woman with vibrant, waist length, fiery red hair sits candidly on a simple stool against a minimalist interior wall. She wears a complex strapless pink mini dress with a prominent, knotted bow detail across the bust and is accessorized with multiple delicate gold chain necklaces and a stacked gold bracelet on her wrist. The video sequence transitions dynamically from a direct, eye level gaze into the lens, to a dramatic, low angle perspective shot, with her legs thrust forward and her body foreshortened against the plain grey concrete background, looking directly down at the camera with an expression of unbothered confidence. Nothing about the physical contact or posture feels synthetic. Extreme low angle perspectives have always been a notorious stress test for generative media pipelines, often serving as the ultimate breaking point. Capturing natural anatomical proportions from below forces a neural network to calculate steep, non-linear vertical perspective, where the hips, knees, and ribcage must align without turning rubbery, disproportionate, or detaching into separate geometric shapes. In earlier models, this radical foreshortening would usually cause the face to warp, the delicate details like the multiple gold chains to dissolve into flickering digital noise, or the entire torso to smear into a warped, abstract object. Here, the micro physics of both body and textile hold absolute structural integrity. The pink jersey cotton fabric of the complex bodice remains completely consistent, its folds across the bust stretching and gathering authentically as she shifts her pose. Crucially, the intricate multi chain gold necklaces track perfectly with her movements and hold their complex geometry, while the stacked gold bracelet remains stable and locked to her wrist. Even at the extreme perspective, the long red hair maintains natural spatial separation from her shoulders, the facial timing carries a genuine human cadence, and the lighting across her skin texture remains photorealistic, completely eliminating the 'uncanny valley' effect. The real leap forward isn’t just rendering a beautiful figure, it's rendering genuine ambient physics and complex spatial logic through radical motion and viewpoint shifts. The comments section will only debate if she’s real, completely unaware that the viral influencer on their timeline exists solely as a latent space projection. This is street level casual perfection, delivered flawlessly. We have officially crossed the threshold where synthetic personas can deliver raw, low effort clips with more physical reality than half the real influencers on your feed.show more

🚀 A better, faster co-folding-based binding affinity model. Predicting... how tightly a drug candidate binds to its target is critical in drug discovery. It also requires massive computational resources. State-of-the-art models can take 20 seconds to a minute per prediction, impractical for the demands of large scale early-stage programs . 💠 Today, Recursion’s Valence Labs is releasing Nesso-1: the fastest open-source co-folding-based binding affinity model available. At 1 second per prediction, it’s roughly 20x faster than our previous collaboration on Boltz-2 while matching or surpassing its accuracy across public and internal benchmarks. By leveraging NVIDIA Healthcare cuEquivariance, we’ve been able to further accelerate both training and inference by an additional 2-3x. We look forward to continuing to improve Nesso-1 in collaboration with NVIDIA. Weights and code are fully open-sourced. The core architectural ideas behind Nesso-1 build on the insight that coarse-grained co-folding representations can match full-atom models for affinity prediction at a fraction of the cost. Nesso-1 is the first open implementation of this approach with no proprietary dependencies, trained entirely on public data, built to be reproducible and extensible. We’re already using Nesso-1 internally in active drug discovery programs. Fast, reliable affinity prediction at scale is foundational to the kind of autonomous design loops that define our vision for Autonomous Precision Design and Nesso-1 is a meaningful step toward that. 👉 Report: 👉 Github: 👉 HF:show more

Household chores created using a movement sheet as reference... to animate the entire scene using ChatGPT Image 2.0 and Seedance 2.0 on Yapper GPT Image 2 Prompt; [VISUAL STYLE] Monochrome grayscale composition featuring a highly detailed 3D-rendered female character. Designed like a professional instructional guide with a technical, diagram-inspired layout. Clean white background, soft studio lighting, and strong contrast to highlight posture, actions, and object interaction. [GRID LAYOUT] Structured 4×4 panel grid (16 frames total), evenly spaced with thin black divider lines. Each panel is identical in size and clearly numbered from 1 to 16, showing a continuous sequence of household activities. [CHARACTER] Use the provided reference image for the face and overall likeness. Same facial features, skin tone, and proportions Natural makeup, soft expression Consistent identity across all 16 panels Realistic proportions and clean hairstyle (loose or tied back) [WARDROBE] Modern, modest casual outfit: Fitted crop top (not revealing, clean neckline, practical for movement) High-waisted straight or slightly wide-leg jeans (full length, neat fit) Optional minimal sneakers or barefoot indoor styling Fabric should react naturally to movement (subtle folds and tension) [SCENE APPROACH] Minimal, clean environment per panel — only essential props related to the chore. No clutter, no complex backgrounds — focus stays on the subject and action. [PANEL STRUCTURE – EACH FRAME] Top-left: Step number + task title (e.g., “Step 4 – Vacuum Floor”) Center: Full-body pose performing the chore Bottom-left: 3–4 concise instruction lines Overlay: Motion arrows and directional guides showing action flow [CHORE SEQUENCE EXAMPLES] Make the Bed Tidy Up Room Dust Surfaces Vacuum Floor Sweep Floor Mop Floor Do Laundry Hang Clothes Fold Clothes Clean Kitchen Counter Wash Dishes Take Out Trash Water Plants Clean Bathroom Organize Shelves Final Room Reset [MOTION INDICATORS] Curved arrows → fluid actions (wiping, folding) Straight arrows → directional movement Circular arrows → repetitive motions (scrubbing, mopping) [RENDER QUALITY] High-detail sculpted 3D style with smooth grayscale shading, soft shadows, and clean linework. Polished, concept-art level finish with clarity in every pose and object interaction. [RESTRICTIONS] No color, no unnecessary background detail, no extra characters, no revealing clothing, no clutter — only the subject, props, and instructional elements.show more

What is XRP Ledger 3.3.0 Upgrade? The XRP Ledger... is moving from version 3.2.1 to 3.3.0, bringing a substantial set of protocol changes. But there is an important distinction. The 3.3.0 software release does not mean every new feature is already active on the XRPL mainnet. The release gives validators the software needed to support new amendments, which must separately pass the XRPL's amendment process. Put simply, this upgrade is less about changing how the entire XRP Ledger works and more about giving it new tools for payments, tokenization, privacy and institutional use. Here are the major changes: (1) Batch Transactions Batch transactions allow multiple transactions to be packaged and executed as a single atomic unit. That means a group of related transactions can either succeed together or fail together. This could be particularly useful for institutional delivery versus payment, where an asset and payment need to settle together without leaving one side completed while the other fails. The XLS-56 standard defines the Batch design and allows multiple transactions to be processed as one atomic unit. (2) Permission Delegation Permission Delegation allows an account to grant specific transaction permissions without handing another party control of its main private key. That could make operational wallets easier to manage for institutions. For example, a business could delegate certain actions to another party while retaining control over the underlying account. The important point is that delegation is limited by the permissions granted, rather than becoming a transfer of complete account ownership. (3) Sponsor Sponsor tackles one of XRPL's less visible barriers for new users. Accounts and certain ledger objects require XRP reserves, while transactions also require fees. Sponsor allows another account to cover those costs on behalf of a user. That could be useful for neobanks, wallets and other applications that want customers to use XRPL without first acquiring XRP simply to satisfy network requirements. The XLS-68 proposal specifically covers sponsored fees and reserves while keeping the user's account control intact. (4) Confidential Transfers Confidential Transfers bring a limited form of privacy to Multi-Purpose Tokens, or MPTs. The feature is designed to hide certain balances and transfer amounts while preserving mechanisms for authorized parties to verify information when required. This is important for institutions handling tokenized assets because complete public visibility can create problems around commercially sensitive transactions. However, this is not full account anonymity. The feature is focused on MPTs. It does not suddenly make XRP transactions private across the entire XRP Ledger. (5) Dynamic MPT Dynamic Multi-Purpose Tokens make MPTs more flexible after issuance. Token issuers can designate certain properties as mutable when creating the token. Those permitted properties can then be changed later without making every part of the token freely editable. That could matter for tokenized real-world assets whose requirements may change over time. The XLS-94 proposal is designed specifically around this controlled flexibility. (6) Fixes And Protocol Improvements Not everything included in XRPL 3.3.0 is a headline feature. The release also contains software fixes, security improvements, performance work and other protocol changes. The official 3.3.0 release includes Batch V1.1, Confidential Transfer for MPTs, Sponsor and Dynamic MPT work, alongside numerous fixes and engineering changes. There is also an important history behind some of these features. Earlier versions of Batch and Permission Delegation encountered security issues before activation, leading developers to work on revised implementations. That means the new versions are not simply new features appearing overnight. They are also the result of the XRPL's amendment and security review process. And this is perhaps the most important thing to understand about XRPL 3.3.0. A software release is not the same thing as a network activation. Validators still need to support individual amendments through the XRP Ledger's governance process before those capabilities become active. So XRPL 3.3.0 should be viewed as an important infrastructure milestone rather than six new features suddenly switching on simultaneously. Together, these changes push the XRP Ledger further toward the infrastructure needed for tokenized finance and institutional blockchain applications.show more

Made this cinematic AI video in minutes using Getvivix... Prompt used below 👇 STORYBOARD 1 "THE KNIGHT" PROJECT TYPE: 10-second cinematic fantasy storyboard CHARACTER LOCK: single consistent knight — original fictional STYLIZED fantasy warrior (not a real person). Full ornate plate armor, VISOR DOWN the entire sequence (face never visible — safe by design), tattered surcoat + banner, mounted on an armored warhorse. Identical armor/horse across all frames. STYLE: epic dark-fantasy, cinematic, painterly film stills PACING & FLOW: slow, weighty, EPIC — no rush. One continuous charge → clash → melee → hero arc. Gradual camera moves; the action carries unbroken from frame to frame (each beat is the next instant of the last). Transitions are match-on-motion — the horse's stride and the sword's arc bridge every cut, never a hard jump. FRAMES (8 shots, 0–10s) — angle | lens | motion | lighting | environment | → into next: 1 (0–1.5s): wide establishing | 24mm | knight reined at a hill crest, banner snapping, slow push-in | cold dawn backlight, mist | battlefield below → camera drifts down as the horse shifts weight 2 (1.5–3s): 3/4-rear tracking | 35mm | horse breaks into a canter down the slope | low sun raking | churned mud, distant ranks → match-on-stride into the gallop 3 (3–4.5s): side tracking | 50mm | full gallop toward the enemy line, dust plume | side rim light, haze | spears + banners ahead → he lowers the lance, carrying the motion 4 (4.5–6s): low-angle hero | 35mm | lance leveled mid-gallop, visor catching light | backlit dust glow | closing on the line → impact begins 5 (6–7s): impact wide | 50mm | lance strikes, enemy hurled back, splinters | harsh flash + sparks | clash of the lines → horse rears from the hit 6 (7–8s): low 3/4 | 35mm | warhorse rears amid the melee, sword drawn | embers, torchlight | swirling battle → the blade sweeps down 7 (8–9s): tracking the blade | 50mm | sweeping arc through foes, motion-blur trail | sparks on steel | bodies + banners → camera settles, pulls back 8 (9–10s): hero hold | 24mm | horse reared, sword raised, banner behind, silhouette | dramatic backlight, battle haze | the field beyond → freeze LAYOUT: film sheet — left: 3 dynamic mounted poses (charging 3/4, rearing, mid-swing — in-scene, visor down); center: 8-frame grid; right: director notes; bottom: 0–10s. VISUAL STYLE: cinematic dark-fantasy, painterly, volumetric dawn light, dust + embers + mist, shallow DOF, motion blur, anamorphic; stylized — NOT photorealistic, not real human skin; FACE NEVER SHOWN (visor down). Seedance on Getvivix lets you generate high-end cinematic visuals for around 1000 credits (~$1), making pro-level video creation cheap and scalable. Try it here:show more