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📕 Update: June 16, 2025 Text Input Blocks Released 🎯 We'll be back with even more exciting updates in the coming weeks! ✅ Added 5 Premium Text Input Blocks w / AlignUI Design System

21,562 次观看 • 1 年前 •via X (Twitter)

10 条评论

Erşad 的头像
Erşad1 年前

Let's take a look at other blocks ⤵️

Erşad 的头像
Erşad1 年前

➡️ Preview:

Stock.Don 的头像
Stock.Don1 年前

@alignui Why do you need the check in the text fileds?

Erşad 的头像
Erşad1 年前

@alignui Actually, the user can complete the action by pressing the Enter key. The icon is just offered as an extra option. Our goal is to make the components as flexible and customizable as possible.

homeboy 的头像
homeboy1 年前

@alignui i love details 😍

Mohd Sohail 的头像
Mohd Sohail1 年前

@alignui Looks promising!

Swati Awasthi 的头像
Swati Awasthi1 年前

@alignui This kind of polish on input blocks is chef’s kiss 👏 Sleek, clean, and super thoughtful UX—can’t wait to see what’s coming next!

VEERA SRIVASTAVA 的头像
VEERA SRIVASTAVA1 年前

@alignui 🌈 The dashboard here is a masterpiece of elegant design

Sajibur Rahman Sagor 的头像
Sajibur Rahman Sagor1 年前

@alignui Amazing Work

Max Kupriianov 的头像
Max Kupriianov1 年前

@alignui Looks insanely fresh. This is so good!!

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Ouroboros "short" Leios Protoyping simulation & visualisation work in progress demonstrated by Duncan Coutts. Full P2P network running Leios Visualisation. You can see 100 different nodes and messages flowing between them all, with representation of Input Blocks (Top left, Endorsement Blocks (20 seconds In these increment), Votes (20 seconds in these increment as the pipeline hits a vote stage) You can see the votes continue to burst at roughly 20 seconds, input blocks also increase, the smoothness of message flows to reduce these bursts is something Duncan details they are working on to improve as-well as resource usage. Input Blocks (IB) - These blocks contain the raw transaction data submitted by users to the blockchain. Endorsement Blocks (EB) - These blocks reference multiple Input Blocks and are used by nodes to validate and vote on the readiness of transactions for inclusion in the blockchain. Votes- Nodes cast votes to achieve consensus on the state of the ledger, enhancing the speed of transaction finality. I believe this is completely off-chain so does not incur transaction costs (Someone keep me honest please) Keep an eye on the diffusion latency chart, as Duncan mentions this is very important, this refers more to time taken for propagation of information across the network I believe. This was an extract from a larger demonstration, really exciting, massive credit to the team working on this. It's going to be extremely impactful to Cardano and I will share as much as I can about it, I really like seeing these visualisations especially during a prototyping stage. What I find interesting is that the visualisation vote count is so much it slows down the browser rendering. Great work! web3innovationnerds & Duncan Coutts what a demonstration that was., keep them coming!

Dave

57,107 次观看 • 1 年前

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,788 次观看 • 1 年前

🚨 Paper Alert 🚨 ➡️Paper Title: Articulate3D: Zero-Shot Text-Driven 3D Object Posing 🌟Few pointers from the paper 🎯Authors of this paper proposed a training-free method, “Articulate3D”, to pose a 3D asset through language control. 🎯Despite advances in vision and language models, this task remains surprisingly challenging. 🎯To achieve this goal, they decomposed the problem into two steps. 🎯They modified a powerful image-generator to create target images conditioned on the input image and a text instruction. 🎯They then align the mesh to the target images through a multi-view pose optimisation step. 🎯 In detail, they introduced a self-attention rewiring mechanism (RSActrl) that decouples the source structure from pose within an image generative model, allowing it to maintain a consistent structure across varying poses. 🎯They observed that differentiable rendering is an unreliable signal for articulation optimisation; instead, they used keypoints to establish correspondences between input and target images. 🎯The effectiveness of Articulate3D is demonstrated across a diverse range of 3D objects and free-form text prompts, successfully manipulating poses while maintaining the original identity of the mesh. 🎯Quantitative evaluations and a comparative user study, in which their method was preferred over 85% of the time, confirm its superiority over existing approaches. 🏢Organization: University of Oxford , Google DeepMind 🧙Paper Authors: Oishi Deb, Anjun Hu, Ashkan Khakzar, Philip Torr, Christian Rupprecht 📝 Read the Full Paper here: 🗂️ Project Page: 🎥 Be sure to watch the attached Demo Video - Sound on 🔊🔊 Find this Valuable 💎 ? ♻️QT and teach your network something new Follow me 👣, naveen manwani , for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements.

naveen manwani

14,334 次观看 • 11 个月前

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GameSir

27,951 次观看 • 14 天前

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