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Time Fri Aug 11 2023 at 07:30 pm to Sat Aug 12 2023 at 02:00 am (GMT+02:00)

La Soa - Vendredi 11 août

Meet the Winners of Startups in North and Central America

In this tutorial, I will show you how you can set up infinite scroll in react native. Infinite Scroll allows you to scroll down a page and see new content without having to click ‘next page.’ It’s like a never-ending magic carpet ride through the internet. You don’t have to click to see new content; it loads automatically as you scroll. It’s used in social media feeds, such as Facebook or Twitter, as well as shopping sites, such as Amazon. While it’s convenient for users, it can also slow you down. If you’re scrolling down a page and there’s a lot of content to view, it can feel like you’re carrying a heavy bag. While there are other ways of displaying content, such as the ‘Load More’ button or splitting content into pages, the idea behind Infinite Scroll is to make your journey through the internet feel like a seamless, never-ending journey. Ways to Implement Infinite Scrolling in React Using React Libraries Install the Library First things first, open your React project in a terminal or command prompt. Use npm or yarn to install the react-infinite-scroll-component library. npm install react-infinite-scroll-component or yarn add react-infinite-scroll-component Import the Necessary Components In your React component where you want to implement Infinite Scroll, import the required components from the library. import React, { useState } from 'react'; import InfiniteScroll from 'react-infinite-scroll-component'; Set Up State and Fetch Function Define your state to hold the items you want to display and create a function to fetch more items when needed. For example: import React, { useState, useEffect } from 'react';

3function YourComponent() 
{
  const [items, setItems] = useState([]);
 // State for items
  const [loading, setLoading] = useState(false);
 // State to track loading status
  const [page, setPage] = useState(1); 
// State to track current page

  // ... Rest of your component code
} Integrate InfiniteScroll Component Utilize the InfiniteScroll component from the library, passing in necessary props such as dataLength, next, hasMore, loader, and endMessage. function YourComponent() 
{
  // Previous state and fetch function here
  return (
    <InfiniteScroll
      dataLength={items.length} 
// Current length of items
      next={fetchMoreData} 
// Function to call for loading more data
      hasMore={hasMore} 
// Boolean to indicate if more data is available
      loader={<h4>Loading...</h4>} 
// Loader displayed while fetching data
      endMessage={<p>No more items to load</p>} 
// Message displayed when all items are loaded
    >
      {/* Render your items here */}
      {items.map((item, index) => (
        <div key={index}>{/* Render individual item */}</div>
      ))}
    </InfiniteScroll>
  );
} Customize and Test Customize the fetchMoreData function to fetch data from your data source, and adjust the appearance or behavior of the Infinite Scroll as needed. Test your component to ensure proper functionality and styling. This implementation provides a basic setup for Infinite Scrolling using the react-infinite-scroll-component library in React. Adjustments and enhancements can be made based on your specific requirements and data fetching logic. I have written a more in-depth piece here where I showcase how you can make custom solutions and also do API integrations. In this tutorial, I will show you how you can set up infinite scroll in react native. Infinite Scroll allows you to scroll down a page and see new content without having to click ‘next page.’ It’s like a never-ending magic carpet ride through the internet. You don’t have to click to see new content; it loads automatically as you scroll. It’s used in social media feeds, such as Facebook or Twitter, as well as shopping sites, such as Amazon. While it’s convenient for users, it can also slow you down. If you’re scrolling down a page and there’s a lot of content to view, it can feel like you’re carrying a heavy bag. While there are other ways of displaying content, such as the ‘Load More’ button or splitting content into pages, the idea behind Infinite Scroll is to make your journey through the internet feel like a seamless, never-ending journey. Ways to Implement Infinite Scrolling in React Using React Libraries Install the Library Install the Library Install the Library First things first, open your React project in a terminal or command prompt. Use npm or yarn to install the react-infinite-scroll-component library. react-infinite-scroll-component npm install react-infinite-scroll-component npm or yarn add react-infinite-scroll-component yarn Import the Necessary Components Import the Necessary Components Import the Necessary Components In your React component where you want to implement Infinite Scroll, import the required components from the library. import React, { useState } from 'react'; import { useState } import InfiniteScroll from 'react-infinite-scroll-component'; import Set Up State and Fetch Function Set Up State and Fetch Function Set Up State and Fetch Function Define your state to hold the items you want to display and create a function to fetch more items when needed. For example: import React, { useState, useEffect } from 'react';

3function YourComponent() 
{
  const [items, setItems] = useState([]);
 // State for items
  const [loading, setLoading] = useState(false);
 // State to track loading status
  const [page, setPage] = useState(1); 
// State to track current page

  // ... Rest of your component code
} import React, { useState, useEffect } from 'react';

3function YourComponent() 
{
  const [items, setItems] = useState([]);
 // State for items
  const [loading, setLoading] = useState(false);
 // State to track loading status
  const [page, setPage] = useState(1); 
// State to track current page

  // ... Rest of your component code
} Integrate InfiniteScroll Component Integrate InfiniteScroll Component Integrate InfiniteScroll Component Utilize the InfiniteScroll component from the library, passing in necessary props such as dataLength, next, hasMore, loader, and endMessage. function YourComponent() 
{
  // Previous state and fetch function here
  return (
    <InfiniteScroll
      dataLength={items.length} 
// Current length of items
      next={fetchMoreData} 
// Function to call for loading more data
      hasMore={hasMore} 
// Boolean to indicate if more data is available
      loader={<h4>Loading...</h4>} 
// Loader displayed while fetching data
      endMessage={<p>No more items to load</p>} 
// Message displayed when all items are loaded
    >
      {/* Render your items here */}
      {items.map((item, index) => (
        <div key={index}>{/* Render individual item */}</div>
      ))}
    </InfiniteScroll>
  );
} function YourComponent() 
{
  // Previous state and fetch function here
  return (
    <InfiniteScroll
      dataLength={items.length} 
// Current length of items
      next={fetchMoreData} 
// Function to call for loading more data
      hasMore={hasMore} 
// Boolean to indicate if more data is available
      loader={<h4>Loading...</h4>} 
// Loader displayed while fetching data
      endMessage={<p>No more items to load</p>} 
// Message displayed when all items are loaded
    >
      {/* Render your items here */}
      {items.map((item, index) => (
        <div key={index}>{/* Render individual item */}</div>
      ))}
    </InfiniteScroll>
  );
} Customize and Test Customize and Test Customize and Test Customize the fetchMoreData function to fetch data from your data source, and adjust the appearance or behavior of the Infinite Scroll as needed. Test your component to ensure proper functionality and styling. This implementation provides a basic setup for Infinite Scrolling using the react-infinite-scroll-component library in React. Adjustments and enhancements can be made based on your specific requirements and data fetching logic. I have written a more in-depth piece here where I showcase how you can make custom solutions and also do API integrations. here

A Quick Way To Implement Infinite Scroll in React Native

Infinite Scrolling vs Pagination: Making the Right Choice for React Apps

This paper is available on arxiv under CC 4.0 license. Authors: (1) Vianney Brouard, ENS de Lyon, UMPA, CNRS UMR 5669, 46 All´ee d’Italie, 69364 Lyon Cedex 07, France; E-mail: vianney.brouard@ens-lyon.f. Table of Links Abstract & Introduction and presentation of the model Main results and biological interpretation First-order asymptotics of the mutant sub-populations for an infinite mono-directional grap First-order asymptotics of the mutant sub-populations for a general finite trait space (Theorem 2.1) Convergence for the stochastic exponents (Theorem 2.2) Acknowledgements & References 3 First-order asymptotics of the mutant sub-populations for an infinite mono-directional grap In this section consider the model described in Section 1 in the particular following infinite monodirectional grap Assume in the case of the infinite mono-directional graph that A graphical representation of the model can be found in Figure 5. In particular it means that any Compared to the general finite graph, for any trait i ∈ N there is only one path from trait 0 to i for this mono-directional graph. In particular it implies that Notice in particular that with such a construction it immediately follows the monotone coupling Denote by see [12] Section 1.1, or [11] Theorem 1. 3.1 The wild-type dynamics Using Ito’s formula and (18) it follows Solving this equation we obtain for all t ≥ 0 Similarly we have Consequently combining (21), (22) and (23) gives that Finally we have which concludes the proof. Proof of Lemma 3.2. Let ε > 0 and for all n ∈ N introduce the event We start by showing that for all 0 < δ1 < δ2 Step 1: from which we obtain Remark 3.1. From Lemma 3.2, it follows the useful result 3.2 The mutant sub-populations dynamics in the deterministic time-scale (Theorem 2.1 (i)) In this subsection, Equations (11) and (12) are proven for the mono-directional graph. It will be done in two steps. The first one will consist in showing the result for a fixed s ∈ R and uniformly in the parameter t. Then in the second step, the result will be proved uniformly in the parameter 3.2.1 Uniform control on the time parameter In this subsection we are going to prove the following proposition, which is a less refine result of (11) and (12), because the result is not uniform on the parameter s. The proof is done by induction on i ∈ N. As long as the proof is similar for the initialization and the inductive part the step considered will not be specified. To make the proof the clearer possible it is cut using several lemmas. All the results are obtained using a martingale approach. In the next Lemma the martingales that are considered for all the mutant sub-populations are introduced, and their quadratic variations are computed. Lemma 3.3. For all i ∈ N define Proof of Proposition 3.1. Let i ∈ N ∗ . For i ≥ 2 assume that Proposition 3.1 is true for i − 1. We start by showing the result when i is a neutral trait, that is to say we are going to prove (27). All the lemmas that we are mentioning in the proof are free from such neutral assumption, and work also for deleterious mutant traits. We are going to show that (33), (34) and (35) converges to 0 when n goes to infinity. (33): Step 1: Convergence to 0 of = 1: (i) Case i Using Equation (38) we have that (40) For i = 1 we prove without condition. It allows to write (44) For i = 1 we prove without condition. Let Proof of Lemma 3.6. Using similar computations as in (46) and (47), it follows (45). First, a natural upper bound on the mean of the growth of each mutant sub-population can be easily obtained. This is stated in the next Lemma. Second, using both the expression of the quadratic variation of the martingale associated to a mutant sub-population given in Equation (30) and the previous Lemma 3.7, a natural upperbound on its mean is obtained and summed up in the next Lemma. First notice that the function is a non-decreasing function implying that Notice that Then for n large enough, and remembering that θ(i) = θ(i − 1) + 1 we have And we have Then for large enough, and remembering that 1) we have n θ(i) = θ(i − Then by hypothesis on (ψn(i), γn(i)) it follows that (41) converges to 0. (35): Step 3: Convergence to 0 of Notice that for all t ≥ t(i) Then it allows to write Then combining (52) and (53) we get as θ(i) ≥ 1 since the vertex i is assumed to be neutral. It ends the proof of the convergence to 0 of the term (49). The term (50) converges to 0 according to the following Lemma. Lemma 3.9. Assume Equation (15). Let i ∈ N, T ≥ t(i) and s ∈ R. For i ≥ 2 we prove that if Proposition 3.1 is true for i − 1 then (54) For i = 1, we prove without condition. Our aim is to prove that for all ε > 0 = 1: We have (i) Case i Let us set 3.2.2 Uniform control on the parameter s In this subsection we will prove (11) and (12) from (27) and (28) using an idea from the proof of Lemma 3 of [15]. We start by showing (12). We will use that It gives that Hence we get for sufficiently large n from which (12) is obtained. Indeed the term (63) converges to 0 according to Equation (28) and (64) converges to 0 since W is finite almost surely (see (20)). We now show Equation (11). We have Finally using (65) and (66) we obtain for all (t, s) ∈ [0, T] × [−M, M] Then it follows that where the different convergences to 0 are obtained in the following way: 3.3 First-order asymptotics of the mutant sub-populations in the random time-scale (Theorem 2.1 (ii))

First-order Asymptotics of the Mutant Sub-populations for an Infinite Mono-directional Grap

Let’s write an essay! But that’s an impossible task. However, we can create a process that, if run forever, would generate an infinitely long essay. . infinitely long Close enough Now, you can obviously generate a long and repetitive essay with a single line of Python code: >>> "This is water. " * 20
'This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. This is water. ' … boooring! Instead, we will generate a much more interesting essay using the State design pattern in this article. Yaawn First, we will understand what state machines are and how they’re related to the State design pattern. Next, we will create a state machine that can generate a (reasonably) interesting and infinite essay. Then, we will briefly go over the state design pattern. Finally, we will translate that state machine into object-oriented code using the state design pattern. Software design patterns are effective ways to solve commonly occurring problems. When applied appropriately, software design patterns like the state pattern can help you write better scalable, maintainable, and testable software. State Machine In essence, the State design pattern translates a State Machine into object-oriented code. If you aren’t familiar with state machines, it’s a pretty simple concept. A state machine has and . States are certain properties of our system of interest, and state transitions are actions that change these properties and thereby also cause a state change. states transitions Since I have a Robotics background (among other things) and because state machines are used extensively in Robotics, I’ll use a simple example of a robot vacuum cleaner to illustrate how state machines work. The state machine diagram paints an intuitive picture of how the robot operates, even if you’ve never encountered state machines. Let’s go over this operation step by step. The robot starts in the state (the black dot indicates the starting state). Docked If the robot detects its battery is low, it begins charging itself ( state) until its battery is full. Once the battery is full, it returns to the state. Charging Docked In the state, if the robot detects that the floor is dirty (and its battery is not low), it starts cleaning the floor ( state). Docked Cleaning In the state, if the robot gets low on battery, then it goes to charge itself. And if the floor is clean, the robot returns to its dock ( state). Cleaning Docked Thus, our robot vacuum cleaner has three states — , , and — and has transitions based on sensory detection of the floor and its battery. Docked Cleaning Charging Simple state machine for infinite essay Now that we understand state machines at a basic level let’s create a state machine capable of writing an infinite essay. Above is a state machine that uses English grammar to generate an essay comprised of short, simple sentences. I promise we’ll get to a more interesting version very soon, but this should serve as a good starting point for understanding. Let’s go over how it works. Starting in the state, we generate a noun by picking from some pre-defined list of nouns. Let’s say our noun is “The World”. (sentence so far: “The World”) Noun Then we end up in the state, generating a verb next (say, “barks”). (sentence so far: “The World barks”) Verb We generate an adjective (say, “red”) in the state. (sentence so far: “The World barks red”) Adjective Then, in the state, we generate one of the terminating punctuation marks, say “!”. (sentence: “The World barks red!”) Endmark Finally, we’re back in the state to generate our next sentence in the essay. Noun This state machine might generate a (nonsensical) essay that looks like this. The World barks red! Cousin Harry rains foul? The tigers shimmer fun. … Non-deterministic state machine for infinite essay Although “non-deterministic” sounds complicated, for our purposes, it just means adding in some randomness. Essentially we’re adding a kind of a coin toss before transitioning to some of the states. You’ll see what I mean. The non-deterministic state machine above is very similar to the one before. The only differences are: Negations are words like “no” or “not,” and conjunctions are words like “and” and “but.” In the state, we generate a verb and then we toss a coin. If it lands heads (50% probability), we go to the state; otherwise we go to the state. Verb Negation Adjective Similarly, in the state, we generate an adjective and then toss a coin. If heads, we go to the state; if it is tails, then we go to the state. Adjective Conjunction Endmark With the introduction of randomness, negation, and conjunctions, we can now generate more interesting and variable-length sentences. State Design Pattern Now, let’s understand how the state design pattern works. Again, remember that we’re trying to translate a state machine to object-oriented code. In the essay generation state machine, observe that every state needs to do two things. Perform some action. In this case, generating a word (noun, adjective, etc.). Transition to the next state. From to , and so on. Noun Verb From the point of view of a particular state, there is that it needs to know about or do. Instead of being bogged down by the complexity of the entire system — all its states and transitions — we can just focus on one state at a time. In my view, this kind of and is the biggest selling point of the State pattern. nothing else isolation decoupling Below, we have a diagram for the State design pattern. There is some context in which each of the states operates, illustrated by the class. The context object has a private state attribute, which it uses to call the current state to perform its action. Each state implements a interface with methods for performing its action or operation and returning the next state. UML Context State If we map this onto the essay generation example, the UML diagram looks like this. is now an abstract class (indicated by italics) instead of an interface. Abstract classes can have some abstract (not implemented) methods and attributes, while others can be defined. Interfaces are totally abstract: all their methods are abstract. I made this change because implementation is the same across all the states, and avoiding duplicate code is good. WordState generateWord Let’s break down each of the attributes and methods above. In the class, we have: EssayContext : Reference to the current object. state WordState : List of all the words generated so far. essayBody : Setter to change the attribute. setState() state : Method to add the next word to the essay body. addWord() : We call this method to generate our essay; we stop when the has length greater than . generateEssay() essayBody length : Returns a string of the generated essay. printEssay() In the abstract class , we have: WordState : Abstract property (indicated by italics) for a list of words from which we choose words to generate. wordList : Method which adds generated word to the essay context. generateWord() : Abstract method for returning the next state. nextState() We’ll use as a representative example for all the other concrete states inherited from . NounState WordState : A list of nouns from which we choose words to generate. wordList : Returns the next state. nextState() Now, we have everything we need to actually implement this in code. Let’s go ahead and do just that! Python Code Let’s first write the class in a file called . We'll ditch camel case and switch to snake case because, well, Python is a... snake (sorry). EssayContext essay_context.py from word_state import WordState


class EssayContext:
    def __init__(self, state: WordState):
        self.state = state
        self.essay_body: list[str] = []

    def set_state(self, state: WordState):
        self.state = state

    def add_word(self, word: str):
        self.essay_body.append(word)

    def generate_essay(self, length: int):
        while len(self.essay_body) < length:
            self.state.generate_word(self)

    def print_essay(self) -> str:
        return " ".join(self.essay_body) Then, let’s add the states in a file called . word_state.py import abc
import numpy as np


class WordState(abc.ABC):
    word_list: list[str]

    @classmethod
    def generate_word(cls, context: "EssayContext"):
        word = np.random.choice(cls.word_list)
        context.add_word(word)
        context.set_state(cls.next_state())

    @classmethod
    @abc.abstractmethod
    def next_state(cls) -> "WordState":
        pass


class NounState(WordState):
    word_list: list[str] = ["everything", "nothing"]

    @classmethod
    def next_state(cls):
        return VerbState


class VerbState(WordState):
    word_list: list[str] = ["is", "was", "will be"]

    @classmethod
    def next_state(cls):
        heads = np.random.rand() < 0.5
        if heads:
            return NegationState
        return AdjectiveState


class NegationState(WordState):
    word_list: list[str] = ["not"]

    @classmethod
    def next_state(cls):
        return AdjectiveState


class AdjectiveState(WordState):
    word_list: list[str] = ["fantastic", "terrible"]

    @classmethod
    def next_state(cls):
        heads = np.random.rand() < 0.5
        if heads:
            return ConjunctionState
        return EndmarkState


class ConjunctionState(WordState):
    word_list: list[str] = ["and", "but"]

    @classmethod
    def next_state(cls):
        return NounState


class EndmarkState(WordState):
    word_list: list[str] = [".", "!"]

    @classmethod
    def next_state(cls):
        return NounState Finally, let’s add code to run everything in . main.py from essay_context import EssayContext
from word_state import NounState


if __name__ == '__main__':
    ctx = EssayContext(NounState)
    ctx.generate_essay(100)
    print(ctx.print_essay()) Running gives us the following output (different each time because of non-determinism): python main.py 'everything is not terrible and nothing was terrible ! everything will be not fantastic but everything is fantastic . everything will be fantastic . nothing will be fantastic and nothing will be terrible ! everything was not fantastic and everything will be not terrible . everything was terrible . nothing was terrible but nothing will be fantastic ! nothing is not terrible . nothing was not fantastic but everything was not fantastic ! everything will be not fantastic but everything will be terrible ! everything will be not fantastic . everything is fantastic but nothing will be not terrible ! everything will be not fantastic but nothing was not fantastic !' Not bad for such a simple system! We can also extend the various word lists or add more states to make the essay generation more sophisticated. We could even introduce some LLM APIs to take our essays to the next level. Final thoughts State machines and the State pattern are a great fit to model and create systems with a well-defined notion of a “state.” That is, there are specific behaviors and properties associated with each state. The robot vacuum cleaner is cleaning, docked or charging. Your TV can be ON or OFF, and the TV remote buttons will act differently based on the TV’s state. It’s also a good fit for generating or identifying sequences with a well-defined pattern. This applies to our essay generation example. Finally, you might ask, “What’s the point of all this?” Why did we go through all the trouble defining the various states and classes to generate this “infinite” essay? We could have written 20 (or fewer) lines of Python code to achieve the same behavior. The short answer is for better . scalability Imagine if, instead of just three or five states, we had 50 or 500 states. This isn’t hyperbole; real enterprise systems do have that level of complexity. Suddenly, the State pattern seems much more appealing because of its decoupling and isolation. We can just focus on one state at a time without having to keep the entire system in our heads. It’s easier to introduce changes since one state won’t affect others. It also allows for easier unit testing, a big part of a scalable and maintainable system. Ultimately, the State pattern is not just about managing states; like all design patterns, it’s a blueprint for building systems that are as scalable and maintainable as they are sophisticated.

Writing an Infinitely Long Essay Using State Pattern in Python

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Einen unendlich langen Aufsatz mit State Pattern in Python schreiben

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पायथन में राज्य पैटर्न का उपयोग करके एक अनंत लंबा निबंध लिखना

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在 Python 中使用状态模式写一篇无限长的论文

For years, as a Laravel developer, I sought the perfect solution for smooth infinite scrolling. Most approaches failed under heavy usage until I optimized a client's site. They needed thousands of products to stream endlessly for shoppers. After implementing my technique, even our most populated categories flew by with ease. Now, I'm ready to share those same secrets that led to their success. In this post, I'll walk through how to build lightning-fast infinite scrolling that satisfies even the most scroll-hungry users. More performance means happier customers and fewer headaches - doesn't that sound appealing? Laying the Groundwork For this project, I chose to work with the latest release for its blade components and improved routing. Additional dependencies included Bootstrap for basic styling, jQuery to simplify DOM manipulation, and the Infinite Scroll library to handle smooth loading. Laravel 8 Folder Structure My file organization followed Laravel's default structure with a few tweaks. Models, controllers, and other app codes reside under app/ while frontend assets go in resources/. app/ Models Http/Controllers database/ migrations seeds resources/ js/ css/ views/ Setting Up the Database The products will be fetched from a . I created a products table with fields like id, title, image using migrations. SQLite database Schema::create('products', function (Blueprint $table) { $table->id(); $table->string('title'); $table->string('image'); //other fields }); Sample data was then seeded into the table to test scrolling large datasets of 10,000 records. Now the foundations were in place to start building out the infinite scroll functionality. Designing the Database For this demo, I created a simple product database with only three fields - id, title, and image. The id is an auto-incrementing primary key, while the title and image are text fields. Creating the Models Eloquent models provide an object-oriented interface to interact with the database. I generated a Product model that corresponds to the products table: php artisan make:model Product This created a Product.php model file under app/Models. I defined the fields that should be mass assignable and established the table name: class Product extends Model { protected $fillable = ['title', 'image']; protected $table = 'products'; } Now I have a model to retrieve, save and delete product data from the database in an ORM manner. With the schema and models ready, the next step is to build a RESTful API to serve the product data. Creating the API Building the Pagination Controller I generated an API controller to handle requests for paginated products data: php artisan make:controller Api/ProductController This created a ProductController under the Http/Controllers folder. Writing the API Endpoint The controller method accepts a page parameter to determine the data slice: public function index($page) { $perPage = 12; $products = Product::paginate($perPage); return response()->json($products); } Testing with Postman With the API built, I fired up Postman to test fetching pages of results. Calls to /api/products?page=1 returned the first 12 products. Increasing the page number loaded the next set. This confirmed my API was correctly paginating large product result sets to support the eventual infinite scrolling of records. Implementing Infinite Scroll
Adding the Infinite Scroll Plugin
I installed the jQuery Infinite Scroll plugin through NPM and added it to my main JS file: import InfiniteScroll from 'infinite-scroll'; Configuring Plugin Settings The plugin needs to know what container to monitor for scrolling and where to insert new content: const div = document.querySelector('.products'); const infScroll = new InfiniteScroll( div, { // settings }); Calling the API on Scroll Each time the user scrolls, a new request is made to fetch the next page: infScroll.on('load', function(response) { // call API infScroll.append(newHtml); }); Displaying Loaded Products The response data is used to build and insert new product elements into the container. Now, infinite scroll is in effect! Additional Enhancements Handling Loading State It's important to provide feedback while new data loads. I added a loading indicator to replace products: Automatic vs Manual Scrolling The plugin supports both behaviors out of the box. I went with manual scrolling for finer user control. Filtering Search Results A search box lets users dynamically filter the results. The API accepts query parameters to modify the dataset. Optimizing Performance To avoid duplicate requests, I check if the user is already loading before subsequent scrolls. Response data is also cached in memory for future identical queries. With these tweaks, I achieved silky smooth infinite loading for catalogs of 1000s of products. Users could now immerse themselves in scanning items without impedance. Conclusion The journey to build a performant infinite scroll in Laravel was quite the learning experience. While it may seem daunting at first, breaking the problem down into incremental steps makes the solution surprisingly straightforward to implement. What began as a frustration with laggy UX became a fascinating technical challenge. I hope you now feel empowered to apply these techniques to your own Laravel projects. Beyond the code, my favorite part was seeing how a small optimization could significantly enhance the end-user experience. Of course, there is always room for improvement. As site traffic and data volumes grow, more advanced caching, database patterns, and front-end logic may be required. But this approach provides an excellent starting point before complex solutions are needed. The next time endless scrolling seems like an impossible feat, remember that with some ingenuity, Laravel makes it surprisingly simple. Keep learning, keep building, and don't forget to savor the satisfaction of projects well-crafted. May your next development adventures be as rewarding as this one was for me.

How to Build Infinite Scrolling in Laravel: A Dev Guide

In recent years, natural language processing has been revolutionized by the advent of large language models ( ). Massive neural networks like GPT-3, , and BlenderBot have demonstrated remarkable proficiency at various language tasks like conversational AI, summarization, and question-answering. However, a major impediment restricts their practical deployment in real-world streaming applications. LLMs PaLM LLMs are pre-trained on texts of finite lengths, usually a few thousand tokens. As a result, their performance deteriorates rapidly when presented with sequence lengths exceeding their training corpus. This limitation renders LLMs incapable of reliably handling long conversations as required in chatbots and other interactive systems. Additionally, their inference process caches all previous tokens' key-value states, consuming extensive memory. Researchers from MIT, Meta AI, and Carnegie Mellon recently proposed StreamingLLM, an efficient framework to enable infinite-length language modeling in LLMs without expensive fine-tuning. Their method cleverly exploits the LLMs' tendency to use initial tokens as "attention sinks" to anchor the distribution of attention scores. By caching initial tokens alongside recent ones, StreamingLLM restored perplexity and achieved up to 22x faster decoding than prior techniques. The says it clearly: paper they published We introduce StreamingLLM, an efficient framework that enables LLMs trained with a to generalize to . We show that StreamingLLM can enable Llama-2, MPT, Falcon, and Pythia to perform stable and efficient language modeling with up to 4 million tokens and more. finite length attention window infinite sequence length without any fine-tuning This blog post explains the key technical findings of this work and their significance in plain terms. The ability to deploy LLMs for unlimited streaming inputs could expand their applicability across areas like assistive AI, tutoring systems, and long-form document generation. However, cautions remain around transparency, bias mitigation, and responsible use of these increasingly powerful models. The Challenges of Deploying LLMs for Streaming Unlike humans who can sustain conversations for hours, LLMs falter beyond short contexts. Two primary issues encumber their streaming application: LLMs based on Transformer architectures cache the key-value states of all previous tokens during inference. This memory footprint balloons with sequence length, eventually exhausting GPU memory. Memory Overhead: More critically, LLMs lose their abilities when context lengths exceed those seen during pre-training. For example, a model trained on 4,000 token texts fails on longer sequences. Performance Decline: Real-world services like chatbots, tutoring systems, and voice assistants often need to maintain prolonged interactions. But LLMs' limited context capacity hampers their deployment in such streaming settings. Prior research attempted to expand the training corpus length or optimize memory usage, but fundamental barriers remained. Windowed Attention and Its Limitations An intuitive technique called windowed attention emerged to mitigate LLMs' memory overhead. Here, only the key values of the most recent tokens within a fixed cache size are retained. Once this rolling cache becomes full, the earliest states are evicted. This ensures constant memory usage and inference time. However, an annoying phenomenon occurs - the model's predictive performance drastically deteriorates soon after the starting tokens fade from the cache. But why should removing seemingly unimportant old tokens impact future predictions so severely? The Curious Case of Attention Sinks Analyzing this conundrum revealed the LLM's excessive attention towards initial tokens, making them act as "attention sinks." Even if semantically irrelevant, they attract high attention scores across layers and heads. The reason lies in the softmax normalization of attention distributions. Some minimal attention must be allocated across all context tokens due to the softmax function’s probabilistic nature. The LLM dumps this unnecessary attention into specific tokens - preferentially the initial ones visible to all subsequent positions. Critically, evicting the key values of these attention sinks warped the softmax attention distribution. This destabilized the LLM's predictions, explaining windowed attention's failure. StreamingLLM: Caching Initial Sinks and Recent Tokens Leveraging this insight, the researchers devised StreamingLLM - a straightforward technique to enable infinite-length modeling in already trained LLMs, without any fine-tuning. The key innovation is maintaining a small cache containing initial "sink" tokens alongside only the most recent tokens. Specifically, adding just 4 initial tokens proved sufficient to recover the distribution of attention scores back to normal levels. StreamingLLM combines this compact set of anchored sinks with a rolling buffer of recent key values relevant for predictions. (There are some interesting parallels to a similar paper in ViT research around registers, .) which you can read here This simple restoration allowed various LLMs like GPT-3, PaLM, and LaMDA to smoothly handle context lengths exceeding 4 million tokens - a 1000x increase over their training corpus! Dumping unnecessary attention into the dedicated sinks prevented distortion, while recent tokens provided relevant semantics. StreamingLLM achieved up to 22x lower latency than prior approaches while retaining comparable perplexity. So by removing this key bottleneck, we may be able to enable practical streaming deployment of LLMs in interactive AI systems. Pre-training with a Single Sink Token Further analysis revealed that LLMs learned to split attention across multiple initial tokens because their training data lacked a consistent starting element. The researchers proposed appending a special "Sink Token" to all examples during pre-training. Models trained this way coalesced attention into this single dedicated sink. At inference time, providing just this token alongside recent ones sufficiently stabilized predictions - no other initial elements were needed. This method could further optimize future LLM designs for streaming usage. Conclusion By identifying initial tokens as attention sinks, StreamingLLM finally enables large language models to fulfill their potential in real-world streaming applications. Chatbots, virtual assistants, and other systems can now leverage LLMs to smoothly sustain long conversations. However, while this removes a technical barrier, concerns around bias, transparency, and responsible AI remain when deploying such powerful models interacting with humans - infinite context window or not. But used judiciously under the right frameworks, the StreamingLLM approach could open up new beneficial applications of large language models. Also published here.

Unveiling Infinite Context Windows: Leveraging LLMs in Streaming Apps with Attention Sinks

SEC v. Binance Court Filing, retrieved on June 5, 2023 is part of . You can jump to any part in this filing . This is part 52 of 69. HackerNoon’s Legal PDF Series here FACTS VIII. THE CRYPTO ASSETS TRADED ON THE BINANCE.COM PLATFORM AND BINANCE.US PLATFORM INCLUDE ASSETS THAT WERE OFFERED AND SOLD AS SECURITIES. I. AXS 486. Axie Infinity Shards (“AXS”) are ethereum tokens that are native to the Axie Infinity (“Axie”) game, a blockchain game that allows players to interact in a virtual world through digital pets called “Axies.” 487. Axie was created by Sky Mavis PTE LTD (Sky Mavis) and launched in 2018. The Sky Mavis team has 40 full-time employees, which include CEO Trung Nguyen and COO Aleksander Leonard Larsen, who are part of the founding team responsible for key decisions regarding Axie, such as product development, marketing, digital design, and software engineering. 488. Players of the Axie game can earn AXS for successfully playing the Axie game and can use AXS to make in-game purchases. AXS can also be staked through Axie. The total AXS token supply is 270 million with over 100 million in circulation. 489. In 2020, Sky Mavis raised about $864,000 in a purportedly private sale of AXS tokens to “strategic investors.” That same year, Sky Mavis conducted a public sale of AXS, resulting in the distribution of 29.7 million AXS tokens, raising $2.9 million for Axie. The AXS tokens sold to “strategic investors” were offered at a 20 percent discount to those sold in the public offering, and were subject to a quarterly unlocking schedule over a two-year period. 490. AXS has been available for buying, selling, and trading on the Binance.com Platform since October 2020, and the Binance.US Platform since November 2021. 491. The information Sky Mavis publicly disseminated has led AXS holders, including those who have purchased AXS since August 2021, reasonably to view AXS as an investment in and to expect to profit from Sky Mavis’s efforts to grow the Axie protocol, which, in turn, would increase demand for and value of AXS. 492. For example, Sky Mavis explained publicly that funds raised in the AXS 2020 offerings were pooled and used to develop and improve the Axie platform. An October 26, 2020 article explains that the “team has used funds raised according to the allocations below: 85% [d]evelopmental expenses; 10% [a]dministrative costs; 5% [b]usiness development and marketing.” 493. Also, on November 19, 2020, the Axie Twitter account stated: “Today, we’re proud to share more info on the $AXS strategic sale! The participants will help open amazing new doors. The capital will help us scale the team so we can better deliver on the gameplay and feature updates you’re all patiently waiting for!” To keep the Sky Mavis team “incentivized to keep building after a successful token sale,” 21 percent of the total AXS tokens—56.7 million AXS—were issued to these individuals, which will be gradually unlocked over a 4.5 year period to ensure that “the team, community and investors have aligned incentives.” 494. An Axie whitepaper further explained that the Sky Mavis team will use its experience and efforts to develop and grow the Axie game. For example, it lists the Axie founding team, their roles, and background experience and touts that “we’ve established a core team to lead product development and oversee most decisions related to the progress of the game. This allows us to build and iterate quickly towards product-market fit.” 495. At the time of the initial sale of AXS, the Axie platform was not complete, and while several features of the platform have been implemented since 2020, others are still in the development phase today. Further, the whitepaper explicitly set growth goals in terms of daily average users of Axie Infinity and average weekly growth rates, that if “not met by the end of 2023, Sky Mavis will lead the formation of a steering committee or similar vehicle to discuss a path forward.” Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 1:23-cv-01599 retrieved on September 6, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. docdroid.net

Binance Faces SEC Probe for Unregistered Axie Infinity Shard Transactions

Explaining the Infinite Potential of Quantum Computing in Marvel-lous English (No Math)

মার্ভেল-লাউস ইংলিশে কোয়ান্টাম কম্পিউটিং এর অসীম সম্ভাবনা ব্যাখ্যা করা (কোন গণিত নয়)

Erklären Sie das unendliche Potenzial des Quantencomputings in wunderbarem Englisch (keine Mathematik)

Explicando el potencial infinito de la computación cuántica en un inglés bastante divertido (sin matemáticas)

Expliquer le potentiel infini de l'informatique quantique dans un anglais plutôt drôle (pas de mathématiques)

क्वांटम कंप्यूटिंग की अनंत संभावनाओं को मज़ेदार अंग्रेजी में समझाना (कोई गणित नहीं)

量子コンピューティングの無限の可能性を面白い英語で説明する (数学なし)

놀라운 영어로 양자 컴퓨팅의 무한한 잠재력 설명(수학 없음)

Explicando o potencial infinito da computação quântica em inglês bastante engraçado (sem matemática)

Kuantum Hesaplamanın Sonsuz Potansiyelini Harika İngilizceyle Açıklamak (Matematiksiz)

Giải thích tiềm năng vô hạn của máy tính lượng tử bằng tiếng Anh khá hài hước (Không có toán học)

用相当有趣的英语解释量子计算的无限潜力（无数学）

Identifying Axion Conversion in Compact Star Magnetospheres With Radio-Wave Polarization Signatures

How Cloud-Based Machine Learning Platforms Streamline AI Development

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Dipankar Sarkar, Cryptuon Research and me@dipankar.name Table of Links Abstract and Introduction
Formal Model - Rollups with Decentralized Common Pool (DCP)
Atomic composability & ZK-proofs
Incorporating Zk-proofs
Application of the Formal Model
Conclusion and References 6 Conclusion The formal model presented serves as a superset for all possible atomic composability across rollups because of its comprehensive nature. This model is a superset because it is designed to encompass every step, every entity, and every possible scenario in the lifecycle of a transaction across rollups. By being exhaustive in its approach, any atomic composability solution across rollups that exists or might be conceived in the future can be mapped onto some subset of this model. It acts as a general framework or blueprint from which specific implementations can be derived, tailored to particular requirements or constraints. Comprehensiveness of Definitions It identifies every rollup, transaction, timestamp, cryptographic key, buffer mechanism, and even the decentralized pool where transactions are posted. This covers all essential tools and entities required for atomic transactions between rollups. Detailed Operations The detailed operation steps ensure that every conceivable action associated with transactions – from their creation, publication, verification, buffering, to dependency resolution – is incorporated: • Publish: Every transaction is committed to a decentralized common pool, capturing the universal broadcast mechanism. • Buffer: Handles the uncertainties and delays. • Resolve: Manages interdependencies, making sure that if one transaction in a set can’t be executed, none in that set are. • Verify: Ensures that only legitimate and valid transactions are processed. Robust Dependency Handling This is perhaps the crux of atomic composability. The model provides: • A method to check the timing (timestamps) of transactions, which is crucial for ensuring order and dependencies. • Buffering mechanisms to account for delays in dependency resolution. • Defined limits for buffering to prevent infinite waits and to give an outcome (acceptance or rejection) within a finite time. Flexibility & Scalability The model doesn’t restrict the number or type of dependencies. It simply provides mechanisms to handle them. This ensures that as blockchain technology evolves and new dependency types emerge or transactions become more intricate, the model remains applicable. Incorporation of Limits By integrating system limits like τmax, Bmax, and Dmax, the model not only accounts for ideal scenarios where all dependencies are quickly resolved but also for edge cases where system constraints come into play. This adds to its universality. Security through Verification The model’s inclusion of cryptographic verification ensures that security concerns are front and center. It recognizes that composability isn’t just about making sure transactions work together, but that they’re also genuine and untampered. References [1] E. Ben-Sasson, A. Chiesa, D. Genkin, E. Tromer, and M. Virza. Snarks for c: Verifying program executions succinctly and in zero knowledge. In Proceedings of the 33rd Annual Cryptology Conference, pages 90–108. Springer, 2013. [2] E. Ben-Sasson, I. Bentov, Y. Horesh, and M. Riabzev. Scalable, transparent, and post-quantum secure computational integrity. IACR Cryptol. ePrint Arch., 2018:46, 2018. [3] N. Bitansky, R. Canetti, A. Chiesa, and E. Tromer. Extractable collision resistance: A new complexity assumption for zero-knowledge. Advances in Cryptology–CRYPTO 2012, pages 416–434, 2012. [4] V. Buterin. A next-generation smart contract and decentralized application platform. White paper, 2014. Available at https://ethereum.org/en/whitepaper/. [5] V. Buterin. Ethereum’s scalability and decentralization challenge, 2020. [6] R. Gennaro, C. Gentry, B. Parno, and M. Raykova. Quadratic span programs and succinct nizks without pcps. In Proceedings of the 32nd Annual International Conference on the Theory and Applications of Cryptographic Techniques, pages 626–645. Springer, 2013. [7] S. Micali. Cs proofs. Journal of Cryptology, 4(3):175–194, 1994. [8] S. Micali. Atomic transactions in blockchain, 2016. [9] A. Narayanan, J. Bonneau, E. Felten, A. Miller, and S. Goldfeder. Bitcoin and Cryptocurrency Technologies. Princeton University Press, 2016. [10] C. Reitwiessner. zk-snarks in a nutshell. Blog post, 2016. Available at https://blog.ethereum.org/2016/12/05/zksnarks-in-a-nutshell/. [11] F. Schär. Decentralized finance: On blockchain- and smart contract-based financial markets. Federal Reserve Bank of St. Louis Review, 2020. [12] R. S. Wahby, I. Tzialla, A. Shelat, J. Thaler, and M. Walfish. Fast and simple constant-size zk-snarks without trusted setup. IACR Cryptol. ePrint Arch., 2019:748, 2019. [13] H. Williamson. Optimistic rollups: Layer 2 scaling. Blog post, 2020. Available at https://optimism.io/. This paper is available on arxiv under CC 4.0 license. Authors: (1) Dipankar Sarkar, Cryptuon Research and me@dipankar.name This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Dipankar Sarkar, Cryptuon Research and me@dipankar.name Table of Links Abstract and Introduction Formal Model - Rollups with Decentralized Common Pool (DCP) Atomic composability & ZK-proofs Incorporating Zk-proofs Application of the Formal Model Conclusion and References Abstract and Introduction Abstract and Introduction Formal Model - Rollups with Decentralized Common Pool (DCP) Formal Model - Rollups with Decentralized Common Pool (DCP) Atomic composability & ZK-proofs Atomic composability & ZK-proofs Incorporating Zk-proofs Incorporating Zk-proofs Application of the Formal Model Application of the Formal Model Conclusion and References Conclusion and References 6 Conclusion The formal model presented serves as a superset for all possible atomic composability across rollups because of its comprehensive nature. This model is a superset because it is designed to encompass every step, every entity, and every possible scenario in the lifecycle of a transaction across rollups. By being exhaustive in its approach, any atomic composability solution across rollups that exists or might be conceived in the future can be mapped onto some subset of this model. It acts as a general framework or blueprint from which specific implementations can be derived, tailored to particular requirements or constraints. Comprehensiveness of Definitions It identifies every rollup, transaction, timestamp, cryptographic key, buffer mechanism, and even the decentralized pool where transactions are posted. This covers all essential tools and entities required for atomic transactions between rollups. Comprehensiveness of Definitions Detailed Operations The detailed operation steps ensure that every conceivable action associated with transactions – from their creation, publication, verification, buffering, to dependency resolution – is incorporated: Detailed Operations • Publish: Every transaction is committed to a decentralized common pool, capturing the universal broadcast mechanism. • Buffer: Handles the uncertainties and delays. • Resolve: Manages interdependencies, making sure that if one transaction in a set can’t be executed, none in that set are. • Verify: Ensures that only legitimate and valid transactions are processed. Robust Dependency Handling This is perhaps the crux of atomic composability. The model provides: • A method to check the timing (timestamps) of transactions, which is crucial for ensuring order and dependencies. • Buffering mechanisms to account for delays in dependency resolution. • Defined limits for buffering to prevent infinite waits and to give an outcome (acceptance or rejection) within a finite time. Flexibility & Scalability The model doesn’t restrict the number or type of dependencies. It simply provides mechanisms to handle them. This ensures that as blockchain technology evolves and new dependency types emerge or transactions become more intricate, the model remains applicable. Incorporation of Limits By integrating system limits like τmax, Bmax, and Dmax, the model not only accounts for ideal scenarios where all dependencies are quickly resolved but also for edge cases where system constraints come into play. This adds to its universality. Security through Verification The model’s inclusion of cryptographic verification ensures that security concerns are front and center. It recognizes that composability isn’t just about making sure transactions work together, but that they’re also genuine and untampered. References [1] E. Ben-Sasson, A. Chiesa, D. Genkin, E. Tromer, and M. Virza. Snarks for c: Verifying program executions succinctly and in zero knowledge. In Proceedings of the 33rd Annual Cryptology Conference, pages 90–108. Springer, 2013. [2] E. Ben-Sasson, I. Bentov, Y. Horesh, and M. Riabzev. Scalable, transparent, and post-quantum secure computational integrity. IACR Cryptol. ePrint Arch., 2018:46, 2018. [3] N. Bitansky, R. Canetti, A. Chiesa, and E. Tromer. Extractable collision resistance: A new complexity assumption for zero-knowledge. Advances in Cryptology–CRYPTO 2012, pages 416–434, 2012. [4] V. Buterin. A next-generation smart contract and decentralized application platform. White paper, 2014. Available at https://ethereum.org/en/whitepaper/. [5] V. Buterin. Ethereum’s scalability and decentralization challenge, 2020. [6] R. Gennaro, C. Gentry, B. Parno, and M. Raykova. Quadratic span programs and succinct nizks without pcps. In Proceedings of the 32nd Annual International Conference on the Theory and Applications of Cryptographic Techniques, pages 626–645. Springer, 2013. [7] S. Micali. Cs proofs. Journal of Cryptology, 4(3):175–194, 1994. [8] S. Micali. Atomic transactions in blockchain, 2016. [9] A. Narayanan, J. Bonneau, E. Felten, A. Miller, and S. Goldfeder. Bitcoin and Cryptocurrency Technologies. Princeton University Press, 2016. [10] C. Reitwiessner. zk-snarks in a nutshell. Blog post, 2016. Available at https://blog.ethereum.org/2016/12/05/zksnarks-in-a-nutshell/. [11] F. Schär. Decentralized finance: On blockchain- and smart contract-based financial markets. Federal Reserve Bank of St. Louis Review, 2020. [12] R. S. Wahby, I. Tzialla, A. Shelat, J. Thaler, and M. Walfish. Fast and simple constant-size zk-snarks without trusted setup. IACR Cryptol. ePrint Arch., 2019:748, 2019. [13] H. Williamson. Optimistic rollups: Layer 2 scaling. Blog post, 2020. Available at https://optimism.io/.

Towards Universal Atomic Composability: Conclusion and References

Happiness Is Based on a Flawed Equation — Here's How to Fix It

সুখ একটি ত্রুটিপূর্ণ সমীকরণের উপর ভিত্তি করে - এটি কীভাবে ঠিক করা যায় তা এখানে

Glück basiert auf einer fehlerhaften Gleichung – so lässt sie sich beheben

La felicidad se basa en una ecuación defectuosa: aquí se explica cómo solucionarla

Le bonheur est basé sur une équation erronée : voici comment y remédier

खुशी एक दोषपूर्ण समीकरण पर आधारित है - इसे ठीक करने का तरीका यहां बताया गया है

幸福は間違った方程式に基づいている — これを修正する方法はこれだ

행복은 잘못된 방정식에 기초합니다. 문제를 해결하는 방법은 다음과 같습니다.

A felicidade é baseada em uma equação falha – veja como consertar

Счастье основано на ошибочном уравнении — вот как это исправить

Mutluluk Kusurlu Bir Denkleme Dayalı - İşte Nasıl Düzeltileceği

Hạnh phúc dựa trên một phương trình sai lầm - Đây là cách khắc phục

幸福建立在一个有缺陷的等式上——下面是如何修复它

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. Table of Links Abstract and Introduction
Grassroots Protocols
Grassroots Implementation of Grassroots Dissemination
Applications of Grassroots Dissemination Supporting Digital Sovereignty
Future Work & Discussion
References
A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems
B. All-to-All Dissemination is not Grassroots
C. Proofs 2 Grassroots Protocols Informally, when agents operate according to a grassroots protocol (i) the possible behaviors and interactions of an isolated community of agents are not constrained by placing the community within a larger community and (ii) agents in an isolated community, when placed within a larger community, may have behaviors and interactions not possible when in isolation. In particular, agents may interact with each other across community boundaries. This notion supports the grassroots deployment of a distributed system – autonomous independent instances deployed at different locations and over time that can possibly (but not necessarily) interoperate once interconnected. Asynchronous distributed multiagent transition systems. We formalize this intuitive notion of a grassroots protocol using asynchronous distributed multiagent transition system [24]. See Appendix A for a brief introduction; we repeat below essential definitions and results. Assume a set Π of agents, each equipped with a single and unique key-pair, and identify an agent p ∈ Π by its public key. While the set of all agents Π could in principle be infinite (think of all the agents that are yet to be born), when we refer to a particular set of agents P ⊆ Π we assume P to be finite. Note that the trivial liveness requirement entails the standard one: A run is live if for every agent p that is enabled infinitely often, the run has infinitely many p-transitions. A transition system is asynchronous if progress by other agents cannot prevent an agent from taking an already-enabled transition. Observation 9. An all-to-all dissemination protocol cannot be grassroots. Sufficient condition for a protocol to be grassroots. Here we define certain properties of a protocol and prove that satisfying them is sufficient for the protocol to be grassroots. Theorem 12 (Grassroots Protocol). An asynchronous, interactive, and non-interfering protocol is grassroots. We note that blockchain consensus protocols with hardcoded miners, e.g. the seed miners of Bitcoin [11] or the bootnodes of Ethereum [12], and permissioned consensus protocols with a predetermined set of participants such as Byzantine Atomic Broadcast [27, 16], are all interfering, as additional participants may be required and/or cannot be ignored. However, Theorem 12 and the examples above do not imply that ordering consensus protocols cannot be grassroots. The problem is not with the consensus protocol as such, but with the choice of its participants. If participants in an instance of an ordering consensus protocol are determined by the agents themselves via a grassroots protocol, rather than being provided externally and a priori as in standard permissioned consensus protocols, or are required to include a predetermined set of initial participants as in Bitcoin and Ethereum, then the participants can reach consensus without violating grassroots. Devising grassroots consensus protocols is a subject of future work. Next, we present the Grassroots Dissemination protocol and prove it to be grassroots. 2.2 The Grassroots Dissemination Protocol The All-to-All Dissemination protocol is asynchronous and interactive, and this is also true of Grassroots Dissemination. But, unlike All-to-All Dissemination, in the Grassroots Dissemination protocol dissemination occurs only among friends. Moreover, agents are free to choose their friends. Hence, additional agents may be ignored by a given group of agents without violating agent liveness, implying that Grassroots Dissemination is also non-interfering and thus grassroots, as proven below. We assume a given payloads function X that maps each set of agents P to a set of payloads X (P). For example, X could map P to all strings signed by members of P; or to all messages sent among members of P, signed by the sender and encrypted by the public key of the recipient; or to all financial transactions among members of P. Remember that here P are not ‘miners’ serving transactions by other agents, but are the full set of agents participating the in protocol. 2.3 Grassroots Implementation Here we define the notion of a grassroots implementation and prove that having a grassroots implementation is a sufficient condition for a protocol to be grassroots. First we recall the notion of implementation among multiagent transition systems [24] (See also Appendix A) The following theorem shatters the hope of a non-grassroots protocol to have a grassroots implementation, and can be used to prove that a protocol is grassroots by providing it with a grassroots implementation. Theorem 20 (Grassroots Implementation). A protocol that has a grassroots implementation is grassroots. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. Table of Links Abstract and Introduction Grassroots Protocols Grassroots Implementation of Grassroots Dissemination Applications of Grassroots Dissemination Supporting Digital Sovereignty Future Work & Discussion References A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems B. All-to-All Dissemination is not Grassroots C. Proofs Abstract and Introduction Abstract and Introduction Grassroots Protocols Grassroots Protocols Grassroots Implementation of Grassroots Dissemination Grassroots Implementation of Grassroots Dissemination Applications of Grassroots Dissemination Supporting Digital Sovereignty Applications of Grassroots Dissemination Supporting Digital Sovereignty Future Work & Discussion Future Work & Discussion References References A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems B. All-to-All Dissemination is not Grassroots B. All-to-All Dissemination is not Grassroots C. Proofs C. Proofs 2 Grassroots Protocols Informally, when agents operate according to a grassroots protocol (i) the possible behaviors and interactions of an isolated community of agents are not constrained by placing the community within a larger community and (ii) agents in an isolated community, when placed within a larger community, may have behaviors and interactions not possible when in isolation. In particular, agents may interact with each other across community boundaries. This notion supports the grassroots deployment of a distributed system – autonomous independent instances deployed at different locations and over time that can possibly (but not necessarily) interoperate once interconnected. Asynchronous distributed multiagent transition systems. We formalize this intuitive notion of a grassroots protocol using asynchronous distributed multiagent transition system [24]. See Appendix A for a brief introduction; we repeat below essential definitions and results. Assume a set Π of agents, each equipped with a single and unique key-pair, and identify an agent p ∈ Π by its public key. While the set of all agents Π could in principle be infinite (think of all the agents that are yet to be born), when we refer to a particular set of agents P ⊆ Π we assume P to be finite. Note that the trivial liveness requirement entails the standard one: A run is live if for every agent p that is enabled infinitely often, the run has infinitely many p-transitions. A transition system is asynchronous if progress by other agents cannot prevent an agent from taking an already-enabled transition. Observation 9. An all-to-all dissemination protocol cannot be grassroots. Observation 9. An all-to-all dissemination protocol cannot be grassroots. Sufficient condition for a protocol to be grassroots. Here we define certain properties of a protocol and prove that satisfying them is sufficient for the protocol to be grassroots. Sufficient condition for a protocol to be grassroots. Theorem 12 (Grassroots Protocol). An asynchronous, interactive, and non-interfering protocol is grassroots. Theorem 12 We note that blockchain consensus protocols with hardcoded miners, e.g. the seed miners of Bitcoin [11] or the bootnodes of Ethereum [12], and permissioned consensus protocols with a predetermined set of participants such as Byzantine Atomic Broadcast [27, 16], are all interfering, as additional participants may be required and/or cannot be ignored. However, Theorem 12 and the examples above do not imply that ordering consensus protocols cannot be grassroots. The problem is not with the consensus protocol as such, but with the choice of its participants. If participants in an instance of an ordering consensus protocol are determined by the agents themselves via a grassroots protocol, rather than being provided externally and a priori as in standard permissioned consensus protocols, or are required to include a predetermined set of initial participants as in Bitcoin and Ethereum, then the participants can reach consensus without violating grassroots. Devising grassroots consensus protocols is a subject of future work. Next, we present the Grassroots Dissemination protocol and prove it to be grassroots. 2.2 The Grassroots Dissemination Protocol 2.2 The Grassroots Dissemination Protocol The All-to-All Dissemination protocol is asynchronous and interactive, and this is also true of Grassroots Dissemination. But, unlike All-to-All Dissemination, in the Grassroots Dissemination protocol dissemination occurs only among friends. Moreover, agents are free to choose their friends. Hence, additional agents may be ignored by a given group of agents without violating agent liveness, implying that Grassroots Dissemination is also non-interfering and thus grassroots, as proven below. We assume a given payloads function X that maps each set of agents P to a set of payloads X (P). For example, X could map P to all strings signed by members of P; or to all messages sent among members of P, signed by the sender and encrypted by the public key of the recipient; or to all financial transactions among members of P. Remember that here P are not ‘miners’ serving transactions by other agents, but are the full set of agents participating the in protocol. 2.3 Grassroots Implementation 2.3 Grassroots Implementation Here we define the notion of a grassroots implementation and prove that having a grassroots implementation is a sufficient condition for a protocol to be grassroots. First we recall the notion of implementation among multiagent transition systems [24] (See also Appendix A) The following theorem shatters the hope of a non-grassroots protocol to have a grassroots implementation, and can be used to prove that a protocol is grassroots by providing it with a grassroots implementation. Theorem 20 (Grassroots Implementation). A protocol that has a grassroots implementation Theorem 20 is grassroots.

Grassroots Distributed Systems for Digital Sovereignty: Grassroots Protocols

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. Table of Links Abstract and Introduction
Grassroots Protocols
Grassroots Implementation of Grassroots Dissemination
Applications of Grassroots Dissemination Supporting Digital Sovereignty
Future Work & Discussion
References
A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems
B. All-to-All Dissemination is not Grassroots
C. Proofs A Preliminaries: Asynchronous Distributed Multiagent Transition Systems Here we introduce definitions and results regarding asynchronous distributed multiagent transition systems [24], needed for the definition and proofs of grassroots protocols. The original reference introduces them in four stages: transition systems; multiagent; distributed; asynchronous, and in addition to proofs it includes examples illustrating the various concepts. Here, we introduce distributed multiagent transition systems at once and simplify other notions by adhering to this special case. Assume a set Π of agents, each equipped with a single and unique key-pair, and identify an agent p ∈ Π by its public key. While the set of all agents Π could in principle be infinite (think of all the agents that are yet to be born), when we refer to a particular set of agents P ⊆ Π we assume P to be finite. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Ehud Shapiro,  Department of Computer Science and Applied Math, Weizmann Institute of Science, Israel and ehud.shapiro@weizmann.ac.il. Table of Links Abstract and Introduction Grassroots Protocols Grassroots Implementation of Grassroots Dissemination Applications of Grassroots Dissemination Supporting Digital Sovereignty Future Work & Discussion References A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems B. All-to-All Dissemination is not Grassroots C. Proofs Abstract and Introduction Abstract and Introduction Grassroots Protocols Grassroots Protocols Grassroots Implementation of Grassroots Dissemination Grassroots Implementation of Grassroots Dissemination Applications of Grassroots Dissemination Supporting Digital Sovereignty Applications of Grassroots Dissemination Supporting Digital Sovereignty Future Work & Discussion Future Work & Discussion References References A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems A. Preliminaries: Asynchronous Distributed Multiagent Transition Systems B. All-to-All Dissemination is not Grassroots B. All-to-All Dissemination is not Grassroots C. Proofs C. Proofs A Preliminaries: Asynchronous Distributed Multiagent Transition Systems Here we introduce definitions and results regarding asynchronous distributed multiagent transition systems [24], needed for the definition and proofs of grassroots protocols. The original reference introduces them in four stages: transition systems; multiagent; distributed; asynchronous, and in addition to proofs it includes examples illustrating the various concepts. Here, we introduce distributed multiagent transition systems at once and simplify other notions by adhering to this special case. Assume a set Π of agents, each equipped with a single and unique key-pair, and identify an agent p ∈ Π by its public key. While the set of all agents Π could in principle be infinite (think of all the agents that are yet to be born), when we refer to a particular set of agents P ⊆ Π we assume P to be finite.

Grassroots Distributed Systems: Preliminaries: Asynchronous Distributed Multiagent Transition System

Grassroots Distributed Systems for Digital Sovereignty: Abstract and Introduction

Of late, there has been quite a lot of interest in increasing/improving the context of LLMs. One good example is the infini-attention paper from Google in early April. While it was more of tweaking the model architecture to improve the context, Microsoft came back with an answer in late April but with a data-driven solution. The paper is titled, “Make your LLM Fully Utilize the Context”. There are mainly two contributions from the paper: A data-driven approach that introduces a novel pipeline to synthesize a novel dataset to train LLMs to cleverly use long contexts. It’s dubbed “Information-Intensive Training” (IN2 in short).


A novel evaluation approach named Various Long-Context (VAL Probing) uses 3 context styles — document, code, and structured data and 3 retrieval patterns — forward, backward, and bi-directional. In this article, let's deep-dive into both the simple ideas followed by some evaluations. Visual Explanation If you are someone who likes audio-visual explanations, here is a video version of this article that explains the paper: https://youtu.be/SA21d_4QQSU?feature=shared&embedable=true IN2 Training The first main idea of the paper is a novel data-driven training approach named IN2 training. There are two types of IN2 training proposed in the paper. Fine-grained Information Awareness. In this case, we first take a raw text dataset and split it into 128 tokens and we call each of this set of 128 tokens a “segment.” We then prompt a powerful LLM model like GPT-4 with an instruction I_f in such a way that the answer a_i to this question q_i requires information in the segment s_i to provide the answer. We then combine the segment s_i with several other segments by shuffling it along with the other segments to create the long context L_i. Figure taken from the paper (figure 2). Integration and Reasoning of Information. There can be situations where the context is located in more than one segment (more than 128 tokens). So we consider a set [s_i] instead of a single s_i. This creates a multi-hop Q&A scenario where the LLM has to do at least 2 passes of QAs. We modify the instruction I_f in the prompt to the LLM accordingly. Figure taken from the paper (figure 2) In both scenarios, the authors vary the long context length L_i from 4K to 32K to prevent length bias in training. During IN2 instruction fine-tuning, the long contexts and questions become input to the LLM, and the answer is shown as the output for supervised training. They fine-tune the Mistral-7B-Instruct-v2 model and call the new model FILl-in-the-Middle(FILM-7B). VAL Probing VArious Long-context Probing or VAL Probing was introduced to overcome some of the problems with the current LLMs with retrieving from long contexts. So how do we even test the long context retrieval capability of any given model? The answer lies with “Needle in a haystack” (NIHS) test. It can be accessed here. Needle in a Haystack NIHS was proposed to pressure test LLMs. It’s a simple analysis to test the in-context retrieval ability of long-context LLMs. The test can be run by following the below 3 steps: Place a random fact or statement (the ‘needle’) in the middle of a long context window (the ‘haystack’).


Ask the model to retrieve this statement.


Iterate over various document depths (where the needle is placed) and context lengths to measure performance. The biggest problem with NIHS these days is that the recent models perform too well making the test less appealing. For example, see below the test result of the proposed FILM-7b model NIHS test on the proposed FILM-7b model The results are near perfect indicating that we really need a new testing approach and so, giving birth to VAL Probing. VAL Probing VAL probing considers 3 data types such as documents, structured data, and code. It also considers three retrieval types — forward, backward, and bi-directional. The below figure from the paper gives examples of all three data types and retrieval directions. Results For evaluation, they used the LongBench evaluation scripts and reported ROGUE-L for summarization and F1-scores for all other tasks. The first problem addressed by the paper is the “Lost-in-the-middle” problem. As shown in the below figure, we can see that there is no “U” shaped dip with the FILM-7b model (in orange) compared to Mistral-7B (green), and even other long-context sota models like LongAlign indicating the strength of FILM-7b. On top of addressing the Lost-in-the-middle problem, FILM-7B seems to outperform other recent models quantitatively too. They show the results of averaging the evaluation metric(Avg) and min-max gap of the evaluation metric (Gap) which is the difference between the minimum and maximum values obtained. In short, the model is comparable to GPT-4’s performance! Summary & Conclusion This paper from Microsoft proposes a simple data-driven solution to addressing the long-context problem in LLMs. On top of proposing a data generation pipeline, it also proposes and tests the usage of the novel VAL Probing that encompasses 3 data types and 3 retrieval methods. Let’s hope this is just the dawn of a new generation of LLMs that are no longer limited by the context length. See you in my next… Of late, there has been quite a lot of interest in increasing/improving the context of LLMs. One good example is the infini-attention paper from Google in early April. While it was more of tweaking the model architecture to improve the context, Microsoft came back with an answer in late April but with a data-driven solution. The paper is titled, “ Make your LLM Fully Utilize the Context ”. infini-attention paper infini-attention paper Make your LLM Fully Utilize the Context Make your LLM Fully Utilize the Context Make your LLM Fully Utilize the Context There are mainly two contributions from the paper: A data-driven approach that introduces a novel pipeline to synthesize a novel dataset to train LLMs to cleverly use long contexts. It’s dubbed “Information-Intensive Training” (IN2 in short). A novel evaluation approach named Various Long-Context (VAL Probing) uses 3 context styles — document, code, and structured data and 3 retrieval patterns — forward, backward, and bi-directional. A data-driven approach that introduces a novel pipeline to synthesize a novel dataset to train LLMs to cleverly use long contexts. It’s dubbed “Information-Intensive Training” (IN2 in short). A data-driven approach that introduces a novel pipeline to synthesize a novel dataset to train LLMs to cleverly use long contexts. It’s dubbed “ Information-Intensive Training ” (IN2 in short). Information-Intensive Training A novel evaluation approach named Various Long-Context (VAL Probing) uses 3 context styles — document, code, and structured data and 3 retrieval patterns — forward, backward, and bi-directional. A novel evaluation approach named Various Long-Context ( VAL Probing ) uses 3 context styles — document, code, and structured data and 3 retrieval patterns — forward, backward, and bi-directional. VAL Probing In this article, let's deep-dive into both the simple ideas followed by some evaluations. Visual Explanation If you are someone who likes audio-visual explanations, here is a video version of this article that explains the paper: https://youtu.be/SA21d_4QQSU?feature=shared&embedable=true https://youtu.be/SA21d_4QQSU?feature=shared&embedable=true IN2 Training The first main idea of the paper is a novel data-driven training approach named IN2 training . There are two types of IN2 training proposed in the paper. data-driven training IN2 training Fine-grained Information Awareness. In this case, we first take a raw text dataset and split it into 128 tokens and we call each of this set of 128 tokens a “segment.” We then prompt a powerful LLM model like GPT-4 with an instruction I_f in such a way that the answer a_i to this question q_i requires information in the segment s_i to provide the answer. We then combine the segment s_i with several other segments by shuffling it along with the other segments to create the long context L_i. Fine-grained Information Awareness. In this case, we first take a raw text dataset and split it into 128 tokens and we call each of this set of 128 tokens a “segment.” We then prompt a powerful LLM model like GPT-4 with an instruction I_f in such a way that the answer a_i to this question q_i requires information in the segment s_i to provide the answer. We then combine the segment s_i with several other segments by shuffling it along with the other segments to create the long context L_i . Fine-grained Information Awareness. I_f a_i q_i s_i s_i L_i Figure taken from the paper (figure 2). Integration and Reasoning of Information. There can be situations where the context is located in more than one segment (more than 128 tokens). So we consider a set [s_i] instead of a single s_i. This creates a multi-hop Q&A scenario where the LLM has to do at least 2 passes of QAs. We modify the instruction I_f in the prompt to the LLM accordingly. Integration and Reasoning of Information. There can be situations where the context is located in more than one segment (more than 128 tokens). So we consider a set [ s_i ] instead of a single s_i. This creates a multi-hop Q&A scenario where the LLM has to do at least 2 passes of QAs. We modify the instruction I_f in the prompt to the LLM accordingly. Integration and Reasoning of Information. s_i s_i. I_f Figure taken from the paper (figure 2) In both scenarios, the authors vary the long context length L_i from 4K to 32K to prevent length bias in training. During IN2 instruction fine-tuning, the long contexts and questions become input to the LLM, and the answer is shown as the output for supervised training. They fine-tune the Mistral-7B-Instruct-v2 model and call the new model FILl-in-the-Middle( FILM-7B ). L_i FILM-7B VAL Probing VArious Long-context Probing or VAL Probing was introduced to overcome some of the problems with the current LLMs with retrieving from long contexts. So how do we even test the long context retrieval capability of any given model? The answer lies with “Needle in a haystack” (NIHS) test. It can be accessed here . here here Needle in a Haystack NIHS was proposed to pressure test LLMs. It’s a simple analysis to test the in-context retrieval ability of long-context LLMs. The test can be run by following the below 3 steps: Place a random fact or statement (the ‘needle’) in the middle of a long context window (the ‘haystack’). Ask the model to retrieve this statement. Iterate over various document depths (where the needle is placed) and context lengths to measure performance. Place a random fact or statement (the ‘needle’) in the middle of a long context window (the ‘haystack’). Place a random fact or statement (the ‘needle’) in the middle of a long context window (the ‘haystack’). Ask the model to retrieve this statement. Ask the model to retrieve this statement. Iterate over various document depths (where the needle is placed) and context lengths to measure performance. Iterate over various document depths (where the needle is placed) and context lengths to measure performance. The biggest problem with NIHS these days is that the recent models perform too well making the test less appealing. For example, see below the test result of the proposed FILM-7b model NIHS test on the proposed FILM-7b model The results are near perfect indicating that we really need a new testing approach and so, giving birth to VAL Probing. VAL Probing VAL probing considers 3 data types such as documents, structured data, and code. It also considers three retrieval types — forward, backward, and bi-directional. The below figure from the paper gives examples of all three data types and retrieval directions. Results For evaluation, they used the LongBench evaluation scripts and reported ROGUE-L for summarization and F1-scores for all other tasks. The first problem addressed by the paper is the “Lost-in-the-middle” problem. As shown in the below figure, we can see that there is no “U” shaped dip with the FILM-7b model (in orange) compared to Mistral-7B (green), and even other long-context sota models like LongAlign indicating the strength of FILM-7b. On top of addressing the Lost-in-the-middle problem, FILM-7B seems to outperform other recent models quantitatively too. They show the results of averaging the evaluation metric(Avg) and min-max gap of the evaluation metric (Gap) which is the difference between the minimum and maximum values obtained. In short, the model is comparable to GPT-4’s performance! Summary & Conclusion This paper from Microsoft proposes a simple data-driven solution to addressing the long-context problem in LLMs. On top of proposing a data generation pipeline, it also proposes and tests the usage of the novel VAL Probing that encompasses 3 data types and 3 retrieval methods. Let’s hope this is just the dawn of a new generation of LLMs that are no longer limited by the context length. See you in my next…

How to Make Your LLM Fully Utilize the Context

As I sit here, enveloped in the hum of technology and surrounded by the glow of screens, I can't help but feel a sense of excitement and wonder. The concept of a digital and virtual world has captivated imaginations for years, and now, the idea of a metaverse is finally materializing. The metaverse, a virtual realm designed to be immersive, interactive, and shared by countless individuals, was once a staple of science fiction but is now emerging as a tangible reality, thanks to advancements in virtual and augmented reality technologies. My first experience with a VR headset was unforgettable. It felt like stepping into an entirely new dimension, with a profound sense of immersion and presence. Today, with companies like Meta Quest3, Apple Vision Pro, Lenovo ThinkReality VRX, and HTC Vive introducing high-quality VR headsets, VR has transcended its gaming origins and found utility across diverse industries such as healthcare, education, and architecture. The metaverse represents a natural evolution from VR, aspiring to create a fully realized digital domain that transcends the constraints of the physical world. It offers a canvas where social norms and value systems can be reimagined and liberated from the shackles of cultural and economic rigidity. The possibilities are endless - envision a world where one can traverse any landscape and undertake any endeavor. Art has always held a special allure for me, and the advent of NFTs has opened up new possibilities. NFTs, digital assets representing unique items like art, music, or collectibles, are stored on a blockchain, ensuring their scarcity and authenticity. Remarkably, artists can now sell their creations at prices that reflect their value. Gamification is a fundamental aspect of the metaverse, fostering the development of captivating and interactive experiences that drive user engagement and loyalty. Within the metaverse, gamification takes myriad forms, including virtual rewards, challenges, social interactions, and role-playing. It enhances the allure of the metaverse, making it a more enjoyable and immersive environment that beckons people to spend more time within its digital confines. Numerous companies are actively developing products and services for the metaverse, spanning virtual reality headsets, augmented reality devices, smart glasses, game development software, and blockchain-based games. Apple introduced its first spatial computer device, Apple Vision Pro, and is making headway with AR technology. Apple is looking towards a vision of mixed reality. Meta Platforms Inc.: Facebook is known for its social media platforms. Meta rebranded to focus on the metaverse, with plans to create virtual office spaces, shopping, events, travel, and socializing. Microsoft: Microsoft Mesh provides a business function that builds on Microsoft Teams. It allows people to meet virtually through avatars or holograms. Nvidia is developing the NVIDIA Omniverse Cloud, a suite of cloud services for development teams and artists to create and operate metaverse apps. Epic Games: Famous for Fortnite, Epic Games invests in the metaverse, with plans to create virtual social experiences and infrastructure for metaverse avatars. Roblox: A platform where people can play games made by Roblox users, with plans to build a metaverse where people can create avatars in games, purchase items, socialize, and use real voice chat. Unity Software Inc. is a game development software company used by those in the metaverse. It provides software to build and run 2D and 3D games and software to monetize creations. Decentraland is a decentralized virtual reality platform that allows users to create, experience, and monetize content and applications. The Sandbox is a metaverse platform owned by Animoca Brands on which users can create, sell, and own digital assets and experiences. Axie Infinity is a blockchain-based game in which players collect, breed, and battle creatures known as Axes. Battle Infinity: A blockchain-based game that allows players to collect, breed, and battle creatures known as Battle Infinity. Bloktopia: A blockchain-based game that allows players to collect, breed, and battle creatures known as Bloktopia. NFT Worlds allows users to create, buy, and sell NFTs (non-fungible tokens) representing digital assets. Lenovo: It aims to make the ThinkReality VRX headset, focusing on virtual conferencing. The journey toward a digital and virtual world is exhilarating, and I am privileged to be a part of it. The metaverse, NFTs, and gamification represent just the beginning of a transformative era. I eagerly anticipate the unfolding of this journey, whether as an artist, a gamer, or simply an enthusiast of cutting-edge technology. Join me on this journey towards a digital and virtual world. Let's explore the metaverse, NFTs, and gamification together. Who knows what the future holds, but I'm excited to find out. As I sit here, enveloped in the hum of technology and surrounded by the glow of screens, I can't help but feel a sense of excitement and wonder. The concept of a digital and virtual world has captivated imaginations for years, and now, the idea of a metaverse is finally materializing. The metaverse, a virtual realm designed to be immersive, interactive, and shared by countless individuals, was once a staple of science fiction but is now emerging as a tangible reality, thanks to advancements in virtual and augmented reality technologies. As I sit here, enveloped in the hum of technology and surrounded by the glow of screens, I can't help but feel a sense of excitement and wonder. The concept of a digital and virtual world has captivated imaginations for years, and now, the idea of a metaverse is finally materializing. The metaverse, a virtual realm designed to be immersive, interactive, and shared by countless individuals, was once a staple of science fiction but is now emerging as a tangible reality, thanks to advancements in virtual and augmented reality technologies. My first experience with a VR headset was unforgettable. It felt like stepping into an entirely new dimension, with a profound sense of immersion and presence. Today, with companies like Meta Quest3, Apple Vision Pro, Lenovo ThinkReality VRX, and HTC Vive introducing high-quality VR headsets, VR has transcended its gaming origins and found utility across diverse industries such as healthcare, education, and architecture. My first experience with a VR headset was unforgettable. The metaverse represents a natural evolution from VR , aspiring to create a fully realized digital domain that transcends the constraints of the physical world. It offers a canvas where social norms and value systems can be reimagined and liberated from the shackles of cultural and economic rigidity. The possibilities are endless - envision a world where one can traverse any landscape and undertake any endeavor. The metaverse represents a natural evolution from VR Art has always held a special allure for me , and the advent of NFTs has opened up new possibilities. NFTs, digital assets representing unique items like art, music, or collectibles, are stored on a blockchain, ensuring their scarcity and authenticity. Remarkably, artists can now sell their creations at prices that reflect their value. Art has always held a special allure for me Gamification is a fundamental aspect of the metaverse, fostering the development of captivating and interactive experiences that drive user engagement and loyalty. Within the metaverse, gamification takes myriad forms, including virtual rewards, challenges, social interactions, and role-playing. It enhances the allure of the metaverse, making it a more enjoyable and immersive environment that beckons people to spend more time within its digital confines. Gamification is a fundamental aspect of the metaverse, Numerous companies are actively developing products and services for the metaverse, spanning virtual reality headsets, augmented reality devices, smart glasses, game development software, and blockchain-based games. Numerous companies Apple introduced its first spatial computer device, Apple Vision Pro, and is making headway with AR technology. Apple is looking towards a vision of mixed reality. Apple introduced its first spatial computer device, Apple Vision Pro , and is making headway with AR technology. Apple is looking towards a vision of mixed reality. Apple Apple Vision Pro Meta Platforms Inc.: Facebook is known for its social media platforms. Meta rebranded to focus on the metaverse, with plans to create virtual office spaces, shopping, events, travel, and socializing. Meta Platforms Inc. : Facebook is known for its social media platforms. Meta rebranded to focus on the metaverse, with plans to create virtual office spaces, shopping, events, travel, and socializing. Meta Platforms Inc. Meta Platforms Inc. Microsoft: Microsoft Mesh provides a business function that builds on Microsoft Teams. It allows people to meet virtually through avatars or holograms. Microsoft : Microsoft Mesh provides a business function that builds on Microsoft Teams. It allows people to meet virtually through avatars or holograms. Microsoft Microsoft Mesh Nvidia is developing the NVIDIA Omniverse Cloud, a suite of cloud services for development teams and artists to create and operate metaverse apps. Nvidia is developing the NVIDIA Omniverse Cloud , a suite of cloud services for development teams and artists to create and operate metaverse apps. Nvidia NVIDIA Omniverse Cloud Epic Games: Famous for Fortnite, Epic Games invests in the metaverse, with plans to create virtual social experiences and infrastructure for metaverse avatars. Epic Games : Famous for Fortnite, Epic Games invests in the metaverse , with plans to create virtual social experiences and infrastructure for metaverse avatars. Epic Games Epic Games invests in the metaverse Roblox: A platform where people can play games made by Roblox users, with plans to build a metaverse where people can create avatars in games, purchase items, socialize, and use real voice chat. Roblox : A platform where people can play games made by Roblox users, with plans to build a metaverse where people can create avatars in games, purchase items, socialize, and use real voice chat. Roblox build a metaverse Unity Software Inc. is a game development software company used by those in the metaverse. It provides software to build and run 2D and 3D games and software to monetize creations. Unity Software Inc . is a game development software company used by those in the metaverse. It provides software to build and run 2D and 3D games and software to monetize creations. Unity Software Inc Unity Software Inc . Decentraland is a decentralized virtual reality platform that allows users to create, experience, and monetize content and applications. Decentraland is a decentralized virtual reality platform that allows users to create, experience, and monetize content and applications. Decentraland Decentraland The Sandbox is a metaverse platform owned by Animoca Brands on which users can create, sell, and own digital assets and experiences. The Sandbox is a metaverse platform owned by Animoca Brands on which users can create, sell, and own digital assets and experiences. The Sandbox The Sandbox Axie Infinity is a blockchain-based game in which players collect, breed, and battle creatures known as Axes. Axie Infinity is a blockchain-based game in which players collect, breed, and battle creatures known as Axes. Axie Infinity Axie Infinity Battle Infinity: A blockchain-based game that allows players to collect, breed, and battle creatures known as Battle Infinity. Battle Infinity : A blockchain-based game that allows players to collect, breed, and battle creatures known as Battle Infinity. Battle Infinity Battle Infinity Bloktopia: A blockchain-based game that allows players to collect, breed, and battle creatures known as Bloktopia. Bloktopia : A blockchain-based game that allows players to collect, breed, and battle creatures known as Bloktopia. Bloktopia Bloktopia NFT Worlds allows users to create, buy, and sell NFTs (non-fungible tokens) representing digital assets. NFT Worlds allows users to create, buy, and sell NFTs (non-fungible tokens) representing digital assets. NFT Worlds NFT Worlds Lenovo: It aims to make the ThinkReality VRX headset, focusing on virtual conferencing. Lenovo : It aims to make the ThinkReality VRX headset, focusing on virtual conferencing. Lenovo Lenovo The journey toward a digital and virtual world is exhilarating, and I am privileged to be a part of it. The metaverse, NFTs, and gamification represent just the beginning of a transformative era. I eagerly anticipate the unfolding of this journey, whether as an artist, a gamer, or simply an enthusiast of cutting-edge technology. Join me on this journey towards a digital and virtual world. Let's explore the metaverse, NFTs, and gamification together. Who knows what the future holds, but I'm excited to find out. Join me on this journey towards a digital and virtual world. Let's explore the metaverse, NFTs, and gamification together. Who knows what the future holds, but I'm excited to find out.

A Journey Towards Digital and Virtual Worlds

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