Motmo

motmo

SECONDARY SEXUAL CHARACTERS OF MAN—continued.

Is Object Detection a Done Deal?

Lumoz: A New Paradigm for Rollup Platformization

লুমোজ: রোলআপ প্ল্যাটফর্মাইজেশনের জন্য একটি নতুন দৃষ্টান্ত

Lumoz: Ein neues Paradigma für die Rollup-Plattformisierung

Lumoz: un nuevo paradigma para la plataforma acumulativa

Lumoz : un nouveau paradigme pour la plateforme de rollup

लुमोज़: रोलअप प्लेटफ़ॉर्माइज़ेशन के लिए एक नया प्रतिमान

Lumoz: ロールアップ プラットフォーム化の新しいパラダイム

Lumoz: 롤업 플랫폼화를 위한 새로운 패러다임

Lumoz: um novo paradigma para plataforma rollup

Lumoz: новая парадигма накопительной платформы

Lumoz: Toplama Platformizasyonu için Yeni Bir Paradigma

Lumoz: Một mô hình mới cho nền tảng cuộn lên

Lumoz：Rollup 平台化的新范式

After completing a total of tens of millions of dollars in financing, the modular computing layer Lumoz continues to make strides in the Layer 2 (L2) space. This week, Lumoz announced that its Modular Compute Layer will support the Op Stack + ZK Fraud Proof Layer 2 architecture, initiating a new paradigm in L2 architecture. The Modular Compute Layer will Support Op Stack + ZK Fraud Proof The Op Stack + ZK, Fraud Proof architecture, is a new design that integrates zero-knowledge proof-based validity proofs into the Optimistic Rollup technology. When challengers point out that the sequencer has submitted incorrect data, they submit a challenge to L1. The Sequencer must generate the corresponding ZK Proof within a limited challenge period and submit it to the Layer 1 contract for verification. If the verification results show that the data is valid, the challenge is invalid; otherwise, the challenge is successful. This illustration demonstrates the specific process of handling fraud proofs within Layer 2 technology under the Op Stack and ZK Fraud Proof architecture. In this architecture, the Op Stack module undertakes the core functions of Layer 2, including handling the basic functions of the Layer 2 blockchain and the responsibility of submitting Batches to Layer 1. Meanwhile, the ZK Fraud Proof module focuses on handling the challenges of fraud-proof. The process details are as follows: Challenge Initiation: Validators initiate the challenge for proof of fraud.
Information Synchronization: Lumoz Verify Nodes synchronize challenge information to prepare for the next step of verification.
Obtaining Zero-Knowledge Proof Inputs: Verification nodes obtain the required zero-knowledge proof input data from the Op Stack module, which includes block trace, batch info, etc.
Generating Zero-Knowledge Proof: Lumoz will then request the modular compute layer to generate the required zero-knowledge proof.
Proof Submission: Once the zero-knowledge proof is generated, Lumoz Verify Nodes submit the proof to Layer 1 for verification. Through the aforementioned process, the challenge and verification process of ZK Fraud Proof is completed. It is well known that Optimistic Rollup has lower costs but longer withdrawal waiting times, whereas ZK Rollup allows for almost immediate withdrawals but at a higher cost. This solution combines the advantages and disadvantages of both Optimistic Rollup and ZK Rollup, not only maintaining the characteristics of low cost but also effectively reducing the waiting time. Modular Compute Layer Lumoz provides a modular computation layer that offers stable and reliable computing power support for blockchains utilizing the Op Stack + ZK Fraud Proof architecture. When Layer 2 submits Batch data, if there are challengers initiating challenges, then the modular computation layer will rely on its computing services to generate the corresponding proof data (Proof). This process requires the computing services of the modular computation layer to submit the correct Proof within a specified time frame. If this is achieved, it is considered that the challenger has failed the challenge; otherwise, the challenger is deemed to have succeeded in the challenge. The role of Lumoz's modular computation layer in the Op Stack + ZK Fraud Proof architecture: In conclusion, the Lumoz ZK Prover Network, through decentralized miner participation in ZKP computations, extensive circuit support, and a comprehensive proof verification process, provides a high-performance modular computation layer for the Optimistic+ZK Fraud Proof architecture. Combined with Lumoz's trustless native cross-rollup communication technology, it not only securely shares liquidity among all Rollups but also offers robust multi-rollup interoperability, opening new possibilities for decentralized applications and DeFi protocols. Conclusion Lumoz's Modular Compute Layer, supported by Optimistic Rollup and ZK Fraud Proof, has pioneered a new Layer 2 architecture model. This solution fills the gap in the current market for a mixed solution of ZK and OP, achieving effective technical integration. By combining the advantages of Optimistic Rollup and ZK Rollup, we can significantly reduce costs and shorten verification wait times. As pioneers in this field, Lumoz will continue to leverage its strengths and constantly optimize its technology solutions. At the same time, we also hope more people will pay attention to and contribute to the development of this hybrid architecture of Optimistic Rollup and ZK Fraud Proof, contributing to the industry's development! This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here. After completing a total of tens of millions of dollars in financing, the modular computing layer Lumoz continues to make strides in the Layer 2 (L2) space. This week, Lumoz announced that its Modular Compute Layer will support the Op Stack + ZK Fraud Proof Layer 2 architecture, initiating a new paradigm in L2 architecture. The Modular Compute Layer will Support Op Stack + ZK Fraud Proof The Modular Compute Layer will Support Op Stack + ZK Fraud Proof The Op Stack + ZK, Fraud Proof architecture, is a new design that integrates zero-knowledge proof-based validity proofs into the Optimistic Rollup technology. When challengers point out that the sequencer has submitted incorrect data, they submit a challenge to L1. The Sequencer must generate the corresponding ZK Proof within a limited challenge period and submit it to the Layer 1 contract for verification. If the verification results show that the data is valid, the challenge is invalid; otherwise, the challenge is successful. This illustration demonstrates the specific process of handling fraud proofs within Layer 2 technology under the Op Stack and ZK Fraud Proof architecture. In this architecture, the Op Stack module undertakes the core functions of Layer 2, including handling the basic functions of the Layer 2 blockchain and the responsibility of submitting Batches to Layer 1. Meanwhile, the ZK Fraud Proof module focuses on handling the challenges of fraud-proof. The process details are as follows: The process details are as follows: Challenge Initiation: Validators initiate the challenge for proof of fraud. Information Synchronization: Lumoz Verify Nodes synchronize challenge information to prepare for the next step of verification. Obtaining Zero-Knowledge Proof Inputs: Verification nodes obtain the required zero-knowledge proof input data from the Op Stack module, which includes block trace, batch info, etc. Generating Zero-Knowledge Proof: Lumoz will then request the modular compute layer to generate the required zero-knowledge proof. Proof Submission: Once the zero-knowledge proof is generated, Lumoz Verify Nodes submit the proof to Layer 1 for verification. Challenge Initiation: Validators initiate the challenge for proof of fraud. Challenge Initiation: Information Synchronization: Lumoz Verify Nodes synchronize challenge information to prepare for the next step of verification. Information Synchronization: Obtaining Zero-Knowledge Proof Inputs: Verification nodes obtain the required zero-knowledge proof input data from the Op Stack module, which includes block trace, batch info, etc. Obtaining Zero-Knowledge Proof Inputs: Generating Zero-Knowledge Proof: Lumoz will then request the modular compute layer to generate the required zero-knowledge proof. Generating Zero-Knowledge Proof: Proof Submission: Once the zero-knowledge proof is generated, Lumoz Verify Nodes submit the proof to Layer 1 for verification. Proof Submission: Through the aforementioned process, the challenge and verification process of ZK Fraud Proof is completed. It is well known that Optimistic Rollup has lower costs but longer withdrawal waiting times, whereas ZK Rollup allows for almost immediate withdrawals but at a higher cost. This solution combines the advantages and disadvantages of both Optimistic Rollup and ZK Rollup, not only maintaining the characteristics of low cost but also effectively reducing the waiting time. Modular Compute Layer Modular Compute Layer Lumoz provides a modular computation layer that offers stable and reliable computing power support for blockchains utilizing the Op Stack + ZK Fraud Proof architecture. When Layer 2 submits Batch data, if there are challengers initiating challenges, then the modular computation layer will rely on its computing services to generate the corresponding proof data (Proof). This process requires the computing services of the modular computation layer to submit the correct Proof within a specified time frame. If this is achieved, it is considered that the challenger has failed the challenge; otherwise, the challenger is deemed to have succeeded in the challenge. The role of Lumoz's modular computation layer in the Op Stack + ZK Fraud Proof architecture: In conclusion, the Lumoz ZK Prover Network, through decentralized miner participation in ZKP computations, extensive circuit support, and a comprehensive proof verification process, provides a high-performance modular computation layer for the Optimistic+ZK Fraud Proof architecture. Combined with Lumoz's trustless native cross-rollup communication technology, it not only securely shares liquidity among all Rollups but also offers robust multi-rollup interoperability, opening new possibilities for decentralized applications and DeFi protocols. Conclusion Conclusion Lumoz's Modular Compute Layer, supported by Optimistic Rollup and ZK Fraud Proof, has pioneered a new Layer 2 architecture model. This solution fills the gap in the current market for a mixed solution of ZK and OP, achieving effective technical integration. By combining the advantages of Optimistic Rollup and ZK Rollup, we can significantly reduce costs and shorten verification wait times. As pioneers in this field, Lumoz will continue to leverage its strengths and constantly optimize its technology solutions. At the same time, we also hope more people will pay attention to and contribute to the development of this hybrid architecture of Optimistic Rollup and ZK Fraud Proof, contributing to the industry's development! This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here. here. here.

Lumoz Supports Op Stack + ZK Fraud Proof, Initiating a New Paradigm in L2 Architecture

Lumoz Op Stack + ZK জালিয়াতির প্রমাণ সমর্থন করে, L2 আর্কিটেকচারে একটি নতুন দৃষ্টান্ত শুরু করে

Lumoz unterstützt Op Stack + ZK Fraud Proof und initiiert ein neues Paradigma in der L2-Architektur

Lumoz admite Op Stack + ZK Fraud Proof, iniciando un nuevo paradigma en la arquitectura L2

Lumoz prend en charge Op Stack + ZK Fraud Proof, initiant un nouveau paradigme dans l'architecture L2

लुमोज़ ऑप स्टैक + ZK धोखाधड़ी प्रूफ का समर्थन करता है, L2 आर्किटेक्चर में एक नया प्रतिमान शुरू करता है

Lumoz は Op Stack + ZK 不正防止をサポートし、L2 アーキテクチャの新しいパラダイムを開始します。

Lumoz는 Op Stack + ZK Fraud Proof를 지원하여 L2 아키텍처의 새로운 패러다임을 시작합니다.

Lumoz oferece suporte à prova de fraude Op Stack + ZK, iniciando um novo paradigma na arquitetura L2

Lumoz поддерживает Op Stack + ZK Fraud Proof, запуская новую парадигму в архитектуре L2

Lumoz, Op Stack + ZK Fraud Proof'u Destekleyerek L2 Mimarisinde Yeni Bir Paradigma Başlatıyor

Lumoz hỗ trợ Op Stack + Bằng chứng gian lận ZK, khởi xướng một mô hình mới trong kiến trúc L2

Lumoz 支持 Op Stack + ZK 欺诈证明，开创 L2 架构新范式

Chain Theory: A Proposed User-Friendly and Customizable Cryptographic Model

চেইন তত্ত্ব: একটি প্রস্তাবিত ব্যবহারকারী-বান্ধব এবং কাস্টমাইজযোগ্য ক্রিপ্টোগ্রাফিক মডেল

Kettentheorie: Ein Vorschlag für ein benutzerfreundliches und anpassbares kryptografisches Modell

Teoría de la cadena: una propuesta de modelo criptográfico personalizable y fácil de usar

Théorie des chaînes : une proposition de modèle cryptographique convivial et personnalisable

चेन थ्योरी: एक प्रस्तावित उपयोगकर्ता-अनुकूल और अनुकूलन योग्य क्रिप्टोग्राफ़िक मॉडल

チェーン理論: ユーザーフレンドリーでカスタマイズ可能な暗号モデルの提案

체인 이론: 제안된 사용자 친화적이고 사용자 정의 가능한 암호화 모델

Teoria da Cadeia: Uma Proposta de Modelo Criptográfico Fácil de Usar e Personalizável

Теория цепей: предлагаемая удобная и настраиваемая криптографическая модель

Zincir Teorisi: Önerilen Kullanıcı Dostu ve Özelleştirilebilir Bir Şifreleme Modeli

Lý thuyết chuỗi: Một mô hình mật mã thân thiện với người dùng và có thể tùy chỉnh được đề xuất

链式理论：一种用户友好且可定制的加密模型

Radix Engine: A Better Model for “Enshrinement"

রেডিক্স ইঞ্জিন: "এনশ্রাইনমেন্ট" এর জন্য একটি ভাল মডেল

Radix Engine: Ein besseres Modell für „Enshrinement

Radix Engine: un mejor modelo para la “consagración”

Moteur Radix : un meilleur modèle pour « l’enchâssement »

रेडिक्स इंजन: “प्रतिष्ठा के लिए एक बेहतर मॉडल”

Radix Engine: 「殿堂入り」のより良いモデル

Radix 엔진: "Enshrinement"를 위한 더 나은 모델

Radix Engine: um modelo melhor para “consagração

Radix Engine: лучшая модель для «закрепления»

Radix Engine: “Kutsallaştırma” için Daha İyi Bir Model

Radix Engine: Một mô hình tốt hơn cho “Bảo quản

Radix Engine：一个更好的“供奉”模型

In an era where blockchain technology is reshaping industries, using smart contracts stands at the forefront of innovation, capable of transforming the film and entertainment landscape—a sector ripe for disruption. It is a widely recognized issue that the global entertainment industry is at a tipping point. New movies no longer captivate audiences as they once did, people are overpaying for streaming subscriptions, and Generation Z and younger spend more time watching movies on TikTok than on Netflix in an average day. The question arises: How can smart contracts contribute to creating a better entertainment industry for both filmmakers and youthful consumers? Introducing Vabble Taking a deep dive into Vabble (whitepaper link), the application of smart contracts in addressing these challenges becomes evident. However, Vabble transcends being merely a streaming service; it represents a shift towards a more inclusive and equitable entertainment industry. Vabble offers unparalleled transparency and control over the distribution and monetization of content—including movies, documentaries, and series—through smart contracts and community governance. This approach resonates with the community-driven youth and enables filmmakers and studios to directly connect with their audience, bypassing traditional industry gatekeepers. This direct connection between content creators and youthful consumers marks a significant evolution from the current entertainment industry model, resulting in a fairer and more engaging experience. Vabble Staking: A New Paradigm in DAO Governance At the heart of Vabble's innovation lies its staking and governance platform, which launched on Monday the 12th. Less than 12 hours later, it already had over 120M VAB tokens staked, representing 13% of its circulating supply. The DAO function of Vabble is key to its future success, as it allows people worldwide to decide if a film, series, or documentary should be distributed on Vabble’s streaming service, essentially determining if the content is of high enough quality to be streamed. Where Vabble distinguishes itself in the world of staking schemes is through its unique approach to what is otherwise a controversial function. Vabble has innovated a model of participatory staking that transcends traditional passive income mechanisms by requiring users to actively participate in the governance of the DAO to receive their staking rewards. This means that stakers must engage; otherwise, they will not receive their rewards at the end of their lock period. While this may initially seem odd, it is clear that Vabble is focused on ensuring that stakeholders are actively involved and making sure users directly influence the types of content distributed on the streaming platform. How Stakers Influence Content Distribution In the Vabble ecosystem, stakers play a critical role in deciding what content gets distributed. Through a participatory process, stakers vote on proposals for new content, directly influencing the streaming platform's offerings. This democratic approach ensures that the content reflects the community's preferences, leading to a more engaged and satisfied audience. Moreover, it rewards creators for producing content that resonates with viewers, creating a virtuous cycle of content creation and consumption. Understanding Vabble's Staking Model Vabble's staking model, designed with fairness and scalability at its core, ensures a dynamic allocation of rewards based on both the size of the stake and the level of participation. This model rewards larger stakes with a proportionally larger share of the rewards pool and encourages active participation in the DAO's governance by all stakeholders, fostering a balanced and engaged community. Practical Example of Staking Rewards Calculation To clarify the rewards distribution mechanism, let's consider an example. Suppose the initial Vabble Staking Pool holds 50,000,000 VAB tokens. The annual emission rate set for this pool is 9%, translating to a 4,500,000 VAB token emission rate per year, or approximately 374,850 VAB tokens per month. Scenario: Before Your Stake: The pool has 100 users staking a total of 1,000 VAB. Your Stake: You decide to stake 1,000 VAB, doubling the total staked VAB to 2,000 VAB. Ownership of the Pool: Immediately after your stake, you own 50% of the reward pool, entitling you to 50% of the monthly rewards, in this case, 187,425 VAB for the month, given the pool's monthly release rate or 374,850. Subsequent Change: If another user stakes 2,000 VAB after you, the total staked VAB increases to 4,000 VAB. Your share of the pool drops to 25%, reducing your monthly reward entitlement to 93,712.5 VAB. To maintain your pool share, you must increase your stake position. This example demonstrates the fluid nature of reward distribution, emphasizing the importance of both the size of your stake and the timing of your participation. Participation Factor in Rewards An integral aspect of Vabble's staking model is the participation factor. This feature adjusts your reward based on your activity within the DAO, ensuring that stakers who actively participate in voting and governance receive the full extent of their rewards. For instance, if 100 content distribution proposals are presented during your 30-day staking period, you need to vote on all 100 proposals to receive 100% of your entitled rewards. Voting on fewer proposals proportionately decreases the reward, aligning incentives with active participation and contributing to the DAO's governance and operational efficacy. A Streaming Vision for the Future Vabble's introduction of participatory staking as a means to build a fair content distribution model marks a significant leap in the evolution of DAO governance and blockchain participation. Once content passes through governance, it is distributed to Vabble’s streaming service, which is a hybrid of Snapchat and Netflix, highlighting their focus on engaging younger generations. Rather than launching another Netflix clone, Vabble has combined social and streaming features, allowing people to watch movies together over video calls or text chat in a smooth and seamless experience. The streaming service has also integrated various utilities for different types of crypto assets, such as NFTs. Decisions made by the filmmaker during the DAO proposal phase of distribution allow them to specify which NFT collections users may use to watch content for free, by holding the NFT in their wallet. The platform also boasts features like Friend Lists, DM’s, and so much more. While its technology is innovative, it is also very competitively priced and operates on a freemium model for features and Peer to Peer, Pay Per View streaming. Does Vabble have the experience to re-invent the entertainment industry? As the landscape of film distribution continues to evolve, having the right people to help Vabble position itself as the leader in blockchain-based OTT streaming, is important. Last week, Vabble was thrilled to announce a powerful new alignment with industry veterans Barry Chamberlain, Philip Groves and Joanna Miles. This dynamic trio joins John C. Hall on at a critical moment, poised to disrupt traditional film distribution models. Vabble’s Advisory Board John C. Hall brings his vast experience as the former Executive Vice President of Distribution & Marketing at His career, spanning several decades, includes working on over 408 theatrical releases, covering a wide spectrum from major franchise titles to Oscar® winners and blockbuster hits. Universal Pictures. Barry Chamberlain, former President of Sales at , brings his expertise in sales, marketing, and intricate deal negotiations across every medium has been a driving force in generating multibillion-dollar revenues and successfully leading global operations through various evolution in the media industry CBS Studios International Philip Groves, celebrated for his work as the writer and producer of the IMAX documentary ASTEROID HUNTERS and former EVP of Worldwide Distribution and SVP of , brings his influence in bringing blockbuster event films to IMAX screens worldwide. His commitment to expanding the reach of IMAX documentaries has set new benchmarks in the industry. IMAX Corporation Further enhancing the board's expertise is Joanna Miles, former SVP of Theatrical Marketing, , Canada. With over two decades of experience in the film industry, Joanna has played a crucial role in marketing major titles such as The Hunger Games franchise and the John Wick franchise to Canadian audiences. Her extensive background in film festival management and talent negotiation further strengthens Vabble's position in the industry. Entertainment One Vabble is gearing up to showcase its groundbreaking approach at film industry events and festivals globally. Alongside these efforts, Vabble is excited to announce that its innovative streaming platform is set to launch soon, and its DAO launched on Monday, February 12th. In anticipation of this launch, filmmakers, especially those in the indie sector, are already invited to utilize Vabble for distributing their content. This early access provides a low-cost, efficient alternative for creators seeking to navigate the distribution landscape. This strategic expansion of Vabble’s Advisory Board marks a new chapter in the company's journey, promising to redefine the paradigms of film distribution and elevate blockchain's role in the entertainment industry. Find more information on Vabble’s by visiting their website, following on X, and joining their community: | | X Telegram Discord This press release is for informational purposes only and should not be considered financial advice. Participating in staking involves risks, and individuals should do their research before engaging in the VAB DAO ecosystem Note:

Vabble: A New Paradigm in DAO and Entertainment

ভ্যাবল: DAO এবং বিনোদনের একটি নতুন দৃষ্টান্ত

Vabble: Ein neues Paradigma in DAO und Unterhaltung

Vabble: un nuevo paradigma en DAO y entretenimiento

Vabble : un nouveau paradigme en matière de DAO et de divertissement

वैबल: डीएओ और मनोरंजन में एक नया प्रतिमान

Vabble: DAO とエンターテイメントの新しいパラダイム

Vabble: DAO와 엔터테인먼트의 새로운 패러다임

Vabbal: um novo paradigma em DAO e entretenimento

Vabble: новая парадигма в DAO и индустрии развлечений

Vabble: DAO ve Eğlencede Yeni Bir Paradigma

Vabble: Một mô hình mới trong DAO và Giải trí

Vabble：DAO 和娱乐的新范式

Large Language Models (LLMs) are a subset of (AI) that are designed to understand and generate natural language. These models are trained on vast amounts of textual data and use complex algorithms to learn patterns in language. LLMs have been used to develop a range of applications, from chatbots to language translation services. In this article, we will discuss how to create an LLM on a single computer and provide source code examples. artificial intelligence Prerequisites Before we begin, it’s essential to ensure that your computer meets the following requirements: A powerful computer with at least 16GB of RAM and 1TB of free storage A modern GPU, such as an NVIDIA GeForce or AMD Radeon, with at least 8GB of video memory A Linux operating system, such as Ubuntu or CentOS Basic knowledge of the command line interface (CLI) and Python programming language Step 1: Install Anaconda Anaconda is a popular open-source platform for data science and machine learning. It includes various tools and libraries that we will use to create an LLM. To install Anaconda, follow these steps: Download the Anaconda installer from the official website: https://www.anaconda.com/products/individual Open the terminal and navigate to the directory where the installer was downloaded Run the installer by typing the following command: bash Anaconda3–2021.11-Linux-x86_ 64.sh Follow the instructions on the screen to complete the installation Step 2: Create a Python environment We will create a Python environment to install the necessary libraries and dependencies for the LLM. To create an environment, follow these steps: Open the terminal and type the following command: conda create — name lm python=3.8 Activate the environment by typing: conda activate lm Step 3: Install TensorFlow TensorFlow is an open-source platform for building and training machine learning models. We will use TensorFlow to create the LLM. To install TensorFlow, follow these steps: Type the following command in the terminal: pip install tensorflow Verify that TensorFlow is installed by importing it in Python: import tensorflow as tf Step 4: Download a pre-trained LLM Training an LLM from scratch requires an enormous amount of computational power and time. Luckily, pre-trained models are available that we can fine-tune for our specific use case. One of the most popular pre-trained LLMs is GPT-2 (Generative Pre-trained Transformer 2). To download the pre-trained GPT-2 model, follow these steps: Open the terminal and type the following command: git clone https://github.com/openai/gpt-2.git Navigate to the gpt-2 directory by typing: cd gpt-2 Download the pre-trained model by typing: python download_ 117M model.py Step 5: Fine-tune the pre-trained LLM Fine-tuning a pre-trained LLM involves training the model on a specific dataset for a specific task. In our example, we will fine-tune the pre-trained GPT-2 model on a text generation task. To fine-tune the model, follow these steps: Create a new directory for the fine-tuned model by typing: mkdir my_model Navigate to the my_model directory by typing: cd my_model Create a new Python file by typing: touch train.py Open the file in a text editor and paste the following code: train.py import tensorflow as tf
import numpy as np
import os
import json
import random
import time
import argparse

# Define the command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--dataset_path", type=str, required=True,
                    help="Path to the dataset")
parser.add_argument("--model_path", type=str, required=True,
                    help="Path to the pre-trained model")
parser.add_argument("--output_path", type=str, required=True,
                    help="Path to save the fine-tuned model")
parser.add_argument("--batch_size", type=int, default=16,
                    help="Batch size for training")
parser.add_argument("--epochs", type=int, default=1,
                    help="Number of epochs to train for")
args = parser.parse_args()
# Load the pre-trained GPT-2 model
with open(os.path.join(args.model_path, "hparams.json"), "r") as f:
    hparams = json.load(f)
model = tf.compat.v1.estimator.Estimator(
    model_fn=model_fn,
    model_dir=args.output_path,
    params=hparams,
    config=tf.compat.v1.estimator.RunConfig(
        save_checkpoints_steps=5000,
        keep_checkpoint_max=10,
        save_summary_steps=5000
    )
)
# Define the input function for the dataset
def input_fn(mode):
    dataset = tf.data.TextLineDataset(args.dataset_path)
    dataset = dataset.repeat()
    dataset = dataset.shuffle(buffer_size=10000)
    dataset = dataset.batch(args.batch_size)
    dataset = dataset.map(lambda x: tf.strings.substr(x, 0, hparams["n_ctx"]))
    iterator = dataset.make_one_shot_iterator()
    return iterator.get_next()
# Define the training function
def train():
    for epoch in range(args.epochs):
        model.train(input_fn=lambda: input_fn(tf.estimator.ModeKeys.TRAIN))
        print(f"Epoch {epoch+1} completed.")
# Start the training
train() In the code above, we define the command-line arguments for the dataset path, pre-trained model path, output path, batch size, and number of epochs to train for. We then load the pre-trained GPT-2 model and define the input function for the dataset. Finally, we define the training function and start the training. Step 6: Generate text using the fine-tuned LLM Once the LLM is fine-tuned, we can use it to generate text. To generate text, follow these steps: Open the terminal and navigate to the my_model directory by typing: cd my_model Create a new Python file by typing: touch generate.py Open the file in a text editor and paste the following code: generate.py import tensorflow as tf
import numpy as np
import os
import json
import random
import time
import argparse

# Define the command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, required=True,
                    help="Path to the fine-tuned model")
parser.add_argument("--length", type=int, default=100,
                    help="Length of the generated text")
parser.add_argument("--temperature", type=float, default=0.7,
                    help="Temperature for text generation")
args = parser.parse_args()
# Load the fine-tuned model
with open(os.path.join(args.model_path, "hparams.json"), "r") as f:
    hparams = json.load(f)
model_fn = model_fn(hparams, tf.estimator.ModeKeys.PREDICT)
model = tf.compat.v1.estimator.Estimator(
    model_fn=model_fn,
    model_dir=args.model_path,
    params=hparams
)
# Define the generation function
def generate_text(length, temperature):
    start_token = "<|startoftext|>"
    tokens = tokenizer.convert_tokens_to_ids([start_token])
    token_length = len(tokens)
    while token_length < length:
        prediction_input = np.array(tokens[-hparams["n_ctx"]:])
        output = list(model.predict(input_fn=lambda: [[prediction_input]]))[0]["logits"]
        logits = output[-1] / temperature
        logits = logits - np.max(logits)
        probs = np.exp(logits) / np.sum(np.exp(logits))
        token = np.random.choice(range(hparams["n_vocab"]), p=probs)
        tokens.append(token)
        token_length += 1
    output_text = tokenizer.convert_ids_to_tokens(tokens)
    output_text = "".join(output_text).replace("▁", " ")
    output_text = output_text.replace(start_token, "")
    return output_text
# Generate text
text = generate_text(args.length, args.temperature)
print(text) In the code above, we define the command-line arguments for the fine-tuned model path, length of the generated text, and temperature for text generation. We then load the fine-tuned model and define the generation function. Finally, we generate text using the generate_text function and print the output, and we are done! Conclusion In this article, we have learned how to create a Large Language Model (LLM) on a single computer using TensorFlow and the GPT-2 architecture. We started by installing TensorFlow and downloading the GPT-2 code from the OpenAI GitHub repository. We then trained a GPT-2 model on a dataset and fine-tuned the pre-trained GPT-2 model using the same dataset. Finally, we generated text using the fine-tuned LLM. By following the steps in this article, you can create your own LLM and generate text for various tasks such as language translation, chatbots, and content generation. References Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9. OpenAI. (2019). GPT-2: Language models are unsupervised multitask learners. Github. Retrieved from . https://github.com/openai/gpt-2 TensorFlow. (2021). TensorFlow: An open source machine learning framework for everyone. Retrieved from . https://www.tensorflow.org/ Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165. Also published here.

How to Host a Large Language Model Locally

স্থানীয়ভাবে একটি বড় ভাষার মডেল কীভাবে হোস্ট করবেন

So hosten Sie ein großes Sprachmodell lokal

Cómo alojar localmente un modelo de lenguaje grande

Comment héberger localement un grand modèle de langage

बड़े भाषा मॉडल को स्थानीय रूप से कैसे होस्ट करें

大規模な言語モデルをローカルでホストする方法

대규모 언어 모델을 로컬로 호스팅하는 방법

Como hospedar um modelo de idioma grande localmente

Büyük Bir Dil Modelini Yerel Olarak Nasıl Barındırırsınız?

Cách lưu trữ một mô hình ngôn ngữ lớn cục bộ

如何在本地托管大型语言模型

Twitter v. Elon Musk Court Filing by Potter Anderson & Corroon LLP, July 12, 2022 is part of . You can jump to any part in this filing . This is part 1 of 31. HackerNoon’s Legal PDF Series here Feature Image: HackerNoon’s Midjourney AI, Prompt “lawyers says Musk is the model of hypocrisy” VERIFIED COMPLAINT Plaintiff Twitter, Inc. (“Twitter”), by and through its undersigned counsel, as and for its complaint against defendants Elon R. Musk, X Holdings I, Inc. (“Parent”), and X Holdings II, Inc. (“Acquisition Sub”), alleges as follows: NATURE OF THE ACTION In April 2022, Elon Musk entered into a binding merger agreement with Twitter, promising to use his best efforts to get the deal done. Now, less than three months later, Musk refuses to honor his obligations to Twitter and its stockholders because the deal he signed no longer serves his personal interests. Having mounted a public spectacle to put Twitter in play, and having proposed and then signed a seller-friendly merger agreement, Musk apparently believes that he — unlike every other party subject to Delaware contract law — is free to change his mind, trash the company, disrupt its operations, destroy stockholder value, and walk away. This repudiation follows a long list of material contractual breaches by Musk that have cast a pall over Twitter and its business. Twitter brings this action to enjoin Musk from further breaches, to compel Musk to fulfill his legal obligations, and to compel consummation of the merger upon satisfaction of the few outstanding conditions. Musk, the Chief Executive Officer of Tesla, Inc. and leader of SpaceX and other entities, opened a Twitter account in 2009. His presence on the Twitter platform is ubiquitous. With over 100 million followers, Musk’s account is one of the most followed on Twitter, and he has Tweeted more than 18,000 times. He has also suggested he would consider starting his own company to compete with Twitter. On April 25, 2022, Musk, acting through and with his solely-owned entities, Parent and Acquisition Sub, agreed to buy Twitter for $54.20 per share in cash, for a total of about $44 billion. That price, presented by Musk on a take-it-or-leave-it basis in an unsolicited public offer, represented a 38% premium over Twitter’s unaffected share price. The other terms Musk offered and agreed to were, as he touted, “seller friendly.” There is no financing contingency and no diligence condition. The deal is backed by airtight debt and equity commitments. Musk has personally committed $33.5 billion. After the merger agreement was signed, the market fell. As the Wall Street Journal reported recently, the value of Musk’s stake in Tesla, the anchor of his personal wealth, has declined by more than $100 billion from its November 2021 peak. So Musk wants out. Rather than bear the cost of the market downturn, as the merger agreement requires, Musk wants to shift it to Twitter’s stockholders. This is in keeping with the tactics Musk has deployed against Twitter and its stockholders since earlier this year, when he started amassing an undisclosed stake in the company and continued to grow his position without required notification. It tracks the disdain he has shown for the company that one would have expected Musk, as its would-be steward, to protect. Since signing the merger agreement, Musk has repeatedly disparaged Twitter and the deal, creating business risk for Twitter and downward pressure on its share price. Musk’s exit strategy is a model of hypocrisy. One of the chief reasons Musk cited on March 31, 2022 for wanting to buy Twitter was to rid it of the “[c]rypto spam” he viewed as a “major blight on the user experience.” Musk said he needed to take the company private because, according to him, purging spam would otherwise be commercially impractical. In his press release announcing the deal on April 25, 2022, Musk raised a clarion call to “defeat[] the spam bots.” But when the market declined and the fixed-price deal became less attractive, Musk shifted his narrative, suddenly demanding “verification” that spam was not a serious problem on Twitter’s platform, and claiming a burning need to conduct “diligence” he had expressly forsworn. Musk’s strategy is also a model of bad faith. While pretending to exercise the narrow right he has under the merger agreement to information for “consummation of the transaction,” Musk has been working furiously — albeit fruitlessly — to try to show that the company he promised to buy and not disparage has made material misrepresentations about its business to regulators and investors. He has also asserted, falsely, that consummation of the merger depends on the results of his fishing expedition and his ability to secure debt financing. On July 8, 2022, a little over a month after first using bad-faith pursuit of spam-related evidence to assert a baseless claim of breach, Musk gave Twitter notice purporting to terminate the merger agreement. The notice alleges three grounds for termination: (i) purported breach of information-sharing and cooperation covenants; (ii) supposed “materially inaccurate representations” in the merger agreement that allegedly are “reasonably likely to result in” a Company Material Adverse Effect; and (iii) purported failure to comply with the ordinary course covenant by terminating certain employees, slowing hiring, and failing to retain key personnel. These claims are pretexts and lack any merit. Twitter has abided by its covenants, and no Company Material Adverse Effect has occurred or is reasonably likely to occur. Musk, by contrast, has been acting against this deal since the market started turning, and has breached the merger agreement repeatedly in the process. He has purported to put the deal on “hold” pending satisfaction of imaginary conditions, breached his financing efforts obligations in the process, violated his obligations to treat requests for consent reasonably and to provide information about financing status, violated his non-disparagement obligation, misused confidential information, and otherwise failed to employ required efforts to consummate the acquisition. Twitter is entitled to specific performance of defendants’ obligations under the merger agreement and to secure for Twitter stockholders the benefit of Musk’s bargain. Musk and his entities should be enjoined from further breaches, ordered to comply with their obligations to work toward satisfying the few closing conditions, and ordered to close upon satisfaction of those conditions. Continue reading here

'Musk’s exit strategy is a model of hypocrisy', says Twitter lawyer Peter Walsh in July 22 lawsuit

"La estrategia de salida de Musk es un modelo de hipocresía", dice el abogado de Twitter Peter Walsh en la demanda del 22 de julio

"La stratégie de sortie de Musk est un modèle d'hypocrisie", déclare l'avocat de Twitter Peter Walsh dans le procès du 22 juillet

22 जुलाई के मुकदमे में ट्विटर वकील पीटर वॉल्श कहते हैं, 'मस्क की निकास रणनीति पाखंड का एक मॉडल है'

「マスク氏の出口戦略は偽善のモデルだ」とツイッター弁護士のピーター・ウォルシュ氏は7月22日の訴訟で述べた

'A estratégia de saída de Musk é um modelo de hipocrisia', diz o advogado do Twitter Peter Walsh no processo de 22 de julho

"Chiến lược rút lui của Musk là một mô hình đạo đức giả", luật sư Twitter Peter Walsh nói trong vụ kiện ngày 22 tháng 7

推特律师彼得沃尔什在 7 月 22 日的诉讼中表示，“马斯克的退出策略是虚伪的典范”

On November 15th, MetaAI and Papers with Code announced the release of Galactica, a game-changer, open-source large language model trained on scientific knowledge with 120 billion parameters. As one of my friends , the model can write whitepapers, reviews, Wikipedia pages, and code. It knows how to cite and how to write equations. It’s kind of a big deal for AI and science. shared on Twitter On November 17th, Galactica was shut down. Why? Because, as with all deep learning models, it didn’t understand the task at hand and was wrong in many cases. This shouldn’t be an issue, especially if we add a warning saying the model may be wrong and not to trust it blindly. Just like nobody trusted Wikipedia, we couldn’t put this as a reference in High School projects. The issue is that . Galactica was wrong or biased but sounded right and authoritative Still, the model is available to researchers, and I believe it is important to keep it open-sourced. As another one of my friend's shared, all the drama around the new model seems a bit excessive. Of course, the model isn’t perfect, just like all others that are currently available online. We need it online to test its limitations, work on it and improve it. We should see these kinds of publications as students and allow for mistakes and improvements without fear of being shut down or canceled. Anyways, we are not here to discuss that. Hopefully, . it will be back online soon We are here to see what Galactica is, or was, and how it could achieve writing papers, reviews, code, and more… Learn more in the video References ►Read the full article: ►Taylor et al., 2022: Galactica, ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/galactica/ https://galactica.org/ https://www.louisbouchard.ai/newsletter/ Video Transcript 0:00 on November 15th Metairie and papers 0:03 with code announced the release of 0:04 galatica a game changer open source 0:07 large language model trained on 0:09 scientific knowledge with 120 billion 0:12 parameters as one of my friends shared 0:14 on Twitter the model can write white 0:16 papers reviews Wikipedia pages and code 0:19 it knows how to cite and how to write 0:22 equations it really is kind of a big 0:24 deal for AI and science on November 17th 0:28 Galactica was shut down why because as 0:31 with all deep learning models it didn't 0:34 understand the task at hand and was 0:36 wrong in many cases this shouldn't be an 0:39 issue especially if we add a warning 0:41 saying the model may be wrong and not to 0:43 trust it blindly just like nobody 0:45 trusted Wikipedia we couldn't put it as 0:48 a reference in high school projects the 0:50 issue was that Galactica was wrong and 0:52 biased but sounded right and uteritative 0:55 still the model is available to 0:57 researchers and I believe it's important 0:59 to keep bit open sourced as another of 1:02 my friends shared all the drama around 1:04 this new model seems a bit excessive of 1:06 course the model isn't perfect just like 1:08 all others that are currently available 1:10 online we need it online to test its 1:13 limitations work on it and improve it we 1:16 should see these kinds of Fabrications 1:18 as students and allow for mistakes and 1:21 improvements without the fear of being 1:22 shut down or canceled anyways we are not 1:26 here to discuss that hopefully it will 1:28 be back online soon we are here to see 1:30 what Galactica is or was and how it 1:33 could achieve writing papers reviews 1:35 code math and more basically Galactica 1:39 is a large language model with a size 1:41 comparable to gpt3 but specialized on 1:44 scientific knowledge more precisely it 1:46 was trained on a large and curated 1:48 Corpus of scientific knowledge including 1:50 over 48 million papers textbooks and 1:54 lecture notes millions of compounds and 1:56 proteins scientific websites 1:58 encyclopedias and more as they highlight 2:00 data were of high quality and highly 2:03 curated which is one of the big 2:05 difference with gpt3 So in theory 2:08 Galactica contains pretty much all of 2:10 Humanity's scientific knowledge imagine 2:12 having an amazing memory and the time to 2:15 read millions of research remembering 2:18 most of it well this is Galactica it 2:21 seems like its memory isn't so good 2:23 after all and it mixes everything even 2:25 though we could assume most information 2:27 present in the training data set was 2:29 accurate even considering all devices 2:31 and failures Galactica stays pretty 2:34 powerful and outperforms pretty much all 2:36 other approaches for scientific related 2:39 tasks it's just not enough for a product 2:41 we can have confidence in still it's 2:44 worth understanding how it works 2:46 especially because it will come back 2:48 even more powerful pretty soon as we 2:51 mentioned Galactica is a large language 2:53 model similar to gpt3 or Bloom 2:55 specifically trained for as they say 2:58 organize science there's also a lot of 3:01 engineering going on in this model 3:03 allowing so much versatility in its 3:05 inputs and outputs like special 3:07 tokenization of citations or protein 3:09 sequences which you can learn more in 3:11 their paper linked below their 3:13 tokenization effort is by far the 3:15 biggest contribution of this work 3:17 tokensation basically means the way the 3:20 model will see the data instead of words 3:23 math or shapes that we understand I 3:26 actually share a video on embedding and 3:28 tokenization later this week so if that 3:30 sounds interesting stay tuned for that 3:33 and subscribe to not miss it so accept 3:35 this weird tokensation and 3:37 pre-processing steps what is Galactica 3:39 and what does it do after taking the 3:42 words or different scientific inputs and 3:44 preparing it for the model doing 3:46 tokenization no surprise Galactica is 3:50 yet another Transformer based 3:52 architecture like gpt3 with a couple of 3:55 variations including the tokenization 3:57 differences so I definitely invite you 3:59 to but one of the many videos I or some 4:02 of my friends made covering the 4:04 Transformer architectures as I won't get 4:06 into how they work once again the second 4:09 major difference between Galactica and 4:11 other large language models is what they 4:13 call the prompt pre-training this means 4:16 that they will include prompts extracted 4:18 from the training data set alongside the 4:21 data itself which has been shown to 4:23 maximize the generality of the model 4:25 while boosting performance on some tasks 4:28 of interest and that's pretty much it as 4:31 I said the architecture is very similar 4:33 to what you already know and mostly the 4:35 training and pre-processing schemes vary 4:37 which shows that the model isn't 4:39 everything but how we preach through the 4:41 data for it might actually matter even 4:43 more you can basically see the 4:45 difference between gpt3 and Galactica as 4:48 the same student with a bad science 4:49 teacher versus a good one it has the 4:52 same capabilities and resources the 4:55 teacher just made it more accessible and 4:57 understandable for him of course this 4:59 was just an overview of the paper and I 5:02 strongly recommend reading it there are 5:04 tons of details about the multiple 5:06 engineering tricks they've implemented 5:08 along with results analysis details on 5:11 all the tasks they tackle using the 5:13 model and how it understood the input 5:15 data and its predictions its limitations 5:18 biases and more I hope you've enjoyed 5:21 this video and I will see you next week 5:23 with another amazing paper and a special video covering what embeddings are

Galactica is an AI Model Trained on 120 Billion Parameters

Galactica es un modelo de IA entrenado en 120 mil millones de parámetros

Galactica est un modèle d'IA formé sur 120 milliards de paramètres

गैलेक्टिका एक एआई मॉडल है जिसे 120 बिलियन पैरामीटर्स पर प्रशिक्षित किया गया है

Galactica は 1200 億のパラメーターでトレーニングされた AI モデルです

Galactica é um modelo de IA treinado em 120 bilhões de parâmetros

Galactica là một Mô hình AI được đào tạo trên 120 tỷ tham số

Galactica 是一个经过 1200 亿个参数训练的 AI 模型

In May, Triller sued the popular H3 Podcast for $50m claiming that the people behind it had pirated the Jake Paul vs. Ben Askren fight and displayed it on YouTube. In a scathing motion to dismiss, the defendants describe Triller's complaint as "fatally flawed" and a "mangled and mangy mess", noting that they are fully protected under the doctrine of fair use. As part of Triller’s legal campaign targeting people and entities that allegedly pirated the Jake Paul vs. Ben Askren fight earlier this year, the company sued the popular H3 Podcast. Filed in May, the alleged two types of copyright infringement, violations of the Federal Communications Act (FCA), conversion, and violations of the Computer Fraud and Abuse Act. complaint Triller said that the operators of the H3 Podcast “unlawfully uploaded, distributed, and publicly displayed” the fight in breach of its rights, causing damages in excess of $50,000,000 after the “unauthorized broadcast” was viewed a reported 1,000,000 times. Later that same month, Triller filed its first amended complaint naming Ted Entertainment Inc. (TEI) and the operators of the H3 Podcast, Ethan and Hila Klein, as defendants. H3 Podcast was removed but the claim for $50m in damages remained. In July, Triller filed its second amended complaint which added Teddy Fresh Inc. and 10 ‘Doe’ defendants. It also cut the allegations of wrongdoing by half, leaving the defendants to answer claims of copyright infringement, vicarious copyright infringement and violations of the FCA. Triller’s Complaint is “Retaliation” to Criticism In a scathing motion to dismiss filed with the court yesterday, the defendants pull no punches in their response to Triller. Describing Triller’s complaint as “fatally defective”, they explain that despite being given numerous opportunities, the company consistently files complaints that are “a mangled and mangy mess that are devoid of any merit.” According to the motion, Triller’s complaint centers around TEI’s use of an excerpt of the ‘broadcast’ for the purposes of commentary and criticism in a video entitled ‘Jake Paul Fight Was a Disaster’. TEI employees reportedly uploaded the excerpt (the ‘reference video’) onto YouTube as an unlisted video and the defendants say that Triller’s lawsuit amounts to retaliation for their broad criticism. “Anyone who watches the 4/22/21 Podcast can instantly recognize the true purpose of Triller’s [second amended complaint]: retaliation against the 4/22/21 Podcast because it excoriated the Broadcast and its main event – the boxing match between Jake Paul and Ben Askren (the ‘Fight’). In other words, Triller’s SAC is a brazen and unabashed attempt to intimidate, punish and silence lawful and protected criticism,” the motion reads. Triller Says Defendants Used a ‘Bootleg’ Video Triller says that the defendants watched a pirated or bootleg version of the broadcast, uploaded it to YouTube, and showed an excerpt and its URL in the 4/22/21 podcast. However, the defendants say that the screenshot provided by Triller failed to show the URL and Triller could not provide it. It was possible to see the URL in the video with some work but ultimately the video received just 65 views. Triller says the defendants profited from their conduct because the H3 Podcast is part of the YouTube partner program, contains sponsorships, and sells merchandise. However, the channel the ‘reference video’ was uploaded to has none of these qualities, the defendants say. From here, things start to get more interesting. While Triller’s complaints have all centered on the fight itself, that famously lasted for under two minutes. The entire broadcast, to which Triller owns the copyright, lasted much longer – four hours in fact – meaning that any use of the Jake Paul fight by the defendants in their two-hour podcast was necessarily small. According to the motion to dismiss, the H3 Podcast spent 12 minutes of the podcast critiquing the broadcast and in the middle of that showed 42 seconds of the ‘reference video’, which included five seconds exclusively of audio, nine seconds of both audio and video, and 28 seconds exclusively of video. Ethan and Hila’s Outreach to Triller Failed Late June, Ethan and Hila wrote to Triller demonstrating how each claim in the first amended complaint failed. Triller then submitted its second amended complaint (SAC), dropping three claims. In July the parties had a telephone conference but Triller remained “unpersuaded” that its SAC contained “fatal defects”. Triller responded by filing a new action against the defendants in the Los Angeles Superior Court claiming that comments made in H3 Podcasts about the fight lawsuit amounted to tortious interference. Copyright Infringement Claims “Fail as Matter of Law” The defendants believe that Triller’s first copyright infringement claim is based on an Ethan Klein statement that he watched a pirated version of the broadcast. According to them, that’s not infringement. “Viewing a transmission – whether it be Triller’s April 17, 2021 transmission of the Broadcast, the Reference Video or the 4/22/21 Podcast – does not constitute copyright infringement. Viewing a transmission is not a public display, public performance, public distribution or derivative work of the original copyrighted work,” the motion to dismiss reads. Triller’s vicarious copyright infringement claim appears to rely on the allegation that third parties watched the ‘reference’ video and the podcast but in common with the first infringement claim, that also fails under the doctrine of fair use, the motion adds. Fair Use Defenses According to the motion, Triller is seeking to separate the ‘reference’ video and Klein’s viewing of the broadcast from the use of the broadcast in the podcast. This, the defendants say, is something explicitly rejected under the doctrine of “intermediate use”, meaning that copying as a preliminary step in the creation of a fair use work also qualifies as fair use. “As such, Triller’s attempt to view Defendants’ alleged acts of infringement in isolation from the 4/22/21 Podcast is precluded as a matter of law.” Weighing in on the first fair use factor, the defendants say that their use was highly transformative and contained significant commentary and criticism, including that relating to the overall quality of the show and the physical appearance of Ben Askren. The podcast also critiqued the referee, pondered whether the fight had been staged, and questioned the amount of revenue the event might make. In respect of whether the use was commercial, the defendants argue that since the use was highly transformative, this factor is much less important. They also believe that any unauthorized access to the broadcast (“bad faith”) should not affect a finding in favor of fair use. “[T]riller’s argument that access to an authorized copy is a precondition for Defendants to invoke the fair use is, itself, a bad faith argument,” the motion adds. The second factor in a determination of fair use is the nature of the work being copied, such as whether it was for factual/informational or entertainment purposes. The defendants only used a section from the broadcast that showed the men fighting so on that basis they argue their use was primarily factual. On the third factor of fair use, which questions whether “the amount and substantiality of the portion used in relation to the copyrighted work as a whole are reasonable in relation to the purpose of the copying”, the defendants believe the law is on their side. “The Broadcast was essentially four hours long…In other words, the 4/22/21 Podcast used less than .3% of the Broadcast,” an amount that under the circumstances was “very reasonable in relation to the 4/22/21 Podcast’s transformative purpose of commentary and criticism of the Broadcast and Fight.” In addressing the fourth factor (whether the copying affects the market value of the copyrighted work), the motion notes that the court must distinguish between “biting criticism that merely suppresses demand and copyright infringement which usurps it.” They also point out that they catered to a different audience than Triller. While the latter’s wanted to sit back and enjoy the event, H3 Podcast’s viewers wanted to hear why the whole thing was a disaster. s Vicarious Infringement Claim Fail To prove a vicarious copyright infringement claim, the defendants say that Triller must show that the defendants had a direct financial interest in the alleged infringing activity by showing a causal relationship. They counter by noting that the ‘reference’ video had no sponsorships and was not monetized on YouTube so Triller’s claim fails. On the allegations relating to the violations of the Federal Communications Act, the defendants say that since Triller acknowledges that the videos were shown on YouTube after the original transmission (i.e they did not extend the point of distribution of the actual broadcast signal), that claim fails too. Conclusion “Triller’s abuse of the judicial process to punish and silence lawful speech can no longer be countenanced. Triller’s lawsuit is retribution for the 4/22/21 Podcast lambasting the Broadcast as a ‘disaster’,” the motion adds. “[T]riller persists in filing “disastrous” and fatally defective complaints – and the SAC is no exception. Since Triller consistently refuses to cease its abuse of the judicial process and Defendants, it is up to this Court to make Triller stop.” The motion to dismiss and associated memorandum can be found and (pdf) here here

What Happens When a Legal Complaint is Fatally Flawed and Overall a Mangled and Mangy Mess?

¿Qué sucede cuando una queja legal es fatalmente defectuosa y, en general, es un desastre destrozado y sarnoso?

Que se passe-t-il lorsqu'une plainte légale est fatalement défectueuse et, dans l'ensemble, un gâchis mutilé et galeux ?

क्या होता है जब एक कानूनी शिकायत गंभीर रूप से त्रुटिपूर्ण होती है और कुल मिलाकर एक उलझी हुई और गड़बड़ होती है?

法的訴状に致命的な欠陥があり、全体的にめちゃくちゃで混乱した場合はどうなりますか?

O que acontece quando uma reclamação legal é fatalmente falha e, no geral, uma bagunça mutilada e sarnenta?

Điều gì sẽ xảy ra khi một Khiếu nại pháp lý bị sai phạm nghiêm trọng và nói chung là một mớ hỗn độn và Mangy?

当法律投诉存在致命缺陷并且总体上是一团糟和一团糟时会发生什么？

If the complex and costly hardware required for traditional mining has been a deterrent for you, or if you’re weary of the relentless grind to boost transaction volumes just to earn fees, now there’s a refreshing alternative. Discover the brand-new mobile mining experience that ‘ ’ provides, where your smartphone becomes your portal to a rewarding ‘verify to earn’ journey. Massive Introduction We’re thrilled to introduce , our innovative mobile mining app, poised for launch alongside the upcoming at beginning of April. As the first project in the blockchain sector to leverage ‘ZKVM+DePIN+AI’ technology, Ola is at the forefront, pioneering a new, fair, open and user-centric approach to blockchain interaction with this initiative, Massive. Massive Ola Testnet Alpha Massive simplifies the blockchain experience by removing the need for expensive hardware or technical expertise. Our vision for Massive is direct and clear: to democratize web3 access by providing a platform where anyone with a smartphone can participate and earn rewards through simple verification tasks. Our commitment with Massive is to lower the entry barriers to blockchain technology, making it accessible and equitable for all users. By introducing the ‘verify to earn’ concept, we’re not only changing how people mine; we’re also transforming their role within the blockchain ecosystem from passive observers to active participants. Massive, as a crucial part of Ola’s development strategy, . Ola is set to open applications for invitation codes on March 18th, releasing the first set of 1,000 codes for node registration. This initial offering is limited to 1,000 nodes, with registrations being accepted on a first-come, first-served basis. Additionally, the first 1,000 nodes will have the privilege of generating invitation codes, allowing them to invite newcomers with these codes to earn rewards. plans to allocate about 10% of Ola’s token to verifiers & miners, with an initial 1% being used for Ola Testnet Alpha Join us as we embark on this exciting journey to make blockchain technology intuitive, inclusive, and rewarding for everyone, everywhere. Background The industry has tirelessly explored various strategies to engage traditional users (those outside the web3 ecosystem), including gaming, infrastructure, and incentivizing through airdrops. Notable efforts like StepN’s ‘move to earn’ and Starkware’s airdrops for non-Web3 developers momentarily captured high interest, hinting at potential breakthroughs. However, their inability to sustain long-term momentum and impact highlighted the critical need for innovative, enduring approaches that truly resonate with and retain users in the blockchain realm. “We recognized the need for a standout feature that resonates deeply with users; thus, Massive was conceived as our bold solution to this industry challenge,” - Simon Chen, co-founder and CEO of Ola Ola’s mission with Massive is to catalyze the widespread adoption of web3, advocating for a decentralized and fair digital ecosystem. By introducing the ‘verify to earn’ concept, where anyone with a smartphone can participate in mining, Massive aims to significantly lower these barriers. Additionally, users can engage with multiple projects over time on their smartphones, enhancing the longevity and diversity of their participation. This approach will foster a more inclusive environment, encouraging a broader demographic to engage with, understand, and contribute to the web3 space, while receiving long-term rewards. The introduction of Massive is a strategic move toward making blockchain more accessible and engaging for the general public, thus infusing the industry with new participants and fresh perspectives. Through this initiative, Ola envisions transforming mere observers into active contributors, nurturing a dynamic and sustainable blockchain community. Massive: Fair Opportunities for All In the realm of traditional mining, such as the Proof of Work (POW) mining mechanism, the requirement for costly hardware presents a significant barrier to ordinary users. When developing Massive, Ola focused on reducing entry barriers, enhancing user experience, and increasing user rewards, all while guaranteeing utmost fairness for EVERY user. Massive is a decentralized authentication network composed of various mobile elements. In terms of user-friendliness, Massive boasts several key features: User Access Threshold: : Users can easily access Massive by simply downloading the necessary app and wallet on their smartphones. Low Entry Barrier : Massive supports a broad range of mobile models, compatible with both Android and iOS (coming soon) systems. Minimal Device Requirements User Rewards (10% of Ola’s tokens): : Users receive rewards by completing verification tasks, providing a straightforward way to earn. Verify to Earn : In addition to earning verification rewards, users can also gain additional returns by inviting newcomers with a referral code and through staking activities. Multiple Reward Streams User Experience: : Users can join or leave the network at their convenience, offering the flexibility to suit individual needs. Flexible Access and Exit : Without the need for complex operations or maintaining high transaction volumes, Massive simplifies user participation. Ease of Use : Compared to distributing funds across multiple addresses, using a smartphone to participate presents a lower barrier for the average user. User-Friendly Enhanced Network Security and Intelligence: : Massive harnesses AI+zk technology to elevate network security and intelligence, utilizing AI for nuanced analysis of node service statuses. This results in refined task distribution and heightened network efficiency. AI + ZK Integration Platform Fairness and Sustainability: : Massive ensures fairness for all users, where everyone’s time and contributions are equally valued. Fair : Users can engage with multiple projects over time on their smartphones, enhancing the longevity and diversity of their participation. Sustainable Massive Changes the Game: Say Hi to Your New Web Roles At Massive, your role is fundamentally transformed. Gone are the days of having to “slave away” by artificially boosting transaction volumes and amounts to earn incentives. Now, all you need is a standard smartphone to connect. By simply staying active and verifying, you can consistently earn rewards. But your role evolves further; once you connect as an Ola node, you become an essential part of maintaining the network’s security. You might have felt the imbalance before, as web3 whales could distribute their funds across multiple addresses to amplify their earnings, a feat not so easily replicated by purchasing multiple smartphones. Hence, Massive levels the playing field, offering a balanced opportunity for both whales and retailers. On the Massive platform, everyone participates and contributes in the same manner, ensuring fairness. This approach incentivizes a broader spectrum of users to engage with the web3 ecosystem, lowering entry barriers and bolstering decentralization and equity within the system. With Massive, every participant has the chance to earn rewards fairly, fostering wider participation and a healthier network. Distinct from other staking or mining models where more staking could mean more chances to validate, Massive champions decentralization and equity. Every node stakes an equal value, and by merely staying online and completing verification tasks, rewards can be earned. Time is valued equally across all participants, ensuring that mining on Massive is inherently fair. This paradigm encourages broader and more equitable user engagement, reinforcing the network’s integrity and community inclusiveness. Looking Ahead: Shaping the Future of Blockchain and Web3 Mobile Experiences with ZK Technology As we gaze into the future, our expectations for the Massive are set even higher: Firstly, as a purely decentralized ZK verification network, Massive is poised to accelerate the modularization of blockchain design. Secondly, with an increasing influx of new users joining the blockchain, we anticipate the emergence of genuine Web3 mobile technology. This evolution promises not only to propel the advancement of blockchain technology and invigorate the ecosystem but also to offer users a more equitable, open, and convenient digital experience. Collectively, these efforts aim to facilitate the widespread adoption and development of blockchain technology, steering the industry toward a more prosperous future. Join the Venture! Ola is tackling blockchain’s biggest hurdles: fairness and accessibility, with its gamechanging ‘Massive’ app, leveraging “ZKVM+DePin+AI” technology. Massive simplify blockchain interactions with a ‘verify to earn’ model, opening the digital door for anyone with a smartphone. 🗓️ March 18 marks the launch of Ola’s first 1,000 node registration codes, with mining, new features, and greater rewards soon to follow. Early nodes gain the advantage of creating invite codes for collaborative reward earning. Join us on this venture and be among the first to benefit! About us is an open-source Hybrid ZK Rollup that delivers programmable scalability and privacy to blockchains, empowering individuals through data ownership. Incubated by cryptographers and engineers of Sin7y Labs in 2022, Ola offers a layer2 ZK Stack, featuring Ola-lang, OlaVM, OlaUI, OlaOS, and a series of developer-friendly tools, facilitating the development of any type of contracts. In 2023, Ola launched the Ambassador Program and OVP Points Rewards, now with 20+ global ambassadors. For 2024, Ola plans to roll out public testnet, mainnet, mining mechanism (PoW, ZKP, & Verifier), and a range of incentive programs steadily. If you are interested in joining or partnering with us to contribute to a fair, decentralized, and permissionless world, please contact us at . Ola contact@olavm.org | | | | | | | | | | Website Twitter Discord Telegram Whitepaper GitHub LinkedIn YouTube HackMD Medium HackerNoon

The First Ola Mobile Mining App: A New ‘Verify to Earn’ Paradigm with a 10% Token Airdrop Plan

প্রথম ওলা মোবাইল মাইনিং অ্যাপ: একটি 10% টোকেন এয়ারড্রপ প্ল্যান সহ একটি নতুন 'ভেরিফাই টু আর্ন' দৃষ্টান্ত

Die erste mobile Mining-App von Ola: Ein neues „Verify to Earn“-Paradigma mit einem 10 %-Token-Airdrop-Plan

La primera aplicación de minería móvil de Ola: un nuevo paradigma de "verificar para ganar" con un plan de lanzamiento aéreo de tokens del 10%

La première application Ola Mobile Mining : un nouveau paradigme « Vérifier pour gagner » avec un plan Airdrop de 10 % de jetons

पहला ओला मोबाइल माइनिंग ऐप: 10% टोकन एयरड्रॉप प्लान के साथ एक नया 'वेरिफाई टू अर्न' प्रतिमान

最初の Ola モバイル マイニング アプリ: 10% トークン エアドロップ プランを備えた新しい「検証して獲得」パラダイム

최초의 Ola 모바일 채굴 앱: 10% 토큰 에어드롭 계획을 통한 새로운 '수익 확인' 패러다임

O primeiro aplicativo de mineração móvel Ola: um novo paradigma de ‘verificar para ganhar’ com um plano de lançamento aéreo de token de 10%

Первое мобильное приложение для майнинга Ola: новая парадигма «подтверди, чтобы заработать» с планом Airdrop 10% токенов

İlk Ola Mobil Madencilik Uygulaması: %10 Token Airdrop Planıyla Yeni Bir "Kazanmak İçin Doğrula" Paradigması

Ứng dụng khai thác di động Ola đầu tiên: Mô hình 'Xác minh để kiếm tiền' mới với kế hoạch thả 10% mã thông báo

第一个 Ola 移动挖矿应用程序：新的“验证赚取”范式和 10% 代币空投计划

The New York Times Company v. Microsoft Corporation Court Filing December 27, 2023 is part of . You can jump to any part in this filing . This is part 8 of 27. HackerNoon’s Legal PDF Series here IV. FACTUAL ALLEGATIONS B. Defendants’ GenAI Products 1. A Business Model Based on Mass Copyright Infringement 55. OpenAI was formed in December 2015 as a “non-profit artificial intelligence research company.” OpenAI started with $1 billion in seed money from its founders, a group of some of the wealthiest technology entrepreneurs and investors and companies like Amazon Web Services and InfoSys. This group included Elon Musk, the CEO of Tesla and X Corp. (formerly known as Twitter); Reid Hoffman, the co-founder of LinkedIn; Sam Altman, the former president of Y Combinator; and Greg Brockman, the former Chief Technology Officer of Stripe. 56. Despite accepting very large investments from enormously wealthy companies and individuals at its founding, OpenAI originally maintained that its research and work would be entirely unmotivated by profit. In a December 11, 2015, press release, Brockman and co-founder lya Sutskever (now OpenAI’s President and Chief Scientist, respectively) wrote: “Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return. Since our research is free from financial obligations, we can better focus on a positive human impact.” In accordance with that mission, OpenAI promised that its work and intellectual property would be open and available to the public, that its “[r]esearchers will be strongly encouraged to publish their work, whether as papers, blog posts, or code” and that its “patents (if any) will be shared with the world.” 57. Despite its early promises of altruism, OpenAI quickly became a multi-billiondollar for-profit business built in large part on the unlicensed exploitation of copyrighted works belonging to The Times and others. Just three years after its founding, OpenAI shed its exclusively nonprofit status. It created OpenAI LP in March 2019, a for-profit company dedicated to conducting the lion’s share of OpenAI’s operations—including product development—and to raising capital from investors seeking a return. OpenAI’s corporate structure grew into an intricate web of for-profit holding, operating, and shell companies that manage OpenAI’s day-to-day operations and grant OpenAI’s investors (most prominently, Microsoft) authority and influence over OpenAI’s operations, all while raising billions in capital from investors. The result: OpenAI today is a commercial enterprise valued as high as $90 billion, with revenues projected to be over $1 billion in 2024. 58. With the transition to for-profit status came another change: OpenAI also ended its commitment to openness. OpenAI released the first two iterations of its flagship GenAI model, GPT-1 and GPT-2, on an open-source basis in 2018 and 2019, respectively. But OpenAI changed course in 2020, starting with the release of GPT-3 shortly after OpenAI LP and other for-profit OpenAI entities were formed and took control of product design and development. 59. GPT-3.5 and GPT-4 are both orders of magnitude more powerful than the two previous generations, yet Defendants have kept their design and training entirely a secret. For previous generations, OpenAI had voluminous reports detailing the contents of the training set, design, and hardware of the LLMs. Not so for GPT-3.5 or GPT-4. For GPT-4, for example, the “technical report” that OpenAI released said: “this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method, or similar.”[3] 60. OpenAI’s Chief Scientist Sutskever justified this secrecy on commercial grounds: “It’s competitive out there …. And there are many companies who want to do the same thing, so from a competitive side, you can see this as maturation of the field.”[4] But its effect was to conceal the identity of the data OpenAI copied to train its latest models from rightsholders like The Times. 61. OpenAI became a household name upon the release of ChatGPT in November 2022. ChatGPT is a text-generating chatbot that, given user-generated prompts, can mimic humanlike natural language responses. ChatGPT was an instant viral sensation, reaching one million users within a month of its release and gaining over 100 million users within three months. 62. OpenAI, through OpenAI OpCo LLC and at the direction of OpenAI Inc., OpenAI LP, and other OpenAI entities, offers a suite of services powered by its LLMs, targeted to both ordinary consumers and businesses. A version of ChatGPT powered by GPT-3.5 is available to users for free. OpenAI also offers a premium service, powered by OpenAI’s “most capable model” GPT-4, to consumers for $20 per month. OpenAI’s business-focused offerings include ChatGPT Enterprise and ChatGPT API tools designed to enable developers to incorporate ChatGPT into bespoke applications. OpenAI also licenses its technology to corporate clients for licensing fees. 63. These commercial offerings have been immensely valuable for OpenAI. Over 80% of Fortune 500 companies are using ChatGPT.[5] According to recent reports, OpenAI is generating revenues of $80 million per month, and is on track to surpass over $1 billion within the next 12 months.[6] 64. This commercial success is built in large part on OpenAI’s large-scale copyright infringement. One of the central features driving the use and sales of ChatGPT and its associated products is the LLM’s ability to produce natural language text in a variety of styles. To achieve this result, OpenAI made numerous reproductions of copyrighted works owned by The Times in the course of “training” the LLM. 65. Upon information and belief, all of the OpenAI Defendants have been either directly involved in or have directed, controlled, and profited from OpenAI’s widespread infringement and commercial exploitation of Times Works. OpenAI Inc., alongside Microsoft, controlled and directed the widespread reproduction, distribution, and commercial use of The Times’s material perpetrated by OpenAI LP and OpenAI Global LLC, through a series of holding and shell companies that include OpenAI Holdings LLC, OpenAI GP LLC, and OAI Corporation LLC. OpenAI LP and OpenAI Global LLC were directly involved in the design, development, and commercialization of OpenAI’s GPT-based products, and directly engaged in the widespread reproduction, distribution, and commercial use of Times Works. OpenAI LP and OpenAI Global LLC also controlled and directed OpenAI, LLC and OpenAI OpCo LLC, which were involved in distributing, selling, and licensing OpenAI’s GPT-based products, and thus monetized the reproduction, distribution, and commercial use of Times Works. 66. Since at least 2019, Microsoft has been, and continues to be, intimately involved in the training, development, and commercialization of OpenAI’s GPT products. In an interview with the Wall Street Journal at the 2023 World Economic Forum, Microsoft CEO Satya Nadella said that the “ChatGPT and GPT family of models … is something that we’ve been partnered with OpenAI deeply now for multiple years.” Through this partnership, Microsoft has been involved in the creation and commercialization of GPT LLMs and products based on them in at least two ways. 67. First, Microsoft created and operated bespoke computing systems to execute the mass copyright infringement detailed herein. These systems were used to create multiple reproductions of The Times’s intellectual property for the purpose of creating the GPT models that exploit and, in many cases, retain large portions of the copyrightable expression contained in those works. 68. Microsoft is the sole cloud computing provider for OpenAI. Microsoft and OpenAI collaborated to design the supercomputing systems powered by Microsoft’s cloud computer platform Azure, which were used to train all OpenAI’s GPT models after GPT-1. In a July 2023 keynote speech at the Microsoft Inspire conference, Mr. Nadella said: “We built the infrastructure to train their models. They’re innovating on the algorithms and the training of these frontier models.” 69. That infrastructure was not just general purpose computer systems for OpenAI to use as it saw fit. Microsoft specifically designed it for the purpose of using essentially the whole internet—curated to disproportionately feature Times Works—to train the most capable LLM in history. In a February 2023 interview, Mr. Nadella said: But beneath what OpenAI is putting out as large models, remember, the heavy lifting was done by the [Microsoft] Azure team to build the computer infrastructure. Because these workloads are so different than anything that’s come before. So we needed to completely rethink even the datacenter up to the infrastructure that first gave us even a shot to build the models. And now we’re translating the models into products.[7] 70. Microsoft built this supercomputer “in collaboration with and exclusively for OpenAI,” and “designed [it] specifically to train that company’s AI models.”[8] Even by supercomputing standards, it was unusually complex. According to Microsoft, it operated as “a single system with more than 285,000 CPU cores, 10,000 GPUs and 400 gigabits per second of network connectivity for each GPU server.” This system ranked in the top five most powerful publicly known supercomputing systems in the world. 71. To ensure that the supercomputing system suited OpenAI’s needs, Microsoft needed to test the system, both independently and in collaboration with OpenAI software engineers. According to Mr. Nadella, with respect to OpenAI: “They do the foundation models, and we [Microsoft] do a lot of work around them, including the tooling around responsible AI and AI safety.” Upon information and belief, such “tooling around AI and AI safety” involves the finetuning and calibration of the GPT-based products before their release to the public.[9] 72. In collaboration with OpenAI, Microsoft has also commercialized OpenAI’s GPTbased technology, and combined it with its own Bing search index. In February 2023, Microsoft unveiled Bing Chat, a generative AI chatbot feature on its search engine powered by GPT-4. In May 2023, Microsoft and OpenAI unveiled “Browse with Bing,” a plugin to ChatGPT that enabled it to access the latest content on the internet through the Microsoft Bing search engine. Bing Chat and Browse with Bing combine GPT-4’s ability to mimic human expression—including The Times’s expression—with the ability to generate natural language summaries of search result contents, including hits on Times Works, that obviate the need to visit The Times’s own websites. These “synthetic” search results purport to answer user queries directly and may include extensive paraphrases and direct quotes of Times reporting. Such copying maintains engagement with Defendants’ own sites and applications instead of referring users to The Times in the same way as organic listings of search results. 73. In a recent interview, Mr. Nadella acknowledged Microsoft’s intimate involvement in OpenAI’s operations and, therefore, its copyright infringement: [W]e were very confident in our own ability. We have all the IPrights and all the capability. If OpenAI disappeared tomorrow, Idon’t want any customer of ours to be worried about it quite honestly, because we have all of the rights to continue theinnovation. Not just to serve the product, but we can go and just dowhat we were doing in partnership ourselves. We have the people,we have the compute, we have the data, we have everything. 74. Through their collaboration in both the creation and the commercialization of the GPT models, Defendants have profited from the massive copyright infringement, commercial exploitation, and misappropriation of The Times’s intellectual property. As Mr. Nadella recently put it, “[OpenAI] bet on us, we bet on them.” He continued, describing the effect of Microsoft’s $13 billion investment: And that gives us significant rights as I said. And also this thing, it’snot hands off, right? We are in there. We are below them, abovethem, around them. We do the kernel optimizations, we build tools,we build the infrastructure. So that’s why I think a lot of theindustrial analysts are saying, ‘Oh wow, it’s really a joint projectbetween Microsoft and OpenAI.’The reality is we are, as I said, very self-sufficient in all of this. Continue Reading . Here [3] OPENAI, GPT-4 TECHNICAL REPORT (2023), https://cdn.openai.com/papers/gpt-4.pdf. [4] James Vincent, OpenAI Co-Founder on Company’s Past Approach to Openly Sharing Research: ‘We Were Wrong’, THE VERGE (Mar. 15, 2023), https://www.theverge.com/2023/3/15/23640180/openai-gpt-4-launch-closedresearch-ilya-sutskever-interview. [5] OpenAI, Introducing ChatGPT Enterprise, OPENAI (Aug. 28, 2023), https://openai.com/blog/introducing-chatgpt-enterprise. [6] Chris Morris, OpenAI Reportedly Nears $1 Billion in Annual Sales, FAST COMPANY (Aug. 30, 2023), https://www.fastcompany.com/90946849/openai-chatgpt-reportedly-nears-1-billion-annual-sales. [7] First on CNBC: CNBC Transcript: Microsoft CEO Satya Nadella Speaks with CNBC’s Jon Fortt on “Power Lunch” Today, CNBC (Feb. 7, 2023), https://www.cnbc.com/2023/02/07/first-on-cnbc-cnbc-transcriptmicrosoft-ceo-satya-nadella-speaks-with-cnbcs-jon-fortt-on-power-lunch-today.html. [8] Jennifer Langston, Microsoft Announces New Supercomputer, Lays Out Vision for Future AI Work, MICROSOFT (May 19, 2020), https://news.microsoft.com/source/features/ai/openai-azure-supercomputer/. 9 SÉBASTIEN BUBECK ET AL., SPARKS OF ARTIFICIAL GENERAL INTELLIGENCE: EARLY EXPERIMENTS WITH GPT-4 (2023), https://arxiv.org/pdf/2303.12712.pdf 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-11195 retrieved on December 29, 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. nycto-assets.nytimes.com

The Times v. Microsoft/OpenAI: A Business Model Based on Mass Copyright Infringement (8)

দ্য টাইমস বনাম মাইক্রোসফ্ট/ওপেনএআই: ব্যাপক কপিরাইট লঙ্ঘনের উপর ভিত্তি করে একটি ব্যবসায়িক মডেল (8)

The Times gegen Microsoft/OpenAI: Ein Geschäftsmodell, das auf massenhaften Urheberrechtsverletzungen basiert (8)

The Times contra Microsoft/OpenAI: un modelo de negocio basado en la infracción masiva de derechos de autor (8)

The Times contre Microsoft/OpenAI : un modèle économique basé sur la violation massive du droit d'auteur (8)

द टाइम्स बनाम माइक्रोसॉफ्ट/ओपनएआई: बड़े पैमाने पर कॉपीराइट उल्लंघन पर आधारित एक बिजनेस मॉडल (8)

タイムズ対マイクロソフト/OpenAI: 大量の著作権侵害に基づくビジネス モデル (8)

The Times v. Microsoft/OpenAI: um modelo de negócios baseado na violação em massa de direitos autorais (8)

The Times против Microsoft/OpenAI: бизнес-модель, основанная на массовом нарушении авторских прав (8)

The Times - Microsoft/OpenAI: Kitlesel Telif Hakkı İhlallerine Dayalı Bir İş Modeli (8)

The Times v. Microsoft/OpenAI: Mô hình kinh doanh dựa trên vi phạm bản quyền hàng loạt (8)

《泰晤士报》诉微软/OpenAI：基于大规模版权侵权的商业模式 (8)

Once, I was tasked with improving the database and general app operations of backend services which were using MongoDB as their main database. These services were part of a huge infrastructure where millions of messages came through the queues and needed to be processed based on the message actions. That means tons of DB ops each second and other additional checks while processing. Real Case Scenario The processing layer of the service was using Pymongo to interact with MongoDB and the service itself was running in a synchronous environment. Even though the database operations were handled in bulk, the performance was still not capable of handling incoming data. Synchronous code was making things even worse. The code execution waits for the result from the current operation to move forward. That's a serious bottleneck in scalable systems. This was causing queue overflows and potential data loss every time. Asynchronous Environment The solution I implemented was a combination of: Motor
Asyncio
Uvloop Let's quickly go through the definitions of these items. PyMongo is the official MongoDB driver for Python, providing a simple and intuitive way to interact with MongoDB databases. It's synchronous, meaning each database operation blocks the execution of your program until it completes it, which can be a bottleneck in I/O-bound tasks. Motor is the asynchronous driver for MongoDB, built on top of PyMongo and designed to take advantage of Python's asyncio library. Motor allows you to perform non-blocking database operations, making it suitable for high-performance applications that require concurrency. To illustrate the performance differences, I prepared a stress test using two scripts: one using Motor (asynchronous) and the other using PyMongo (synchronous). Both scripts performed the same task of reading and writing documents to MongoDB in batches. Both scripts read 300k documents from the source collection and migrated them to the new target collection. Asynchronous Script (Motor) import logging
import asyncio
import time
from bson import ObjectId
from motor.motor_asyncio import AsyncIOMotorClient

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# MongoDB setup
MONGO_URI = 'mongodb://root:root@localhost:27019'
DB_NAME = 'products'
COLLECTION_NAME = 'gmc_products'

client = AsyncIOMotorClient(MONGO_URI)
db = client[DB_NAME]
collection = db[COLLECTION_NAME]
target_collection = db["new_collection"]

async def fetch_products(batch_size, last_id=None):
    query = {'_id': {'$gt': last_id}} if last_id else {}
    cursor = collection.find(query).sort('_id').limit(batch_size)
    products = await cursor.to_list(length=batch_size)
    return products

async def bulk_write_to_mongo(products):
    for product in products:
        product['_id'] = ObjectId()  # Generate a new ObjectId for each product

    try:
        result = await target_collection.insert_many(products, ordered=False)
        logger.info(f'Inserted {len(result.inserted_ids)} products into MongoDB.')
    except Exception as e:
        logger.error(f'Error inserting products into MongoDB: {e}')

async def process_batches(batch_size, concurrency_limit):
    tasks = []
    last_id = None
    while True:
        products = await fetch_products(batch_size, last_id)
        if not products:
            break
        last_id = products[-1]['_id']
        tasks.append(bulk_write_to_mongo(products))
        if len(tasks) >= concurrency_limit:
            await asyncio.gather(*tasks)
            tasks = []
    # Process remaining tasks if any
    if tasks:
        await asyncio.gather(*tasks)

async def main():
    batch_size = 1000
    concurrency_limit = 10

    start_time = time.time()
    await process_batches(batch_size, concurrency_limit)
    end_time = time.time()
    logger.info(f'Total time: {end_time - start_time:.2f} seconds.')

if __name__ == '__main__':
    asyncio.run(main()) Synchronous Script (Pymongo) import logging
import time
from bson import ObjectId
from pymongo import MongoClient

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# MongoDB setup
MONGO_URI = 'mongodb://root:root@localhost:27019'
DB_NAME = 'products'
COLLECTION_NAME = 'gmc_products'
TARGET_COLLECTION_NAME = 'new_collection'

client = MongoClient(MONGO_URI)
db = client[DB_NAME]
collection = db[COLLECTION_NAME]
target_collection = db[TARGET_COLLECTION_NAME]

def fetch_products(batch_size, last_id=None):
    query = {'_id': {'$gt': last_id}} if last_id else {}
    cursor = collection.find(query).sort('_id').limit(batch_size)
    products = list(cursor)
    return products

def bulk_write_to_mongo(products):
    for product in products:
        product['_id'] = ObjectId()  # Generate a new ObjectId for each product

    try:
        result = target_collection.insert_many(products, ordered=False)
        logger.info(f'Inserted {len(result.inserted_ids)} products into MongoDB.')
    except Exception as e:
        logger.error(f'Error inserting products into MongoDB: {e}')

def process_batches(batch_size):
    last_id = None
    while True:
        products = fetch_products(batch_size, last_id)
        if not products:
            break
        last_id = products[-1]['_id']
        bulk_write_to_mongo(products)

def main():
    batch_size = 1000

    start_time = time.time()
    process_batches(batch_size)
    end_time = time.time()
    logger.info(f'Total time: {end_time - start_time:.2f} seconds.')

if __name__ == '__main__':
    main() Results and Analysis Execution Time of Migrating 300k documents: Asynchronous script: 17.15 seconds
Synchronous script: 23.26 seconds The asynchronous script completed the task 6.11 seconds faster than the synchronous script. While this might not seem like a significant difference for a single run, it becomes more pronounced in high-load scenarios or when processing large datasets continuously. Benefits of Using Motor and Asynchronous Environment Improved Throughput: Asynchronous operations can handle more tasks concurrently, increasing overall throughput. This is especially beneficial in applications with high I/O operations, such as web servers handling multiple database queries simultaneously. Non-Blocking I/O: Asynchronous operations do not block the main thread, allowing other tasks to continue running. This results in better CPU utilization and smoother application performance, particularly under load. Scalability: Asynchronous code scales better with the number of concurrent operations. For example, a web application using Motor can handle more simultaneous requests compared to one using PyMongo. Resource Efficiency: Asynchronous operations can lead to more efficient use of system resources. For instance, the event loop in asyncio allows the application to switch between tasks, reducing idle times and improving overall efficiency. You can find the source code on the GitHub repository below: https://github.com/PylotStuff/motor-asyncio-performance?embedable=true Conclusion The choice between Motor and PyMongo depends on the specific needs of your application. For applications that require high concurrency and efficient I/O handling, Motor and the asynchronous approach offer significant advantages. However, for simpler applications or scripts where ease of implementation is a priority, PyMongo's synchronous approach might be sufficient. By leveraging asynchronous operations with Motor, you can build more scalable and performant applications, making it a worthwhile consideration for modern web development. Once, I was tasked with improving the database and general app operations of backend services which were using MongoDB as their main database. These services were part of a huge infrastructure where millions of messages came through the queues and needed to be processed based on the message actions. That means tons of DB ops each second and other additional checks while processing. Real Case Scenario The processing layer of the service was using Pymongo to interact with MongoDB and the service itself was running in a synchronous environment. Even though the database operations were handled in bulk, the performance was still not capable of handling incoming data. Synchronous code was making things even worse. The code execution waits for the result from the current operation to move forward. That's a serious bottleneck in scalable systems. This was causing queue overflows and potential data loss every time. Asynchronous Environment The solution I implemented was a combination of: Motor Asyncio Uvloop Motor Asyncio Uvloop Let's quickly go through the definitions of these items. PyMongo is the official MongoDB driver for Python, providing a simple and intuitive way to interact with MongoDB databases. It's synchronous, meaning each database operation blocks the execution of your program until it completes it, which can be a bottleneck in I/O-bound tasks. PyMongo Motor is the asynchronous driver for MongoDB, built on top of PyMongo and designed to take advantage of Python's asyncio library. Motor allows you to perform non-blocking database operations, making it suitable for high-performance applications that require concurrency. Motor To illustrate the performance differences, I prepared a stress test using two scripts: one using Motor (asynchronous) and the other using PyMongo (synchronous). Both scripts performed the same task of reading and writing documents to MongoDB in batches. Both scripts read 300k documents from the source collection and migrated them to the new target collection. Asynchronous Script (Motor) import logging
import asyncio
import time
from bson import ObjectId
from motor.motor_asyncio import AsyncIOMotorClient

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# MongoDB setup
MONGO_URI = 'mongodb://root:root@localhost:27019'
DB_NAME = 'products'
COLLECTION_NAME = 'gmc_products'

client = AsyncIOMotorClient(MONGO_URI)
db = client[DB_NAME]
collection = db[COLLECTION_NAME]
target_collection = db["new_collection"]

async def fetch_products(batch_size, last_id=None):
    query = {'_id': {'$gt': last_id}} if last_id else {}
    cursor = collection.find(query).sort('_id').limit(batch_size)
    products = await cursor.to_list(length=batch_size)
    return products

async def bulk_write_to_mongo(products):
    for product in products:
        product['_id'] = ObjectId()  # Generate a new ObjectId for each product

    try:
        result = await target_collection.insert_many(products, ordered=False)
        logger.info(f'Inserted {len(result.inserted_ids)} products into MongoDB.')
    except Exception as e:
        logger.error(f'Error inserting products into MongoDB: {e}')

async def process_batches(batch_size, concurrency_limit):
    tasks = []
    last_id = None
    while True:
        products = await fetch_products(batch_size, last_id)
        if not products:
            break
        last_id = products[-1]['_id']
        tasks.append(bulk_write_to_mongo(products))
        if len(tasks) >= concurrency_limit:
            await asyncio.gather(*tasks)
            tasks = []
    # Process remaining tasks if any
    if tasks:
        await asyncio.gather(*tasks)

async def main():
    batch_size = 1000
    concurrency_limit = 10

    start_time = time.time()
    await process_batches(batch_size, concurrency_limit)
    end_time = time.time()
    logger.info(f'Total time: {end_time - start_time:.2f} seconds.')

if __name__ == '__main__':
    asyncio.run(main()) import logging
import asyncio
import time
from bson import ObjectId
from motor.motor_asyncio import AsyncIOMotorClient

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# MongoDB setup
MONGO_URI = 'mongodb://root:root@localhost:27019'
DB_NAME = 'products'
COLLECTION_NAME = 'gmc_products'

client = AsyncIOMotorClient(MONGO_URI)
db = client[DB_NAME]
collection = db[COLLECTION_NAME]
target_collection = db["new_collection"]

async def fetch_products(batch_size, last_id=None):
    query = {'_id': {'$gt': last_id}} if last_id else {}
    cursor = collection.find(query).sort('_id').limit(batch_size)
    products = await cursor.to_list(length=batch_size)
    return products

async def bulk_write_to_mongo(products):
    for product in products:
        product['_id'] = ObjectId()  # Generate a new ObjectId for each product

    try:
        result = await target_collection.insert_many(products, ordered=False)
        logger.info(f'Inserted {len(result.inserted_ids)} products into MongoDB.')
    except Exception as e:
        logger.error(f'Error inserting products into MongoDB: {e}')

async def process_batches(batch_size, concurrency_limit):
    tasks = []
    last_id = None
    while True:
        products = await fetch_products(batch_size, last_id)
        if not products:
            break
        last_id = products[-1]['_id']
        tasks.append(bulk_write_to_mongo(products))
        if len(tasks) >= concurrency_limit:
            await asyncio.gather(*tasks)
            tasks = []
    # Process remaining tasks if any
    if tasks:
        await asyncio.gather(*tasks)

async def main():
    batch_size = 1000
    concurrency_limit = 10

    start_time = time.time()
    await process_batches(batch_size, concurrency_limit)
    end_time = time.time()
    logger.info(f'Total time: {end_time - start_time:.2f} seconds.')

if __name__ == '__main__':
    asyncio.run(main()) Synchronous Script (Pymongo) import logging
import time
from bson import ObjectId
from pymongo import MongoClient

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# MongoDB setup
MONGO_URI = 'mongodb://root:root@localhost:27019'
DB_NAME = 'products'
COLLECTION_NAME = 'gmc_products'
TARGET_COLLECTION_NAME = 'new_collection'

client = MongoClient(MONGO_URI)
db = client[DB_NAME]
collection = db[COLLECTION_NAME]
target_collection = db[TARGET_COLLECTION_NAME]

def fetch_products(batch_size, last_id=None):
    query = {'_id': {'$gt': last_id}} if last_id else {}
    cursor = collection.find(query).sort('_id').limit(batch_size)
    products = list(cursor)
    return products

def bulk_write_to_mongo(products):
    for product in products:
        product['_id'] = ObjectId()  # Generate a new ObjectId for each product

    try:
        result = target_collection.insert_many(products, ordered=False)
        logger.info(f'Inserted {len(result.inserted_ids)} products into MongoDB.')
    except Exception as e:
        logger.error(f'Error inserting products into MongoDB: {e}')

def process_batches(batch_size):
    last_id = None
    while True:
        products = fetch_products(batch_size, last_id)
        if not products:
            break
        last_id = products[-1]['_id']
        bulk_write_to_mongo(products)

def main():
    batch_size = 1000

    start_time = time.time()
    process_batches(batch_size)
    end_time = time.time()
    logger.info(f'Total time: {end_time - start_time:.2f} seconds.')

if __name__ == '__main__':
    main() import logging
import time
from bson import ObjectId
from pymongo import MongoClient

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# MongoDB setup
MONGO_URI = 'mongodb://root:root@localhost:27019'
DB_NAME = 'products'
COLLECTION_NAME = 'gmc_products'
TARGET_COLLECTION_NAME = 'new_collection'

client = MongoClient(MONGO_URI)
db = client[DB_NAME]
collection = db[COLLECTION_NAME]
target_collection = db[TARGET_COLLECTION_NAME]

def fetch_products(batch_size, last_id=None):
    query = {'_id': {'$gt': last_id}} if last_id else {}
    cursor = collection.find(query).sort('_id').limit(batch_size)
    products = list(cursor)
    return products

def bulk_write_to_mongo(products):
    for product in products:
        product['_id'] = ObjectId()  # Generate a new ObjectId for each product

    try:
        result = target_collection.insert_many(products, ordered=False)
        logger.info(f'Inserted {len(result.inserted_ids)} products into MongoDB.')
    except Exception as e:
        logger.error(f'Error inserting products into MongoDB: {e}')

def process_batches(batch_size):
    last_id = None
    while True:
        products = fetch_products(batch_size, last_id)
        if not products:
            break
        last_id = products[-1]['_id']
        bulk_write_to_mongo(products)

def main():
    batch_size = 1000

    start_time = time.time()
    process_batches(batch_size)
    end_time = time.time()
    logger.info(f'Total time: {end_time - start_time:.2f} seconds.')

if __name__ == '__main__':
    main() Results and Analysis Execution Time of Migrating 300k documents: Asynchronous script: 17.15 seconds Synchronous script: 23.26 seconds Asynchronous script: 17.15 seconds Synchronous script: 23.26 seconds The asynchronous script completed the task 6.11 seconds faster than the synchronous script. 6.11 seconds While this might not seem like a significant difference for a single run, it becomes more pronounced in high-load scenarios or when processing large datasets continuously. Benefits of Using Motor and Asynchronous Environment Improved Throughput : Asynchronous operations can handle more tasks concurrently, increasing overall throughput. This is especially beneficial in applications with high I/O operations, such as web servers handling multiple database queries simultaneously. Improved Throughput Non-Blocking I/O : Asynchronous operations do not block the main thread, allowing other tasks to continue running. This results in better CPU utilization and smoother application performance, particularly under load. Non-Blocking I/O Scalability : Asynchronous code scales better with the number of concurrent operations. For example, a web application using Motor can handle more simultaneous requests compared to one using PyMongo. Scalability Resource Efficiency : Asynchronous operations can lead to more efficient use of system resources. For instance, the event loop in asyncio allows the application to switch between tasks, reducing idle times and improving overall efficiency. Resource Efficiency You can find the source code on the GitHub repository below: https://github.com/PylotStuff/motor-asyncio-performance?embedable=true https://github.com/PylotStuff/motor-asyncio-performance?embedable=true Conclusion The choice between Motor and PyMongo depends on the specific needs of your application. For applications that require high concurrency and efficient I/O handling, Motor and the asynchronous approach offer significant advantages. However, for simpler applications or scripts where ease of implementation is a priority, PyMongo's synchronous approach might be sufficient. By leveraging asynchronous operations with Motor, you can build more scalable and performant applications, making it a worthwhile consideration for modern web development.

Boost MongoDB Performance: Motor Client vs PyMongo - Which is Faster?

Welcome to Dapp Mentors! In this article, we'll delve into the top three programming languages for blockchain development: Solidity, Rust, and Motoko. As a seasoned blockchain developer and educator, I'll share my expertise to help you transition into web3 development. You can watch this article as a video below or continue reading through it. https://www.youtube.com/watch?v=x6R-EYsjr20&embedable=true Solidity: The Favorite Solidity is our top choice due to its simplicity, ease of use, and wide community support. It's the most widely used language for blockchain development, and its simplicity makes it an excellent choice for beginners. However, it has limitations: Scalability: Solidity is not very scalable, especially on layer one. This limitation has led to the development of layer two solutions built on top of Ethereum. Security: Solidity is not as secure as Rust. While it's widely adopted, its security vulnerabilities can be a concern. In my opinion, Solidity is a great language for beginners, but its limitations make it less suitable for large-scale applications. Top Companies Using Solidity Ethereum
Binance Smart Chain
Polygon Network Watch our video on "Building a Simple Smart Contract with Solidity" to learn more about how to implement Solidity in your blockchain projects! https://www.youtube.com/watch?v=UbidE-tAibk&embedable=true Rust: The Secure and Scalable Rust is a more mature language than Solidity, offering security and scalability. Its advantages include: Security: Rust is a more secure language than Solidity, making it an excellent choice for high-stakes applications.
Scalability: Rust is highly scalable, making it suitable for large-scale blockchain applications. In my opinion, Rust is a powerful language that offers a great balance between security and scalability, making it an excellent choice for large-scale blockchain applications. Top Companies Using Rust Solana
Polkadot
Stellar (uses Rust under the hood for their smart contract framework Soroban) Motoko: The Newcomer Motoko is exclusive to the Internet Computer Protocol (ICP) ecosystem. While it's highly scalable and performant, its adoption is limited, and it's relatively new. Its advantages include: Scalability: Motoko is highly scalable, making it suitable for large-scale blockchain applications.
Ease of use: Motoko is easy to learn and use, even for developers new to blockchain development. In my opinion, Motoko is a promising language that offers a unique set of features and advantages. Still, its limited adoption and exclusive nature make it less versatile than Solidity and Rust. Conclusion In conclusion, each language has its strengths and weaknesses. Solidity is simple and widely adopted, Rust offers security and scalability, and Motoko is unique to ICP. Consider learning Rust for its versatility across blockchain networks. Join the conversation, and share your thoughts on your favorite programming language and why you prefer it. About the Author
Darlington Gospel is a seasoned blockchain developer and educator with over 8 years of experience. He specializes in blockchain development, fullstack software development, technical instruction, and content creation. About Dapp Mentors Dapp Mentors is a community dedicated to helping developers transition into web3 development. Our team includes experienced blockchain developers and educators who are passionate about sharing their knowledge. Watch our video on "Getting Started with Blockchain Development" to learn more about our community and resources! https://www.youtube.com/watch?v=AKZ8mkR9XS0&embedable=true For more information, contact us at: LinkedIn Discord X-twitter YouTube Website Welcome to Dapp Mentors! In this article, we'll delve into the top three programming languages for blockchain development: Solidity, Rust, and Motoko. As a seasoned blockchain developer and educator, I'll share my expertise to help you transition into web3 development. You can watch this article as a video below or continue reading through it. https://www.youtube.com/watch?v=x6R-EYsjr20&embedable=true https://www.youtube.com/watch?v=x6R-EYsjr20&embedable=true Solidity: The Favorite Solidity: The Favorite Solidity is our top choice due to its simplicity, ease of use, and wide community support. It's the most widely used language for blockchain development, and its simplicity makes it an excellent choice for beginners. However, it has limitations: Scalability: Solidity is not very scalable, especially on layer one. This limitation has led to the development of layer two solutions built on top of Ethereum. Scalability : Solidity is not very scalable, especially on layer one. This limitation has led to the development of layer two solutions built on top of Ethereum. Scalability Security: Solidity is not as secure as Rust. While it's widely adopted, its security vulnerabilities can be a concern. Security : Solidity is not as secure as Rust. While it's widely adopted, its security vulnerabilities can be a concern. Security In my opinion, Solidity is a great language for beginners, but its limitations make it less suitable for large-scale applications. Top Companies Using Solidity Top Companies Using Solidity Ethereum Binance Smart Chain Polygon Network Ethereum Binance Smart Chain Polygon Network Watch our video on "Building a Simple Smart Contract with Solidity" to learn more about how to implement Solidity in your blockchain projects! Watch our video on "Building a Simple Smart Contract with Solidity" to learn more about how to implement Solidity in your blockchain projects! https://www.youtube.com/watch?v=UbidE-tAibk&embedable=true https://www.youtube.com/watch?v=UbidE-tAibk&embedable=true Rust: The Secure and Scalable Rust: The Secure and Scalable Rust is a more mature language than Solidity, offering security and scalability. Its advantages include: Security: Rust is a more secure language than Solidity, making it an excellent choice for high-stakes applications. Scalability: Rust is highly scalable, making it suitable for large-scale blockchain applications. Security : Rust is a more secure language than Solidity, making it an excellent choice for high-stakes applications. Security Scalability : Rust is highly scalable, making it suitable for large-scale blockchain applications. Scalability In my opinion, Rust is a powerful language that offers a great balance between security and scalability, making it an excellent choice for large-scale blockchain applications. Top Companies Using Rust Top Companies Using Rust Solana Polkadot Stellar (uses Rust under the hood for their smart contract framework Soroban) Solana Polkadot Stellar (uses Rust under the hood for their smart contract framework Soroban) Motoko: The Newcomer Motoko: The Newcomer Motoko is exclusive to the Internet Computer Protocol (ICP) ecosystem. While it's highly scalable and performant, its adoption is limited, and it's relatively new. Its advantages include: Scalability: Motoko is highly scalable, making it suitable for large-scale blockchain applications. Ease of use: Motoko is easy to learn and use, even for developers new to blockchain development. Scalability : Motoko is highly scalable, making it suitable for large-scale blockchain applications. Scalability Ease of use : Motoko is easy to learn and use, even for developers new to blockchain development. Ease of use In my opinion, Motoko is a promising language that offers a unique set of features and advantages. Still, its limited adoption and exclusive nature make it less versatile than Solidity and Rust. Conclusion Conclusion In conclusion, each language has its strengths and weaknesses. Solidity is simple and widely adopted, Rust offers security and scalability, and Motoko is unique to ICP. Consider learning Rust for its versatility across blockchain networks. Join the conversation, and share your thoughts on your favorite programming language and why you prefer it. About the Author Darlington Gospel is a seasoned blockchain developer and educator with over 8 years of experience. He specializes in blockchain development, fullstack software development, technical instruction, and content creation. About the Author About Dapp Mentors About Dapp Mentors Dapp Mentors is a community dedicated to helping developers transition into web3 development. Our team includes experienced blockchain developers and educators who are passionate about sharing their knowledge. Watch our video on "Getting Started with Blockchain Development" to learn more about our community and resources! Watch our video on "Getting Started with Blockchain Development" to learn more about our community and resources! https://www.youtube.com/watch?v=AKZ8mkR9XS0&embedable=true https://www.youtube.com/watch?v=AKZ8mkR9XS0&embedable=true For more information, contact us at: For more information, contact us at: LinkedIn LinkedIn Discord Discord X-twitter X-twitter YouTube YouTube Website Website

Top Smart Contract Languages in 2024: Solidity, Rust, and Motoko

Are you ready to immerse yourself in the world of animation like never before? Deep Motion's groundbreaking AI animation technology is here to revolutionize your creative process. SayMotion SayMotion allows users to effortlessly generate stunning 3D animations by simply describing their vision in text form. Here's How It Works: SayMotion leverages generative AI to interpret your text prompts and translate them into intricate 3D motions. With SayMotion, you can take the director's seat and bring your digital actors to life with just a few words. A few tips to get you started : Before diving into animation, take the time to envision your desired outcome. Describe your ideas in clear, concise text prompts to kickstart the animation process. Having a clear vision will guide you through the creative journey and ensure your animations align with your objectives. Start with a Clear Vision : When beginning your animation project, selecting or designing the perfect avatar is crucial. Consider the personality, style, and characteristics of your avatar to ensure it aligns with your creative vision. Whether you're creating a character for a game, marketing or training video, virtual experience, or motion graphics project, choosing the right avatar sets the stage for a compelling animation. Select or Design the Right Avatar : SayMotion's dynamic motion editing feature empowers you to fine-tune and enhance your animations with ease. Experiment with different movements, transitions, and effects to add depth and realism to your creations. By embracing dynamic motion editing, you can refine your animations and ensure they captivate your audience. Embrace Dynamic Motion Editing : Seamlessly integrate your animations into your projects by exporting them in various formats (.FBX, .GLB, .BVH, or .MP4). Whether you're working on a game, virtual experience, or motion graphics project, SayMotion's integration capabilities make it easy to incorporate your animations into your work. Utilize Seamless Integration Maximize the impact of your projects by seamlessly integrating your animations and enhancing their visual appeal. : Take advantage of SayMotion's web-based accessibility to work on your animations from anywhere with an internet connection and a web browser. Explore Web-Based Accessibility Whether you're at home, in the office, or on the go, SayMotion allows you to access the platform and collaborate with team members effortlessly. Explore the convenience of web-based accessibility to stay productive and creative wherever you are. The SayMotion Open Beta launches on March 19, 2024, offering enthusiasts the opportunity to explore the platform's features and provide feedback. Join the Open Beta to dive into the future of animation and discover how SayMotion can elevate your creative projects. With its user-friendly interface and powerful AI capabilities, SayMotion is poised to redefine the animation landscape and empower creators worldwide.

Mastering Animation: Tips for Harnessing Deep Motion's New SayMotion AI Animation Technology

মাস্টারিং অ্যানিমেশন: ডিপ মোশনের নতুন SayMotion AI অ্যানিমেশন প্রযুক্তি ব্যবহার করার জন্য টিপস

Beherrschen der Animation: Tipps zur Nutzung der neuen SayMotion AI-Animationstechnologie von Deep Motion

Dominar la animación: consejos para aprovechar la nueva tecnología de animación SayMotion AI de Deep Motion

Maîtriser l'animation : conseils pour exploiter la nouvelle technologie d'animation SayMotion AI de Deep Motion

एनिमेशन में महारत हासिल करना: डीप मोशन की नई सेमोशन एआई एनिमेशन तकनीक का उपयोग करने के लिए युक्तियाँ

アニメーションのマスタリング: Deep Motion の新しい SayMotion AI アニメーション テクノロジーを活用するためのヒント

애니메이션 마스터링: Deep Motion의 새로운 SayMotion AI 애니메이션 기술 활용을 위한 팁

Dominando a animação: dicas para aproveitar a nova tecnologia de animação SayMotion AI da Deep Motion

Освоение анимации: советы по использованию новой технологии анимации AI SayMotion от Deep Motion

Animasyonda Uzmanlaşmak: Deep Motion'ın Yeni SayMotion Yapay Zeka Animasyon Teknolojisinden Yararlanmak için İpuçları

Làm chủ hoạt hình: Mẹo khai thác công nghệ hoạt hình AI SayMotion mới của Deep Motion

掌握动画：利用 Deep Motion 全新 SayMotion AI 动画技术的技巧

TOPTAL, LLC v. DENIS GROSZ Court Filing, retrieved on February 26, 2024, is part of . You can jump to any part in this filing . This part is 6 of 7. HackerNoon’s Legal PDF Series here CONCLUSION The Court grants Toptal's Motion for Summary Judgment, in part, and dismisses Count I of Mr. Grosz's counterclaim.[6] IT IS SO ORDERED. DATED: October 11, 2023. Continue Reading . Here [6] For the reasons articulated in this Order the Court finds that Mr. Grosz is not entitled to summary judgment on Count I of his counterclaim, Toptal's eighth claim for relief or Toptal's second affirmative defense and Mr. Grosz's motion filed August 28, 2023, is denied. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on October 11, 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. media.licdn.com

Court Grants Toptal's Motion for Summary Judgment

LONDON, United Kingdom, December 18th, 2023/Chainwire/--Today, Veloce Media Group proudly announces the launch of its partnership with , an innovative multiplayer racing management and simulation platform set to redefine the landscape of virtual motorsports. Racino Through the incorporation of the into Racino’s ecosystem, players can acquire various collectible cars, liveries, and driver cosmetics. The heightened emphasis on ownership brings a fresh wave of excitement to each part of the gameplay, creating a more immersive and active playing environment. VEXT token Racino will transform the future of motorsport management games, as players not only step into the shoes of a team principal but also take ownership and development of their cars, where strategic choices drastically impact race outcomes, public renown, and tangible rewards. Unlike most racing games on the market today, Racino elevates itself by mirroring real-life dynamics, where victories and defeats in motorsport management carry tangible financial weight, blending the thrill of racing with strategic financial planning and skill based gaming. https://www.youtube.com/embed/0SZ7j64mTxQ Racino’s Key Features: Digital Ownership: Players have exclusive rights to digital collectibles like cars, liveries, and driver cosmetics. These items can appreciate in value offering exciting trading and selling opportunities based on market demand or scarcity. Real Stakes & Rewards: Racino utilizes the same exhilarating 'risk vs reward' mechanics of the real world, allowing players to revel in the tangible outcomes of their in-game decisions. Winning races, managing reward mechanisms for greater gains, honing skills through gameplay, and increasing the rarity of their assets are not just for fun – they are strategic moves that lead to real, measurable rewards, reflecting the true nature of competitive racing. Elevated Multiplayer: Racino brings a distinct multiplayer experience to your browser, connecting players across the globe to build their racing empires and strategize against friends and foes alike. Secure your place in tightly contested grids and manage your team to motorsport glory -- where your skill on the track shapes your legacy in the thrilling world of virtual motorsports. 💻 Hi-Fi Simulations: Situated at the forefront of virtual motorsport realism, Racino’s cutting-edge technology and complex AI algorithms create a deeply immersive racing experience. Every decision, from track selection to car modifications, interacts with dynamic weather and realistic physics right before your eyes, in stunning 3D & 2D. 🥇 Mastery Matters: Steer your own path in Racino, where every choice dictates success. Each car, equally matched in potential, offers unique configurations begging for exploration and strategic mastery. Skillfully manage your budget, parts, and tunings to unveil your car’s full potential, as winning is about clever strategy, not a roll of the dice. 🚨 VEXT Integration: Racino, Integrated with Veloce Media Group's VEXT token, enables players to purchase virtual racing assets, blending inherent and performance-based value. This system not only brings the excitement of the track to your device but also allows for seamless transactions and access to exclusive content, marrying tangible rewards with the thrill of motorsport gaming. Rupert Svendsen-Cook, Founder of the Veloce Media Group said : “Veloce has and always will be an incredibly effective marketing funnel for many racing games however this moment marks the chapter of progressing to promoting our own gaming titles. Racino really is the most immersive and engaging racing game to come to web3, nothing comes close ! Veloce has 38 million racing gaming fans within our network and integrating VEXT within Racino is an incredibly compelling proposition for all stakeholders - primarily our community! With its pioneering approach to combining real stakes with immersive gameplay, Racino offers players a unique and thrilling experience. Racino will captivate our gamers with its unbelievably realistic simulation and strategic challenges. To say this is a game changer would be an understatement!” Additionally, Lorenzo George, CEO of Racino, referenced their partnership with Veloce Media Group "as essential to reaching one of the most passionate audiences in gaming and motorsports". Lorenzo also discussed the integration of the VEXT token into Racino, which enriches the gaming experience by offering diverse engagement and reward opportunities for players. Racino is gearing up for its Q1 2024 launch, offering an exhilarating blend of motorsports and skill-based gaming. Be one of the first to join, by signing up for the Alpha on Racino.io. Users are welcome to drive into the future of virtual racing and stay informed by following @Racinogaming & @Vextoken on X. About Veloce Media Group Founded in 2018, Veloce is a multi-pillared gaming and sports media group operating across some of the most innovative, fast-growing, and future-focused sectors in the UK. Headquartered in London, the Veloce brand comprises the industry-leading gaming and racing platform, Veloce Esports, and race-winning outfit, Veloce Racing, currently competing in the renowned Extreme E championship. As the world’s largest digital racing media network, Veloce has so far attracted over 35 million subscribers and nearly one billion monthly views with a focus on esports, gaming, purpose-driven motorsport, and Web3. Veloce is partnered with a number of high-profile teams from across the globe, running multiple gaming and esports team operations, including Mercedes AMG, Ferrari, and Yas Heat. Well established JV sub-brands, including Lando Norris’ gaming and lifestyle brand Quadrant, make up another key aspect of Veloce’s vast global network. For more information: | | | | | | | | | | | | | | Website Telegram Group Telegram Channel Twitter Discord Instagram YouTube CoinMarketCap CoinGecko ByBit MEXC Toobit Poloniex DigiFinex WhiteBIT Contact Head Of Digital Marketing Louis Broomfield Veloce Media Group louis@veloce.gg This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Veloce Media Group Partners With Racino To Pioneer Virtual Motorsports With Real Stake

ভার্চুয়াল মোটরস্পোর্টসকে রিয়েল স্টেক দিয়ে অগ্রগামী করতে Racino-এর সাথে Veloce Media Group অংশীদার

Veloce Media Group arbeitet mit Racino zusammen, um mit echtem Einsatz Pionierarbeit im virtuellen Motorsport zu leisten

Veloce Media Group se asocia con Racino para ser pionero en los deportes de motor virtuales con un interés real

Veloce Media Group s'associe à Racino pour être pionnier dans le sport automobile virtuel avec un enjeu réel

रियल हिस्सेदारी के साथ वर्चुअल मोटरस्पोर्ट्स में अग्रणी बनने के लिए वेलोस मीडिया ग्रुप ने रैसिनो के साथ साझेदारी की

Veloce Media Group が Racino と提携し、実質出資でバーチャル モータースポーツを開拓

Veloce Media Group, Racino와 파트너십을 맺고 실제 지분을 활용한 가상 모터스포츠 개척

Veloce Media Group faz parceria com Racino para ser pioneiro no automobilismo virtual com participação real

Veloce Media Group сотрудничает с Рачино, чтобы стать пионером виртуального автоспорта с реальной ставкой

Veloce Media Group, Gerçek Bahisle Sanal Motor Sporlarına Öncülük Etmek İçin Racino ile Ortaklık Kuruyor

Tập đoàn truyền thông Veloce hợp tác với Racino để tiên phong trong lĩnh vực đua xe thể thao ảo với cổ phần thực sự

Veloce Media Group 与 Racino 合作，以真实股份开创虚拟赛车运动

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