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    Understanding the System 2 Model: OpenAI’s New Approach to LLM Reasoni

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    Understanding the System 2 Model: OpenAI’s New Approach to LLM Reasoni


    OpenAI recently launched two new models, OpenAI o1-preview and OpenAI o1-mini, representing a significant step forward in large language models (LLMs). These models are being hailed as the first commercial implementations of “System 2” reasoning models, a concept that contrasts with the traditional “System 1” AI models we’ve been using since the release of ChatGPT in 2022. But what exactly is a System 2 model, and how does it differ from System 1? This article dives into the techniques, concepts, and innovations behind this new wave of reasoning-based AI.

    What Is the System 2 Model?

    The idea of System 1 and System 2 thinking originates from Daniel Kahneman’s 2011 book Thinking, Fast and Slow. System 1 refers to fast, intuitive thinking, while System 2 involves slower, more deliberate, and analytical thinking. Similarly, in AI, System 1 models respond quickly to prompts based on learned patterns, whereas System 2 models engage in more thoughtful, step-by-step reasoning.

    Until now, most of the AI models we have interacted with fall into the System 1 category, offering immediate responses based on previous training. System 2 models, like the new OpenAI o1, are designed to break down complex tasks, analyze different scenarios, and deliver more reasoned responses—mimicking a more human-like reasoning process.

    The Shift from System 1 to System 2 in AI

    When OpenAI launched ChatGPT in November 2022, it quickly became clear that AI models could handle a wide variety of tasks but often struggled with more complex, multi-step problems. System 1 models are excellent for straightforward queries, but tasks that require deeper analysis have often been challenging.

    System 2 models, by contrast, approach problems methodically. They break tasks into smaller steps, assess different approaches, and evaluate outcomes before delivering a final response. This transition from reactive to deliberate problem-solving can revolutionize how AI handles more nuanced, never-before-seen problems.

    Key Concepts Behind System 2 Models

    1. Chain of Thought (CoT) Reasoning

    The foundation of System 2 models lies in their ability to use Chain of Thought (CoT) reasoning. This involves generating intermediate steps before arriving at a final answer, helping the model process complex problems more effectively. This approach, popularized by papers such as Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022), allows the model to reason through a problem, much like a human would break down a difficult question.

    2. Tree of Thoughts

    Another technique integrated into System 2 models is the Tree of Thoughts (2023). This method expands on the CoT approach by exploring multiple paths of reasoning simultaneously. The model can evaluate different strategies in parallel, selecting the most promising path based on logical outcomes.

    3. Branch-Solve-Merge (BSM)

    A more recent innovation is the Branch-Solve-Merge (2023) technique. This allows the model to branch off into different potential solutions, work through each one, and then merge the best elements to form a final, optimized solution.

    4. System 2 Attention

    System 2 Attention is another key aspect of these models. While traditional models use attention mechanisms to focus on important words or tokens in a prompt, System 2 models pay attention to the most critical steps in a reasoning process. By weighing certain reasoning paths more heavily, these models can make more informed decisions throughout the problem-solving process.

    What Are Reasoning Tokens?

    One of the biggest breakthroughs in System 2 models is the introduction of reasoning tokens. These tokens serve as a guide for the AI, directing it through each step of the reasoning process. Rather than simply responding to a prompt, the model uses these tokens to think through a problem more thoroughly.

    Types of Reasoning Tokens

    There are several types of reasoning tokens used in System 2 models, each designed for a specific purpose:

    Self-Reasoning Tokens: These tokens help the model reason about the problem by itself, almost like a self-guided brainstorming session.

    Planning Tokens: These tokens help the model plan out its steps in advance, ensuring that it follows a logical path toward solving the problem.

    Examples of reasoning tokens might include commands like <Analyze_Problem>, <Generate_Hypothesis>, <Evaluate_Evidence>, and <Draw_Conclusion>. These tokens are invisible to the user but are crucial in guiding the AI through a complex reasoning process.

    System 2 models often generate intermediate outputs or temporary conclusions during reasoning. These outputs allow the model to assess its progress before giving a final answer. However, these intermediate steps are removed before the user sees the final output. This behind-the-scenes reasoning process makes System 2 models capable of solving more intricate problems than their System 1 predecessors.

    The Role of Reinforcement Learning (RL)

    OpenAI has also integrated Reinforcement Learning (RL) into its System 2 models. RL helps the model focus on the most promising reasoning paths while avoiding less fruitful ones. By continuously learning from its mistakes, the model improves over time, improving at solving complex problems with each iteration.

    This learning mechanism allows the AI to excel at tasks involving uncertainty or long-term planning—areas where traditional models tend to falter. RL ensures that the model doesn’t waste resources exploring unproductive paths and instead zeroes in on the best solutions faster.

    Decision Gates: Ensuring Thoughtful Responses

    System 2 models also use Decision Gates, which act as checkpoints during the reasoning process. These gates determine whether the model has engaged in sufficient reasoning before responding. If the reasoning is incomplete, the model continues to process the task until a satisfactory solution is found.

    How System 2 Models Excel at Complex Tasks

    Thanks to their CoT reasoning, planning tokens, and reinforcement learning techniques, System 2 models are particularly well-suited for complex, never-seen-before tasks. For example, deciphering ancient texts or installing a Wi-Fi network in a large stadium can be broken down into manageable steps by using specialized reasoning tokens.

    Example: Deciphering Corrupted Texts

    In a scenario where a System 2 model is tasked with deciphering a corrupted text, the reasoning tokens might include:

    <analyze_script>: Directs the model to analyze the text’s structure.

    <identify_patterns>: Guides the model in looking for recurring themes or patterns.

    <cross_reference>: Prompts the model to compare the corrupted text with known texts.

    These tokens help the model approach the task step-by-step, just as a human expert would.

    System 2 in Action: Complex Wi-Fi Installations

    Similarly, when designing a Wi-Fi installation in a complex environment like a stadium, the model could use tokens like:

    <Analyze_Environment>: To understand the stadium’s layout.

    <Determine_AP_Locations>: To decide the best places to install access points.

    <Simulate_Traffic>: To simulate a full stadium and assess Wi-Fi performance.

    By simulating different scenarios and solutions, the model ensures that the final outcome is optimized for real-world conditions.

    Conclusion: The Future of AI with System 2 Models

    System 2 models represent a major leap forward in AI capabilities, offering a new level of reasoning and problem-solving that traditional models couldn’t achieve. These models can tackle more complex, multi-step tasks with greater accuracy by utilizing techniques like Chain of Thought reasoning, reinforcement learning, and planning tokens. Although System 2 AI is still evolving, its potential to reshape industries like engineering, science, and data analysis is undeniable.

    FAQs

    What is the difference between System 1 and System 2 models?

    System 1 models provide immediate, intuitive responses, while System 2 models engage in slower, more deliberate reasoning processes.

    What are reasoning tokens in System 2 AI?

    Reasoning tokens guide the model through each step of solving complex problems, breaking down tasks into smaller, manageable steps.

    How does reinforcement learning improve System 2 models? Reinforcement learning helps the model focus on the most promising reasoning paths, learning from mistakes to improve over time.

    What are Decision Gates in System 2 models?

    Decision Gates ensure that the model has completed sufficient reasoning before delivering a final response.

    How does the Chain of Thought technique help System 2 models?

    Chain of Thought allows the model to break down complex tasks into intermediate steps, enabling a more thorough and reasoned approach.



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    Hyperledger Web3j: HSM support for AWS KMS

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    Hyperledger Web3j: HSM support for AWS KMS


    In the world of digital security, protecting sensitive data with robust encryption is essential. AWS Key Management Service (KMS) plays a crucial role in this space. It serves as a highly secure, fully managed service for creating and controlling cryptographic keys. What many may not realize is that AWS KMS itself operates as a Hardware Security Module (HSM), offering the same level of security you’d expect from dedicated hardware solutions.

    An HSM is a physical device designed to securely generate, store, and manage encryption keys, and AWS KMS delivers this functionality in a cloud-native way. Beyond key management, AWS KMS with HSM support can also be used to sign cryptographic transactions. This provides a trusted, hardware-backed way to secure blockchain interactions, digital signatures, and more. This article will cover  how AWS KMS functions as an HSM, the benefits of using it to sign crypto transactions, and how it fits into a broader security strategy.

    In Hyperledger Web3j, support for HSM was introduced two years ago, providing users with a secure method for managing cryptographic keys. For more details, you can refer to the official documentation.

    However, despite this integration, many users have encountered challenges in adopting and implementing HSM interfaces, particularly when using the AWS KMS module. To address these difficulties, a ready-to-use implementation has been added specifically for AWS KMS HSM support. This simplifies the integration process, making it easier for users to leverage AWS KMS for secure transaction signing without the complexity of manual configurations.

    The class, HSMAwsKMSRequestProcessor, is an implementation of the HSMRequestProcessor interface, which is responsible for facilitating interaction with an HSM. This newly implemented class contains all the essential code required to communicate with AWS KMS, enabling the retrieval of data signed with the correct cryptographic signature. It simplifies the process of using AWS KMS as an HSM by handling the intricacies of signature generation and ensuring secure transaction signing without additional development overhead.

    Here is a snippet with the most important actions of the callHSM method:


    @Override
    public Sign.SignatureData callHSM(byte[] dataToSign, HSMPass pass) {

    // Create the SignRequest for AWS KMS
    var signRequest =
    SignRequest.builder()
    .keyId(keyID)
    .message(SdkBytes.fromByteArray(dataHash))
    .messageType(MessageType.DIGEST)
    .signingAlgorithm(SigningAlgorithmSpec.ECDSA_SHA_256)
    .build();

    // Sign the data using AWS KMS
    var signResult = kmsClient.sign(signRequest);
    var signatureBuffer = signResult.signature().asByteBuffer();

    // Convert the signature to byte array
    var signBytes = new byte[signatureBuffer.remaining()];
    signatureBuffer.get(signBytes);

    // Verify signature osn KMS
    var verifyRequest =
    VerifyRequest.builder()
    .keyId(keyID)
    .message(SdkBytes.fromByteArray(dataHash))
    .messageType(MessageType.DIGEST)
    .signingAlgorithm(SigningAlgorithmSpec.ECDSA_SHA_256)
    .signature(SdkBytes.fromByteArray(signBytes))
    .build();

    var verifyRequestResult = kmsClient.verify(verifyRequest);
    if (!verifyRequestResult.signatureValid()) {
    throw new RuntimeException(“KMS signature is not valid!”);
    }

    var signature = CryptoUtils.fromDerFormat(signBytes);
    return Sign.createSignatureData(signature, pass.getPublicKey(), dataHash);
    }

    NOTE!

    In order to use this properly, the type of key spec created in AWS KMS must be ECC_SECG_P256K1. This is specific to the crypto space, especially to EVM. Using any other key will result in a mismatch error when the  data signature is created.

    Example

    Here is a short example of how to call the callHSM method from the library:

    public static void main(String[] args) throws Exception {
    KmsClient client = KmsClient.create();

    // extract the KMS key
    byte[] derPublicKey = client
    .getPublicKey((var builder) -> {
    builder.keyId(kmsKeyId);
    })
    .publicKey()
    .asByteArray();
    byte[] rawPublicKey = SubjectPublicKeyInfo
    .getInstance(derPublicKey)
    .getPublicKeyData()
    .getBytes();

    BigInteger publicKey = new BigInteger(1, Arrays.copyOfRange(rawPublicKey, 1, rawPublicKey.length));

    HSMPass pass = new HSMPass(null, publicKey);

    HSMRequestProcessor signer = new HSMAwsKMSRequestProcessor(client, kmsKeyId);
    signer.callHSM(data, pass);
    }

    Conclusion

    AWS KMS, with its built-in HSM functionality, offers a powerful solution for securely managing and signing cryptographic transactions. Despite initial challenges faced by users in integrating AWS KMS with Hyperledger Web3j, the introduction of the HSMAwsKMSRequestProcessor class has made it easier to adopt and implement. This ready-to-use solution simplifies interactions with AWS KMS, allowing users to securely sign data and transactions with minimal configuration. By leveraging this tool, organizations can enhance their security posture while benefiting from the convenience of AWS’s cloud-native HSM capabilities.

     



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    Mt. Gox Moves $2.2 Billion in Bitcoin Following Repayment Timeline Extension – Decrypt

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    Mt. Gox Moves .2 Billion in Bitcoin Following Repayment Timeline Extension – Decrypt



    Mt. Gox moved another $2.2 billion worth of Bitcoin on Monday amid an extended period of volatility that has seen the crypto oscillating between $73,000 and $65,000 over the past few weeks.

    The defunct crypto exchange’s recent transfer was identified through wallets tracked by blockchain analytics firm Arkham Intelligence, which disclosed the movement of 32,371 BTC, with the majority—30,371 BTC—directed to wallet address “1FG2C…Rveoy.” 

    An additional 2,000 BTC was initially moved to a Mt. Gox cold wallet before being transferred to a separate unmarked wallet, Arkham data shows.

    It comes as Bitcoin briefly slid below $68,000 during Asian market trading, recording a 1% decline over 24 hours. The asset has since clawed back losses, trading at $68,700.

    Market analysts anticipate heightened volatility this week, projecting potential price swings of up to $8,000 as U.S. election activities add to market uncertainty.

    Monday’s significant movement also follows a smaller transfer of 500 BTC to two unidentified wallets in late September, which marked the exchange’s first activity since that period. 

    These transfers historically precede distributions to creditors through established crypto exchanges, including Bitstamp and Kraken.

    Notably, the timing of this latest transfer coincides with Mt. Gox’s recent announcement that it is extending its repayment deadline by one year. 

    This extension affects thousands of creditors who lost assets during the exchange’s 2014 security breach, which resulted in the theft of approximately 850,000 BTC—valued at over $15 billion at current market prices.

    Mt. Gox’s historical significance in the crypto industry ecosystem adds weight to these movements as well. 

    Founded in 2010, the exchange once dominated Bitcoin trading, handling over 70% of global transactions before its collapse after a series of hacks between 2011 and 2014. 

    The security breach marked one of the industry’s most significant setbacks, leading to years of legal proceedings and recovery efforts.

    In any case, the repayment process represents one of the cryptocurrency industry’s longest-running recovery efforts, with implications extending beyond immediate market dynamics. 

    While short-term volatility is expected, the market’s maturity since Mt. Gox’s 2014 collapse may help buffer against dramatic price swings, with Bitcoin often displaying resilience against such events.

    Edited by Sebastian Sinclair

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    Run Language Models Locally with Ollama: A Comprehensive Guide

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    Run Language Models Locally with Ollama: A Comprehensive Guide


    Ollama is an open-source platform that simplifies the process of setting up and running large language models (LLMs) on your local machine. With Ollama, you can easily download, install, and interact with LLMs without the usual complexities.

    To get started, you can download Ollama from here. Once installed, open a terminal and type:

    ollama run phi3

    OR

    ollama pull phi3
    ollama run phi3

    This will download the required layers of the model “phi3”. After the model is loaded, Ollama enters a REPL (Read-Eval-Print Loop), which is an interactive environment where you can input commands and see immediate results.

    To explore the available commands within the REPL, type:

    /?

    This will show you a list of commands you can use. For example, to exit the REPL, type /bye. You can also display the models you have installed using:

    ollama ls

    If you need to remove any model, use:

    ollama rm

    For a complete list of available models in Ollama, you can visit their model library, which contains details about model sizes, parameters, and more. Additionally, Ollama has specific hardware requirements. For instance, to run a 7B model, you’ll need at least 8 GB of RAM; 16 GB for a 13B model, and 32 GB for a 33B model. If you have a GPU, Ollama supports it—more details can be found on their GitHub page. However, if you’re running on a CPU, expect it to perform slower.

    Ollama also allows you to set a custom system prompt. For example, to instruct the system to explain concepts at a basic level, you can use:

    /set system Explain concepts as if you are talking to a primary school student.

    You can then save and reuse this setup by giving it a name:

    /save forstudent

    To run this system prompt again:

    ollama run forstudent

    Integration with LangChain

    Ollama can be used with LangChain, a tool that enables complex interactions with LLMs. To get started with LangChain and Ollama, first, pull the required model:

    ollama pull llama3

    Then, install the necessary packages:

    pip install langchain langchain-ollama ollama

    You can interact with the model through code, such as invoking a basic conversation:

    from langchain_ollama import OllamaLLM
    model = OllamaLLM(model=”llama3″)
    response = model.invoke(input=”What’s up?”)
    print(response)

    The model might respond with something like:

    “Not much! Just an AI, waiting to chat with you. How about you? What’s new and exciting in your world?”

    Building a Simple Chatbot

    Using LangChain, you can also build a simple AI chatbot:

    from langchain_ollama import OllamaLLM
    from langchain_core.prompts import ChatPromptTemplate

    template = “””
    User will ask you questions. Answer it.

    The history of this conversation: {context}

    Question: {question}

    Answer:
    “””

    model = OllamaLLM(model=”llama3″)
    prompt = ChatPromptTemplate.from_template(template)
    chain = prompt | model

    def chat():
    context = “”
    print(“Welcome to the AI Chatbot! Type ‘exit’ to quit.”)
    while True:
    question = input(“You: “)
    if question.lower() == “exit”:
    break
    response = chain.invoke({“context”:context, “question”: question})
    print(f”AI: {response}”)
    context += f”\nUser: {question}\nAI: {response}”

    chat()

    This will create an interactive chatbot session where you can ask the AI questions, and it will respond accordingly. For example:

    You: What’s up?
    AI: Not much, just getting started on my day. How about you?

    Using AnythingLLM with Ollama

    AnythingLLM is another useful tool that acts as an AI agent and RAG (retrieval-augmented generation) tool, which can also run locally. To try this out, pull a model, such as:

    ollama pull llama3:8b-instruct-q8_0

    In AnythingLLM, you can select Ollama in the preferences and assign a name to your workspace. Although running models can be slow, the system works efficiently once set up.

    You can also interact with Ollama via a web UI by following the installation instructions provided.

    For more details, visit Ollama’s official pages and documentation to explore the full range of features and models available.

    Several alternatives and complementary tools to LangChain and AnythingLLM provide capabilities for working with language models (LLMs) and building AI-powered applications. These tools help orchestrate interactions with LLMs, enabling more advanced AI-driven workflows, automating tasks, or integrating AI into various applications. Here are some notable examples:

    1. Haystack by Deepset

    Haystack is an open-source framework that builds search engines and question-answering systems using LLMs. It enables developers to connect different components, such as retrievers, readers, and generators, to create an information retrieval pipeline.

    Key Features:

    Offers a pipeline-based approach for search, Q&A, and generative tasks.

    Supports integration with models from Hugging Face, OpenAI, and local models. Can combine LLMs with external data sources such as databases, knowledge graphs, and APIs.

    Great for production-grade applications with robust scalability and reliability.

    Link: Haystack GitHub

    2. LlamaIndex (formerly GPT Index)

    LlamaIndex (formerly GPT Index) is a data framework that helps you index and retrieve information efficiently from large datasets using LLMs. It’s designed to handle document-based workflows by structuring data, indexing it, and enabling retrieval when interacting with LLMs.

    Key Features:

    Integrates with external data sources such as PDFs, HTML, CSVs, or custom APIs.

    Builds on top of LLMs for more efficient data querying and document summarization. Helps optimize the performance of LLMs by constructing memory-efficient indices.

    Provides compatibility with LangChain and other frameworks.

    Link: LlamaIndex GitHub

    3. Chroma

    Chroma is an open-source embedding database designed for LLMs. It helps store and query high-dimensional vector embeddings of data, enabling you to work with semantic search, retrieval-augmented generation (RAG), and more.

    Key Features:

    Embedding search for documents or large datasets using models like OpenAI or Hugging Face transformers.

    Scalable and optimized for efficient retrieval of large datasets with millisecond latency.

    Works well for semantic search, content recommendations, or building conversational agents.

    Link: Chroma GitHub

    4. Hugging Face Transformers

    Hugging Face provides a library of pretrained transformers that can be used for various NLP tasks such as text generation, question-answering, and classification. It offers easy integration with LLMs, making it a great tool for working with different models in a unified way.

    Key Features:

    Supports a wide range of models, including GPT, BERT, T5, and custom models.

    Provides pipelines for quick setup of tasks like Q&A, summarization, and translation.

    Hugging Face Hub hosts a large variety of pre-trained models ready for deployment.

    Link: Hugging Face Transformers

    5. Pinecone

    Pinecone is a managed vector database that allows you to store, index, and query large-scale vectors produced by LLMs. It is designed for high-speed semantic search, vector search, and machine-learning applications.

    Key Features:

    Fast, scalable, and reliable vector search for applications requiring high performance.

    Integrates seamlessly with LLMs to power retrieval-based models.

    Handles large datasets and enables search across millions or billions of vectors.

    Link: Pinecone Website

    6. OpenAI API

    OpenAI’s API gives access to a wide range of LLMs, including the GPT series (like GPT-3.5 and GPT-4). It provides text generation, summarization, translation, and code generation capabilities.

    Key Features:

    Access to state-of-the-art models like GPT-4 and DALL-E for image generation.

    Offers prompt engineering for fine-tuning and controlling model behavior.

    Simplifies AI integration into applications without needing to manage infrastructure.

    Link: OpenAI API

    7. Rasa

    Rasa is an open-source framework for building conversational AI assistants and chatbots. It allows for highly customizable AI assistants trained on specific tasks and workflows, making it a good alternative to pre-trained LLM chatbots.

    Key Features:

    Supports NLU (Natural Language Understanding) and dialogue management.

    Highly customizable for domain-specific applications.

    Can integrate with LLMs to enhance chatbot capabilities.

    Link: Rasa Website

    8. Cohere

    Cohere offers NLP APIs and large-scale language models similar to OpenAI. It focuses on tasks like classification, text generation, and search, providing a powerful platform for LLM-based applications.

    Key Features:

    Provides easy access to LLMs through an API, allowing developers to implement NLP tasks quickly.

    Offers fine-tuning options for domain-specific applications.

    Well-suited for tasks like customer support automation and text classification.

    Link: Cohere Website

    9. Vercel AI SDK

    Vercel AI SDK provides tools for building AI-powered applications using frameworks like Next.js. It simplifies the development process by integrating APIs from OpenAI, Hugging Face, and other AI providers into web applications.

    Key Features:

    Seamless integration with AI models in serverless environments.

    Supports building interactive applications with fast deployments using Vercel’s infrastructure.

    Focuses on web-based applications and LLM-powered front-end experiences.

    Link: Vercel AI SDK

    Conclusion

    Beyond LangChain and AnythingLLM, many powerful tools and frameworks cater to different needs when working with LLMs. Whether you want to build conversational agents, semantic search engines, or specialized AI applications, platforms like Haystack, LlamaIndex, Chroma, and others offer flexible and scalable solutions. Depending on your specific use case, you can choose the most suitable tool for integrating LLMs into your projects.



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    Where Do Kamala Harris and Donald Trump Stand on Crypto? – Decrypt

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    Where Do Kamala Harris and Donald Trump Stand on Crypto? – Decrypt



    Neary 65 million Americans have already cast their votes ahead of next week’s election—and the race between the two candidates is tightening up.

    Cryptocurrency has been a significant issue in the presidential race, with former president and Republican nominee Donald Trump pivoting from a skeptic to a self-proclaimed crypto candidate, while current Vice President Kamala Harris—who took over for President Joe Biden as the Democrats’ pick in July—has signaled an intent to break from the anti-crypto policies of the current administration in which she serves.

    At Decrypt, we’ve been covering the ins and outs of the role of cryptocurrency in American politics throughout the entire election cycle. But with just three days left until Election Day, here’s a primer on where Trump and Harris stand on crypto, and what you might expect if either secures the win next week.

    Donald Trump

    Ex-President Donald Trump has been far louder than Harris on the topic of crypto. 

    Previously anti-Bitcoin and skeptical of the crypto space, the business and real estate mogul has taken a sharp U-turn on the topic, coming out as an advocate for the industry and picking up ample support and donations along the way.

    Fast-forward to 2024 and Trump has released multiple sets of NFT collectibles, called for the Americanization of Bitcoin, and even has backed a decentralized finance (DeFi) project called World Liberty Financial alongside his sons. World Liberty hasn’t gotten off to a great start with prospective investors, though sources tell Decrypt that it plans to issue a stablecoin.

    Like some other Republicans, Trump has railed against central bank digital currencies (CBDCs), or digital dollars—effectively government-backed cryptocurrencies that don’t yet exist in the U.S., but frighten the libertarian wing of the GOP and large parts of the digital asset space due to fears of increased government surveillance.

    His promise to help Bitcoin mining—a big business formerly dominated by China, but now with a lot of American players—perfectly fits Trump’s fiery protectionist brand. As does his desire to fire crypto bogeyman Gary Gensler, the crypto-targeting U.S. Securities and Exchange Commission chairman.

    Top executives in the crypto space have since backed Trump for his apparent passion for the industry, or at least his willingness to publicly engage with an industry that most politicians have avoided.

    If elected to a second term, will Trump live up to his promises to protect crypto in the U.S. and advance the industry?

    Kamala Harris

    Democratic nominee Kamala Harris was initially quiet on the topic, and unlike Trump, certainly hasn’t been seen tossing burgers to Bitcoiners at a BTC-themed New York City bar.

    But crypto is a part of the former attorney general’s agenda. 

    In October, Harris said she had plans for the space when a part of her “Kamala Harris Will Deliver for Black Men” platform included a commitment for specifically for the African American community. 

    A document for the campaign said it was for “supporting a regulatory framework for cryptocurrency and other digital assets so Black men who invest in and own these assets are protected.”

    The framing proved controversial, particularly since it was arguably her most specific comments to date about crypto. But a spokesperson later clarified that such plans were intended for all Americans, and wouldn’t be limited by race.

    That isn’t the only evidence that the crypto industry will fit into her presidential plans, however. Harris has said that blockchain, AI, and other emerging technologies will be innovated upon in America, and before that told donors at a fundraising event that she would encourage growth for the digital assets space in the country.

    Billionaire businessman and crypto enthusiast Mark Cuban previously told Decrypt in July that the Harris campaign reached out to the former Dallas Mavericks owner with questions about digital assets. He later said the Harris camp was “far more open” to the space than the Biden administration—and he’s not the only crypto heavyweight who’s optimistic about Kamala.

    Edited by Andrew Hayward

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    Meet Recraft V3: The Best AI Image Generator You Never Heard Of – Decrypt

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    Meet Recraft V3: The Best AI Image Generator You Never Heard Of – Decrypt


    Stand aside Flux and MidJourney: There’s a new player that just shot to the top of AI image generation rankings. A mystery model formerly known as Red Panda—which had AI watchers scratching their heads on Artificial Analysis’s leaderboards—finally revealed itself as Recraft V3, a fresh release from a little-known London startup.

    The model enjoyed the top score on the ELO rating system for image generators, outperforming Flux 1.1 Pro and MidJourney. In terms of efficiency, Recraft V3 matches SDXL’s generation speed of under 10 seconds while delivering what blind tests indicate is unmatched superior image quality.

    Artificial Analysis leaderboard. Image: Screenshot

    In four days of benchmark testing, Recraft V3 demonstrated superiority in text generation quality, anatomical accuracy, and prompt comprehension. It stands as the sole model capable of generating images with extended text passages, beyond simple word or phrase integration.

    In fact, it was so good that even former Stability AI researcher Joe Penna, who worked on the development of SDXL—the undisputed king of open source image generation until Flux appeared—publicly praised the model on the company’s Discord server.

    “Wow! Amazing new model, Recraft,” he said, “I’m very impressed.”

    Stability AI researcher Joe Penna on Recraft V3. Image: Discord screenshot
    Stability AI researcher Joe Penna on Recraft V3. Image: Discord screenshot

    You may not have heard about it, unless you are deeply into generative AI or digital design, but London startup Recraft AI was founded in 2022 and started as a niche player focused on AI-powered tools for graphic designers rather than general image generation. Its trajectory shifted after securing $11 million in funding from the likes of Khosla Ventures and former GitHub CEO, Nat Friedman, earlier this year.

    Recraft V3 excels in creating realistic images, handling fine details and imperfections with notable precision and operates on a subscription model similar to MidJourney, Leonardo, or Ideogram.

    Digital design is at the core of Recraft’s values. This model is also capable of processing text to vector generations, meaning users can prompt a model to generate images that can be upscaled infinitely without losing quality.

    SVG Image generated with Recraft V3

    Free users receive 50 daily credits which is enough for 50 images. However—and this is a new business model, users don’t retain ownership of their creations. That right is reserved for paid subscribers, with plans starting at $10 monthly for 1,000 credits.

    How to use Recraft V3

    Users can access Recraft V3 through three channels: a web interface, Discord commands, or mobile apps available on iOS and Android.

    Fire up Discord, join Recraft’s server, and you’ll find yourself in familiar territory if you’ve ever used MidJourney. Head to the #image-gen channel, type /recraft, and watch the magic happen.

    You can also use different modifiers after the prompt. Want a widescreen masterpiece? Throw in –ar 16:9. Need a portrait? Type –ar 3:4, and it has your back.

    Other useful additions are the –style command that lets users choose the specific visuals of their generations whether it is photorealism, 3D, or even kawaii. Got a specific look in mind? The –sref command lets you upload reference images to guide the AI’s artistic vision.

    Once the image is generated, users can choose the image they like the most between 2 generations and then they can either save it or upscale it to 4 times its size

    Recraft's Discord-based UI
    Recraft’s Discord-based UI

    The web interface at recraft.ai flexes some serious muscle. To start, simply go to Recraft.AI and sign in.

    Once on the image generation UI, users will just need to place their prompt on the text box on the left size.

    They also have sliders to change the aspect ratio and the number of images.

    Users can also change the style by clicking on the button with the icon on top of the text box and choosing their preferred option from a popup menu with a lot of examples.

    Recraft’s Web-based UI

    The interface is a lot more sophisticated than other sites and it is clear just at first glance that it’s aimed at designers. It lets users generate frames, product mockups, sets of images, deal with backgrounds, vectorize images, etc.

    Mobile creators haven’t been forgotten. The official apps on iOS and Android are available, offering the same quality generations. Simply download the app, login, click on the top middle button, and generate an image by putting the prompt on on the text box on the lower side of the screen and clicking the generate button

    Recraft Mobile UI

    Users can choose how detailed the image will be, the aspect ratio, the styles and references on the same interface. It is pretty intuitive.

    Testing the model

    We tested the model in different areas, both in terms of style and technical capabilities. Here is how it stacked up against its competitors—both open source and close source.

    Realism

    Prompt: A projection of the word “Emerge” on a woman’s face

    Recraft V3:

    Emerge image created with Recraft V3
    Emerge image created with Recraft V3

    Recraft shows the best understanding of natural skin texture, facial expressions, and environmental lighting. The projection appears well-integrated with the skin, and crucially, there are real imperfections—visible pores, slight skin blemishes, and natural hair flyaways. The candid expression and background context add significant authenticity.

    Stable Diffusion 3.5:

    Emerge image created with Stable Diffusion 3.5
    Emerge image created with Stable Diffusion 3.5

    SD 3.5 comes in a close second place. It is a big improvement vs SD3 medium and even the best realistic SDXL finetunes. It shows a strong dramatic presence with the orange-tinted lighting and bold red lipstick. While the facial features are well-defined, there’s a noticeable artificial quality to the skin texture. The projection appears more like a sharp overlay, and the expression feels somewhat posed and synthetic.

    MidJourney:

    Emerge image created with MidJourney
    Emerge image created with MidJourney

    As always, MidJourney creates a moody, cinematic look with strong technical execution. However, the woman’s skin has a glossy, almost ethereal quality that, while beautiful, feels less natural than Recraft’s attempt. The projection blends well, but the overall perfection of the features and textures—and the clear lack of authenticity in the expressions—reveals its AI origin.

    Winner: Recraft

    Prompt Adherence and Spatial Awareness

    Prompt: A dog standing on top of a TV showing the word “Decrypt” on the screen. On the left there is a woman in a business suit holding a coin, on the right there is a robot standing on top of a first aid box. The overall scenery is surreal

    Recraft V3:

    Decrypt image created with Recraft V3
    Decrypt image created with Recraft V3

    The model failed in terms of spatial awareness. However, it managed to achieve the surreal style in the overall composition. This is a departure from other models that exhibited great adherence and spatial awareness in the elements, but the overall mood or style of the scene was questionable.

    This can be seen as a good tradeoff for some since it is easier to inpaint and edit elements in a composition than to restyle a whole image. However, it is important to consider this as a major limitation when compared against other models.

    Decrypt images created with Flux, Auraflow, and SD3 Medium
    Decrypt images created with Flux, Auraflow, and SD3 Medium

    Winner: Flux

    Illustration and Style:

    Prompt: Hand-drawn illustration of a giant spider chasing a woman in the jungle, extremely scary, anguish, dark and creepy scenery, horror, hints of analog photography influence, sketch

    The model has a lot of different styles to choose from, but we went with Recraft RAW for this generation. At first we thought the “hand drawn” style was the best option, but… no, it wasn’t.

    Image created with Recraft RAW
    Image created with Recraft RAW

    After trying different preset styles, the good old RAW (the most versatile one) was the best fit for what we were looking for.

    Image created with Recraft RAW
    Image created with Recraft RAW

    Compared to the other models, Recraft generated an interesting composition, and was accurate at showing the key message of the scene: A giant spider chasing a woman. However, the overall art looked more like a digital illustration instead of a hand-drawn illustration.

    Aesthetically, the most accurate model for this specific prompt seems to be the latest Stable Diffusion Model, which generated a hand drawn illustration, and was able to convey the anguish of a woman running away from a giant spider.

    Images created with SD3, SDXL, MidJourney, and Ideogram
    Images created with SD3, SDXL, MidJourney, and Ideogram

    Winner: SD3

    Conclusions

    It is easy to see why Recraft V3 claims the top spot in the Image Generation Leaderboard. Unlike competitors like MidJourney and Flux, which often fall into predictable, stylized patterns—the smooth “Flux face” or the lifeless “MidJourney look”—Recraft leans into realism. Its outputs are compelling, showing intricate details like natural skin texture, subtle imperfections, and nuanced lighting. This aesthetic balance, favoring authenticity without sacrificing polish, gives Recraft an edge that other models struggle to match.

    The pricing strategy is also important to consider. Recraft offers a free tier with generous daily credits, and it’s the only model supporting text-to-SVG generation, a boon for illustrators looking for scalable, professional-quality vectors. It is also priced similarly to MidJourney’s cheapest plan—but unless you are looking for the MidJourney aesthetics, Recraft is a lot more versatile and powerful, so it is the better option.

    That said, Recraft isn’t perfect. When it comes to complex scenes with multiple elements, spatial awareness sometimes falters. Prompts requiring precise composition can result in minor misalignments, and users may find themselves inpainting or adjusting positions more than expected. But for those who prioritize realism and versatility, this shortfall is easily overlooked.

    Also, free users not owning their creations may be a major flaw to take into consideration.

    In general, Recraft V3 does seem to be the best closed-source option, delivering superior value and flexibility at a price point that respects creators’ budgets. For anyone in search of high-quality realistic images without the trademark “AI look,” Recraft is a clear winner.

    However, those capable of running AI models locally, may be just fine enough with Flux or SD 3.5.

    Generally Intelligent Newsletter

    A weekly AI journey narrated by Gen, a generative AI model.



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    Starseed AI Partners with Microsoft for Startups, Named AI Company of the Year in Los Angeles, and Expands Operations to Scottsdale, Arizona | Web3Wire

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    Starseed AI Partners with Microsoft for Startups, Named AI Company of the Year in Los Angeles, and Expands Operations to Scottsdale, Arizona | Web3Wire


    Starseed AI Partners with Microsoft for Startups, Named AI Company of the Year in Los Angeles, and Expands Operations to Scottsdale, Arizona | Web3Wire

    Los Angeles, CA – October 31, 2024 – Starseed AI, a trailblazer in artificial intelligence, proudly announces its recognition as the AI Company of the Year in the Los Angeles area. This award acknowledges the company’s groundbreaking research in hair disease diagnostics and AI-powered counterfeit detection, emphasizing its commitment to solving real-world problems at the intersection of healthcare and commerce.

    Founded in 2020, Starseed AI applies graph neural networks (GNNs) and cognitive science-based AI technologies to deliver powerful, scalable solutions. The company’s success is fueled by a strategic partnership with Microsoft for Startups, enabling it to leverage cloud technologies to accelerate development, streamline operations, and expand the reach of its innovative products.

    To meet growing market demand, Starseed AI is also excited to announce the expansion of its operations to Scottsdale, Arizona. This new office will support research and development efforts and foster new partnerships, ensuring continued growth.

    “Our mission is to create meaningful, practical solutions-whether it’s through diagnostics for hair diseases or protecting brands from counterfeit threats,” said Ariel Rostami, CEO at Starseed AI. “Being recognized as AI Company of the Year and expanding to Scottsdale are significant milestones that reflect our progress and the impact of our work.”

    Connecting Healthcare and Commerce through AI

    Starseed AI bridges the worlds of healthcare and consumer protection through cutting-edge technologies. Its solutions are designed to work seamlessly together, tackling distinct challenges with a unified approach.

    Revolutionizing Hair Disease Diagnostics: Using an advanced knowledge graph platform, Starseed AI predicts disease pathways, identifies symptoms, and recommends treatments in real time. This AI-powered diagnostic tool transforms hair care, equipping healthcare professionals, beauty brands, and consumers with actionable insights to improve outcomes.

    Safeguarding Brands with AI-Driven Anti-Counterfeit Solutions: Starseed AI’s computer vision-based counterfeit detection system monitors digital marketplaces, identifying unauthorized sellers and fraudulent products. This solution ensures product integrity, protects brand reputation, and enhances consumer trust by proactively reducing counterfeit risks.

    Expanding Impact and Delivering Real-World Solutions

    Starseed AI’s dual focus on healthcare and brand protection exemplifies its ability to merge AI research with practical applications. The synergy between these solutions allows the company to address diverse challenges, from identifying complex medical conditions to securing consumer markets from counterfeit goods.

    Recognition as AI Company of the Year reflects Starseed AI’s dedication to delivering innovative, impactful solutions. With the opening of its new office in Scottsdale, Arizona, the company is positioned to scale its capabilities further, fostering new partnerships and driving continued success.

    For the latest updates, visit http://www.starseed.ai [http://www.starseed.ai/] or follow @StarseedAI on social media

    About Starseed AI

    Founded in 2023, Starseed AI is a pioneering technology company that develops advanced artificial intelligence solutions for healthcare diagnostics and consumer protection. Through graph neural networks and cognitive AI, Starseed AI offers tools for predictive hair disease diagnosis and counterfeit detection. With the support of Microsoft for Startups, the company continues to expand its capabilities and deliver real-world impact. Now with offices in Scottsdale, Arizona, Starseed AI is poised for further growth, driving innovation at the intersection of healthcare and commerce.

    Media ContactCompany Name: Starseed AIContact Person: Brandon LeeEmail:Send Email [https://www.abnewswire.com/email_contact_us.php?pr=starseed-ai-partners-with-microsoft-for-startups-named-ai-company-of-the-year-in-los-angeles-and-expands-operations-to-scottsdale-arizona]Phone: 323-744-0087Country: United StatesWebsite: http://www.starseed.ai

    This release was published on openPR.

    About Web3Wire Web3Wire – Information, news, press releases, events and research articles about Web3, Metaverse, Blockchain, Artificial Intelligence, Cryptocurrencies, Decentralized Finance, NFTs and Gaming. Visit Web3Wire for Web3 News and Events, Block3Wire for the latest Blockchain news and Meta3Wire to stay updated with Metaverse News.



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    Megan Thee Stallion Files Suit Against Influencer Over Deepfake Harassment – Decrypt

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    Megan Thee Stallion Files Suit Against Influencer Over Deepfake Harassment – Decrypt



    Rapper Megan Thee Stallion sued Milagro Gramz on Tuesday, alleging the Texas-based internet personality defamed her following a separate court case with Canadian rapper Tory Lanez.

    Born Megan Pete, the rapper alleges Gramz (Milagro Cooper) stalked her online, causing emotional distress and spreading AI-generated deepfake pornography featuring her likeness.

    In court documents filed in the Southern District Court of Florida, Pete’s attorneys acknowledged that while it’s unknown who created the deepfake video, they argue Cooper published a video on YouTube that addressed an X post related to the explicit content.

    “The lengths to which Defendant Cooper goes to harass Ms. Pete knows no bounds, attorneys for Pete wrote. “In June 2024, Defendant Cooper encouraged her 27,000 X followers to view an X post by Bimbella that shared a doctored, artificially created video of Ms. Pete purportedly engaged in sexual acts without Ms. Pete’s knowledge or consent.”

    It’s the latest development in the ongoing feud between Lanez and Pete, which began in 2020 when Lanez was charged with shooting Pete in the feet during an altercation in Los Angeles.

    Lanez was convicted on three felony counts, including assault with a semiautomatic handgun, according to a report by the New York Times.

    The complaint further asserts that Cooper’s alleged harassment was at the behest of Lanez (born Daystar Peterson) and accused Cooper of running an “online rumor mill” that spread false claims about Pete, including questioning the rapper’s mental state and having a “severe drinking problem.”

    Taking to X (formerly Twitter), Cooper said she was informed of the lawsuit by Pete’s attorney, Alex Spiro. “Of course, we’ll chat about it,” she wrote. “They threw in the tape, too.” Cooper did not say if she intends to counter-sue.

    Attorneys for Pete are seeking compensatory and punitive damages, legal fees, and a court order to prevent future harassment by Cooper.

    Pete’s lawsuit is the most recent concerning AI-generated deepfakes of high-profile artists.

    Last year, actress Scarlett Johansson filed a lawsuit against image generator Lisa AI, which posted a deepfake of Johansson promoting the platform.

    In May, Johansson, who starred as the voice of the AI Samantha in the 2013 film “Her,” took legal action against ChatGPT creator OpenAI after it released a voice-enabled version of the chatbot that sounded eerily similar to the actress.

    Representatives for both Cooper and Pete did not immediately respond to Decrypt’s request for comment.

    Edited by Sebastian Sinclair

    Generally Intelligent Newsletter

    A weekly AI journey narrated by Gen, a generative AI model.



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    Introducing Chainlink Runtime Environment (CRE)

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    Introducing Chainlink Runtime Environment (CRE)


    Key Takeaways:

    The Chainlink Platform is evolving to give developers substantially more power, freedom, and reach than ever before through a highly self-serve, scalable, and programmable architecture.
    The core functions of oracle networks are becoming reusable modular capabilities that developers can compose in any way into workflows and run via the new Chainlink Runtime Environment (CRE).
    Developers will be able to seamlessly combine all Chainlink capabilities to create customized apps and unlock use cases not bound by any chain, offchain resource, or product integration.
    The upgrade of the Chainlink Platform is key to expanding Chainlink to thousands of blockchains and meeting the growing demand from capital markets and Web3.

    On the Main Stage at SmartCon 2024 today, we announced a major upgrade to the Chainlink Platform. This upgrade is designed to scale Chainlink across thousands of blockchains, meet the growing demand from financial institutions, and empower developers to build with Chainlink faster, more easily, and with more reach and flexibility than ever before.

    Underpinning this initiative is a deep re-architecture of the Chainlink Platform. Drawing inspiration from microservices architecture, the Chainlink node software that manages decentralized oracle networks (DONs) is being broken down into distinct, modular capabilities (e.g., read chain, perform consensus, etc.) that are each secured by independent DONs. Developers can seamlessly combine these capabilities in any number of ways into executable workflows that run via the newly developed Chainlink Runtime Environment (CRE)—the system of DON-based capabilities, DON-to-DON communications, capability orchestration, and code execution on which workflows run with the appropriate consensus model. 

    The Chainlink Platform architecture will have DONs that each specialize in a capability, as well as empower developers to string together these capabilities to form workflows that the Chainlink Runtime Environment (CRE) DON executes.

    The result of this upgrade is developers being able to build substantially quicker, connect their apps seamlessly across all chains connected to the Chainlink Platform, and create more powerful applications, including purpose-built financial apps that interact with capital markets infrastructure, incorporate custom compliance policies, and handle sensitive information in a privacy-preserving manner. 

    While developers will continue to write core application logic as onchain smart contracts, CRE enables them to deploy code directly on the Chainlink Platform for building and composing capabilities, removing the need to add Chainlink-specific code to their onchain contracts. This allows developers to leverage Chainlink’s capabilities regardless of which blockchains their application is deployed to, leading to unified applications secured end-to-end by consensus computing. 

    The Evolution Toward a Modular Developer Platform

    Existing Platform

    To date, the Chainlink Platform consists of a series of prepackaged services, with each service akin to a set of pre-assembled lego pieces that form a single design pattern (i.e., workflow). For example, Chainlink Automation combines 5-6 separate capabilities into a smart contract automation workflow. Each capability has its own parameters, and capabilities must be executed in order to produce a valid workflow output.

    Chainlink Automation = Cron/log trigger → read chain → (optional) fetch from Data Streams aggregation network → simulate → consensus → write chain

    This service-oriented architecture helped scale Chainlink from 0 to 1, and in the process enabled Chainlink to become the most widely used oracle platform, with the most secure and reliable services across data, smart contract automation, verifiable randomness, cross-chain interoperability, and more. 

    However, to hyperscale Chainlink to thousands of chains, support millions of new developers at faster development speeds, and unlock a wider range of use cases and customizations across DeFi and fast-emerging TradFi adoption, an upgrade to the architecture of the Chainlink Platform is necessary. And since Chainlink is currently enabling trillions of dollars in transaction value, this transition must take place without any disruption to the security and reliability of existing Chainlink services. 

    CRE-Based Platform

    Chainlink has embarked on a multi-phased initiative to re-architect the Chainlink Platform so developers can build their own custom workflows in a self-serve manner. Essential to this vision is distilling the bare essential functions of an oracle network (e.g., chain read, chain write, fetch API, do compute, etc.) into modular capabilities that developers can directly piece together into their own workflows.

    Each capability in a workflow is run by a separate DON (i.e., akin to a microservice) as opposed to the previous architecture where the same DON executes all the capabilities of a particular workflow. For example, instead of having a single DON responsible for executing all 5-6 capabilities of Chainlink Automation, there is one DON per capability and all capability DONs are combined to form a workflow. 

    With DONs purpose-built to perform one capability, they are able to provide highly reliable and predictable services and quickly scale their support to many different users. Furthermore, the platform itself becomes more efficient as already developed capabilities can be reused as opposed to building the same ones from scratch.

    Chainlink Platform Upgrade
    The upgraded Chainlink Platform enables developers to compose individual capabilities of the Chainlink Network into workflows rather than only having access to a prepackaged service.

    Chainlink Workflows

    Workflows are the new programs that developers build and run on the Chainlink Platform. Instead of integrating a prepackaged service, developers can build their own workflows using different Chainlink capabilities. Capabilities can be bucketed into two categories: 1) trigger capabilities that start the workflow and 2) execution capabilities that compose and constitute the workflow.

    We plan to support workflow development in Go, TypeScript, and other programming languages, which the platform compiles into WASM for execution by Chainlink nodes. Developers can create and manage their workflows using their IDE and the Chainlink SDK and CLI, as well as view and manage them in a UI. 

    During the initial launch phases, pre-built capabilities will be provided to devs that they can use to create their custom workflows. The longer-term plan is to enable anyone to create and deploy their own capabilities (e.g., custom self-serve chain integrations, connectivity to permissioned systems, etc.).

    Chainlink Workflow
    A Chainlink Workflow that calls an API, performs a consensus computation, and then writes the result onchain for a smart contract to consume.

    Chainlink Runtime Environment

    The Chainlink Runtime Environment (CRE)—the engine of the Chainlink Platform—executes developers’ workflows in a decentralized manner by interacting with different capability DONs. CRE provides the coordination of the DONs for each of the capabilities invoked in a workflow, as well as combines them with the right consensus overlay.

    “The Chainlink Runtime Environment pulls all of the capabilities together by executing the workflows whenever their triggers fire and using DON-to-DON communications to connect the various capability DONs.” —Uri Sarid, Chainlink Labs Chief Architect 

    *For a deeper understanding of the different technical terms, refer to the References section at the end of this blog.

    The Benefits of the Upgraded Chainlink Platform

    The upgraded Chainlink Platform powered by CRE unlocks numerous benefits for developers, the Chainlink Platform itself, and the industry as a whole. 

    Limitless Developer Innovation

    Easy to use: Effortlessly create workflows with programming languages you already know via a comprehensive set of SDKs and an intuitive CLI.
    Customizable and programmable: Build to fit your bespoke needs with fully programmable workflows.
    Seamless integration: Connect with offchain APIs and multiple blockchains within a single workflow using standardized components.
    Secure: Safeguard your users by leveraging Chainlink’s proven security, providing consensus guarantees for offchain applications.

    In the previous architecture, for example, standing up a single Proof of Reserve (POR) feed required carefully coordinated operational processes across multiple teams and components. This involved complex customization, deployment, and ongoing maintenance. Chainlink’s new architecture removes the complexity of customizing, setting up, and linking disparate components and reduces the required ongoing maintenance. In a few hours, a single developer can express a fully customized POR feed that writes to multiple chains as a workflow and leverages CRE to monitor and reliably execute it. This frees up precious development and maintenance time, so teams can focus more on meeting customer needs.

    Next-Generation Platform

    Hyper-scaling: Since capabilities can be long-standing and easily reused for new integrations, new chains can be adopted by simply creating a new read chain / write chain capability, which can then be leveraged by all other Chainlink capabilities to interact with those chains. Instead of a new EVM chain integration for multiple Chainlink products taking weeks, developers can compose workflows that use all Chainlink capabilities within a number of days. 
    Financial market workflows: Banks can connect the Chainlink Platform to their internal private chains and systems and seamlessly interface across other private and public chains. Financial institutions can also create workflows that work in compliance prior to onchain execution, such as building custom policy capabilities into their workflows.
    Limitless use cases: Developers’ full creative potential is unlocked as capabilities can be programmed and combined in ways currently not possible to expand to new offchain resources and unlock innovative use cases. 
    Increased network efficiency: Optimized DON configurations mean less operational overhead for both Chainlink and Node Operators (NOPs). For example, existing DONs can be reused as Chainlink grows rather than the linear DON growth of today. Other efficiencies include more optimized utilization across DON deployments, more economical and efficient products, more sustainable NOP business models, and more efficient provisioning and revenue generation through a compute marketplace.

    Overall Industry Growth

    With app composability being a main driver in the expansion of DeFi, the composability of offchain services and onchain smart contracts across all blockchains can supercharge a similar expansion in onchain innovation. Every chain stands to benefit, as blockspace becomes more in demand thanks to more users, more transactions, and easier access and deployment to chains.

    Making Consensus Computing the Way All Markets Work

    The underlying power of Chainlink is greatly expanding the use of consensus computing, with the goal of making it an industry standard throughout financial markets, user applications, and beyond. 

    Consensus computing is when a decentralized network of nodes must form consensus as part of the network storing and executing code. It’s an evolution in computing because it provides users with unique guarantees such as tamper-resistance, hyper-availability, trust minimization, enhanced composability, and permissionless and universal accessibility. 

    On the foundation of consensus computing, truly secure and reliable automated services can begin to thrive, opening up major efficiency and utility gains and increasing global connectivity.

    Blockchains first introduced consensus computing to store and maintain a permissionless and immutable asset ledger. Blockchain-based consensus computing then expanded to include smart contracts, where ledger transactions could have conditions attached to their execution, making way for decentralized applications (dApps). 

    While blockchains will continue to power asset ledgers and dApps, they have limitations. Blockchain-based consensus is only focused on the validity and ordering of transactions, and produce deterministic results, which can be reproduced by anyone based on historical state. However, there is a much broader set of things that consensus could be generated about that blockchains are not applicable for, such as consensus with a median output based on data sources not available onchain (e.g., calculating the current temperature using data from multiple offchain APIs).

    Chainlink expands consensus computing to virtually anything and enables the use of any offchain data and offchain computing method. This includes consensus computing on the current price of an asset, transmission of data between disparate networks, triggering smart contracts based on events, and now coordinating consensus across onchain and offchain systems. This expansion enables consensus computing to secure the entire application—such as its offchain data, offchain computation, and interoperability—and not just the state of its onchain code. Through doing so, consensus computing can fulfill a much wider range of use cases while bringing users newfound levels of confidence and verifiability to how the world actually works.

    Consensus computing
    Blockchains use consensus computing to order transactions and validate the state of a ledger, while Chainlink is applying consensus computing to any offchain service.

    Rolling Out the Upgraded Chainlink Platform

    Similar to how Ethereum uses a phased-upgrade model, the Chainlink Platform upgrade is rolling out in phases to ensure that existing users of Chainlink services are unaffected throughout the transition. This is critical since Chainlink services are currently enabling trillions of dollars in value and securing critical functions for many of the most widely used onchain applications.

    The initial phase involves the transition of Chainlink services like CCIP to the upgraded platform architecture. This will help Chainlink scale to chains faster and meet unique and immediate customer requirements. In parallel, the upgraded platform architecture is being implemented into new chain integrations, such as the integration of the Aptos blockchain with Chainlink. Furthermore, the upgraded Chainlink Platform architecture is also being leveraged by financial institutions to seamlessly connect existing infrastructure to blockchains for workflows such as Delivery vs. Payment.

    If you are a developer, established application, or financial institution and want to start building and testing workflows using the Chainlink Runtime Environment, sign up for early access.

    To learn more about Chainlink, visit chain.link, subscribe to the Chainlink newsletter, and follow Chainlink on Twitter and YouTube.

    —

    References

    Consensus Computing—The broader computing paradigm that requires decentralized consensus as part of executing software and storing information.
    Chainlink Platform—The totality of software and node networks that enable development and perform capabilities on Chainlink.
    Capabilities—Individual functions of decentralized oracle networks on Chainlink, such as read chain, write chain, call an API, execute compute, apply a policy, etc.
    DONs—Decentralized Oracle Networks that execute the capabilities requested by users. 
    Chainlink Network—All Chainlink nodes and DONs that are currently in operation.
    Chainlink Workflows—What developers build in the Chainlink Platform. Developers combine Chainlink capabilities into their own workflows.
    Chainlink Runtime Environment (CRE)—The engine of the Chainlink Platform, which executes workflows and provides a programming model on how to program workflows.

    —

    Disclaimer: This post is for informational purposes only and contains statements about the future, including anticipated product features, development, and timelines for the rollout of these features. These statements are only predictions and reflect current beliefs and expectations with respect to future events; they are based on assumptions and are subject to risk, uncertainties, and changes at any time. There can be no assurance that actual results will not differ materially from those expressed in these statements, although we believe them to be based on reasonable assumptions. All statements are valid only as of the date first posted. These statements may not reflect future developments due to user feedback or later events and we may not update this post in response. Please review the Chainlink Terms of Service, which provides important information and disclosures.



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    Top Web3 Crypto Wallets of 2024: Pros and Cons Analyzed | Web3Wire

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    Top Web3 Crypto Wallets of 2024: Pros and Cons Analyzed | Web3Wire


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    In the rapidly evolving landscape of blockchain technology, Web3 wallets have emerged as indispensable tools for crypto enthusiasts and investors. With the rise of decentralized finance (DeFi) and non-fungible tokens (NFTs), choosing the right crypto wallet is paramount. In 2024, several Web3 wallets have distinguished themselves as leaders in the field, offering users a range of features tailored to their needs. In this article, we delve into the top Web3 wallets of 2024, analyzing their pros and cons to help you make an informed decision.

    MetaMask

    MetaMask has long been a favorite among crypto users and continues to lead the pack in 2024. Known for its user-friendly design and seamless integration with DeFi applications, MetaMask offers a versatile and secure experience for both beginners and seasoned traders.

    Pros of MetaMask

    User-Friendly Interface: MetaMask’s intuitive design makes it easy for users to manage their assets and explore decentralized applications (dApps).Wide Compatibility: Compatible with major browsers like Chrome and Firefox, as well as a dedicated mobile app, providing versatile access options.Strong Security Features: Integration with hardware wallets and advanced encryption ensures high-level security for users.Extensive dApp Ecosystem: With access to thousands of dApps, MetaMask provides a gateway to the broader Ethereum ecosystem and beyond.

    Cons of MetaMask

    Gas Fees: Users may find transaction fees on the Ethereum network to be high at times, affecting cost-efficiency.Limited Multi-Chain Support: While MetaMask supports some blockchains, it’s heavily Ethereum-focused, which may limit users seeking diverse blockchain interactions.

    Coinbase Wallet

    Coinbase Wallet, an offshoot of the reputable Coinbase exchange, continues to make waves in 2024. It offers users the ability to manage their crypto assets independently of the main Coinbase platform, featuring an intuitive mobile application to support on-the-go management.

    Pros of Coinbase Wallet

    Integration with Coinbase Exchange: Seamlessly connects with the Coinbase exchange for easy transfers between wallet and trading accounts.Strong Security Protocols: Built on established security measures synonymous with the Coinbase brand.Support for Multiple Cryptocurrencies: Users can manage a broad range of digital assets beyond just Ethereum-based tokens.Direct dApp Access: The wallet includes a built-in dApp browser for direct interaction with various decentralized applications.

    Cons of Coinbase Wallet

    Custodial Challenges: Some users prefer non-custodial solutions, and while it offers some decentralization, Coinbase Wallet retains some custodial elements.Privacy Concerns: Integrating with a major exchange could present privacy challenges, as data may be shared across connected accounts.

    Trust Wallet

    Trust Wallet has steadily gained traction due to its reputation for supporting a wide array of cryptocurrencies and ease of use. Acquired by Binance in 2018, it has benefited from ongoing development and innovation.

    Pros of Trust Wallet

    Multi-Currency Support: Trust Wallet supports a vast array of cryptocurrencies, including those on the Binance Smart Chain, Ethereum, and more.Non-Custodial: Users have full control over their private keys, enhancing the security and autonomy of their crypto holdings.DeFi and NFT-Friendly: With built-in services to interact with DeFi platforms and NFT marketplaces, it’s highly versatile for different use cases.Seamless User Experience: Its intuitive interface and compatibility with various blockchains make it accessible for both new and experienced users.

    Cons of Trust Wallet

    Mobile-Only Access: Trust Wallet primarily functions as a mobile app, which might limit users who prefer desktop applications.Potential Security Risks: As with all mobile wallets, users must remain vigilant against potential security threats like phishing and malware.

    Concluding Thoughts

    As we glide through 2024, Web3 wallets like MetaMask, Coinbase Wallet, and Trust Wallet continue to provide robust solutions for storing and managing digital assets. Each wallet has its unique strengths and challenges, catering to different user needs and preferences. When selecting a Web3 wallet, consider your specific requirements such as currency support, ease of use, and security features.

    Choosing the best wallet ultimately boils down to personal priorities. Whether you’re an enthusiast diving deep into DeFi and NFTs, or a novice starting your crypto journey, there’s an ideal solution out there for you. Stay informed, assess your options, and enjoy the exciting world of decentralized finance with confidence in 2024.

    “`

    This blog post is structured to provide an informative and SEO-optimized overview of the top Web3 crypto wallets in 2024, with a focus on their pros and cons, to assist readers in making an informed decision.

    About Web3Wire Web3Wire – Information, news, press releases, events and research articles about Web3, Metaverse, Blockchain, Artificial Intelligence, Cryptocurrencies, Decentralized Finance, NFTs and Gaming. Visit Web3Wire for Web3 News and Events, Block3Wire for the latest Blockchain news and Meta3Wire to stay updated with Metaverse News.



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