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Binance vs SEC: why is XRP missing from the list of cryptos cited by the SEC?

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Binance, SEC and XRP logos

Amid growing tension between financial regulators and crypto platforms, the announcement of a new lawsuit by the SEC (Securities and Exchange Commission) against Binance has thrown a lot of water. The SEC accuses Binance of having artificially inflated its transaction volumes and of having violated several rules, in particular with regard to the access of American users to its platform. However, one detail particularly challenged some observers: the unexpected absence of Ripple’s XRP from the list of cryptocurrencies that the SEC now considers to be securities. While XRP has been under attack from the SEC for two years, this absence raises a multitude of questions. Why is XRP missing from this list? Is this a deliberate strategy on the part of the SEC? What might be the repercussions of this decision?

Binance vs SEC article: why is XRP missing from the list of cryptos cited by the SEC? appeared first on Cointribune.

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OpenAI announces free fine-tuning for GPT-4o mini model

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OpenAI is offering free fine-tuning on its new GPT-4o mini model, allowing users to train the model on additional data at no charge to enable higher performance for specific use cases.

GPT-4o mini fine-tuning is available to developers in OpenAI’s Tier 4 and 5 usage tiers, which are the highest-priced tiers among OpenAI’s plans. OpenAI plans to gradually expand access to free fine-tuning to all tiers. Free fine-tuning will be offered now through September 23.

Developers can start fine-tuning GPT-4o mini for free by visiting their fine-tuning dashboard, clicking “create,” and selecting “GPT-4o mini” from the base model drop-down menu. Each organization gets 2M training tokens per 24-hour period to train the model. Any overage will be charge $3.00 per 1M tokens. More details on free fine-tuning are offered in OpenAI’s fine-tuning docs.

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Visual Studio Code Java extension backs JDK 23

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Oracle has updated its Java development extension for Microsoft’s Visual Studio Code editor, with the extension supporting early-access builds of the planned JDK 23 release of Java. The extension also adds the ability to manage dependencies in projects that do not require a build tool.

Available in the Visual Studio Marketplace, the latest Java platform extension for Visual Studio Code was announced July 24. Support for JDK 23 means the extension backs the next version of Java, due September 17, which includes a third preview of structured concurrency. The extension also can manage dependencies sans a build tool, which helps projects that do not use the Maven or Gradle build tools. Added support for Markdown in JavaDoc documentation allows developers to focus on content, rather than formatting, and makes it easier to maintain and read documentation directly within code. The extension overall is positioned to provide full-featured development support for VS Code.

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Why Meta’s Llama 3.1 is a boon for enterprises and a bane for other LLM vendors

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the necessary compute resources on a pay-as-you-go basis, reducing upfront investments, Patel added.

Meta said it had partnered with the likes of Accenture, AWS, AMD, Anyscale, Cloudflare, Databricks, Dell, Deloitte, Fireworks.ai, Google Cloud, Groq, Hugging Face, IBM watsonx, Infosys, Intel, Kaggle, Microsoft Azure, Nvidia DGX Cloud, OctoAI, Oracle Cloud, PwC, Replicate, Sarvam AI, Scale.AI, SNCF, Snowflake, Together AI, and the UC Berkeley vLLM Project to make the Llama 3.1 family of models available and simpler to use.  

While cloud service providers such as AWS and Oracle will provide the latest models, partners such as Groq, Dell, and Nvidia will allow developers to use synthetic data generation and advanced retrieval augmented generation (RAG) techniques, Meta said, adding that Groq has optimized low-latency inference for cloud deployments, and that Dell has achieved similar optimizations for on-prem systems.

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What is GraphQL? Better APIs by design

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mutation CreateMovie ($title: String!, $released: Date!) {
    createMovie (title: $title, released: $released){
        id
        title
        released
    }
}

[submitted data]

{
    “title”: “Seven Samurai”
    “released”: “1950”
}

All queries, including mutation queries, can return data. Here, the list of fields in the curly braces after createMovie specifies what we want to see returned from the server after a new record is created with this request. The value for the id field, in this case, would be created by the database; the values for the other fields are submitted in the query.

Another thing to keep in mind is that the queries and data types used to return data are by design different from those used to request data. Mutation queries need to validate incoming data, so the types used for those queries are meant to serve that function. Likewise, the fields used in returned query objects are for display, not validation. If you take in a GraphQL object as returned from a query, it might have fields with circular references or other issues that make it unusable as a query parameter.

Why use GraphQL?

A key reason to choose GraphQL over REST is the explicit, declarative nature of GraphQL queries. Having a formal definition for how queries and returned data should look has advantages aside from being consistent across APIs and implementations.

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Full-stack development with Java, React, and Spring Boot, Part 1

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import './App.css';
import React, { useState, useEffect } from 'react';

function App() {
  const [todos, setTodos] = useState([]);

  // Fetch todos on component mount
  useEffect(() => {
    fetch('http://localhost:8080/todos')
      .then(response => response.json())
      .then(data => setTodos(data))
      .catch(error => console.error(error));
  }, []);

  // Function to add a new TODO item
  const addTodo = (description) => {
    fetch('http://localhost:8080/todos', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ description }),
    })
      .then(response => response.json())
      .then(newTodo => setTodos([...todos, newTodo]))
      .catch(error => console.error(error));
  };

  // Toggle completion
  const toggleTodoComplete = (id) => {
    const updatedTodos = todos.map(todo => {
      if (todo.id === id) {
        return { ...todo, completed: !todo.completed };
      }
      return todo;
    });
    setTodos(updatedTodos);

    // Update completion 
    fetch(`http://localhost:8080/todos/${id}`, {
      method: 'PUT',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ completed: !todos.find(todo => todo.id === id).completed }),
    })
      .catch(error => console.error(error));
  };
  const deleteTodo = (id) => {
    const filteredTodos = todos.filter(todo => todo.id !== id);
    setTodos(filteredTodos);
    fetch(`http://localhost:8080/todos/${id}`, {
      method: 'DELETE'
    })
    .catch(error => console.error(error));
  };

We have functions for creation, toggling completion, and deletion. To load the to-dos initially, we use the useEffect effect to call the server for the initial set of to-dos when React first loads the UI. (See my introduction to React hooks to learn more about useEffect.)

useState lets us define the todos variable. The Fetch API makes it pretty clean to define the back-end calls and their handlers with then and catch. The spread operator also helps to keep things concise. Here’s how we set the new todos list:

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How to create an operational data store with TiDB

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TiDB provides an advanced feature called Resource Control, which is a fine-grained resource usage control mechanism. This resource control mechanism allows for precise management of compute and storage resources, ensuring that each service receives the necessary resources to perform optimally without interfering with others. This capability not only enhances the overall user experience by maintaining high performance and availability but also optimizes operational costs. Services can scale resource usage up or down based on real-time demands, making the operational data store both flexible and cost-effective.

By implementing TiDB’s resource control features, organizations can ensure a balanced and efficient distribution of resources, which is critical for maintaining the stability and responsiveness of shared data environments.

Making the most of data

In an era of fast-paced innovation and global competition, data plays an instrumental role in driving business decisions and operational efficiency. An operational data store like the one described in this article can help businesses achieve seamless connectivity between varied data sources, data warehouses, data lakes, and end-user applications while harnessing real-time insights for strategic decision-making. TiDB’s robust data ingestion, data synchronization, and extensive compatibility with BI tools, along with its ability to handle large-scale data operations with minimal latency, make it a formidable choice for this purpose.

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Microsoft .NET Aspire automates Dockerfile builds

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Microsoft has released .NET Aspire 8.1, an update to the cloud-ready stack for building distributed applications that adds support for building container images from Dockerfiles, as well as support for launching Python-based services.

NET Aspire 8.1 was unveiled on July 23. Developers can find instructions on installing .NET Aspire at learn.microsoft.com.

For building containers, .NET Aspire 8.1 adds two extensions, AddDockerfile(...) and WithDockerfile(...), that allow .NET Aspire to automatically build a Dockerfile when the app host runs. This enables developers to quickly edit Dockerfiles and rely on .NET Aspire to build them without having to manually build these themselves, Microsoft said. In addition to Dockerfile build support, Microsoft has added the ability to provide build arguments and build secrets.

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Shiny for Python adds chat component for generative AI chatbots

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Shiny for Python 1.0 launched this week with built-in chatbot functionality. The Chat() component is aimed at making it “easy to implement generative AI chatbots, powered by any large language model (LLM) of your choosing,” according to today’s announcement. “The ai_model can be anything, but Chat makes it especially easy to use interfaces from OpenAI, Anthropic, Google, LangChain, and Ollama.”

Shiny 1.0 can be installed with the Python package manager of your choice, such as

pip install shiny

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Agentic AI drives enterprises away from public clouds

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AI agents are nothing new. AI itself was old but is new again, and AI agents and agentic AI are another chapter. Agentic AI, known for its autonomous decision-making and complex goal management, radically transforms enterprise operations. The significant change is that public cloud providers are often not the desired platform of choice. Companies big and small are instead looking for smaller, more distributed platforms, including on-premises hardware servers and smaller devices. Let’s explore the driving forces behind this transition and its future implications for enterprise AI.

Agents are on the rise

Agentic AI refers to artificial intelligence systems designed with autonomous decision-making capabilities, enabling them to act independently to achieve specific goals. We’ve seen many instances of this idea over the years, more recently with personal digital assistants on our phones and devices and automated everything, from home HVAC systems to automobiles.

These systems possess advanced reasoning, learning, and adaptive functionalities, allowing them to process complex information, make informed choices, and execute tasks without continuous human oversight. Using sophisticated algorithms and vast data sets, agentic AI can analyze environments, predict outcomes, and initiate real-time actions. This form of AI aims to enhance efficiency and effectiveness by providing intelligent, goal-directed solutions across various domains, such as healthcare, finance, and transportation.

Historically, public cloud services from AWS, Microsoft Azure, Google Cloud, and others have dominated the cloud landscape. However, the unique demands of agentic AI are now leading enterprises to reconsider and ultimately move away from public cloud solutions for several reasons:

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Google Cloud Spanner gets dual-region configuration option

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Each region runs two read/write replicas and a witness replica to maintain availability, Sagar explained, adding that when a zonal outage occurs affecting any one replica in a region, Spanner, due to the dual-region configuration setup, would be able to maintain availability as at least two replicas would be functional.

However, the product manager pointed out that in the event that an entire region faces outage and enterprises are in dual-region configuration setup for Spanner, the databases will lose availability.

“To recover availability, databases must switch to single-region mode, consisting of three replicas in a single region,” Sagar wrote, adding that Google continuously monitors for outages and upon detection automatically fails over and reconfigures the dual-region setup to a single-region setup to ensure availability.  

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