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Bitget Launches New AI-Enhanced Martingale Strategy



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Victoria, Seychelles, June 7, 2023 – Bitget, the world’s leading crypto derivatives and copy trading platform, has announced the addition of the Martingale IA strategy within its suite of Martingale investment strategy services. This new instrument aims to provide cryptocurrency newcomers with a convenient and sophisticated investment strategy made possible by AI.

bitget unveils its new ai program

Bitget has developed the AI ​​Martingale feature to make Martingales more accessible to novice traders and to allow all users to get more out of this investment strategy. The Martingale trading strategy involves increasing the investment amount after a decline in an attempt to recoup losses and make a profit. This strategy relies on historical data that reveals the tendency of markets to grow over time, helping investors gain a broader view of market behavior and adjust their trading accordingly, as well as to effectively allocate capital.

The Martingale AI enables automated trading after traders set their risk tolerance and investment frequency based on parameters recommended by Bitget’s AI. The parameters are calculated with Bitget’s back-end algorithms based on historical market prices and asset fluctuations, providing reliable investment benchmarks for traders.

Both novice and experienced traders can find a number of advantages to the Martingale strategy on Bitget, including two-way trading, which is suitable for both bulls and bears. After selecting the Martingale strategy, traders can make profits by going long or short, buying lows, or taking advantage of price callbacks. Another important advantage is the customizable and controllable risk, which allows traders to adjust various parameters of the Martingale strategy according to their own trading habits and risk tolerance, such as target profit and multiples of additional positions. . Novice traders can try strategies designed with a lower threshold by taking advantage of AI and selecting based on the types of strategy designed by the system, namely: conservative, moderate and aggressive.

Bitget’s Martingale Futures strategy supports leverage up to x100, allowing traders to access substantial funds and meet the needs of different risk tastes. Such an approach opens new horizons of opportunities in digital asset trading to a diverse audience of users looking to experiment with cryptocurrency trading.

The launch of the Martingale IA product represents Bitget’s commitment to providing its users with the tools and strategies necessary to invest confidently and in an informed manner, in accordance with their needs.. »

Delivering both simple and sophisticated offerings that leverage the power of AI aligns with Bitget’s mission to help people safely engage with digital assets. We will continue to explore new ways to integrate AI into our services and products to empower investors. These new features come as Bitget strengthens its range of services provided in light of a global expansion strategy and beyond derivatives. »

Gracy Chen – Managing Director of Bitget

About Bitget

Established in 2018, Bitget is one of the world’s leading cryptocurrency exchanges, whose flagship features are Futures trading and copy trading services. Serving over 8 million users in over 100 countries and regions, the platform is committed to helping users trade smarter by providing them with a versatile and secure trading solution. It also inspires the general public to embrace cryptocurrency through collaborations with reputable partners, including legendary Argentine footballer Lionel Messi, legendary Juventus Turin, and official esports event organizer PGL.

For more information: Website | Twitter | Telegram | LinkedIn | Discord

For media inquiries, please contact: [email protected]

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The Editor C.

The Cointribune editorial team unites its voices to express themselves on topics specific to cryptocurrencies, investment, the metaverse and NFTs, while striving to best answer your questions.


The content and products mentioned on this page are in no way endorsed by Cointribune and should not be interpreted as its responsibility.

Cointribune strives to provide readers with all useful information available, but cannot guarantee its accuracy and completeness. We urge readers to educate themselves before taking any action relating to the company, and to take full responsibility for their decisions. This article should not be considered investment advice, an offer or an invitation to purchase any product or service.

Investing in digital financial assets involves risks.

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Developer productivity poorly understood, report says



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Developer productivity is neither well-understood nor enabled, according to Atlassian’s just-released State of Developer Experience Report 2024. The report also found that interest in the developer experience, or DX, is growing but efforts are lagging.

Announced July 15 and accessible at, the State of Developer Experience Report 2024 is based on a survey of 1,250 engineering leaders in the US, Germany, France, and Australia, and 900 developers around the world. The survey was intended to find out what practices keep software development work flowing smoothly and what practices introduce friction. Feelings about work environments in the age of generative AI and microservices also were assessed. Among the findings were that less than half of developers believe their organizations prioritize developer experience and two out of three developers lose more than eight hours a week due to inefficiencies in their roles. Also, two out of three developers are not seeing significant productivity gains from using AI tools yet.

The top five contributors to developer role complexity, according to engineering leaders, include understaffing, expansion of the developer role, new technology, switching context between tools, and collaboration with other teams. The top five contributors to developer time loss cited include technical debt, insufficient documentation, build processes, lack of time for deep work, and lack of clear direction. Almost all engineering leaders surveyed, 99%, acknowledge that the developer role has become more complex. The top five practices leaders believe will improve developer productivity and satisfaction include AI automation, provision of new collaboration tools, risk taking and experimentation, streamlining decision-making, and hosting hackathons.

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Talk of GitLab sale highlights growing importance of DevSecOps platforms



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By connecting these two capabilities, he said, development teams can release and monitor their applications with a single platform. “Whether that makes sense from a business perspective is a matter for those two companies to decide, but most definitely, enterprise platform teams are linking those capabilities together on their own.”

Platform compatibility

John Annand, research practice lead at Info-Tech Research Group, also thinks that, if a Datadog deal occurred, it could be a good match.

“GitLab is a very popular DevSecOps tool that lets enterprises optimize their pre-production activities rather than having to build their own CI/CD and testing pipelines from multiple vendors’ products and from scratch,” he said in an email. “Datadog’s product is all the information about how a deployed application behaves once it’s left the dev environment and is forced to deal with real-world conditions.”

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How to use HybridCache in ASP.NET Core



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The key benefits of HybridCache are greater simplicity, improved performance, and enhanced efficiency.

HybridCache is an abstract class pertaining to the Microsoft.Extensions.Caching.Hybrid namespace. To store data to the cache and retrieve data from the cache, you use the GetOrCreateAsync method of the HybridCache class. This method takes advantage of the cache key to retrieve an object from the primary cache.

Now, if the object requested by the application is not available in the cache, i.e., in the case of a cache miss, the method checks the secondary cache if it is configured. If the data is not available there, the method stores the object in both the primary and secondary caches.

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Building next-generation applications with the Windows Application SDK



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Download channels show upcoming features

The Windows App SDK is available in three different channels; you can choose between the current stable release, a preview release for features that should ship soon, or an experimental channel where new features are trialed. The current supported release is Version 1.5.5, released early in July 2024. The next major release will be in six months or so. The current preview release predates the current build, so a new version should be available soon. Experimental builds are based on the development tree of the planned 1.6 release, with a second release in July 2024.

Microsoft provides a list of the features available in each channel, along with the current support life cycle. Versions are supported for a year from initial release, so the current 1.5 release will be in support until the end of February 2025, with Version 1.4 dropping out of support at the end of August 2024. There’s one unusual caveat to the Windows App SDK support: Although it offers compatibility back to Windows 10 Version 1809, technically only in-support versions of Windows are covered.

The Windows App SDK and the Copilot Runtime

The Windows App SDK is intended to be a key component of the Copilot Runtime, hosting, among other features, artificial intelligence APIs based on the Phi Silica local generative AI model and AI-powered OCR services. However, these promised features are still missing from the current Version 1.6 experimental builds, two months after Build. If Microsoft wants to shift developers’ attention to Windows, it needs to capitalize on the capabilities of its new hardware and ship Copilot Runtime APIs as soon as possible and accelerate the transition of Version 1.6 from experimental to production.

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Google’s Genkit for Go brings AI app development to Go language



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Google has unveiled Genkit for Go, an open-source framework for building AI-powered applications and cloud services natively in Go. The project is a collaboration between Google’s Firebase and Go language teams.

Introduced July 17 and currently in an alpha state, Genkit for Go enables developers to build generative AI applications by combining Go’s performance and concurrency advantages with Genkit’s libraries and tools. Potential use cases include intelligent assistants that understand complex requests, customer support agents using retrieval-augmented generation (RAG), and data transformation tools that convert unstructured data such as natural language into structured formats (SQL, queries, tables) for deeper analysis.

A guide to getting started with Genkit for Go can be found at Genkit for Go follows the May introduction of Firebase Genkit for Node.js, for JavaScript and TypeScript developers. Genkit provides lightweight, composable abstractions to simplify development of sophisticated AI workflows without sacrificing control and customizability, Google said. The framework features:

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Google rolls out new dev tools focusing on open source and GenAI



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IDC’s Dayaratna said he was particularly intrigued by Project Vanni because it directly addresses a particularly difficult coding obstacle, especially when development teams exist in many different countries speaking many different languages. The problem can also exist in countries with many different local languages, such as when widespread Indian teams have speakers writing in, for example, Hindi, Bengali, Marathi and Kannada.

The Vanni project is dealing with two difficult challenges. One is translating coding references without missing key technical details, especially when it is being translated into multiple languages. The second, potentially more severe, problem is doing these translations within a GenAI environment, where the algorithm can hallucinate or even simply try to extrapolate meaning where it might predict the next word incorrectly. If that happens, the translations become confusing and possibly destructive.

“What (coders) need is translation that is contextual and is specific to natural language prompts in software,” Dayaratna said, adding that translation specialized for developers “does not exist” yet. 

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Deno adds workspaces for managing monorepos



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The latest update to the Deno TypeScript /JavaScript and WebAssembly runtime features workspace support along with Node.js compatibility improvements.

Deno 1.45 was introduced July 11. The release introduces workspaces for managing monorepos, simplifying dependency management, configuration sharing, and module organization across large code bases, Deno Land said. There are two forms of supported workspaces: Deno-first workspaces defined in deno.json and backwards-compatible NPM workspaces. Global configuration for these workspaces is applied to each member package but can be overridden by members. Developers can mix and match NPM and Deno workspaces, with an NPM package inside a Deno workspace or vice versa.

For Node.js compatibility, Node-API support has been revamped, fixing issues with packages such as prisma, sqlite3,  and  paper. Other Node.js compatibility improvements include working on support for dd.trace and support for fs.lutimes and fs.lutimesSync. Also, node:crypto and node:zlib were added. The node:http module was updated, with capabilities such as Server#close() now doing a graceful shutdown, allowing in-flight requests to finish. In another improvement, the node:vm will consume less memory. For NPM support, types shipped with the package are now preferred over types from @types scope.

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Salesforce previews Einstein-powered service agent



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In order to create an Einstein Service Agent customized for an enterprise, its developers need to access the Einstein 1 platform, start creating a new copilot by giving its specific name and purpose, and then expose it to service topics, Nichols explained, adding that once the service topics are mapped and a LLM is selected, developers can assign knowledge-based actions to the agent.

These knowledge-based actions, according to the company, make the agent autonomous or allow the agent or chatbot to take actions without having a human in the loop, differentiating it from regular programmed chatbots or service agents.

One of the main differences between an older predictive chatbot and the Einstein Service Agent is its ability to understand intent because of its LLM base, said Keith Kirkpatrick, research director at The Futurum Group.

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Mistral’s new Codestral Mamba to aid longer code generation



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The company tested Codestral Mamba on in-context retrieval capabilities up to 256k tokens — twice the number seen in OpenAI’s GPT4o — and found its 7B version performing better than open source models in several benchmarking tests, such as HumanEval, MBPP, Spider, and CruxE.

The larger 22B parameter version of the new model also performed significantly better than CodeLlama-34B with the exception of the CruxE benchmark.

While the 7B version is available under the Apache 2.0 license, the larger 22B version is available under a commercial license for self-deployment or community license for testing purposes.

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Frequently sought solutions for JavaScript



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class CampingSpot {
  constructor(name, location) { = name;
    this.location = location;
    describeWater() {
      console.log("The water at " + + " is very cold.");

Now we can create instances of the CampingSpot object at will:

let willowCreek = new CampingSpot("Willow Creek", "Big Sur");
let sunsetStateBeach = new CampingSpot(“Sunset State Beach”, “Santa Cruz”);

6. Using remote APIs

Accessing APIs hosted on services is another common need in JavaScript programs. APIs provide access to external data and functionalities. Developers often seek solutions to fetch data from remote APIs, parse responses, and integrate them into their applications.

Fortunately, modern JavaScript includes the Fetch API in both client and server environments. This is a simple and direct way to make HTTP calls. For example, here’s how we’d get the list of Star Wars movies from the SWAPI service:

async function getStarWarsFilms() {
  try {
    const response = await fetch('');
    if (!response.ok) {
      throw new Error(`Error fetching Star Wars films: ${response.status}`);
    const data = await response.json();
  } catch (error) {
    console.error("Error:", error);


Because this is a GET request, we can just use fetch(''). Notice we use await, which I introduced earlier. That lets us pause while the request goes out and the response comes back.

We use the response object to determine if the request was good (an HTTP 200 response), using response.ok.

7. String manipulation

Strings are fundamental and used in all kinds of situations. Consider the following string:

const taoTeChingVerse1 = 
  `The Tao that can be told is not the eternal Tao. 
  The name that can be named is not the eternal name. 
  The nameless is the beginning of heaven and earth. 
  The named is the mother of all things,`;

Let’s say we wanted only the first line of the first verse:

const firstLine = taoTeChingVerse1.slice(0, 48);

This says: Give us the substring between the first character (0) until the 48th character, inclusive.

To search for the first occurrence of the word “Tao”, enter:

taoTeChingVerse1.indexOf("Tao"); // returns 4

Now, if you want to replace words, you use some simple regex. Here, we replace all occurrences of “told” with “spoken”:

const newText = text.replace(/told/g, "spoken");

The slashes indicate a regular expression, which we match on “told.” The suffix g indicates “global,” meaning all occurrences. The second argument to replace() is the token to swap in. (We wind up with “The Tao that can be spoken is not the eternal Tao.”)

If we need to concatenate two strings, a simple plus (+) operator will do the trick:

let fullVerse = taoTeChingVerse1 + “Having both but not using them, Think of them as the constant.”);

And, you can always count the string length with:

fullVerse.length; // return 261

8. Converting JSON objects to strings

Another common need is to take an actual JSON object and make it a string, or vice versa. Here’s how to take a live JSON object and make it a string:

let website = {
  name: “InfoWorld”,
  url: “”

let myString = JSON.stringify(website);

And here’s how to take the string and return it to an object:


9. Simple date operations

There’s a lot you can do (and must do) with JavaScript’s built-in Date object.

To start, you can get today’s date like so:

const today = new Date();

And get its constituent parts like this:

console.log(today.getFullYear());  // Get the year (e.g., 2024)
console.log(today.getMonth());    // Get the month (0-indexed, e.g., 4 for May)
console.log(today.getDate());     // Get the day of the month (e.g., 21)
console.log(today.getHours());    // Get hours (e.g., 13 for 1 PM)
console.log(today.getMinutes());  // Get minutes (e.g., 27)
console.log(today.getSeconds());  // Get seconds (e.g., 46)

Here’s how to get seven days in the future:

date.setDate(date.getDate() + 7);

10. Creating, finding, and counting numbers

JavaScript today is pretty good with numbers. It natively handles most numbers and offers a built-in library, Math, for extra power.

You can create numbers:

let age = 30;
let pi = 3.14159;
let billion = 1000000000;

Or convert from a string:

let convertedNumber = Number(“42”);

Normal operations behave in obvious ways:

let addition = pi + 3; // addition now equals 6.14159

You can round numbers quickly:

Math.ceil(pi); // round to nearest upper integer (4)
Math.floor(pi); // round to lowest int (3)

Find the largest number:

Math.max(100, 5, 10, 73);  // returns 100

If you start with an array, you can use the spread operator:

let numbers = [100, 5, 10, 73];
Math.max(...numbers); // returns 100

Finally, here’s how to check for evenness:

let isEven = 10 % 2 === 0; // true (checks if remainder is 0)

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