Apsy has unveiled the pioneering AI-powered app builder, enabling users to transform their ideas into functional apps just by speaking their minds, guided by the AI for customization

Apsy

Welcome to HackerNoon’s interview series. This section is curated by our Editorial team to spotlight bold and disruptive startups across the globe. If you’ve been nominated, and answer these questions Startups-Of-The-Year create a brand profile here. Hey Hackers, Apsy has been nominated in HackerNoon's annual Startup of the Year awards in Los Angeles, US. Please vote for us . here Read more about us below to understand why we deserve your vote. Meet Apsy Our start-up is called , a cool name rooted in Apps, but to us, it means “making app-building a breeze!” We are the first to introduce a fully AI-powered app builder. Our users speak their minds as our AI builds them. Apsy What makes us a different startup from others who offer tools to build apps is that we're all about letting your ideas flow while our AI does the heavy lifting. You dream it, speak it, and our AI builds it. Unlike traditional DIY tools, we take care of all the techy stuff, so our users don't have to. With Apsy, your creativity is the only limit. My Role Tooraj, a double Ph.D. dynamo in Economics and Computer Science, is a seasoned tech exec with a penchant for AI algorithms. A Y-Combinator and Techstars alumni is now driving his second startup, Apsy, towards reimagining software development. With a vision to make it simpler and cost-effective through AI, Tooraj is steering tech's future. How We're Disrupting/Improving the app development industry Building an app with traditional methods feels like a tough mountain to climb, right? But at Apsy, we're turning that hike into a fun walk in the park! Just chat with our AI about your brilliant app idea, answer a few clever questions it throws at you, and ta-da! Your app is up and running in a snap - we're talking hours, not months! We're shaking up the app-making game, making it a piece of cake for everyone, not just the coding wizards. Apsy isn't just transforming app development but the whole software landscape and tech entrepreneurship. Exciting times are ahead! Standing Out from The Crowd Do you remember how difficult building an app was in the good old days? Costly developers, scary DIY journeys, and months of learning only to end up with an 'okay-ish' app. Well, not anymore! We Apsy all you need to do is share your big idea, answer a few questions from our smarty-pants AI, and boom! It starts crafting screen options for you. And the best part? You can add your finishing touches and have a fabulous, tailor-made app ready to shine in just a few hours. And all this without burning a hole in your pocket! Building an app has never been this fun and easy, right? Our Predictions/Thoughts on the App Development Industry in 2023 The tech industry, specifically app development, is experiencing a significant shift towards no-code and low-code solutions. However, their complexity often presents challenges. Here, AI-driven platforms like Apsy come into play, simplifying app creation without needing users to be coding experts. As we move further into 2023 and beyond, we predict a surge in AI integration into app development owing to the rising demand for rapid, cost-effective digital solutions. The growth of AI sophistication will enable a better understanding of user needs and the delivery of high-quality, personalized apps. This AI infusion will transform the technological landscape and democratize the digital space, fostering innovation and promoting tech entrepreneurship. As Apsy, we're excited to pioneer this transformative journey, making app development more accessible and enjoyable. What word defines the state of app development in 2023? If you're asking about the vibe of app development in 2023, it's all 'Mind-to-Machine'! Just picture this - you come up with a killer app idea, chat about it, and voila, our AI buddy gets it done. No fuss, no code, just pure creativity! This exciting 'Mind-to-Machine' era is giving everyone the power to turn their app dreams into reality. It's like having a chat with a friend who happens to be a coding genius. So, buckle up, folks! We're on a fun-filled, game-changing journey that's turning the tech world on its head! Why we decided to participate in HackerNoon's Startup of the Year awards We jumped into HackerNoon's Startup of the Year awards because we're shaking things up with our 'Mind-to-Machine' tech at Apsy and wanted to share this cool innovation with fellow tech enthusiasts. Plus, who doesn't love a good challenge? We saw it as a great chance to learn, grow, and have some fun while competing with other innovative startups. So, here we are, all set to dazzle at the awards. Fingers crossed, folks! Final Thoughts At Apsy, we're on an exciting mission in 2023, transforming app development and making it accessible for all. We're thrilled to bring our innovation to HackerNoon's Startup of the Year awards, showcasing our approach, learning from peers, and embracing challenges. So, we invite you to on this thrilling journey, where we'll turn dreams into digital realities together! join us Don’t forget to ! Vote for us today

Vote for Apsy in HackerNoon's Startups of the Year 2023

To help researchers and developers in academia and beyond, Alibaba has now published its Cluster Data V2018 for all to explore In IT articles about internet applications, you will often find words such as “large scale” and “mass requests”. These applications all run in large-scale data centers, and readers generally have many questions about those environments. For example, what is the operating status of each machine in the data center? What kinds of applications are running? What are the characteristics of these applications? With the exception of a few senior experts, it is rare for students and corporate researchers to understand these details. Today, Alibaba shares a real dataset for computer clusters: (attached at the end of article). This dataset provides a full record of details for the servers and running tasks in Alibaba’s production cluster. With the release of this data, Alibaba hopes to engage its peers in academia and the wider industry and to promote further development within the industry. Alibaba Cluster Data V2018 Drawing on insights from Lin Shi, a technical expert at the Ali System Software Division, this article offers an in-depth introduction to this unique dataset, as well as findings from academic research with the previous year’s data. Releasing the Dataset: A Resource for Exploration In 2015, Alibaba tried to deploy latency insensitive batch computing tasks and latency sensitive online services to the same batch of machines in its data center, with the goals of allowing redundant resources to be fully utilized and improving the overall utilization of these machines. After more than three years of trial demonstrations, structural adjustments, and optimization of resource isolation, this program has now moved into the mass production phase. Average resource utilization of clusters has since increased from 10% to 45% using colocation technology (i.e. the technology that allows running online service and batch workloads on same machine). In addition, through various optimization methods, more tasks can now be run in the data center, and the average transaction cost of the 11.11 Global Shopping Festival has been reduced by 17% per 10,000 transactions. Though some readers will understand this achievement and how it was accomplished, many are likely to have questions such as what exactly the computer cluster looks like after optimization, or what colocation means and why it matters. In releasing the Alibaba Cluster Data V2018 dataset, Alibaba hopes to help interested students and researchers answer those questions and better understand large-scale data centers. Using the dataset, individuals can learn more about how Alibaba has increased resource utilization to 45% via colocation; how many tasks Alibaba runs each day; and what the resource requirements of the business are. How individuals use this dataset depends entirely on their needs. Making Use of the Data At 50 GB compressed and 270 GB uncompressed, Alibaba Cluster Data V2018 is a large dataset with many different uses. In six files, it has data for 4000 servers, corresponding online application containers, and operation of offline computing tasks for up to eight days. With Alibaba Cluster Data V2018, individuals can do the following: · Understand the servers and the characteristics of running tasks in today’s advanced data centers. · Experiment with various algorithms for task management and cluster optimization in scheduling and operations, and write papers about them. · Use the data to learn how to conduct data analysis, and reveal more techniques and methodologies that have not yet been discovered. The following are examples of questions the data can be used to answer: · E-commerce businesses face different pressures during daytime and nighttime. How can we improve overall resource utilization with these peaks and valleys? · How many dependencies does Alibaba’s longest DAG have? · How long does a container typically exist? · What is the typical lifetime of a computational task? · Multiple instances of a task are theoretically similar to each other, but do they all run at the same time? In fact, academics have already used Alibaba cluster data for a range of crucial analysis and research. In 2017 and 2018, a number of prominent academic papers were published based on of the first wave of data shared in Alibaba Cluster Data V2017. The following sections show examples of academic uses of the dataset, many of which have been featured in academic conferences such as OSDI. LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation — Yizhou Shan, Yutong Huang, Yilun Chen, and Yiying Zhang; Purdue University. (OSDI’18 Best Paper Award recipient) Imbalance in the Cloud: An Analysis on Alibaba Cluster Trace — Chengzhi Lu et al. (BIGDATA 2017) Characterizing Co-located Datacenter Workloads: An Alibaba Case Study — Yue Cheng, Zheng Chai, Ali Anwar. (APSys2018) The Elasticity and Plasticity in Semi-Containerized Co-locating Cloud Workload: a View from Alibaba Trace — Qixiao Liu and Zhibin Yu. (SoCC2018) Looking Forward: Updates in V2018 There are two major differences that distinguish the new V2018 dataset from the previous year’s V2017 dataset featured in the above research. First, the V2018 dataset includes the DAG information of some of our production batch workloads. This means that offline computing tasks like those commonly used in Map Reduce, Hadoop, Spark, and Flink are arranged in the form of a Directed Acyclic Graph (DAG), which shows parallelism, dependencies between tasks, and so on. As such, this dataset is the now the largest available DAG dataset from an actual production environment. The following figure illustrates and example of a DAG: Second, the V2018 dataset is considerably larger in scale than the previous set, which contained data for approximately 1300 machines over a 24-hour period. The new version includes data for 4000 machines over a period of 8 days. With so much having been accomplished using V2017 dataset, Alibaba looks forward to the developments to come as researchers begin work with the new dataset in 2019. (The original article is written by Ding Haiyang丁海洋) Alibaba Cluster Data V2018 Download link: [machine_meta]( http://clusterdata2018pubus.oss-us-west-1.aliyuncs.com/machine_meta.tar.gz) [machine_usage]( http://clusterdata2018pubus.oss-us-west-1.aliyuncs.com/machine_usage.tar.gz) [container_meta]( http://clusterdata2018pubus.oss-us-west-1.aliyuncs.com/container_meta.tar.gz) [container_usage]( http://clusterdata2018pubus.oss-us-west-1.aliyuncs.com/container_usage.tar.gz) [batch_task]( http://clusterdata2018pubus.oss-us-west-1.aliyuncs.com/batch_task.tar.gz) [batch_instance]( http://clusterdata2018pubus.oss-us-west-1.aliyuncs.com/batch_instance.tar.gz) Alibaba Tech First hand and in-depth information about Alibaba’s latest technology → Facebook: . Twitter: . “Alibaba Tech” “AlibabaTech”

Open Season for Research: Alibaba Releases Cluster Data from 4000 Servers

Introducing ApyHub Fusion: The All-in-One API Client Solution 🚀✨

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

Grassroots Distributed Systems: Preliminaries: Asynchronous Distributed Multiagent Transition System

Virtual Meetings have become an essential part of corporate collaboration, serving as the de facto way that ideas are exchanged, decisions are made, and strategies are devised. However, the true value of meetings is often diluted by their length and the complexity of the discussions. This is why note-taking and effective summaries and action points are so important. Converting long and often pointless discussions into short (actionable) summaries, meeting attendees can quickly catch up on meetings they missed, review decisions that were made, and understand discussions without having to sift through hours and hours of recordings. We thought it would be cool to create our summarizer. If you don't have one already, keep reading. This technical tutorial will guide you through creating a simple web application that summarizes daily meeting Transcripts using ApyHub’s AI Summarize Document API using NodeJS. Pre-requisites: Access to Google Meet and permission to access meeting transcripts.
A preferred programming language environment (e.g., Python).
ApyHub Account.
GitHub CLI is installed on your computer. Step 1: Clone the GitHub repository Open your terminal and run this command: git clone https://github.com/iamspathan/AI-Transcript-Summarizer.git This command will clone the project in your local environment. Once the project is cloned, you can open the repository in the code editor. Step 2. Install Required Packages: Here, in Node Package Manager (npm), we will install all the dependencies that our application needs to summarize the meeting transcribe. npm install Step 3: Add your API Key to the index.js file. Here, we will add our Secret API key from ApyHub. To generate an API key, you can visit the documentation page of AI Summarize Document API. Your secret looks like this ➡️
apy-token: APY****************************************** Copy this token and paste it in as shown in the code comment below. const express = require('express');
const fileUpload = require('express-fileupload');
const cors = require('cors');
const axios = require('axios');
const FormData = require('form-data');
const open = require('open');
const app = express();
const port = 3000;

app.use(cors());
app.use(fileUpload());

// Serve static files from the 'public' directory
app.use(express.static('public'));

app.post('/summarize', (req, res) => {
    console.log("summarixe called", req.files.file);
    if (!req.files || Object.keys(req.files).length === 0) {
        return res.status(400).send('No files were uploaded.');
    }

    // Assuming the file is attached to the request under the key 'file'
    const file = req.files.file;

    // Prepare the file to be sent using form-data
    const formData = new FormData();
    formData.append('file', file.data, file.name);

    // Set up the request headers, including the dynamic headers from form-data and the secret-apy token.
    const requestHeaders = {
        ...formData.getHeaders(),
        'apy-token': 'ADD YOUR OWN APY-TOKEN HERE'
    };

    // Call the AI Summarize API
    axios.post('https://api.apyhub.com/ai/summarize-documents/file', formData, { headers: requestHeaders })
        .then(apiResponse => {
            // Assuming the API returns JSON with a summary field
            console.log(apiResponse.data.data.summary);
            res.send({ summary: apiResponse.data.data.summary });
        })
        .catch(error => {
            console.error('Error calling the AI Summarize API:', error);
            res.status(500).send('Failed to summarize the document.');
        });
});

app.listen(port, () => {
    console.log(`Server running at http://localhost:${port}`);
    // Open the index.html file in the default browser
    open(`http://localhost:${port}`);
}); Once you’ve added the API key. Save the changes using the Ctrl+S Key. Step 4: Running Your Application Once you’ve added the API key. Save the changes using Ctrl+S in your terminal, and run this command.: node index.js That’s it :) As you can see, Now you can generate a meeting transcript anytime you want :) Virtual Meetings have become an essential part of corporate collaboration, serving as the de facto way that ideas are exchanged, decisions are made, and strategies are devised. However, the true value of meetings is often diluted by their length and the complexity of the discussions. This is why note-taking and effective summaries and action points are so important. Converting long and often pointless discussions into short (actionable) summaries, meeting attendees can quickly catch up on meetings they missed, review decisions that were made, and understand discussions without having to sift through hours and hours of recordings. We thought it would be cool to create our summarizer. If you don't have one already, keep reading. This technical tutorial will guide you through creating a simple web application that summarizes daily meeting Transcripts using ApyHub’s AI Summarize Document API using NodeJS. ApyHub’s AI Summarize Document API Pre-requisites: Access to Google Meet and permission to access meeting transcripts. A preferred programming language environment (e.g., Python). ApyHub Account. GitHub CLI is installed on your computer. Access to Google Meet and permission to access meeting transcripts. Google Meet A preferred programming language environment (e.g., Python). ApyHub Account . ApyHub Account GitHub CLI is installed on your computer. Step 1: Clone the GitHub repository Open your terminal and run this command: git clone https://github.com/iamspathan/AI-Transcript-Summarizer.git git clone https://github.com/iamspathan/AI-Transcript-Summarizer.git This command will clone the project in your local environment. Once the project is cloned, you can open the repository in the code editor. Step 2. Install Required Packages: Here, in Node Package Manager (npm), we will install all the dependencies that our application needs to summarize the meeting transcribe. npm install npm install Step 3: Add your API Key to the index.js file. Here, we will add our Secret API key from ApyHub. To generate an API key, you can visit the documentation page of AI Summarize Document API . Your secret looks like this ➡️ apy-token: APY****************************************** AI Summarize Document API apy-token: APY****************************************** Copy this token and paste it in as shown in the code comment below. const express = require('express');
const fileUpload = require('express-fileupload');
const cors = require('cors');
const axios = require('axios');
const FormData = require('form-data');
const open = require('open');
const app = express();
const port = 3000;

app.use(cors());
app.use(fileUpload());

// Serve static files from the 'public' directory
app.use(express.static('public'));

app.post('/summarize', (req, res) => {
    console.log("summarixe called", req.files.file);
    if (!req.files || Object.keys(req.files).length === 0) {
        return res.status(400).send('No files were uploaded.');
    }

    // Assuming the file is attached to the request under the key 'file'
    const file = req.files.file;

    // Prepare the file to be sent using form-data
    const formData = new FormData();
    formData.append('file', file.data, file.name);

    // Set up the request headers, including the dynamic headers from form-data and the secret-apy token.
    const requestHeaders = {
        ...formData.getHeaders(),
        'apy-token': 'ADD YOUR OWN APY-TOKEN HERE'
    };

    // Call the AI Summarize API
    axios.post('https://api.apyhub.com/ai/summarize-documents/file', formData, { headers: requestHeaders })
        .then(apiResponse => {
            // Assuming the API returns JSON with a summary field
            console.log(apiResponse.data.data.summary);
            res.send({ summary: apiResponse.data.data.summary });
        })
        .catch(error => {
            console.error('Error calling the AI Summarize API:', error);
            res.status(500).send('Failed to summarize the document.');
        });
});

app.listen(port, () => {
    console.log(`Server running at http://localhost:${port}`);
    // Open the index.html file in the default browser
    open(`http://localhost:${port}`);
}); const express = require('express');
const fileUpload = require('express-fileupload');
const cors = require('cors');
const axios = require('axios');
const FormData = require('form-data');
const open = require('open');
const app = express();
const port = 3000;

app.use(cors());
app.use(fileUpload());

// Serve static files from the 'public' directory
app.use(express.static('public'));

app.post('/summarize', (req, res) => {
    console.log("summarixe called", req.files.file);
    if (!req.files || Object.keys(req.files).length === 0) {
        return res.status(400).send('No files were uploaded.');
    }

    // Assuming the file is attached to the request under the key 'file'
    const file = req.files.file;

    // Prepare the file to be sent using form-data
    const formData = new FormData();
    formData.append('file', file.data, file.name);

    // Set up the request headers, including the dynamic headers from form-data and the secret-apy token.
    const requestHeaders = {
        ...formData.getHeaders(),
        'apy-token': 'ADD YOUR OWN APY-TOKEN HERE'
    };

    // Call the AI Summarize API
    axios.post('https://api.apyhub.com/ai/summarize-documents/file', formData, { headers: requestHeaders })
        .then(apiResponse => {
            // Assuming the API returns JSON with a summary field
            console.log(apiResponse.data.data.summary);
            res.send({ summary: apiResponse.data.data.summary });
        })
        .catch(error => {
            console.error('Error calling the AI Summarize API:', error);
            res.status(500).send('Failed to summarize the document.');
        });
});

app.listen(port, () => {
    console.log(`Server running at http://localhost:${port}`);
    // Open the index.html file in the default browser
    open(`http://localhost:${port}`);
}); Once you’ve added the API key. Save the changes using the Ctrl+S Key. Ctrl+S Step 4: Running Your Application Once you’ve added the API key. Save the changes using Ctrl+S in your terminal, and run this command.: node index.js node index.js That’s it :) As you can see, Now you can generate a meeting transcript anytime you want :)

Building an AI-Powered Summarizer using NodeJS and ApyHub API To Make Meetings Easy

Designed for developers at the intersection of curiosity and professional growth, this primer aims to illuminate how asynchronous programming can elevate application performance, scalability, and responsiveness. By focusing on the universal principles of non-blocking operations and event loops, we venture beyond specific technologies like AsyncIO, Node.js, or Go. This exploration is crafted for software developers eager to grasp the efficiencies driving modern software without the complexity of technical jargon. Think of how a single thread handling requests is like a single-lane road in a busy city: each request is a car, and when there's only one lane, cars line up, causing delays. But what if our city — our service — could manage traffic more cleverly? That's where the magic of an asynchronous runtime comes into play. It's like adding an intelligent traffic system to our city, guiding cars through multiple lanes and intersections without waiting. This system keeps traffic flowing smoothly, ensuring no car (or task) waits too long, which is precisely how an asynchronous runtime keeps our software running efficiently. Let's consider an example of a service that completes I/O operations synchronously. For clarity, these operations are shown outside the main flow of execution in the diagram: Blocking I/O during your service's startup phase might be acceptable, but avoiding this approach when processing external requests is advisable. The following diagram illustrates how service efficiency can be enhanced through the adoption of non-blocking I/O operations: These examples highlight scenarios where the server's performance gains are maximized. Nevertheless, the advantage of non-blocking operations persists across any frequency of incoming requests. Even in less-than-ideal conditions, performance benefits from integrating dedicated I/O threads into the processing workflow. The total time required to process a request (from the client's initial request to the final response, as depicted by the blue number on the right) will invariably decrease, provided there are sufficient threads to handle all requests. In the worst-case scenario, this duration will not surpass that of synchronous processing methods. We then encounter a practice that might seem counterintuitive to many developers. When I/O operations constitute a significant portion of request processing time, optimizing other code segments may yield slight improvement. The duration to fetch data from a cache might align closely with the time dedicated to business logic and template rendering. Employing caching or an in-process database can reduce data retrieval times compared to other processing activities. To segment code effectively and facilitate callback execution, one can instruct the runtime to proceed to the next event loop cycle. Below is an illustrative example of how this concept is applied: // blocking callbacks
function func1_cb(str, cb) {
 var res = func1(str);
 cb(res);
}

function func2_cb(str, cb) {
 var res = func2(str);
 cb(res);
}

// non-blocking callbacks
function func1_cb(str, cb) {
 var res = func1(str);
 process.nextTick(function () {
   cb(res);
 });
}

function func2_cb(str, cb) {
 var res = func2(str);
 process.nextTick(function () {
   cb(res);
 });
}

// usage example
func1_cb(content, function (str) {
 func2_cb(str, function (result) {
   // work with result
 });
}); By adopting this methodology to split two parts of calculations, the overall processing time for scenarios with nearly concurrent request arrivals remains the same. However, the response to the first request is delayed: This scenario represents the least favorable outcome when employing the “next tick” strategy. As shown in the initial example, the “next tick” method proves benign for infrequent requests. Should requests arrive at a moderate pace, leveraging this technique enhances processing speed by allowing the initiation of new requests and the start of non-blocking operations to be interleaved during execution breaks. This approach effectively reduces both the total processing time and the average time per request: In conclusion, adopting non-blocking I/O is crucial for enhancing application performance and beneficial in environments with both sparse and heavy incoming request volumes. Additionally, effectively sequencing the execution flow — illustrated by concepts similar to the "next tick" technique — significantly improves server efficiency. Embracing these asynchronous programming practices offers clear advantages over traditional, synchronous methods.

Asynchronous Runtimes: A Primer for the Perplexed Developer

Async/await in C# is a framework used for writing asynchronous C# code that is both readable and maintainable. These tips will help you to integrate async/await programming more effectively in the # projects: 1. ValueTask for Lightweight Operations Use ValueTask instead of Task for asynchronous methods that often complete synchronously, reducing the allocation overhead. public async ValueTask<int> GetResultAsync()
    {
        if (cachedResult != null)
            return cachedResult;
        int result = await ComputeResultAsync();
        cachedResult = result;
        return result;
    } 2. ConfigureAwait for Library Code Use ConfigureAwait(false) in the library code to avoid deadlocks by not capturing the synchronization context. public async Task SomeLibraryMethodAsync()
    {
        await SomeAsyncOperation().ConfigureAwait(false);
    } 3. Avoiding async void Prefer async Task over async void except for event handlers, as async void can lead to unhandled exceptions and is harder to test. public async Task EventHandlerAsync(object sender, EventArgs e)
    {
        await PerformOperationAsync();
    } 4. Using IAsyncDisposable For asynchronous cleanup, implement IAsyncDisposable and use await to ensure resources are released properly. public async ValueTask DisposeAsync()
    {
        await DisposeAsyncCore();
    }
    
    private async ValueTask DisposeAsyncCore()
    {
        if (resource != null)
        {
            await resource.DisposeAsync();
        }
    } 5. Efficiently Combine Tasks Use Task.WhenAll for running multiple tasks in parallel and waiting for all to complete, which is more efficient than awaiting each task sequentially public async Task ProcessTasksAsync()
    {
        Task task1 = DoTask1Async();
        Task task2 = DoTask2Async();
        await Task.WhenAll(task1, task2);
    } 6. Cancellation Support Support cancellation in asynchronous methods using CancellationToken. public async Task DoOperationAsync(CancellationToken cancellationToken)
    {
        await LongRunningOperationAsync(cancellationToken);
        cancellationToken.ThrowIfCancellationRequested();
    } 7. State Machine Optimization For performance-critical code, consider structuring your async methods to minimize the creation of state machines by separating synchronous and asynchronous paths. public async Task<int> FastPathAsync()
    {
        if (TryGetCachedResult(out int result))
        {
            return result;
        }
        return await ComputeResultAsync();
    } 8. Avoid Blocking Calls Avoid blocking on async code with .Result or .Wait(). Instead, use asynchronous waiting through the stack. public async Task WrapperMethodAsync()
    {
        int result = await GetResultAsync();
    } 9. Eliding Async/Await In simple passthrough scenarios or when returning a task directly, you can elide the async and await keywords for slightly improved performance. public Task<int> GetResultAsync() => ComputeResultAsync(); 10. Custom Task Schedulers For advanced scenarios, like limiting concurrency or capturing synchronization contexts, consider implementing a custom TaskScheduler. public sealed class LimitedConcurrencyLevelTaskScheduler : TaskScheduler
    {
        // Implement the scheduler logic here.
    } 11. Using Asynchronous Streams Leverage asynchronous streams with IAsyncEnumerable<T> for processing sequences of data asynchronously, introduced in C# 8.0. public async IAsyncEnumerable<int> GetNumbersAsync()
    {
        for (int i = 0; i < 10; i++)
        {
            await Task.Delay(100); // Simulate async work
            yield return i;
        }
    } 12. Avoid Async Lambdas in Hot Paths Async lambdas can introduce overhead. In performance-critical paths, consider refactoring them into separate async methods. // Before optimization
    Func<Task> asyncLambda = async () => await DoWorkAsync();
    
    // After optimization
    public async Task DoWorkMethodAsync()
    {
        await DoWorkAsync();
    } 13. Use SemaphoreSlim for Async Coordination SemaphoreSlim can be used for async coordination, such as limiting access to a resource in a thread-safe manner. private readonly SemaphoreSlim semaphore = new SemaphoreSlim(1, 1);
    
    public async Task UseResourceAsync()
    {
        await semaphore.WaitAsync();
        try
        {
            // Access the resource
        }
        finally
        {
            semaphore.Release();
        }
    } 14. Task.Yield for UI Responsiveness Use await Task.Yield() in UI applications to ensure the UI remains responsive by allowing other operations to process. public async Task LoadDataAsync()
    {
        await Task.Yield(); // Return control to the UI thread
        // Load data here
    } 15. Asynchronous Lazy Initialization Use Lazy<Task<T>> for asynchronous lazy initialization, ensuring the initialization logic runs only once and is thread-safe. private readonly Lazy<Task<MyObject>> lazyObject = new Lazy<Task<MyObject>>(async () =>
    {
        return await InitializeAsync();
    });
    
    public async Task<MyObject> GetObjectAsync() => await lazyObject.Value; 16. Combining async and LINQ Be cautious when combining async methods with LINQ queries; consider using asynchronous streams or explicitly unwrapping tasks when necessary. public async Task<IEnumerable<int>> ProcessDataAsync()
    {
        var data = await GetDataAsync(); // Assume this returns Task<List<int>>
        return data.Where(x => x > 10);
    } 17. Error Handling in Async Streams Handle errors gracefully in asynchronous streams by encapsulating the yielding loop in a try-catch block. public async IAsyncEnumerable<int> GetNumbersWithErrorsAsync()
    {
        try
        {
            for (int i = 0; i < 10; i++)
            {
                if (i == 5) throw new InvalidOperationException("Test error");
                yield return i;
            }
        }
        catch (Exception ex)
        {
            // Handle or log the error
        }
    } 18. Use Parallel.ForEachAsync for Asynchronous Parallel Loops Utilize Parallel.ForEachAsync in .NET 6 and later for running asynchronous operations in parallel, providing a more efficient way to handle CPU-bound and I/O-bound operations concurrently. await Parallel.ForEachAsync(data, async (item, cancellationToken) =>
    {
        await ProcessItemAsync(item, cancellationToken);
    }); 22. Avoid Excessive Async/Await in High-Performance Code In performance-critical sections, minimize the use of async/await. Instead, consider using Task.ContinueWith with caution or redesigning the workflow to reduce asynchronous calls. Task<int> task = ComputeAsync();
    task.ContinueWith(t => Process(t.Result)); 23. Async Main Method Utilize the async entry point for console applications introduced in C# 7.1 to simplify initialization code. public static async Task Main(string[] args)
    {
        await StartApplicationAsync();
    } 24. Optimize Async Loops For loops performing asynchronous operations, consider batching or parallelizing tasks to improve throughput. var tasks = new List<Task>();
    for (int i = 0; i < items.Length; i++)
    {
        tasks.Add(ProcessItemAsync(items[i]));
    }
    await Task.WhenAll(tasks); More Cheatsheets Cheat Sheets - .Net C# Programming🚀 Thank you for being a part of the C# community! Before you leave: Follow us: | | | Visit our other platforms: More content at Youtube X LinkedIn Dev.to GitHub C# Programming Also published . here

Async/Await For Basic to Advanced C# Developers: 24 Essential Tips

Introduction The article highlights the importance of limiting the concurrent asynchronous operations, which in turn improves performance. Learning Objectives The common mistake all developers do How to use limit concurrent async operations Best Practices Prerequisites for Developers Basic understanding of C# programming language Basic understanding of asynchronous programming using async await Getting Started The common mistake all developers do Consider an example where the user wants to load data asynchronously within a method, and it highlights the common mistake developers make. (async item => await ProcessItem(item));
/// <summary>
/// Old approach with classic async await 
/// </summary>
/// <returns></returns>
public async static Task OldApproach(List<string> items)
{
    var tasks = items.Select(async item => await ProcessItem(item));
    await Task.WhenAll(tasks);
} The approach may look clean and simple, but it initiates tasks for each item in the collection concurrently. This can cause system strain under heavy List<string> items, which will produce poor application performance. Optimized approach with Concurrency Limit Let’s transform the above method using SemaphoreSlim to limit the number of concurrent asynchronous operations. The following code snippet demonstrates the more refined approach. /// <summary>
/// Optimized approach with limit concurrency
/// </summary>
/// <returns></returns>
public static async Task OptimizedApproachAsync(List<string> items, int maxConcurrency = 10)
{
    using (var semaphore = new SemaphoreSlim(maxConcurrency))
    {
        var tasks = items.Select(async item =>
        {
            await semaphore.WaitAsync(); // Limit concurrency by waiting for the semaphore.
            try
            {
                await ProcessItem(item);
            }
            finally
            {
                semaphore.Release(); // Release the semaphore to allow other operations.
            }
        });

        await Task.WhenAll(tasks);
    }
} The aforementioned code prevents the system from being choked by too many concurrent tasks. Best Practices Please find below the best practices Limit Concurrency To balance system load and resources, it is recommended to use SemaphoreSlim Avoid Blocking Calls Avoid using  .Result or .Wait(), as they can lead to deadlocks and degrade performance. Async all the way Avoid mixing async and sync code. Ensure all methods are async from top to bottom to prevent deadlock and make optimal use of resources. Conclusion Asynchronous programming in C# involves more than just understanding the async and await keywords; it requires more features like concurrency, resource utilization, and code structure Complete Code on GitHub GitHub — ssukhpinder/30DayChallenge.Net C# Programming🚀 Thank you for being a part of the C# community! Before you leave: If you’ve made it this far, please show your appreciation with a clap and follow the author! 👏️️ Follow us: | | | | | X LinkedIn Dev.to Hashnode Newsletter Tumblr Visit our other platforms: | | | | GitHub Instagram Tiktok Quora Daily.dev More content at C# Programming Also published . here

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