Deworkis in the Decentralized Autonomous Organizations Industry

Dework

Roxana Commands

If you have ever wondered how a Decentralised Autonomous Organisation (DAO) is run, grab a seat and read on! Do you know that a DAO, just like a traditional organization, employs a variety of tools or digital applications for different purposes? But the key difference is that a DAO’s tools are mostly decentralized applications or programs running on a blockchain. Decentralized applications, dApps for short, have a crypto wallet interface that enables DAOs to connect and navigate their websites. Dapps and other tools can altogether be found in a DAO tool kit. Here are some popular DAO tools to get familiar with! Snapshot Launched in 2020, Snapshot is a gasless web3 voting platform for DAOs. It enables DAOs to track the opinions and votes of their members openly. It is a free-to-use decentralized protocol with a simple wallet integration, and it allows DAOs to customize their experience. To vote on Snapshot, voters must be accredited (i.e., they must hold the governance token of a DAO). JuiceBox JuiceBox is the go-to platform for token launch and fundraising on the Ethereum blockchain. It is specifically built for crypto projects that want to raise funds using Ether. In 2021, Constitution DAO raised $47 million on JuiceBox from over 17,000 donors to buy a rare copy of the US constitution. It provides the infrastructure for DAOs to deploy their tokens and raise money. Safe Formerly known as Gnosis Safe, Safe is a decentralized protocol that provides on-chain custody of digital assets on the Ethereum blockchain and the EVM. Ethereum-based DAOs have millions of dollars in assets secured by Safe. Safe was initially launched in 2018 to support Ethereum, but it has now expanded to such blockchain protocols as Polygon and Binance Smart Chain. Safe has a smart contract multi-sig wallet which is suitable for DAOs as it requires a minimum number of accredited members or core contributors or a threshold to approve a transaction. DeWork DeWork is a DAO-friendly dapp that allows freelancers and members of a DAO community to apply for bounties and grants. It currently has over 500 DAOs listed on its platform, and it provides a simple wallet integration that enables successful applicants to earn rewards. DeWork makes finding job opportunities in web3 easy, and it enables DAO communities to collaborate. It is also an open-source platform where DAOs can source for quality contributors whose track records are visible. Coordinape Coordinape is specifically designed to help DAOs allocate rewards fairly to their core contributors. In other words, it is a Dapp that allows participants in a DAO to reward one another for their efforts over a given period of time. Coordinape makes the fair distribution of resources easy, transparent, and quick through ‘Gift Circles’. Discord Server The membership of a DAO can range from 50 to 20 thousand members or more. Every DAO needs a large platform such as a Discord server to bring its members together. Today, most DAO communities can be found on Discord which is a social media app for gaming, collaboration, and discussion. Discord enables DAO and other crypto-native communities to customize their experience, and it also allows them to interact with other applications outside Discord. Other DAO tools include Utopia, Deep DAO, Kali DAO, Yerba, SuperDAO, Notion, Aragon, Telegram, etc. Read more DAO articles: https://hackernoon.com/do-daos-have-the-ability-to-change-the-world

        DAO: Tool Kit for Starters

According to the Book of Genesis, Noah's descendants built a magnificent city near Babylon. They also built a tower of Babel to make a name for themselves. God was offended by the arrogance of man and decided to make a confusion of tongues so that they could not speak to one another. There is no mention in Genesis of whether God destroyed the tower, but in many deductive versions, God did so. So humans began to wander around the ruins, unable to communicate and understand each other. Source:  Daniel Larimer The story of the Tower of Babel is the best metaphor for the fragmentation of the world since the pandemic. Serious problems have cropped up between countries. We lose trust, fail to recognize the same truths, and cut ourselves off from each other and the past. For a long time, most people have been coy about cryptocurrency's encroachment on real power systems. It was not until the Russia-Ukraine War that the DAO was directly elevated to the status of national strategic organization, which also meant that the social and political experiments based on DAO were no longer castles in the air in the ideological sense, but actually entered into the practice of decentralized governance. In this paper, we will explore the possibility of productivity liberation and production relations optimization from the perspective of social consensus and proof of physical work. Look for colonial incentives Every bull market has a new growth narrative. The next cycle is bound to see many countries trying to establish new forms of violent institutions and new international economic organizations. DAOs in the decentralized sovereign category are more like national experiments. The tokenomics of cryptocurrency defines the distribution laws of the country on a macro level, while also defining the rights and obligations of its citizens on a micro level. It no longer needs to be demonstrated that bad monetary policy can wreck a country's economy. From the perspective of international law, people, territory, government, and sovereignty constitute the key elements of a sovereign state. Judging from the development of DAO today, DAO has already taken the embryonic form of a sovereign state. Mainly reflected in the following two points: This is the basis of sovereignty, coordinating the exchange of benefits according to rules written into the code of the smart contract. Ethereum is the most typical value protocol. Tokens are universally recognized and enforceable units of value, including governance tokens representing voting rights, DeFi tokens representing fiscal rights, NFTs representing the sanctity of private property rights, and security tokens representing mapping the real physical world. All of these tokens operate as code, meaning that the distribution of power should follow programmable logic. - Protocols and tokens: These are groups organized around government affairs, that is, the government in general. Allocation relationships are coordinated through sharing rules enforced on the blockchain. If DAOs and protocol tokens can establish good engagement and interconnected value networks in the ecosystem, new forms of digital organizations and states may emerge. - DAOs: Source: Andy Martin, IBM We need to understand how value is created in newly sovereign states because the sustainability of value creation and the moat of governance determine the number of citizens. Similarly, creators of sovereign DAOs must understand the investor perspective to focus most of the DAO's resources on core assets that improve the sovereign value and encourage members to contribute ideas from the bottom up. It is challenging for protocols and DAOs to keep similar companies efficient and profitable and to include token holders in sovereign governance decisions. There is a misconception that token holders should not enjoy too much of the value of the agreed income. This is classic web2 exploitative thinking. Citizens in the Web3 era enjoy absolute data ownership, which not only means that a richer application ecosystem can flourish through growing user interactions, but also that sources of power and value accumulation can be transferred completely freely. Mature DeFi protocols currently look most likely to be transformed into successful sovereign DAOs. These protocols have steadily growing cash flows and ample treasuries. They offer a variety of financial products, from lending to structured products, in addition to the fees charged by the core DEX (decentralized exchange). DeFi is therefore the most prosperous colony in the digital world. The DAO, as the coordinator of the exchange of colonial interests, creates more value through the open construction of applications within the ecosystem, captures more value through tokens, and establishes token-driven network effects. Avoid the Illusion of Power Blockchain protocols are expected to replace the constitutions and governing laws of nation-states, and since DAO participants are citizens of the network, they must be subject to cyber laws and policies. As a government, DAO also needs to establish governance rules, a legal system, an organizational structure of power balance, and high-quality public products and services. Cryptocurrencies have raced through millennia of human simulations of social governance in a few years, and experiments that bring different power systems around the world to the blockchain at Internet speed are bound to experience similar growing pains. The biggest mistake of all would be the failure of the project founders to abide by the basic principles of economics and to believe in the illusion of power derived from the short-term token boom. The determinants of the  value of DAO are: 1. Sustainability of the underlying ecosystem. 2. The long-term intrinsic utility of tokens in the ecosystem. The traditional nation-state aims to increase productivity and build a competitive advantage to achieve the long-term utility of population, trade, and monetary policies. Developed countries are rich because the government is more of an inclusive economic institution. Countries with so-called "inclusive politics" expand individual political power and property rights as widely as possible, and this is their growth engine. By contrast, if power is held by a small elite, wealth either fails to grow for long or gradually falls into a debt crisis after a brief economic expansion. We should take these lessons seriously when designing sovereign DAOs. From a development economics perspective, the network of decentralized political and economic power fits well with the design of blockchain. The value of a blockchain network is largely determined by the "resource endowment" and the national demand for goods and services, which we call block space and Gas. At this point, the similarities in power structure between DAO and the state are obvious. Start from the proof of physical work One of the most powerful features of DAOs is their ability to create colonial-like social incentive structures that allow anyone in the world to contribute to a set of governance goals without permission, and that can be frictionless and fine-tuned to facilitate large-scale inter-state coordination. These innovations link the digital and physical worlds through the token economy, known as "proof of physical work." Projects that conform to this theory motivate people to perform verifiable physical work to build the infrastructure of the physical world. One of the most notable projects is Helium and STEPN. Taking the Russia-Ukraine War as an example, the biggest feature of this war lies in the asymmetric game between the two sides' intelligence systems. The Ukrainian government directly introduced the proof of physical work mode into intelligence warfare and realized the real decentralized dimensionality reduction. Mykhailo Fedorov, who heads the digital transformation ministry of Ukraine, has proposed a project called "Smartphone Nation." Using social media as its main front, Ukraine's digital authorities have developed an intelligence App that combines technology, hacking, cryptocurrencies and the power of public opinion to provide a "find the enemy" feature -- every resident using the App can photograph and upload information about the enemy and provide a location whenever and wherever they want. Sourced ideas for crowdsourced apps can hardly be attributed to DAO. The intelligence information collected by residents is sent to soldiers on the front line through another App, which constitutes a clear "battle order". Ukrainian soldiers who receive these "battle orders" can quickly complete their missions. This governance thinking, which explicitly uses decentralized products and distributed concepts in the state apparatus, approaches the government as one of many DAOs for producing goods and services. Source: Messari Maybe you will refute me and think that UkraineDAO is the real DAO organization. But you cannot deny that DAO can be an option for a nation's digital transformation, even during wartime. Physical work proves that citizens have direct control over the machinery of violence. If the government that controls violence were in ideal shape, it would not take protection money. But in reality, the organization of commercial activities that charge "protection money" is not controlled by both producers and consumers, so it will not serve consumers by maximizing the quality of service and reducing the price charged to the greatest extent. For them, scalability is more important than maximizing profit. Think of most governments as a sort of hybrid organization; the payments that citizens make to these governments are the prices they have to pay to avoid more serious losses. If the DAO charges significantly less protection money than the government and can provide all the public services you need without even charging protection money, how will you choose? In the world of cryptocurrency, cyber citizenship is almost defined by decentralized tokens, meaning that the cost of digital migration for users is almost zero. As long as you hold the token, you are a citizen of the protocol. That's why we're seeing Web3 reputation systems like Station and social graph protocols like Lens grow in popularity because it's easier to get onto a new DAO than it is to get into the U.S. One person may be an active citizen of dozens of DAOs, which will greatly contribute to thriving international trade among DAOs and even solve the impossible triangle in international trade, which you can read more about . here Where should we go? First, a successful sovereign DAO should have the following three components: 1. Network incentive model (liquidity source); 2.Community engagement (value interaction); 3. Adequate decentralization (governance capacity); As proof of physical work mechanisms is refined, many components of DAO activities, including frictional costs and computational rules in production and trading activities, will be replaced by hardware machines and smart contracts. This is why DeFi is the first application direction for sovereign DAOs to break out. In summary, the primary goal of DAOs is to attract and retain high-quality digital citizens, thereby attracting sustainable demand and adding value to the network. Of course, the success of a country is more complex than the token economy and DAO governance design. But there is no doubt that DAOs provide builders with a much richer toolbox than traditional states. Anything in a legal document or monetary policy can run on the blockchain. StepN is a move-to-earn application built on Solana. Its success lies in building rich economic models, vibrant in-app activities, and vibrant cross-chain interactions. I think they could try new governance models that not only reward token holders for ownership but also think about rewarding people for participation and contribution. If there is a major innovation in their governance paradigm, I believe this model based on economic governance can be extended to outer space colonies. Nation3 had a more specific goal, creating a community designed to provide services similar to nation-state services, such as money, education, health care, and so on. The project hopes to build a fair nation of encryption on the cloud. Their unique "union + proposal + Dework + community members" model is working, and it is still cash flow issues that affect the development of the project. Because building a Web3 country often seems like a grand narrative rather than a clear source of cash flow for Web3 apps. Therefore, such projects should still prioritize the operation of Treasury assets and the value interaction with DeFi assets. The construction of a national DAO should start from the following points; - Territory is not necessary for a cloud state, but it must have the ability to protect its citizens, which means it can skip the territory and run the machine of violence, but there is no new exploration mode for this track; - Government is the institution that keeps society functioning. DAO amounts to suggesting an efficient and equitable governance mechanism and a functioning social system, which is very difficult to balance; - Physical proof of work mechanisms that meet the interface needs of digital citizens and the real world. These interfaces need to include a currency exchange interface, physical authentication interface, and physical transaction interface. This is an era of confusion and injustice, but it is also an era of reflection and reset. On a longer timescale, the order of magnitude and opportunities for Web3 countries will be much greater than they are today. DAOs can structure any current economy and governance pattern through programmable currencies, code-based laws, and composability. Of course, there are plenty of problems, including decentralized decisions that tend to be inefficient, code errors that lead to money being stolen, and so on. But when we stop focusing on dysfunctional, centralized government and talk directly to DAO members, we are already transforming ourselves and society.

Rebuilding the Tower of Babel: A Theory of New National Development Based on DAO

Reconstruyendo la Torre de Babel: una teoría del nuevo desarrollo nacional basada en DAO

There are plenty of opportunities to earn and grow as a contributor to blockchain tech even without being a developer fluent in Solidity, Rust or some other complex foreign computer language. To find these opportunities, you really have to know where to look, and it helps to think “outside the box”. Work of all types is being sought after by blockchain-based companies. The work can range from simple jobs like making memes to more skilled tasks like POAP (Proof of Attendance Protocol) Design or in-depth research articles. There are several websites where you can search and find hundreds of jobs or tasks, and a person seeking earning opportunities in any of the places I’ll mention is sure to find some. DAO Bounties The relatively new concept and creation of DAOs (Decentralized Autonomous Organizations) have created a flood of new jobs needing contributors to join in and contribute to community growth, marketing needs, content creation, design tasks and social media growth, management, etc. One such site is Dework, which currently offers a list of over 300 open bounties for a wide range of varying skill sets. The companies and DAOs that are using these sites could either be startups or established companies that have been around for a couple of years. New bounties are being listed daily and are practically permissionless. Once completed, bounties need only approval before they can be added to your portfolio, demonstrating proof of work and reputation as a blockchain contributor. Dework’s user interface and overall website structure are both pretty easy to understand and navigate. You’ll find plenty of open bounties and many accepting multiple submissions. Other bounties require applications or are by request only, so they’ll need to be assigned by a DAO member or company representative. All communication regarding these bounties can be made through the Discord community for the project. There is no shortage of jobs needing completion or ways to earn crypto for your contributions. Crypto-Based Contests Another great place to find some nifty options from some of the most up-and-coming projects is to look on Layer3 at the contests page. There are always some crypto-incentivized jobs that are available to find here. The bounties are more of a source to learn from and gain hands-on knowledge using several blockchain products available. I have noticed an occasional monetary bounty pop up here and there, but most ways to get crypto on Layer3 are on the contest page. Competition is growing, meaning high-quality work is necessary to win. It’s an effective way to see how your work stands up compared to your peers. The third source for potential work I want to mention is Kleoverse. The ecosystem is new, and the setup is not yet optimal, but there is value to be found here. Various DAOs are using this platform, making it grow to create more profit potential. Those are my top 3 choices for websites I regularly used to earn or learn. They are all pretty straightforward to use, so they are the best sources for finding bounties, small tasks, and even longer or ongoing projects needing contributors to complete them. Outside The Box Earlier, I mentioned thinking “outside the box” helps find more chances to earn, and I’ll mention two ideas that will hopefully inspire or even point you towards becoming an active and valued contributor to blockchain growth. The first place to check out is the forum Bitcoin Talk under the thread called Bounty Good. In this forum, you’ll find a fairly reliable list of new projects needing simple tasks that can be performed by anybody and pay in their native cryptocurrency. Not all the projects found here are going to the moon. However, the curator of this list finds valid projects needing quick tasks for the startup’s launch, making it a useful and “outside the box” source of possibilities. My last and final suggestion is to look within publications just like this one, Hackernoon. Often you’ll see contests being sponsored by blockchain companies like or BrickTrade that generally are offering cash prizes. So leave no stone unturned and no path not ventured on your journey to becoming involved with the emerging blockchain technology. Tatum This article was first published here.

Where You Can Go To Create Value And Earn Money With The Blockchain

After 7 years working as Senior Data Manager at Criteo and as Head of Data at Payfit, and after interviewing 200+ data leaders for user research, I am starting to have a good overview of data stacks in action. So, by popular demand, I’m dropping here and there some advice on the trends I observed. Earlier in March, I had two different calls: one from a former colleague, now head of data at , and one from a friend, now head of data at . Both had taken a new job and had to build a complete data stack, from scratch! I assume this could interest others. Here is the piece of advice I gave them. Swile Cajoo Before we start, here is a Decision Matrix that you can use for benchmark purposes: Ease of Use Integration in Cloud environment Community & Documentation Governance Capabilities Pricing To keep things simple, we’ll just follow the data, from source to reporting. Data Warehouse Where you store your data for analytics needs A data warehouse is a place you store and organize your data before analysts use it My recommendation is simple: If your dev stack runs on Google Cloud, then go for BigQuery (BQ) If your dev stack runs on AWS, then go for Snowflake (SF) Here are a few reasons. Redshift was indeed the first big player on the Cloud Data Warehouse market, but it means they’ve made mistakes that followers could avoid, and they haven’t changed much for the last few years. Redshift has a poor UI/UX, especially for end-users (data consumers). BigQuery and Snowflake are quite similar in terms of features and pricing models (based on usage: BQ bills you by scanned data, Snowflake bills you by query time). A slight difference is that the first one is stronger when the whole stack is run on Google Cloud while the second is slightly more “Analytics” focused, making it a perfect match for AWS users. Snowflake is heading towards being a full data platform (multi-cloud, Snowpipe, jobs, data share, python/js functions) with a friendly user interface. BigQuery’s strength is its integration within the Google Cloud systems: read/write from GCS, IAM rights management, billing, Google Spreadsheets native integration, …) Ingestion Get Data in your Data Warehouse Stitch and Fivetran are extracting data from all your business sources and bring them to your Data Warehouse For data originating from a SaaS, use or . These two companies do a great job at maintaining these data dumps for their thousands of clients, and their pricing is linear as well as predictable. The sources they can retrieve data from is numerous, check these and . StitchData Fivetran here here You may want to cherry-pick some tables to ingest yourself, for frequency reasons or whatever. Make sure to leverage the numerous open-source taps made available . here Want a mix between the fully managed and the fully homemade? Check . You’ll have to host it but it comes with the necessary bunch of features, sources, and destinations. Airbyte provides similar features, depending on the plan you’re on. But focuses more on event tracking, it’s a must-have to sync your marketing campaigns with user activity. Think of Segment as the big ears listening to what is happening on your website/app. It also demands an ongoing tagging plan. An alternative is which does not integrate that well with other data sources but does not require a tagging plan Segment Contentsquare Do not worry, there is still plenty of DE work to do: you’ve got ingestion pipelines to build for all your data. If your PROD is in NoSQL (such as Mongo), then you’ve got the transformation to do before ingesting the data in your data warehouse. If your PROD is already in a SQL format, then you can dump all needed data in your analytics warehouse, and directly move on to the next paragraph production Transformation From raw data to model/reports ready Like any raw material, you want to transform it to deliver value. Use DBT to transform data. First things first, (note the L now in the middle). In recent years, storage costs decreased to a point (< $30 per Tb/month) where you can now load all raw data in your data warehouse, and then do the transformation in SQL. Keep in mind that storage in SF and BQ is as cheap as S3 storage is, it’s computing that costs money. ETL is dead, long live ELT Now, putting the spotlight on what became the obvious tool in 90% of the data teams I’ve talked to: the notorious . There is even a new job title that comes with it: . DBT is open source, quite easy to set up, and comes with a bunch of interesting features oriented on quality, such as unit tests. DBT Analytics Engineer The general idea is to load all your data in your warehouse, then use DBT to create your datamart ie: refined data, ready to be used for analytics purposes. As a side note, DBT is not a scheduler, you’ll still have to schedule it. It’s your scheduler who’ll launch the DBT executions… which naturally leads me to the scheduler section below. One last note: end of last year Google , it’s now free and well-integrated with Big Query, but the community is not that big. acquired Dataform Scheduler Run job 1 then run job 2 In any transformation process, there is a schedule. Things have to happen in a certain order. Use Airflow for data transformation scheduling. The market standard for scheduling really boils down to Airflow, the question is which deployment setup you want? You can go for: Self-Managed: dockerized airflow Managed: AWS version / GCP Version Fully-Managed: Astronomer Astronomer helps you get off the ground faster. But there is a cost for that… Data Viz Make your shiny dashboards Once you have a high-value dataset, you will want to show the output in the sexiest and most understandable way. Data visualization with , , or is the best option. Tableau Looker Metabase This one is tough, as competition is fierce within this segment. This will also be the only part your business users will see, out of the whole stack you are building. Strong options are , , or . I know that there are a lot of other options out there, that’s just my opinion Tableau Looker Metabase I’ll make a dedicated article on the matter, but to start, go for Metabase. First, on the data Viz side, it’s simple and straightforward, it takes a few minutes to go from a table to a dashboard. Keep in mind that for a while your dashboards will be simple, as you need to educate the different teams. Oh, and it’s free as long as you host it yourself. Data Discovery Find, understand and use data faster High-value datasets or analyses are useless if no one knows they exist. Make sure people do. Castor is the tool for that. Do you think that data discovery is only for people with too much data or many people? Did you know that after a few data dumps from Stitch or Segment, you’ll already have hundreds of tables? On top of that, I’m quite sure that the first data analyst or engineer you’ll hire will make their own data documentation on a google doc or else. Wouldn’t it be better to have that info accessible to everyone and especially to the second hire? At Castor, we provide just that! is a data documentation tool for the Notion-Figma-Slack generation. Or data-wise, for the Fivetran-Looker-Snowflake-DBT aficionados. We designed our catalog to be . Castor easy to use, automated, delightful, and friendly Want to check it out? and we will show you a demo. Reach out to us

How to Build a Data Stack from Scratch

A Guide to Troubleshooting Frequent Compile-Time Errors: C# for Beginners

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

Dependency Injection in is a lifesaver when organizing dependencies — especially in more complex ASP.NET Core applications. And if you’re already or just want to stick as close as possible to the already provided DI offerings, is a great enhancement. C# familiar with IServiceCollection Scrutor in C# In this article, I’ll provide you with a high-level overview of Dependency Injection and Scrutor in C#. But from there, we’re jumping right into 3 tips that you can use with Scrutor that could prove to be very helpful in your application! What is Scrutor in C#? Scrutor is a powerful NuGet package in C# that enhances dependency injection. It simplifies the registration and resolution of dependencies, making it easier for you to manage and organize your code. Dependency injection is a popular approach to configuring applications that promotes loose coupling and improves testability and maintainability. It involves injecting dependencies into a class rather than directly creating them within the class. This means that the responsibility of creating the dependency is not owned by the class requiring it, but instead, owned by someone up the call stack. Eventually, this pushes nearly all dependency creation to the entry point of an application, making it unwieldy. However,  dependency injection frameworks help . clean up and organize all of this logic Scrutor takes this concept a step further by providing a simple and intuitive way to register and resolve dependencies. With Scrutor, you no longer need to manually register each dependency one by one. Instead, you can use conventions and attributes to automate the process. 1 – Using Scrutor for Registration and Resolution To illustrate how to use Scrutor in C# for registration and resolution, let’s consider a simple scenario. Imagine we have an application that requires the use of different data repositories, such as UserRepository and ProductRepostiory. First, we need to install the Scrutor NuGet package in our project. Open the Package Manager Console and run the following command: Install-Package Scrutor Next, we need to define our repositories and their corresponding interfaces. This example is essentially empty, but we’ll be referring to these in a moment: public interface IUserRepository
{
    // Interface methods
}
public class UserRepository : IUserRepository 
{
    // Implementation
}

public interface IProductRepository
{
    // Interface methods
}

public class ProductRepository : IProductRepository
{
    // Implementation
} Now, let’s wire up our dependencies using Scrutor. In your startup code (such as in the ConfigureServices method in ASP.NET Core), add the following code: services.Scan(scan => scan
    .FromAssemblyOf<Startup>()
    .AddClasses(classes => classes.AssignableToAny(
        typeof(IUserRepository),
        typeof(IProductRepository)))
    .AsImplementedInterfaces()
    .WithScopedLifetime()); This code uses the Scan method from Scrutor to scan the assembly that contains the Startup class. It then filters out the classes that are assignable to IUserRepository or IProductRepository interfaces. Finally, it maps the implementations of these interfaces and registers them with their respective interfaces. Now, we can inject these dependencies into our classes. For example, let’s say we have a UserService class that requires IUserRepository: public class UserService 
{
    private readonly IUserRepository _userRepository; 
    
    public UserService(IUserRepository userRepository) 
    { 
        _userRepository = userRepository;
    }

    // Rest of the class implementation
} By declaring IUserRepository as a dependency in the constructor, Scrutor along with IServiceCollection will automatically resolve and inject the UserRepository instance for us. 2 – The Decorator Pattern – Example of Logging for Services Imagine you have a service interface and an implementation . You want to log the entry and exit of each method call in without polluting its business logic with logging concerns. IService MyService MyService First, define the interface and its implementation: IService public interface IService
{
    void DoWork();
}

public class MyService : IService
{
    public void DoWork()
    {
        // Business logic here
    }
} Next, create a decorator class that implements . This class will wrap the actual service and add logging around the delegated method calls: LoggingDecorator IService public class LoggingDecorator : IService
{
    private readonly IService _decorated;
    private readonly ILogger<LoggingDecorator> _logger;

    public LoggingDecorator(IService decorated, ILogger<LoggingDecorator> logger)
    {
        _decorated = decorated;
        _logger = logger;
    }

    public void DoWork()
    {
        _logger.LogInformation("Starting work.");
        _decorated.DoWork();
        _logger.LogInformation("Finished work.");
    }
} Now, use Scrutor to register your service and its decorator in the or wherever you configure your services: Startup.cs public void ConfigureServices(IServiceCollection services)
{
    // Register the base service
    services.AddScoped<IService, MyService>();

    // Use Scrutor to apply the decorator
    services.Decorate<IService, LoggingDecorator>();

    // Make sure to register the ILogger or ILogger<T> dependencies if not already done

} This setup uses Scrutor to wrap the registration with the . When resolving , the DI container will provide an instance of , which in turn wraps an instance of . This approach achieves the separation of concerns by keeping the logging logic outside of your business logic. IService LoggingDecorator IService LoggingDecorator MyService 3 – Feature Toggle for Service Implementations When we’re building complex systems, there are often cases where we want to introduce something like feature flags. These allow us to take different code paths based on configuration and not have to recompile and deploy an entire application. In some situations, it’s not just about picking a different code path, it’s about swapping an entire implementation of something! Let’s try out an example together! Assume you have an interface with multiple implementations: (a new, experimental feature) and (the current, stable feature). You want to switch between these implementations at runtime based on a configuration flag. IFeatureService NewFeatureService StandardFeatureService First, define the interface and its implementations: IFeatureService public interface IFeatureService
{
    void ExecuteFeature();
}

public class NewFeatureService : IFeatureService
{
    public void ExecuteFeature()
    {
        // New feature logic
    }
}

public class StandardFeatureService : IFeatureService
{
    public void ExecuteFeature()
    {
        // Standard feature logic
    }
} Next, you need a way to determine which implementation to use based on a feature toggle setting. This could be a value in , a database setting, or any other configuration source. appsettings.json Now, use Scrutor to dynamically register the appropriate service implementation based on the feature toggle: public void ConfigureServices(IServiceCollection services)
{
    // Assume GetFeatureToggleValue() retrieves the current feature toggle setting
    var useNewFeature = GetFeatureToggleValue("UseNewFeature");

    // Dynamically register the appropriate implementation based on the feature toggle
    if (useNewFeature)
    {
        services.AddScoped<IFeatureService, NewFeatureService>();
    }
    else
    {
        services.AddScoped<IFeatureService, StandardFeatureService>();
    }

    // Optional: You could combine this with the previous example
    // to use the decorator pattern too!
    // services.Decorate<IFeatureService, FeatureServiceProxy>();
}

private bool GetFeatureToggleValue(string featureName)
{
    // This method would typically check your
    // configuration source to determine if the feature is enabled
    // For simplicity, this is just a placeholder
    return false; // or true based on actual configuration
} Taking an approach like this can help us with: : Allows the application to switch between feature implementations without needing to redeploy. You can enable or disable features with just a configuration change. Flexibility : You can test new features in a production environment with a limited set of users by toggling the feature on for those users. Testing in Production : Helps in rolling out features gradually to monitor the impact and ensure stability before a wider release. Gradual Rollouts : Quickly revert to the old implementation if issues arise with the new feature, minimizing downtime and impact on users. Risk Mitigation Wrapping Up Scrutor in C# In this article, I provided you with 3 simple tips for dependency injection with Scrutor in C#. After a brief overview of what dependency injection is and how Scrutor fits into it, we jumped right into our examples. We got to see assembly scanning, how to decorate dependencies, and even how to consider feature flagging entire implementations! Dependency injection is something that can greatly simplify your applications as they grow in complexity, and Scrutor can be a huge help if you want to stick to the . If you found this useful and you’re looking for more learning opportunities, consider and check out my ! Join me and the ! built-in IServiceCollection offering from Microsoft subscribing to my free weekly software engineering newsletter free videos on YouTube other community members on Discord Also published here.

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