flox

After months of wait, Frax Finance is finally launching Fraxtal, their own chain. from an algorithmic stablecoin protocol, Frax has reinvented itself after the crash of Luna, leading to the creation of new primitives, including Liquid Staking (frxETH), bonds, Real World Assets, and more. This is proof of their resilience and vision to revolutionize DeFi: a modular blockchain with a unique scaling roadmap and incentives to incentivize user growth and development. Fraxtal is the culmination of their vision: Fraxtal is an EVM-compatible roll-up built on the OP Bedrock stack, guaranteeing full EVM equivalence, Ethereum alignment, and security, and facilitating the deployment of dApps. The particularity of Fractals is being infinitely complex, with patterns that seemingly repeat themselves forever. Furthermore, every part of the fractal, regardless of how zoomed in, looks very similar to the whole image. This concept is reflected in the strategic vision for Fraxtal. As Sam has mentioned repeatedly, Fraxtal is a tech thesis-driven chain and is not intended to be a standalone app-chain. Instead, Fraxtal wishes to be a modular L2 rollup incentivizing others to build on it. Currently, there are two main currents of thought with regard to the future architectural developments on blockchains: monolithic versus modular. To sum it up, the modular view sees the main components of the blockchain stack (validation, execution, Data Availability, etc.) as something that can be separated or combined, to tailor to specific use cases and improve the efficiency of blockchain networks. This short piece introduces Fraxtal, the way they incentivize users to build on their L2, and the role of FXS within this new ecosystem. Introducing Fraxtal Fraxtal benefits from the backing of Frax Finance, one of the most resilient DeFi protocols, with tentacles in almost all primitives. This provides Fraxtal with a , with the presence of several primitives that users can take advantage of. strong network and utility from the beginning The purpose of Fraxtal can therefore be intended as a general compute chain, with a roadmap of plenty of future integrations with Data Availability protocols, more L1, and other blockchain components. One such integration regards Eclipse, a protocol using a Solana Virtual Machina but settling on Ethereum. As a modular rollup, Fraxtal will feature several infrastructural and middleware components, “for other chains and networks to use, connect to, deploy L3s, and build on top of.” As an example, Fraxtal uses an developed by the Frax Team. in-house data availability module From its launch, expected in the 2nd week of March, Fraxtal will have a buffer period to allow developers to launch dApps, reflecting their paced-down approach, and making sure that everything is perfectly set. This is often the case for Frax, which has preferred to focus on long-term goals, rather than a short-term view. In the words of Sam, the founder of Frax, this approach consists in seeing “where the industry is going and make sure that you can create the most value there, with a long term grow oriented mindset.” This also means not jumping on trend following or catching, and maintaining a core focus on the long-term vision of the protocol. To achieve this purpose and incentivize development, Fraxtal has chosen a different path compared to many L2 launching these days. Instead of bootstrapping Fraxtal with the promises of an airdrop, the Frax team has come up with FLOX, a series of blockspace incentives for users and developers that are using the Fraxtal chain. FLOX is a system where users who transact on the chain and developers who deploy popular smart contracts get to earn FLOX points at every epoch. Epochs will be initially set with a weekly length, but their length can be further customized to avoid gaming of the mechanisms and have more granular time frames. Until now, gas rebates have been relatively inefficient in attracting users, as they often happen one-shot and not over a long period, and their money contribution is often negligible for projects. The same can be said for airdrops, which are a one-off stimuli that fail to contribute to user stickiness and retaining users. For this reason, Fraxtal has created FLOX points, which can be intended as a rewarding users for real usage. “perpetual airdrop” To avoid having mercenary capital or users gaming the system using their own contracts, Fraxtal introduces 3 mitigation mechanisms: : How much blockspace is this contract contributing to? How much fees is it creating for the Fraxtal network? Blockspace usage : This is similar to how Google ranks pages for domains, taking into account the popularity and ranking of the contract within the Fraxtal ecosystem. This ensures that a single user cannot game the system, making an average of how many users are effectively using this contract and how much each is depositing and spending. Contract Ranking Similar to how Data Availability Sampling works, Fraxtal uses a random block sampling to ensure the availability of assets at any point in time. Random Block Sampling: Combined, these mechanisms will reduce Sybill attacks, and prioritize organic sources of demand and a fair rewarding mechanism. This is much more powerful than “gas rebates” or “sequencer fee sharing” because projects earn much more rewards per $ of gas spent on their contracts than any fee sharing program can match." To sum it up, FLOX can be regarded as recurring incentives for usage and block space consumed. FLOX incentives will be represented by the ticker FXTL and will be“tokenized no later than 12 months after Fraxtal chain genesis.” Currently, the team has left open whether “FXTL points will be tokenized as a separate staking token for the chain (FXTL) or convert to FXS tokens at a specified ratio (or a combination of both).” The FLOX incentives will go live on March 13th, with an airdrop of FLOX points to veFXS stakers. Which brings us to the next question: what happens to FXS now? The Role of FXS Within the Fraxtal Ecosystem Dear FXS holders, fear not, for FXS will still have a central role within the Fraxtal ecosystem. In fact, FXS will assume the role of native token for Fraxtal, accruing value from the network and having the purpose of long-term staking tokens — with several flywheels as incentives. However, it is important to mention that gas fees on Fraxtal are paid in frxETH. Fraxtal also brings further utility to veFXS. Users can now stake veFXS on Fraxtal with unified balances with Ethereum Mainnet, which will serve to get FLOX points, vote on governance, and more. More on Fraxtal Fraxtal will also launch a Frax version of Bitcoin, named frxBTC. How does frxBTC get yield? Frax’s goal is to be the larger issue of the most important economic units of account and become a general-purpose chain. As such, one of the largest addressable markets, aside from the ones already captured by Frax (USDT, ETH), is Bitcoin. From launch, Fraxtal integrates Redstone Oracles: https://twitter.com/redstone_defi/status/1758506563416068121 Great explanation on FLOX by the one and only Flywheel team: https://twitter.com/FlywheelDeFi/status/1757845971751567617 Fraxtal launch thread by Frax Finance: I https://twitter.com/fraxfinance/status/1755386501926433087 The incentive systems, rewards, and FXTL points will start later in March, with the purpose of allowing partners & developers enough time to deploy their dApps and stake FXS to become eligible. The author of this post is not associated with the projects mentioned in this article in any way. All of the content above is not financial advice, and you are always encouraged to do your own research. Also published here

Frax Finance's New Layer 2: Everything You Need to Know About Fraxtal

The Coolest VueJS Resources: Developers' Choice👨‍💻

Expert tips to create a highly functional chatbot. How To Make A Great Chatbot From Scratch These days, companies find it very hard to enhance their customer experience, thus, they want to know how to make a chatbot. And, they invest almost all to develop the best chatbot for their business benefits. Here, we will extend you the necessary information and resources to create a chatbot. Moreover, you will also know how to improve your chatbot after reading this blog. What is a chatbot? Chatbots are Artificial Intelligence-powered systems used to interact with human beings by means of a text or voice interface with the purpose to answer their questions. Such interactions can be simple, for example: asking a chatbot about the weather report, or there could be something complex, like asking a chatbot to troubleshoot a problem with the Internet service. How to build a chatbot The steps given below will help you out in the chatbot development: 1. Know the opportunities for an AI-powered chatbot All of first, make yourself aware of the opportunities for an AI-powered chatbot. Companies always think of utilizing new Bot technologies in their business, so they need a way of thinking about which types of work can be augmented or automated by . AI solutions Artificial Intelligence solutions can be given attention as per the two criteria: Data and work complexity for a specific type of work activity. The dual analysis of data and work complexity brings about four types of activity models: innovation, expert, efficiency, and effectiveness. 2. Knowing your customers’ goals First, you must know exactly why you want to get a chatbot developed, and what the chatbot is expected to do. Having a clear idea of this will be helpful for the designer to create conversations targeted at fulfilling the goals. If the designer knows the purpose of chatbot development, it will be much easier for them to design the conversation with the chatbot. 3. Chatbot conversation designing Interactions of chatbot are split into structured and unstructured interactions. The structured conversation is more about the logical flow of information. It includes choices, menus, and forms into account. On the other hand, the unstructured conversation flow has freestyle plain text. The conversation with workmates, family members, friends, and other known people are categorized in this segment. Creating scripts for these messages need to follow suit. On the contrary, developing the scripts for messages, it must be kept in mind that the conversation topics must be close to the purpose fulfilled by the chatbot. It’s important for the designer to interpret the answer of the users to create scripts for a conversational user interface. Designers also pay attention to close-ended conversations much easier to handle and loose conversations which make possible for customers to have conversations naturally. 4. Picking the appropriate chatbot platform Although there is easy availability of the most powerful platforms to build a chatbot. All platforms have their own advantages and disadvantages and specific purposes. To facilitate your selection, we have provided you a list of the best platform to : develop a chatbot This platform utilizes a drag and drop template to develop bots i.e to make a chatbot. Some famous features like simple integrations through , plugins, machine learning and analytics integration are available too. 1. Botsify : Smart AI Chatfuel offers features like adding content cards and sharing updates with your followers automatically, collecting information in Messenger chats with forms, and allowing users to request information and interact with your chatbot with buttons. 2. Chatfuel : Beep Boop is known for providing an end-to-end developer experience that makes it possible for users to pay attention to developing great chatbots. 3. Beep Boop : This platform is considered as the best way of developing slack bots though it contains some complex and technical features as well which makes it a best platform in true terms. For this, I will suggest you should take help of a . good machine learning app developer Flow XO turns out to be the only chatbot platform to offer more than 100 integrations. It is famous for its easy-to-use visual editor. The platform also offers numerous pre-built templates for a quick beginning. 4. Flox XO : This platform boasts of visually building, training, and deploying chatbots on Slack, FB Messenger, Smooch, or your website. 5. Motion AI : It will make possible for you to diagram your conversation flow like a flowchart to have a visual overview of the outcomes of the query of the bot. This platform extends you an option to embed your bot on your website. It will also be possible for you to add data from a Medium, Wikipedia site, or Wordpress for better data coverage. This is an easy to use tool to make a chatbot 6. Bottr : Microsoft has developed QnA bot in which you need to share the URL of your FAQ page and the bot will be developed in a short while (some minutes) as per the information on the FAQ page and structured data. 7. QnA Maker : It has predefined chatbots ripe for starting-off. The website has templates for customer support, e-commerce, and food business. 8. Chattypeople : If you pick an e-commerce chatbot, then you just need to add Q&A, Products, and some general settings. This platform has many useful tools including boilerplate app starter kits, Botkit Studio, a core library, and plugins for the extension of your bot capabilities. 9. Botkit : This platform can allow you to train, develop, and run your bot. Developing and managing your conversation logic with Bot Builder and visual flow interface assists you in setting up responses as quick as a flash. 10. Recast.AI | Collaborative Bot Platform : The platform has pre-built features which make it simple for you to add messages, content, filling out forms, discussions, showcasing merchandise, and the like to your bot. 11. Octane.ai : Chatter can assist you in building the bot flow and setting up the AI by entering a few examples of the expected conversation between the bot and users. 12. ChatterOn : This platform’s innovative smart-messages has structured data and intelligence that is not in plain-text messages which is an out of the box feature to make a chatbot. Accordingly, it makes possible advanced messaging workflows and automation. 13. Gupshup : This platform is integrated with multiple platforms. It includes Request & Conversation Tracking, complete User, and it has its own NLP parsing engine. 14. Converso.ai : Summary Due to innumerable chatbot benefits, chatbots are regarded as the future. And, every business is seeking how to create a chatbot to enhance their customers’ experience. Here, we also mentioned the and enlisted etc. listed above. best platforms to make a chatbot 14 chatbot development platforms including Botsify, Chatfuel, Beep Boop, Flox XO Final Words: If you want to take your business to the next level, then you need to get a chatbot developed. And, to do so, you must who can build smart chatbots in order to perform complex tasks in simple ways. Afterall, chatbots are meant to make our lives easier. hire chatbot developers

How To Make A Chatbot From Scratch

Group of researchers from Cisco Talos that CCleaner App v5.33 which was available for download between August and September came with the Floxif malware. recently reported The Floxif malware collects information such as computer name, installed softwares, etc and send it back to the hacker’s server. This also has the capability to download and run other programs like key loggers . Millions of systems are suspected to be compromised. Researchers identified the app was making calls to suspicious domains. Normally, this would happen when the app is download from an untrusted source. However, in the case of CCleaner, the installer was obtained from the official site and was signed by a valid digital certificate. This makes the situation even worse because, the development or signing process of the company seems to be compromised. Don’t panic, just update your CCleaner version to v5.34. This should fix the issue. Only Windows/Cloud offering of CCleaner seems to be affected. If you are on macOS, you should be just fine. Follow Hackernoon and me ( ) for more stories. I am also writing a book to raise awareness on the Blue Whale Challenge, which has claimed lives of many teenagers in several countries. It is intended to help parents understand the threat of the dark web and to take actions to ensure safety of their children. The book is available for pre-order on Amazon. The title will be released on 20th of this month. Febin John James Fight The Blue Whale

CCleaner compromised, your computer could be infected

The Heroku team has long been an advocate of CI/CD. Their platform integrates with many third-party solutions like GitLab CI/CD or GitHub Actions. In a previous article, I demonstrated how you can configure your Heroku app with GitLab CI/CD to automatically deploy your app to production. In a follow-up article, I walked you through a slightly more nuanced setup involving both a staging environment and a production environment. But if you want to go all in on Heroku, you can use a series of solutions called Heroku Flow to configure all your CI/CD without any third parties. Heroku Flow brings together Heroku pipelines, Heroku CI, Heroku review apps, a GitHub integration, and a release phase. In this article, I’ll show you how to set this up for your own projects. Getting Started Before we begin, if you’d like to follow along, you’ll need a Heroku account and a GitHub account. You can create a Heroku account here, and you can create a GitHub account here. The demo app shown in this article is deployed to Heroku, and the code is hosted on GitHub. Running Our App Locally You can run the app locally by forking the repo in GitHub, installing dependencies, and running the start command. In your terminal, do the following after forking the repo: $ cd heroku-flow-demo
$ npm install
$ npm start After starting the app, visit http://localhost:5001/ in your browser, and you’ll see the app running locally: Creating Our Heroku Pipeline Now that we have the app running locally let’s get it deployed to Heroku so that it can be accessed anywhere, not just on your machine. We’ll create a Heroku pipeline that includes a staging app and a production app. To create a new Heroku pipeline, navigate to your Heroku dashboard, click the “New” button in the top-right corner of the screen, and then choose “Create new pipeline” from the menu. In the dialog that appears, give your pipeline a name, choose an owner (yourself), and connect your GitHub repo. If this is your first time connecting your GitHub account to Heroku, a second popup will appear in which you can confirm giving Heroku access to GitHub. After connecting to GitHub, click “Create pipeline” to finish the process. With that, you’ve created a Heroku pipeline. Well done! Creating Our Staging and Production Apps Most engineering organizations use at least two environments: a staging environment and a production environment. The staging environment is where code is deployed for acceptance testing and any additional QA. Code in the staging environment is then promoted to the production environment to be released to actual users. Let’s add a staging app and a production app to our pipeline. Both of these apps will be based on the same GitHub repo. To add a staging app, click the “Add app” button in the “Staging” section. Next, click “Create new app” to open a side panel. In the side panel, give your app a name, choose an owner (yourself), and choose a region (I left mine in the United States). Then click “Create app” to confirm your changes. Congrats, you’ve just created a staging app! Now, let’s do the same thing, but this time for our production app. When you’re done configuring your production app, you should see both apps in your pipeline: Configuring Automatic Deploys We want our app to be deployed to our staging environment any time we commit to our repo’s main branch. To do this, click the dropdown button for the staging app and choose “Configure automatic deploys” from the menu. In the dialog that appears, make sure the main branch is targeted, and check the box to “Wait for CI to pass before deploy.” In our next step, we’ll configure Heroku CI so that we can run tests in a CI pipeline. We don’t want to deploy our app to our staging environment unless CI is passing. Enabling Heroku CI If we’re going to require CI to pass, we better have something configured for CI! Navigate to the “Tests” tab and then click the “Enable Heroku CI” button. Our demo app is built with Node and runs unit tests with Jest. The tests are run through the npm test script. Heroku CI allows you to configure more complicated CI setups using an app.json file, but in our case, because the test setup is fairly basic, Heroku CI can figure out which command to run without any additional configuration on our part. Pretty neat! Enabling Review Apps For the last part of our pipeline setup, let’s enable review apps. Review apps are temporary apps that get deployed for every pull request (PR) created in GitHub. They’re incredibly helpful when you want your code reviewer to review your changes manually. With a review app in place, the reviewer can simply open the review app rather than having to pull down the code onto their machine and run the app locally. To enable review apps, click the “Enable Review Apps” button on the pipeline page. In the dialog that appears, check all three boxes. The first box enables the automatic creation of review apps for each PR.
The second box ensures that CI must pass before the review app can be created.
The third box sets a time limit on how long a stale review app should exist until it is destroyed. Review apps use Heroku resources just like your regular apps, so you don’t want these temporary apps sitting around unused and costing you or your company more money. When you’re done with your configuration, click “Enable Review Apps” to finalize your changes. Seeing It All in Action Alright, you made it! Let’s review what we’ve done so far. We created a Heroku pipeline.
We created a staging app and a production app for that pipeline
We enabled automatic deploys for our staging app
We enabled Heroku CI to run tests for every PR
We enabled Heroku review apps to be created for every PR Now, let’s see it all in action. Create a PR in GitHub with any code change you’d like. I made a very minor UI change, updating the heading text from “Heroku Flow Demo” to “Heroku Flow Rules!” Right after the PR is created, you’ll note that a new “check” gets created in GitHub for the Heroku CI pipeline. You can view the test output back in Heroku on your “Tests” tab: After the CI pipeline passes, you’ll note another piece of info gets appended to your PR in GitHub. The review app gets deployed, and GitHub shows a link to the review app. Click the “View deployment” button, and you’ll see a temporary Heroku app with your code changes in it. You can also find a link to the review app in your Heroku pipeline: Let’s assume that you’ve gotten a code review and that everything looks good. It’s time to merge your PR. After you’ve merged your PR, look back at the Heroku pipeline. You’ll see that the staging app was automatically deployed since new code was committed to the main branch. At this point in the software development lifecycle, there might be some final QA or acceptance testing of the app in the staging environment. Let’s assume that everything still looks good and that you’re ready to release this change to your users. Click the “Promote to production” button on the staging app. This will open a dialog for you to confirm your action. Click “Promote” to confirm your changes. After promoting the code, you’ll see the production app being deployed. And with that, your changes are now in production for all of your users to enjoy. Nice work! Conclusion What a journey we’ve been through! In this short time together, we’ve configured everything we need for an enterprise-ready CI/CD solution. If you’d like to use a different CI/CD tool like GitLab CI/CD, GitHub Actions — or whatever else you may prefer — Heroku supports that as well. But if you don’t want to reach out to a third-party CI/CD provider, you can now do it all within Heroku with Heroku Flow. The Heroku team has long been an advocate of CI/CD. Their platform integrates with many third-party solutions like GitLab CI/CD or GitHub Actions. In a previous article , I demonstrated how you can configure your Heroku app with GitLab CI/CD to automatically deploy your app to production. In a follow-up article , I walked you through a slightly more nuanced setup involving both a staging environment and a production environment. previous article follow-up article But if you want to go all in on Heroku, you can use a series of solutions called Heroku Flow to configure all your CI/CD without any third parties. Heroku Flow brings together Heroku pipelines, Heroku CI, Heroku review apps, a GitHub integration, and a release phase. configure all your CI/CD In this article, I’ll show you how to set this up for your own projects. Getting Started Before we begin, if you’d like to follow along, you’ll need a Heroku account and a GitHub account. You can create a Heroku account here , and you can create a GitHub account here . create a Heroku account here create a GitHub account here The demo app shown in this article is deployed to Heroku, and the code is hosted on GitHub . the code is hosted on GitHub Running Our App Locally You can run the app locally by forking the repo in GitHub, installing dependencies, and running the start command. In your terminal, do the following after forking the repo: $ cd heroku-flow-demo
$ npm install
$ npm start $ cd heroku-flow-demo
$ npm install
$ npm start After starting the app, visit http://localhost:5001/ in your browser, and you’ll see the app running locally: http://localhost:5001/ Creating Our Heroku Pipeline Now that we have the app running locally let’s get it deployed to Heroku so that it can be accessed anywhere, not just on your machine. We’ll create a Heroku pipeline that includes a staging app and a production app. To create a new Heroku pipeline, navigate to your Heroku dashboard, click the “New” button in the top-right corner of the screen, and then choose “Create new pipeline” from the menu. In the dialog that appears, give your pipeline a name, choose an owner (yourself), and connect your GitHub repo. If this is your first time connecting your GitHub account to Heroku, a second popup will appear in which you can confirm giving Heroku access to GitHub. After connecting to GitHub, click “Create pipeline” to finish the process. With that, you’ve created a Heroku pipeline. Well done! Creating Our Staging and Production Apps Most engineering organizations use at least two environments: a staging environment and a production environment. The staging environment is where code is deployed for acceptance testing and any additional QA. Code in the staging environment is then promoted to the production environment to be released to actual users. Let’s add a staging app and a production app to our pipeline. Both of these apps will be based on the same GitHub repo. To add a staging app, click the “Add app” button in the “Staging” section. Next, click “Create new app” to open a side panel. In the side panel, give your app a name, choose an owner (yourself), and choose a region (I left mine in the United States). Then click “Create app” to confirm your changes. Congrats, you’ve just created a staging app! Now, let’s do the same thing, but this time for our production app. When you’re done configuring your production app, you should see both apps in your pipeline: Configuring Automatic Deploys We want our app to be deployed to our staging environment any time we commit to our repo’s main branch. To do this, click the dropdown button for the staging app and choose “Configure automatic deploys” from the menu. main In the dialog that appears, make sure the main branch is targeted, and check the box to “Wait for CI to pass before deploy.” In our next step, we’ll configure Heroku CI so that we can run tests in a CI pipeline. We don’t want to deploy our app to our staging environment unless CI is passing. main Enabling Heroku CI If we’re going to require CI to pass, we better have something configured for CI! Navigate to the “Tests” tab and then click the “Enable Heroku CI” button. Our demo app is built with Node and runs unit tests with Jest. The tests are run through the npm test script. Heroku CI allows you to configure more complicated CI setups using an app.json file, but in our case, because the test setup is fairly basic, Heroku CI can figure out which command to run without any additional configuration on our part. Pretty neat! npm test app.json Enabling Review Apps For the last part of our pipeline setup, let’s enable review apps. Review apps are temporary apps that get deployed for every pull request (PR) created in GitHub. They’re incredibly helpful when you want your code reviewer to review your changes manually. With a review app in place, the reviewer can simply open the review app rather than having to pull down the code onto their machine and run the app locally. To enable review apps, click the “Enable Review Apps” button on the pipeline page. In the dialog that appears, check all three boxes. The first box enables the automatic creation of review apps for each PR. The second box ensures that CI must pass before the review app can be created. The third box sets a time limit on how long a stale review app should exist until it is destroyed. Review apps use Heroku resources just like your regular apps, so you don’t want these temporary apps sitting around unused and costing you or your company more money. The first box enables the automatic creation of review apps for each PR. The second box ensures that CI must pass before the review app can be created. The third box sets a time limit on how long a stale review app should exist until it is destroyed. Review apps use Heroku resources just like your regular apps, so you don’t want these temporary apps sitting around unused and costing you or your company more money. When you’re done with your configuration, click “Enable Review Apps” to finalize your changes. Seeing It All in Action Alright, you made it! Let’s review what we’ve done so far. We created a Heroku pipeline. We created a staging app and a production app for that pipeline We enabled automatic deploys for our staging app We enabled Heroku CI to run tests for every PR We enabled Heroku review apps to be created for every PR We created a Heroku pipeline. We created a staging app and a production app for that pipeline We enabled automatic deploys for our staging app We enabled Heroku CI to run tests for every PR We enabled Heroku review apps to be created for every PR Now, let’s see it all in action. Create a PR in GitHub with any code change you’d like. I made a very minor UI change, updating the heading text from “Heroku Flow Demo” to “Heroku Flow Rules!” Right after the PR is created, you’ll note that a new “check” gets created in GitHub for the Heroku CI pipeline. You can view the test output back in Heroku on your “Tests” tab: After the CI pipeline passes, you’ll note another piece of info gets appended to your PR in GitHub. The review app gets deployed, and GitHub shows a link to the review app. Click the “View deployment” button, and you’ll see a temporary Heroku app with your code changes in it. You can also find a link to the review app in your Heroku pipeline: Let’s assume that you’ve gotten a code review and that everything looks good. It’s time to merge your PR. After you’ve merged your PR, look back at the Heroku pipeline. You’ll see that the staging app was automatically deployed since new code was committed to the main branch. main At this point in the software development lifecycle, there might be some final QA or acceptance testing of the app in the staging environment. Let’s assume that everything still looks good and that you’re ready to release this change to your users. Click the “Promote to production” button on the staging app. This will open a dialog for you to confirm your action. Click “Promote” to confirm your changes. After promoting the code, you’ll see the production app being deployed. And with that, your changes are now in production for all of your users to enjoy. Nice work! Conclusion What a journey we’ve been through! In this short time together, we’ve configured everything we need for an enterprise-ready CI/CD solution. If you’d like to use a different CI/CD tool like GitLab CI/CD, GitHub Actions — or whatever else you may prefer — Heroku supports that as well. But if you don’t want to reach out to a third-party CI/CD provider, you can now do it all within Heroku with Heroku Flow. do it all

How I Got My CI/CD Act together With Heroku Flow

Gartner, a leading research and advisory firm, recently predicted a significant rise in the value-add of enterprise blockchain by 2030, fueled by the increasing need for scalable business solutions, secure transactions, and the inherent benefits of decentralization offered by blockchain technology. Although B2B blockchain SaaS solutions are still in the early stages of adoption, they have the potential to revolutionize how companies interact and conduct business transactions. In this interview, Florian Ehrbar, CEO of OnchainLabs, takes us through the nitty-gritty of B2B blockchain SaaS and how it is shaping the future of business. It’s great to have you, Florian. Can you briefly tell us about yourself and your route to web3? Thank you for having me. My crypto journey started back in 2017 when Bitcoin got close to 20k for the first time. I was super fascinated by this new asset class and all the potential that came with the associated technology. With over a decade of experience in management roles within tech startups, I found myself increasingly drawn to the potential of blockchain technology to revolutionize how businesses operate. Before founding OnchainLabs, I delved into a range of web3 investment strategies with a private Hong Kong family office, where I gained valuable insights into crypto projects. However, ultimately I really wanted to be at the heart of my own project, which I can now finally do with OnchainLabs. This background laid a solid foundation for my transition into the web3 world, where I'm now focused on leveraging blockchain to create value for businesses and consumers alike. What is B2B blockchain SaaS, and why does it matter? A Software as a Service platform we offer businesses the tools and infrastructure they need to harness the benefits of blockchain technology in their operations. Our clients can choose the solutions they need, from creating digital twins of their products to managing loyalty programs, digital warranty management, and more. This is relevant because blockchain has the potential to improve the way businesses operate by enhancing transparency, security, and efficiency in customer and product management, as well as gaining more data insights to improve the quality of services and, with that, grow revenues. Can you explain how blockchain technology can benefit B2B businesses? Absolutely. Blockchain technology offers several advantages for businesses. Together with our NFC technology it provides a tamper-proof connection between a physical product and its digital twin, this enhances trust between parties and reduces the risk of fraud. Moreover, it enables streamlined and automated processes through smart contracts, optimizing internal and external processes for businesses. Additionally, blockchain enhances data security and privacy, which is crucial in industries handling sensitive information. Overall, it allows businesses to optimize their operations, improve collaboration with partners, and unlock new revenue streams. What are the challenges B2B blockchain SaaS companies often face? Making blockchain solutions user-friendly is still a big challenge. There is still a lot of education that needs to be done, but more importantly, the ultimate goal is to integrate the technology in a way that end consumers get the benefits of the blockchain without needing to handle all the hurdles. This is one reason why we integrate in our SaaS solution OnchainID, which is a login that automatically comes with a non-custodial wallet that allows the owner of a digital twin to manage their portfolio seamlessly. What can you tell us about the founding of OnchainLabs? We founded OnchainLabs because of a deep conviction in the transformative power of blockchain technology. We recognized a growing need for businesses to harness blockchain's potential but often lacked the expertise and infrastructure to do so effectively. Thus, we set out to bridge this gap by developing scalable B2B blockchain solutions tailored to the specific needs of each client. Our goal is to support businesses and brands seeing the potential within Web3 and help them with an easy gateway to onboard their first services via our B2B SaaS platform. What does ‘helping businesses to leverage blockchain’ entail? This involves identifying areas where blockchain can add value, whether it's enhancing transparency in ownership, improving customer engagement, or streamlining data management processes. We work closely with our clients to integrate blockchain seamlessly into their operations and provide them with the necessary infrastructure, tools, and support. Every client has different needs, and that’s why our SaaS portal includes different applications clients can choose from. This way, clients can put together the services that are needed for them to grow and manage their business most effectively. Can you briefly share one of your success stories in the B2B blockchain SaaS landscape? One standout example is our partnership with Zai, a luxury ski brand. We are supporting them with a blockchain-driven solution to authenticate Zai's ski products while also implementing a loyalty program. By leveraging NFC technology seamlessly embedded within the ski layers, we ensure tamper-proof verification. This integration is not only a secure way to connect the ski with the NFT that gives customers access to their product warranty but also is a gateway to access exclusive offers for loyal customers. It's a prime illustration of how strategic blockchain implementation can yield tangible benefits for B2B enterprises like Zai. Can you walk us through the process of designing and implementing a secure and scalable B2B blockchain solution? Designing and implementing a secure and scalable B2B blockchain solution begins with a proper assessment of the client's needs and objectives. We work closely with our clients to identify pain points and opportunities where blockchain can add value. These inputs help us to improve our SaaS portal and also help us to add additional applications that clients can choose to be integrated in their portal. Moreover, we believe that an ecosystem should also involve integrations with third-party providers, which is why we also focus strongly on partnerships to build an ever-better solution for our clients. Can you share your experience as CEO of OnchainLabs? Leading OnchainLabs has been an incredibly rewarding journey. As CEO, my focus is on driving innovation, fostering a culture of collaboration, and delivering value to our clients. I'm fortunate to lead a talented team of experts who share a passion for blockchain technology and a commitment to excellence. Together, we're striving to empower businesses in the web3 era, leveraging blockchain to unlock new opportunities for businesses and brands. It's a privilege to be at the forefront of this transformative industry, and I'm excited about the future. Any parting words? I would like to thank you for the opportunity to share our experiences as well as give some insights into what we are developing. As we continue to navigate the rapidly evolving landscape of web3, I encourage businesses to explore the potential of blockchain technology and, with that, find out new opportunities for growth. Gartner, a leading research and advisory firm, recently predicted a significant rise in the value-add of enterprise blockchain by 2030, fueled by the increasing need for scalable business solutions, secure transactions, and the inherent benefits of decentralization offered by blockchain technology. Although B2B blockchain SaaS solutions are still in the early stages of adoption, they have the potential to revolutionize how companies interact and conduct business transactions. In this interview, Florian Ehrbar, CEO of OnchainLabs, takes us through the nitty-gritty of B2B blockchain SaaS and how it is shaping the future of business. It’s great to have you, Florian. Can you briefly tell us about yourself and your route to web3? It’s great to have you, Florian. Can you briefly tell us about yourself and your route to web3? Thank you for having me. My crypto journey started back in 2017 when Bitcoin got close to 20k for the first time. I was super fascinated by this new asset class and all the potential that came with the associated technology. With over a decade of experience in management roles within tech startups, I found myself increasingly drawn to the potential of blockchain technology to revolutionize how businesses operate. Before founding OnchainLabs, I delved into a range of web3 investment strategies with a private Hong Kong family office, where I gained valuable insights into crypto projects. However, ultimately I really wanted to be at the heart of my own project, which I can now finally do with OnchainLabs. This background laid a solid foundation for my transition into the web3 world, where I'm now focused on leveraging blockchain to create value for businesses and consumers alike. What is B2B blockchain SaaS, and why does it matter? What is B2B blockchain SaaS, and why does it matter? A Software as a Service platform we offer businesses the tools and infrastructure they need to harness the benefits of blockchain technology in their operations. Our clients can choose the solutions they need, from creating digital twins of their products to managing loyalty programs, digital warranty management, and more. This is relevant because blockchain has the potential to improve the way businesses operate by enhancing transparency, security, and efficiency in customer and product management, as well as gaining more data insights to improve the quality of services and, with that, grow revenues. Can you explain how blockchain technology can benefit B2B businesses? Can you explain how blockchain technology can benefit B2B businesses? Absolutely. Blockchain technology offers several advantages for businesses. Together with our NFC technology it provides a tamper-proof connection between a physical product and its digital twin, this enhances trust between parties and reduces the risk of fraud. Moreover, it enables streamlined and automated processes through smart contracts, optimizing internal and external processes for businesses. Additionally, blockchain enhances data security and privacy, which is crucial in industries handling sensitive information. Overall, it allows businesses to optimize their operations, improve collaboration with partners, and unlock new revenue streams. What are the challenges B2B blockchain SaaS companies often face? What are the challenges B2B blockchain SaaS companies often face? Making blockchain solutions user-friendly is still a big challenge. There is still a lot of education that needs to be done, but more importantly, the ultimate goal is to integrate the technology in a way that end consumers get the benefits of the blockchain without needing to handle all the hurdles. This is one reason why we integrate in our SaaS solution OnchainID, which is a login that automatically comes with a non-custodial wallet that allows the owner of a digital twin to manage their portfolio seamlessly. What can you tell us about the founding of OnchainLabs? What can you tell us about the founding of OnchainLabs? We founded OnchainLabs because of a deep conviction in the transformative power of blockchain technology. We recognized a growing need for businesses to harness blockchain's potential but often lacked the expertise and infrastructure to do so effectively. Thus, we set out to bridge this gap by developing scalable B2B blockchain solutions tailored to the specific needs of each client. Our goal is to support businesses and brands seeing the potential within Web3 and help them with an easy gateway to onboard their first services via our B2B SaaS platform. What does ‘helping businesses to leverage blockchain’ entail? What does ‘helping businesses to leverage blockchain’ entail? This involves identifying areas where blockchain can add value, whether it's enhancing transparency in ownership, improving customer engagement, or streamlining data management processes. We work closely with our clients to integrate blockchain seamlessly into their operations and provide them with the necessary infrastructure, tools, and support. Every client has different needs, and that’s why our SaaS portal includes different applications clients can choose from. This way, clients can put together the services that are needed for them to grow and manage their business most effectively. Can you briefly share one of your success stories in the B2B blockchain SaaS landscape? Can you briefly share one of your success stories in the B2B blockchain SaaS landscape? One standout example is our partnership with Zai, a luxury ski brand. We are supporting them with a blockchain-driven solution to authenticate Zai's ski products while also implementing a loyalty program. By leveraging NFC technology seamlessly embedded within the ski layers, we ensure tamper-proof verification. This integration is not only a secure way to connect the ski with the NFT that gives customers access to their product warranty but also is a gateway to access exclusive offers for loyal customers. It's a prime illustration of how strategic blockchain implementation can yield tangible benefits for B2B enterprises like Zai. Can you walk us through the process of designing and implementing a secure and scalable B2B blockchain solution? Can you walk us through the process of designing and implementing a secure and scalable B2B blockchain solution? Designing and implementing a secure and scalable B2B blockchain solution begins with a proper assessment of the client's needs and objectives. We work closely with our clients to identify pain points and opportunities where blockchain can add value. These inputs help us to improve our SaaS portal and also help us to add additional applications that clients can choose to be integrated in their portal. Moreover, we believe that an ecosystem should also involve integrations with third-party providers, which is why we also focus strongly on partnerships to build an ever-better solution for our clients. Can you share your experience as CEO of OnchainLabs? Can you share your experience as CEO of OnchainLabs? Leading OnchainLabs has been an incredibly rewarding journey. As CEO, my focus is on driving innovation, fostering a culture of collaboration, and delivering value to our clients. I'm fortunate to lead a talented team of experts who share a passion for blockchain technology and a commitment to excellence. Together, we're striving to empower businesses in the web3 era, leveraging blockchain to unlock new opportunities for businesses and brands. It's a privilege to be at the forefront of this transformative industry, and I'm excited about the future. Any parting words? Any parting words? I would like to thank you for the opportunity to share our experiences as well as give some insights into what we are developing. As we continue to navigate the rapidly evolving landscape of web3, I encourage businesses to explore the potential of blockchain technology and, with that, find out new opportunities for growth.

Blockchain SaaS And The Future Of Business: Exclusive Interview With OnchainLabs CEO Florian Ehrbar

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.edu;
(2)  Ricardo Baptista, Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA and rsb@caltech.edu;
(3) Youssef Marzouk, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and ymarz@mit.edu;
(4) Lars Ruthotto, Department of Mathematics, Emory University, Atlanta, GA and lruthotto@emory.edu;
(5) Deepanshu Verma, Department of Mathematics, Emory University, Atlanta, GA and deepanshu.verma@emory.edu. Table of Links Abstract & Introduction
Background and Related Work
Partially Convex Potential Maps (PCP-Map) for Conditional OT
Conditional OT flow (COT-Flow)
Implementation and Experimental Setup
Numerical Experiments
Discussion and Reference 4. Conditional OT flow (COT-Flow). In this section, we extend the OT-regularized continuous normalizing flows in [36] to the conditional generative modeling problem introduced in section 2. Training Problem. Following the general approach of continuous normalizing flows [11, 19], we express the transport map that pushes forward the reference distribution ρz to the target π(x|y) via the flow map of an ODE. That is, we define g(z, y) = u(1) as the terminal state of the ODE where the evolution of u: [0, 1] → R n depends on the velocity field v : [0, 1] × R n × R m → R n and will be parameterized with a neural network below. When the velocity is trained to minimize the negative log-likelihood loss in (3.2), the resulting mapping is called a continuous normalizing flow [19]. We add y as an additional input to the velocity field to enable conditional sampling. Consequently, the resulting flow map and generator g depend on y. One advantage of defining the generator through an ODE is that the loss function can be evaluated efficiently for a wide range of velocity functions. Recall that the loss function requires the inverse of the generator and the log-determinant of its Jacobian. For sufficiently regular velocity fields, the inverse of the generator can be obtained by integrating backward in time. To be precise, we define g −1 (x, y) = p(0) where p : [0, 1] → R n satisfies (4.1) with the terminal condition p(1) = x. As derived in [58, 19, 56], constructing the generator through an ODE also simplifies computing the log-determinant of the Jacobian, i.e., Penalizing transport costs during training leads to theoretical and numerical advantages; see, for example, [58, 56, 36, 17]. Hence, we consider the OT-regularized training problem where α1 > 0 is a regularization parameter that trades off matching the distributions (for α1 ≪ 1) and minimizing the transport costs (for α1 ≫ 0) given by the dynamic transport cost penalty This penalty is stronger than the static counterpart in (3.4), in other words However, the values of both penalties agree when the velocity field in (4.1) is constant along the trajectories, which is the case for the optimal transport map. Hence, we expect the solution of the dynamic problem to be close to that of the static formulation when α1 is chosen well. To provide additional theoretical insight and motivate our numerical method, we note that (4.3) is related to the potential mean field game (MFG) Here, the terminal and running costs in the objective functional are the L2 anti-derivatives of JNLL and PDOT, respectively, and the continuity equation is used to represent the density evolution under the velocity v. To be precise, (4.3) can be seen as a discretization of (4.6) in the Lagrangian coordinates defined by the reversed ODE (4.14). Note that both formulations differ in the way the transport costs are measured: (4.4) computes the cost of pushing the conditional distribution to the Gaussian, while (4.6) penalizes the costs of pushing the Gaussian to the conditional. While these two terms do not generally agree, they coincide for the L2 optimal transport map; see [16, Corollary 2.5.13] for a proof for unconditional optimal transport. For more insights into MFGs and their relation to optimal transport and generative modeling, we refer to our prior work [43] and the more recent work [57, Sec 3.4]. The fact that the solutions of the microscopic version in (4.3) and the macroscopic version in (4.6) agree is remarkable and was first shown in the seminal work [28]. By Pontryagin’s maximum principle, the solution of (4.3) satisfies the feedback form where Φ : [0, 1] × R n × R m → R and Φ(·, ·, y) can be seen as the value function of the MFG and, alternatively, as the Lagrange multiplier in (4.6) for a fixed y. Therefore, we model the velocity as a conservative vector field as also proposed in [36, 43]. This also simplifies the computation of the log-determinant since the Jacobian of v is symmetric, and we note that Another consequence of optimal control theory is that the value function satisfies the Hamilton Jacobi Bellman (HJB) equations with the terminal condition which is only tractable if the joint density, π(x, y), is available. These n-dimensional PDEs are parameterized by y ∈ R m. Neural network approach. We parameterize the value function, Φ, with a scalar-valued neural network. In contrast to the static approach in the previous section, the choice of function approximator is more flexible in the dynamic approach. The approach can be effective as long as Φ is parameterized with any function approximation tool that is effective in high dimensions, allows efficient evaluation of its gradient and Laplacian, and the training problem can be solved sufficiently well. For our numerical experiments, we use the architecture considered in [36] and model Φ as the sum of a simple feed-forward neural network and a quadratic term. That is, As in [36], we model the neural network as a two-layer residual network of width w that reads Sample generation. After training, we draw i.i.d. samples from the approximated conditional distribution for a given y by sampling z ∼ ρz and solving the ODE (4.1). Since we train the inverse generator using a fixed number of integration steps, nt, it is interesting to investigate how close our solution is to the continuous problem. One indicator is to compare the samples obtained with different number of integration steps during sampling. Another indicator is to compute the variance of ∇Φ along the trajectories. Hyperparameters. As described in section 5, we randomly sample the width, w, of the neural network, the number of time integration steps during training, the penalty parameters α1, α2, and the hyperparameters of the Adam optimizer (batch size and initial learning rate) from the values listed in Table 2. This paper is available on arxiv under CC 4.0 license. Authors: (1) Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.edu; (2)  Ricardo Baptista, Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA and rsb@caltech.edu; (3) Youssef Marzouk, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and ymarz@mit.edu; (4) Lars Ruthotto, Department of Mathematics, Emory University, Atlanta, GA and lruthotto@emory.edu; (5) Deepanshu Verma, Department of Mathematics, Emory University, Atlanta, GA and deepanshu.verma@emory.edu. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.edu; (2)  Ricardo Baptista, Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA and rsb@caltech.edu; (3) Youssef Marzouk, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and ymarz@mit.edu; (4) Lars Ruthotto, Department of Mathematics, Emory University, Atlanta, GA and lruthotto@emory.edu; (5) Deepanshu Verma, Department of Mathematics, Emory University, Atlanta, GA and deepanshu.verma@emory.edu. Table of Links Abstract & Introduction Background and Related Work Partially Convex Potential Maps (PCP-Map) for Conditional OT Conditional OT flow (COT-Flow) Implementation and Experimental Setup Numerical Experiments Discussion and Reference Abstract & Introduction Abstract & Introduction Background and Related Work Background and Related Work Partially Convex Potential Maps (PCP-Map) for Conditional OT Partially Convex Potential Maps (PCP-Map) for Conditional OT Conditional OT flow (COT-Flow) Conditional OT flow (COT-Flow) Implementation and Experimental Setup Implementation and Experimental Setup Numerical Experiments Numerical Experiments Discussion and Reference Discussion and Reference 4. Conditional OT flow (COT-Flow). In this section, we extend the OT-regularized continuous normalizing flows in [36] to the conditional generative modeling problem introduced in section 2. Training Problem. Following the general approach of continuous normalizing flows [11, 19], we express the transport map that pushes forward the reference distribution ρz to the target π(x|y) via the flow map of an ODE. That is, we define g(z, y) = u(1) as the terminal state of the ODE where the evolution of u: [0, 1] → R n depends on the velocity field v : [0, 1] × R n × R m → R n and will be parameterized with a neural network below. When the velocity is trained to minimize the negative log-likelihood loss in (3.2), the resulting mapping is called a continuous normalizing flow [19]. We add y as an additional input to the velocity field to enable conditional sampling. Consequently, the resulting flow map and generator g depend on y. One advantage of defining the generator through an ODE is that the loss function can be evaluated efficiently for a wide range of velocity functions. Recall that the loss function requires the inverse of the generator and the log-determinant of its Jacobian. For sufficiently regular velocity fields, the inverse of the generator can be obtained by integrating backward in time. To be precise, we define g −1 (x, y) = p(0) where p : [0, 1] → R n satisfies (4.1) with the terminal condition p(1) = x. As derived in [58, 19, 56], constructing the generator through an ODE also simplifies computing the log-determinant of the Jacobian, i.e., Penalizing transport costs during training leads to theoretical and numerical advantages; see, for example, [58, 56, 36, 17]. Hence, we consider the OT-regularized training problem where α1 > 0 is a regularization parameter that trades off matching the distributions (for α1 ≪ 1) and minimizing the transport costs (for α1 ≫ 0) given by the dynamic transport cost penalty This penalty is stronger than the static counterpart in (3.4), in other words However, the values of both penalties agree when the velocity field in (4.1) is constant along the trajectories, which is the case for the optimal transport map. Hence, we expect the solution of the dynamic problem to be close to that of the static formulation when α1 is chosen well. To provide additional theoretical insight and motivate our numerical method, we note that (4.3) is related to the potential mean field game (MFG) Here, the terminal and running costs in the objective functional are the L2 anti-derivatives of JNLL and PDOT, respectively, and the continuity equation is used to represent the density evolution under the velocity v. To be precise, (4.3) can be seen as a discretization of (4.6) in the Lagrangian coordinates defined by the reversed ODE (4.14). Note that both formulations differ in the way the transport costs are measured: (4.4) computes the cost of pushing the conditional distribution to the Gaussian, while (4.6) penalizes the costs of pushing the Gaussian to the conditional. While these two terms do not generally agree, they coincide for the L2 optimal transport map; see [16, Corollary 2.5.13] for a proof for unconditional optimal transport. For more insights into MFGs and their relation to optimal transport and generative modeling, we refer to our prior work [43] and the more recent work [57, Sec 3.4]. The fact that the solutions of the microscopic version in (4.3) and the macroscopic version in (4.6) agree is remarkable and was first shown in the seminal work [28]. By Pontryagin’s maximum principle, the solution of (4.3) satisfies the feedback form where Φ : [0, 1] × R n × R m → R and Φ(·, ·, y) can be seen as the value function of the MFG and, alternatively, as the Lagrange multiplier in (4.6) for a fixed y. Therefore, we model the velocity as a conservative vector field as also proposed in [36, 43]. This also simplifies the computation of the log-determinant since the Jacobian of v is symmetric, and we note that Another consequence of optimal control theory is that the value function satisfies the Hamilton Jacobi Bellman (HJB) equations with the terminal condition which is only tractable if the joint density, π(x, y), is available. These n-dimensional PDEs are parameterized by y ∈ R m. Neural network approach. We parameterize the value function, Φ, with a scalar-valued neural network. In contrast to the static approach in the previous section, the choice of function approximator is more flexible in the dynamic approach. The approach can be effective as long as Φ is parameterized with any function approximation tool that is effective in high dimensions, allows efficient evaluation of its gradient and Laplacian, and the training problem can be solved sufficiently well. For our numerical experiments, we use the architecture considered in [36] and model Φ as the sum of a simple feed-forward neural network and a quadratic term. That is, As in [36], we model the neural network as a two-layer residual network of width w that reads Sample generation. After training, we draw i.i.d. samples from the approximated conditional distribution for a given y by sampling z ∼ ρz and solving the ODE (4.1). Since we train the inverse generator using a fixed number of integration steps, nt, it is interesting to investigate how close our solution is to the continuous problem. One indicator is to compare the samples obtained with different number of integration steps during sampling. Another indicator is to compute the variance of ∇Φ along the trajectories. Hyperparameters. As described in section 5, we randomly sample the width, w, of the neural network, the number of time integration steps during training, the penalty parameters α1, α2, and the hyperparameters of the Adam optimizer (batch size and initial learning rate) from the values listed in Table 2.

Efficient Neural Network Approaches for Conditional Optimal Transport:Conditional OT flow (COT-Flow)

Exceptions are designed to handle unexpected situations rather than controlling the application flow. Using exceptions during input validation can affect your application's readability and performance. Learning Objectives The inefficient use of exceptions
A better approach using TryParse Prerequisites for Developers Basic understanding of C# programming language. 30 Day .Net Challenge Getting Started The inefficient use of exceptions Using exceptions for flow control, especially in a loop or frequently called code, may lead to severe performance bottlenecks. It also makes code hard to understand. Exceptions are really expensive in terms of system resources because when an exception is triggered, .Net runtime captures the stack trace and the process is resource-intensive. try
    {
        int.Parse(input); // Attempt to parse input
    }
    catch (FormatException)
    {
        // Handle the invalid input
    } A better approach using TryParse Please find below the refactored version of the previous code snippet if (int.TryParse(input, out int result))
    {
        // Use the parsed value
    }
    else
    {
        // Handle the invalid input
    } The aforementioned code attempts to parse the input supplied from the console and returns a boolean whether it's a success or failure. Complete Code Create another class named AvoidExceptions and add the following code snippet public static class AvoidExceptions
    {
        public static void BadWay(string input)
        {
            // Inefficient way: Using exceptions for flow control
            try
            {
                int number = int.Parse(input);
                Console.WriteLine($"You entered (Exception method): {number}");
            }
            catch (FormatException)
            {
                Console.WriteLine("Invalid input! Please enter a valid integer.");
            }
        }
    
        public static void GoodWay(string input)
        {
            // Efficient way: Using TryParse for flow control
            if (int.TryParse(input, out int result))
            {
                Console.WriteLine($"You entered (TryParse method): {result}");
            }
            else
            {
                Console.WriteLine("Invalid input! Please enter a valid integer.");
            }
        }
    } Execute from the main method as follows #region Day 24: Avoid Exceptions in Flow Control
    static string ExecuteDay24()
    {
        Console.WriteLine("Enter a number:");
    
        string input = Console.ReadLine();
        AvoidExceptions.BadWay(input);
        AvoidExceptions.GoodWay(input);
        return "Executed Day 24 successfully..!!";
    }
    
    #endregion Console Output Invalid input! Please enter a valid integer.
    Invalid input! Please enter a valid integer. Complete Code here: GitHub — ssukhpinder/30DayChallenge.Net C# Programming🚀 Thank you for being a part of the C# community! Before you leave: Follow us: Youtube | X | LinkedIn | Dev.to Visit our other platforms: GitHub More content at C# Programming Exceptions are designed to handle unexpected situations rather than controlling the application flow. Using exceptions during input validation can affect your application's readability and performance. Learning Objectives The inefficient use of exceptions A better approach using TryParse The inefficient use of exceptions A better approach using TryParse Prerequisites for Developers Basic understanding of C# programming language. Basic understanding of C# programming language. C# programming language. 30 Day .Net Challenge 30 Day .Net Challenge Getting Started The inefficient use of exceptions Using exceptions for flow control, especially in a loop or frequently called code, may lead to severe performance bottlenecks. It also makes code hard to understand. Exceptions are really expensive in terms of system resources because when an exception is triggered, .Net runtime captures the stack trace and the process is resource-intensive. try
    {
        int.Parse(input); // Attempt to parse input
    }
    catch (FormatException)
    {
        // Handle the invalid input
    } try
    {
        int.Parse(input); // Attempt to parse input
    }
    catch (FormatException)
    {
        // Handle the invalid input
    } A better approach using TryParse Please find below the refactored version of the previous code snippet if (int.TryParse(input, out int result))
    {
        // Use the parsed value
    }
    else
    {
        // Handle the invalid input
    } if (int.TryParse(input, out int result))
    {
        // Use the parsed value
    }
    else
    {
        // Handle the invalid input
    } The aforementioned code attempts to parse the input supplied from the console and returns a boolean whether it's a success or failure. Complete Code Create another class named AvoidExceptions and add the following code snippet public static class AvoidExceptions
    {
        public static void BadWay(string input)
        {
            // Inefficient way: Using exceptions for flow control
            try
            {
                int number = int.Parse(input);
                Console.WriteLine($"You entered (Exception method): {number}");
            }
            catch (FormatException)
            {
                Console.WriteLine("Invalid input! Please enter a valid integer.");
            }
        }
    
        public static void GoodWay(string input)
        {
            // Efficient way: Using TryParse for flow control
            if (int.TryParse(input, out int result))
            {
                Console.WriteLine($"You entered (TryParse method): {result}");
            }
            else
            {
                Console.WriteLine("Invalid input! Please enter a valid integer.");
            }
        }
    } public static class AvoidExceptions
    {
        public static void BadWay(string input)
        {
            // Inefficient way: Using exceptions for flow control
            try
            {
                int number = int.Parse(input);
                Console.WriteLine($"You entered (Exception method): {number}");
            }
            catch (FormatException)
            {
                Console.WriteLine("Invalid input! Please enter a valid integer.");
            }
        }
    
        public static void GoodWay(string input)
        {
            // Efficient way: Using TryParse for flow control
            if (int.TryParse(input, out int result))
            {
                Console.WriteLine($"You entered (TryParse method): {result}");
            }
            else
            {
                Console.WriteLine("Invalid input! Please enter a valid integer.");
            }
        }
    } Execute from the main method as follows #region Day 24: Avoid Exceptions in Flow Control
    static string ExecuteDay24()
    {
        Console.WriteLine("Enter a number:");
    
        string input = Console.ReadLine();
        AvoidExceptions.BadWay(input);
        AvoidExceptions.GoodWay(input);
        return "Executed Day 24 successfully..!!";
    }
    
    #endregion #region Day 24: Avoid Exceptions in Flow Control
    static string ExecuteDay24()
    {
        Console.WriteLine("Enter a number:");
    
        string input = Console.ReadLine();
        AvoidExceptions.BadWay(input);
        AvoidExceptions.GoodWay(input);
        return "Executed Day 24 successfully..!!";
    }
    
    #endregion Console Output Invalid input! Please enter a valid integer.
    Invalid input! Please enter a valid integer. Invalid input! Please enter a valid integer.
    Invalid input! Please enter a valid integer. Complete Code here: GitHub — ssukhpinder/30DayChallenge.Net Complete Code here: GitHub — ssukhpinder/30DayChallenge.Net GitHub — ssukhpinder/30DayChallenge.Net C# Programming🚀 Thank you for being a part of the C# community! Before you leave: Follow us: Youtube | X | LinkedIn | Dev.to Visit our other platforms: GitHub More content at C# Programming Youtube X LinkedIn Dev.to GitHub

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