Online auction system github


  • Online Auction System project in PHP
  • How to build an online auction website like eBay
  • GitHub Copilot and the Rise of AI Language Models in Programming Automation
  • Android Auction System App Project
  • eAuction – Creating a sample auction house CorDapp from scratch! (Part1)
  • Online Auction System project in PHP

    As we see more and more of them coming to life, one such interesting area is auctions. New to Corda? A great way to start with Corda is to take a look at one of our online bootcamp webinars. So, what do we want our auction app to do? The first thing that is quite evident is we need something to put on auction. So we should be able to both issue an asset on the ledger, and then auction that asset.

    We should be able to create an auction and the auction should be visible to everyone in the network. The Auction should be able to accept bids from the network participants. The auction should have a deadline at which it should stop accepting bids, so we need a way to schedule an action at a pre-determined time to end the auction. The auction should finally be settled, i. And finally, we should be able to remove finished auctions.

    That would conclude an auction. And, it should be good enough for our prototype. Here are a few snapshots of the final prototype we are aiming at. What is it that we want to store on the ledger? I think storing the asset and the auction information should be good enough. Implementing LinearState would require you to introduce a linearId which remains consistent across state evolution. We also need to override a method called withNewOwner. This method should be called from a flow when there is a need to transfer ownership.

    So, we expect this method to return the updated state and the command for transfer action. This is wrapped in a CommandAndState object.

    As you can easily guess, this updated state becomes the output of the transaction and the command becomes the command of the transaction being built to perform the ownership transfer. TransferAsset , new Asset this. StatePointer The first thing that we want to concentrate on is linking this Auction to an Asset.

    For that, we are going to use StatePointer. It allows you to point to a ContractState without directly including it in the ContractState. Corda allows you to schedule events using SchedulableState. For that to happen we need to implement the SchedulableState interface. But when do we update it? So, we need to trigger a flow that would execute that particular transaction.

    Luckily for us, as we implement the SchedulableState we are also required to override a method called nextScheduledActivity which could be used to trigger a flow at a particular instant. This method returns a SechduledActivity which is used by the scheduler to schedule the execution of a flow at a particular time. In our case, we schedule the EndAuctionFlow. And also good to check that the auction has ended. We need to make sure that the auction is inactive If the auction has got bids, both the auctioneer and the winner must sign the transaction and also, the auction must be settled.

    Want to continue to build the Auction CorDapp? Find the Part-2 of this blog post here , where I discuss how to implement the flows. The final completed source code is available below. Source Code The source code discussed in the post can be found below.

    It contains the completed Cordapp with the states, contracts and flows all implemented. It also has a UI and a client implemented to play around. Check it out! Thanks for reading through, hopefully, you like this post.

    To learn how to implement the flows of the Auction CorDapp checkout the Part-2 of this blog post here. Want to learn more and connect with other CorDapp developers? You may consider joining us in our public slack channel. Learn more about Corda at corda. Official documents can be found at docs. Follow Ashutosh on Twitter: iashutoshmeher , LinkedIn: iashutoshmeher.

    Should I Use Github Copilot? If you are a software engineer, or count any of them among your circle of acquaintances, then you're probably already aware at some level of Copilot. Copilot is GitHub's new deep learning code completion tool.

    Autocomplete tools for programmers are nothing new, and Copilot is not even the first to make use of deep learning nor even the first to use a GPT transformer. Microsoft which owns GitHub has packaged their own IntelliSense code completion tool with programming products since at least , and autocomplete and text correction has been an active area of research since the s.

    Since its inception, Copilot has fueled a heated discussion about the product and its potential copyright implications. In large part this is due to the way the model was trained. This is a principal source of controversy surrounding the project, surpassing the discussion about automating away software engineering and the impact of tools like Copilot on the future of programming. More technical information about the model and its limitations can be found in the paper on Arxiv.

    Portrait of Edmond de Belamy. Some programmers are simply upset that their code contributed to what is likely to become a paid product without their explicit permission, with a few commenters on hacker news discussing leaving the platform.

    The art piece was created on top of open source contributions with a lineage of multiple authors, none of whom received any compensation that we know of as reward for the noteworthy success of the work at auction. GitHub Copilot is demonstrably capable of reproducing extended sections of copyleft code, which many in the open source community consider a violation of the terms of licenses like GPL.

    Copilot was trained on all public code, including permissive open source licenses like the MIT License. However, it also copyleft licenses like the Affero General Public License AGPL that allows use and modification, but requires modified works to be made available under the same license.

    In some interpretations, code generated by GitHub Copilot can be considered derivative of the original training data, and perhaps more problematically Copilot can sometimes reproduce code from the training dataset verbatim.

    That makes Copilot a trickier case than, say, the Google book-scanning precedent often cited as a cornerstone of fair use for scraping copyrighted data. The discussion on potential legal issues continues with little consensus from either side of the debate for now, and the subject may very likely become an issue for the courts to resolve.

    Even if we assume that Copilot is totally in the clear legally, there may be other risks to using the product.

    This will automatically suggest the next word as you begin to type it, and may suggest a slightly longer continuation such as to finish the current sentence. The default autocomplete in vim, for example, will simply offer a list of suggested completions based on the words that a user has entered previously. More recently developed code completion tools like TabNine or Kite are a little more sophisticated and can suggest the completion of the rest of a line or two.

    The Kite website suggests this is enough to make a programmer nearly twice as efficient in terms of the number of keystrokes used, but Github Copilot takes this one step further, albeit with a very long stride. Copilot can interpret the contents of a docstring and write a function to match, or given a function and the start of an appropriately named test function it can generate unit tests.

    Taken to its logical conclusion, when Copilot works perfectly it turns the job of a software engineer into something that looks a lot more like constant code review than writing code. Several programmer-bloggers with early access to the technical preview version have put Copilot to the test by essentially challenging the model to solve interview-level programming problems.

    Copilot is pretty impressive in how well it can solve these types of challenges, but not good enough to warrant using its output without carefully reviewing it first. To evaluate the performance of this model, OpenAI built what they call the HumanEval dataset: a collection of hand-written programming challenges with corresponding unit tests, the sort you might find on a coding practice site like CodeSignal , Codeforces, or HackerRank.

    In HumanEval, the problem specifications are included in function docstrings, and the problems are all written for the Python programming language. While an undifferentiated GPT-3 without code-specific was unable to solve any of the problems in the HumanEval dataset at least on the first try , the fine-tuned Codex and Codex-S were able to solve By cherry-picking from the top attempts at the problems, Codex-S was further able to solve One way to interpret this is that if a programmer was using Codex, they could expect to find a valid solution to a problem at roughly the level of complexity encountered in technical interviews by looking through the first suggestions, or even blindly throwing attempted solutions at a valid set of unit tests until they pass.

    This method has a lower variance than reporting pass k directly. The Best Codex Model Still Under-Performs a Computer Science Student The authors of Codex note that, being trained on over GB in hundreds of millions of lines of code from GitHub, the model has been trained on significantly more code than a human programmer can expect to read over the course of their careers.

    However, the best Codex model Codex-S with 12 billion parameters still under-performs the abilities of a novice computer science student or someone who spends a few afternoons practicing interview-style coding challenges. In particular, Codex performance degrades rapidly when chaining together several operations in a problem specification. In fact, the ability of Codex to solve several operations chained together drops by a factor of 2 or worse for each additional instruction in the problem specification.

    To quantify this effect, the authors at OpenAI built an evaluation set of string manipulations that could operate sequentially change to lowercase, replace every other character with a certain character, etc.

    The rapid drop-off in solving multi-step problems was seen by an early Copilot reviewer Giuliano Giacaglia on Medium. Copilot did, however, manage to write a test that failed for its own implementation. Although not sticking to the multi-step string manipulation paradigm used by Giuliano and OpenAI to test Copilot, Kumar Shubham discovered an impressive result when Copilot successfully solved a multi-step problem description that involved calling system programs to take a screenshot, run optical character recognition on the image, and finally extract email addresses from the text.

    That does, however, raise the issue that Copilot may write code that relies on unavailable, out-of-date, or untrusted external dependencies. Other reviews of Copilot by YouTubers DevOps Directive and Benjamin Carlson found impressive results when challenging Copilot with interview-style questions from leetcode. The difference in the complexity of code that Copilot can generate and the complexity of problem specifications that Copilot can understand is striking. Perhaps the prevalence of code written in the style of interview practice questions in the training dataset leads to Copilot overfitting those types of problems, or perhaps it is just more difficult to chain together several steps of modular functionality than it is to churn out a big chunk of complex code that is very similar to something the model has seen before.

    Poorly described and poorly interpreted specifications are already a common source of complaints for engineers and their managers of the human variety, so perhaps it should not be so surprising to find an AI coding assistant fails to excel at parsing complicated problem specifications. Originally built by Jacob Jackson and now owned by codota , TabNine was able to solve 7. TabNine has been around since and has both free and paid versions.

    Kite is another code completion tool in the same vein as TabNine, with free desktop and paid server versions that differ in the size of the model used by a factor of Going by the animated demos on their website, Kite definitely suggests shorter completions than both TabNine and Copilot.

    This differs in degree from TabNine, which suggests only slightly longer completions for the most part, but it's qualitatively different from Copilot: Copilot can suggest extended blocks of code and changes the experience from choosing the best completion to code reviewing several suggested approaches to the problem. In reality this is very unlikely to be the case for many years as there is more to programming than just writing code. On the other hand, Copilot and other natural language code completion tools like it and trust us, more are coming are indeed likely to have a big impact on the way software engineers do their jobs.

    Engineers will probably spend more time reviewing code and checking tests, whether the code under scrutiny was written by an AI model or a fellow engineer.

    Additionally, most contemporary interpretations of intellectual property require a human author for a work to be eligible for copyright.

    As more code is written in larger proportions by machine learning models instead of humans, will those works legally enter the public domain upon their creation?

    Who knows? Perhaps the open source community will finally win in the end, as the great-great-great successor to Copilot becomes a staunch open source advocate and insists on working only on free and open source software.

    Bio: Kevin Vu manages Exxact Corp blog and works with many of its talented authors who write about different aspects of Deep Learning. Reposted with permission.

    As you can easily guess, this updated state becomes the output of the transaction and the command becomes the command of the transaction being built to perform the ownership transfer. TransferAssetnew Asset this. StatePointer The first thing that we want to concentrate on is linking this Auction to an Asset.

    How to build an online auction website like eBay

    For that, we are going to use StatePointer. It allows you to point to a ContractState without directly including it in the ContractState. Corda allows you to schedule events using SchedulableState. For that to happen we need to implement the SchedulableState interface. But when do we update it? So, we need to trigger a flow that would execute that particular transaction.

    Luckily for us, as we implement the SchedulableState we are also required to override a method called nextScheduledActivity which could be used to trigger a flow at a particular instant. This method returns a SechduledActivity which is used by the scheduler to schedule the execution of a flow at a particular time.

    In our case, we schedule the EndAuctionFlow. And also good to check that the auction has ended. We need to make sure that the auction is inactive If the auction has got bids, both the auctioneer and the winner must sign the transaction and also, the auction must be settled. Want to continue to build the Auction CorDapp? Find the Part-2 of this blog post herewhere I discuss how to implement the flows.

    Even if we assume that Copilot is totally in the clear legally, there may be other risks to using the product. This will automatically suggest the next word as you begin to type it, and may suggest a slightly longer continuation such as to finish the current sentence. The default autocomplete in vim, for example, will simply offer a list of suggested completions based on the words that a user has entered previously.

    More recently developed code completion tools like TabNine or Kite are a little more sophisticated and can suggest the completion of the rest of a line or two. The Kite website suggests this is enough to make a programmer nearly twice as efficient in terms of the number of keystrokes used, but Github Copilot takes this one step further, albeit with a very long stride.

    Copilot can interpret the contents of a docstring and write a function to match, or given a function and the start of an appropriately named test function it can generate unit tests.

    Taken to its logical conclusion, when Copilot works perfectly it turns the job of a software engineer into something that looks a lot more like constant code review than writing code. Several programmer-bloggers with early access to the technical preview version have put Copilot to the test by essentially challenging the model to solve interview-level programming problems.

    Copilot is pretty impressive in how well it can solve these types of challenges, but not good enough to warrant using its output without carefully reviewing it first. To evaluate the performance of this model, OpenAI built what they call the HumanEval dataset: a collection of hand-written programming challenges with corresponding unit tests, the sort you might find on a coding practice site like CodeSignalCodeforces, or HackerRank.

    In HumanEval, the problem specifications are included in function docstrings, and the problems are all written for the Python programming language. While an undifferentiated GPT-3 without code-specific was unable to solve any of the problems in the HumanEval dataset at least on the first trythe fine-tuned Codex and Codex-S were able to solve By cherry-picking from the top attempts at the problems, Codex-S was further able to solve One way to interpret this is that if a programmer was using Codex, they could expect to find a valid solution to a problem at roughly the level of complexity encountered in technical interviews by looking through the first suggestions, or even blindly throwing attempted solutions at a valid set of unit tests until they pass.

    This method has a lower variance than reporting pass k directly. The Best Codex Model Still Under-Performs a Computer Science Student The authors of Codex note that, being trained on over GB in hundreds of millions of lines of code from GitHub, the model has been trained on significantly more code than a human programmer can expect to read over the course of their careers.

    However, the best Codex model Codex-S with 12 billion parameters still under-performs the abilities of a novice computer science student or someone who spends a few afternoons practicing interview-style coding challenges. In particular, Codex performance degrades rapidly when chaining together several operations in a problem specification. In fact, the ability of Codex to solve several operations chained together drops by a amputee pretender fiction of 2 or worse for each additional instruction in the problem specification.

    GitHub Copilot and the Rise of AI Language Models in Programming Automation

    To quantify this effect, the authors at OpenAI built an evaluation set of string manipulations that could operate sequentially change to lowercase, replace every other character with a certain character, etc. These tools create valuable connections between groups of people.

    However, it is common to use not a single community builder tool. But a bunch of tools. These tools include visual graphics, social networking, media, spreadsheets, forms. As well as software tools for automated emails or advertisements, and integrations for your marketplace. Photo Gallery A clear and extensive image gallery is your little key to success. Basically, the gallery is one of the most viewed features in any online marketplace. Not only in auction-type platforms.

    Such a tool helps your customers with a better understanding of what your products are. So people are more likely to choose and purchase the products. NB: To check what kind of elements or pictures your users pay attention to. You can install a special behavior analytics tools like Hotjar or Crazyegg. Such tools help to check out how exactly your users interact with your website.

    You should keep not only tech features in mind. Choose the most appropriate business model for your auction website. There are a few types of business models. They, in turn, influence the monetization models and marketing approaches.

    Buy it Now The product has a bid and a fixed price. It is sold when the best price is offered. Buy-it-now allows merchants to set the product price right away.

    The customers can buy a product for its actual price if they want to. Reserve Price The bid starts from the base auction price. The product is sold to the highest bidder. In this model, a merchant sets a hidden minimum price.

    He agrees to sell the product for that. So the product can be sold in two cases. Proxy Bidding The buyer uses special software applications for bidding.

    The software has information about the maximum price. The concept of marketplaces became popular and handy. And thus ready-made solutions for marketplaces also appear on the market. The reason is there are lots of business app patterns for faster website creation. No need to hire a team No need to look for, interview and choose developers.

    Truth to say, it saves both time and money. Lower prices Naturally, good ready-made tools do not need that much additional development and customization. And appears to be less expensive than hiring a team Cons of Ready-Made Solutions for Auction Website Development No choice of self-expression Using a ready-made software you have not that many abilities to customize your auction website. This causes issues starting with the impossibility to choose your own design or functional solutions.

    Therefore, it is hard to stand out from other auction websites. Limitation of business development Using ready-made solutions can limit you not only in technical but also in business goals. Choosing a ready-made solution? Adapt your business to it. Custom development, on the contrary, allows you to conduct a discovery phase first.

    Android Auction System App Project

    And create an exact product you and your audience need. Big players require fees External solutions may be faster and easier to work with. But all of them require fees for maintenance. The bigger and quality provider you choose, the more expensive may be a final price. At this point, custom development may cost you the same money.

    But custom development keeps you in control of the whole development process. Tools to Build your own Auction Website Here we come to the mainline of the article. How to build an online auction website for your business? We are here to answer this question.

    And help solve the related problems in the future. The variety of software languages, frameworks, and tools for web development is quite extensive. Web solutions are the point of interest of many people worldwide nowadays.

    eAuction – Creating a sample auction house CorDapp from scratch! (Part1)

    But when it comes to the development of an online auction website, what to choose? But things look not so well when diving deeper. For example, into WordPress profitability for auction-model websites. There is an opinion. CMS is a really bad solution to create a website like eBay.


    Online auction system github