Aiwa (eye-WAH, stylised aiwa) is a Japanese consumer electronics brand of Aiwa Co. Ltd., a subsidiary of Towada Audio holdings. The current company was established in 2017 and creates mainly audio products; the brand is also licensed to or owned by other companies in different regions of the ...

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Darine Afyouni (Arabic: دارين افيوني; born 20 August 1983), known by the mononym Darine (Arabic: دارين), is a Lebanese-Swedish singer-songwriter. With two albums in account, her debut single was &quot;Aiwa&quot; on her album Ma Rulez. In 2006, she released her second studio album My ...

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Aiwa company, the experienced and professional TV supplier, prides itself on being uncompromising even while pledging ultimate value to the consumers. It is reflected strongly within its display range, and so do the smart TVs Aiwa company makes. With considerate service and high quality, the ...

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Hot Products Over the years, Aiwa, as a professional LED TV manufacturer, has become an all-around household appliance provider, offering from Aiwa smart TV to air conditioners. The followings are domestic appliances from Aiwa, including aiwa smart TV, LCD monitors, Speakers, Display, ...

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TOKYO -- A Japanese electronics manufacturing service company is breathing new life into the Aiwa brand, reviving a once-beloved household name k

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By Project Manager Jon Purdy If you built your own or forked an existing one, one of your top priorities should be to make it easy for users and developers to interact with. enabled Ethereum users and developers to easily use ETH and ERC-20 tokens, and use DApps without needing to use command-line tools or run their own node (thanks to Infura). blockchain MetaMask Aion realized this early on, which is why they had BlockX Labs build , a wallet and DApp interaction tool for Aion, for them. As Project Manager for AIWA, I’d like to go over the process that we undertook when building “MetaMask for Aion”; specifically, both the inspiration as well as the differentiation. I’ve written this for product people and executives so I’ve kept the jargon quite light. AIWA Why do you need a wallet? Firstly, you need to justify why your blockchain needs its version of MetaMask. As mentioned above, you want developers and users to interact with it. They need to read and write data and possibly send and receive cryptocurrency, If your blockchain is like Ethereum and Aion, you’ve got a distributed app ecosystem that needs to interact with it. Without a wallet, it’s a lot more difficult for both developers and users to use your blockchain, so reduce the friction and provide them with tool(s) to make it easier. What do you love about MetaMask? Since you’ve got a reason to build a MetaMask-like tool, think about what you love about MetaMask and decide whether you want or need those features. At , we like that MetaMask is honest with how it maps Ethereum’s features to its UI. MetaMask doesn’t hide or abstract away complexity but exposes it to the user. For example, it shows the raw gas price (in GWei) rather than abstracting it away by converting it to USD. BlockX Labs It also differentiates between seed-generated and imported wallets, so that the user can know which wallets are recoverable from the seed phrase. We also love that MetaMask doesn’t keep the user inside MetaMask; it provides links to Etherscan for viewing more transaction details and embeds the ability to use Coinbase and ShapeShift for acquiring Ether. It supports third-party services and doesn’t try to hold the user captive by doing everything itself. Forget about MetaMask! Once you’ve found the features that you love about MetaMask, it’s time to forget about it. Here at BlockX Labs, we use Agile with Scrum. I recommend writing user stories to figure out what user and developers will do with your tool and build your UI around that. In our case, some of the basic features like the receive screen and how the DApp popped up ended up working pretty similarly to MetaMask. But by focusing on our user stories and not trying to clone MetaMask, we came up with a unique hierarchical interface that is modeled around the Token < Wallet < Vault metaphor. As a result, we came up with the idea of a Vault to contain both seed-generated wallets and imported wallets in a single exported file. This makes it super easy for users to backup all of their wallets regardless of whether they’re seed-generated or imported from private keys. Of course, we also built the option to export individually using the standard wallet format as well. </end> Although it’s heavily influenced from my experience building AIWA, hopefully this article has given a decent overview of building something like MetaMask for your own blockchain. I didn’t mention some things that aren’t part of the core product but help make it easier to use (like Infura, and NodeSmith in our case). But perhaps a follow-up article could address the ecosystem partners that would be useful. Have any suggestions on building Metamask for your own blockchain? We’d love to hear them! Leave a comment below! Jon Purdy is the resident Project Manager at BlockX Labs. He has been in the cryptocurrency space for years (with building a mining rig in 2014, investing in ICOs in 2017, and automated Korean price arbitrage just a few months ago). You can contact him at jpurdy@blockxlabs.com

Why and How to build Metamask for your own Blockchain

By Ben Xiang, SVP, Product & Engineering, Veritone Artificial Intelligence (AI), particularly Large Language Models (LLMs), has emerged as a transformative force in business technology. However, the true value of these advanced AI tools lies in their strategic application to address specific business needs. The first–and critically important–step for businesses is to effectively evaluate their needs and align them with appropriate AI solutions, maximizing the impact of AI integration. The place to begin is by examining business operations through a thorough evaluation of your current challenges and opportunities. This process involves assessing areas where efficiency, accuracy, or innovation could be enhanced. Assessing Current Challenges and Opportunities Start by conducting a comprehensive analysis of your business processes to identify bottlenecks or inefficiencies. Are there repetitive language tasks that consume valuable human resources? Are there obvious “low-hanging fruit” opportunities for innovation in customer engagement or product development that require extensive language processing? For instance, consider a customer service department inundated with routine queries. This scenario often leads to long wait times and overworked staff. Identifying such challenges can highlight the potential for LLMs to automate responses, thus improving efficiency and customer satisfaction. LLMs excel in automating routine tasks and enhancing more complex ones, making customer support a prime area where AI can manage routine inquiries, allowing human agents to focus on more intricate issues. Similarly, in content creation, LLMs can produce high-quality content rapidly, freeing up time for more strategic human activities. For example, an e-commerce company might struggle with creating unique product descriptions for thousands of items. An LLM can generate these descriptions automatically, ensuring consistency and saving significant time and effort. Evaluating Data Availability The success of LLM implementation hinges on access to substantial and relevant data. Businesses must assess whether they generate enough data to feed and train the LLM effectively. Data such as customer interactions, product descriptions, or research reports are invaluable. Consider a healthcare provider with vast amounts of patient data. By leveraging this data, an LLM can assist in diagnosing conditions or providing personalized patient care recommendations, thus enhancing service quality and operational efficiency. Considering Scalability and Impact When choosing use cases, focus on areas where LLMs can significantly improve outcomes or scale operations. For instance, LLMs can process vast amounts of text data in analytics, offering deeper insights and enhancing decision-making. A financial services firm, for instance, might use LLMs to analyze market trends and generate reports. This capability not only speeds up the process but also provides more comprehensive insights, aiding better investment decisions. Common Use Cases of LLMs Across Industries LLMs are versatile and can be applied in numerous sectors. Here are some impactful use cases demonstrating their diverse capabilities: Document Summarization In academic and corporate research settings, LLMs can process extensive texts to extract key information and themes. This significantly reduces the time required to digest large volumes of information, enhancing productivity and decision-making. For example, a law firm could use LLMs to summarize lengthy legal documents, making it easier for lawyers to review key points quickly and efficiently. Creative Writing Assistance Authors can leverage LLMs to generate ideas, suggest narrative structures, and even draft content. This helps overcome writer’s block and enhances creativity, allowing writers to produce more refined and imaginative work. Email and Communication Management LLMs streamline email sorting, drafting, and scheduling, preserving context and personalizing communication. This boosts productivity by managing routine communications efficiently, freeing up time for more critical tasks. Personalized Content Recommendation LLMs analyze user preferences to suggest relevant content, enhancing user engagement on streaming services and online platforms through tailored content suggestions. Technical Support Automation LLMs generate technical support guides and troubleshooting tips, enhancing customer support in technology sectors by providing instant and accurate technical assistance. Copywriting and Advertising LLMs craft creative marketing copy and promotional material, which are crucial for marketing agencies and businesses and help create compelling and targeted advertising content. Scripting and Code Generation LLMs assist developers by generating code scripts, aiding in debugging, streamlining software development processes, and reducing errors. Resume Screening and HR Automation LLMs automate the screening of resumes and job applications, making the hiring process more efficient and fair by integrating with applicant tracking systems and utilizing bias-free evaluation algorithms. Essential Features of LLM Applications Each use case requires specific LLM features to be effective. Here are key features to consider: Conversational Response: Essential for chatbots and virtual assistants, enabling natural, fluid dialogue with users.
Coding and Math Capabilities: Crucial for code generation and mathematical problem-solving in technical applications.
Reasoning and Comprehension: Vital for logical analysis, problem-solving, and understanding complex texts.
Word Knowledge and Fast Response: Important for tasks requiring factual accuracy and real-time interactions.
Grounded Knowledge and Fine-Tuning: Ensures accuracy and relevance, particularly in professional and educational settings.
Extended Context and Content Moderation: Maintains coherence over long interactions and ensures safe, reliable usage. By strategically aligning your business needs with the right LLM applications, you can harness the power of AI to transform operations, enhance efficiency, and drive innovation. The key lies in a thoughtful evaluation of your challenges and opportunities, coupled with a deep understanding of the capabilities of LLMs, ensuring a marriage of business needs with the right AI solutions. About Ben Xiang Ben Xiang is SVP of Corporate Development & Strategy and General Manager of aiWARETM for Veritone, Inc. Ben is responsible for overseeing Veritone’s AI Solutions group, product development and Veritone’s proprietary AI operating system. In addition, he also leads corporate development and strategy. Prior to Veritone, Ben held multiple roles with Ingram Micro as the global executive for the IoT, AI, and Mixed-Reality business as well as roles in strategy and corporate development. He has extensive experience in global business operations and cross-border M&A. About Veritone Veritone (NASDAQ: VERI) designs human-centered AI solutions. Serving customers in the talent acquisition, media, entertainment, and public sector industries, Veritone's software and services empower individuals at the world's largest and most recognizable brands to run more efficiently, accelerate decision-making, and increase profitability. Veritone's leading enterprise AI platform, aiWARETM, orchestrates an ever-growing ecosystem of machine learning models, transforming data sources into actionable intelligence. By blending human expertise with AI technology, Veritone advances human potential to help organizations solve problems and achieve more than ever before, enhancing lives everywhere. By Ben Xiang, SVP, Product & Engineering, Veritone By Ben Xiang, SVP, Product & Engineering, Veritone Veritone Veritone Artificial Intelligence (AI), particularly Large Language Models (LLMs), has emerged as a transformative force in business technology. However, the true value of these advanced AI tools lies in their strategic application to address specific business needs. The first–and critically important–step for businesses is to effectively evaluate their needs and align them with appropriate AI solutions, maximizing the impact of AI integration. The place to begin is by examining business operations through a thorough evaluation of your current challenges and opportunities. This process involves assessing areas where efficiency, accuracy, or innovation could be enhanced. transformative force Assessing Current Challenges and Opportunities Assessing Current Challenges and Opportunities Start by conducting a comprehensive analysis of your business processes to identify bottlenecks or inefficiencies. Are there repetitive language tasks that consume valuable human resources? Are there obvious “low-hanging fruit” opportunities for innovation in customer engagement or product development that require extensive language processing? For instance, consider a customer service department inundated with routine queries. This scenario often leads to long wait times and overworked staff. Identifying such challenges can highlight the potential for LLMs to automate responses, thus improving efficiency and customer satisfaction. LLMs excel in automating routine tasks and enhancing more complex ones, making customer support a prime area where AI can manage routine inquiries, allowing human agents to focus on more intricate issues. Similarly, in content creation, LLMs can produce high-quality content rapidly, freeing up time for more strategic human activities. For example, an e-commerce company might struggle with creating unique product descriptions for thousands of items. An LLM can generate these descriptions automatically, ensuring consistency and saving significant time and effort. Evaluating Data Availability Evaluating Data Availability The success of LLM implementation hinges on access to substantial and relevant data. Businesses must assess whether they generate enough data to feed and train the LLM effectively. Data such as customer interactions, product descriptions, or research reports are invaluable. Consider a healthcare provider with vast amounts of patient data. By leveraging this data, an LLM can assist in diagnosing conditions or providing personalized patient care recommendations, thus enhancing service quality and operational efficiency. Considering Scalability and Impact Considering Scalability and Impact When choosing use cases, focus on areas where LLMs can significantly improve outcomes or scale operations. For instance, LLMs can process vast amounts of text data in analytics, offering deeper insights and enhancing decision-making. A financial services firm, for instance, might use LLMs to analyze market trends and generate reports. This capability not only speeds up the process but also provides more comprehensive insights, aiding better investment decisions. Common Use Cases of LLMs Across Industries Common Use Cases of LLMs Across Industries LLMs are versatile and can be applied in numerous sectors . Here are some impactful use cases demonstrating their diverse capabilities: numerous sectors Document Summarization Document Summarization In academic and corporate research settings, LLMs can process extensive texts to extract key information and themes. This significantly reduces the time required to digest large volumes of information, enhancing productivity and decision-making. For example, a law firm could use LLMs to summarize lengthy legal documents, making it easier for lawyers to review key points quickly and efficiently. Creative Writing Assistance Creative Writing Assistance Authors can leverage LLMs to generate ideas, suggest narrative structures, and even draft content. This helps overcome writer’s block and enhances creativity, allowing writers to produce more refined and imaginative work. Email and Communication Management Email and Communication Management LLMs streamline email sorting, drafting, and scheduling, preserving context and personalizing communication. This boosts productivity by managing routine communications efficiently, freeing up time for more critical tasks. Personalized Content Recommendation Personalized Content Recommendation LLMs analyze user preferences to suggest relevant content, enhancing user engagement on streaming services and online platforms through tailored content suggestions. Technical Support Automation Technical Support Automation LLMs generate technical support guides and troubleshooting tips, enhancing customer support in technology sectors by providing instant and accurate technical assistance. Copywriting and Advertising Copywriting and Advertising LLMs craft creative marketing copy and promotional material, which are crucial for marketing agencies and businesses and help create compelling and targeted advertising content. Scripting and Code Generation Scripting and Code Generation LLMs assist developers by generating code scripts, aiding in debugging, streamlining software development processes, and reducing errors. Resume Screening and HR Automation Resume Screening and HR Automation LLMs automate the screening of resumes and job applications, making the hiring process more efficient and fair by integrating with applicant tracking systems and utilizing bias-free evaluation algorithms. Essential Features of LLM Applications Essential Features of LLM Applications Each use case requires specific LLM features to be effective. Here are key features to consider: Conversational Response: Essential for chatbots and virtual assistants, enabling natural, fluid dialogue with users. Coding and Math Capabilities: Crucial for code generation and mathematical problem-solving in technical applications. Reasoning and Comprehension: Vital for logical analysis, problem-solving, and understanding complex texts. Word Knowledge and Fast Response: Important for tasks requiring factual accuracy and real-time interactions. Grounded Knowledge and Fine-Tuning: Ensures accuracy and relevance, particularly in professional and educational settings. Extended Context and Content Moderation: Maintains coherence over long interactions and ensures safe, reliable usage. Conversational Response: Essential for chatbots and virtual assistants, enabling natural, fluid dialogue with users. Coding and Math Capabilities: Crucial for code generation and mathematical problem-solving in technical applications. Reasoning and Comprehension: Vital for logical analysis, problem-solving, and understanding complex texts. Word Knowledge and Fast Response: Important for tasks requiring factual accuracy and real-time interactions. Grounded Knowledge and Fine-Tuning: Ensures accuracy and relevance, particularly in professional and educational settings. Extended Context and Content Moderation: Maintains coherence over long interactions and ensures safe, reliable usage. By strategically aligning your business needs with the right LLM applications, you can harness the power of AI to transform operations, enhance efficiency, and drive innovation. The key lies in a thoughtful evaluation of your challenges and opportunities, coupled with a deep understanding of the capabilities of LLMs, ensuring a marriage of business needs with the right AI solutions. About Ben Xiang About Ben Xiang About Ben Xiang Ben Xiang is SVP of Corporate Development & Strategy and General Manager of aiWARETM for Veritone, Inc. Ben is responsible for overseeing Veritone’s AI Solutions group, product development and Veritone’s proprietary AI operating system. In addition, he also leads corporate development and strategy. Prior to Veritone, Ben held multiple roles with Ingram Micro as the global executive for the IoT, AI, and Mixed-Reality business as well as roles in strategy and corporate development. He has extensive experience in global business operations and cross-border M&A. About Veritone About Veritone About Veritone Veritone (NASDAQ: VERI) designs human-centered AI solutions. Serving customers in the talent acquisition, media, entertainment, and public sector industries, Veritone's software and services empower individuals at the world's largest and most recognizable brands to run more efficiently, accelerate decision-making, and increase profitability. Veritone's leading enterprise AI platform, aiWARETM, orchestrates an ever-growing ecosystem of machine learning models, transforming data sources into actionable intelligence. By blending human expertise with AI technology, Veritone advances human potential to help organizations solve problems and achieve more than ever before, enhancing lives everywhere. Veritone Veritone

How to Get Started Harnessing the Power of AI: Aligning Business Needs with the Right Solutions

By: Jesse AbramowitzBlockchain Developer It has been a while since I had been truly excited about a new blockchain. This is not to say applications. I think the applications on the blockchain are crushing it and there are some truly amazing products. However, Platforms themselves…other than Ethereum have not excited me. In my mind they either sacrifice decentralization for scalability or copy Ethereum’s road map and call themselves a 3rd generation blockchain. One current way to scale blockchains is horizontally. This means to create more blockchains that can burden the load. For this to truly work seamlessly users must be able to walk in between those blockchains as easily as they switch websites on the internet. We also can see further issues with horizontal scaling. It is the difficulty, not to create a blockchain, that is actually really easy with the tools we have today. However, to truly decentralize, bridge and secure a chain is a vastly more complex situation. You need to balance incentives and convince both users and network maintainers to hop on board whatever you are building. Once that is complete then you can actually build whatever application you had in mind. Polkadot offers a fascinating solution to this. They do scale vertically with a higher TPS than Ethereum but they also offer a horizontal scaling solution. Polkadot was built with Substrate. Substrate is a bunch of node modules that allow you to build your own blockchain fairly fast and customize it. In the you are connected to Polkadot. As well, you can borrow from Polkadot’s consensus security. Thus, if I now want to build my own chain I do not have to worry about the bridge or the security. core substrate modules I can customize and deploy a specific chain for a specific use case. We have seen this model pop up with Ethereum and some other really cool projects. For example, Burner wallet is all the rage and for good reason. It allows you to spend DAI by bridging over to the PoA network. Thus the burdens of gas are reduced astronomically. With Polkadot the creation of specific blockchains for this use purpose will have a lot of friction removed. Well, not only does Polkadot excite me, it left me staring up at my bed unable to sleep imagining a world with as many blockchains as websites communicating with each other. After spending a few hours deploying my own demo parachain, I knew I wanted to get my hands wet with Polkadot itself, so it’s been added to our ! Universal Faucet Blockchain Polkadot Tokens Chain: Alexander DOTs Why this Blockchain? They didn’t have a faucet yet and it will be an amazing chain. I love anything by Parity Getting coins The was super nice, after we demoed what we built people were tripping over themselves to send us more DOTs community Sending a Transaction This wasn’t bad at all, the was pretty decent. I personally had an issue with some older packages documentation The JS are getting updated quite frequently, the blockchain is new not even mainnet so constant updates are a good thing, makes it a tad bit challenging trying to use it though package <a href="https://medium.com/media/7eaec0170358cb483332116cc7fee411/href">https://medium.com/media/7eaec0170358cb483332116cc7fee411/href</a> Technical notes The Endpoint we were using was a websocket which was really cool. Should make updates from the network easier We had to force a disconnection from the websocket after we send a transaction. if (provider.isConnected()) provider.disconnect() — - Jesse Abramowitz is a Blockchain Developer at BlockX Labs. He has worked on multiple DApps, projects, and Blockchain Networks. Currently, he is also a professor at George Brown College in Toronto. He is always looking to help, teach and build on the blockchain. You can reach him at: jesse@blockxlabs.com specializes in building developer tools and solutions for blockchain ecosystems.We aim to sift through the noise to bring some sense and clarity into the Blockchain space. Our accomplishments include — a wallet and DApp interaction tool for the Aion Network, and — a test token faucet for Ethereum, Aion, and Tron. BlockX Labs AIWA Universal Faucet Follow Us on Twitter: @BlockXLabs Disclaimer: BlockX Labs is a recipient of a Web3 Foundation Grant to build Enzyme — a dApp wallet for the Polkadot Network. This was announced on Wednesday March 29, 2019.

Universal Faucet Integration: Polkadot

By: Blockchain Developer Jesse Abramowitz Ethereum’s latest last fork has successfully been integrated into Ethereum and with that comes extra features. This article will cover what the optcode is, why it was added, and what you can do with it. Lastly, I will go through an in-depth demonstration of how to execute it. Constantinople create2 Let’s get started! What is Create2? This EIP (Ethereum Improvement Proposal) was created by Vitalik on 2018–04–20 and can be found . Currently there are two types of addresses in Ethereum: wallet addresses and contact addresses. All of these addresses already exist and can have funds sent to them already (there is no button or switch or message required to initiate these account). To create a wallet, a randomly generated private key is required, which will map it to the corresponding public key, then address on chain. here When creating a contract address with the regular create optcode, the address is determined from the address and the nonce of the sender. It would be very dangerous to pre interact with a contract because, (1) the nonce can get out of order and all the funds would be lost and (2) the address is not tied to the contract code so any contract can take that address. This is what the Create2 optcode can fix. Any contract that is deployed with it can have a deterministic address that is calculated by: keccak256( 0xff ++ address ++ salt ++ keccak256(init_code))[12:] Since everything there is pre-known a user can calculate it yourself off chain then send the parameters on chain and create the contract. Why is this important? With this method users can interact with a contract that does not yet exist. Their funds will be safe at that address and when they are ready, they can deploy a contract that fills that address and interact with those funds. This solution is secure and (unlike the Create optcode) allows for binding of that address to the initiating contract code. Use cases The big use cases for this optcode started in the state channel scalability domain. Specifically with pre-creating and funding contracts before they exist. The whole idea of state channels are to reduce cost. If people can interact with a contract before it exists, then when they are ready to exit the state channel they can create, settle and destroy the contract all in one call, that would significantly reduce the cost. Anyways, Counterfactual is cool and if you want to read more about it you can start . here What I always find so great about the crypto and the Ethereum community is how they can turn a slit into a whole. Weird metaphor, but I mean it with utmost admiration. It has been less than a month and people have started theorizing using this optcode for other cool use cases. For example, James Young from his medium post “ ” describes how to onboard new users with Create2 in a more seamless way: One Weird Trick to Fix User On-Boarding Example Steps for Ethereum New User Flow (ENUF) User generated key pair locally (keys never leave the device) Key pair used to generate contract address Funds sent to address to contract deployment prior Contract deployed user has funds to at least pay for gas after For novice users, centralize key recovery (ex. ) Chaperone Wallet Concept Let’s Calculate it Ok, enough talk, let’s see this in action. I found some sample code . And after some alterations, I was able to deploy it to the rinkeby network . You can see a transaction which is my factory contract deploying the Create2 contract. here here My final outcome looked like this: We see the factory contract that I deployed first, then we pre-calculate my new contract, we check to make sure it is not on chain (just for our knowledge) we can see false. Then we run the transaction which calls the factory contract’s create2 optcode. That gives us the transaction hash and from that we get the contract address. Now we can check to see if the contract exists on chain and we can see it is true. The function we are most interested in here it this: function buildCreate2Address(creatorAddress, saltHex, bytecode) {return `0x${web3.utils.sha3(`0x${[‘ff’,creatorAddress,saltHex,web3.utils.sha3(bytecode)].map(x => x.replace(/0x/, ‘’)).join(‘’)}`).slice(-40)}`.toLowerCase()} It takes the creator of the contract’s address, a hex of the salt (any random number of certain length) and the init code to create the contract and then it hashes them. This is the same math the EVM does and thus produces the same contract code. Possible Attack Vectors Since the generation of the contract address relies on the initcode it is not tied to the constructor which gets called in the contract code at run time. A great explanation of that can be found . here This means that there is a possible attack where user’s agree on a contract, pre interact with it. Yet this contract has a suicide code in it, and after it is killed, it is redeployed with different inputs for the constructor. Thus you are re-deploying different code than was thought to be expected. Which can lead to potential poor behaviour. An example of this was launched in this Reddit . thread Conclusion Create2 is an awesome improvement I can’t wait to see what people build with it. Even more, I can’t wait until I can suggest it to a client as the best possible solution and go build something with it myself. — - Jesse Abramowitz is a Blockchain Developer at BlockX Labs. He has worked on multiple DApps, projects, and Blockchain Networks. Currently, he is also a professor at George Brown College in Toronto. He is always looking to help, teach and build on the blockchain. You can reach him at: jesse@blockxlabs.com specializes in building developer tools and solutions for blockchain ecosystems.We aim to sift through the noise to bring some sense and clarity into the Blockchain space. Our accomplishments include — a wallet and DApp interaction tool for the Aion Network, and — a test token faucet for Ethereum, Aion, and Tron. BlockX Labs AIWA Universal Faucet Follow Us on Twitter: @BlockXLabs

Create2: A Tale of Two Optcodes

By: Blockchain Developer Jesse Abramowitz This article is part two of the non fungible token article . Probably best to read that first if the term ERC721 is confusing to you. This article is for an intermediate crowd. here Last time I wrote about the idea of what is a non fungible token we didn’t get into any use cases, this part will address that gap. One of the first industries to push forward any cool new tech is usually video games. Whether it be because they are relatively low stakes, they are already either technology-driven or the users tend to be techno literate. In this case let us look at a video game and how they can employ both ERC20 and ERC721. Let’s take a random game, say, Fortnite for example, a game which I am a god at…actually I am horrible at it but you all have no way of verifying that. So there. In Fortnite, players receive an in-game currency called Vbucks. These could be considered fungible as each of them have the same value as the others and can be used to redeem things like skins in the game. tokens What do we know about ERC721 tokens? Well, they are unique and they can hold metadata. This means they can hold some data and can have that be populated on the front end. So the tokens can literally be the skins themselves. This means users could actually interoperate between games and a company can create a secondary market for their skins. If you need any proof of how a secondary market can boost a brand, ask Nike. The potential here also allows for individual developers to negate certain gatekeepers and have an easier pathway to licensing their game. All they need is to do is publish the rights on this token, as long as the user holds the token they hold access to the game. When we boil the video game example down what we are doing is using a cryptographic proof to prove the owner of a piece of data. This proof is done on a public so it is decentralized, immutable, and auditable. blockchain A lot of things fit this mould. How do we prove someone owns a house? Usually this comes in the form of a deed which is given out by some Canadian (I am Canadian by the way) government branch (I am too lazy to look up) and stored by them somewhere…also you get a copy and put it in your bank. Proving that you are own the Deed involves you having to gain access to the government’s database which can be slow tedious and painful. A government agency can just launch an ERC721 token and mint you a token with some metadata that points to a database. That token can point to a file in a database that is unlocked by the user’s private key. Furthermore, transferring the token (ownership of your house) would be vastly less painful than before. In this world there would probably still be safeguards, needing a witness watch the transfer etc. However, the process as a whole would be a lot smoother. Now apply this same logic to other things like patents, identity, or supply chains. It can work, it can all work. The limitations now are in things like UX, scalability, mass adoption etc. Conclusion Whether it be ERC721 or another standard the possibilities of non fungibles are endless. The groundwork for some amazing technological breakthroughs are being laid and we are only scratching the surface. — Jesse Abramowitz is a Blockchain Developer at BlockX Labs. He has worked on multiple DApps, projects, and Blockchain Networks. Currently, he is also a professor at George Brown College in Toronto. He is always looking to help, teach and build on the blockchain. You can reach him at: jesse@blockxlabs.com BlockX Labs specializes in building developer tools and solutions for blockchain ecosystems.We aim to sift through the noise to bring some sense and clarity into the Blockchain space. Our accomplishments include AIWA — a wallet and DApp interaction tool for the Aion Network, and Universal Faucet — a test token faucet for Ethereum, Aion, and Tron. Follow Us on Twitter: @BlockXLabs

Non-fungible tokens vs Fungible Tokens: What’s the difference? Part II

By: Jesse Abramowitz & Laura Marissa Cullell Last week, MIT Technology Review released an article titled “ ” authored by which has been making the rounds around blockchain/crypto Twitter. Once hailed as unhackable, blockchains are now getting hacked Mike Orcutt We wanted to provide a response because we felt that it lacked context and a proper explanation to what a hack actually means and what it entails. blockchain 1. “Blockchains are particularly attractive to thieves because fraudulent transactions can’t be reversed as they can be in the traditional financial system”. This is one actual inaccuracy in the article: Ethereum Classic itself was created because of a fraudulent transaction that was reversed which happened on the . A lot of money that was locked up in the DAO (a Decentralized Autonomous Organization) that relied on smart contracts to govern its decisions was hacked. After long debate amongst the community, the chain was forked, the hacked transaction was rewritten, and the Ethereum chain split in two. One chain where the DAO hack never happened and one chain (Ethereum Classic) which it did. Ethereum chain The 51% attack that was described earlier in the article involves rewriting transactions on a blockchain. This is when a rogue party can gain a majority of the compute power of the network and rewrite past transactions. The crucial point here is as follows: 51% attacks rely solely on a blockchain’s hash strength. Ethereum Classic’s hash rate is under hashes per second, in comparison Bitcoin’s hash rate is closer to which is around 4747007 factors larger. 9 trillion 4.27e19 That would be like me saying “Oh nice! I ran a 5km today, that means I can run to the moon and back no problem.” Although Orcutt does mention this later in the article we wanted to point out how large the chasm is between the two blockchains. It is also important to note that blockchains seem to be moving away from the proof-of-work model that creates this vulnerability to a proof-of-stake model which gives this type of attack (mass coordination between large parts of the network) a different paradigm. To learn more about 51% attacks we’ve covered it in detail in this . article 2. “Hacking” a blockchain First, it is important to note: Hacking an is like a bank robber robbing a bank. application on the Blockchain Hacking the would be like a foreign government destabilizing another government’s monetary system. Blockchain itself The differences are night and day. If a bank robber robs my bank then the money in my pocket and my neighbour’s pocket is still worth the same. Nobody is going to say “Wow the monetary system of our country sucks!”. However, if a foreign entity destabilizes our currency then everyone trading in that currency is affected. The recent successful known hack to a blockchain which Orcutt brings up in his article is a poor example. Mr. Orcutt mainly talks about hacking applications on the blockchain and fails to separate the two accurately. This really comes down to the crux of the bad taste left in my mouth by this article. When talking to people who aren’t experienced in blockchain it is important to separate the idea of hacking a blockchain and hacking an application built on the blockchain. 3. Bugs in the Core Software Orcutt does bring up two examples of bugs in core software problems: “Earlier this month, the company in charge of Zcash — a cryptocurrency that uses extremely complicated math to let users transact in private — revealed that it had secretly fixed a “ ” accidentally baked into the protocol.” subtle cryptographic flaw and this one: “In September, developers of Bitcoin’s main client, called Bitcoin Core, had to scramble to fix a bug ( ) that could have let attackers mint more bitcoins than the system is supposed to allow.” also in secret Each of these software bugs were fixed and not exploited. These fixes may be considered a strength not a weakness due to the open nature of bitcoin and cryptocurrencies as a whole means that all day everyday the code is under review by both nefarious and genuine people from every corner of the world with different perspectives. In the end we believe that will make for a strong system. Conclusion We want to stress that nothing was factually incorrect in this article. However, it did feel it to be misleading. Although Orcutt brings up the points we’ve made in this article, they seem more of an afterthought leading to a very unbalanced article. Blockchain hacks may make for a great article title but are not necessarily accurate, genuine or cover the entire story. — - Jesse Abramowitz is a Blockchain Developer at BlockX Labs. He has worked on multiple DApps , projects, and Blockchain Networks. Currently, he is also a professor at George Brown College in Toronto. He is always looking to help, teach and build on the blockchain. You can reach him at: jesse@blockxlabs.com specializes in building developer tools and solutions for blockchain ecosystems.We aim to sift through the noise to bring some sense and clarity into the Blockchain space. Our accomplishments include — a wallet and DApp interaction tool for the Aion Network, and — a test token faucet for Ethereum, Aion, and Tron. BlockX Labs AIWA Universal Faucet Follow Us on Twitter: @BlockXLabs

Hacking a Blockchain vs. Hacking a DApp: A Response to Mike Orcutt’s MIT Article

By: Blockchain Developer Jesse Abramowitz In order for a ecosystem to thrive, developers need to be on board to create and foster a community. Accomplishing that feat requires the appropriate tools to handle the job. From creating DApps using Ethereum, Hyperledger or even , a blockchain becomes mainstream only when the proper tools are in place for developers to allow for mass adoption. Blockchain Bitcoin Here at , some of my favourite work is building developer tools for blockchains. We are passionate about tools and are always looking for more to build. I personally joined the company almost 4 months ago. I am what is known as a blockchain native developer as in I am self taught with solidity as a base language. BlockX Labs As a lot of people may know the gold standard for this is Ethereum. The job they have done making it easy for developers is nothing short of a miracle and is the secret-not-so-secret key to their success. We’ve curated the following list that every blockchain developer should know, and blockchains need for their ecosystems to succeed. Take a look at our Top 10 below! 1. Language This can be any language, but the virtual machine needs to be able to read it and I need to be able to write it. It can go the route of blockchains like or and write their own or it can be like and use Javascript. Regardless, it has to be functioning. Ethereum Cardano BUMO An example is from Ethereum and is called . Solidity 2. Documentation and Education This is hands down the most important and sometimes most overlooked part of the equation. This is true for every tool but especially the language. How do I use it? What are the caveats? Is it an existing language? If so how is it different (for example can’t use non-deterministic code)? The more details the better. Documentation is key to allowing a Blockchain ecosystem to thrive because it allows developers everything they need to know about a blockchain. 3. Compiler Whatever virtual machine the specific blockchain uses it is not actually running the language that we are writing in. The code needs to be compiled down to bytecode then the virtual machine can act on it, so we need a tool to do that, which brings me to the next one. 4. Integrated Developer Environment for Ethereum is a very powerful tool. It is great for writing code and finding errors. It is also a great testing environment. Now technically you do not need to launch a whole browser based IDE like remix, instead you can create a plug in for existing IDE’s like VS code or Atom. However, I can attest to consistently using Remix for building and testing even though I have moved on to using for live deployments. Remix Truffle 5. Node as an API service To understand why this is needed, you have to understand some fundamental truths about the blockchain. One is that you can’t just connect and interact to this network. A blockchain (like the ones we are talking about) is a global state machine, so how can you read or interacting with it without knowing the state? You can’t. So how do you get the state?, That’s easy, you just run a node and sync it up on your laptop. Is that hard? Well lets just put it like this: To do this you need to start at the genesis block and run through every transaction locally that has ever happened on the blockchain. So, yes, very. Also do you want to turn off your laptop and go to bed? No problem, you just need to re-sync every new block the next day and dedicate a lot of laptop resources to it. This is ridiculous and very few developers would actually do this, so what we need is an endpoint. Someone who hosts their node over web sockets and allows you to access it to make calls and send transactions. For Ethereum the main endpoint service is called . Infura 6. Block Explorer How am I going to read the blockchain? It is a lot of code. For example, to figure out a list of transactions from an account you would have to iterate through the whole blockchain and then search for all instances of the . A block explorer can log and tag these files for searching to make it easy to access. A block explorer really is a visual representation of the blockchain. It is important both for users and developers. account 7. Browser Extensions Interface (Wallet) This extends to the idea of a new type of internet. One that uses peer-to-peer value transfer protocols as a standard at the very least. Preferably it will be used for more things. Currently DApps like use the blockchain for this exact purpose. One day, our browsers and phones will build in this functionality however; today we need to use a browser extension. Something that allows us to make constant calls to nodes and then send transactions to those nodes. gitcoin Example: AIWA for the Aion Network 8. Front-end to back-end library This one is mainly for developers. What we want are libraries. Things to make our front end easier and more flexible to write. By using Web3 js library I can easily make calls to the node I’m connected to. This makes developing the DApps that will bring value to your blockchain easier. 9. Testnet/Faucet There are many ways to test contracts and dapps. One way is to test it locally. This however lacks the dynamics of a live network and can only be considered as partial testing. You need a network that is free and acts in the exact same way as the mainnet you are going to. The closest we can get to this is testnets. Networks that are voluntary maintained and act in the same way as the mainnet. This also helps with testing forks as you can fork a test net before the mainnet. Example: Ropsten testnet. Although we have a testnet, I still need some of the native token to the blockchain to interact with it. I could always mine it but I want to move fast and reduce points of friction. You need a faucet a DApp that gives out tokens for free so developers can test. Example: **(**Built with ♥ by BlockX Labs) My Faucet 10. Token Standards (at least ERC20, ERC721 equivalents) ICOs dominated the crypto-sphere in 2017 and 2018. However, it i possible to use tokens for a multitude of reasons. Tokens themselves are complicated in terms of the rigorous security standards required. Not every developer can make a totally secure token nor should they have to use one in a larger application. The blockchain is new and hard to fully understand, it is also more permanent than other systems. Having standards that are well tested by a multitude of parties really help reduce security errors, to bring a blockchain into the mainstream and avoid hacks like the batch Overflow. Example: ERC-20 Bonus Couldn’t resist adding just one more! 11. Gas station Each transaction costs gas (for most cryptocurrencies). It is both an awesome feature and a drawback to these networks. It is important for users to be able to calculate how much gas they want to use based on the current state of the network. A nice visualization is beneficial. However, more important there needs to be an API that wallets can call to, to suggest an adequate gas price for users or else there will be a lot of dropped transactions. Example: Ethgasstation Conclusion I believe that these are the critical tools we believe will allow early-stage blockchain ecosystems to thrive. With these tools, developers will be able to experiment with different blockchains, develop DApps, and more. Are there any you think we missed? Let us know in the comments below! Jesse Abramowitz is a Blockchain Developer at BlockX Labs. He has worked on multiple DApps, projects, and Blockchain Networks. Currently, he is also a lab assistant at a local college and is always looking to help, teach and build on the blockchain. You can reach him at: jesse@blockxlabs.com

Top 10 Developer Tools for Blockchain Ecosystems

Having the most accurate and useful machine learning is not about having the best algorithms or the biggest data set. That’s so 2017! Artificial intelligence and machine learning are fantastic new tools, and I’ve personally seen them used to solve a myriad of use cases and generate a lot of productivity for companies big and small. It enables things that weren’t previously possible like , , and . tagging faces in millions photos filtering out spam from your inbox making vast swaths of media content more searchable But if you ask me to give you the most accurate machine learning model for your particular use case, I’m going to cheat. I have aces up my machine learning sleeve Allow me to explain. Previously, the explosion of interest in machine learning has been partly due to the prevalence, availability, and ease-of-use of API services that connect users’ data with pre-trained models. Upload a photo of a celebrity, get back a tag of that celebrities name — simple! These tools worked for some generic use cases, and also made for a great demonstration of the capabilities of machine learning, but this is only the very beginning of what is possible. As some of you are finding out the hard way, giant, pre-trained machine learning models underperform on some use cases. The reason is that these big models weren’t trained on your use case, they were trained on generic datasets in an attempt to server as many different kinds of use cases as possible. You can throw a lot of different problems at these models, but at the cost of a higher degree of accuracy. So what I do to improve the accuracy is cheat. I train a model on YOUR dataset, not some generic dataset I’ve gathered independent of your use case. How can I do that too? Machine learning is becoming ever more portable. With tools like , one can start orchestrating thousands of machine learning models in their stack pretty easily. When you get to this kind of scale, you can start to spin up models for specific use cases on the fly, train them live, and then spin them down when they’re no longer needed or relevant. Machine Box This puts the spotlight on the dataset and the training. My goal is to give you as many tools as possible to turn your existing data into a training dataset, use it to train a model, deploy the model, validate it, and then let you improve it live (without having to redeploy). This is where the best accuracy comes from. Let me give you an example; Here, I’ve used the platform to upload a photo of Bill Murray and Dan Ackroyd to use for face recognition training. Veritone Using Library within the aiWARE platform When I then run the trained recognition against a video clip from Ghostbusters, I end up with some of the faces not being recognized. Instead of trying to find a better algorithm, I just take a screenshot FROM that clip, add it to the Veritone library for that person, and re-run the engine. Taking a screenshot with COMMAND-SHIFT-4 on a Mac Notice that there are fewer unknown faces now as they’ve been correctly associated with the people they represent. You are not going to find better training data for an algorithm than your own data. Before and , unlocking the training potential of one’s own data was extremely difficult. But these tools have the amount of training data you need, and they also solve deployment, scale, portability, and security so you can focus on making the best model for your use case. Machine Box Veritone special technology to minimize What about errors? Your first model won’t be perfect, in fact, no machine learning will be correct 100% of the time. What you do with the examples where it got the answer wrong is where you can differentiate your implementation of machine learning. At , we encourage developers to build a feedback workflow back into the engine (the API has wonderful endpoints for this) to correct mistakes that come up. Does it say Dan Ackroyd when it should be Bill Murray? Snap the offending frame, label it and teach it to . You’ll be astonished at just how much this will improve the model. Machine Box Facebox Try it Don’t just take my word for it, go and download the tools now, or to try it on the platform to see for yourself. It’s really easy to do, all you need is a problem to solve. Machine Box sign up for Veritone

To win at AI you have to cheat

At Machine Box, we’ve always been about empowering you to build amazing things with simple machine learning tools. One important aspect of this approach is ownership. When you use Machine Box, you get complete control of all of your data, and nowhere is this more important than in surveillance and face recognition. Back in May of 2018, of our minds. this came to the forefront [T]he American Civil Liberties Union led a group of more than two dozen civil rights organizations that asked Amazon to stop selling its image recognition system, called Rekognition, to law enforcement. At the time, I remember trying to wrap my head around the implications of this backlash for face recognition in general. On the one hand, I am a strong advocate for personal privacy, not just from government organizations but from large companies attempting to track my every thought. On other hand, I’ve been the victim of a crime that could have either been prevented just by the presence of surveillance cameras — or at least the resulting video could have helped law enforcement identify him later. So just like everyone else, I have to grapple with the age-old question of security vs. liberty. The reason I bring this up is because the company that acquired Machine Box two new applications that can use to recognize known offenders in law enforcement video. recently announced Facebox A natural extension to the aiWARE application suite, agencies are now empowered to not only intelligently search to find pertinent evidence but identify suspects and redact sensitive materials within that evidence prior to distribution. I’ve also been a strong advocate for the use of video in law enforcement, as it can be a superior arbiter of truth. But capturing all of that video won’t help much if law enforcement agencies have to sift through it manually. Furthermore, compliance with evidentiary laws and other considerations become even more difficult without the assistance of AI-powered capabilities to make sense of what video has been captured. There’s simply too much video. We want video, but we also have to draw a line between helping law enforcement keep us safe and enabling Big Brother. This is why I prefer these applications use Facebox. Facebox is a Docker container that can run in either a private or public cloud environment, where the ingress and egress of data is 100% controllable by the application. In either deployment option, there’s no open-ended cloud endpoint into which face data simply disappears. Instead, you as the developer are in 100% control of your data — from training data, state files, to any subsequent data you present to Facebox. You’ll note that in the press release it mentions the fact that the only faces that are used to train the face recognition are those of existing offenders or persons of interest provided by the specific law enforcement agency using the app. Because runs in such a controlled manner, all of the training data never leaves the chain of custody of the law enforcement organization. It is treated like a fingerprint found at the scene of a crime. Facebox This is congruent with our mission of being able to let organizations keep their data private and within their control. Additionally, Facebox enables the face data and all the biometric properties therein to be deleted or forgotten. Other face recognition services might not allow this, either because they want to maintain that data forever due to their business goals, or because they technically don’t have those capabilities. For example, in it states: Google Vision’s API terms of service b. Submission of ContentSome of our APIs allow the submission of content. Google does not acquire any ownership of any intellectual property rights in the content that you submit to our APIs through your API Client, except as expressly provided in the Terms. For the sole purpose of enabling Google to provide, secure, and improve the APIs (and the related service(s)) and only in accordance with the applicable Google privacy policies, Before you submit content to our APIs through your API Client, you will ensure that you have the necessary rights (including the necessary rights from your end users) to grant us the license. you give Google a perpetual, irrevocable, worldwide, sublicensable, royalty-free, and non-exclusive license to Use content submitted, posted, or displayed to or from the APIs through your API Client. “Use” means use, host, store, modify, communicate, and publish. (emphasis mine) it offers us the ability to expand not just the reach of Machine Box, but the democratization of the power of these tools. Part of that step is to address these use cases head on, and to do our best to uphold our values. We joined Veritone because

How we approach face recognition and law enforcement as ethically as we can

By: Kush PatelChief Technical Officer This week we would like to showcase a series of guest posts from fellow Co-Founder and resident CTO here at . BlockX Labs of this Guest Series included an in-depth explanation of the Maker and Taker concept and the distrust that happens in Decentralized exchanges. In this second installment, we will explore available solutions and limitations of distrust by 0x protocol, Waves, and EtherDelta. Part I What are 0x protocol, EtherDelta, and Waves? Firstly, all three represent examples of Decentralized Exchanges. For those that are unfamiliar, and are decentralized exchanges that allow users to trade different types of Ethereum-based tokens directly. 0x protocol EtherDelta is a blockchain platform used to create custom application tokens (aka CATs), but also serves as a decentralized exchange in which users can trade newly created coin in a trading pair with any other Waves token. Waves Although these examples are normally presented as decentralized, many of them do not actually meet the thresholds of ‘true decentralization’. Normally, 0x protocols are typically kept on a centralized book, while settlement is on a decentralized ledger. How do 0x and EtherDelta Work? In the first article of this series, we explained the concept of Makers and Takers. Most decentralized exchanges such as 0x protocol, EtherDelta have a lifecycle like the image below: In this example, Mary Maker sends the order, which then enters on the exchange running on 0x APIs. The order then resides in a central book hosted by each exchange. The order then shows up on a screen to be taken by Terry Taker and filled. One of the only pieces in this model that is decentralized is the Execution of the Smart Contract on both sides. In this instance, the settlement happens right on a distributed ledger (in this case it would be Ethereum). The order is then completed by Buyer and Seller account directly. This is the decentralized settlement. In EtherDelta, the order is similar to 0x with a few exceptions. The Order is firstly submitted to decentralized Contract. Terry Taker sees the order by reading the Contract and selects an order to fill. Trades are settled directly between Mary and Terry by their accounts. The central book is decentralized as is settlement. What the Advantages of these Decentralized Exchanges? Not everything is doom and gloom for decentralized exchanges. Anybody can trade, there is no central exchange, and settlement occurs directly between the participants themselves. According to ., having a single Decentralized Exchange hold the assets for all point-to-point and broadcast orders and allowing multiple Relayers will likely lead to a more liquid market. They further believe that competition among Relayers may lower the fees that user pay. Iddo Bentov, et al EtherDelta has the added benefit of reputation as it was created by well-respected Ethereum developers. The settlement is decentralized, and liquidity in this exchange is central. What are some disadvantages? Iddo Bentov and co’s article expertly outlines some of the disadvantages and flaws of decentralized exchanges some of which include: exposure to arbitrage, vulnerability to miner frontrunning, exposure to exchange abuses, etc. on the subject is highly recommended. Their article We would like to add that in both 0x and EtherDelta, the Central book still exists in each exchange. The overall liquidity is thus diluted across the exchanges as well. According to , to prevent scaling issues, the Waves platform uses a two-tier architecture with both lightweight and full nodes maintaining the network. Waves has an orderbook like the EtherDelta and 0x. In order to post an order, access to the Waves nodes are required while are responsible for pairing orders and executing trades quickly, whilst they are still settled on the blockchain Coincentral Matcher nodes Disadvantages: The Central book still exists in each of the exchange. Furthermore, in both exchanges, the Overall liquidity is diluted. Both also only accept Ethereum, which vastly decreases the access to other cryptocurrency. Conclusion Although there are downsides to centralized exchanges, it is important to recognize the limitations each exchange provides. There are a lot of unanswered questions concerning governance, and the negative implications regarding the current limitations. Decentralized exchanges such as 0x, EtherDelta, and Waves, have made an impact in the blockchain space, but ensuring adequate performance, access to other currencies and ensuring security, is still yet to be seen. Do you think there are better Decentralized Exchanges than 0x, EtherDelta and Waves? Let us know in the comments below! Kush Patel has been a consultant for some of Canada’s top banks. His experience spans from developing financial products to scaling complex technical architectures. Kush has also built and maintained Exchanges which transferred approximately $1bn worth of assets on a daily basis. Nowadays, he is focused on working with our clients to build developer tools, DEXs, and innovative blockchain products. specializes in building developer tools and solutions for blockchain ecosystems.We aim to sift through the noise to bring some sense and clarity into the Blockchain space. Our accomplishments include — a wallet and DApp interaction tool for the Aion Network, and — a test token faucet for Ethereum, Aion, and Tron. BlockX Labs AIWA Universal Faucet Follow Us on Twitter: @BlockXLabs

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