Trade Jobs

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Customer Behavior: Change From Freelancer Hiring to AI and LLMs The emergence of large language models and AI in general, such as ChatGPT, is redefining how business is done in several ways. The gig economy is experiencing this massive shift in consumer behavior seen on platforms such as Fiverr, which initially connected clients to freelancers to offer their range of services. Yet, a breadth of users nowadays prefer AI-driven solutions to human freelancers because of several compelling factors. This article delves into the motivations and implications behind this pronounced behavioral shift. Cost Efficiency Among the AI and LLMs, the greatest advantage ought to be cost efficiency. For example, hiring a freelance writer on Fiverr costs a variable amount depending on the complexity and level of expertise of the task. In other ways, solutions grounded in AI always tend to be more cost-effective. For example, a lot of graphic design or content creation tools, though backed by AI, usually bring out good results and that's at a fraction of what their human freelancers would charge for the same work. This is further enticing to low-budget-restricted small businesses and individual owners. Speed and Ease of Access With machine learning, the execution speed regarding the outcome of LLM is unrivaled. For example, it might take a freelancer a couple of days, sometimes even weeks, to get a project delivered, but AI can churn out results almost on the spur of the moment. This will be a valuable asset for your business, which surely works at a clip. In addition, since AI tools operate 24/7, there is no need to adjust your time based on a freelancer in a different time zone or one who is not available because of their schedule. Quality and Consistency Consistency in output is also a major factor driving the shift towards solutions using AI. Humans, whether skilled or not, can have off days or produce variably in quality. AI, on the other hand, delivers consistent results based on its programming and the data it was trained on. In such tasks where high accuracy and uniformity are important, such as data analysis or transcription, AI technologies provide an alternative to human labor. In particular, advanced LLMs like GPT-4 are capable of maintaining a standard quality, and this further raises the level at which users are encouraged to go for such technologies. Automating Routine Job Activities AI does handle a lot of routine and repetitive nature of jobs, mostly oppressive and time-consuming tasks given to human freelancers. Activities such as data entry, basic customer service interaction, and scheduling can be handled effectively by AI, thus leaving human resources free for more difficult and creative work. ore productivity and less negativity would ensue if correctly done, thus diminishing the risk of human error in those repetitive processes. Enhanced Capabilities and Features Artificial Intelligence and LLMs can lead the way by possessing the most leading-edge functionalities, at times, even surpassing human skills. For example, design tools will leverage AI and bring in a large repository of design templates, providing automated suggestions, while content generation models create well-structured and engaging text based on minimal inputs provided by the writer. These shield users from untalented freelancers while supporting them to acquire sophisticated professional tools; in fact, this surpasses anything an inexperienced freelancer might deliver. Case Studies and Illustrations Several real-world examples exemplify the shifting customer behavior. Most of them today are using artificial intelligence-driven platforms like Midjourney and DALL-E to meet the needs of the many graphics that were in the past very common on Fiverr. Other services have become a favorite—due to particularly quick, accurate outputs that obviate the human input needed for them—like Otter for transcription services. Freelance writers and content creators also use tools like LLMs for coming up with ideas and drafting, and this cuts the time and costs of hiring human freelancers. Implications for the Freelance Market This has tremendous, large-scale implications for the work profile in the freelance market. As demand for AI solutions and services continues to rise, there might be a relative decline in the demand for human freelancers on platforms such as Fiverr, mainly for routine tasks that are easily automated. This would make human service providers up their games and only engage where creativity and critical thinking are indispensable, such as problem-solving, strategic consulting, and highly customized creative work. Moreover, these AI tools will enable freelancers to serve better on their own, meaning they can move toward a hybrid model of servicing, which leverages both human expertise and AI efficiencies. This kind of adaptability will become essential for freelancers to keep being competitive in an evolving market. Ethical and Practical Issues Even though AI and Legal Machine Learning have many advantages, several ethical issues have to be brought to the forefront and practically considered, from data privacy to algorithmic bias which could include the loss of human jobs. Transparency and fairness in carrying out business and individual activities with the deployed AI solutions are characterized by efficiency and ethical responsibility. In addition, while AI is able to process many tasks of high specificity with effectiveness, there yet remain areas where without the intervention of human intuition, emotional intelligence, and creative insight, it is difficult to act. Understanding this limitation and capitalizing on human skills in such a domain is quite important for optimal outcomes. Will Freelancing Soon Be Limited to Physical and Skilled Trade Jobs? With AI and automation fast taking effect, you would be convinced that pretty soon, the notion of freelancing will largely indicate physical task-based work and specialized trades. As AI continues to intrude into the field of digital and creative services, the very essence of freelance work may observe a situation quite soon, whereby only labor related to a physical feature would be associated with freelancing. Examples could be rideshare driving, food delivery, home repairs, and skilled trades like plumbing and electrical work, all of which remain areas where automation has not significantly disrupted work. This sort of competitive pressure will displace the current makeup of jobs comprising the freelance world. This possible evolution brings the question about the nature of the gig economy which might be different in the future and accordingly changes the nature of freelancing to include only those jobs that AI cannot easily do. Conclusion This behavior change is powered by the concrete benefits such AI applications offer: speed, consistency, and advanced capabilities. On the other hand, it also poses challenges and raises ethical concerns which need to be balanced and therefore, mindfully considered to permit the integration of AI into the bicycle of the gig economy. When businesses and freelancers operate in this emerging scenario, adaptability and the ability to leverage the complementary strengths of each other will be important in making the most of the forthcoming AI age. Customer Behavior: Change From Freelancer Hiring to AI and LLMs Customer Behavior: Change From Freelancer Hiring to AI and LLMs The emergence of large language models and AI in general, such as ChatGPT, is redefining how business is done in several ways. The gig economy is experiencing this massive shift in consumer behavior seen on platforms such as Fiverr, which initially connected clients to freelancers to offer their range of services. Yet, a breadth of users nowadays prefer AI-driven solutions to human freelancers because of several compelling factors. This article delves into the motivations and implications behind this pronounced behavioral shift. Cost Efficiency Among the AI and LLMs, the greatest advantage ought to be cost efficiency. For example, hiring a freelance writer on Fiverr costs a variable amount depending on the complexity and level of expertise of the task. In other ways, solutions grounded in AI always tend to be more cost-effective. For example, a lot of graphic design or content creation tools, though backed by AI, usually bring out good results and that's at a fraction of what their human freelancers would charge for the same work. This is further enticing to low-budget-restricted small businesses and individual owners. Speed and Ease of Access With machine learning, the execution speed regarding the outcome of LLM is unrivaled. For example, it might take a freelancer a couple of days, sometimes even weeks, to get a project delivered, but AI can churn out results almost on the spur of the moment. This will be a valuable asset for your business, which surely works at a clip. In addition, since AI tools operate 24/7, there is no need to adjust your time based on a freelancer in a different time zone or one who is not available because of their schedule. Quality and Consistency Consistency in output is also a major factor driving the shift towards solutions using AI. Humans, whether skilled or not, can have off days or produce variably in quality. AI, on the other hand, delivers consistent results based on its programming and the data it was trained on. In such tasks where high accuracy and uniformity are important, such as data analysis or transcription, AI technologies provide an alternative to human labor. In particular, advanced LLMs like GPT-4 are capable of maintaining a standard quality, and this further raises the level at which users are encouraged to go for such technologies. Automating Routine Job Activities AI does handle a lot of routine and repetitive nature of jobs, mostly oppressive and time-consuming tasks given to human freelancers. Activities such as data entry, basic customer service interaction, and scheduling can be handled effectively by AI, thus leaving human resources free for more difficult and creative work. ore productivity and less negativity would ensue if correctly done, thus diminishing the risk of human error in those repetitive processes. Enhanced Capabilities and Features Artificial Intelligence and LLMs can lead the way by possessing the most leading-edge functionalities, at times, even surpassing human skills. For example, design tools will leverage AI and bring in a large repository of design templates, providing automated suggestions, while content generation models create well-structured and engaging text based on minimal inputs provided by the writer. These shield users from untalented freelancers while supporting them to acquire sophisticated professional tools; in fact, this surpasses anything an inexperienced freelancer might deliver. Case Studies and Illustrations Several real-world examples exemplify the shifting customer behavior. Most of them today are using artificial intelligence-driven platforms like Midjourney and DALL-E to meet the needs of the many graphics that were in the past very common on Fiverr. Other services have become a favorite—due to particularly quick, accurate outputs that obviate the human input needed for them—like Otter for transcription services. Freelance writers and content creators also use tools like LLMs for coming up with ideas and drafting, and this cuts the time and costs of hiring human freelancers. Implications for the Freelance Market This has tremendous, large-scale implications for the work profile in the freelance market. As demand for AI solutions and services continues to rise, there might be a relative decline in the demand for human freelancers on platforms such as Fiverr, mainly for routine tasks that are easily automated. This would make human service providers up their games and only engage where creativity and critical thinking are indispensable, such as problem-solving, strategic consulting, and highly customized creative work. Moreover, these AI tools will enable freelancers to serve better on their own, meaning they can move toward a hybrid model of servicing, which leverages both human expertise and AI efficiencies. This kind of adaptability will become essential for freelancers to keep being competitive in an evolving market. Ethical and Practical Issues Even though AI and Legal Machine Learning have many advantages, several ethical issues have to be brought to the forefront and practically considered, from data privacy to algorithmic bias which could include the loss of human jobs. Transparency and fairness in carrying out business and individual activities with the deployed AI solutions are characterized by efficiency and ethical responsibility. In addition, while AI is able to process many tasks of high specificity with effectiveness, there yet remain areas where without the intervention of human intuition, emotional intelligence, and creative insight, it is difficult to act. Understanding this limitation and capitalizing on human skills in such a domain is quite important for optimal outcomes. Will Freelancing Soon Be Limited to Physical and Skilled Trade Jobs? With AI and automation fast taking effect, you would be convinced that pretty soon, the notion of freelancing will largely indicate physical task-based work and specialized trades. As AI continues to intrude into the field of digital and creative services, the very essence of freelance work may observe a situation quite soon, whereby only labor related to a physical feature would be associated with freelancing. Examples could be rideshare driving, food delivery, home repairs, and skilled trades like plumbing and electrical work, all of which remain areas where automation has not significantly disrupted work. This sort of competitive pressure will displace the current makeup of jobs comprising the freelance world. This possible evolution brings the question about the nature of the gig economy which might be different in the future and accordingly changes the nature of freelancing to include only those jobs that AI cannot easily do. Conclusion This behavior change is powered by the concrete benefits such AI applications offer: speed, consistency, and advanced capabilities. On the other hand, it also poses challenges and raises ethical concerns which need to be balanced and therefore, mindfully considered to permit the integration of AI into the bicycle of the gig economy. When businesses and freelancers operate in this emerging scenario, adaptability and the ability to leverage the complementary strengths of each other will be important in making the most of the forthcoming AI age.

Navigating AI: Fiverr and the Future of the Gig Economy

The Comeback of IDOs and Launchpads in Web3: A 2024 Perspective

Bitfinex, , has more than 180 open-source repositories in GitHub–from a to . The most sophisticated trading toolkit developed by Bitfinex, though, is Honey Framework with many useful packages such as , , , and . one of the largest cryptocurrency digital exchanges Tezos JS library UI goodies bfx-hf-ui bfx-hf-strategy bfx-hf-indicators bfx-hf-strategy-exec In this tutorial, we'll use the Honey Framework ecosystem to define and backtest a trend trading strategy on historic data for the past 500 days. Requirements Let's install the libraries we're going to use. First, run the following command to install Honey Framework libraries. npm install bfx-hf-strategy bfx-api- bfx-hf-util bfx-hf-indicators node -models We'll also need the following packages to display the trades on the command line. npm install cli- colors table Eventually, install library as well to retrieve the data from Bitfinex API. node-fetch npm install node -fetch Strategy We use a simple trend trading strategy based on the following logic: position if the price is between -1 SD and -2 SD Bollinger Bands® and short SMA is below the long SMA. Open short position if the candle is green and more than 75% of its body crossed above the -1 SD band. Close short position if the price is between +1 SD and +2 SD bands and the short simple moving average is above the long simple moving average. Open long position if the candle is red and more than 75% of its body crossed below +1 SD band. Close long The logic behind the strategy is fairly straightforward: if the price is between  -1 SD and +1 SD bands (No Man's Land), we close our position. If the price is in the sell-zone (between the lower bands) and the market trend is bearish (short SMA is below long SMA), we open a short position. On the other hand, if the price is between upper bands and the market is bullish, we open a long position (in other words, we only trade in direction of the overall trend). Check to find more information about trend trading based on Bollinger Bands®. this article from Investopedia Code Let's get our hands dirty. First, create that defines a dummy strategy. strategy.js { SYMBOLS, TIME_FRAMES } = ( ); { BollingerBands, SMA } = ( ); HFS = ( ); .exports = HFS.define({ : , : ,
    symbol,
    tf, : { : BollingerBands([ , ]), : BollingerBands([ , ]), : SMA([ ]), : SMA([ ]),
    },
  }); // strategy.js const require "bfx-hf-util" const require "bfx-hf-indicators" const require "bfx-hf-strategy" module ( ) => { symbol = SYMBOLS.BTC_USD, tf = TIME_FRAMES.ONE_DAY } id "bbands" name "bbands" indicators bbands2 new 20 2 bbands1 new 20 1 smaShort new 25 smaLong new 100 Let's break it down. We define the default symbol (BTCUSD) and timeframe (one day). Then, we define the indicators we're going to use. We need two Bollinger Band® bands, both in the period of 20 days. We give a multiplier of 2 to the first (±2 standard deviations) and a multiplier of 1 to the second one  (±1 standard deviations). In the end, we add two SMAs, one in a 25-day period (short SMA) and the other in a 100-day period (long SMA). to find a list of all indicators available in Bitfinex Honey Framework. Check this We can enter the market now. The strategy we defined accepts five handler methods to update positions and make trades on each event: - called when no position is open onEnter - called when a long position is open onUpdateLong - called when a short position is open onUpdateShort - called when any position is open onUpdate - called on every tick onPriceUpdate Event handlers receive two parameters, (the strategy context) and (point-in-time data), they look for trade opportunities and update if there's a trade to be made. state update state Enter Into the Market Let's define the strategy to enter the market. First, add to encapsulate the reusable logic. Define in it to extract Bollinger Band® bands values from the state. helper.js getBBands HFS = ( ); getBBands = { indicatorValues = HFS.indicatorValues(state); { bbands1, bbands2 } = indicatorValues; { : plusOne, : minusOne } = bbands1; { : plusTwo, : minusTwo } = bbands2; {
    plusOne,
    plusTwo,
    minusOne,
    minusTwo,
  };
}; .exports = {
  getBBands
} // helpers.js const require "bfx-hf-strategy" const ( ) => state const const const top bottom const top bottom return module returns a map of all indicators defined in the strategy. We then extract the Bollinger Bands objects from them, which include three values: top, bottom, and middle. Eventually, the method returns ±1 and ±2 SD bands. indicatorValues Define another method to extract uptrend/downtrend data from the state. checkIfIsUptrend = { indicatorValues = HFS.indicatorValues(state); { smaShort, smaLong } = indicatorValues; isUpTrend = smaShort > smaLong; isUpTrend;
}; .exports = {
  getBBands,
  checkIfIsUptrend,
} // helpers.js /*
getBBands here
*/ const ( ) => state const const const return module The method simply checks if short SMA is above long SMA and infers the trend from it. Add . We'll use it to find trade opportunities. look_for_trade.js HFS = ( ); { getBBands, checkIfIsUptrend } = ( ); .exports = (state = {}, update = {}) => { { price, : timestamp } = update; orderParams = { : timestamp, : ,
    price,
  }; bands = getBBands(state); isBuyZone = bands.plusTwo >= price && price >= bands.plusOne; isSellZone = bands.minusTwo <= price && price <= bands.minusOne; isUptrend = checkIfIsUptrend(state); (isSellZone && !isUptrend) { HFS.openShortPositionMarket(state, orderParams);
  } (isBuyZone && isUptrend) { HFS.openLongPositionMarket(state, orderParams);
  } state;
}; // look_for_trade.js const require "bfx-hf-strategy" const require "./helpers" module async const mts const mtsCreate amount 1 const // between +1 SD and +2 SD from mean, the trend is up const // between -1 SD and -2 SD from mean, the trend is down const const if return if return return The method checks if the price is in the trade zone (between +1 and +2 SD or -1 SD and -2 SD bands). If the price is in the sell zone and the trend is bullish, it opens a short position, and if the price is in the sell zone and the trend is bearish, it opens a long position. If none is true, it simply returns the previous state. Notice that we use Honey Framework helper functions to open positions. You can find the list of all helper functions . Add to use the function we just defined to handle onEnter events. here on_enter.js lookForTrade = ( ); .exports = (state = {}, update = {}) => {
  newState = lookForTrade(state, update); newState;
}; // on_enter.js const require "./look_for_trade" module async await return Now add the handler to the strategy. on_enter = ( ); .exports = HFS.define({ onEnter: on_enter,
  }); // strategy.js /* other imports */ //  handlers const require "./on_enter" module ( ) => { symbol = SYMBOLS.BTC_USD, tf = TIME_FRAMES.ONE_DAY } /*
    other strategy properties
    */ Update Positions Well done! We've just exposed ourselves to the market. But we should add other handlers to react to market updates while we have an open position. Define another method in to check if 75% or more of the candle body crossed the inner bands. helpers.js HFS = ( ); checkIfShouldClose = { candleBody = .abs(close - open); closeToNoMansLandDist =
    position == ? close - bands.minusOne : bands.plusOne - close; shouldClose = closeToNoMansLandDist / candleBody >= ; shouldClose;
}; .exports = {
  getBBands,
  checkIfIsUptrend,
  checkIfShouldClose,
}; // helpers.js const require "bfx-hf-strategy" /*
 Other methods
*/ const ( ) => close, open, bands, position const Math const "short" // check if 75% of candle body entered to the No Man's Land. // see: https://www.investopedia.com/thmb/AtmrjGx96Tbz8f1cfnGOGzpqHqU=/2378x0/filters:no_upscale():max_bytes(150000):strip_icc():format(webp)/dotdash_Final_Using_Bollinger_Bands_to_Gauge_Trends_Oct_2020-02-f76c639116734ccfb8493dce32ed149a.jpg const 0.75 return module calculates the candle body height by finding the distance between open and close values. Then, it checks how much of the candle entered the No Man's Land. If more than 75% of the candle body is in between two inner bands, it closes the position. checkIfShouldClose It's time to create . on_long.js HFS = ( ); lookForTrade = ( ); { getBBands, checkIfShouldClose } = ( ); .exports = (state = {}, update = {}) => { { candle, : timestamp, price } = update; { open, close } = candle; orderParams = { : timestamp, : ,
    price,
  }; bands = getBBands(state); isCandleGreen = open <= close; (isCandleGreen) state; isCloseAbove1Std = bands.plusOne < close; (isCloseAbove1Std) state; shouldClose = checkIfShouldClose(close, open, bands, ); (!shouldClose) state; newState = HFS.closePositionMarket(state, orderParams); newState = lookForTrade(newState, update); newState;
}; // on_long.js const require "bfx-hf-strategy" const require "./look_for_trade" const require "./helpers" module async const mts const const mtsCreate amount 1 const const if return const if return //  check if 75% of candle body is below 1 std const "long" if return let await //  look if should open short await return The method we just defined checks if: The candle is green. The candle is above the No Man's Land (i.e., the closing value is above the +1 SD band). Less than 75% of the candle is in the No Man's Land. And if any of these are correct, it doesn't touch the position. Otherwise, it closes the long position using helper method and then calls to check if a short position should be opened. Now, let's define our strategy while shorting. closePositionMarket lookForTrade HFS = ( ); lookForTrade = ( ); { getBBands, checkIfShouldClose } = ( ); .exports = (state = {}, update = {}) => { { candle, : timestamp, price } = update; { open, close } = candle; orderParams = { : timestamp, : ,
    price,
  }; bands = getBBands(state); isCandleRed = open >= close; (isCandleRed) state; isCloseBelowStd = bands.minusOne > close; (isCloseBelowStd) state; shouldClose = checkIfShouldClose(close, open, bands, ); (!shouldClose) state; newState = HFS.closePositionMarket(state, orderParams); newState = lookForTrade(newState, update); newState;
}; // on_short.js const require "bfx-hf-strategy" const require "./look_for_trade" const require "./helpers" module async const mts const const mtsCreate amount 1 const const if return const if return //  check if 75% of candle body is below 1 std const "short" if return let await //  look if should open long await return checks if: on_short.js The candle is red. The candle is below the No Man's Land (i.e., the closing value is below the -1 SD band). Less than 75% of the candle is in the No Man's Land. And if all above are false, it closes the short position and looks for new trade opportunities. That's it. We've finished defining the strategy. Add the new event handlers to . Then we can start testing the algorithm. strategy.js on_enter = ( ); on_long = ( ); on_short = ( ); .exports = HFS.define({ onEnter: on_enter, : on_long, : on_short,
  }); // strategy.js /* other imports */ //  handlers const require "./on_enter" const require "./on_long" const require "./on_short" module ( ) => { symbol = SYMBOLS.BTC_USD, tf = TIME_FRAMES.ONE_DAY } /* 
      Other strategy properties
    */ onUpdateLong onUpdateShort Test on Historical Data Add to retrieve historic data from Bitfinex API. get_data.js ; fetch = ( ); url = ; get_500_days = { date = (); end = date.getTime();

  date.setDate(date.getDate() - ); start = date.getTime(); { start, end };
}; .exports = { { { start, end } = get_500_days(); pathParams = ; queryParams = ; req = fetch( ); response = req.json(); response;
  } (err) { .log(err);
  }
}; // get_data.js "use strict" const require "node-fetch" const "https://api-pub.bitfinex.com/v2/" const => () let new Date const 500 const return module async ( ) function getData { symbol, tf } try const const `candles/trade: : /hist` ${tf} ${symbol} const `start= &end= &limit=500` ${start} ${end} const await ` / ? ` ${url} ${pathParams} ${queryParams} const await return catch console fetches OHLC data of the past 500 days, given a symbol and a timeframe.  You can modify the parameters to meet your needs. Find more information about Bitfinex API's public endpoint for candle data . getData here Add to define the execution logic. exec.js process.env.DEBUG = ; { Candle } = ( ); { SYMBOLS, TIME_FRAMES } = ( ); logTrades = ( ); HFS = ( ); getData = ( ); BBandsStrategy = ( ); market = { : SYMBOLS.BTC_USD, : TIME_FRAMES.ONE_DAY,
}; getCandles = () => { rawCandleData = getData(market); candles = rawCandleData
    .sort( a[ ] - b[ ])
    .map( ({
      ...new Candle(candle).toJS(),
      ...market,
    })); candles;
}; run = () => { candles = getCandles(); strategyState = BBandsStrategy(market); ( i = ; i < candles.length; i += ) {
    strategyState = HFS.onCandle(strategyState, candles[i]);
  }

  logTrades(strategyState);
}; {
  run();
} (e) { .error(e);
} // exec.js "*" const require "bfx-api-node-models" const require "bfx-hf-util" const require "bfx-hf-strategy/lib/debug/log_trades" const require "bfx-hf-strategy" const require "./get_data" const require "./strategy" const symbol tf const async const await // attach market data const ( ) => a, b 0 0 ( ) => candle return const async const await let for let 0 1 await try catch console method retrieves the data and attaches market information to them. During live execution, we may receive data from multiple sources for different symbols and different timeframes, thus, it's necessary to attach this information to raw data. getCandles Then, gets candle data and the strategy and passes them to helper method that orchestrates relevant strategy event handlers for the new price action data. Eventually, logs the results using function. run onCandle run logTrades Let's test the algorithm. Open the command line in the project's directory and run the following command. node exec The result will be something like this: You can find a list of all positions along with the amount, price, fee, and profit/loss figure of each one. At the bottom, general information about the strategy is logged. The bot has made 37 trades, opened 19 positions, and gained 3960.91$. You can modify and change how it displays the results. helper function log_trades.js That's it! We've defined and backtested a simple trading strategy using Bitfinex Honey Framework toolkit. The example pieces of code in the article are available . here

Define and Backtest Crypto Algo Trading Strategies With Bitfinex Honey Framework Toolkit

Put tokenized items for sale in a decentralized classifieds board In this article, I’m going to show you how to code Craigslist for the Ethereum blockchain. Before Gumtree, Ebay and Craigslist, classified boards were mostly made of cork or paper. There were classifieds boards in school corridors, newspapers, streetlights, storefronts. All that changed with the internet. The number of people that could see a specific classified board multiplied by many orders of magnitude. With that, the markets they represent became much more efficient and scaled to global size. Ebay is a massive business which traces its origins to these physical classifieds boards. With blockchain these markets are set to change once more, let me show you how. Monetization The business model of a public blockchain classifieds board will need to be different from that of Ebay and company. First, there is the decentralization angle. Existing platforms need to maintain their own servers. A decentralized platform is maintained by its users, so the cost of running the core platform drops to zero for the platform owner. Then there is the front end, the website or interface that gives access to the platform. Here there are many options. The platform owners can restrict access and force everyone to use their interface, charging a fee. The platform owners can also decide to open access (Power to the People!) and let anyone build interfaces to the platform. Or the owners could decide any approach in the middle of those extremes. Business leaders with more vision than I will know how to monetize this. All I see is that this is different from the status quo and probably profitable. Furthermore, there is the automation and payments angle. Some things can be very and traded in a classifieds board. Tokenized assets are easily transferred in a blockchain. Highly complex payment methods can be easily implemented in a blockchain. effectively tokenized I’m just smelling a business opportunity here. A classifieds board with no running costs can easily be implemented, with complex payment paths included in each transaction. I’m sure someone will come up with an idea about what to use this for. I’m just happy building it. Let’s have a look at the code. Implementation Some time ago we started an with business case example implementations and other goodies, please have a look. open source repository The code for this is there, please use it and abuse it. Just be aware that the code hasn’t been audited and you need to do your own due diligence before letting money go into it. Ethereum Classifieds Board The basics of the board are not complex. All the adverts in the board will be just a struct with a few fields: struct Trade {
  address poster;
  uint256 item;
  uint256 price;
  bytes32 status; } // Open, Executed, Cancelled So there is someone posting the advert. An item for sale. A price for the item. The status of the trade which can be open, executed or cancelled. All these trades will be kept in a mapping. Because everything in Solidity seems to be a mapping. Also because it is convenient. mapping( Trade) public trades; => uint256 Using a mapping just means that we have to come up with an id for each advert before posting it, and we will need to know the id of an advert before we can operate on it. There are multiple ways of dealing with this either in the smart contract or in the front-end. Please ask if you need some pointers. Next comes the question of what are those items we deal with, and what is this currency that is used to pay for the transaction. For the items, we are just going to ask that they implement the interface, which really is just a way of representing real world items in a blockchain, although it . We are going to specify our own ERC721 contract in the constructor, meaning that any assets in our classifieds board need to have been tokenized beforehand. IERC721 works best with digital assets For the payments, we are going to do something similar. Most blockchain projects define their own cryptocurrency. Some others prefer to use a mainstream one like . In this classifieds board, you just need to decide on construction what your currency will be. Easy. ERC20 DAI (
  address _currencyTokenAddress, address _itemTokenAddress
) public {
  currencyToken = IERC20(_currencyTokenAddress);
  itemToken = IERC721(_itemTokenAddress);
  tradeCounter = ;
} constructor 0 We are getting there. We’ve got adverts, items for trade and a currency for payments. To make an advert means to put an item in escrow to show both that you have it and that you haven’t posted it twice, possibly in a different board. The code below does exactly that. Puts the item in escrow, creates the advert, does some housekeeping. {
  itemToken.transferFrom(msg.sender, address( ), _item);
  trades[tradeCounter] = Trade({ : msg.sender, : _item, : _price, : });
  tradeCounter += ;
  emit TradeStatusChange(tradeCounter - , );
} ( ) function openTrade uint256 _item, uint256 _price public this poster item price status "Open" 1 1 "Open" To accept the trade means to choose an advert (trade), pay the price, receive the item. The code below retrieves a trade. Checks it’s available. Pays the item. Retrieves the item. Updates the advert. {
  Trade memory trade = trades[_trade]; (trade.status == , );
  currencyToken.transferFrom(msg.sender, trade.poster, trade.price);
  itemToken.transferFrom(address( ), msg.sender, trade.item);
  trades[_trade].status = ;
  emit TradeStatusChange(_trade, );
} ( ) function executeTrade uint256 _trade public require "Open" "Trade is not Open." this "Executed" "Executed" Finally, we have an option for sellers to back out of a trade before a buyer accepts it. In some models, adverts would instead be live for a period of time before they expire. Your choice, depending on the design of your market. The code is very similar to that used to execute a trade, only that there is no currency changing hands and the item goes back to the advert poster. {
  Trade memory trade = trades[_trade]; (
    msg.sender == trade.poster, ); (trade.status == , );
  itemToken.transferFrom(address( ), trade.poster, trade.item);
  trades[_trade].status = ;
  emit TradeStatusChange(_trade, );
} ( ) function cancelTrade uint256 _trade public require "Trade can be cancelled only by poster." require "Open" "Trade is not Open." this "Cancelled" "Cancelled" That’s it. You made it to the end of the implementation. It is quite surprising how compact some business concepts are when expressed in code, and this is one of those cases. Check the complete contract . in our repo Conclusion Classifieds boards are a common market configuration that scaled massively with the internet, becoming a hugely popular business model with a few monopolistic winners. Classifieds boards also happen to be an easy tool to replicate in a blockchain environment, with very specific features that will make a challenge to the existing giants possible. In this article, I made an attempt to bridge the business reality of a classifieds board business with the technological implementation. This knowledge should help you to create a vision and a roadmap for implementation if you have the right skills. As always, if you are out to build anything fun and would welcome some advice, I’m always happy to help. please drop me a line!

Implementing an ERC721 Market [A How-To Guide]

Disclosure: the ecoysystem that links crypto to traditional finance, has previously sponsored Hacker Noon. INGOT Coin , It is no secret that everybody wants to make their own ICOs, raise funds and provide better and more efficient services. With the revolution of Blockchain it seems that we are headed towards a better, more complete and innovative future. Companies across all industries are pushing hard to be the early bird that eats the worm and achieving their legacy and create history. So why have we not yet gained the momentum we are all waiting for? Don’t you feel like we were supposed to move even faster with all this fund raising and innovative implementations? Can we truly compare this to the revolution of the internet? Masterminds are coming together, creating new projects and ways to venture into the world of Blockchain, Cryptocurrencies & Smart Contracts, so the real question is, how do we gain the true momentum of this technology and use it to better our lives and our future. Same Technology; Different Race The truth is, as humans we compete to always finish first and eventually find out that all the other participants are in a totally different race. It starts with innovation and the intention to improve the industry but surely turns to a competition on who finishes the fastest, and makes the biggest buzz. What if we told you we can change that? What if it were possible to work together, hand in hand while sharing resources and knowledge? There are a few reasons why people are unable to partner up and work together: Ego. People think it’s a weakness to cooperate or even think they are too good and can accomplish everything by themselves. Fear. Market participants are scared of companies and other ICOs they do not know a lot about. There is always the assumption that any ICO could be a scam and would affect you negatively. Competition, in which everyone solely focuses on themselves and any ICO trying to find an ally, believes there are many others that are doing better and can land better partnerships. Collaboration Vs. Competition has collaborated with , which recently completed a successful USD31 million ICO, to address the above issues. INGOT Coin Trade.io Iman Mutlaq, INGOT Coin Founder. “C vs C — I believe in Collaboration, I don’t believe in Competition. Let’s collaborate” Founder of , formed a company with the objective to cultivate other innovative companies and market leading entrepreneurs. These companies and entrepreneurs consist of many reputable organizations including successful and promising upcoming ICOs, experienced advisers, proven visionaries, well-accredited universities and other organizations. Iman Mutlaq INGOT Coin It is good to compete as long as there is efficiency in the competition. Competition becomes harmful when companies are no longer focused on the main goal; how can we improve our services while using Blockchain and other technological advancement to further enhance the services provided to the people. Iman has a great deal of experience in the financial sector. She believes in collaborating and innovating to pioneer the industry What to Expect Next? “We’re very happy to be offering Ingot our consulting and advisory services. We feel that Ms. Iman has done a wonderful job thus far with Ingot, and with trade.io’s assistance we can get her and her firm to even greater heights,” stated Jim Preissler, CEO of trade.io Who said the sky is the limit? INGOT has very strong teams backing them up, not to mention great Advisers that are very experienced. The following don’t even cover half of Ingot’s strong team, Founder Iman Mutlaq @INGOTCoin: ICO Adviser & Co-Founder HalalChain Saad Mohib @INGOTCoin : CEO Ali Shibib @INGOTCoin: ICO Marketing Adviser Sydney Ifergan @INGOTCoin: : CMO Ahmed Khawanky @INGOTCoin ICO Business Adviser Victor Chow @INGOTCoin: ICO Tech Adviser Bogdan Fiedur @INGOTCoin: ICO Adviser John Van der Vos @INGOTCoin: Investment Adviser Darrell Emmanuel @INGOTCoin: ICO Legal Adviser George Mentz @INGOTCoin: Blockchain R&D Adviser Pranav Bhatia @INGOTCoin: Blockchain ICO Adviser Naviin Kapoor @INGOTCoin: PR & Marketing Adviser Warren Whitlock @INGOTCoin: will be advised by to enhance their ICO and leverage their established stakeholders with trade.io’s expert consulting services to maximize value to Ingot’s ICO participants. They both share similar attributes in that they work with respectable universities, quality ICOs and respectable individuals within the blockchain sector. INGOT trade.io “ We are expecting this week to be full of surprises and new recruitment to the group. We have almost finalized with from Bahrain as well as multiple organization that I can not mention, just yet.” H.E Shaikh Ali Al Khalifa Iman Mutlaq Stay tuned to watch us make history!

Assembling the A Team — The Start of a New Era- INGOT Coin Leverages Trade.io

The Financial Token and Trading Platform Image Caption: Before reading this article, please read our full disclaimer found and at the bottom of this page. This Paid Story is brought to you by trade.io here Introduction is a Swiss company that wants to revolutionize the exchange trading system, through a decentralized, traceable, transparent platform, that enables its users to trade through the blockchain. The platform eventually wants to enable users to trade both digital, and physical assets, such as cryptocurrencies, precious metals, oil, and other commodities. Trade.io The team has a long history of working on trading. The advisors of trade.io have founded successful brokerage firms, such as , and Primus Capital Markets UK. The former started 9 years ago, and is a regulated foreign exchange and CFD trading platform with over $180B worth of transactions traded per year. FXPRIMUS At the time of writing, Trade.io is going through their Pre-ICO. The company is looking to raise 200,000M ETH in exchange of 200M TIO (their Trade Tokens), or roughly $100M USD. Unsold tokens from the Pre-ICO + 75M more TIO tokens will be added to the ICO, scheduled December 5th for a total possible value of over . $100M USD Note: The company’s White Paper defers a lot from the numbers on their website. After an interview we verified that the website numbers are the most up to date and accurate in terms of token pricing and quantity. The Idea Trade.io is introduced in the white paper as a replacement trading platform for the existing system. It aims to be more efficient and transparent by leveraging a decentralized network such as the blockchain. The system is building a shared liquidity pool, where users are able to invest their assets through their trade.io membership (by the use of the token), trade.io will in return, share 50% of the profits generated through this pool, but will also share the losses. Joining this network requires the user to own 2,500 TIO Trade Tokens (approx $875 USD pre-ICO or $1200 USD post-ICO). The company is looking to provide startups with a number of investment and trading options, including the possibility of enabling companies to ICO and IPO through the platform. There is little information about how the company will handle commissions received from those activities, or what the current status is, in the accreditation to enable IPOs or ICOs to happen through the platform. The company is looking to provide multiple trading and investing features, including: : Their trading token acts as a membership to use the trading platform, in order to trade, a user has to hold 2,500 Trade Tokens Trading : Wins and losses from the trading activities are shared amongst traders Liquidity Pool : The platform will invest for the users and distributes wins and losses at 50% Investment Banking : Every day the platform will distribute the results of its investment amongst the users holding a membership Daily Distributions : The company claims to be (or will be) compliant with securities rules, and regulations in multiple jurisdictions, as well as Know Your Customer, and Anti money Laundry regulations Compliance There is currently not a lot of information regarding how the profits will be generated in the first place. The company will also be able to trade physical assets by virtualizing them on the network through their own token. This is a very similar practice to the one we discussed on , and asset-backed cryptocurrencies. Stable Tokens Token The pre-ICO launches the trade token which is an ERC 20 token and follows the standard specification required by the ethereum clients. It is used as the internal currency while performing trading and investment actions on the trade.io platform. Trade.io drives the usage of trade tokens over other cryptocurrencies by providing preferential payouts for investments with trade tokens over other cryptocurrencies or fiat. The smart contracts that underlie this token are written using the Majoolr system. The team at Majoolr are well versed in decentralized applications and have a good understanding of the technical side of things. However, due to the ambitious nature of this project it is highly unlikely that just four developers would be able to produce the required platform according to the timelines given by trade.io. It is also unclear if they have a plan in place to attract the requisite blockchain engineers to work on the platform. Since technical talent is highly sought after in this industry, this is a very important question that must be addressed with high priority. Use of Funds One of the most complicated aspects of this ICO, is the fact that the amount of money raised is completely dependent on when the users buy the tokens. The price of the Trade Token during the Pre-ICO is a fixed amount, while the price during the ICO will change depending on the time the user purchases the token. This is not that uncommon, what’s strange is that there seems to be a huge disconnect between the White Paper and the website. The White Paper states that each ETH will give the user 1000Trade Tokens. On their website, trade.io provides 1000 Trade Tokens per ETH during the Pre-ICO, and 625 Trade Tokens per 1 ETH during the ICO . This complex and unclear system forces trade.io to give an estimate on the use of funds based on a percentage of how much money they raised, instead of an amount. The reason this system is difficult, is that companies scale their budget by percentages. Having twice as many funds doesn’t mean you need to spend twice as much in legal, audits, or advisory. We wished that the company had 3–5 different scenarios, and the use of funds on each scenario, say, one for raising $5–10M USD, another for $50–99M USD, and one for > $200M USD. do not The following is the estimated use of funds for the platform: Image Caption: Image from: Page 26 of trade.io White Paper Since the White Paper, trade.io has expanded their use of funds through their website. The website now displays a set of goals or milestones that are reached, as funds increase. Image Caption: Image from: trade.io website as of November 22nd, 2017 Token Distribution The tokens of this ICO will be distributed based on the following chart: Image Caption: Image from: Page 23 of trade.io White Paper The company has an interesting strategy to distribute tokens during the Pre-ICO and ICO. Their plan is to launch 200 million tokens during the Pre-ICO and if there are any unsold tokens, they will proceed to add the leftovers to the ICO and increase the prices, which is a very common practice. The ICO itself will have 75 million tokens plus the leftovers of the Pre-ICO. Any tokens that are not purchased through the ICO (concluding December 15th, 2017) will be destroyed. Assuring that the platform has a stable supply of tokens. Pre-ICO Details The Pre-ICO period for token.io is November 7th to November 25th, 2017. During this period they have raised over $9M USD. Based on their websites statement, each 1 ETH = 1,000 Trade Tokens. With a price of $360USD, this means that over 25 million Trade Tokens have been sold, out of the possible 200 Million. ICO Details As previously mentioned, all the leftover Trade Tokens from the Pre-ICO + 75 Million will be for sale during the December 5th ICO. As it stands, there should be around 175 Million Trade Tokens remaining from the Pre-ICO plus another 75 million, or approximately 250 million Trade Tokens will be available for sale. Blockchain Test the decentralization of transactions allows for shifting of trust from a central organization (like a bank) to the entire node network, and this is the prime use of the blockchain. A trading platform also can benefit from the same trust transfer, and can avoid dangerous situations where large amounts of funds are lost due to hacking or improper asset management by the trading platform (like Mt. Gox). Certainly Trade.io’s trading platform is definitely a very good use of a blockchain and utilizes its decentralized nature to the fullest extent. However, how they will execute certain classical trading features like marginal trades and stop loss remains to be seen. (There is no existing implementation of marginal trading on a decentralized exchange). Counter to this however is Trade.io’s liquidity pool which requires entrusting liquid assets (trade tokens) to the trading platform (something which entirely negates the decentralized aspect), and while trades can still happen in a decentralized manner, the storage of the shared liquidity pool is still subject to hacks and arbitrary draining by the signing keyholders of the liquidity pool (i.e the platform). Traction One of the weakest parts of the trade.io White Paper is the lack of information on traction. From the company’s timeline, the intend is to launch the trade.io exchange by April 2018. With only 5 months to go, we would like to see an established MVP or some sort of technical update. Team The team at trade.io has clearly worked in the trading industries for a number of years. The brokerage firms and Primus Capital Markets UK were started by some of their advisors. This means that the founders have a good understanding of what is required in order to trade, and the regulations and due diligence that they will possibly be facing. FXPRIMUS The White Paper references 15 members of the management team, and board. As well as 10 advisors. Some of their team members are based in Cyprus, EU. Transparency The company claims to use blockchain technology to increase transparency for transactions. However, we have certain doubts about complete transparency regarding as claimed by their promise of no backroom trades. The Raiden network (and any off-chain networks) enable backroom trades, and while they publish the final transaction to the blockchain, it still enables some manipulation to happen. However, we recognize that off-chain payment channels are necessary to enable thousands of transactions to happen in a blockchain-hybrid environment. Moving forward, the platform could demonstrate how they are planning to achieve this, or the steps they are taking to improve transparency. Conclusion The trade.io whitepaper is one of the most ambitious ICOs we have seen. The document leaves a lot of questions in terms of regulations, current status of the token and platform, and a clear plan of how they are planning on mitigating risks and delays. Furthermore, there currently exist decentralized exchanges with a platform that has been built and thus competition in this category is quite stiff, and the business plan for trade.io has failed to address how they will retain market-share when they are late to market. There are also questions regarding regulation and status, while trade.io White Paper explains how the token is a utility and not a security, we still recommend this decision to be stated by a securities regulator, or at least some information from securities attorneys. Furthermore the raiden network is currently untested and would require heavy development to be able to launch to fit in the timeline provided by trade.io. The company is also looking to go public in only 12 months. That’s approximately 8 months after their launch, and less than 6 before the anticipated swiss banking license and within 6 months of the MetaTrader 4 platform incorporation. Our concern will be that without having the platform fully launched for even 12 months, it will be very difficult for investors on the public market to determine the price of the company and expectation of sales. It is also currently unclear what would happen in the case that the company IPOs in December 2018, what would happen to the tokens, and how would token holders be rewarded? What if the token is regarded to be a security? What if they don’t receive swiss banking license? Or if the MT4 platform connection has security vulnerabilities, like last week’s $300M Ethereum Hack. If the platform is successful, it would be a monumental development on the financial system, as creating a physical/virtual token system could potentially revolutionize the ways people trade, crowdfund, and support companies. Looking to help? 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