You’ve probably noticed a meme that’s gone around the cryptosphere for the last few years. Basically, it’s “give somebody bitcoin and they’ll realize how good it is.” does this and many people think it will get the mainstream to use bitcoin. Lolli A few months ago, Michael Nye tweeted some good, brief tips on talking to people about crypto—including his own version of that meme: In general, Nye provides great content, perspective, and advice on all his platforms. Also, I appreciate the hell out of him for giving me a little of his time and taking an interest in my book, despite my small profile and his many other commitments. Several people tell me they’ve done what he suggests. I’ve done it, too. It doesn’t work. Like pushing an ox up a hill Your average person does not have any clue how to use bitcoin nor do they have a reason to learn. It’s slow, expensive, complicated, and they can’t use it for most of the things they want to do. Of course, you can say the same about checks, but not really. There’s a big difference. Everybody’s used to checks. They’re normal. When you handle checks, you don’t have to learn anything new. You don’t even need to know how to balance a checkbook anymore—most banks handle all the paperwork and tracking for you. In fact, banks spend a lot of time and effort to make checking as easy as possible. They design an experience so you don’t think about fees, costs, and risks. Some even throw in a complimentary starter pack when you open your account (replacement books cost $10 and up). Usually, banks include debit cards and free ATM access. That way, you never have to actually use those checks—which is good, because most places don’t take checks anyway. Unless you’re poor, banks will usually waive your first bounced-check fees and rarely charge to stop payment on a check. If you give them enough money, they won’t even charge maintenance fees. Traditional banking has several centuries head start on bitcoin when it comes to customer service. And, banking is so common that most people have no other frame of reference. Over 90% of Americans have a bank account, as does half of the rest of the world. Giving away bitcoin only makes people wonder WTF the big deal is. Allow me to share three short stories about three real people who’ve gotten bitcoin as gifts. I changed their names because somebody said that’s what I’m supposed to do when I write about people online without their permission. Louise, the baby boomer Louise got 1 bitcoin from her son. She keeps it in a Coinbase account on her phone. Despite multiple tutorials and lots of handholding, she still does not know how to use it. She wonders why its price goes up and down like crazy. When I asked her about what she plans to do with it, she said she will keep it in her Coinbase wallet because her son says it might be worth something someday. “Who knows? It went from nothing to $20,000, it might do that again!” She doesn’t care about open, public, permissionless, borderless, neutral, and censorship-resistant financial networks. Her son gave her a bitcoin and said, “hold onto it.” She has plenty of money and did nothing to earn that bitcoin. She can’t use it anywhere and doesn’t even know how. Why would she care about it? Olso, the curious Oslo asked me for advice about buying bitcoin. I helped him out. Even sent him a few thousand sats as a gift after he set up his wallet. Now that he has his bitcoin, he complains about how hard it was to set up and that nobody else uses it. He also got upset about paying a 4% credit card charge plus spread to buy it. That’s much more expensive than his other investments. He told me he’ll keep the bitcoin for his kids. After all, it might be useful one day and everybody says it’ll go up. That’s another person who could’ve turned into a bitcoiner but didn’t. Maybe his kids will, but that doesn’t do anybody any good right now. Mallory, the idiot Mallory won’t mind me calling him an idiot because he’s an old friend of mine. And really, he’s not an idiot—he’s just simple. I bought him $18 worth of bitcoin in 2018. I told him he could have the bitcoin or $18 any time he wanted, on demand, any time. My gift to him. His choice. Didn’t set him up with a wallet or anything, just sent .0021 bitcoin to a new wallet I created. I have the keys, ready to turn them over as soon as he asks. That bitcoin’s still sitting in that wallet. The keys are still securely mine. At one point, the price of that .0021 bitcoin dipped down to $7.35. He could’ve taken the $18, bought himself his own bitcoin, and ended up way ahead. Nope. If we get that $1 million bitcoin like everybody says, he’ll have $2,100. Just for being my friend. I’ll bet he won’t collect it. Why? “I don’t know how to use it” and “it might go down.” (And, no, I will not give him $2,100 and keep the bitcoin. That defeats the whole point!) A silver lining Giving away bitcoin does have one benefit: It creates Instant-HODLers. Almost all gifted bitcoin ends up getting HODLed. There’s just no incentive for most people to do anything else with it. They don’t care about it, don’t know how to use it, and didn’t sacrifice anything to get it. But its price might go up, so they don’t sell it. They keep it in their wallets. While that’s literally the opposite of adoption, it keeps sellers out of the market. This helps keep bitcoin’s price from collapsing. So, gift away! Just don’t expect converts. If that bothers you, look on the bright side. If bitcoin’s really as good as everybody says it is, those people will come around. It just may take them longer than you’d like. Relax and enjoy the ride. Mark Helfman is a top writer on Medium for cryptocurrency, finance, and bitcoin topics. His book, Consensusland , explores the social, cultural, and business challenges of a fictional country that runs on cryptocurrency. In a past life, he worked for U.S. House Speaker Nancy Pelosi.

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People Will Not Use Bitcoin Just Because You Gave it to Them (And That’s OK)

While regarding how to form a DAC, people are too optimistic or ideal. It seems DAC will start from scratch by itself when connecting people (it's definitely not, remember the Guild that the Mandalorian has joined, it is a typical DAC, you can see how complicated it is), no clear path was given or discussed so far. DAC would be the mainstream value creation entity in the Value Internet Era. In my last article, I talked about why the new DAC style organization should be the study focus of the Token Economy. As a new paradigm, we've also noticed that a lot of people have discussed what DAO/DAC is. You know, just as people could incorporate a limited liability company in only a few days, there should be a handbook to guide how to establish a DAC so that people who have interest could follow the process, design the mechanism, and make it done. So, today we will present a minimum viable model for building up a DAC, which is a combination of the Token Economy, DAC Smart Contract, DevOps, Microservices, and Governance Protocol. Some context we should know in advance. 1. DAC will be the primary value creation entity in the value internet era, every of which serves to accomplish a clear business goal. 2. The competitive collaboration among multidisciplinary stakeholders in each DAC and among different DACs is the course of how the value will be created. 3. Although DAC is also a business organization, and I mentioned "forming" a DAC, DAC is not formed. DAC is an organic organization that will grow and evolves in the progress of value creation (stakeholders collaborate to reach consensus). 4. DAC is also a kind of Smart Organization, which means all the business activities in the DACs are programmable. The DAC digests value(input) and produces a new value(output), so the input and output value needs to be digitalized and tokenized. 5. Then, we need to redefine the scope of "Value" because, in the traditional business model, all the economic resources, no matter how many different dimensions they have, will be reduced only to one dimension, which is evaluated by Money. While in the DAC, all the various aspects of value can be recognized and represented (thanks to Blockchain and Cryptoeconomics). Generally speaking, we can define a model containing a simplified two dimensions of "value". : the mapping of the value in the real world, which can be divided into three categories: X Token (XT) Digital Currency: the value representation in payment scenario, i.e., DC/EP, Libra, etc. Digital Asset: the value representation of tokenized assets, i.e., Big Data, Real Estate, etc. Digital Commodity: the value representation of tokenized commodities or its derivatives, i.e., vouchers for picking up, order deposits, etc. generated by the algorithm, which is the representation of values created in the cyber world. Utility Token (UT): It represents the benefits of DAC community members as the consideration of the work, time, or other resources the community members have contributed in the course of value creation. It also represents the privileges for the UT holding community members to exert in the community operation, governance (e.g., voting power), offering priority of service or products, and the use of data assets. The generating of UT is dependent on the production, exchange, and trading process of XT, but UT is not directly related to the assets in the real world, it is a value-added value. For example, a community member will get a different amount of UT if he is in below value creation scenarios: Example of UT releasing in different scenarios 6. All these tokens (XT and UT) will be evaluated and priced by the decentralized market, and the value of UT is restricted by XT's value to ensure the credibility of UT. 7. To regulate the relationship among different stakeholders in the collaboration, a new governance model will be introduced to the model (I will bring more details in the next article). 8. We will introduce different Microservice tools into the process to make the value creation activities programmable. A microservice [1] architectural style is an approach to developing a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms, often an HTTP resource API. These services are built around business capabilities and independently deployable by fully automated deployment machinery. There is a bare minimum of centralized management of these services, which may be written in different programming languages and use diverse data storage technologies. So, the value creation activities in the real world would be defined as different Microservices, and the execution results will be stored as Data Assets, which are recognizable, auditable, and referable. During the collaboration, community members will use a series of microservice tools to record and store the values they created, such as the open-source tools of data input, data management, data storage, collaborative software, wallet, real-world data connection, etc. 9. The traditional Smart Contract is only a blank template, which offers flexibility but also is very hard to use , just as Conner Brown said . So we upgrade the traditional Smart Contract to the new DAC Smart Contract via introducing DevOps methodology, which includes seven factors: [2] [3] Context: Describe the time and space boundaries of the DAC; Boundaries: When and How the boundaries of the previously defined "Context" could be broken Goals: Mission Statement regarding the detailed problems that DAC aims to solve, and can be broken down to small goals in different stages; Measurable Effects: Conditional statements of the observable implementation outcomes that satisfy the "Goals"; Input: Values input into DAC, including XT and UT; Activities: Detailed implementation process, including the production, exchange, and trading of the values, executed by various Microservices; Output: The output value of these activities, acting as the value input for the next step. Triangle Logic Model With all the context bearing in mind, the overall logic of growing a DAC is illustrated as a triangle model, in which Time and Space will be connected by the upgraded Smart Contract to trigger the evolution of the DAC. A new paradigm to form the DAC So, what do Time and Space stand for? As for "Time", it means the values in the real world and the cyber world, will be regulated by the traditional law and governance methods (Auditing, Compliance, Tax, etc.), registered on the chain, and represented by XT and UT separately. In this mechanism, Time is seen as an inaccessible coordinate, and the apparent passage of time arises as a consequence of correlations between the subsystems of a global state, while with the global consensus mechanism of the blockchain, these tokens are unique on the chain of time consequence and cannot be tampered with. [4] As for "Space", it means the business activities in the DAC, which are observable, auditable, and can be processed through various microservices. The "Smart Contract" is used to connect "time" and "space" to complete the business activities in the DAC, but this is not a one-way process of assigning tasks and execution tasks, it's a DevOps process of continuous integration, delivery, and improvement. DAC Smart Contract Template It is a complicated procedure to accomplish the business goals for a DAC, and each step (containing one or more microservices) in the process can be administered by the DAC Smart Contract, with clear goals and observable, measurable outcomes, and the output will become the input for the next step, so all the business activities are connected, and that's the way a DAC can initiate. In the value creation activities, the community members implement different tasks by contributing time and resources. As a result, they will get UT as the incentives, and the more they help, the more UT they can get. UT holders will have the right to get involved with the DAC operation, governance, and the use of community data assets. This is a virtuous circle, which will incentivize people to join the community, to reach the consensus, and to contribute. That's the way a DAC can grow its consensus community. With the contribution and feedback from the participated community members, the complicated process will be templated and marked as data assets for all the members to refer, to optimize, which will significantly facilitate the communication and collaboration among community members, and that's the way a DAC can develop and evolve. Token Economic Model of DAC UT is generated during the process of microservices dealing with XT, so the relationship between XT and UT is not independent but coupled. Whether a token economic model is well-founded for a DAC depends on the interaction design between XT and UT. DAC Token Economic Model Firstly, UT can facilitate value creation for XT. With the existence of UT, the contribution of community members will no longer be measured randomly, and holding different amounts of UT will have different kinds of rights to exert over the XT, such as the priority in trading and picking up the products represented by XT, discount on transaction fees, etc. So, UT will encourage community members to participate in the value production process of XT. Secondly, the holding and trading of UT in the market will influence the pricing of XT, which means UT will act as a price evaluator for XT in some circumstances. If the longer in time, the more in quantity that a community member holds the UT, the more discounts he can get for purchasing the XT, then whether he would like to keep UT will be an index for judging the demand of XT, just like the interaction between the spot market and futures market in the traditional finance. If a plenty number of community members would like to hold UT, which reflects in the market is the price of UT is getting higher, and it also shows that the demands for XT are also very high, then the price of XT will increase as well. When people find it profitable for holding UT and XT, more and more people will join the community and participate in the value creation activities, and that's the virtuous circle. So, , to grow a DAC from the beginning, you will need to define the goals and measurable outcomes for the DAC, and what the XT and UT are within your DAC, what kind of business activities can be categorized as Microservices, what type of process you will follow in the execution of XT, and then compose these context into the DAC smart contract. key takeaways Of course, you also should design the token economic model to regulate the relationship between XT and UT. Just as the Mandalorian said, this is the way. In the next article, I will give more details on the governance protocol, and also present two use cases. Please stay tuned! References: [1] https://martinfowler.com/articles/microservices.html#footnote-etymology [2] https://medium.com/@Conner_/bitcoin-the-blockchain-for-truly-smart-contracts-f7100b73da01 [3] https://en.wikipedia.org/wiki/DevOps [4] https://royalsocietypublishing.org/doi/10.1098/rspa.2019.0470 (Contributed by Kevin Liu from Metis Lab)

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Blockchain-Verified Sentiment

A step by step tutorial to analyse sentiment of Amazon product reviews with the FastText API This blog provides a detailed step-by-step tutorial to use FastText for the purpose of text classification. For this purpose, we choose to perform sentiment analysis of customer reviews on Amazon.com and also elaborate on how the reviews of a particular product can be scraped for performing sentiment analysis on them hands on, the results of which may be analysed to decide the quality of a product based on the given feedback, before purchase. What is FastText? Text classification has become an essential component of the commercial world; whether it is used in spam filtering or in analysing sentiments of tweet sor customer reviews for E-Commerce websites, which are perhaps the most ubiquitous examples. FastText is an open-source library developed by the Facebook AI Research (FAIR), exclusively dedicated to the purpose of simplifying text classification. FastText is capable of training with millions of example text data in hardly ten minutes over a multi-core CPU and perform prediction on raw unseen text among more than 300,000 categories in less than five minutes using the trained model. Pre-Labelled Dataset for Training A manually annotated dataset of amazon reviews obtained from Kaggle.com containing few million reviews was collected and used for training the model after conversion to FastText format. The data format for FastText is as follows: __label__ __label__ ... < > X < > Y < > Text where X and Y represent the class labels. In the dataset we use, we have the review title prepended to the review, separated by a ‘ ’ and a space. : A sample from the training data file is given below, the datasets for training and testing the models can be found here in the website. Kaggle.com __label__2 Great CD: My lovely Pat has one the GREAT voices her generation. 
I have listened this CD YEARS I
still LOVE IT. I makes feel better. A bad mood just
evaporates sugar the rain. This CD
just oozes LIFE. Vocals are jusat STUUNNING lyrics just kill. One life gems. This a desert isle CD my book.
Why she never made it big just beyond .
everytime I play this, no matter black,
white, young, old, male, female EVERYBODY
says one thing of of to for and When 'm in a good mood it me like in and of 's hidden is in is me "Who was that singing ?" Here, we have only two classes 1 and 2, where signifies that the reviewer gave either 1 or 2 stars for the product, while indicates a 4 or 5 star rating. __label__1 __label__2 Training FastText for Text Classification Pre-process and Clean Data Execute the following command to generate a preprocessed and cleaned training data file after normalizing text case and removing unwanted characters. <path training > | sed - “s/$[.\!?,’/()]$/ \ /g” | “[:upper:]”
“[:lower:]” > 
<path -processed output > cat to file e 1 tr to pre file Setup FastText Let us start by downloading the : most recent release $ wget https://github.com/facebookresearch/fastText/archive/v0.1.0.zip $ unzip v0.1.0.zip Move to the fastText directory and build it: $ fastText-0.1.0 cd $ make Running the binary without any argument will print the high level documentation, showing the different use cases supported by fastText: >> ./fasttext
usage: fasttext < > < >
The commands supported fasttext are:
  supervised              train supervised classifier
  quantize                quantize model reduce memory usage
  test                    evaluate supervised classifier
  predict                 predict most likely labels
  predict-prob            predict most likely labels probabilities
  skipgram                train skipgram model
  cbow                    train cbow model
  print- -vectors      print vectors given trained model
  print- -vectors  print vectors given trained model
  nn                      query nearest neighbors
  analogies               query analogies command args by a a to the a with a a word word a sentence sentence a for for In this tutorial, we mainly use the , and subcommands, which corresponds to learning (and using) text classifier. supervised test predict Training the model The following command is used to train a model for text classification: ./fasttext supervised - < pre-processed training file> -output
< save model> -label __label__ input path to path to The command line option refers to the training file, while the option refers to the location where the model is to be saved. After training is complete, a file , containing the trained classifier, is created in the given location. -input -output model.bin Optional parameters for improving models Increasing number of epochs for training By default, the model is trained on each example for 5 epochs, to increase this parameter for better training, we can specify the -epoch argument. Example: ./fasttext supervised - < pre-processed training file> -output 
< save model> -label __label__ -epoch input path to path to 50 Specify learning rate Changing learning rate implies changing the learning speed of our model is to increase (or decrease) the learning rate of the algorithm. This corresponds to how much the model changes after processing each example. A learning rate of 0 would means that the model does not change at all, and thus, does not learn anything. Good values of the learning rate are in the range . 0.1 - 1.0 The default value of lr is 0.1. Here’s how we specify this parameter. ./fasttext supervised - <path -processed training > -output
 <path save model> -label __label__ - input to pre file to lr 0.5 Using n-grams as features This is a useful step for problems depending on word order, especially sentiment analysis. It is to specify the usage of the concatenation of consecutive tokens in a n-sized window as features for training. We specify parameter for this (ideally value between 2 to 5): -wordNgrams ./fasttext supervised - < pre-processed training file> -output 
< save model> -label __label__ -wordNgrams input path to path to 3 Test and Evaluate the Model The following command is to test the model on a pre-annotated test dataset and compare the original labels with the predicted labels of each review and generate evaluation scores in the form of precision and recall values. The precision is the number of correct labels among the labels predicted by fastText. The recall is the number of labels that successfully were predicted. ./fasttext test < model> < test file> k path to path to where the parameter represents that the model is to predict the top labels for each review. k k The results obtained on evaluating our trained model on a test data of 400000 reviews are as follows . As observed, a precision, recall of 91% is obtained and the model is trained in a very quick time. N Number of examples: 400000 P@ 1 0.913 R@ 1 0.913 400000 Analyse Sentiments of Real-Time Customer Reviews of Products on Amazon.com Scrape Amazon Customer Reviews We use an existing python library to scrape reviews from pages. To setup the module, In your command prompt/terminal type: pip amazon-review-scraper install Here’s a sample code to scrape review of a particular product, given the url of the web page: amazon_review_scraper amazon_review_scraper

url = input( )
start_page = input( )
end_page = input( )
time_upper_limit = input( )
file_name = scraper = amazon_review_scraper.amazon_review_scraper(url, start_page, end_page, time_upper_limit)
scraper.scrape()
scraper.write_csv(file_name) from import "Enter URL: " "Enter Start Page: " "Enter End Page: " "Enter upper limit of time range (Example: Entering the value 5 would mean the program will wait anywhere from 0 to 5 seconds before scraping a page. If you don't want the program to wait, enter 0): " "amazon_product_review" : While entering the URL of the customer review page of a particular product, ensure that you append if it does not exist already, for the scraper to function properly. NOTE &pageNumber=1 The above code scrapes the reviews from the given url and creates an output csv file in the following format: ( Dataset Link ) From the above csv file, we extract the Title and the Body and append them together separated by a and a space as in the training file, and store them in a separate txt file for prediction of sentiments. ‘: Prediction of Sentiments of Scraped Data ./fasttext predict < model> 
< test file> 
k > < prediction file> path to path to path to where signifies that the model will predict the top labels for each review. k k The labels predicted for the above reviews are as follows: 2 1 2 2 2 2 2 2 1 2 2 __label__ __label__ __label__ __label__ __label__ __label__ __label__ __label__ __label__ __label__ __label__ Which are quite accurate as verified manually. The prediction file can then be used for further detailed analysis and visualization purposes. Thus, in this blog, we learnt using the FastText API for text classification, scraping Amazon Customer Reviews for a Given Product and predicting their sentiments with the trained model for analysis. If you have any queries or suggestions, I would love to hear about it. Please write to me at abhishek.narayanan@dataturks.com.

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Facebook

Text Classification Simplified with Facebook’s FastText

The objective of the problem is to implement classification and localization algorithms to achieve high object classification and labelling accuracies, and train models readily with as least data and time as possible. The solution to the problem is considered in the following blog. The efficiency of a model is dependent on various parameters, including the architecture of the model, number of weight parameters in the model, number of images the net has been trained on, and the computational power available to test the models in real time. The third parameter can’t be controlled, thus leaving us dependent on the first two parameters. Thus transfer learning works the best in this scenario, for the pre-trained weights are adjusted according to our dataset, although minimal errors and reliable accuracies are obtained. Model For image classification, we use a keras model with the model summary obtained by running the code below The model’s parameters are tuned to suit the maximum change in information for as minimum data as possible. Thus, we have the batch normalization layers, that randomly shake up the weights to make the model generalized. . Experiment We use the MobileNet model for training on our dataset. The dataset has been taken from HackerEarth deep learning challenge to classify animals. If you need any other domain-specific dataset: . You can find thousands of such open datasets here We choose 10 random classes from the dataset and change the number of images per class and the size of the fully connected layers, and report the results. keras applications keras.preprocessing.image ImageDataGenerator keras optimizers keras.models Sequential, Model keras.layers Dropout, Flatten, Dense, GlobalAveragePooling2D keras backend k keras.callbacks ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping keras.models load_model os pickle keras.models model_from_json matplotlib.pyplot plt

image_width, image_height= , nb_train_samples= nb_validation_sample= batch_size = model = applications.mobilenetv2(weights= , include_top=False, input_shape=(image_height, image_width, ))

x=model.layers[ ].output
#take the first layers the model
x=Flatten()(x)
x=Dense( , activation= )(x)
x=Dropout( )(x)
x=Dense( , activation= )(x)
x=Dropout( )(x)
x=Dense( , activation= )(x)
x=Dropout( )(x)
predictions = Dense( , activation= )(x)


model_final =Model(input=model.input, output=predictions)

model_final = load_model( )
model_final.compile(loss= , optimizer=optimizers.nadam(lr= ), metrics=[ ])

train_datagen = ImageDataGenerator(rescale = / ,
                                   shear_range = ,
                                   zoom_range = ,
                                   horizontal_flip = True,
                                   fill_mode= ,
                                   width_shift_range= ,
                                   height_shift_range= ,
                                   rotation_range= )

test_datagen = ImageDataGenerator(rescale = / ,
horizontal_flip = True,
fill_mode = ,
zoom_range = ,
width_shift_range = ,
height_shift_range= ,
rotation_range= )

training_set = train_datagen.flow_from_directory( , target_size = ( , ), batch_size = ,class_mode = )
test_set = test_datagen.flow_from_directory( , target_size = ( , ), batch_size = , class_mode = ) 
model_final.fit_generator(training_set, steps_per_epoch = ,epochs = , validation_data = test_set,validation_steps= )
print(model.summary())
#uncomment the follwoing to save your weights and model. model.json r categorical_crossentropy nadam accuracy from import from import from import from import from import from import as from import from import import import from import import as 256 256 11000 2000 8 "imagenet" 3 7 5 of 1024 "relu" 0.5 384 "relu" 0.5 96 "relu" 0.5 30 "softmax" "weights_Mobile_Net.h5" "categorical_crossentropy" 0.00001 "accuracy" 1. 255 0.2 0.2 "nearest" 0.3 0.3 30 1. 255 "nearest" 0.3 0.3 0.3 30 './HE_Chal' 256 256 8 'categorical' './Validation' 256 256 8 'categorical' 1000 80 1000 '' 'model_json=model_final.to_json()

with open("model.json", "w") as json_file:
    json_file.write(model_json)
model_final.save_weights("weights_VGG.h5")
model_final.save("model_27.h5")
#model_final.predict(test_set, batch_size=batch_size)
' '' '' '
json_file = open(' ', ' ')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("weights_VGG.h5",by_name=True)
print("Loaded model from disk")
 
# evaluate loaded model on test data
loaded_model.compile(loss=' ', optimizer=' ', metrics=[' '])

#print(loaded_model.summary())
loaded_model.fit_generator(training_set,                         steps_per_epoch = 1000,epochs = 100,                         validation_data = test_set,validation_steps=1000)
#score = loaded_model.evaluate(training_set,test_set , verbose=0)
' '' Results The models were run for 15 epochs on an Intel i7 Processor. Model 1: Mobilenet : 1000 steps/epoch: Larger FC Layers: Training Time: 18 mins/epoch : dataset: 50 images/average: accuracy : 82.2% Model 2: Mobilenet: 500 steps/epoch: Smaller FC layers: Training time : 12 mins/epoch: dataset: 50 images/average : 82.47% Model 3: Mobilnet 500 steps/epoch: Smaller FC layers: Training time: 11 mins/epoch: dataset: 30 images per class: accuracy: 76% Image Detection There are a few methods that pose detection as a regression problem. Two of the most popular ones are YOLO and SSD. These detectors are also called single shot detectors. Let’s have a look at them: You Only Look Once. YOLO divides each image into a grid of S x S and each grid predicts N bounding boxes and confidence. The confidence reflects the accuracy of the bounding box and whether the bounding box actually contains an object(regardless of class). YOLO also predicts the classification score for each box for every class in training. You can combine both the classes to calculate the probability of each class being present in a predicted box. So, total SxSxN boxes are predicted. However, most of these boxes have low confidence scores and if we set a threshold say 30% confidence, we can remove most of them as shown in the example below. (YOLO predicts one type of class in one grid! Hence small objects are not identified…) Single Shot Detectors SSD runs a convolutional network on input image only once and calculates a feature map. Now, we run a small 3×3 sized convolutional kernel on this feature map to predict the bounding boxes and classification probability. SSD also uses anchor boxes at various aspect ratio similar to Faster-RCNN and learns the off-set rather than learning the box. In order to handle the scale, SSD predicts bounding boxes after multiple convolutional layers. Since each convolutional layer operates at a different scale, it is able to detect objects of various scales. We compared two models, initially YOLO(darknet) and later SSDs and compared their accuracies and speeds. Since our inputs are images, the FPS parameter is not used to differentiate the models. Moreover, the SSDs are a balance between the Faster — RCNN model and the YOLO model. Let’s see what the experiment tells us? The SSD model is implemented using the dnn module, with the help of Adrian Rosebrock, in openCV’s library. # the necessary packages numpy np argparse cv2

# construct the argument parse and parse the ap = argparse.ArgumentParser()
ap.add_argument( , , required=True,
	help= )
ap.add_argument( , , required=True,
	help= )
ap.add_argument( , , required=True,
	help= )
ap.add_argument( , , type=float, = ,
	help= )
args = vars(ap.parse_args())
# load our serialized model disk
print( )
net = cv2.dnn.readNetFromCaffe(args[ ], args[ ])

# load the input image and construct an input blob the image
# by resizing to a fixed x300 pixels and then normalizing it
# (note: normalization is done via the authors the MobileNet SSD
# implementation)
image = cv2.imread(args[ ])
(h, w) = image.shape[: ]
blob = cv2.dnn.blobFromImage(cv2.resize(image, ( , )), ,
	( , ), )
  
# pass the blob through the network and obtain the detections and
# predictions
print( )
net.setInput(blob)
detections = net.forward()

# loop over the detections i np.arange( , detections.shape[ ]):
	# extract the confidence (i.e., probability) associated the
	# prediction
	confidence = detections[ , , i, ]

	# filter out weak detections by ensuring the is
	# greater than the minimum confidence confidence > args[ ]:
		# extract the index the = int(detections[ , , i, ])
		box = detections[ , , i, : ] * np.array([w, h, w, h])
		(startX, startY, endX, endY) = box.astype( )

		# display the prediction
		label = .format(CLASSES[idx], confidence * )
		print( .format(label))
		cv2.rectangle(image, (startX, startY), (endX, endY),
			COLORS[idx], )
		y = startY - startY - > startY + cv2.putText(image, label, (startX, y),
			cv2.FONT_HERSHEY_SIMPLEX, , COLORS[idx], )
      
     # show the output image
cv2.imshow( , image)
cv2.waitKey( ) import import as import import arguments "-i" "--image" "path to input image" "-p" "--prototxt" "path to Caffe 'deploy' prototxt file" "-m" "--model" "path to Caffe pre-trained model" "-c" "--confidence" default 0.2 "minimum probability to filter weak detections" from "[INFO] loading model..." "prototxt" "model" for 300 of "image" 2 300 300 0.007843 300 300 127.5 "[INFO] computing object detections..." for in 0 2 with 0 0 2 `confidence` if "confidence" of ` `,
		# ( , )- # class label from the detections then compute the x y coordinates of the bounding box for the object idx 0 0 1 0 0 3 7 "int" "{}: {:.2f}%" 100 "[INFO] {}" 2 15 if 15 15 else 15 0.5 2 "Output" 0 The YOLO pre-trained weights were downloaded from the author’s website where we choose the YOLOv3 model. Since it is the darknet model, the anchor boxes are different from the one we have in our dataset. Hence we initially convert the bounding boxes from VOC form to the darknet form using code from . Then we train the network by changing the config file. here Results: SSDs: IOU= 0.74, mAP: 0.83 Time /epoch: 12 minutes YOLOs: IOU= 0.69, mAP: 0.85 Time/epoch: 11 minutes Output Images (SSDs used for Vehicle Detection) (Output with YOLOv3 Pretrained Weights) Conclusion The overall problem is stated as one where we need to trade off the speed and accuracy. The overall solution is proposed as two different models for various types of images. The trade-off between speed and accuracy is accompanied with computational power available. The YOLO model is suitable for high-speed outputs, where accuracy is not that high… whereas SSDs provide higher accuracies with high-speed outputs with a higher computation time. Hence choose SSDs on good microprocessors, else YOLO is the goto for microprocessor-based computations. Shameless plugin: We are an online tool to make it super easy for you to build ML datasets like doing image bounding boxes, NER tagging etc. Check us out: the best image labeling tool !

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This year, over 4.1 billion records were exposed through data breaches. When it comes to identity and access management, are companies and users doing enough? 444,602. That's almost as many people as living in Miami. All in all, they lost a staggering can happen to anyone and lead to problems that could damage your credit history, impact your loans, mortgage and tax refund and sometimes even exhaust your savings account. Worse yet, it may even lead to a wrongful arrest. But it’s not just about the money - it can include highly sensitive data such as private pictures as well. And you could be affected too! That’s the number of people in the US that got their identities stolen in 2018 . $200 million. Identity manipulation and theft What is online identity theft? The common identity theft definition is to . It may not be a simple case of spam emails - consider the impact if your credit card details are compromised and you run the risk of fraudulent activity under your name? In the US, . take over another person’s identity without authority to use their sensitive data for fraudulent actions identity theft happens almost every minute How can identity theft happen online? A few years ago, it was reported that , a 32 year old waiter stole more than 200 digital identities including ones that belonged to Oprah Winfrey, Steven Spielberg and Warren Buffett to withdraw money and make online purchases. This was made possible by technology that churned out fake credit cards and later, registered new accounts under different addresses. In today’s digital space, there are common and easier ways to get access to sensitive data, highlighting the critical need to build ample security for stronger privacy and data controls. Abraham Abdallah Phishing is one of the more commonly used methods of identity thefts online. It’s the act of sending emails with links which auto-downloads spy software upon clicking on it, handing over control to a large amount of your sensitive data. The stolen data can be leveraged to make payments, order things online or create new accounts, so you wouldn't even notice it. Trojans and viruses can also take over your account and block you from accessing it. Fraudsters then try to access other accounts through your account by using the same password. It's not far from likely that your account was hacked last year when over . 800+ Million Records were exposed Another trick often used by online identity thefts is to create new profiles or accounts under your name, backed by similar email addresses and pictures. Here’s a look at some of the most common types of identity thefts in the US: Credit card fraud is the most common one. In the latest report from 2018, the FTC registered around 157,000 cases where people claimed credit card fraud. In 130,000 cases, new accounts had been created, whereas the remaining cases had leveraged existing accounts to siphon money from. Identity theft followed closely behind, with nearly 122,000 cases reported through everyday activities like online shopping, social media or medical data theft, followed by employment and tax related frauds with 67,000 registered cases. Number four were phone or utility frauds where thefts stole around 67,000 identities. Although the number of incidents surrounding online identity theft stagnated since 2015, that’s not the case - the graph only shows three major frauds. The total number has been increasing over the last couple of years, and the only category recording a decline is the number of employment or tax-related fraud. Even though the number is that high, it seems like identity theft is still not taken seriously. But why? Even though the number is that high, it seems like identity theft is still not taken seriously. But why? is an underestimated necessity even though it’s crucial because it not only builds mutual trust, it also ensures that fraud is prevented. There are two reasons for that. Identity theft prevention First, a large part is attributed to people’s approach - where they’re not careful enough and carry a casual "this happens to others but not to me" thinking. Think about it - have you ever had a second look at a web site before sharing sensitive data? And do you have a different (and strong) password for every account you are using? Secondly, it's not quite true that nobody cares about it, but today there is no easy and secure way for people to online when identifying themselves online to other businesses. This leaves companies with the issue at hand, where they’re forced to resort to intrusive, expensive and cumbersome processes for or ones that simply cannot be scaled to solve this problem. This has enabled fake accounts to proliferate, industrial-scale identity theft and manipulation, and complicated onboarding and access management processes. prevent identity theft identity verification So what will the future of online identity theft look like? And what does a digital identity have to do with it? Identity theft online will be a lot harder in the future. such as try to solve this problem by fully digitizing the way people identify themselves online by creating a biometrically secured, verified and portable digital identity. will play an in the way we verify ourselves over the internet. Digital identity platforms Passbase Digital identities important role Passbase uses features like liveness detection, ID authenticity checks and to provide additional layers of security for digital identities while giving users control over which sensitive data they want to share and with whom. facial recognition Interested? Try Passbase now Without a highly secure, robust identity verification platform, consumers should pay attention to the following questions. How can you prevent identity theft online? Only use trusted websites (https instead of http) to share your information Use different, strong passwords for every account you have Use two-factor authentication Never use public Wifi to share sensitive information such as credit card numbers Always check your bank account and take immediate action if something looks suspicious Check what you share on social media Check whether there are similar accounts with your name and pictures Don't click on email links from people you don't know Set up a mail spam Use tools for identity theft prevention such as Passbase Where to check if your identity has been stolen online? Can you still log into your online bank account? Can you still log into your social media accounts? You get random emails from your bank You get recipes for things you didn't order Google yourself and search for your name on social media What to do if your identity has been stolen online? Reach out to the social media platform, go to “help” and report the online identity theft Reach out to your bank, explain the situation and block your account Reach out to your local police to press charges For more information go to the FTC website Are you a business owner? Help your customers by avoiding identity theft on your platform! You can try out Passbase for free.

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These are some AI-generated project names, courtesy of Namelix . Our naming story I write about developer innovation and new technologies for a VC firm called Project A. Every now and then, we develop projects that we’d like to open source. Coming up with names for these projects is always a challenge and each team has a different approach. I’d like to describe how we named a couple of our projects and what we learned along the way. One of these projects was initiated by our data engineering team and another by our back-end team. Despite having different use cases, both of these projects have a similar theme — they enable developers to efficiently process data from different sources and pipe it to different systems. Naming is hard but important Coming up with a name is never an easy decision. Babies, bands, top-secret projects, new products, and companies — someone, somewhere has to name them. Naming an open source project isn’t quite as “high-stakes” as naming a baby but it ain’t a walk in the park either. You can give an internal development project whatever crappy name you want, but if there’s a chance you’re going to go public with it, there are several reasons why you should put some effort into a decent name (besides simply avoiding ridicule). “A good name can help a company or product become successful, of course, but it can also help the lowliest code library find an audience, help formalize an informal process, and propel ideas about the world toward becoming talking points throughout it.” – Greg Leppert and Willem Van Lancker, Onym Developers can learn a lot from marketing pros Admittedly, we didn’t ask our marketing team for any tips about naming. They’re busy folks and we didn’t want to bother them with a highly technical project that we were going to share for free. For open source projects, you probably won’t need to worry about traditional marketing anyway — at least not when starting off. However, earlier in my career, I had collaborated with marketing teams on product naming and got to know some of the experts that they trusted. I started reading what these experts had written about naming and branding. Some of their ideas and concerns are still highly relevant for open source software. For example, I love this quote: “ “ We should lower our sights when it comes to naming. Because company names, like pretty much everything in life, fall on a bell curve. There are a few fantastic names, a whole bunch of okay-to-good names and a few truly awful names. The problem is, the surest way to end up with an awful name is by aiming for a fantastic one. — Doug Kessler, Velocity Partners It addresses how much time you can potentially waste trying to come up with the perfect name, and foreshadows some of the problems that we personally encountered. The first problem is… Who to involve in the naming process Whatever you do, you should keep the group small — I would recommend no more than 5 people. Any larger and you’re going end up with costly review cycles and discussions. Also, decide the group in advance and stick to it — gradually involving more and more people can undo any previous work and bring you back to square one. Or you could also leave the entire responsibility up to a senior member of your team. The creator of Maria DB, Max DB, and MySQL simply named those projects after his kids: “Maria”, “Max” and “My”. However, this strategy didn’t go so well for us. The backend project was first conceived a couple of years ago, by our CTO and a few of his colleagues. They were joking around and thought it would be fun to name the future project “Wayne” (no one had any children named Wayne). Now, I don’t want to offend any people with that name, but we felt it had certain connotations that we wanted to avoid. Not really the association we were going for — Wayne on YouTube (Image: YouTube) In all fairness to our CTO, “Wayne” was just a working title. He quickly needed a name so that he could discuss the thing that we wanted the team to build…without calling it “the thing”. However, most of us agreed that we needed a better name. Avoid a free-for-all naming frenzy Many development teams have flat hierarchies and try to involve everyone in big decisions. So, of course, we sought input from everyone in the team. Unfortunately, this strategy can lead to a lot of…shall we say…“noise”. Think of what happens whenever an organization decides to let the internet suggest a name for a product or project. As the New York Times wrote in 2016: happens. Nerds will be nerds. Boaty McBoatface And so it was, that names like “Crunchy McCrunchFace” and “BusinessObject McBusinessObjectFace” were some of the first suggestions when we tried to name our own projects (full disclosure, the second one was mine). People are also going to suggest names from popular culture such as “Gargamel”, “Tyrion”, and “Optimus Prime” — but good luck if you want people to find your “Tyrion” project in Google. Of course, it’s fun to do some free association but it probably wouldn’t do any harm to skip this part. Automating the brainstorming process Marketing teams have all sorts of complex methodologies that they like to use for this. For example, , Doug Kessler describes how they plotted words on scales such as “evocative vs descriptive”, “emotional vs rational” and “playful vs enterprisey”. They also arranged words that sent “the right signals” into a sphere of associations. in his article on rebranding If this stuff floats your boat, then by all means, give it a try — but I’m guessing you’d rather be coding than pinning names to an ideas board. And luckily, some of these processes have been automated by AI. While we were trying to figure out how to come up with a name, our CTO discovered an interesting tool called . According to their website, Namelix “uses artificial intelligence to create a short, brandable business name”. Namelix We weren’t after a business name but were game to give it a try. Namelix wants you to enter a few keywords about your business, so I entered the most generic words that related to our tool “backend” “database” “software”. I opted for a medium length name and chose a “name style”. There are 8 styles to choose from and I opted for “Misspellings” (like Lyft and Flickr). Some of the suggestions were baffling: “Scraink”, “Boonry”, or “Treesus”? If you need to come up with a name for your emo rap trio, the “Misspellings” option might work for you. But there was no way we were going to name our project “treesus”. I tried again with the “Compound Word” option (like “FedEx” and “Instagram”). Those suggestions were even odder: “Nannystate”, “Spyware”, or “Failapp”? In all fairness to Namelix, I did cherry-pick the funniest examples, but nonetheless, I couldn’t find anything that I liked and I didn’t want to spend hours tinkering with the settings. However, Namelix did help with clarifying the of words that we should consider. To see what I mean, have a look at their “name style” options: types Incidentally, our data engineering team had structured their naming process around on similar “naming styles”. Which leads me to the next point: Agree on your naming styles before you start brainstorming Our data engineering team had people propose names in categories such as “Italian words”, “Swahili words”, “Minerals”, “Characters from Roman Mythology”, “Plant Names” and “verbs”. If you agree on the categories that you want to use first, you can avoid the task of filtering through a whole bunch of random junk. Focus on Verbs The “verbs” category is particularly important — you want to build a list of verbs that describe what your project Even if none of them are suitable for a name, verbs are especially useful for triggering inspiration. does. To start off, our data engineering came up with simple verb-object pairs that described their project. Here are some examples: Import data Build dashboards Display data Transform data Extract, transform, load (ETL) data For each of those principal “actions”, they brainstormed further verb “associations”. For example, here are some examples of verbs that they associated with the “ETL” action. organize manage sort merge gather fold They then translated these verbs into Swahili. Swahili seems to be a favored language when it comes to naming projects. I suspect that Swahili words appeal to English and German speakers because they have a melodic texture and are pleasing to vocalize (maybe this has something to do with the ). bouba/kiki effect Again, here are some of the ETL verbs with their Swahili translations. organize: kuandaa extract: dondoo manage: kusimamia sort: aina merge: kuunganisha gather: kukusanya fold: mara The name that the data engineering team liked best was the last one: ‘mara’ — it was short, simple and easy to pronounce. Sometimes a good backstory provides the best option Our backend team had more of an intuitive approach. For them, it was more important that their name had a good backstory or anecdote that they could share. Our lead backend developer tried to think of metaphors for his team’s project. Then he hit upon a childhood memory — he remembered how much he loved making herbariums at school. The Herbarium — our lead developer’s childhood passion. He felt that the “herbarium” was a good metaphor for his team’s project. The project was an application for defining business objects independent of any database or tool. Different systems could then pull data whatever structure they require. For our lead backend developer, each business object was kind of like a pressed plant. Each plant has its own shape and attributes, but it gets flattened into a consistent form so that all of these plant samples can be stored and compared together in one place. He shared this idea with the team and everyone liked the metaphor. Some people felt like “herbarium” was a bit of a mouthful, so they shortened it to the more diminutive “Herbie”. But their naming quest wasn’t over yet… Do the Google Test Before you make a final decision on the name, make sure you find out what shows up on the search results page when you search for that word online. Branding professionals do this so that they can find out how stiff the competition is for the search results rankings. This isn’t a huge priority for most open source projects — you’re not trying to build a brand, you just want to share your project. However, you don’t want to find out too late that your project name has some unexpected connotations or associations (For example, Ikea had to deal with this problem when they ). expanded into Thailand Incidentally, our data engineering team was not immediately aware that “mara” also refers to an odd-looking mammal. That was until our Chief Data Officer went to the zoo with his family and spotted this sign: Fun fact: Here in Germany, there’s a very logical system for naming animals (as illustrated by this German animal names flowchart ) so in this neck of the woods, the “mara” is also called “Pampas Hare” (Pampashase). Fortunately, the Mara is kind of cute and it made for a great logo. Not everyone comes off so lucky, however. The point being, it’s better to be safe than sorry — even for a small open source project. What’s more important is searching on Github. Do the Github test Try checking the following URL patterns to see what comes up for your chosen name: https://github.com/<name> https://<name>.github.io We like to store our projects under their own organizations in Github so that they’re independent entities from the main “Project A” organization. These special projects consisted of several repos and we didn’t want them to get mixed up with all the other Project A repos. That’s why it was important for our data engineering team to get the account and for our backend team to get . https://github.com/mara https://github.com/herbie However, these short snappy names are often taken, but if you’re lucky, the name might belong to an inactive account. You can find out by reviewing the account activity. If there’s little to no activity, you might be able to claim the name according to . This was indeed the case for both the “mara” and “herbie” accounts. After contacting GitHub support, both names were made available for our projects. Github’s username policy Unfortunately for the backend team, they hadn’t paid enough attention to the general search results in Github. After everyone agreed on the name and set up the repos, people started discovering other “herbies” such as a German project and another from the University of Washington. Maybe “treesus” wasn’t such a bad idea after all? flat-file CMS small project While a double up is not ideal, it’s also not as critical as having a duplicate brand name. As long as the projects are in separate niches of the open source ecosystem, you can probably get away with it for a while. However, if another project with the same name becomes astronomically popular, your project risks drowning in a sea of content about its more well-known namesake. Check relevant top-level domain names This is not really a “must-have” but if you think that your project might gain traction, you could also check top-level domains that are popular with open source projects, such as “.org” or “.io”. Just be aware that some domains can come with a hefty price tag. At the time of writing, the domain “mara.io” was being sold for $1,999 — which definitely wasn’t an option for our data engineering team. Try out some usage examples As a final precaution, try to use the name in at least 5 sentences and make sure that there are no contexts where it sounds weird. For example, have a look at these sentences containing the term “Splunk” — the name of a popular application for analyzing machine-generated big data. “…to your new data, run the demo script…” “…you have the data but how will it be ?…” “…see our tutorial on Perforce Data…” “…check to see if knows about your script” “…both apps are enabled on this instance..” splunk splunked splunking Splunk Splunk Splunk is definitely a memorable name and a strong brand, but for British English speakers, there’s something disconcerting about using the word “Splunk” as a verb — it sounds somehow vulgar. A list of usage examples can help uncover such linguistic idiosyncrasies and it can also give you ideas on rules for correct usage. Further Resources Of course, I couldn’t finish a blog post about names without mentioning the indispensable naming resource “ ”. It’s a fantastic website which links to curated content about every aspect of the naming process, including many more tools that can help you with brainstorming. Onym I wish we had found out about this resource a lot earlier — we could have saved ourselves a lot of time!. But hopefully, these tips help you avoid some of the naming pitfalls that we encountered. Also, if you have any interesting stories about how you came up with a project name, let us know! A Disclosure Note On The Author and Project A Merlin blogs about developer innovation and new technologies at Project A — a venture capital investor focusing on early-stage startups. provides operational support to its portfolio companies, including developer expertise. As part of the IT team, he covers the highlights of Project A’s work helping startups to evolve into thriving success stories. Project A

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And why should you care? Well, annotations are a brand-spanking-new way to add context to stories in a user-friendly way, augmenting the reading experience. Annotations, how do they work? Right now, if you , you'll be able to highlight story text on any page and submit a note, comment, or annotation. You can do this on your own stories, or someone else's. visit the super-top-secret annotation activation page Then what? When your story has received an annotation, you'll see it pending on your dashboard at . — if you reject them, they go away, but if you accept them, they show up on your story as a little yellow highlight, under the selected text. app.hackernoon.com You, the author, have the power to accept or reject these annotations When you hover over this highlighted text, you'll see the annotation! Easy as pie. Why should I use this? You can use annotations to: Define acronyms and jargon Provide context Make jokes Explain complex concepts Provide a backstory Define difficult words Help people better understand technical terms Provide footnotes Where do we go from here? As we continue to build on, iterate, and improve Hacker Noon, our goal is to be more transparent (check out ). We hope you'll continue to follow us on this journey, and see where it goes. Dane's recent product update recap Happy hacking, y'all!

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It’s January, and most of you have probably enjoyed a break from work over the festive season. Less time coding and more time gaming or film watching. All that time with colleagues is replaced with family, friends and pets rather than coworkers. But it’s Monday, and the working week is back with a vengeance. Did you return to your job this week with feeling is enthusiasm or dread? The beginning of the year can be a time of reckoning. You might be considering looking for a new job. Or taking steps towards the next level in your career. Or moving into another sector altogether. Here at Codemotion, we’re here to help. We have a dedicated track at our conferences to career development. We also have a platform dedicated to meetups. These are great places to sharpen your skills and find out about jobs in your sector. Let’s take a look at some resources from speakers from the conference series: community Codemotion 1. When you’re a junior developer and frustrated by job rejections Many people are working in tech who arrived from other careers or degrees. is one of them, juggling a career in photography with learning to code, eventually becoming an engineer. She shared with us about the challenge of getting a foot in the door as a junior developing noting, Berta Devant “There is a common misconception that junior developers are a burden and won’t be delivering value for the first months or even years.” Berta experienced a struggle many of us are familiar with. She was applying for jobs that come with a grocery list of job requirements, most of which require years of experience. More than one employer told her “Just come back again in three years” She shared: “One thing that got to me after being a developer for like an actual company with a team for about a year and a half now I realized that half of my job more than half of all the time it’s not even code, it’s about people and it’s about learning." 2. You’re part of a team with infighting Conflict at work can be a distraction from doing actual work, and offer plenty of entertainment value and fodder for gossip. Still, ultimately conflicts are destructive and can result in lost jobs, lost reputations and lost credibility. However, Lea Böhm, CEO – AllesRoger UG encourages workplaces to take a different look at workplace conflict. Avoiding it might not be the answer – or quitting. She notes: “Always after quitting a job I looked back and was thinking to myself what is it that learnt from that experience was what is it that I could have done differently maybe to not leaving this situation but just make it better for myself so then another job.” Whether its teams fighting over slack or leaving disparaging comments in GitHub, there might be a way forward. Find out how you can embrace conflict resolution and build a bridge between warring teammates. You can also view Lea’s . slides 3. You’re struggling to keep your knowledge up to date We’re in an era where innovation cycles are evolving at a faster speed than any other time in modern history. As a developer, the challenge is not only to maintain your knowledge but anticipate and prepare for what comes next. took a critical, detailed look last year at how developers can keep learning, positioning devs as expert beginners. He offers a series of takeaway strategies including: Dennis Nerush “I have a board of sticky notes, and I add a term every time I hear something at the office, but I don’t really understand -some framework or some buzzword whatever. Later, when I have time, I google it and try to understand what they  mean. Usually, I find my answers in different blog posts.” Take a look to see what other kinds of resources are crucial to developer learning. You can also follow along with Dennis’ slides 4. You’re new to managing a team asserts that: Alessandro Cinelli “Becoming an engineering manager or a leader organizational leader is not a promotion, it’s a total career change.” He shares his belief that being an engineering manager is not cool and the value of the servant-leader style of leadership. He also offers some great ideas tools and tips to get team feedback and insights – the two monitor story is particularly memorable! Watch to see if an engineer manager should code. 5. You’re sick of working at a job for a soulless organisation is the CEO/Co-founder of , the world’s first non-profit computer consultancy company. She presented a fantastic talk last year at about post-growth entrepreneurship. She challenges the ideology of profit and asks why there haven’t been walkouts of business school or startup incubators. Take a look for a deep dive into social entrepreneurship and how you could change your life – and industry – with a new kind of enterprise: Dr Melanie Rieback Radically Open Security security Codemotion Rome

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A well-planned influencer campaign can yield much better returns than traditional banner ads. For a fintech project, collaborating with influencers can be a key to success – but only if you manage to negotiate all the stumbling blocks. , 89% of marketers believe that investment in influence marketing generates a higher ROI than other channels. According to a Mediakix study In some cases, every dollar spent on influencers yields . Therefore, it’s not surprising that this market is growing so rapidly. Experts predict that it will reach . up to $8 $10 billion in 2020 The potential of Influencer Marketing is particularly high in the blockchain industry. With dozens of projects launched every month, investors physically can't research all of them. Moreover, most token buyers aren't blockchain experts, so it's hard for them to evaluate the potential of each platform. When choosing where to invest, they rely on the opinion of the influencers they trust most. This makes influencer marketing a great way to win over your target audience and create a buzz around your project. But what's the most efficient way to use this marketing channel? Blockchain influencers: price and quality Among the most popular services offered by influencers are YouTube reviews, tweets, and Telegram posts. By the way, YouTube accounts for almost generated by blockchain-centered social media channels (see the diagram). Twitter comes second, followed by Facebook – in spite of the fact that advertising crypto projects is banned on the latter platform. 40% of all the traffic Credits: Crypto exchanges' marketing activity – August 2018, BDCenter Digital Those startups that can afford working with famous influencers like or often get great results – as it happened with the videos in the screenshot below, which scored thousands of views: CryptoLark Ivan on Tech Credits: Ivan on Tech' vlog. All three videos contain promos. Tweets also allow projects to reach out to dozens of thousands of potential investors. The most notorious example is John McAfee, who has over a million followers on Twitter. In 2018, the creator of the famous antivirus software said that he charges . However, McAfee only accepts those proposals that he finds interesting. Some of them are rather unexpected – like this project of a zombie farm in China: over $100 000 for one promotional tweet Startups with smaller budgets can choose among dozens of influencers who charge between $200 and $400 per tweet. They usually have from 20 thousand to 50 thousand followers. By the way, you don't contract an influencer with a huge audience to get good results. So-called micro influencers often have more loyal and engaged followers and thus generate more conversions. Choosing the right influencer Blockchain influencer marketing only works if you pick the right person to promote your project. You need to find out the answers to the following questions: Does the influencer's audience overlap with your target audience? What's the ratio of the number of followers and the price of one post or review? What's the average number of views, likes, and retweets per one post or review? Does the influencer promote clearly poor-quality projects? (You'll hardly want to find yourself in the company of losers). Does the influencer conduct a real in-depth analysis of the projects under review – or does he simply read the copy from the official website? And the most important question: could this be a scam? Beware of fake influencers Dubious practices and scams are common in influence marketing. For example, hundreds of shady characters pretend to be famous figures like or Crypto Bobby. Louis Thomas A tweet by Crypto Bobby, warning of scammers impersonating him A marketing offer from a fake Crypto Bobby on Telegram Credits: MinedBlock.io official Telegram chat Of course, such an «influencer» will immediately disappear without a trace after receiving your money. One more issue plaguing startup chats in Telegram is the never-ending stream of marketing offers. They mostly come from people who claim to be influencers – though they are nothing of the sort. Such characters tend to own minor channels on Telegram and YouTube. Most of them aren't experts at all, and their audiences are small. Their prices can seem attractive, but don't count on a lot of conversions. Study the market before making a decision Even though the risks of working with influencers are quite high, you shouldn't neglect this marketing channel. There have been cases when influencers generated over half of the ICO revenue. But in order to get such a dramatic effect, you need to invest not only money, but your time – lots of time. The best strategy here is #DYOR, or Do Your Own Research. You can also delegate the task to professionals you trust. One thing you should never do is agree to an incoming offer without thinking it through. You shouldn't pick the first influencer you see on a marketplace like Coinbound, either. Spend as much time as it takes to do a due diligence of a large number of influencers. It's better to delay your campaign launch and carry out a comprehensive evaluation than waste thousands of dollars on influencers who can't convert. As long as you stick to a systematic approach, you'll be able to find an influencer who will check all your boxes in terms of fees, audience size, and expertise. However, your role doesn't end here. You can face quite a few challenges when working with influencers. In one of our next posts, we'll explain further.

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Influencer Marketing can Generate up to 700% RoI But Comes with Risks!

or how to open your project to the world Hello and welcome to the second part of the “Open Source” journey. For those of you who haven’t read the or wonder what is the planned content for the next parts: first part Table of content Intro Starting a project Documentation Publicity Issues and PRs (WIP) Automation (WIP) Versions management (WIP) Recap In the previous part we’ve discussed what is Open Source and why anyone would bother with that. In this part we’re going to get more practical and we’ll discuss how one actually starts an open source project. So you’re in scenario #1, you have a solution for a specific problem and you’re eager to share it with the world. Let’s emphasize it again: It’s not about your ego You’re not hoping to benefit from it You truly want to help others with the same problem If you answered yes to all of these then here is a quick checklist for you to make sure you’re doing the right thing: Make sure that open source is the right format. If it’s something small that you want to share with the world then a small blog post might be just enough. Double check that a similar project doesn’t exist already. Maybe your solution makes a perfect PR for an established open source project. Be prepared for what’s coming As I mentioned, owning an open source project carries a lot of difficulties. One difficulty that stands out is that it requires a lot of your time. Everything that you do for your open source requires time, whether it’s writing code, managing issues, updating dependencies, talking to people, answering questions and etc. Every minute that you invest into your open source is a minute that you could have but didn’t invest in your family, your hobby, your health and what not. The only thing that you can do about it is delegation. When (or should I say “if”) you have enough collaborators you can outsource part of your responsibilities to the people you trust. Code separation So here we go, you have a solution for your specific problem and you think others can benefit from that. It is still integrated within your code base and you probably don’t want to make the whole code base an open source (unless you do). First you have to separate this code from the rest of your code base. Start with refactoring …which will eventually cause all the code that is going to be opened to reside in a separate directory. Make it generic Make sure that the code in the new directory is generic and is not bound to your specific problem, make an abstraction layer if needed. As an example I started with a very specific need (coming from ) of adding custom loader for native modules to Angular build. angular-builders one of my other open source projects I could have created which would serve solely this very purpose. However, I realized that at a relatively low cost I can create a more generic solution that would solve similar (but not the same!) problems. This is how builder was born. native-module-builder custom-webpack Keep it simple Generic is great but be careful not to get too excited about that. Premature optimization and over-generalization are two very well known problems in software engineering. You should find this sweet spot where your solution solves problems other than yours but not all the problems in the world. If you build a scale where the solution for your specific problem is 1 and a solution for all the world problems is 100 then you should start with 2. Your generic code should be able to solve a a few more problems than your specific code. Eat your own dog food Keep using this generic code in your code base at every step — doing so makes sure you eliminate the unnecessary parts and leave only what’s needed. It also ensures that the code you’re going to open is working properly. Remember, you are the very first user of your open source project. Don’t get sued If you’re extracting the code from your company code base consult with your superiors and if needed with the legal department. Make sure that they support your initiative and that the code you’re going to open is not subject to the IP (intellectual property) of your company. This will also help you to decide which is more suitable for your project. open source license When everything is working, the code is separated and is generic enough, you have all the approvals (if needed) - it is time to open it to the world. Opening the project to the world Once your open source code is separated and generalized it’s time to disconnect it completely from your code base. Going public First, you have to open the source code of your project (at the end of the day that what makes it an Open Source Project!). There are for hosting source code online but we’ll go with the default — Github. different options on Github Create a new repo Clone the repository Move the sources from the directory you previously created (don’t remove the directory yet). Commit & push — voila it’s now an open source project. Or is it? Creating a package Your project is publicly available, but no one is using it (including you, since you’re using a copy of this code within your larger code base), and no one is aware of its existence. Furthermore the only format in which your project is publicly available on the web is the , and the only way to consume it is copy-pasting the code into ones code base. Not a very convenient way, don’t you think? source code In order to properly distribute your project you have to: Create a package out of the source code Make this package available on one of the public package registries (depends on your ecosystem, for example, for Java it might be , in the case of JavaScript it might be and etc.) Maven Central Repository Npm Package Registry This is when you add a build chain to your new shiny repository, define project name etc. I’m not going to break down the whole process because it is very dependent on your ecosystem, set of tools and language you are using. You might be an all around person to whom defining a new project as well as adding a build chain and publishing the package is a piece of cake. If this is the case — good for you! You also might be a person that is used to only write code but never faced all these definitions, configurations, artifacts and stuff like that. It might be a whole new world to you. Well, time to learn. Not going to be quick, I promise you that. In any case When you’re done with filling all the missing puzzle pieces in your head, you’ve learned everything about the relevant package registry and your package is actually published, can you truly consider your project open source. then and only then At this point you can actually tell people: “Hey, I already have a solution to your problem, just add this package to your project and use it!” Sanity check Before your project goes viral you better make sure it works. A sanity check for your package would be actually removing the generic directory from your larger code base and use the publicly available package instead. After all, . you’re the very first user of your open source project Further development When you start using the package in your code base the development flow is likely to change: previously the now-open-source-code has been a part of your code base; you could consume the changes right away but now it’s as much of an external package as any other 3rd party package used in your code. Thus, when you develop a new feature in your shiny new open source project you’ll have to publish it first in order to be able to consume it in your larger code base. However you cannot publish it if you aren’t positive it works, because once published it might affect other users. So here are a few things you can do in order to avoid publishing broken versions: Cover your code with tests, both unit tests and end-to-end tests. I don’t think I have to convince you how important the tests are. Package and install the new version of the package locally, into your larger code base. Once verified that everything works as expected you may publish it. Publish a beta version which is available only for those who explicitly want it rather than to the whole world. For example in npm package registry there are dist tags that can be used for this purpose. The default tag is latest and when you run npm install mypackage it effectively runs npm install mypackage@latest . When you publish a new version under another tag, for instance beta , one will have to explicitly install from this tag in order to get the latest version: npm install mypackage@beta . Wrapping it up Unlike the previous part, which was completely theoretical, this part actually requires some work from you. Depending on your experience and learning abilities it might take you a few days or even weeks to complete this mandatory step. And we didn’t even start yet. This is why it is my duty to ask you again: Are you really prepared to give a decent amount of your precious time to the community? Thanks for reading, make sure you follow me here or on if you don’t want to miss the next part in which we’re going to talk about . Twitter Documentation

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Open Source Series: Starting a Project

I will get redis and redis commander up and running using docker. Source files can be found here: in folder recipe-04 https://github.com/ikknd/docker-study 1. Create docker-compose.yml file: Here I do several things: create volume - redis-data. This is where all redis data will be stored, even if container is restarted, data will be there. command: [" ", " ", " "] - I start redis in persistent storage mode redis-server --appendonly yes - tells redis commander how to connect to redis REDIS_HOSTS=local:redis:6379 and - protect redis commander interface with login/password HTTP_USER HTTP_PASSWORD " " - prevents container to start before other container, on which it depends depends_on 2. Go to /var/www/docker-study.loc/recipe-04/docker/ and execute: docker-compose up -d If I now try - I will see Redis Commander interface. localhost:8081 I can login using root/qwerty credentials. Note: if I need to use my own redis.conf config, I can add to redis section: volumes: - /var/www/docker-study.loc/recipe-04/docker/redis.conf:/usr/local/etc/redis/redis.conf and change: command: ["redis-server", "--appendonly" , "yes" , "/usr/local/etc/redis/redis.conf" ] Stay Tuned!

How To Configure Redis + Redis Commander + Docker

Are you new to coding? Have you been struggling to come to grips with your first programming language? If so, allow me to illuminate an early step on your path to becoming a software developer. Coding is an easily accessible field for everyone with an internet connection. It’s a great medium for anyone thinking of switching fields. It’s also great for anyone looking to adopt a new hobby that mentally engages the mind. There is a wealth of online resources that allow you to start building a base of knowledge. HTML and CSS are the recommended languages when diving into the realm of web development. They are easy to grasp. Their syntaxes are straight-forward and their rules and properties bear intuitive names. So you’ve done your research and you’ve decided to begin with the (insert tutorial name here) program. Everything is going fine so far. You’ve spent a few hours each day, reading web notes. You’ve gotten into the groove of watching tutorial videos on your favourite platform. Now it’s time to start coding. It’s usually at this point that things start getting hazy. You write the page skeleton and start adding headings and ‘p’ tags. Before long you have content on the page but everything looks a bit funky. Time to apply some CSS. After adding some text colour, font styling, and an input field, your excitement builds. You now have something resembling a very basic 1990’s website. It’s not what you had in mind. You were trying to get that 2019 goodness going on, but here you are. Here’s what you should do next. Slow Down Trying to run a race isn’t going to get you anywhere. When building any webpage, it pays to first take stock of what you’re trying to build. Break the webpage down into sections. Does it have a navigation bar or a sidebar? How many sections are there? How is the footer structured? Try breaking down the webpage into semantic sections first. If your mind struggles to do this, break out a pen and paper. Draw out the website as a collection of boxes (also known as a blueprint or ). You could also check out this for a list of awesome tools to achieve this in digital form. wireframe article Once you have your wireframe built, you’ll have a better idea of how to move forward. Also, don’t try designing everything at once. Start at the top and work your way down in a systematic fashion. Rewind So you’ve got your first few page sections built. You’re unsure of how to position those pesky divs in the way you want to. Rewind and revisit those tutorials. Go through each new and relevant piece of information step by step. Keep trying things out and testing concepts until you have a good handle on them. Think of the web page as your own, personal scrapbook. Declare blank divs and use them to figure out how each CSS rule works. Trial and error is the best teacher. Get Help You’re trying to clone a website and the tutorial instructs you to use the browser’s developer tools. This affords you extra help in seeing how things are organised on the page. Yet, it doesn’t quite explain the finer details and you’re still a bit confused by what you’re seeing. Don’t be afraid to ask for help. The developer community is quite vibrant. You’ll find answers to most of the problems you will encounter when now starting. Websites like and have tons of user answers to developer questions. Programmers, both novice and experienced alike, have used these resources over the years. You’ll find answers for both simple and complex problems. Also, if you’re not finding a very specific answer to the problem you’re having, feel free to ask a question. There’s a very high possibility that someone out there knows the solution you seek. Reddit Stackoverflow Stick To The Process Coding can get frustrating at times. Information, while easy to come by can sometimes be hard to understand and apply. This is especially true if you’re now starting. Don’t let these early frustrations get the best of you. If you’re stuck on something or you’re simply burnt out. Take a break. Consecutive hours spent learning a new concept can take their toll. Leave the computer and get a snack, drink some water or get some rest. More times than not, progress lies on the other side of a refreshed mind. You’re anxious to become a crack-shot web developer, and I understand. Rome was not built in a day. This process will take the kind of time and effort that few other things do. These simple principles will ensure that you build a solid foundation. One in which you learn and test those new things as you learn them. Before you know it, you’ll get into a rhythm of improvement. You will develop a level of comfort as you continue on the path to becoming an expert programmer. Happy coding!

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The Coding Barrier: A Simple Guide to Clearing The First Hurdle

It’s a fact that software developers are in great demand these days, to say the least… The growing need for building more tech products vs. well-trained resources is out of balance. There’s plenty of studies and articles explaining why that happens and that as the number of tech companies increases, we have more job offerings every year for almost the same number of computer science students. But that’s not the point of this article! X If you’re a developer you already know this as you’re constantly being harassed with “come to the dark side” messages from nagging HR recruiters trying to lure you into changing your job. So what are your criteria to evaluate if you should change your job? Is getting higher pay, higher responsibility or just a change of scenery? Well, what I’m about to tell you is beyond all of those reasons! If money or status is all you care about, then you should stop reading here as I’m not adding relevant information for your decision criteria… 1. Building a product from scratch Yes, it is easier to do improvements on an existing product. It sure releases you from several headaches, but bear with me for a second: Imagine that you could build Twitter from scratch. Or GitHub? Can you imagine the feeling of belonging and the sentiment of ? Let’s ignore the crying on their first months of life… 😅 Taking it into perspective it almost feels like magic and that anything is possible if you put your mind to it. calling a product a baby of your own 2. Being able to work with several technologies or areas of expertise Working with a big company as a developer means you’ll have a very limited scope in what you do and the role you play within the company. Usually, you’re assigned to developing a specific feature or a component within a feature (featureception? 🧐) reporting to a manager that reports to another manager until the chain is completed. This may seem like it takes most of the responsibility out of you, but I’m sure that it will eventually make you bored or even hate your job. On the other hand, working in a small company (especially in a startup), having fewer resources to hire loads of people makes you have an important role in what the company develops. It will make you learn new things as emerging tech, owning a big chunk of the product development as well as articulating with other areas of the company, which will increase your skill set and make you a more valuable professional! 3. Working remote, flexible hours and a fun environment It is a fact that big companies are trying to adapt to this new future of work environment in which you can work from home, enter and leave work when you please or simply have a table tennis game during lunch break. But changing a company’s culture (even if you’re improving it) is something that takes a while. In newborn companies (especially tech ones) that culture is already built-in: working remotely is an option, having to leave early to go to a doctor’s appointment or just to , having and other fun activities… collect your kids from school lunches, beers, dinners, go surfing More than that, you can and not afraid of that scary corporate boss that doesn’t like your yellow flipflops or your black hoodie. Working in a place where everybody works for passion and not as an obligation will make you want to wake up to go to work everyday! be yourself 4. Long term opportunities are enhanced or created As a wise man said once “with great power, comes great responsibility”, Uncle Ben (or Voltaire if you’re old school). Responsibility of building a product or leading a whole department brings you invaluable experience that would be difficult for you to replicate outside of the startup ecosystem. So if you take a step back and visualize the whole picture, even if you get paid more now, working in a very narrow function and with the same tech stack every day for 10 years will bring you a lot less than being able to learn new tech, lead projects and build new products, in which you’ll have a and even if your startup tanks, your experience will be worth . faster learning curve at least 3x more in the market 5. Accepting that risk is everywhere, whether it is a big company or a small one Failing is what makes us humans. And if you think that only startups fail, you’re wrong. Remember MySpace? What about Blockbuster or Kodak? All big companies that failed to innovate and eventually went bankrupt. It is true that as they’re building new products or services that may or may not have market fit. But sometimes, (remember Lehman Brothers?). statistically, startups have a bigger tendency to fail a big bank also goes kaput Instead of running away from risk you should evaluate it and embrace it as part of your decision. If you truly believe in a company’s vision isn’t it riskier to stick with a company that you don’t like, even if it brings more financial stability? Risk is not only financial, but it is also a bunch of factors that you should consider, like emotional stability or happiness, for example. 6. You can make a difference in the company’s vision and culture Last, but not least… Joining a small company is not just about creating a good product, learning faster or having more long term opportunities. When you’re in a company with less than 50 people YOU will make a difference. You can set the course of a company to success or failure, but assuming you’re in it to drive it to success you’re leaving your mark that eventually will last for a long time, even if you leave it. This is something that you won’t get anywhere, I’ll assure you that! To sum it all up… These are the reasons why choosing a startup over a big corp might change your life for good! Statistically, it will make you happier, train you to be a better professional and a better individual, and of course, if it all works out, it will make you get better pay in the long run! Bonus Conclusion This works for software developers as it works for any other field, such as business developers, marketers, even executive management! 😎 Our Culture This is what we’re building at . Not only as a product but as a team. It may sound like a cliché, but I truly see my team as a family that is building a future together and that is reflected in our culture. Don’t trust my word for it! Ask anyone who’s in the team! TAIKAI If you feel like you relate to this somehow, reach out to us at or find more info on our careers ! hello@taikai.network here TL;DR: The smaller the company the happier you’ll be! Bye for now! Previously published at https://medium.com/taikai/developer-are-you-looking-for-the-freedom-you-will-never-have-in-your-company-fed91d5fa706

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2020 - Hacker Noon Contributor of the Year in INNOVATION

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