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1Fit is a dynamically growing startup offering fitness classes via a mobile app. Our goal is to make healthy lifestyle as affordable as possible.

1FIT

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NAV edges down 1 per cent to $199mn (632¢) $6.3mn positive revaluations of four smaller investee companies offsets $5.26mn write-downs on four unlisted holdings 1.3 per cent stake in Bolt rises $1.5mn to $71.2mn Investors' increased focus on start-ups ...

Despite write-downs, TMT is a bargain way to own start-ups

44,000 startups. 4,553 cities. “4 billion votes” It was such a blessing to get to know so many of your ideas and innovations from every corner of the world in 2021. The startup community amazed all of us with your hard work and persistence despite all of the challenges that we faced globally throughout last year. Not gonna lie, the competition got our hearts racing like Eurovision. We won’t let you wait for much longer, below are long-awaited results! ✨✨✨ To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review.) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement Series 🔥 Central Europe Central Europe has always caught our attention with the talented pool of people in tech community. But were they able to get through 2021 despite the pandemic? Let’s cheers to the most outstanding amongst 4,500+ startups who have been working hard this year: Basel, Switzerland An overwhelming vote for ~ in their mission of helping machinery companies drive more service revenues from their installed base MachiQ Software Berlin, Germany We have with a whooping 538 votes! Let’s support Plan A with its mission to help companies to measure, monitor and reduce their environmental footprint. Plan A Düsseldorf, Germany It doesn’t get any easier with Kicking off in helping people organize online schedules more easily. EasyWeek! Geneva, Switzerland Manage code quality? We have to give props to for their startup! SonarSource Hamburg, Germany Taking over Hamburg is , an acoustic device to reduce noises in vehicle cabins. No more annoying sounds on the road 🚗 ReCalm Köln, Germany Small business? Hoping to manage your marketing campaign? is winning the vote! rankingCoach Lausanne, Switzerland is a Swiss-based company focused on cryptocurrency management solutions built for financial institutions. Metaco Leipzig, Germany empowers companies to operate and utilize drones for the most effective industrial data applications. FlyNex Mainz, Germany is growing sustainable digital mobility startups! Next Mobility Labs Munich, Germany wins for top-line, high-security international Identity Management! ORY Stuttgart, Germany Remote desktop application? Ask anywhere. AnyDesk Vienna, Austria , an IT company enabling privacy-preserving big data applications in the digital age. Mostly AI Zug, Switzerland Way to go 1762 votes for the first provably live pure Proof-of-Stake (PoS) CBC-Casper blockchain designed for real-world applications. CasperLabs! Nyon Calling for robust data solutions for impact-driven organizations! Havuta LLC Zürich is revolutionizing small business lending to give easy access to capital to those who need it most: SMEs ✨ Teylor Heidelberg, Germany is an innovative HR startup providing a solution for fast and efficient recruiting of employees with simple job profiles. Talk'n'Job Stuttgart, Germany Congratulations for winning this title! AnyDesk Moving on to our buddies in... 🔥 Eastern Europe Eastern Europe is home to many familiar big names in our start-up world. Trace history for Miro, Telegram, and Grammarly and you know where they are from. Who is the next generation for Eastern European startups? Budapest, Hungary is a platform for mobile app development, enabling tens of thousands of developers to automate integration, testing, and deployment. Bitrise I__ __ stanbul, Turkey Leading here is - a brand new software intelligence platform- helps organizations deliver faster, high-quality products by connecting their DevOps tools. Oobeya Kraków, Poland is a human cloud platform that connects global enterprises with European software houses, offering tech talent as a service. Talent Alpha Kyiv, Ukraine 281 votes for , an AI powered app where people can swap faces in the videos, GIFs and images in just a few seconds. Reface Sofia, Bulgaria 387 votes for __ __378, leading Blockchain and DLT solutions company that specializes in helping enterprises and blockchain startups design, build and scale innovative solutions using Blockchain & DLT! LimeChain Warsaw, Poland is a next-generation blockchain document management software (DMS). DoxyChain Yerevan, Armenia Whoever clicked to vote for was sure a vote, because it ended up winning as a full-service software provider delivering Artificial Intelligence & Machine Learning solutions for businesses. SmartClick smart Almaty Just like the name suggested, provides one pass to dozens of gyms and studios. 1FIT Debrecen got all the coders ranked figured out! CodersRank Kharkiv is complete solution digital assets management! Pics.io Minsk, Belarus with 243 votes is the first blockchain-powered sports media platform. Scorum Moscow, Russian Federation 1 e-grocery company in Russia with 26 votes and #1 on our HackerNoon board for Minsk. SberMarket Saint Petersburg, Russian Federation leads this area as a personal finance application that rewards responsible spending and savings. CoinKeeper Kazan, Russian Federation Smelling a sweet victory? 3d App designers for flower bouquets is in the house! Flowito Western Europe 🔥 Brexit? Pandemic? It seems like startups in Western Europe still keep their spirits regardless. Startups in Western Europe that stood out in votes this year go to: Antwerp is on a streak! As a bug bounty platform, it ensures online security through a crowd-sourced platform for computer experts. intigriti Belfast A record-breaking 10,003 votes? is an independent coffee shop. Come grab a coffee! Bobs & Berts Birmingham What’s happening in Birmingham? We have , an innovative UK-based advanced lithium battery developer aiming to accelerate the global shift to cleaner. Aceleron Bordeaux 30 is an open-source web framework for building modern apps and websites with Vue.js. NuxtJS Boulogne-billancourt is a software company that provides an in-store analytics platform to optimize operations. DiGEiZ Brighton In for some jam? offers musical education services. BIMM Bristol Another tasty victory for Western Europe is enabling the strongest digital encryption on the internet. KETS Quantum Security Brussels A fashionable win for , a social fashion network. You scan barcodes in fashion shops and see every influencer wearing those outfits! Share your wishlist with your friends and earn cashback. SCANDALOOK Cardiff is a technology company developing end-to-end digital connectivity for the point-of-care diagnostics market. Bond Digital Health London A whooping number of 1085 vote__ __ offers hardware based cyber-security solution for authentication and encryption in a wired local network. Crypto Quantique Edinburgh Hopping up on the crypto movement, just launched last year but they are already rocking it at making crypto accessible to everyone. Zumo Ghent 5379 votes for ! A Belgian InsurTech startup that helps insurance providers all over the world to better connect with their digital customers. WeGroup Glasgow specializes in single-cell analysis transforming the life sciences. Biomage Paris Show some love in the city of love for , a sports platform that is connected to a mobile sports coaching application. AirFit Aberystwyth enables artificial intelligence, data science & digital innovation solutions for the agriculture, biotech & life sciences industries. Agxio Norwich Congratulations for the amazing win of 1124 votes! WATR Charleroi Congratulations for your amazing win! Hirering Gibraltar Give a round of applause to Fortknoxter! Waltham Abbey is ruling the area with its amazing 72 votes! RPAIR Antwerpen Rocking up Antwerpen area is the rising startup ! Willow Egham What’s happening in Egham? Proudly announcing winning the Noonies this year! ATA.ONE Leicester Leicester is calling the name of as the winner for the region! DeadHappy Macclesfield Who else but ? Congratulations on dominating the award! AeroCloud Northern Europe 🔥 Want to listen to some music? Spotify. Need some games to kill time? Candy Crush. What about a quick call to someone you haven’t seen in a long time? Skype. These are all startups that originated from Northern Europe! Noonies, let’s take a look at the upcoming generation of startups from Northern Europe that hopefully will see an even bigger and brighter future. Gothenburg, Sweden Shipping was never made for small shippers, so took on the mission to built an end-to-end logistics platform to empower small businesses to spend less time and money on shipping and more time on growing their businesses. Sendify The Netherlands provides a virtual platform for many kinds of conferences and events. Let's Get Digital Helsinki, Finland A curated collection of public basketball courts in the Helsinki Metropolitan Area called . Everyone is welcome to submit to their local court. Koripallopaikat Lund, Sweden develops an antibiotic-free, safe and effective treatment of vaginal fungal infections. Gedea Biotech Luxembourg, Luxembourg 🚀 is a job-matching platform that connects developers and tech experts with CTOs and team leads from the best tech companies across Europe. 👾 nexten.io Malmö, Sweden Feeling studious, Noonies? __ __e is a tool for Google Classroom, that enables formative matrix assessment towards course learning objectives directly. StudyBe Oslo, Norway is the platform supports a unified API across all connected device categories, removing significant friction for app developers. Domos Labs Reykjavík, Iceland __ __s is a mobile-first technologically advanced neo-bank. Indo service #S__ __ tockholm, Sweden is the biggest blockchain start-up in Sweden. Double winner of the most prestigious nomination in Nordics 'NyTeknik 33-listan', winner of the Deloitte Fast 50, and now Noonies Award! ChromaWay AB T__ __ allinn, Estonia Passing 1000 votes is a fully digital and decentralized ecosystem for all actors in supply chain. OBORTECH Vilnius, Lithuania is an easy-to-use money app for cards and accounts. Via Genome, users can open a business or a merchant account or a personal one with dedicated IBANs, access transfers, exchange currency, process payments, and manage the funds. Genome Aalborg Digura A legaltech offering tenants fast, digital and reliable legal assistance Aarhus Partisia Market Design provides secure, simple, efficient solutions for auctions and exchanges realizing the potential of the market. Helsingborg is a hi-tech company developing cardiac occlusion devices and other vascular implants. Occlutech Tallinna is an AI-powered system for marketing automation in social networks. POSTOPLAN 🔥 Southern Europe Southern Europe’s economy went through a hard time this year due to a huge reduction in tourism. But our startups are of course always headstrong, from Spain to Italy and all the way through Greece. Who are the heroes rising amidst changes of Southern Europe? Skopje is a versatile software development company that solves the business challenges of SMBs, big brands, and brave hearts with awesome ideas. CodeChem Sliema Sounds like just a relaxing exhale. is a holistic health and wellbeing app built to allow users work on the development of their physical, mental and spiritual health Few Tajonar is a company of experienced business & industry entrepreneurs offering solutions to industries based on cutting-edge technologies. das-Nano Tarragona is a technology-based company focusing on the production of integrated microsystems. IMICROQ Terni is an ICT-based integrated solution that is able to modulate, respond, assist and learn to control most home activities. Mind Tirana is a video content and e-commerce platform for the Balkans built on top of an Albanian language specialized search engine. Gjirafa Torino develops and commercializes algorithms based on Artificial Intelligence to increase the autonomy of small satellite missions. AIKO T__ __ orres Buy online groceries like you would buy offline with personalized picking as fast as in 4 hours with Vedras 360hyper! Trento is a music-based platform built around streaming platforms that allow users to discover new emerging artists and make new friends. Brots 2021, what a YEAR. Congratulations to all of the startups that made it to the top winning list for Europe! Stay tuned for Startup awards in 2022 and let’s cheers to a new year of more innovative ideas, bold innovations, and robust creations from you, Europe 🌷 More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written by An Le and edited by Natasha Nel. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Europe

That's likely a question that you've never asked yourself, but as a manager, it's an important one. Career conversations are an important part of We’re going to define 5 levels that will make it easier for you to have a better career conversation. Through self-reflection or 360 feedback, it should be relatively easy to know where you stand. You can then set the next highest level as a tangible goal to improve. career development. Each level builds on top of the previous one. Let’s dive in. Level 0 Description: Manager never initiates any conversations with employees about their careers or development. If the employee brings up a career conversation the manager is not helpful. They’re either dismissive, unsupportive, ignorant, or don’t offer any insights. Grade: F You don’t want to be this manager! The good news is that it’s hard to be at this level. The most likely explanation is that you don’t believe in supporting your employees’ careers and development. You may believe that it’s not your job or that it’s not in the best interest of the organization. If so, I think you’re mistaken, though I would be curious to understand that point of view better. Here are some of the potential risks of not supporting them: Decreased engagement Decreased motivation Decreased innovation and creativity Lack of trust and goodwill Tips for improvement: Be prepared to answer the following questions from your employees: How do promotions work? What are the different career paths? What is my next role, and what does it take to get there? What should I be working on to progress in my career? Discuss this with your manager, HR, and peers. You want your answers to be accurate and consistent with the rest of the department. Level 1 Description: Manager doesn’t initiate conversations about careers and development. Manager is helpful when the employee initiates a career conversation. Grade: C This is better than nothing. At Level 1 you may think that you don’t have time to support their careers and that it’s their responsibility. You’re at least providing value to them. If you’re good at responding when they initiate a career conversation, you may even add a lot of value. You can do much better though. Here are some of the downsides of this level: It’s not equitable. The people that are comfortable and skilled at initiating career conversations will benefit more than those who aren’t. It’s hard to know what you don’t know. The employee can only bring up career topics and options that they’re familiar with. This could leave some valuable conversations out. The employee is making all the effort which is not as good at building trust and goodwill. If there’s little trust the employee is less likely to share their career goals. Tips for improvement: Schedule multiple times throughout the year to have career conversations. Prepare for each conversation. Make sure you understand career progressions in your department well. Anticipate common questions. Level 2 Description: Manager initiates some career conversations, usually at review time. Manager discusses the employee’s career options in the department (e.g. career ladder and/or different tracks). Manager discusses the employee’s next role, where they stand, and how to get there. Grade: B This is pretty good. You could consider this your target level or your minimum requirement. At Level 2 you're taking an active role in the career conversations. Your focus is helping employees progress their careers at your company. This is reasonable and will provide solid value to the employee. You can still do better! Here are some of the downsides of this level: The default progressions in your department may be limiting to some employees. Focusing on the next role may feel transactional and less inspiring. The focus is on the benefit to the company which is sub-optimal for building trust and goodwill. Tips for improvement: Expand the scope of your career conversations. Get curious about them as individuals and about their career goals: What are their personal goals? How much is career growth important to them? What kind of growth are they looking for right now? What are they passionate about? What do they want to learn? Expand the career options that you’re comfortable discussing beyond the next role in your department. Talk about career and growth frequently in your 1-1s. Experiment with a variety of different conversations and questions. Level 3 Description: Manager discusses the employee’s Career with a capital C. Manager takes a more holistic view of their career unconstrained by the roles available in the department, or even the company. Career conversations explore short-term and long-term goals. Conversations occur more frequently throughout the year. Employees trust the manager enough to shares their true career goals. Grade: A- This is a great target level for all managers. The big difference at this level is that for the first time we’re putting the goals of the individual on par with the goals of the company. We’ve made the transition to in the belief that the best way to serve the company is to serve the company the individual. We're proactively supporting the individual. This not only builds trust and goodwill; it also adds a lot more value to them! If they trust you, they are more likely to tell you what they really want. This gives you a much better chance to find them work they love. It’s not crazy to say that if you do this well you could have a profound effect on someone’s career, maybe even their life. servant leadership and Tips for improvement: At this level improvement becomes harder. Progressing through the earlier levels is mostly a question of taking the time to perform various actions. To progress beyond Level 3 you have to view career conversations as a and get serious about working on it. skill Read Radical Candor Three Powerful Conversations Managers Must Have to Develop Their People Three Questions Every Manager Struggles with Making Career Development Plans Different 1-1 question banks , , and here here here Practice Have career conversations often with different people and reflect. What went well, what could have gone better? Have mock conversations with peers or a coach. Work on your . listening skills Use mindfulness training to improve your . emotional intelligence Get feedback Ask your team members for feedback. What was helpful to them? What wasn’t? Use an and pay attention to the scores related to career support. upward feedback survey Get feedback from a coach, your manager, or HR. Get a coach or a mentor Level 4 Description: Manager inspires the employee. Manager motivates the employee. Manager expands the employee’s realm of career possibilities. Manager sees potential that the employee doesn’t see in themselves. Grade: A+ The Picasso of career conversations! This is Level 3 executed at an expert level. The other levels are attainable through effort and knowledge. This one is not. This level requires superb emotional intelligence, listening, and job crafting skills. You need to have a great feel for asking insightful questions. You have to know when and how to push people out of their comfort zones and when to reassure and build up their confidence. You have the humility to coach people to find their own path instead of imposing your vision. This level requires the skills and the dedication to apply them, one good career conversation at a time. This is really hard to do consistently. Which is exactly what you want for an expert level! Final thoughts These levels are not perfect They’re not going to be neat and precise, and they don’t capture all the subtleties of career conversations. You may find that you don’t fit nicely in any of them. You may reach a higher level with one employee versus another. As imperfect as they are, they can be helpful The benefit of these levels is using them for a quick self-assessment: . . What level are you at with each of your team members? What would you have to change to move up one level? If you realize that you’re not at the level you want to be, don’t be too hard on yourself. You have a lot on your plate and chances are you’ve had little to no training on career conversations. Being a better career conversationalist is a great way to add value to each of your team members. Each career conversation is an opportunity to build trust, increase engagement, and job satisfaction. If you can have them often and skillfully your team will be more likely to stay, perform at a high level, and be happy. Challenge yourself to get to the next level!

Have You Hacked Level 4 of Career Conversations Yet?

Predicting the Attrition of Valuable Employees….. In an IT firm, there are many Employee Architectures available. Some IT firms or at particular departments or certain levels follow the structure, in which there is a “star” organisation around a “chief” position designated to the Engineer who best understands the system requirements. chief programmer While, some follow an ( ) structure, where all the Engineers are at the same level designated for different jobs like Front-End Design, Back-End Coding, Software Testing etc. But, this architecture is not followed by very big or Multi-National Software Giants. But all in all, this is a very successful and working environment-friendly architecture. egoless democratic Egoless (Democratic) Architecture 3rd Type of architecture is the structure, which is the combination of the above 2 types. This is the mostly followed architecture and very common among software giants. mixed Mixed Controlled Architecture Likewise, International Business Machine Corporation (IBM) probably follows either or structures. So, for the HR Department, an important responsibility is to measure the attrition of the Employees at specific time-gaps. The factors on which the Employee Attrition depends upon are: egoless mixed Age of the Employee Monthly Income Overtime Monthly Rate Distance from Home Years at Company and so on… IBM also made their Employee Information publicly available, with the problem statement: “ Predict the Attrition of the Employees i.e., will there be attrition of the employees or not, given the Employee Details i.e., the factors responsible for attrition” The Employee Dataset is made available at Kaggle: _Predict attrition of your valuable employees_www.kaggle.com IBM HR Analytics Employee Attrition & Performance A possible solution to solve this problem is by applying Machine Learning i.e., by imparting Machine Intelligence which involves development of a Predictive Model by training it, using the data available and validating it for Model Performance Analysis…. Given below is a step-by-step procedure of Machine Learning Model Development using Python and Scikit-Learn Machine Learning Toolbox: Model Development: # import numpy as npimport pandas as pdimport matplotlib.pyplot as plt%matplotlib inlineimport pylab as plfrom sklearn.metrics import roc_curve, auc importing all the libraries #**loading the dataset using Pandas**df = pd.read_csv('WA_Fn-UseC_-HR-Employee-Attrition.csv')df.head()# Output shown below Pandas Dataframe Output of the Dataset # df.shape == df.dropna().shape # Output shown below checking whether the dataset contains any missing values... Hence, there are no missing values present in the dataset. This is a Binary Classification Problem, so the Distribution of instances among the 2 classes, is visualized below: y_bar = np.array([df[df['Attrition'] 'Yes'].shape[0]])x_bar = ['No (0)', 'Yes (1)' 'No'].shape[0],df[df['Attrition'] # plt.bar(x, y)plt.xlabel('Labels/Classes')plt.ylabel('Number of Instances')plt.title('Distribution of Labels/Classes in the Dataset')# Output shown below Bar Visualization Bar Visualization of the Class Distribution # X = df.iloc[:,[0] + list(range(2,35))].valuesy = df.iloc[:,1].valuesfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder Label Encoding for Categorical/Non-Numeric Data labelencoder_X_1 = LabelEncoder()X[:,1] = labelencoder_X_1.fit_transform(X[:,1])X[:,3] = labelencoder_X_1.fit_transform(X[:,3])X[:,6] = labelencoder_X_1.fit_transform(X[:,6])X[:,10] = labelencoder_X_1.fit_transform(X[:,10])X[:,14] = labelencoder_X_1.fit_transform(X[:,14])X[:,16] = labelencoder_X_1.fit_transform(X[:,16])X[:,20] = labelencoder_X_1.fit_transform(X[:,20])X[:,21] = labelencoder_X_1.fit_transform(X[:,21])y = labelencoder_X_1.fit_transform(y) #**Feature Selection using Random Forest Classifier's Feature**#**Importance Scores**from sklearn.ensemble import RandomForestClassifiermodel = RandomForestClassifier()model.fit(X,y) # Output shown below list_importances=list(model.feature_importances_)indices=sorted(range(len(list_importances)), key=lambda k:list_importances[k])feature_selected=[None]*34k=0for i in reversed(indices):if k<=33:feature_selected[k]=ik=k+1X_selected = X[:,feature_selected[:17]]l_features=feature_selectedi=0for x in feature_selected:l_features[i] = df.columns[x]i=i+1l_features = np.array(l_features) #**Extracting 17 most important features among 34 features**l_features[:17] #Output shown below #**Selecting the 17 most important features**df_features = pd.DataFrame(X_selected, columns=['Age','MonthlyIncome', 'OverTime','EmployeeNumber', 'MonthlyRate',,                       'DistanceFromHome', 'YearsAtCompany','TotalWorkingYears', 'DailyRate','HourlyRate', 'NumCompaniesWorked','JobInvolvement', 'PercentSalaryHike','StockOptionLevel','YearsWithCurrManager','EnvironmentSatisfaction','EducationField', 'Attrition']]df_selected.head() # Output shown below So, again label encoding has to be done for the selected categorical features: #**Label Encoding for selected Non-Numeric Features:**X = df_selected.iloc[:,list(range(0,17))].valuesy = df_selected.iloc[:,17].values X[:,2] = labelencoder_X_1.fit_transform(X[:,2])X[:,16] = labelencoder_X_1.fit_transform(X[:,16])y = labelencoder_X_1.fit_transform(y) Now the Data Pre-Processing Steps are over. Let’s move on to Model Training:- from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2, random_state=1753) #80-20 splitting where 80% Data is for Training the Model#and 20% Data is for Validation and Performance Analysis #**Using Logistic Regression Algorithm for Model Training**from sklearn.linear_model import LogisticRegressionclf= LogisticRegression(verbose = 3) #**Training the Model**clf_trained = clf.fit(X_train, y_train) #Output shown below This is the Library of Parameter Optimization Strategy used by Logistic Regression 2. Model Performance Analysis: => Training Accuracy clf_trained.score(X_train,y_train) # Output shown below Training Accuracy of 84.44% is achieved by the model => Validation Accuracy clf_trained.score(X_test,y_test) # Output shown below Validation Accuracy of 89.12% is achieved by the model => , and Precision Recall F1-Score # predictions=clf_trained.predict(X_test) getting the predictions... print(classification_report(y_test,predictions)) Classification Report of the model => Confusion Matrix # MODULE FOR CONFUSION MATRIX import matplotlib.pyplot as plt%matplotlib inlineimport numpy as npimport itertoolsdef plot_confusion_matrix(cm, classes,normalize=False,title='Confusion matrix',cmap=plt.cm.Blues):"""This function prints and plots the confusion matrix.Normalization can be applied by setting `normalize=True`."""if normalize:cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]print("Normalized confusion matrix")else:print('Confusion matrix, without normalization') print(cm) plt.imshow(cm, interpolation='nearest', cmap=cmap)plt.title(title)plt.colorbar()tick_marks = np.arange(len(classes))plt.xticks(tick_marks, classes, rotation=45)plt.yticks(tick_marks, classes) fmt = '.2f' if normalize else 'd'thresh = cm.max() / 2.for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):plt.text(j, i, format(cm[i, j], fmt),horizontalalignment="center",color="white" if cm[i, j] > thresh else "black") plt.tight\_layout()  
plt.ylabel('True label')  
plt.xlabel('Predicted label') #**Generating the Confusion Matrix**plt.figure() cm = np.array([[252, 1], [31, 10]]) plot_confusion_matrix(confusion_matrix(y_test,predictions),classes=[0,1], normalize=True, title='Normalized Confusion Matrix')# Output shown below Normalized Confusion Matrix => Receiver Operator Characteristic Curve: # y_roc = np.array(y_test)fpr, tpr, thresholds = roc_curve(y_roc, clf_trained.decision_function(X_test))roc_auc = auc(fpr, tpr)pl.clf()pl.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)pl.plot([0, 1], [0, 1], 'k--')pl.xlim([0.0, 1.0])pl.ylim([0.0, 1.0])pl.xlabel('False Positive Rate')pl.ylabel('True Positive Rate')pl.legend(loc="lower right")pl.show() # Output shown below Plotting the ROC Curve Receiver Operator Characteristic Curve (ROC Curve) According to the Performance Analysis, it can be concluded that the Machine Learning Predictive Model has been successful in effectively classifying 89.12% unknown (Validation Set) examples correctly and has shown quite descent statistical figures for different performance metrics. Hence, in this way an Employee Attrition Predictive Model can be developed using Data Analysis and Machine Learning. This model has been deployed in a Web Application by me using php ( ) as back-end with the help of . The link to the Web-App is given below: PHP: Hypertext Preprocessor PHP-ML navocommerce.in IBM-HR-ANALYTICS For Personal Contacts regarding the article or the Web-App or discussions on Machine Learning or any department of Data Science, feel free to reach out to me on LinkedIn . _View Navoneel Chakrabarty's profile on LinkedIn, the world's largest professional community. Navoneel has 3 jobs listed…_www.linkedin.com Navoneel Chakrabarty - Founder - Road To Financial Data Science | LinkedIn

A Machine Learning Approach to IBM Employee Attrition and Performance

When I first came across , I liked how easy it was to get started and that it’s built with React. I write React code on a daily basis and why not use it to create presentations? All went well for a while until I realized that the file started to exceed a few hundred lines. Spectacle ./presentation/index.js There must be a way to modularize my slides! The code is available at tl;dr: https://github.com/mikenikles/presentations/tree/master/packages/blog-post-source Get Started With `spectacle-boilerplate` As recommended in the Spectacle Github repo, I started by cloning the repo and removed the folder: spectacle-boilerplate .git $ git clone git@github.com:FormidableLabs/spectacle-boilerplate.git blog-post-source$ cd blog-post-source$ rm -fr .git At this point, you can install dependencies with and start the presentation with . It will be available at npm install npm start http://localhost:3000. Create a Folder For Each Slide There are four slides in the boilerplate presentation. Let’s create a directory where we’ll move each slide in its own subfolder. ./presentation/slides $ cd ./presentation$ mkdir slides && cd slides$ mkdir 1 && mkdir 2 && mkdir 3 && mkdir 4 We also want a file in each folder. This is where each slide’s content is going to be. index.js $ touch ./1/index.js && touch ./2/index.js && touch ./3/index.js && touch ./4/index.js This leaves us with the following directory structure: : Commit https://github.com/mikenikles/presentations/commit/5d208f669c633da7d95424d48e62588081bd7d56 Populate Each Slide’s Content The spectacle-boilerplate repo already provides each slide’s content in . All we need to do is move each React component into its corresponding file. ./presentation/index.js <Slide /> ./presentation/slides/[slide-number]/index.js Let’s do that together for the first slide. Cut and paste that to ./presentation/slides/1/index.js We also have to add a few import statements to . Also, let’s make sure we export the code for this slide. The final file looks like this: ./presentation/slides/1/index.js import React from "react";import { Heading, Slide, Text } from "spectacle"; export default (<Slide transition={["zoom"]} bgColor="primary"><Heading size={1} fit caps lineHeight={1} textColor="secondary">Spectacle Boilerplate</Heading><Text margin="10px 0 0" textColor="tertiary" size={1} fit bold>open the presentation/index.js file to get started</Text></Slide>); Follow the same steps for the remaining slides. : Commit https://github.com/mikenikles/presentations/commit/a45f144247d0e4f0c39d922a9a23cf73c05c0a32 Load Slides Dynamically Lastly, we have to load each slide dynamically. This sounds trickier than it is. At a high-level, the following steps are required: Load all slides dynamically with . import() Provide the loaded slides to the component’s state. Presentation Populate the state with placeholder components while step 1 and 2 above are in progress. Provide a unique prop to each dynamically loaded slide. key 1. Load all slides dynamically with import() In , we define a list of all slides and their order. ./presentation/index.js const slidesImports = [import("./slides/1"),import("./slides/2"),import("./slides/3"),import("./slides/4")]; Each statement returns a promise. So is an array of Promises. We can leverage that and use the to wait until all slides have been imported. More on that shortly. import() slidesImports Promise.all() function 2. Provide the loaded slides to the component’s state. Presentation The component needs a where we provide the loaded slides once they’re available. We populate an empty array in the and replace it with the actual slides’ content in the lifecycle method. The new component now looks like this: Presentation state constructor() componentDidMount() Presentation export default class Presentation extends React.Component {constructor(props) {super(props); this.state = {  
  slides: \[\] // A placeholder for slides once they're loaded.  
}; } componentDidMount() {const importedSlides = [];Promise.all(slidesImports).then((slidesImportsResolved) => {slidesImportsResolved.forEach((slide) => {importedSlides.push(slide.default);});this.setState({ slides: importedSlides });});} render() {return (<Deck transition={["zoom", "slide"]} transitionDuration={500} theme={theme}></Deck>);}} We’re almost done. Next up, let’s update the function and actually render all slides. render() render() {const { slides } = this.state;return (<Deck transition={["zoom", "slide"]} transitionDuration={500} theme={theme}>{slides.map((slide) => {return slide;})}</Deck>);} When we look at , we see a blank screen and the following error in the browser console: http://localhost:3000/ Browser console error based on the current code A closer look at on line 415 reveals the error is caused by the following line of code: manager.js children: _react.Children.toArray(child.props.children), Based on the error message, we know that is . That’s an easy fix. child undefined 3. Populate the state with placeholder components while step 1 and 2 above are in progress When the component’s function is called for the first time, is set to an empty array. Spectacle doesn’t like that, so let’s provide some placeholder slides until our real slides are imported and added to the state. Presentation render() this.state.slides We could provide an empty slide until is available, along the lines of: this.state.slides render() {const { slides } = this.state;return (<Deck transition={["zoom", "slide"]} transitionDuration={500} theme={theme}>{slides.length ? slides.map((slide) => {return slide;}) : <Slide />}</Deck>);} That actually works fine when we load the first slide at . However, try to navigate to the second slide and reload the page at . . http://localhost:3000/ http://localhost:3000/#/1 Error What this teaches us is that Spectacle needs to know the exact number of slides a presentation requires upfront upon first calling the function of the component. render() Presentation Easy, let’s make it happen by changing the function from: constructor() constructor(props) {super(props); this.state = {slides: []};} to: constructor(props) {super(props); this.state = {slides: Array(slidesImports.length).fill(<Slide key="loading" />)};} We basically populate the property of the state with the exact number of slides that we’ll import. In the code snippet above, we render an empty component, but we could just as well design a nice slide that displays a “Loading…” spinner. slides <Slide /> Now head back to and enjoy the second slide of your presentation without the nasty error we saw earlier. http://localhost:3000/#/1 Wait a minute, I spoke too soon… Each <Slide /> component requires a unique “key” prop Oh yeah, that’s right. in the error message explains why that prop is important. The link key 4. Provide a unique prop to each dynamically loaded slide key We have two options to do that: Add a prop to each component within . key <Slide /> ./presentation/slides/[slide-number]/index.js Provide the prop dynamically in the component’s function. key Presentation render() Option 1 sounds simple, but we’ll go for option 2 because it makes it easier to rearrange slides later. If the individual slide files are unaware of their position within the presentation, we can simply rename the slide’s folder from to to move a slide from the first to the third position in the presentation. index.js 1 3 The function’s updated component now looks like this: render() <Deck /> <Deck transition={["zoom", "slide"]} transitionDuration={500} theme={theme}>{slides.map((slide, index) => {return React.cloneElement(slide, {key: index});})}</Deck> : Commit https://github.com/mikenikles/presentations/commit/2c8630086548405e7d7ac2394d087fcfe504b06c Conclusion With this approach, I can now easily modularize my Spectacle presentations. In fact, I can take this to a whole new level… I could create a NPM module with a collection of commonly used slides, such as an “About Me” slide I use for every presentation. Whenever I want to use that in a presentation, I could simply add it as a dependency to my file and import it into my presentation at the correct index in my deck. package.json If you have any questions, don’t hesitate to reach out!

Presentations With Spectacle — How I Modularize My Deck

How to Create a Blog using Golang

Building a (simple) Blog with Rust

The Gainesville-based American artist and software developer is the founder of Mirror NFT Gallery, a five-story glass building(70,29) located in Decentraland’s Crypto Valley. Vision Jay Rosen has a visible footprint in the worlds of art and technology. In 2021, he was commissioned to design two NFT galleries for the DCentral Miami NFT Conference and was honored at the 2021 Art Basel Miami Week for designing the blueprint floor plans for the largest NFT gallery of the year. As a developer, Jay has a shining reputation. He was hired by the O & P Edge to build a database and web application for tracking amputee athletes competing in the 2020 Tokyo Paralympics and in the 2022 Beijing Winter Paralympics Events. Read on to learn more about why he believes in the future of art merged with emerging technology in this recent interview with Olayimika Oyebanji. You were interviewed in 2021 by Bloomberg and Vice at the Miami Art Basel Week. I am genuinely disposed to believe that your ideas piqued their interest in a great way. How do you feel sharing the stage with other builders in web 3 space? The key to metaverse success and mass adoption is to collaborate with other web3 builders - not just compete with patents and market domination like web2 companies. If we are to build an interoperable digital ecosystem for everyone, we need to do it together. Since getting into blockchain in 2020, I have been involved with the live conferences and met many of the developers and innovators in the space. I am perpetually an emerging artist, the unknown trendsetter - never selling my own artwork or NFTs - but somehow I am Director for art, metaverse and video game projects. Sharing the stage with millionaires and degenerates feels ridiculous since we are all clowns in this endless circus of life. The intersection between art and technology represents a shift and a milestone in the evolution of art itself. If you were to switch places with someone from a hundred years ago, how would you react to technological disruption taking place today? Many of my personal heroes lived a hundred years ago at the turn of the century. I’m very inspired by Italian Futurism, Dada, Cubism, Neo-Impressionism, Modern Architecture and individuals such as Umberto Boccioni, Picasso, Dali, Frank Lloyd Wright, the Wright Brothers, Thomas Eddison, Albert Einstein  - whom all dared to build their impossible dream machines. The Futurists were wild for technology and innovation - many of them died prematurely because their fascination for technology led them to blindly charge into war. I probably too would be on that first tank or wooden airplane charging headfirst into a brand new world as I did with building crypto mining servers, going all-in very fast. I feel like some crypto and NFT communities draw parallels to Italian Futurism - rejecting traditional art for pixel PFPs, memecoins, and pushing way for utopian decentralized tech. If I had the mindset of a 20th-century American seeing the world of 2022, I would be amazed by the illuminated streets, automobiles, and electric newspapers. I imagine it would be baffling to grasp the concept of globalization through wires, and that there are man-made science labs flying through the solar system taking pictures of the cosmos - let alone the helicopter drone and rovers on Mars. Let’s talk about Mirror Visionwear. I’m personally thrilled by the fact that it doesn’t require too much effort to understand that it is art merged with technology in one piece of a kaleidoscopic device. What makes it really unique? Mirror Visionwear has been my ongoing experiment with light, optics, mirrors, and playing with the invisible laws of nature for the past 10 years. It literally is cutting-edge wearable tech - glass fashion, luxury virtual reality. I began creating wearable ocular apparatuses as a low-tech satire to the Oculus Rift and Google Glasses in 2012. The geometric mirrored masks allowed the wearer to see the omnipresent vision - can see ghostly images of their environment superimposed into a natural immersive hologram - a surprisingly twisted 360 vision of the world. To outsiders, the Mirror vision wear appeared as an obscure fashion accessory, reflecting the ever-changing environment on top of the wearer's face in a very flashy eye-catching display. When Google Cardboard was announced, I made several prototypes of a mirrored or glass virtual reality headset. This modification allows the VR device to be visible to others, inviting others to see the virtual world projected on the wearer’s face. Mirror vision wear has played a low profile in the Anonymous community, being a way to mask identity, and the mirrors make it difficult for computer vision and surveillance to track the subject properly. In retrospect, the semi-transparent mirrored material I have been working with is perfect for Augmented Reality and holograms, as the silvered material allows for projected pixel reflection, both physical and digital - testing the limits of art and tech. As someone who believes in the vast potential of the metaverse and web 3, how do you define your contributions to it? With backgrounds as a software developer, cryptominer, and concept artist - I challenge myself and my peers to think of innovative solutions to change the world and our lives through the metaverse. Beyond the MirrorVisionwear art project, I have been quietly involved with several web3 projects including ED3N, Public ArtChain, NFT Hotel, Underworld Technology, and DCentral Conference. ED3N is developed by Interplay Entertainment Corp. and the creative team behind the recently-announced Earthworm Jim animated series. I am the co-creator and Metaverse Architect for this web3 game. ED3N is intended as a game where players can create life and trade genetic stocks. It is being built with a Grow-to-Earn gameplay that involves terraforming various exo-planets with alien vegetation and colorful bugs. The project centers around NFT-linked Cans of Dirt hosted on the Ethereum blockchain. Eventually, our goal is to release a companion coin to incentivize trades between players. Similar to Nintendo’s Pokémon or Bandai Namco’s Tamagotchi, ED3N players care for and influence the evolution of their creatures. The game combines machine learning AI with an integrated genetics engine, Pixar-level aesthetics, and larger-than-life simulations of physics. As for DCentral Conference & NFTs , it was the first DCentral event in Miami 2021. My friend and producer of the event Rob Kowalski asked for help in designing the floor plans for the NFT galleries. This was a fun exercise in using Maya to envision what a gallery would look like with just TVs and LED wall panels. In exchange they allowed me to display my own artwork on the TVs. This month-long scramble to create a nice 3D animated NFT resulted in me transforming my 36 GPU ethereum mining rigs into a render farm to quickly produce the highly reflective 3D scenes. The Prototypes NFT series was on display at the event and led to me being discovered by Interplay. Public ArtChain is a collaboration with Monochronicle Network, the “AirBNB for public art” connecting a network of 4,000+ professional international artists with property owners and government agencies to create public murals, sculptures, and large-scale artworks. Public ArtChain will add a web3 component to the software stack in an attempt to standardize the Call-to-Artists process via smart contracts, making the contractual payments between artists and institutions more streamlined and easier for international settlements. This also adds transparency and DAO governance to how an art proposal is voted to win a competition. By having the Call-to-Arts on chain, every aspect of the artwork from idea to creation to unveiling is historically captured in the NFT metadata, serving as a digital twin to real-world art. Digitizing public artworks also allows for artists or institutions to unlock equity or fractionalize shares if used in DeFi protocols, and makes the artwork portable in that they can display their artwork in the metaverse. We are hoping that Public ArtChain can pave the way toward allowing government agencies to possess cryptocurrency and NFTs. NFT Hotel is a physical structure such as a 12 ft cube of transparent LED walls and projections to create volumetric 3D displays of digital content such as NFTs. NFT Hotel design was first used in a proposal for Sankofa, a public artwork of a digital bird in Gainesville, FL and a similar design is again being proposed to Jacksonville Jaguars Stadium for a digital Jaguar. Underworld Technology is my impossible vision to build a community larger than Meta, by creating a global network of all cemeteries, reanimating our ancestors through AI and AR using public records.  I conceptualize an app that you bring to a graveyard to help find a specific site and essentially “see dead people” as they walk round their graves, waiting to tell the visitor their story. Surprisingly, cemetery cartography is not fully digitized or searchable online even though the gravesites are capable of being geo-located and coordinated permanently. Not only do I want to have conversations with our past ancestors, but to simulate what it would be like to chat with our future ancestors - to bring everyone together in the Underworld metaverse. Why are tech giants in a rat race to invest in the metaverse project and dominate the conversation? Do you treat it as a web 3 thing? They are investing or building metaverse projects because it is seen as the next phase of the internet. As a business owner in crypto valley? Was it a strategic business decision to share the same business district with Binance, Polygon, and the famous Tokens.com tower? Decentraland’s I first started investing in Decentraland in mid-October 2021, just 2 weeks before Facebook became Meta. I became fascinated with the concept of creating my own architecture and showcasing my 3D artworks in an NFT gallery. I originally was interested in setting up shop in the Fashion District or the Arts/Museum District, but it just felt cluttered and not cohesive. I looked at Crypto Valley as a possible future Silicone Valley of the metaverse, and was lucky to get a plot of land 2 squares away from Binance. At the time, there was a large empty reserved lot next to mine for a few months, but one day I logged into the game in  February, and saw the gigantic Tokens.com Tower as my true neighbor. It is an honor to be positioned next to the star destination on the map as that is the HQ for Metaverse Architects Group, the talented team behind most of the metaverse architecture projects. Tokens.com Tower hosts several metaverse fashion events so it does bring traffic. For the longest time I was showcasing my own NFTs in my gallery, but now currently running a curated show for West Coast NFT. These are the creative folks behind Doodles - the 10th largest NFT project. What advice would you give to someone who is exploring web 3 as a starter? Research and invest slowly into a project such as dollar cost averaging and never give anyone your seed phrase. A large portion of crypto projects and advertisements are literally scams that target newcomers. The technology and ideas of web3 are revolutionary, but who knows when the revolution will come where people actually use cryptocurrency for daily purchases or interact with smart contracts in daily routines. There are plenty of great resources online to learn about web3 - and plenty to avoid! I suggest newcomers to check out Youtube channels such as Coin Bureau who give a good unbiased overview of many projects and good weekly news updates. Avoid influencer channels who seem to be talking highly of crypto projects or only talk about price speculation (without technical analysis) - they are often paid to talk about the crypto project even if it is a pump and dump. Newcomers should look up crypto Media Kit, this is a saved screenshot on Reddit and Twitter which exposes how much these Youtube channels profit from talking about coins. What does your creative process look like ? I am old school and carry around a sketchbook for when the ideas strike. I draw my ideas very quickly and write notes, none of my drawings look finished. My degree in Drawing was more for concept design and ideation so I always enjoy working on brand-new projects and facing the unknown. I tend to go from sketches to making prototype 3D models in Maya and Blender software. If the concept art gets approved by a client, I will work on a more detailed version or begin crafting a sculpture with EVA foam, clay, or 3D printing.  I’m not the best craftsman, so I do hire others to professionally craft or fabricate my designs.  For software development, I spend a lot of time creating diagrams in Draw.Io of how the program will work to try and visualize all the ways it could be played. I use most of the Adobe CC apps and lately have been using Canva to quickly create mockups. What inspired you to establish Mirror Visionwear and how challenging was it to get it off the ground? Mirror Visionwear was inspired by my academic research on the emerging genre of light installation art. In college, I was fascinated by the architectural works of Olafur Eliasson and James Turrell, how they use light to induce a phenomenological effect within the viewer. Light is physical and yet illusionary, and captivated my imagination for the different ways to explore its unique properties. Mirrors are typically used in scientific experiments to control light, and so I started using mirrors in my art practice. My first Light installations involved using a channel or series of mirrors to create a“Portal” effect in different buildings, to see through the walls like X-ray vision. It was breaking an architectural boundary using optics. I then started creating polyhedron sculptures with mirrors and became obsessed with the Octahedron back in 2012 (this happens to now be the Ethereum logo). I envisioned a fractal of floating mirrored octahedrons in an infinite space and built small 1ft infinity cube boxes of glass to create the visual effect. I realized I needed a semi-transparent mirror on one side to create the illusion of infinity and still be able see inside the infinity mirror box - but when I created a cube of all semi-transparent mirrors is when Mirror Visionwear was discovered. The 1st cube was created by accident. I decided to put the glass cube over my head and walk around and saw the world through a whole new kaliedoscopic experience - I was able to see things behind me overlapping with my peripheral and  frontal vision. It was an interesting art performance and scientific experiment at the same time. Over time I developed more fashion styles inspired by the Platonic Solids and Virtual Reality headsets. It inspired me to make 3D models on the computer, and the style fit in with the vaporwave aesthetic movement of the 2016. Mirror Visionwear was adopted by the synth-pop band TIME,and they toured with the masks in 150 shows throughout America and Canada. For their 2nd tour they requested a mirror stage built for them, and that is how the Mirror Vision Geodesic Dome was created - a collapsible 12ft modular dome composed of cardboard triangles that could be pieced together into different formations depending on the venue size. Mirror Visonwear has been featured in short films Sight Seers, Loading Circle, and the MTV music video Automatic by Lovari. In 2021, I brought Mirror Visionwear to the DCentral Confence in Miami, and subsequently invaded the attention of the media such as Bloomberg, Vice, and entered livestreams of BitBoy Crypto and Altcoin Daily. In 2022, the same thing happened again, wearing the new Mirror Visionwear AR headset in front row as a controversial moment happened during the Blockchain Awards ceremony. 10. Is the metaverse here to stay? The metaverse has always existed, we are just creating more doors and pathways that lead into it. Some doors may close over time, but the metaverse will still be alive and doing its own thing. I believe mass adoption of metaverse will happen by having a killer app or utility such as low-cost Augmented Reality. My personal favorite metaverses are VRChat and Decentraland because these can be played without having to pay, and anyone can play them on older computers as well. My Mirror Visionwear AR headsets are an open-source 3D printed design made with inexpensive parts such as PLA filament and raspberry pi. Ideally, the price of the headset will be $20, using your own smartphone as the display and computer. Unlike the Google Cardboard design, the AR design allows for visible passthrough and serves as an overlay to the world in front of us. All these tech companies working on AR or VR are targeting high tech for a high price. I believe that lowering the cost of entry via DIY 3D printing will allow more people to enjoy and explore the metaverse from an augmented/mixed reality perspective.  By using the device they already own and are invested in will be easy to pick up and play with new worlds from the novel perspective of 50/50 digital physical.

“I Am Pushing The Boundaries Of Art and Tech”, Jay Rosen On Bridging Art And Technology

Product-Market Fit Tips for Startups: Insights From 20 Experts

Behavioral interviews are the most uncertain type of interview in the tech industry. I've seen many people underestimate the importance of preparing for these interviews, only to fail to receive an offer precisely because of their poor preparation. Fortunately, nailing behavioral interviews does not require months of preparation. In this guide, I will describe the key steps for success in such interviews. I’m a software development manager at Amazon. This article is a reflection of my thoughts and has nothing to do with Amazon or its subsidiaries. Why Do Behavioral Interviews Exist? Companies use behavioral interviews to assess whether you would be a successful hire based on your previous experiences. Thus, the most popular types of questions are example-based, such as “ . Companies test whether: Tell me about a time…” you are a “culture fit,” your previous are sufficient for the role you applied for. scope and impact Not all companies place equal importance on behavioral interviews. You can forget about receiving an offer from Netflix or Amazon if you haven't read about their principles or values. Meanwhile, companies like Google or Microsoft may place less emphasis on this aspect, and you might pass if you avoid showing red flags. Do your research to understand how important the behavioral part is at a particular company. Step 1. Prepare Examples It can be incredibly hard to come up with a good story during an interview, so I’d recommend preparing at least two examples for each bucket of behavioral interview questions. What are the buckets? A I like includes: categorization — challenges involving specific technical skills or knowledge. Technical Problem Solving — learning new skills, mentoring, taking feedback, managing failures, or achieving growth. Professional Growth — situations where you delivered results that impacted the business or project outcomes. Impact and Results — managing conflicts, stakeholders, negotiations, or leading people. Communication and Team Dynamics — handling ambiguity, adapting to change, or offering innovative solutions. Adaptability and Innovation Research the typical question buckets for the company you're applying to. You can’t prepare for all questions, but with a good set of stories, adapting them during the interview becomes quite easy. Don't have enough examples to prepare strong stories? Some might suggest making them up. It works, but I don't like that approach for many reasons. It is not ethical, and it takes more effort than building upon real stories. A better strategy would be to slightly augment your existing stories. That way, you can be more confident in your responses, answer follow-up questions more effectively, and spend less time on preparation. If you don't have a good story to tell during the interview, consider skipping the question. It is much better than giving an inappropriate example. Skipping five questions in a row isn't good, but skipping one or two doesn't hurt, especially if these aren't mainstream questions. Step 2. Use Examples Appropriate for Your Level Telling a story about a conflict with an intern over class naming might be acceptable for a junior-level engineer but not for a senior. Your stories should reflect your seniority level. For a principal or staff level, the examples should impact multiple organizations; for a senior level — at least your team; for a middle level — several people; and for an entry-level position, impacting even one person is acceptable. Strive to broaden your impact as much as possible. Name all organizations and teams impacted, and mention senior-level people involved. Failing to do so may result in being downleveled. Note that having many people involved is not a universal rule. Saving millions by introducing a clever optimization may be within the scope of your team, but this story could be strong enough for a principal-level position. Keep in mind the level you aim to land at and calibrate your answers. Step 3. Demonstrate "Culture Fit" Each company adheres to a specific set of values or principles, which they might call , , or . In essence, these are sets of attitudes and behaviors companies want to see in their employees. Google these values before the interview, and try to frame your answers according to these values. Culture Memos Leadership Principles Core Values Many companies do not document their values; however, behavioral interviews remain as crucial as any other type of interview. Even some well-known businesses, such as , fail to clearly communicate their corporate culture. In that case, I'd recommend the following: Apple Find a YouTube interview with the company's founder or a top manager discussing its culture. Talk with someone from this company, such as a friend of a friend or someone on LinkedIn. Research questions and answers on platforms like Glassdoor. Step 4. Structure Your Answers The standard recommendation for behavioral interviews is to use the (Situation, Task, Action, Result). Unfortunately, even candidates familiar with this method often fail to apply it effectively. The sections that usually present challenges are actions and results. STAR method For actions, it's critical to clarify your personal contribution — use “I” to articulate this. Statements like "We optimized this..." or "We escalated to..." do not clarify your specific role and contribution. Were you the one who optimized this, or was it a team member? Did you personally escalate the issue to a product manager, or was the escalation handled by your manager? Ideally, spend about half of your story's time discussing your actions. For the results, avoid vague statements like “it became faster” or “the project was delivered.” Instead, articulate the specific impact with numbers, even if approximate. More compelling answers might be: “The loading time decreased from 1s to 500ms” or “The project received 4.5 stars on the Apple Store and generated $5 million in revenue.” Adding a brief summary at the beginning of your response can enhance the STAR method. Briefly describing your story from start to finish helps the interviewer follow along and quickly determine if they need to hear more, potentially saving you several minutes of interview time. Step 5. Answer the Specific Question It's easy to misunderstand the question or drift into irrelevant stories due to nerves or misinterpretation. This often happens when candidates fail to actively listen or assume they know what the interviewer is asking without seeking clarification. Providing an accurate and relevant response is critical for demonstrating that you understand the requirements of the role and have the necessary skills. For complex multi-part questions writing them down is a good idea. That way, you can ensure you're answering all parts of the question, not just what you’ve remembered. If the interviewer asks about contributions outside of your area of responsibility, do not describe how you refactored a piece of code belonging to your team or resolved a bug introduced by a colleague. These are your core responsibilities. If the interviewer asks about a conflict, don't describe a brief disagreement where you accepted either point of view after five minutes. Conflict implies prolonged periods of disagreement, such as several weeks. Got a multi-part question about a difficult situation with a customer that significantly affected the business. Don't only describe why this situation was difficult and how it was solved. Write down the question and also explain how it affected the business. Step 6. Ask the Right Questions At the end of interviews, you have 5-10 minutes for your questions. Don’t fall into the trap of thinking the interview has ended — it has not. During the interview, everything matters, and the questions you ask at the end can influence the interviewer's decision on whether you pass or not. To use this time to your benefit, it's important to understand which questions to ask and which to avoid. Avoid HR questions about compensation, work from home, etc. Avoid personal questions about the country of origin, gender, mood, etc. Avoid mainstream questions such as “What does this company do?” or “What is the culture like here?” These show a lack of preparedness on your part. So, what should you ask? Choose questions that prove your interest in the company and suggest that you would be a valuable employee. For instance, ask, “What does success look like for this position?” “How do you manage to stay innovative despite being in the market for decades?” “What does a typical day look like for this position?” and “If I have an idea for a product feature or technical improvement, what is the process to implement it?.” Show that you've done your homework and are knowledgeable about the company and the position. Demonstrate your desire to succeed in this role and to be a team member that everyone would appreciate. Your future manager needs to see your independence (so they don't have to micromanage you) and your desire to achieve shared goals. Step 7. Practice Having impactful stories is critical, but effectively communicating them is key to resonating with interviewers. You might forget a story or struggle with detailing it due to anxiety. Interviewing can be stressful, and sometimes, is the optimal solution as many issues there can often be resolved. Fortunately, practicing behavioral interviews, also known as mock interviews, can significantly reduce stress during actual interviews and better prepare you to describe your experiences in a structured and clear manner. staying at your current job Many online platforms offer mock interviews on a paid basis. There, you can even choose interviewers from a specific company who can provide valuable feedback. This can be a bit expensive, so as an alternative, you can ask your friends to conduct such an interview. I’d recommend having at least three mock interviews before the real ones. Step 8. Impress and Connect Behavioral interviews are subjective by nature. The same story might be evaluated differently by different interviewers or even by the same interviewer under different circumstances. What can you do about this? Connect with your interviewer. Treat the interview as a dialogue, and listen attentively for any hints from the interviewer. Ask if your example fits well if they would like more details about the numbers or impact, etc. Demonstrate engagement and passion in your answers. I’d like to see that you’re interested in working with me. After all, this step is about making a positive impression on your interviewers. Final Thoughts Interviewing is a separate skill. You can be great at your day-to-day work but struggle with interviewing, and vice versa. It is a great investment to develop your interviewing skills, and every interview is a learning opportunity. Reflect on the feedback you receive and continuously refine your approach to become a more compelling candidate in future interviews.

Behavioral Interview: The Guide to Fitting In

আচরণগত ইন্টারভিউ: ফিটিং এর জন্য গাইড

Verhaltensinterview: Der Leitfaden zur Anpassung

Entrevista conductual: la guía para adaptarse

Entretien comportemental : le guide pour s'intégrer

व्यवहारिक साक्षात्कार: फिट होने की मार्गदर्शिका

行動面接：適応するためのガイド

행동 인터뷰: 적응 가이드

Entrevista comportamental: o guia para se adaptar

Поведенческое собеседование: руководство по адаптации

Davranışsal Röportaj: Uyum Sağlama Rehberi

Phỏng vấn hành vi: Hướng dẫn hòa nhập

行为面试：融入指南

Debugging is not just about identifying errors—it's about instituting a reliable process for ensuring software health and longevity. In this post, we discuss the role of software testing in debugging, including foundational concepts and how they converge to improve software quality. https://www.youtube.com/watch?v=yap509UZz6M&embedable=true As a side note, if you like the content of this and the other posts in this series, check out my Debugging book, which covers this subject. If you have friends who are learning to code, I'd appreciate a reference to my Java Basics book. If you want to get back to Java after a while, check out my Java 8 to 21 book. The Intersection of Debugging and Testing Debugging and testing play distinct roles in software development. Debugging is the targeted process of identifying and fixing known bugs. Testing, on the other hand, encompasses an adjacent scope, identifying unknown issues by validating expected software behavior across a variety of scenarios. Both are a part of the debug fix cycle, which is a core concept in debugging. Before we cover the cycle, we should first make sure we're aligned with the basic terminology. Unit Tests Unit tests are tightly linked to debugging efforts, focusing on isolated parts of the application—typically individual functions or methods. Their purpose is to validate that each unit operates correctly in isolation, making them a swift and efficient tool in the debugging arsenal. These tests are characterized by their speed and consistency, enabling developers to run them frequently, sometimes even automatically, as code is written within the IDE. Since software is so tightly bound, it is nearly impossible to compose unit tests without extensive mocking. Mocking involves substituting a genuine component with a stand-in that returns predefined results, thus a test method can simulate scenarios without relying on the actual object. This is a powerful yet controversial tool. By using mocking, we're, in effect, creating a synthetic environment that might misrepresent the real world. We're reducing the scope of the test, which might perpetuate some bugs. Integration Tests Opposite to unit tests, integration tests examine the interactions between multiple units, providing a more comprehensive picture of the system's health. While they cover broader scenarios, their setup can be more complex due to the interactions involved. However, they are crucial in catching bugs that arise from the interplay between different software components. In general, mocking can be used in integration tests, but it is discouraged. They take longer to run and are sometimes harder to set up. However, many developers (myself included) would argue that they are the only benchmark for quality. Most bugs express themselves in the seams between the modules and integration tests are better at detecting that. Since they are far more important, some developers would argue that unit tests are unnecessary. This isn't true; unit test failures are much easier to read and understand. Since they are faster, we can run them during development and even while typing. In that sense, the balance between the two approaches is the important part. Coverage Coverage is a metric that helps quantify the effectiveness of testing by indicating the proportion of code exercised by tests. It helps identify potential areas of the code that have not been tested, which could harbor undetected bugs. However, striving for 100% coverage can be a case of diminishing returns; the focus should remain on the quality and relevance of the tests rather than the metric itself. In my experience, chasing high coverage numbers often results in bad test practices that persist in problems. It is my opinion that unit tests should be excluded from coverage metrics due to the importance of integration tests to overall quality. To get a sense of quality coverage, we should focus on integration and end-to-end tests. The Debug-Fix Cycle The debug-fix cycle is a structured approach that integrates testing into the debugging process. The stages include identifying the bug, creating a test that reproduces the bug, fixing the bug, verifying the fix with the test, and finally, running the application to ensure the fix works in the live environment. This cycle emphasizes the importance of testing in not only identifying but also in preventing the recurrence of bugs. Notice that this is a simplified version of the cycle with a focus on the testing aspect only. The full cycle includes a discussion of the issue tracking and versioning as part of the whole process. I discuss this more in-depth in other posts in the series and my book. Composing Tests with Debuggers A powerful feature of using debuggers in test composition is their ability to "jump to line" or "set value." Developers can effectively reset the execution to a point before the test and rerun it with different conditions without recompiling or rerunning the entire suite. This iterative process is invaluable for achieving desired test constraints and improves the quality of unit tests by refining the input parameters and expected outcomes. Increasing test coverage is about more than hitting a percentage; it's about ensuring that tests are meaningful and that they contribute to software quality. A debugger can significantly assist in this by identifying untested paths. When a test coverage tool highlights lines or conditions not reached by current tests, the debugger can be used to force execution down those paths. This helps in crafting additional tests that cover missed scenarios, ensuring that the coverage metric is not just a number but a true reflection of the software's tested state. In this case, you will notice that the next line in the body is a rejectValue call, which will throw an exception. I don’t want an exception thrown as I still want to test all the permutations of the method. I can drag the execution pointer (arrow on the left) and place it back at the start of the method. Test-Driven Development How does all of this fit with disciplines like Test-Driven Development (TDD)? It doesn't fit well. Before we get into that, let's revisit the basics of TDD. Weak TDD typically means just writing tests before writing the code. Strong TDD involves a red-green-refactor cycle: Red: Write a test that fails because the feature it tests isn't implemented yet.


Green: Write the minimum amount of code necessary to make the test pass.


Refactor: Clean up the code while ensuring that tests continue to pass. This rigorous cycle guarantees that new code is continually tested and refactored, reducing the likelihood of complex bugs. It also means that when bugs do appear, they are often easier to isolate and fix due to the modular and well-tested nature of the codebase. At least, that's the theory. https://www.youtube.com/watch?v=yImkjlm08Cw&embedable=true TDD can be especially advantageous for scripting and loosely typed languages. In environments lacking the rigid structure of compilers and linters, TDD steps in to provide the necessary checks that would otherwise be performed during compilation in statically typed languages. It becomes a crucial substitute for compiler/linter checks, ensuring that type and logic errors are caught early. In real-world application development, TDD's utility is nuanced. While it encourages thorough testing and upfront design, it can sometimes hinder the natural flow of development, especially in complex systems that evolve through numerous iterations. The requirement for 100% test coverage can lead to an unnecessary focus on fulfilling metrics rather than writing meaningful tests. The biggest problem in TDD is its focus on unit testing. TDD is impractical with integration tests as the process would take too long. But as we determined at the start of this post, integration tests are the true benchmark for quality. In that test, TDD is a methodology that provides great quality for arbitrary tests, but not necessarily great quality for the final product. You might have the best cog in the world, but if it doesn't fit well into the machine, then it isn't great. Final Word Debugging is a tool that not only fixes bugs but also actively aids in crafting tests that bolster software quality. By utilizing debuggers in test composition and increasing coverage, developers can create a suite of tests that not only identifies existing issues but also guards against future ones, thus ensuring the delivery of reliable, high-quality software. Debugging lets us increase coverage and verify edge cases effectively. It's part of a standardized process for issue resolution that's critical for reliability and prevents regressions. Debugging is not just about identifying errors—it's about instituting a reliable process for ensuring software health and longevity. In this post, we discuss the role of software testing in debugging , including foundational concepts and how they converge to improve software quality. debugging https://www.youtube.com/watch?v=yap509UZz6M&embedable=true https://www.youtube.com/watch?v=yap509UZz6M&embedable=true As a side note, if you like the content of this and the other posts in this series, check out my Debugging book, which covers t his subject. If you have friends who are learning to code, I'd appreciate a reference to my Java Basics book. If you want to get back to Java after a while, check out my Java 8 to 21 book . Debugging book , Debugging book t Java Basics book. Java Basics book. Java 8 to 21 book Java 8 to 21 book . The Intersection of Debugging and Testing Debugging and testing play distinct roles in software development. Debugging is the targeted process of identifying and fixing known bugs. Testing, on the other hand, encompasses an adjacent scope, identifying unknown issues by validating expected software behavior across a variety of scenarios. Both are a part of the debug fix cycle, which is a core concept in debugging. Before we cover the cycle, we should first make sure we're aligned with the basic terminology. Unit Tests Unit tests are tightly linked to debugging efforts, focusing on isolated parts of the application—typically individual functions or methods. Their purpose is to validate that each unit operates correctly in isolation, making them a swift and efficient tool in the debugging arsenal. These tests are characterized by their speed and consistency, enabling developers to run them frequently, sometimes even automatically, as code is written within the IDE. Since software is so tightly bound, it is nearly impossible to compose unit tests without extensive mocking. Mocking involves substituting a genuine component with a stand-in that returns predefined results, thus a test method can simulate scenarios without relying on the actual object. This is a powerful yet controversial tool. By using mocking, we're, in effect, creating a synthetic environment that might misrepresent the real world. We're reducing the scope of the test, which might perpetuate some bugs. Integration Tests Opposite to unit tests, integration tests examine the interactions between multiple units, providing a more comprehensive picture of the system's health. While they cover broader scenarios, their setup can be more complex due to the interactions involved. However, they are crucial in catching bugs that arise from the interplay between different software components. In general, mocking can be used in integration tests, but it is discouraged. They take longer to run and are sometimes harder to set up. However, many developers (myself included) would argue that they are the only benchmark for quality. Most bugs express themselves in the seams between the modules and integration tests are better at detecting that. Since they are far more important, some developers would argue that unit tests are unnecessary. This isn't true; unit test failures are much easier to read and understand. Since they are faster, we can run them during development and even while typing. In that sense, the balance between the two approaches is the important part. Coverage Coverage is a metric that helps quantify the effectiveness of testing by indicating the proportion of code exercised by tests. It helps identify potential areas of the code that have not been tested, which could harbor undetected bugs. However, striving for 100% coverage can be a case of diminishing returns; the focus should remain on the quality and relevance of the tests rather than the metric itself. In my experience, chasing high coverage numbers often results in bad test practices that persist in problems. It is my opinion that unit tests should be excluded from coverage metrics due to the importance of integration tests to overall quality. To get a sense of quality coverage, we should focus on integration and end-to-end tests. The Debug-Fix Cycle The debug-fix cycle is a structured approach that integrates testing into the debugging process. The stages include identifying the bug, creating a test that reproduces the bug, fixing the bug, verifying the fix with the test, and finally, running the application to ensure the fix works in the live environment. This cycle emphasizes the importance of testing in not only identifying but also in preventing the recurrence of bugs. Notice that this is a simplified version of the cycle with a focus on the testing aspect only. The full cycle includes a discussion of the issue tracking and versioning as part of the whole process. I discuss this more in-depth in other posts in the series and my book. Composing Tests with Debuggers A powerful feature of using debuggers in test composition is their ability to " jump to line " or " set value ." Developers can effectively reset the execution to a point before the test and rerun it with different conditions without recompiling or rerunning the entire suite. This iterative process is invaluable for achieving desired test constraints and improves the quality of unit tests by refining the input parameters and expected outcomes. jump to line jump to line set value set value Increasing test coverage is about more than hitting a percentage; it's about ensuring that tests are meaningful and that they contribute to software quality. A debugger can significantly assist in this by identifying untested paths. When a test coverage tool highlights lines or conditions not reached by current tests, the debugger can be used to force execution down those paths. This helps in crafting additional tests that cover missed scenarios, ensuring that the coverage metric is not just a number but a true reflection of the software's tested state. In this case, you will notice that the next line in the body is a rejectValue call, which will throw an exception. I don’t want an exception thrown as I still want to test all the permutations of the method. I can drag the execution pointer (arrow on the left) and place it back at the start of the method. Test-Driven Development How does all of this fit with disciplines like Test-Driven Development (TDD)? It doesn't fit well. Before we get into that, let's revisit the basics of TDD. Weak TDD typically means just writing tests before writing the code. Strong TDD involves a red-green-refactor cycle: Red: Write a test that fails because the feature it tests isn't implemented yet. Green: Write the minimum amount of code necessary to make the test pass. Refactor: Clean up the code while ensuring that tests continue to pass. Red: Write a test that fails because the feature it tests isn't implemented yet. Red : Write a test that fails because the feature it tests isn't implemented yet. Red Green: Write the minimum amount of code necessary to make the test pass. Green : Write the minimum amount of code necessary to make the test pass. Green Refactor: Clean up the code while ensuring that tests continue to pass. Refactor : Clean up the code while ensuring that tests continue to pass. Refactor This rigorous cycle guarantees that new code is continually tested and refactored, reducing the likelihood of complex bugs. It also means that when bugs do appear, they are often easier to isolate and fix due to the modular and well-tested nature of the codebase. At least, that's the theory. https://www.youtube.com/watch?v=yImkjlm08Cw&embedable=true https://www.youtube.com/watch?v=yImkjlm08Cw&embedable=true TDD can be especially advantageous for scripting and loosely typed languages. In environments lacking the rigid structure of compilers and linters, TDD steps in to provide the necessary checks that would otherwise be performed during compilation in statically typed languages. It becomes a crucial substitute for compiler/linter checks, ensuring that type and logic errors are caught early. In real-world application development, TDD's utility is nuanced. While it encourages thorough testing and upfront design, it can sometimes hinder the natural flow of development, especially in complex systems that evolve through numerous iterations. The requirement for 100% test coverage can lead to an unnecessary focus on fulfilling metrics rather than writing meaningful tests. The biggest problem in TDD is its focus on unit testing. TDD is impractical with integration tests as the process would take too long. But as we determined at the start of this post, integration tests are the true benchmark for quality. In that test, TDD is a methodology that provides great quality for arbitrary tests, but not necessarily great quality for the final product. You might have the best cog in the world, but if it doesn't fit well into the machine, then it isn't great. Final Word Debugging is a tool that not only fixes bugs but also actively aids in crafting tests that bolster software quality. By utilizing debuggers in test composition and increasing coverage, developers can create a suite of tests that not only identifies existing issues but also guards against future ones, thus ensuring the delivery of reliable, high-quality software. Debugging lets us increase coverage and verify edge cases effectively. It's part of a standardized process for issue resolution that's critical for reliability and prevents regressions.

How Software Testing Fits Into The Debugging Process

Decoding GymShark's Marketing: How a Fitness Brand Navigated Sales vs. Communication Goals

জিমশার্কের মার্কেটিং ডিকোডিং: কিভাবে একটি ফিটনেস ব্র্যান্ড নেভিগেট সেলস বনাম যোগাযোগ লক্ষ্য

GymSharks Marketing entschlüsselt: Wie eine Fitnessmarke Verkaufs- und Kommunikationsziele unter einen Hut brachte

Decodificando el marketing de GymShark: cómo una marca de fitness navegó entre las ventas y los objetivos de comunicación

Décoder le marketing de GymShark : comment une marque de fitness a géré ses objectifs de vente et de communication

जिमशार्क की मार्केटिंग को समझना: एक फिटनेस ब्रांड ने बिक्री बनाम संचार लक्ष्यों को कैसे पार किया

GymShark のマーケティングを解読: フィットネス ブランドが販売目標とコミュニケーション目標をどのように両立させたか

GymShark의 마케팅 해석: 피트니스 브랜드가 판매와 커뮤니케이션 목표를 탐색하는 방법

Decodificando o marketing da GymShark: como uma marca de fitness navegou pelas vendas versus metas de comunicação

Расшифровка маркетинга GymShark: как фитнес-бренд управлял продажами и коммуникационными целями

GymShark'ın Pazarlamasının Çözümü: Bir Fitness Markası Satış ve İletişim Hedeflerini Nasıl Yönlendirdi?

Giải mã hoạt động tiếp thị của GymShark: Cách một thương hiệu thể hình điều hướng doanh số bán hàng so với các mục tiêu truyền thông

解读 GymShark 的营销：健身品牌如何实现销售与沟通目标

It is well-known (and backed by ) that over 90% of startups fail. So, out of 100 startups, only 10 will survive for longer than 5-10 years. To be more exact, 20 of them won't even make it a year, and 50 will fail within the first 5 years. Yes and no. On the one hand, they are usually talented individuals who want and can create brilliant products. On the other hand, they often make the same mistake: they overlook PMF - statistics Why does this happen? Are the founders to blame? Product Market Fit. Why does PMF matter so much? First, let’s start by giving it a definition. Simply put, a good PMF is a scenario where your product satisfies the needs of a specific group of consumers. In other words, it's when the product solves a problem that people face, and they are willing to pay to use the product, and even better - are willing to recommend it to others. PMF is the foundation of any startup. Without it, even the most technically advanced product with great potential is doomed to fail. What do I need to achieve a good PMF? To achieve good PMF, you only need to consider 3 criteria: Value Proposition Consumer communication channels Monetization But let’s look at each one of these in detail. At the end of the day, we are all here to build great products right? Value proposition The value proposition is what you offer to the consumer through your product. The value proposition should be clear, well-formulated, and easy to understand for the wider audience. The product should either solve an important problem for the audience or satisfy a specific need - that is the golden rule of value proposition. Consumer communication channels Consumer communication channels are the ways by which you deliver your product to your target audience. They have to be convenient, efficient, and economically viable. Here are some tips for choosing the best communication channels for your audience: Keep in mind the preferences of your target audience. Where do they reside in the digital space? Is it email, , or , or is it an offline post subscription? Twitter Facebook Diversify the channels you use. This will help you reach a wider audience. Test and analyze how effective the channels you choose are. This way you can track which ones yield the best results at the lowest cost. Monetization Monetization is the way a company earns revenue from its product. It should be efficient and sustainable. The value proposition and communication channels play a crucial role in product monetization, and if your consumer is convinced of the value of the product and the communication channels work efficiently, they will be more inclined to spend their hard-earned money on your product. To achieve good PMF, it's important to make the consumer want to buy the product, to make sure this purchase brings them happiness. Then, they will surely recommend the product to others - which will be evidence of good PMF. 5 Steps to Achieving Product Market Fit Now that we’ve dealt with the main criteria for PMF, let’s check out the steps that will lead you to creating a product that truly fits the market. Step 1. A good business model This is the key to ticking off the first criterion. A good business model will not only help you identify and formulate a good value proposition but will also help you understand through which tools and actions you can convey the value to your potential consumer. Tip: Use the Business Model Canvas template. The Business Model Canvas is a visual tool that helps companies structure their business plan. It consists of nine blocks that represent the main components of the business model. Start with market research, find who your audience is, and what their needs and problems are. Then, highlight the key advantages of your product. What specifically makes your product unique, unlike other competitors? Continue by formulating a clear, direct, and simple value proposition, so that you can potentially reach a wider audience and still build that connection. Bonus points if it’s memorable! Wonder what a good value proposition looks like? Here’s a great example from Loom: Next, now that you can consider your target audience and the value you bring with your product you can go through the Business Model Canvas blocks one-be-one to create an overall experience that is your business. Here are some tips for when you decide to fill out the blocks: Avoid general phrases and abstract concepts. Specificity: Use vivid images and persuasive phrases to create the impression for consumers that your product or service can solve their problems. Persuasiveness: Whenever possible, use measurable indicators (metrics) to demonstrate the value of your product or service. Measurability: Perhaps, the most important thing is to constantly revisit your business model to account for changes in the audience preferences and needs. Often, the first version of the business model is just a hypothesis - it may work or it may not. Therefore, it's very important to continue experimenting and testing different options. Segments will change and evolve: this is normal. The worst thing is when a founder doesn't listen to their customers and acts based on their ideas rather than customers’ real problems and needs. Step 2. Market validation This is a very important step that will help you maximize your chances of success. Before starting product development, it's important to answer the following questions: Does the problem your product solves exist? (1. Value proposition) Can you attract potential customers to your product with minimal costs? (2. Communication channels) Are your potential customers willing to pay for your product? (3. Monetization) Some entrepreneurs fall in love with their idea (understandably so, we’ve all been there) and forget to test their product before launch which inevitably leads to failure. To test the market, you can create a landing page with your value proposition and unique product features, and add a button that leads to an email subscription (so you can collect those email addresses later on). Buy online ads to drive traffic and analyze your conversions. Done. A landing page gives you the opportunity (and ability) to really perfect your first impression on your potential customer. In order to make it effective, your value proposition needs to be clear as day. Talk about the problem your product solves and present a solution that is bound to make your clients’ lives better and easier. Once again, the landing page has to be simple and easy to understand to drive a potential customer’s attention to all the right places (such as leaving his contact info). A great example of this - Ahrefs (pictured above).\ . If your percentage is lower, something needs to change. Try changing wordings or blocks with offers, and if you get no conversions at all, perhaps you should go back to step 1 and once again reconsider your business model and value proposition. Maybe the problem or need you are addressing is not as concerning to the consumer as you might have anticipated. It happens! A good landing page conversion rate is 5% Step 3. Audience interviews Now it’s time to do audience interviews. Reach out to those who have left their contact information and organize 5 to 10-minute calls. These interviews are important no matter your conversion rates, they still give you an insight into what works - and what doesn’t. If you see low conversion rates, interviews will help you understand and come up with ideas on how to improve the product or service to increase its attractiveness to consumers. On the other hand, if conversions are high, these calls will give you a manual on retaining and increasing the number of satisfied and interested customers.\ Lost on what to discuss? Here are a few things you might want to ask your customers: How did you hear about the product? (Channels) Why did you decide to subscribe? (Value proposition) Are you currently paying for an alternative? (Monetization) These interviews are an opportunity to learn, not sell. It’s the perfect time to listen to your audience and ask questions to gather facts instead of opinions. Use your discernment and don't fall into biases about your product. It’s also not the time for solutions just yet! Step 4. Product Creation and Customer Base Building After gathering all that information on what works and what doesn’t for your audience you can move on to the next step - creating the product and building your customer base. When creating the product, it's important to start with a Minimum Viable Product (MVP). An MVP is an early version of the product that has enough features and capabilities to attract early adopters. Audience feedback will provide insight into which specific feature needs to be added to the product (or removed). This feature should address the most pressing problem or need of the audience, and the product itself should be simplified as much as possible and given to your audience to test. While you are creating the final version of your product, keeping an eye on creating a customer base is just as important. Customers won't come on their own, so alongside product development, you should be testing various communication channels to understand which ones are most profitable for you. A systematic approach is key. **For that, you can use a Bullseye Framework:
\ Here you can choose the main 3 channels out of this list and write them in the middle of the diagram. Then, pick 6 more in the next round. Once you test them, cross out the ones that don’t work and do so until you find the ones that do. Step 5. Analyze results Once the product is launched, analyzing how effective it is is key. This will help you understand what works and what doesn't, and give you the tools to make necessary changes. Even if there were many conversions at the second stage, it's not a given that all of them will start paying you money. One of the most important tools for product analysis is the sales funnel–a model that describes the user's journey from the first contact with the product down to the purchase. Analyzing the sales funnel helps identify "gaps" that hinder users from progressing to the next stage. Here are a few questions you can ask yourself when analyzing the sales funnel: How many users visit your website or app? How many of them made an account? How many of the registered users made a purchase? How many of the buyers became repeat customers? Answers to these questions will help you understand where people drop off in the sales funnel and what steps you can take to improve its effectiveness. Measuring PMF: the 40% rule Now, after completing all 5 steps, you can try to measure whether you have achieved a good PMF. One way to determine this is to conduct customer surveys. Ask them this question: "How would you feel if you could no longer use our product?" If 40% or more of the customers say that they would be very disappointed: the product solves an important problem for them, PMF is achieved. If the percentage is below 40%, it may mean that either the product does not yet provide a complete solution to the problem, or the product does not meet the needs of the target audience. If the survey results indicate that PMF has not been achieved, then you need to return to step 3 and conduct interviews with customers again. This will help you understand where the product does not solve the problem well enough, or that the product does not meet the needs of the target audience. During the interviews, ask customers the following questions: What do you like about our product? What do you dislike about our product? What would you like to change about our product? What problems are you trying to solve with our product? How much does our product help you solve these problems? Answers to these questions will help you determine what needs to be changed or added to the product. Or maybe you will have to change the criteria of the target audience. If you decide to make changes to the product, it is important to choose the most important aspects and focus on them. You can use the to evaluate ideas, where: ICE system Impact - How much will this change affect users? Confidence - How confident are you that this change will be successful? Ease - How easy will it be to implement it? To evaluate the ICE of a feature/idea, rate each of these items from 1 to 10, and multiply them together. For example, for the first feature I = 7, C = 6, and E = 5, and for the second - I = 9, C = 2, and E = 2. ICE scores will be 210 for the first feature and 126 for the second. Ideas with a high ICE value should be a priority! You can also try other channels for more productive communication with customers. For example, you can conduct a survey or launch a pilot project with a small group of customers. After you make changes to the product or target audience, you need to conduct the survey again to evaluate the results. If the percentage of satisfied customers is still below 40%, then it is necessary to repeat the cycle of changes and testing. The wheel turns again. The ideal recipe is: return to steps 3, 4, and 5 again and again until the 40% rule (or a convincing LTV:CAC ratio) is achieved. LTV:CAC Another way to determine PMF is to calculate the [LTV:CAC ratio](https://www.klipfolio.com/resources/kpi-examples/saas/customer-lifetime-value-to-customer-acquisition-cost#:~:text=The%20Customer%20Lifetime%20Value%20(LTV,is%20a%20signal%20of%20profitability). is the amount of money a customer spends on the product over the entire period of its use. CAC (customer acquisition cost) is the cost of acquiring a new customer. This way, you can assess the effectiveness of communication channels and monetization. LTV (lifetime value) If LTV is higher than CAC, it means that one user generates more revenue during product usage than what was spent to acquire them. A good LTV:CAC ratio is 3:1 or higher. If it’s so straightforward, why do so many startups fail to achieve PMF? There are many reasons that can explain this issue. Many startups begin product development without first conducting market research. They think they know what customers need, but in reality, they don’t. Ignoring market needs/pains. Even if a startup has conducted market research, it is important to continue communicating with customers throughout the whole product development process to stay up to date with your customers’ needs. This will also help you understand what works and what doesn't and will help you make necessary changes. Lack of communication with consumers. Many startups focus only on product development and overlook the importance of proper marketing. To achieve PMF, it is important to test various customer acquisition channels and find the ones that work best – for you. Focusing solely on the product without testing channels early on and regularly during development. Some startups believe that adding new features is a sign of progress. However, this is not always the case. Sometimes it is better to focus on improving existing features rather than adding new ones. Adding new features to replace real progress. So, PMF is a key component for any startup, and the 40% rule is a simple way to determine whether it has been achieved. Without PMF, a product will not be successful and will become one of the 90% that fail.

5 Steps To Achieving Product Market Fit And The 40% Rule

This paper is available on arxiv under CC 4.0 license. Authors: (1) Matu´s Medo, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern and matus.medo@unifr.ch; (2)  Michaela Medova, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern. Table of Links Abstract & Introduction Results Discussion Materials and methods Acknowledgement & References Supporting Information Supporting Information This paper is available on under CC 4.0 license. Arxiv

Extra Information on the Comprehensive Comparison of Tools for Fitting Mutational Signatures

This paper is available on arxiv under CC 4.0 license. Authors: (1) Matu´s Medo, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern and matus.medo@unifr.ch; (2)  Michaela Medova, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern. Table of Links Abstract & Introduction Results Discussion Materials and methods Acknowledgement & References Supporting Information Acknowledgement We thank Charlotte Ng and Peter Degen for their useful feedback. References Alexandrov, L.B., Kim, J., Haradhvala, N.J., Huang, M.N., Tian Ng, A.W., Wu, Y., Boot, A., Covington, K.R., Gordenin, D.A., Bergstrom, E.N., et al., 2020. The repertoire of mutational signatures in human cancer. Nature 578, 94–101. Alexandrov, L.B., Nik-Zainal, S., Wedge, D.C., Aparicio, S.A., Behjati, S., Biankin, A.V., Bignell, G.R., Bolli, N., Borg, A., Børresen-Dale, A.L., et al., 2013. Signatures of mutational processes in human cancer. Nature 500, 415–421. Berger, B., Yu, Y.W., 2022. Navigating bottlenecks and trade-offs in genomic data analysis. Nature Reviews Genetics , 1–16. Blokzijl, F., Janssen, R., van Boxtel, R., Cuppen, E., 2018. MutationalPatterns: Comprehensive genome-wide analysis of mutational processes. Genome Medicine 10, 1–11. Brady, S.W., Gout, A.M., Zhang, J., 2022. Therapeutic and prognostic insights from the analysis of cancer mutational signatures. Trends in Genetics 38, 194–208. Cannataro, V.L., Mandell, J.D., Townsend, J.P., 2022. Attribution of cancer origins to endogenous, exogenous, and preventable mutational processes. Molecular Biology and Evolution 39, msac084. Degasperi, A., Zou, X., Dias Amarante, T., Martinez-Martinez, A., Koh, G.C.C., Dias, J.M., Heskin, L., Chmelova, L., Rinaldi, G., Wang, V.Y.W., et al., 2022. Substitution mutational signatures in whole-genome–sequenced cancers in the UK population. Science 376, abl9283. Diaz-Gay, M., Vangara, R., Barnes, M., Wang, X., Islam, S.A., Vermes, I., Narasimman, N.B., Yang, T., Jiang, Z., Moody, S., et al., 2023. Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment. bioRxiv , 548264doi:10. 1101/2023.07.10.548264. bioRxiv 2023.07.10.548264. Gori, K., Baez-Ortega, A., 2020. sigfit: Flexible Bayesian inference of mutational signatures. bioRxiv , 372896doi:10.1101/372896. bioRxiv, 372896. Gulhan, D.C., Lee, J.J.K., Melloni, G.E., Cortes-Ciriano, I., Park, P.J., 2019. Detecting the mutational signature of homologous recombination deficiency in clinical samples. Nature Genetics 51, 912–919. Hubschmann, D., Jopp-Saile, L., Andresen, C., Kr ¨ amer, S., Gu, Z., Heilig, C.E., Kreutzfeldt, S., Teleanu, V., Fr ¨ ohling, S., Eils, R., et al., 2021. ¨ Analysis of mutational signatures with yet another package for signature analysis. Genes, Chromosomes and Cancer 60, 314–331. Islam, S.A., D´ıaz-Gay, M., Wu, Y., Barnes, M., Vangara, R., Bergstrom, E.N., He, Y., Vella, M., Wang, J., Teague, J.W., et al., 2022. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell Genomics 2, 100179. Kim, Y.A., Leiserson, M.D., Moorjani, P., Sharan, R., Wojtowicz, D., Przytycka, T.M., 2021. Mutational signatures: From methods to mechanisms. Annual Review of Biomedical Data Science 4, 189–206. Koh, G., Degasperi, A., Zou, X., Momen, S., Nik-Zainal, S., 2021. Mutational signatures: Emerging concepts, caveats and clinical applications. Nature Reviews Cancer 21, 619–637. Li, S., 2020. sigLASSO: Optimizing cancer mutation signatures jointly with sampling likelihood. URL: https://github.com/gersteinlab/ siglasso. R package version 1.1. Li, S., Crawford, F.W., Gerstein, M.B., 2020. Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood. Nature Communications 11, 3575. Maura, F., Degasperi, A., Nadeu, F., Leongamornlert, D., Davies, H., Moore, L., Royo, R., Ziccheddu, B., Puente, X.S., Avet-Loiseau, H., Campbell, P.J., Nik-Zainal, S., Campo, E., Munshi, N., Bolli, N., 2019. A practical guide for mutational signature analysis in hematological malignancies. Nature Communications 10, 1–12. Missa, O., Dytham, C., Morlon, H., 2016. Understanding how biodiversity unfolds through time under neutral theory. Philosophical Transactions of the Royal Society B: Biological Sciences 371, 20150226. Nik-Zainal, S., Alexandrov, L.B., Wedge, D.C., Van Loo, P., Greenman, C.D., Raine, K., Jones, D., Hinton, J., Marshall, J., Stebbings, L.A., et al., 2012. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993. Nik-Zainal, S., Davies, H., Staaf, J., Ramakrishna, M., Glodzik, D., Zou, X., Martincorena, I., Alexandrov, L.B., Martin, S., Wedge, D.C., et al., 2016. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54. Omichessan, H., Severi, G., Perduca, V., 2019. Computational tools to detect signatures of mutational processes in DNA from tumours: A review and empirical comparison of performance. PLoS ONE 14, e0221235. Pandey, P., Arora, S., Rosen, G.L., 2022. MetaMutationalSigs: Comparison of mutational signature refitting results made easy. Bioinformatics 38, 2344–2347. Riva, L., Pandiri, A.R., Li, Y.R., Droop, A., Hewinson, J., Quail, M.A., Iyer, V., Shepherd, R., Herbert, R.A., Campbell, P.J., et al., 2020. The mutational signature profile of known and suspected human carcinogens in mice. Nature Genetics 52, 1189–1197. Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B.S., Swanton, C., 2016. DeconstructSigs: Delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biology 17, 1–11. Rustad, E.H., Nadeu, F., Angelopoulos, N., Ziccheddu, B., Bolli, N., Puente, X.S., Campo, E., Landgren, O., Maura, F., 2021. mmsig: A fitting approach to accurately identify somatic mutational signatures in hematological malignancies. Communications Biology 4, 1–12. Schumann, F., Blanc, E., Messerschmidt, C., Blankenstein, T., Busse, A., Beule, D., 2019. SigsPack, A package for cancer mutational signatures. BMC Bioinformatics 20, 1–9. Van Hoeck, A., Tjoonk, N.H., van Boxtel, R., Cuppen, E., 2019. Portrait of a cancer: Mutational signature analyses for cancer diagnostics. BMC Cancer 19, 1–14. Wang, S., Li, H., Song, M., Tao, Z., Wu, T., He, Z., Zhao, X., Wu, K., Liu, X.S., 2021. Copy number signature analysis tool and its application in prostate cancer reveals distinct mutational processes and clinical outcomes. PLoS Genetics 17, e1009557. Wu, Y., Chua, E.H.Z., Ng, A.W.T., Boot, A., Rozen, S.G., 2022. Accuracy of mutational signature software on correlated signatures. Scientific Reports 12, 1–12. This paper is available on under CC 4.0 license. Arxiv

The People We Have to Thank In Our Comparison of Tools for Fitting Mutational Signatures

This paper is available on arxiv under CC 4.0 license. Authors: (1) Matu´s Medo, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern and matus.medo@unifr.ch; (2)  Michaela Medova, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern. Table of Links Abstract & Introduction Results Discussion Materials and methods Acknowledgement & References Supporting Information 4. Materials and methods 4.1. Reference signatures We used all signatures from version 3.0 of the COSMIC catalog (COSMICv3, https://cancer.sanger.ac. uk/signatures/sbs/), in particular the SBS signatures for human genome assembly GRCh38. COSMICv3 contains 67 mutational signatures (18 are artifact signatures) defined in 96 mutational contexts. While newer COSMIC versions (the latest version is 3.3.1 from June 2022) added more signatures, profiles of the COSMICv3 signatures have not been altered (all absolute weight differences are below 10−7 ). To measure the diversity of signature profiles, we compute their exponentiated Shannon index which is a common measure of diversity (Missa et al., 2016) that can be understood as the effective number of active mutational contexts. For a signature whose all weight is concentrated in one context, the exponentiated Shannon index is one. For a signature with the same weight 1/96 of all contexts, the exponentiated Shannon index is 96. For COSMICv3 signatures, the exponentiated Shannon index ranges from 2.7 (SBS48) to 80.3 (SBS3). 4.2. Synthetic mutational catalogs Absolute contributions of signatures to sequenced tissues from various cancer types were downloaded from the Catalogue Of Somatic Mutations In Cancer (COSMIC, https://cancer.sanger.ac.uk/signatures/sbs/, catalog version 3.2) on November 14, 2021 for all 46 non-artifact SBS signatures that have tissue contribution data available. These data included only samples with reconstruction accuracy above 0.90 and at least 10 mutations attributed to the specific SBS signature. Using the provided unique sample identifiers, we used these files to compute the relative signature contributions in individual WGS-sequenced samples that are stratified by the cancer type. We chose eight different cancer types for further analysis: Hepatocellular carcinoma (Liver-HCC, n = 422), stomach adenocarcinoma (Stomach-AdenoCA, n = 170), head and neck squamous cell carcinoma (Head-SCC, n = 57), colorectal carcinoma (ColoRect-AdenoCA, n = 72), lung adenocarcinoma (Lung-AdenoCA, n = 62), cutaneous melanoma (Skin-Melanoma, n = 280), non-Hodgkin Lymphoma (Lymph-BNHL, n = 117), and glioblastoma (CNS-GBM, n = 63). The approach described above allows us to reproduce empirical signature weights in previously analyzed samples without resorting to assumptions such as a log-normal distribution of the number of mutations due to a given signature (Alexandrov et al., 2020, Islam et al., 2022) or adding additional zeros to the Poisson distribution to reproduce signatures that are not active in a sample (Omichessan et al., 2019). The code for generating synthetic mutational catalogs and evaluating the signature fitting tools is available at https://github.com/8medom/SigFitTest. For Figure 6, we created synthetic mutational catalogs with a systematic difference in the activity of signature SBS40. After generating sample weights as described in the previous paragraph (assuming the CNS-GBM cancer type), the relative weights of SBS40 were multiplied by 1.3 in the first half of the samples and divided by 1.3 in the second half of the samples. The relative weights of all signatures in each sample were then normalized to one. Note that signature SBS40 is active in nearly all CNS-GBM samples (Fig. SF9 in SI) and its median relative weight is close to 50%. By the described process, the median weights in the two groups of samples become 42% and 55%, respectively. 4.3. Tools for signature fitting The task of fitting known mutational signatures to a mutational catalog consists of finding the combination of signatures from a reference set that “best” matches the given catalog. Denoting the matrix with reference signatures as S, where S cs is the relative weight of context c for signature s, and the given normalized mutational catalog as m, where mci is the fraction of mutations in context c in sample i, this amounts to solving where wsi is the relative weight of signature s in sample i. Mathematically, this is an over-determined system as the number of reference signatures is smaller than the number of mutational contexts (96 contexts for the common SBS signatures). Several fitting tools are therefore based on minimizing the difference between m and Sw through nonnegative least squares (as the signature weights cannot be negative) or quadratic programming. We evaluated several tools that belong to this class: MutationalPatterns v3.4.1 (Blokzijl et al., 2018), YAPSA v1.20.1 (Hubschmann et al. ¨ , 2021), SigsPack v1.8.0 (Schumann et al., 2019), and sigminer v2.2.0 (Wang et al., 2021) which has three separate methods based on quadratic programming, non-linear least squares, and simulated annealing. We find that all these tools produce similar results. Other tools use various iterative processes by which the provided set of reference signatures is gradually reduced by removing the signatures, for example, whose inclusion does not considerably improve the match between the observed and reconstructed mutational catalogs (or, opposite, signatures are gradually added as long as the reconstruction accuracy sufficiently improves). We evaluated deconstructSigs v1.8.0 (Rosenthal et al., 2016), SigProfilerSingleSample v0.0.0.27 (Alexandrov et al., 2020), SigProfilerAssignment v0.0.30 (Diaz-Gay et al., 2023), mmsig v0.0.0.9000 (Rustad et al., 2021), and signature.tools.lib v2.2.0 (Degasperi et al., 2022), that all belong to this category. The newest tool, SigProfilerAssignment, combines backward and forward iterative adjustment of the reference catalog and these steps are repeated until convergence. Finally, SigLASSO v1.1 combines the data likelihood in a generative model with L1 regularization and prior knowledge (Li, 2020, Li et al., 2020). To allow for a fair comparison with other tools, we did not use prior knowledge when evaluating sigLASSO. In our experience, this tool sometimes fails to halt but starting it again with the same input data resolves the issue. A similar Bayesian framework is used by sigfit v2.2 (Gori and Baez-Ortega, 2020). The authors of sigfit recommend setting all signatures whose lower bounds of the estimated 95% confidence intervals are below 0.01 to zero. The authors of deconstructSigs recommend setting all signatures whose relative weights are below 0.06 to zero. All results shown for sigfit and deconstructSigs follow these recommendations. 4.4. Trimming reference signatures It has been argued that the removal of irrelevant reference signatures can improve the fitting results (Kim et al., 2021). To assess this hypothesis, we use a two-step process where: (1) We fit signatures using all COSMICv3 signatures as a reference and (2) keep only the signatures whose relative weight exceeds threshold w0 for at least 5 samples in our cohorts with 100 samples. We use thresholds w0 = 0.05 (m = 100), w0 = 0.03 (m = 2, 000) and w0 = 0.01 (m = 50, 000) which correspond to absolute signature contributions 5, 60, and 500, respectively. This pruning of reference signatures is beneficial when the number of mutations per sample is small (see m = 100 in Fig. S17 in SI). However, the converse is true when the number of mutations per sample is high, in particular for tools that perform well with COSMICv3 as a reference (m = 50, 000 in Fig. S17 in SI). To overcome this problem, we have also tested a less strict pruning where we keep all signatures whose relative weight exceeds w0 for at least one sample (see Fig. S18 in SI for the results). We have also tested the approach based on de novo extraction of signatures as suggested in (Maura et al., 2019). For signature extraction, we used SignatureAnalyzed v0.0.7 (https://github.com/getzlab/SignatureAnalyzer) with L1 prior on both W and H and a Poisson objective function for optimization. As recommended in (Maura et al., 2019), each extracted signature was then compared with individual signatures from COSMICv3 as well as linear combinations of two signatures from COSMICv3. The signature (or a pair of signatures) that yielded the smallest cosine distance was then added to the list of trimmed reference signatures for the given input data. When the number of mutations is small (m = 100), less than five signatures are selected, on average. When the number of mutations is large (m = 50, 000), 89% of the active signatures are selected, on average. A comparison of the results obtained by the two reference-trimming schemes is shown in Fig. S19 in SI. 4.5. Evaluation metrics Recent literature often classifies fitting results as true positives, false positives, true negatives, and false negatives, which allows computing evaluation metrics such as precision and specificity Omichessan et al. (2019), Islam et al. (2022), Diaz-Gay et al. (2023). We use instead quantitative metrics that measure the differences between the true relative signature weights (which are used to generate the input mutational catalogs) and the estimated relative signature weights. For tools that estimate absolute signature weights, those are first normalized by the number of mutations in each sample. and average it over all samples in the cohort to obtain fitting error. The lowest fitting error, 0, is achieved when the estimated signature weights are exact for all signatures and all samples. The highest fitting error, 1, is achieved when the estimated signature weights sum to one for each sample but they are all false positives. For example, when a sample has 40% contribution of SBS1 and 60% of SBS4 but a tool estimates 20% contribution of SBS2 and 80% contribution of SBS3, the fitting error is 1. There are some tools whose signature weight estimates do not sum up to one for each sample, typically as a result of the uncertainty implied by a small number of mutations in samples. The fitting error of those tools can be inflated due to these tools’ conservativeness. However, we probed whether additionally normalizing the estimated weights to one improves the fitting error achieved by those tools and found that this is not the case. We can thus conclude that these tools are not disfavored by measuring their fitting error. Another way to quantify the agreement between the true and estimated signature weights is by computing their Pearson correlation. For each sample where at least three signatures had positive either true or estimated weights, we computed the Pearson correlation coefficient between these two weight vectors whilst excluding all signatures that are zero for both of them. The obtained values were then averaged over all samples in the cohort. We finally computed false positive weight which quantifies how much weight is assigned to the signatures that are not active in a given sample, and then this quantity is averaged over all samples. This quantity too ranges from 0 (when no weight is assigned to inactive signatures) to 1 (when all weight is assigned to inactive signatures). This paper is available on under CC 4.0 license. arxiv

The Materials and Methods That We Used to Compare Tools for Fitting Mutational Signatures

This paper is available on arxiv under CC 4.0 license. Authors: (1) Matu´s Medo, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern and matus.medo@unifr.ch; (2)  Michaela Medova, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern. Table of Links Abstract & Introduction Results Discussion Materials and methods Acknowledgement & References Supporting Information 3. Discussion The broad range of tools for fitting mutational signatures makes it difficult to understand which tool to choose for a given project. In this work, we provide a comprehensive assessment of eleven different tools on synthetic mutational catalogs. Using mutational catalogs where only one signature is active allows us to quantify the differences in the fitting difficulty of individual signatures. We find that flat signatures whose average similarity with other signatures is high are the most difficult to fit. To assess fitting tools, we use synthetic mutational catalogs whose signature activities are modeled based on eight distinct cancer types. We find that when the number of mutations is small (100 mutations per sample), SigProfilerSingleSample is the best tool to use for all cancer types. As the number of mutations increases, SigProfilerAssignment becomes the best tool for all cancer types with mmsig best for some cancer types for an intermediate number of mutations (2,000 mutations per sample). The results change little when Pearson correlation is used instead of fitting error for the evaluation (Figs. S22 and S23 in SI). The risk of overfitting the data by including too many signatures in the reference catalog is often discussed in the literature (Maura et al., 2019, Koh et al., 2021, Degasperi et al., 2022). We show that three methods (SigProfilerSingleSample, SigProfilerExtractor, and mmsig) are robust to such overfitting when the number of mutations per sample is not too small (Figs. S12 and S13 in SI). Crucially, we find that the common practice of excluding signatures from the reference catalog because they do not seem to be active (or are little active) in the analyzed data is little effective or even harmful, unless the number of mutations per sample is small (Fig. S16 in SI). In most cases it is preferable to let the built-in statistical methods of these tools to decide using the complete COSMIC catalog as a reference. While our work gives clear recommendations on which tools to consider and which to avoid, many issues can be addressed in the future to further improve the fitting of mutational signatures. First, a similar assessment of fitting tools can be done for other types of signatures: doublet base substitutions, small insertions and deletions, and copy number variations. Then, some tools (e.g., sigfit and mmsig) can compute confidence intervals for their estimates. For sigfit, we used these estimates in the way recommended by the authors: When the lower bound of the confidence interval for the relative signature weight is below 0.01, the signature is marked as absent in the sample (its relative weight is set to zero). Our results show that this recommended practice indeed improves the results achieved by sigfit. By bootstrapping (resampling the original mutational catalogs with replacement), confidence intervals could be computed also for the tools that do not compute them by themselves. It would be worth investigating whether thus-determined confidence intervals could also improve the performance of other fitting tools. To fit mutational signatures, it is commonly required that each sample has at least 50 (Riva et al., 2020) or 200 (Blokzijl et al., 2018) single base substitutions. However, different signatures are differently difficult to fit (Figure 1), so it is unlikely that such a universal threshold is meaningful. Based on the simulation framework established here, it would be possible to study the minimum necessary number of mutations for different signatures of interest and different cancer types. To best match the needs of practitioners, it would be possible to extend the simulation framework so that it finds the best-performing fitting tool for a given cancer type, a list of signatures of interest, and a given distribution of sample mutation counts. When an active signature is missing from the reference catalog because it is new or is falsely deemed inactive in a given set of samples, most fitting tools cannot cope with this situation and distribute all (or nearly all) mutations among signatures from the reference set (Fig. S15 in SI). Three tools do not suffer from this drawback—deconstructSigs, signature.tools.lib, and sigfit—but their performance is generally weak. One could aim to enhance one of the bestperforming tools, such as SigProfilerAssignment, with an additional step that would quantify whether given signatures are indeed likely to produce given mutational catalogs. Another approach to this problem is offered in (Maura et al., 2019) where the authors suggest a multistep process to simultaneously avoid under- and over-fitting. Mutational signatures are first extracted de novo, then they are assigned to existing signatures from a reference catalog, and thusdetermined active signatures are finally used to fit the original input data. While there are well-defined statistical principles for the first and third steps of this process, the second assignment step is largely ad hoc. A more principled approach could improve the outcome. More generally, COSMIC signatures are the result of analyzing tumor tissues from many different organs, so they can be viewed as an average across them (Degasperi et al., 2022). It might be more appropriate to construct organ-specific reference catalogs that would better reflect mutational processes that occur in these biologically diverse systems. The fast-growing number of sequenced samples would make such an effort possible. This paper is available on under CC 4.0 license. Arxiv

Discussing Tools for Fitting Mutational Signatures: A Comprehensive Comparison

Comparing Tools for Fitting Mutational Signatures: The Results of Our Work

This paper is available on arxiv under CC 4.0 license. Authors: (1) Matu´s Medo, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern and matus.medo@unifr.ch; (2)  Michaela Medova, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern. Table of Links Abstract & Introduction Results Discussion Materials and methods Acknowledgement & References Supporting Information Abstract Mutational signatures connect characteristic mutational patterns in the genome with biological processes that take place in the tumor tissues. Analysis of mutational signatures can help elucidate tumor evolution, prognosis, and therapeutic strategies. Although tools for extracting mutational signatures de novo have been extensively benchmarked, a similar effort is lacking for tools that fit known mutational signatures to a given catalog of mutations. We fill this gap by comprehensively evaluating eleven signature fitting tools (well-established as well as recent) on synthetic input data. To create realistic input data, we use empirical signature weights in tumor tissue samples from the COSMIC database. The study design allows us to assess the effects of the number of mutations, type of cancer, and the catalog of reference signatures on the results obtained with various fitting tools. We find substantial performance differences between the evaluated tools. Averaged over 120,000 simulated mutational catalogs corresponding to eight different cancer types, SigProfilerSingleSample and SigProfilerAssignment perform best for small and large numbers of mutations per sample, respectively. We further show that ad hoc constraining the list of reference signatures is likely to produce inferior results and that noisy estimates of signature weights in samples with as few as 100 mutations can still be useful in downstream analysis. Cancer genomics, statistical methods, mutational signatures, computational models Keywords: 1. Introduction Since their introduction a decade ago by Nik-Zainal et al. (2012), Alexandrov et al. (2013), mutational signatures have become a widely used tool in genomics (Maura et al., 2019, Koh et al., 2021). They allow researchers to move from individual mutations in the genome to biological processes that take place in living tissues (Kim et al., 2021). In (Cannataro et al., 2022), for example, signature activities have been used to attribute mutations to endogenous, exogenous, and preventable mutational processes. The activity of various mutational signatures can also serve as prognostic or therapeutic biomarkers (Van Hoeck et al., 2019, Brady et al., 2022). Homologous recombination defficiency leads to the accumulation of DNA damage and manifests itself in a specific mutational signature (signature SBS3 from the COSMIC catalog) (Nik-Zainal et al., 2016, Gulhan et al., 2019). Mutational signatures can be introduced for single base substitutions (SBS), doublet base substitutions (DBS), small insertions and deletions (ID), as well as for copy number variations (CN). We focus here on SBS-based mutational signatures which are most commonly used in the literature. Current SBS signatures are defined using 6 possible classes of substitutions (C>A, C>G, C>T, T>A, T>C, and T>G) together with their two immediate neighboring bases, thus giving rise to 6 × 4 × 4 = 96 different nucleotide contexts into which all SBS mutations in a given sample are classified. De novo extraction of signatures from somatic mutations in sequenced samples has been used to gradually map the landscape of mutational signatures in cancer tissues. Over time, the initial catalogue of 22 SBS-based mutational signatures in the first version of the Catalogue Of Somatic Mutations In Cancer (COSMIC) released in August 2013 has expanded to 80 signatures in the COSMICv3.3 version released in June 2022. This expansion was possible owing to the increased availability of WES and WGS sequencing data as well as improved tools for signature extraction. Extensive benchmarking of extraction tools on synthetic data has recently shown that SigProfilerExtractor outperforms other methods in terms of sensitivity, precision, and false discovery rate, particularly in cohorts with more than 20 active signatures (Islam et al., 2022). A two-step process consisting of first extracting common signatures and then rare signatures has been recently recommended in (Degasperi et al., 2022). Nevertheless, with the existing signatures extracted from cohorts including thousands of sequenced samples (Alexandrov et al., 2020, Islam et al., 2022, Degasperi et al., 2022), the authors of smaller studies are unlikely to discover new signatures. For example, the analysis of more than 23,000 WGS and WES cancers in (Islam et al., 2022) has only discovered four new signatures. The much more relevant task is thus that of fitting existing signatures to given sequenced samples. In this process, the catalogs of somatic mutations are used to determine the signature contributions to each individual sample. Many different tools have been developed for this task (Koh et al., 2021) (see Methods for their description and classification). However, while tools for extracting mutational signatures have been recently extensively benchmarked on synthetic data by various studies (Omichessan et al., 2019, Alexandrov et al., 2020, Islam et al., 2022, Wu et al., 2022), a similar quantitative comparison is lacking for tools for fitting mutational signatures. This need is further exacerbated by substantial variations between results obtained by different methods (Pandey et al., 2022). Furthermore, newly introduced tools for fitting mutational signatures are commonly compared with only a few existing tools, rarely the most recent ones, and a standardized comparison methodology is lacking. In this study, our aim is to fill this gap and provide a comprehensive evaluation of a broad range of fitting tools on synthetic data motivated by various types of cancer. We constrain on fitting SBS signatures as they are the most widely used signature type. Many of the included tools can be nevertheless used for other types of signatures as their fitting is mathematically not different from the fitting of SBS signatures. We generate two classes of synthetic mutational catalogs. In the first one, only one mutational signature is responsible for all mutations (single-signature cohorts). In the second one, signature activities in each sample are modeled after the activities found in real tumor samples from various cancers (heterogeneous cohorts). We have collected eleven tools for fitting mutational signatures, from earlier tools such as deconstructSigs to very recent ones such as SigProfilerAssignment. We assess their performance by comparing the known true signature activities in the created catalogs with results obtained by various fitting tools. We find that there is no single fitting tool that performs best regardless of how many mutations are in the samples and which cancer type is chosen to model signature activities. Averaged across all cancer types, two related tools (SigProfilerSingleSample and SigProfilerAssignment) perform best when the number of mutations per sample is small (below 1000, roughly) and large (above 1000), respectively. We also compare the tools by how prone they are to overfitting caused by increasing the size of the reference signature catalog and evaluate whether it is beneficial to constrain the reference catalog based on which signatures seem to be absent or little active in the analyzed samples. We close with a discussion of open problems in fitting mutational signatures. Figure 1: (a, b) The bars show the 95th percentile range of the observed fraction of mutations in synthetic data for SBS mutational contexts (horizontal axis) and different total mutation counts (100, 1,000 and 10,000 represented with red, blue and gray bars, respectively). The effect of the number of mutations in single-signature cohorts. Panels a and b show signature SBS1 with four distinct C>T peaks and signature SBS5 with a flat mutational spectrum, respectively. (c) The fitting errors of the evaluated fitting tools for synthetic data corresponding to 100 samples with 100% contributions of a single signature; the catalog of all 67 COSMICv3 signatures was used for fitting. We show results for ten common signatures (horizontal axis) and various mutation counts (vertical axis). The tools are ordered by the error averaged over the displayed signatures achieved for 51,200 mutations. This paper is available on under CC 4.0 license. arxiv

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