After working with hundreds of data science teams, Rasgo's founders were dismayed by the failure rate of data science projects

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What if your avatar in the Genies Ecosystem encoded not only your visual representation but also who you are – your personality, quirks, and all? The Traits Framework at Genies is our approach to bringing multi-layered depth and dynamism to your digital identity: depicting not just what you look like, but also describing what your personality is like. Why do we want to do this? Because we think your online persona should grow and change just like you do in real life. It's all about making the digital world more personal and responsive to who you are. There’s nowhere else where you can be part of an ecosystem that not only comes with amazing high-fidelity avatars but also a high-fidelity description of the inner self and personality of someone in the digital world. We describe a little bit of this in our previous , but we’d like to dive deeper into our methodology so you can understand how we’ve designed our Traits Framework and how it describes your digital avatar’s inner self. It's like giving your digital self its own unique flavor. blog post The Origins of Our Traits Framework We had two initial requirements for our Traits Framework that our product team set out as goals It must be comprehensive enough to describe a person relatively accurately but also uniquely. It must be simple & straightforward enough for developers to build innovative custom experiences based on your unique Trait profile. Based on these two requirements, we knew we had to bring in experts to do proper discovery work in building out the foundation of Traits. Therefore, we brought in a team of psychologists to work directly with our product team in building the foundation of our innovative framework. The psychologists helped build a clinical and scientific foundation for our framework and helped audit our creative process to ensure rigor in design. The Game Designer helped provide a developer perspective in architecting this framework and is now developing flagship examples that will make it straightforward for a new genre of personality-based Experiences to be built by anyone building within the Genies Ecosystem. We first worked with the psychologists to pull inspiration and research from other personality systems (Jungian Archetypes, the 7 Chakra System, 5 elements in Chinese medicine, and the 9 Enneagram Personality Types), but in parallel, we also looked to innovate in creating our own structure that would work specifically for our ecosystem where we’re able to have a bit of creative leeway to make personality profiles much more fun and engaging especially as we need to pair them with Experiences. Eventually, our focus narrowed to combining the 7 Chakras System with a gaming approach of multi-layered Traits (i.e., FIFA’s DEF or “Defensive” stat is actually a composition of multiple sub-traits each with their own values), forming the bedrock for the current Traits Framework. Clinical judgment from psychologists and qualitative research methods helped us to distill each of the 7 Chakras into 7 categories, each with three descriptive Traits that uniquely describe a different facet of that respective category. We then worked with the psychologists to generate a list of 21 Traits in total (3 Traits per category, 7 categories) making sure that we don’t generate so many Traits that it’s impossible for developers to build against, but making sure we have enough variety in Traits such that every individual can eventually build up a unique Trait DNA profile. As seen in the graphic above, these 21 Traits can also be beautifully grouped by “Mind”, “Gut”, and “Heart” type Traits where Mind are the more intellectual Traits, Gut Traits are the instinctive Traits, and Heart Traits are the emotional Traits. We’ve also color-coded each category based on the chakra system to give each personality type more expression and something for both users and developers to hold on to. As we started to leverage this framework to create custom personality-tailored experiences, our Game Designer gave us valuable feedback to simplify the system as much as possible to make the development experience as easy for devs as possible in the near term. Thus, we then worked to focus on 7 Traits to start off with - one from each category that the psychologists felt epitomized each category the best and was most accessible to users. We are not removing the remaining 14 Traits; eventually, we will want to layer in the two additional Traits within each category as we expand our Framework to create better fidelity. These are the final starting 7 Traits that we ended up with along with their illustrative Trait stones that can be gained by users within the Genies Party ecosystem. This personality Trait dimension presents a unique opportunity for users to learn about themselves from spiritual, psychological, somatic, and material perspectives. It’s an invitation to reflect on who you are, who you want to be, and to see aspects of yourself reflected in the Genies ecosystem. This is not the end though! As we build out the Genies Party ecosystem, there will not only be these Foundational 21 personality Traits but also many other Traits added that will gradually create more fidelity as more complex Traits grow from these Foundational Traits. Some of these you can start to experience in our ! personality quiz Additionally, we are looking to expand and test additional Traits like Physical Traits, which are useful for games within the Genies Party ecosystem as developers may find these Traits easier to understand. The first physical Traits that you will be able to gain for your avatar in Genies Party are “Strength”, “Stamina”, and “Speed.” How Will a Genies Party User See These Traits? As a user builds out their Trait profile in Genies Party, they will be able to collect and build on the Trait stones that correspond to their personality, with their most prominent Traits shown closer to their Avatar. There’s no “best” Trait value or combination for users, you are your own unique person and every profile will be a relatively unique combination of scores for each Trait. It’s like a Trait DNA that’s created for each user and constantly evolving based on your actions within Genies Party. Your Trait profile will be developed through what you say and do in Genies Party. Every conversation and every experience could shape your profile based on your behavior. It’s also fun seeing how your Trait profile could evolve and change over time, maybe even to characteristics you wouldn’t have expected! Don’t worry, if you don’t like your Trait profile, you can always reset your Traits and start over just as you would be able to completely change up your avatar appearance. Because this Trait profile is a result of your actions taken in the digital world, it’ll truly become a horoscope of your personality. Through Genies Party, we’d also capture insights about your inner self that you may not have been able to realize through any other platform, allowing you to compare your true self to friends and Creators. “Am I more similar to my friend John or Claire?” “Would I be compatible with Timothee Chalamet?” Finally, the personalized experiences brought to life by your Traits in Genies Party are set to redefine avatar-based gaming. They unlock a new realm of customized games that not only enhance how you can socialize your identity but also add a whole new tailored-to-you dimension of multiplayer interactions. We’ll touch on this in the next section along with some challenges of balance that we’ve thought of between Traits. It’s important to note that although the creation of this Framework is rooted in empirical and scientific work, our aim is to still make it fun with somewhat mystical/metaphysical leanings, rather than a more standard personality profile measure like the Myers-Briggs or the Big Five personality Traits. How Will Custom Experiences Be Built Against These Traits? Devs should generally not be using Traits to modify the difficulty of a game. Our recommendation for types of ways 3rd party devs can develop against Traits are as such: - Through cosmetic changes in avatar, environment, and VFX, devs can provide unique Trait-based experiences that don’t directly impact game mechanics. This rewards players with a sense of achievement without disrupting the game flow. Cosmetic - Through narrative changes in game flow, story, or progression, a user’s Traits can change the way NPCs interact or how the game progresses in story. Narrative - Through specific Trait values, a user may be given a shortcut through a game experience. Shortcuts/Paths If devs have created multiple archetypes of characters in their game, Traits can be used to assign an archetype to that player. Archetypes - - Things are the smart interoperable 3D objects of the Genies Ecosystem. Traits can be attached to Things developers create, adding another depth and dynamism to not just avatars, but also the objects that you carry around. Things For example, if users have a high Calm Trait in a Tamagotchi-type game, they may be more likely to find calmer pets to raise. Similarly, if a user picks a calmer pet to raise within that same game, their Calm Trait value may increase. Another example is if in a racing game, a user comes in with a specific Trait, they may get 3 race bonuses to pick from based on their specific Trait, whereas someone coming in with a different Trait may be selecting from 3 different race bonuses. Cosmetically, you may notice that Caring Trait users may have a pink heart-particle aura and Ambitious Trait users may have glowing red exhausts. We do not want users to use Traits for gating or power level purposes within their games to avoid balance issues. It’s also important not to only have direct player competition within games based on Traits to not have to contend with “which Trait is better” type of questions for each game. If you have concerns about being assigned a Trait profile that doesn't resonate with you, we have a thoughtful solution: Things x Traits. Envision this: each item your avatar equips carries specific Traits. This allows you the flexibility to customize your Trait profile to suit various experiences, ensuring that each interaction can be shaped by your choices and the items you choose to equip, giving you the power to own your gaming experience. We’ll touch on our innovative Things Framework and the intricacies of that system another time. How do Developers Access Traits in Our Developer Kit? We've integrated an API into our devkit scripting, allowing developers to access Trait values ranging from -100 to 100. While the exact number of a Trait isn't directly shown, this range simplifies the application of Traits in-game logic. For instance, a developer can use a formula like `if(max(calm, 0) * RNG > 50) success! Reading Traits: Else failure to apply the positive aspect of the calm Trait in a random number generator (RNG) check. This approach simplifies complex calculations, making the development process more efficient. Our API allows developers to adjust Traits in terms of Large, Medium, and Small increments, either positively or negatively. For example, a period of low actions per minute (APM) with successful outcomes might prompt a Positive Medium adjustment to the Calm Trait. Writing Traits: Conversely, a high APM with few successes could lead to a Negative Medium adjustment. We're also exploring a 'batching' API for more granular games, allowing developers to accumulate Trait changes and send them in batched API calls. This ensures smoother and more efficient Trait management in our devkit. To further simplify Trait adjustments, we're considering offering developers various controllers that they can easily integrate and configure. These controllers would respond to different gameplay aspects, like APM and success rates, to automatically adjust Traits. By simplifying Trait management through intuitive APIs and developer-friendly tools, our goal is to empower developers to create a completely new genre of more engaging and dynamic games that are effectively customized to each user's Trait profile. Stay tuned for more updates on these exciting developments in our developer toolkit, and look forward to our first flagship Trait-based games built internally by Genies and by 3rd party devs in our Developer Incubator. If you’re interested in building on our innovative Traits Framework and being an early creator within the Genies Ecosystem, apply for our today. You can also learn about what we’re doing with AI Avatars on our . Developer Incubator website . Developer Incubator todayDeveloper Incubator todayDeveloper Incubator today

A Deep Dive on Digital Identity: Why You Need to Check Out Genies Traits Framework

ডিজিটাল আইডেন্টিটি সম্পর্কে গভীর ডুব: কেন আপনাকে জেনেসিসের বৈশিষ্ট্য কাঠামো পরীক্ষা করতে হবে

Ein tiefer Einblick in die digitale Identität: Warum Sie sich das Traits Framework von Genesis ansehen sollten

Una inmersión profunda en la identidad digital: por qué es necesario consultar el marco de rasgos de Génesis

Une analyse approfondie de l'identité numérique : pourquoi vous devez consulter le cadre de traits de Genesis

डिजिटल पहचान पर एक गहन जानकारी: आपको जेनेसिस के लक्षण ढांचे की जांच करने की आवश्यकता क्यों है

デジタル アイデンティティの詳細: Genesis の特性フレームワークをチェックする必要がある理由

디지털 신원에 대한 심층 분석: Genesis의 특성 프레임워크를 확인해야 하는 이유

Um mergulho profundo na identidade digital: por que você precisa verificar a estrutura de características do Genesis

Глубокое погружение в цифровую идентичность: почему вам нужно ознакомиться с системой черт Genesis

Dijital Kimliğe Derin Bir Bakış: Genesis'in Özellikler Çerçevesine Neden Göz Atmanız Gerekiyor?

Tìm hiểu sâu về nhận dạng kỹ thuật số: Tại sao bạn cần kiểm tra Khung đặc điểm của Genesis

深入探讨数字身份：为什么您需要查看 Genesis 的特征框架

Traits were introduced in Mojo recently, so I thought I'd try them out. Currently, the built-in traits include , , , , , , and (looks like the "-able" suffix is a thing in these naming conventions!). Traits work on structs. To implement a trait, you simply add a method to a struct that matches the trait; then pass the trait name as a parameter: CollectionElement Copyable Destructable Intable Movable Sized Stringable @value  
struct Duck(Quackable):      
  fn quack(self):          
     print("Quack!") The decorator in Mojo inserts the lifecycle methods like , and into the struct, simplifying our life a bit as we don’t have to add them ourselves. @value __init__() __copyinit__() __moveinit__() Traits in Mojo don't support default implementations for methods yet, hence, the in the body of the method above. You can also use , which will have the same effect, as in Python. ... Quackable pass Mojo Traits vs. Go Interfaces Despite the different name, the basic approach to traits in Mojo reminds me of Go interfaces. In Go, you would define a struct and implement a interface like this: Duck Quackable type Quackable interface {
	quack()
} And to make a struct that satisfies this interface: type Duck struct {}

func (d Duck) quack() {
    fmt.Println("Quack!")
} Compare it to a Mojo implementation: trait Quackable:  
    fn quack(self):  
        ...

@value  
struct Duck(Quackable):  
    fn quack(self):  
        print("Quack!") I think the Mojo version is even more concise, nesting the method definition inside the struct type. In addition to working on structs, traits in Mojo don't require an keyword, just like in Go. quack() Duck implements Interestingly, the point is made in Mojo docs that default implementations are not allowed , meaning they might be allowed in the future. This means that Mojo might pursue an approach different from Go, which focuses on structs an interface, not it. yet satisfying implementing Default method implementations are not needed in Go, and a similar effect is achieved with embedding, as interface/trait implementation is simply a concept that doesn't exist in Go. Things might be different in Mojo. How to Use Mojo Traits The value of traits and interfaces is to make code reusable. For instance, in Mojo, you can write functions that accept trait types… fn make_it_quack[T: Quackable](could_be_duck: T): 
    could_be_duck.quack() And then pass different structs that implement the same trait as input - everything will just work! For example, here's a struct that implements : Goose Quackable @value  
struct Goose(Quackable):  
    fn quack(self):  
        print("Honk!") And here, to call on both the and the (remember, you need the function in Mojo as an entry point to your program): make_it_quack Goose Duck main def main():  
    make_it_quack(Duck())  
    make_it_quack(Goose()) The output of this will be Quack!
Honk! Trait Errors If I tried to pass in something that doesn't implement the trait to the function, let's say , the program won't compile: Quackable make_it_quack StealthCow @value
struct StealthCow():
    pass make_it_quack(StealthCow()) The error message below could have been more descriptive; maybe the Mojo team will improve it? error: invalid call to 'make_it_quack': callee expects 1 input parameter, 
but 0 were specified Same if I remove the method from ; here, we get a nice, descriptive error: quack Goose struct 'Goose' does not implement all requirements for 'Quackable'
required function 'quack' is not implemented The advantage of this kind of static type checking over the pure Python approach is that we can catch errors easily and avoid shipping any mistakes to production as the code will simply not compile. Inheritance Traits in Mojo already support inheritance, so our `Quackable` trait can extend an `Audible` trait like so: trait Audible:
    fn make_sound(self):
        ... trait Quackable(Audible):  
    fn quack(self):  
        ... This means that a struct will have to implement both and to conform to the trait. Duck quack make_sound Quackable This is similar to the concept of "Interface embedding" in Go, where to make a new interface that inherits from other interfaces, you would embed parent interfaces like this: type Quackable interface {
	Audible  // includes methods of Audible in Quackable's method set
} Static Methods Traits also accept static methods that work without creating an instance of a struct: trait Swims:  
    @staticmethod  
    fn swim():  
        ...

@value  
struct Duck(Quackable, Swims):  
    fn quack(self):  
        print("Quack!")  

    @staticmethod  
    fn swim():  
        print("Swimming") fn make_it_swim[T: Swims]():  
    T.swim() You call a static method like this: def main():
	make_it_quack(Duck())
	make_it_quack(Goose())
	Duck.swim() Which will output: Quack!
Honk!
Swimming Notice that in the last method call, a Duck instance is not created. This is how static methods in Python work, departing somewhat from object-oriented programming. Mojo builds on this Python functionality. Limitations of Mojo Traits Compared to Go Interfaces Interestingly, the Go trick with an empty that allows to pass any type and was popular with the Go community before Go Generics were introduced, will not work with a Mojo-typed function . interface{} fn Your struct has to implement at least one of the lifecycle methods like, or , which in this case, would make it conform to or , to be used with functions that accept trait types. __len__ __str__ Sized Stringable This is actually not a real limitation and makes a lot of sense in Mojo, since, with Mojo, if you need dynamic typing, you can just fall back onto the more flexible function, and then apply the usual Python magic to work with unknown types. def The more strict Mojo functions also work on generic structs using types like ; read more about that . fn DynamicVector here Another limitation I see has to do with structs and their methods rather than with traits and is a potential obstacle to implementing Clean Architecture/separating code into different parts. Consider one of the previous examples with a Go vs. Mojo struct method definition: type Duck struct {}

func (d Duck) quack() {
    fmt.Println("Quack!")
} @value  
struct Duck(Quackable):  
    fn quack(self):  
        print("Quack!") The Mojo example, following a more Python-like syntax, nests the method definition directly inside the struct, while the Go version allows it to separate it from the struct itself. In this version, if I have a very long struct with many types and methods, it will be somewhat harder to read. It's not a critical difference though, just something to be aware of. Mojo traits are already pretty useful, despite the language being in its early days. While Go philosophy is all about simplicity and tries to keep the features to a minimum, it's likely that we'll see more functionality added to Mojo traits in the future, making them even more powerful and enabling tons of different use cases.

Mojo Traits: How Do They Compare to Go Interfaces?

মোজো বৈশিষ্ট্য: তারা গো ইন্টারফেসের সাথে কীভাবে তুলনা করে?

Mojo-Eigenschaften: Wie vergleichen sie sich mit Go-Schnittstellen?

Rasgos de Mojo: ¿Cómo se comparan con las interfaces Go?

Traits Mojo : comment se comparent-ils aux interfaces Go ?

मोजो लक्षण: उनकी तुलना गो इंटरफेस से कैसे की जाती है?

Mojo の特性: Go インターフェイスとの比較は?

Mojo 특성: Go 인터페이스와 어떻게 비교됩니까?

Traços do Mojo: como eles se comparam às interfaces Go?

Особенности Mojo: как они соотносятся с интерфейсами Go?

Mojo Özellikleri: Go Arayüzleriyle Nasıl Karşılaştırılır?

Đặc điểm Mojo: Chúng so sánh với giao diện Go như thế nào?

Mojo 特征：它们与 Go 接口相比如何？

In the world of tech, innovation is at its heart, and product managers are right at its core. We had a chance to sit down with , the brains behind and a celebrated Product Management Advisor & Coach. With more than 20 years under her belt, Wendy-Lynn has seen it all. As we chatted, she distilled the essence of a top-notch PM into three foundational traits: Communication, Curiosity, and Data-Driven Decision Making. Wendy-Lynn McClean intrico.io Communication: More Than Just Words " " Wendy-Lynn mentions. " " For her, effective communication goes beyond simple words. It's about active listening, crafting compelling narratives, and simplifying concepts. She recalls a moment when an executive's simple act of glancing at their phone spoke volumes about their disinterest. " " she notes. Being a product manager is like being the ultimate translator of ideas, We constantly bridge gaps – whether it's between teams, stakeholders, or end-users. Sometimes, the silent cues like body language or the atmosphere in a room convey more than any words ever could, She adds, " " A product’s story can inspire teams, align visions, and ignite passion. But remember, storytelling isn't just about the story itself, but how effectively you communicate it. Curiosity: The Bedrock of Innovation Wendy-Lynn believes that curiosity is the often-overlooked champion in product management. " " she explains with enthusiasm. 'Why does this challenge keep coming up for users? Or what draws one group to our product but turns another away?' It's what connects us from seeing a problem to actually solving it, " " she says. A product manager's world is filled with ambiguities. If you aren’t innately curious, you might find it challenging to navigate these uncertainties, Data-Driven Decision Making: The Compass in Chaos While data might seem like a modern catchword, Wendy-Lynn insists it's the compass guiding a PM's journey. " " However, she is quick to note that being data-driven doesn't imply being data-blinded. "It's not just about what the numbers tell you, but also about what they might not be saying." The ocean of product management is vast and often loud. Data becomes your North Star, offering insights and direction. Drawing from Douglas Hubbard’s wisdom, Wendy-Lynn reiterates that while data is a powerful tool, intuition born out of experience and knowledge often fills in the gaps that raw data might miss. Beyond The Core: A Holistic View When asked about other often-cited traits like empathy or adaptability, Wendy-Lynn believes that they naturally intertwine with the three core traits she emphasizes. " " she explains. A curious PM will inherently be empathetic. They'll seek to understand user pain points, team dynamics, and broader market shifts, Reflecting on her unconventional transition from the wine industry to the echelons of tech giants like Amazon and Google, Wendy-Lynn's story is a testament to the power of adaptability, fueled by inherent curiosity and informed by data. Her passion extends beyond her role, deeply rooted in mentoring emerging talent, especially from underrepresented backgrounds. She remarks, " " Coaching isn’t about sugarcoating. My approach is direct, ensuring that my clients, fortified with honest feedback, are ready to face any challenge. As our conversation wraps up, it's evident that Wendy-Lynn McClean’s insights offer invaluable guidance for aspiring product managers. With a steadfast focus on nurturing communication skills, fostering curiosity, and cultivating a data-driven mindset, PMs can indeed steer their products—and careers—toward unparalleled success.

A Conversation with Wendy-Lynn McClean: The 3 Most Important Traits of a Product Manager

ওয়েন্ডি-লিন ম্যাকক্লিনের সাথে একটি কথোপকথন: একটি পণ্য পরিচালকের 3টি সবচেয়ে গুরুত্বপূর্ণ বৈশিষ্ট্য

Ein Gespräch mit Wendy-Lynn McClean: Die 3 wichtigsten Eigenschaften eines Produktmanagers

Una conversación con Wendy-Lynn McClean: los 3 rasgos más importantes de un gerente de producto

Une conversation avec Wendy-Lynn McClean : les 3 traits les plus importants d'un chef de produit

वेंडी-लिन मैक्लेन के साथ बातचीत: एक उत्पाद प्रबंधक के 3 सबसे महत्वपूर्ण लक्षण

ウェンディ・リン・マクリーンとの会話: プロダクト マネージャーの 3 つの最も重要な特徴

Wendy-Lynn McClean과의 대화: 제품 관리자의 가장 중요한 3가지 특성

Uma conversa com Wendy-Lynn McClean: as três características mais importantes de um gerente de produto

Разговор с Венди-Линн МакКлин: 3 самых важных качества менеджера по продукту

Wendy-Lynn McClean ile Söyleşi: Bir Ürün Yöneticisinin En Önemli 3 Özelliği

Cuộc trò chuyện với Wendy-Lynn McClean: 3 đặc điểm quan trọng nhất của Giám đốc sản phẩm

与 Wendy-Lynn McClean 的对话：产品经理的 3 个最重要的特质

Underrated Founder Traits: The Unspoken Variables in Startup Success. In the venture capital ecosystem, there's a persistent allure for formulas, models, and hard data that investors routinely rely upon to gauge a startup's worthiness. It's not unjustified; numbers are seductive in their promise of certainty. , product-market fit, and financial projections — these are the well-trodden paths that lead us to believe we have reduced the odds of failure. Market size But what if this mathematical sanctuary is a mirage? What if the x-factors in a startup's success or failure are less quantifiable, tucked away in the nuanced attributes of its founder? As an investor, it's convenient to put faith in spreadsheets because they offer an illusion of control. But startups are not merely algebra problems waiting to be solved. They are more akin to organisms, complex and unpredictable, thriving or decaying based on a multitude of variables. A significant part of that equation is the founder, specifically traits that most overlook: Adaptability Emotional Intelligence Domain Expertise Execution Capabilities. The absence of these traits can subtly or explicitly lead to a startup's downfall, creating a negative ripple effect that can dampen an entire investment portfolio, erode investor reputation, and subtly chip away at the industry's spirit of innovation. Adaptability Adaptability in a founder is not a luxury — It's a form of insurance against the inherent volatility of startup life. An adaptable founder can pivot from a failing strategy without the cognitive baggage of sunk cost fallacy, making them invaluable in crisis situations. Adaptability is about the founder's capacity to learn quickly, to unlearn even quicker, and to make decisions with a composite view that takes into account changing variables. An adaptable founder doesn't just make a startup resilient; they make it nearly antifragile, improving its capability to capitalize on unexpected challenges or changes. Emotional Intelligence Emotional intelligence often gets categorized as a "soft skill," a sideline in the arena of startup gladiators. But this dismissal couldn't be more misguided. Founders with high emotional intelligence create environments where talent thrives, where conflicts are resolved constructively, and where the cultural fabric of the startup remains robust, even when stretched thin by crises. More importantly, emotional intelligence translates to a founder's capacity for empathy — a critical trait for understanding customers deeply, navigating investor relations effectively, and fostering a workspace culture that attracts top talent. An emotionally intelligent founder doesn't just manage a startup; they nurture it. Domain Expertise Domain expertise, too, is often marginalized in favor of broad strategic ability. However, a founder, well-versed in the nuances of the specific market domain, operates with a granularity of insight that is profoundly advantageous. This isn't merely about having '10,000 hours' in a field. It's about a nuanced understanding of customer pain points, emerging trends, and, crucially, seeing the dead-ends before they appear. Founders with strong domain expertise can not only steer the startup effectively but can also serve as a magnet for other industry talents and even for niche investors. When a founder knows the 'what' and 'why' of the market intricacies, it significantly de-risks the investment and fast-tracks the journey to product-market fit and beyond. Execution Focus on execution over ideas represents the final cornerstone trait that is often left out of investor evaluations. Ideas are abundant; it’s the disciplined execution of those ideas that's a scarce commodity. There's a reason why 'execution risk' is one of the most pervasive deal-breakers in the world of venture capital. It’s not because investors are averse to risk; it’s because they know that even a mediocre idea with brilliant execution can trump a brilliant idea burdened by poor execution. Look at Friendster and Facebook. Virtually identical in their core propositions, but galaxies apart in their fates — solely due to differences in execution. An idea is nothing but a hypothesis; execution is the experiment that validates it. Examples Here are some examples of founders & executives who combine these traits masterfully: Sheryl Sandberg ●      Adaptability: Transitioned from Google to Facebook, and from economics to tech. ●      Emotional Intelligence: A thought leader on leadership and workplace culture. ●      Domain Expertise: Skilled in business operations and people management. ●      Execution: Instrumental in Facebook's immense growth and profitability. Brian Chesky ●      Adaptability: Pivoted Airbnb's offerings in response to the COVID-19 crisis. ●      Emotional Intelligence: Maintains a positive internal culture. ●      Domain Expertise: Deep understanding of the sharing economy and travel industry. ●      Execution: Expanded Airbnb into a global brand. Whitney Wolfe Herd ●      Adaptability: Pivoted from Tinder to launch Bumble, a competitor with a twist. ●      Emotional Intelligence: Advocates for gender equality and healthy relationships. ●      Domain Expertise: In-depth understanding of online dating market dynamics. ●      Execution: Took Bumble public, reaching multi-billion-dollar valuation. Reed Hastings ●      Adaptability: Transitioned Netflix from DVD rentals to streaming to original content. ●      Emotional Intelligence: Known for a unique, performance-oriented culture. ●      Domain Expertise: Entertainment industry and software algorithms. ●      Execution: Transformed the way we consume media globally. Stewart Butterfield ●      Adaptability: Pivoted from a gaming startup to launch Slack. ●      Emotional Intelligence: Fostered a company culture that values empathy and teamwork. ●      Domain Expertise: Deep knowledge of software development and team collaboration. ●      Execution: Grew Slack into a tool used by millions globally. Melanie Perkins ●      Adaptability: Evolved Canva from a school project to a design platform. ●      Emotional Intelligence: Maintains a strong workplace culture. ●      Domain Expertise: Graphic design and online software. ●      Execution: Grew Canva to over 60 million monthly active users. In closing, the traits of adaptability, emotional intelligence, domain expertise, and a focus on execution in founders are not just 'nice-to-haves.' They are critical performance variables that, when ignored, can result in not just financial loss but a cascading failure that disrupts lives, ruins reputations, and stifles innovation. Investors should be as rigorous in evaluating these traits as they are with financial models and market research. Not doing so isn’t merely an oversight; it’s a disservice to the entrepreneurial spirit that drives the innovation economy.

Underrated Founder Traits: The Unspoken Variables in Startup Success

আন্ডাররেটেড ফাউন্ডারের বৈশিষ্ট্য: স্টার্টআপ সাফল্যে না বলা ভেরিয়েবল

Unterschätzte Gründereigenschaften: Die unausgesprochenen Variablen für den Startup-Erfolg

Rasgos del fundador subestimados: las variables tácitas en el éxito de una startup

Caractéristiques sous-estimées du fondateur : les variables tacites du succès d'une startup

कम मूल्यांकित संस्थापक लक्षण: स्टार्टअप की सफलता में अनकहे परिवर्तन

過小評価されている創業者の特徴: スタートアップの成功における暗黙の変数

과소평가된 창업자의 특성: 스타트업 성공의 무언의 변수

Características subestimadas do fundador: as variáveis não ditas no sucesso de uma startup

Недооцененные качества основателя: невысказанные факторы успеха стартапа

Yeterince Değerlendirilmeyen Kurucu Özellikleri: Startup Başarısında Söylenmeyen Değişkenler

Những đặc điểm của người sáng lập bị đánh giá thấp: Những biến số không thể nói ra trong thành công của công ty khởi nghiệp

被低估的创始人特质：初创企业成功中不言而喻的变量

We work hard to improve our tech and soft skills. But our character traits, our mindset - we take it for granted. An innate, fixed part of who we are. And thus, we let it run on autopilot. This way of thinking is harmful. Not only is your mindset critical for a successful software development career (maybe even more than your skills), but it's also under your control. True senior devs acknowledge that character traits are malleable, are self-aware of their mindset, and deliberately work on it. This is the superpower that makes them stand out from the crowd and accelerates their career. In this post, I'll discuss the 10 most critical traits of a successful developer, why they are important, and share a few tips on how you can shape them. 3 Things You Need to Succeed as a Software Developer Professional software development is a complex discipline that requires a diverse set of abilities to succeed. We can group them into three main categories: Technical Skills This is the most obvious group. To be a successful developer, you need to be good at your trade: programming languages and frameworks, clean code principles, architecture, testing, debugging, and so on. You also need to be skillful with your tools: version control, command line, and IDE. However, technical skills alone won't get you far in a professional, team-based setup. That's why you need also soft skills. Soft Skills To be able to work on big, commercial projects, you need a solid set of soft skills: teamwork, communication, project management and planning, remote work, self-organization, learning, personal productivity, and similar. Many of us find these skills less fun than the technical ones, and thus, we often neglect them - so they can already separate solid, professional developers from the crowd. But at least we acknowledge them. There's also a third category, though, which is equally important but flies under the radar of almost all, even the otherwise good, developers. Mindset Mindset is kinda similar to soft skills, but the "skills" it’s comprised of are more fundamental, core traits: curiosity, patience, empathy, grit, adaptability, and so on. On some level, we know these traits are important for a successful software development career. But because they seem an innate, fixed part of our personality, we do not attempt to deliberately learn them in the same way we learn project management or personal productivity techniques. This makes us blind to a huge and important part of our skillset and can badly hurt our progression. [For an even more comprehensive perspective on advancing your career as a professional developer, check out my on GitHub] "Definitive Guide to Becoming a TRUE Senior Dev" Why Being Blind to Your Mindset Can Jeopardize Your Career Software development is a complex, sometimes frustrating, and insanely fast-moving field. Learning, growing, and staying relevant as a developer (and not burning out in the process) requires a specific mix of character traits. At the same time, it's a 100% team game. Lone-wolf hackers have no place in professional software development, no matter how good they are technically. And functioning well in a team requires another, seemingly opposite, set of traits. Mindset is something different than skills. Understanding Agile development principles is not the same as being adaptable and open to frequent change. And knowing the debugging techniques is not the same as having the curiosity and persistence to actually enjoy chasing the problem for several hours without getting burned out or discouraged. But while these traits may seem fixed and innate to our personality, they are equally learnable as skills. Without understanding what traits are critical for a programmer and deliberately honing them over time, you'll never reach your full potential. True senior developers know this, are self-aware of their mindset, and continuously work on improving it. This is the differentiator, the superpower that separates the best developers from the crowd. Ok, but which traits are the most important? Let's dive a bit deeper into the details. 10 Traits of a True Senior Developer 1. Curiosity If I had to summarize in one sentence what software development is about, I'd say it's about learning. You need to stay up-to-date with constantly evolving technologies and software development processes. You need to learn the deep details of complex business domains (several ones throughout your career). Analyzing and clarifying requirements is learning. Research is learning. Performance optimization and debugging - in other words, poking deeply into the inner workings of code - is a kind of learning as well. Software development is also about working with people (and people). for You'll be working on cross-functional teams with a diverse set of people from different specializations and backgrounds (nowadays, in the age of remote, most probably from multiple countries and cultures). You'll have to understand "business." You'll have to understand and solve user pain points and problems. Without a healthy dose of curiosity, you'll not only be less effective at all those things, but you'll also burn out pretty quickly. 2. Affinity for Problem-Solving Software development is a giant puzzle - an infinite stream of problems to solve. The reason you are hired as a developer is to solve your company's and customers' problems. To do this, you need to solve organizational problems (how to function as a team, how to organize your work, what processes to use) and technical problems (logic, architecture, performance, and so on). These problems consist of even smaller, nested problems, down to atomic problems like how to design and name a particular function or unit test. If you don't enjoy solving such puzzles, if you don't have a knack for breaking down and untangling problems, your software development career will be a struggle. 3. Patience Becoming a true senior developer requires years of deliberate practice. You'll also experience a lot of setbacks along the way. Developing your career is rewarding but also a slow and sometimes painful process. To achieve excellence and get to the top, you must be ready for lifelong dedication. And this requires a lot of patience. Patience is also critical for a lot of things adjacent to our job: handling technical support, working with not-very-tech-savvy users, and coping with organizational bureaucracy. Plus, it's a great problem-solving aid. And you won't sustain working in such a fast-moving, constantly changing industry as tech without patience. 4. Grit (In Just the Right Amount) Software development requires a lot of persistence. Hunting bugs. Deciphering poorly documented APIs and libraries. Untangling legacy code. Tracking down performance bottlenecks. Even simply sustaining a deep focus for extended periods of time. You'll struggle, fail, get stuck, and get frustrated a lot - no matter how senior you are. And you'll need a lot of grit to plow through and not get burned out. But you also need to understand what's the right amount of grit. What's the sweet spot between unproductively banging your head against the wall for hours and days vs constantly disrupting your team by requesting assistance immediately when you hit even the smallest bump in the road? 5. Emotional Intelligence Software development revolves around people and teams. You'll work very closely with your colleagues at an individual level: pair programs, debug together, and review their code. You'll also work with them in a team setup: brainstorm, plan, and make decisions as a group. And this collaboration is messy: your work will overlap or conflict, and you'll have different opinions. You'll negotiate your roadmap with management. Finally, to build a great product, you'll have to put yourself in your users' shoes. On top of that, all these people come from diverse backgrounds, both technical and non-technical. They are passionate. They have strong opinions. They may sometimes have difficult characters. And your success as a developer depends on how well you can build rapport with them. Without high emotional intelligence, it'll simply be impossible. 6. Ability to Keep Your Ego in Check Software development (and working in a team in general) is a balancing act. On one hand, you're hired for your expertise. You're expected to have strong opinions and to guide less tech-savvy or more junior people. On the other hand, you'll work with equally experienced and opinionated teammates who will challenge your point of view, and with whom you'll have to make group decisions. Your ego will often get hurt in the process. You must be able to keep it in check - but without getting withdrawn and disengaged. You must be opinionated but not zealot. Have a strong point of view but hold it weakly; be open to getting convinced otherwise. You must be ready to defend your opinion but also know when to let go, to not be a condescending, brilliant jerk. You need to respect the team, business, and customers. Be able to disagree but commit. And gracefully take constructive (and even purely negative) feedback. Otherwise, you won't be able to effectively work in a team. 7. Adaptability Everything in software development is moving so fast. Technologies are constantly changing. New methodologies get popular. Companies pivot. Throughout your career, you'll be also changing projects, teams, companies, and business domains. Even a single project is a constant act of inspecting and adapting (especially in agile approaches). And your team will constantly self-reorganize, too. Most people are allergic to change. Change is hard. It's uncomfortable. It's stressful. Being adaptable and open to change will instantly set you apart. It will not only let you climb to the top of the seniority ladder, but it will also let you for a long time. stay there 8. Reliability I'm repeating it ad nauseam, but software development is a team game. Your colleagues, manager, and company - they all count on you to do your part. Nobody will ever consider you a true senior developer - no matter your tech expertise - if they can't rely on you to take care of your work and deliver on your promises without having to be micromanaged. It doesn't mean you can never make any mistakes. Failures happen. And the best companies see them as valuable learning opportunities. But to enable this, you need to be able to pick up a dropped ball, gracefully recover, and be trusted to learn from your failure and not repeat it in the future. 9. Pragmatism Professional software development is an art of tradeoffs. You constantly need to compromise between development speed and quality. Balance new and promising with proven and stable. Walk a thin line between under- and over-engineering. To succeed in professional software development, you need to be very pragmatic. You need to understand that nothing is black and white, and that no principle or pattern holds true in every situation. You must have great intuition for making tradeoffs between different approaches, technologies, and solutions; feel comfortable cutting corners but have a good sense of how much. 10. Positive Outlook Your life as a programmer is not all sunshine and rainbows. You'll meet annoying customers. Face tight deadlines. Your project may get canceled. You may disagree with your team or management but still have to commit and execute. You'll also work with ugly code. (Yes, every codebase has good and bad parts, even at the top companies like Google.) You'll get tired, frustrated, and upset. If you let negativity take over, if you start criticizing and complaining, you'll not only demotivate yourself, but you'll also kill the morale of your team - which won't take your career very far. You need to be biased toward the positive. Be optimistic and cheerful. Always look for a silver lining. Be the person who rallies their team, kills the bad mood, and restores morale. It'll not only get you noticed and promoted, but it'll also make your career more pleasant and sustainable. Bonus: A Few Tips on How to Deliberately Shape Your Mindset Skills, obviously, can be trained - both soft and technical ones. You can get better at debugging as well as at communication. But what about such seemingly innate traits like curiosity or a positive outlook? Can you really train yourself to be more optimistic or curious? Yes, you can! This is a vast topic, worth several books ("Mindset" by Carol S. Dweck is a great starting point). But let me quickly share a couple of tips: Acknowledge that your mindset is not fixed, and that your traits are malleable. Build self-awareness. Observe how you react in different situations. Try to understand what makes you feel in a particular way (curious vs bored, positive vs grumpy, eager vs defensive). Retrospect your behavior from a perspective of a day or two. Was your opinion really pragmatic, or was it your ego talking? How could you have acted differently? Prepare in advance. Pre-plan up-front how you'll behave next time in a similar situation, and identify the right trigger that will remind you about your plan when the time comes. Expose yourself to situations that will let you exercise and strengthen desired traits. Actively look for such opportunities. Focus on a single trait for some time. It will make it easier to find opportunities to exercise it and to increase your self-awareness of this trait. Reframe. Be conscious and deliberate about how you talk to yourself. The idea isn't stupid, it's surprising. That shiver isn't anxiety, it's excitement. The problem isn't frustrating, it's interesting. Enlist help. Don't shy from asking your colleague or manager for feedback - or even to become your "accountability partner" who will catch and point out your unwanted behavior. I also encourage you to learn a bit about cognitive biases and habit forming. This is a well-developed discipline, with a huge body of knowledge, and pretty fun to explore. Bottom line If you want to be a , you have to be a complete one. You must have the right mix of tech skills, soft skills, and character traits, and you can't ignore any part of this trio. true senior developer Character traits are the most tricky part. Most developers neglect them because they are either not self-aware of them, don't know which ones are important, or don't believe they are malleable. But if you can overcome this flawed way of thinking - if you can change your mindset - you can turn it into a superpower that will elevate your career and make you truly stand out.

What Separates the Best Devs From the Crowd: 10 Critical Traits

ভিড় থেকে সেরা দেবকে কী আলাদা করে: 10টি সমালোচনামূলক বৈশিষ্ট্য

Was die besten Entwickler von der Masse unterscheidet: 10 entscheidende Merkmale

Lo que separa a los mejores desarrolladores de la multitud: 10 rasgos críticos

Ce qui sépare les meilleurs développeurs de la masse : 10 traits critiques

भीड़ से सर्वश्रेष्ठ देवों को क्या अलग करता है: 10 महत्वपूर्ण लक्षण

最高の開発者と群衆を分けるもの: 10 の重要な特徴

최고의 개발자와 일반 개발자를 구별하는 요소: 10가지 중요한 특성

O que separa os melhores desenvolvedores da multidão: 10 características críticas

En İyi Geliştiricileri Kalabalıktan Ayıran Nedir: 10 Kritik Özellik

Điều gì phân biệt các nhà phát triển giỏi nhất với đám đông: 10 đặc điểm quan trọng

是什么让最好的开发者脱颖而出：10 个关键特征

Suspicion and Confrontation: A Meeting Between Raskolnikov and Svidrigaïlov

The Burden of Paranoia: Raskolnikov's Struggle with Intrusive Thoughts

is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here: . Part IV, Chapter VI. Crime and Punishment, by Fyodor Dostoyevsky [LINK TO TABLE OF LINK] Part IV, Chapter VI When he remembered the scene afterwards, this is how Raskolnikov saw it. The noise behind the door increased, and suddenly the door was opened a little. “What is it?” cried Porfiry Petrovitch, annoyed. “Why, I gave orders...” For an instant there was no answer, but it was evident that there were several persons at the door, and that they were apparently pushing somebody back. “What is it?” Porfiry Petrovitch repeated, uneasily. “The prisoner Nikolay has been brought,” someone answered. “He is not wanted! Take him away! Let him wait! What’s he doing here? How irregular!” cried Porfiry, rushing to the door. “But he...” began the same voice, and suddenly ceased. Two seconds, not more, were spent in actual struggle, then someone gave a violent shove, and then a man, very pale, strode into the room. This man’s appearance was at first sight very strange. He stared straight before him, as though seeing nothing. There was a determined gleam in his eyes; at the same time there was a deathly pallor in his face, as though he were being led to the scaffold. His white lips were faintly twitching. He was dressed like a workman and was of medium height, very young, slim, his hair cut in round crop, with thin spare features. The man whom he had thrust back followed him into the room and succeeded in seizing him by the shoulder; he was a warder; but Nikolay pulled his arm away. Several persons crowded inquisitively into the doorway. Some of them tried to get in. All this took place almost instantaneously. “Go away, it’s too soon! Wait till you are sent for!... Why have you brought him so soon?” Porfiry Petrovitch muttered, extremely annoyed, and as it were thrown out of his reckoning. But Nikolay suddenly knelt down. “What’s the matter?” cried Porfiry, surprised. “I am guilty! Mine is the sin! I am the murderer,” Nikolay articulated suddenly, rather breathless, but speaking fairly loudly. For ten seconds there was silence as though all had been struck dumb; even the warder stepped back, mechanically retreated to the door, and stood immovable. “What is it?” cried Porfiry Petrovitch, recovering from his momentary stupefaction. “I... am the murderer,” repeated Nikolay, after a brief pause. “What... you... what... whom did you kill?” Porfiry Petrovitch was obviously bewildered. Nikolay again was silent for a moment. “Alyona Ivanovna and her sister Lizaveta Ivanovna, I... killed... with an axe. Darkness came over me,” he added suddenly, and was again silent. He still remained on his knees. Porfiry Petrovitch stood for some moments as though meditating, but suddenly roused himself and waved back the uninvited spectators. They instantly vanished and closed the door. Then he looked towards Raskolnikov, who was standing in the corner, staring wildly at Nikolay and moved towards him, but stopped short, looked from Nikolay to Raskolnikov and then again at Nikolay, and seeming unable to restrain himself darted at the latter. “You’re in too great a hurry,” he shouted at him, almost angrily. “I didn’t ask you what came over you.... Speak, did you kill them?” “I am the murderer.... I want to give evidence,” Nikolay pronounced. “Ach! What did you kill them with?” “An axe. I had it ready.” “Ach, he is in a hurry! Alone?” Nikolay did not understand the question. “Did you do it alone?” “Yes, alone. And Mitka is not guilty and had no share in it.” “Don’t be in a hurry about Mitka! A-ach! How was it you ran downstairs like that at the time? The porters met you both!” “It was to put them off the scent... I ran after Mitka,” Nikolay replied hurriedly, as though he had prepared the answer. “I knew it!” cried Porfiry, with vexation. “It’s not his own tale he is telling,” he muttered as though to himself, and suddenly his eyes rested on Raskolnikov again. He was apparently so taken up with Nikolay that for a moment he had forgotten Raskolnikov. He was a little taken aback. “My dear Rodion Romanovitch, excuse me!” he flew up to him, “this won’t do; I’m afraid you must go... it’s no good your staying... I will... you see, what a surprise!... Good-bye!” And taking him by the arm, he showed him to the door. “I suppose you didn’t expect it?” said Raskolnikov who, though he had not yet fully grasped the situation, had regained his courage. “You did not expect it either, my friend. See how your hand is trembling! He-he!” “You’re trembling, too, Porfiry Petrovitch!” “Yes, I am; I didn’t expect it.” They were already at the door; Porfiry was impatient for Raskolnikov to be gone. “And your little surprise, aren’t you going to show it to me?” Raskolnikov said, sarcastically. “Why, his teeth are chattering as he asks, he-he! You are an ironical person! Come, till we meet!” “I believe we can say good-bye!” “That’s in God’s hands,” muttered Porfiry, with an unnatural smile. As he walked through the office, Raskolnikov noticed that many people were looking at him. Among them he saw the two porters from the house, whom he had invited that night to the police station. They stood there waiting. But he was no sooner on the stairs than he heard the voice of Porfiry Petrovitch behind him. Turning round, he saw the latter running after him, out of breath. “One word, Rodion Romanovitch; as to all the rest, it’s in God’s hands, but as a matter of form there are some questions I shall have to ask you... so we shall meet again, shan’t we?” And Porfiry stood still, facing him with a smile. “Shan’t we?” he added again. He seemed to want to say something more, but could not speak out. “You must forgive me, Porfiry Petrovitch, for what has just passed... I lost my temper,” began Raskolnikov, who had so far regained his courage that he felt irresistibly inclined to display his coolness. “Don’t mention it, don’t mention it,” Porfiry replied, almost gleefully. “I myself, too... I have a wicked temper, I admit it! But we shall meet again. If it’s God’s will, we may see a great deal of one another.” “And will get to know each other through and through?” added Raskolnikov. “Yes; know each other through and through,” assented Porfiry Petrovitch, and he screwed up his eyes, looking earnestly at Raskolnikov. “Now you’re going to a birthday party?” “To a funeral.” “Of course, the funeral! Take care of yourself, and get well.” “I don’t know what to wish you,” said Raskolnikov, who had begun to descend the stairs, but looked back again. “I should like to wish you success, but your office is such a comical one.” “Why comical?” Porfiry Petrovitch had turned to go, but he seemed to prick up his ears at this. “Why, how you must have been torturing and harassing that poor Nikolay psychologically, after your fashion, till he confessed! You must have been at him day and night, proving to him that he was the murderer, and now that he has confessed, you’ll begin vivisecting him again. ‘You are lying,’ you’ll say. ‘You are not the murderer! You can’t be! It’s not your own tale you are telling!’ You must admit it’s a comical business!” “He-he-he! You noticed then that I said to Nikolay just now that it was not his own tale he was telling?” “How could I help noticing it!” “He-he! You are quick-witted. You notice everything! You’ve really a playful mind! And you always fasten on the comic side... he-he! They say that was the marked characteristic of Gogol, among the writers.” “Yes, of Gogol.” “Yes, of Gogol.... I shall look forward to meeting you.” “So shall I.” Raskolnikov walked straight home. He was so muddled and bewildered that on getting home he sat for a quarter of an hour on the sofa, trying to collect his thoughts. He did not attempt to think about Nikolay; he was stupefied; he felt that his confession was something inexplicable, amazing—something beyond his understanding. But Nikolay’s confession was an actual fact. The consequences of this fact were clear to him at once, its falsehood could not fail to be discovered, and then they would be after him again. Till then, at least, he was free and must do something for himself, for the danger was imminent. But how imminent? His position gradually became clear to him. Remembering, sketchily, the main outlines of his recent scene with Porfiry, he could not help shuddering again with horror. Of course, he did not yet know all Porfiry’s aims, he could not see into all his calculations. But he had already partly shown his hand, and no one knew better than Raskolnikov how terrible Porfiry’s “lead” had been for him. A little more and he might have given himself away completely, circumstantially. Knowing his nervous temperament and from the first glance seeing through him, Porfiry, though playing a bold game, was bound to win. There’s no denying that Raskolnikov had compromised himself seriously, but no facts had come to light as yet; there was nothing positive. But was he taking a true view of the position? Wasn’t he mistaken? What had Porfiry been trying to get at? Had he really some surprise prepared for him? And what was it? Had he really been expecting something or not? How would they have parted if it had not been for the unexpected appearance of Nikolay? Porfiry had shown almost all his cards—of course, he had risked something in showing them—and if he had really had anything up his sleeve (Raskolnikov reflected), he would have shown that, too. What was that “surprise”? Was it a joke? Had it meant anything? Could it have concealed anything like a fact, a piece of positive evidence? His yesterday’s visitor? What had become of him? Where was he to-day? If Porfiry really had any evidence, it must be connected with him.... He sat on the sofa with his elbows on his knees and his face hidden in his hands. He was still shivering nervously. At last he got up, took his cap, thought a minute, and went to the door. He had a sort of presentiment that for to-day, at least, he might consider himself out of danger. He had a sudden sense almost of joy; he wanted to make haste to Katerina Ivanovna’s. He would be too late for the funeral, of course, but he would be in time for the memorial dinner, and there at once he would see Sonia. He stood still, thought a moment, and a suffering smile came for a moment on to his lips. “To-day! To-day,” he repeated to himself. “Yes, to-day! So it must be....” But as he was about to open the door, it began opening of itself. He started and moved back. The door opened gently and slowly, and there suddenly appeared a figure—yesterday’s visitor from underground. The man stood in the doorway, looked at Raskolnikov without speaking, and took a step forward into the room. He was exactly the same as yesterday; the same figure, the same dress, but there was a great change in his face; he looked dejected and sighed deeply. If he had only put his hand up to his cheek and leaned his head on one side he would have looked exactly like a peasant woman. “What do you want?” asked Raskolnikov, numb with terror. The man was still silent, but suddenly he bowed down almost to the ground, touching it with his finger. “What is it?” cried Raskolnikov. “I have sinned,” the man articulated softly. “How?” “By evil thoughts.” They looked at one another. “I was vexed. When you came, perhaps in drink, and bade the porters go to the police station and asked about the blood, I was vexed that they let you go and took you for drunken. I was so vexed that I lost my sleep. And remembering the address we came here yesterday and asked for you....” “Who came?” Raskolnikov interrupted, instantly beginning to recollect. “I did, I’ve wronged you.” “Then you come from that house?” “I was standing at the gate with them... don’t you remember? We have carried on our trade in that house for years past. We cure and prepare hides, we take work home... most of all I was vexed....” And the whole scene of the day before yesterday in the gateway came clearly before Raskolnikov’s mind; he recollected that there had been several people there besides the porters, women among them. He remembered one voice had suggested taking him straight to the police-station. He could not recall the face of the speaker, and even now he did not recognise it, but he remembered that he had turned round and made him some answer.... So this was the solution of yesterday’s horror. The most awful thought was that he had been actually almost lost, had almost done for himself on account of such a trivial circumstance. So this man could tell nothing except his asking about the flat and the blood stains. So Porfiry, too, had nothing but that delirium, no facts but this psychology which cuts both ways, nothing positive. So if no more facts come to light (and they must not, they must not!) then... then what can they do to him? How can they convict him, even if they arrest him? And Porfiry then had only just heard about the flat and had not known about it before. “Was it you who told Porfiry... that I’d been there?” he cried, struck by a sudden idea. “What Porfiry?” “The head of the detective department?” “Yes. The porters did not go there, but I went.” “To-day?” “I got there two minutes before you. And I heard, I heard it all, how he worried you.” “Where? What? When?” “Why, in the next room. I was sitting there all the time.” “What? Why, then you were the surprise? But how could it happen? Upon my word!” “I saw that the porters did not want to do what I said,” began the man; “for it’s too late, said they, and maybe he’ll be angry that we did not come at the time. I was vexed and I lost my sleep, and I began making inquiries. And finding out yesterday where to go, I went to-day. The first time I went he wasn’t there, when I came an hour later he couldn’t see me. I went the third time, and they showed me in. I informed him of everything, just as it happened, and he began skipping about the room and punching himself on the chest. ‘What do you scoundrels mean by it? If I’d known about it I should have arrested him!’ Then he ran out, called somebody and began talking to him in the corner, then he turned to me, scolding and questioning me. He scolded me a great deal; and I told him everything, and I told him that you didn’t dare to say a word in answer to me yesterday and that you didn’t recognise me. And he fell to running about again and kept hitting himself on the chest, and getting angry and running about, and when you were announced he told me to go into the next room. ‘Sit there a bit,’ he said. ‘Don’t move, whatever you may hear.’ And he set a chair there for me and locked me in. ‘Perhaps,’ he said, ‘I may call you.’ And when Nikolay’d been brought he let me out as soon as you were gone. ‘I shall send for you again and question you,’ he said.” “And did he question Nikolay while you were there?” “He got rid of me as he did of you, before he spoke to Nikolay.” The man stood still, and again suddenly bowed down, touching the ground with his finger. “Forgive me for my evil thoughts, and my slander.” “May God forgive you,” answered Raskolnikov. And as he said this, the man bowed down again, but not to the ground, turned slowly and went out of the room. “It all cuts both ways, now it all cuts both ways,” repeated Raskolnikov, and he went out more confident than ever. “Now we’ll make a fight for it,” he said, with a malicious smile, as he went down the stairs. His malice was aimed at himself; with shame and contempt he recollected his “cowardice.” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. . Urbana, Illinois: Project Gutenberg. Retrieved April 2022, from Fyodor Dostoyevsky. 2001. CRIME AND PUNISHMENT https://www.gutenberg.org/files/2554/2554-h/2554-h.htm#link2HCH0026 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

The Confession: A Surprising Turn of Events in Raskolnikov's Tale

Raskolnikov's Reckoning: A Battle of Conscience and Conviction

A Sudden Calm: Raskolnikov's Determination to End it All

Lost Memories and Strange Visitors: Raskolnikov's Recovery

The Uninvited Visitor: A Confrontation in Raskolnikov's Cabin

The Many Agitations of Raskolnikov

Raskolnikov Harbored a Deep Hatred for Crowds 

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References REFERENCES [1] Toril Aalberg and Anders Todal Jenssen. 2017. Gender Stereotyping of Political Candidates. Nordicom Review 28, 1 (2017), 17–32. https://doi.org/10.1515/nor-2017-0198 [2] Pushkal Agarwal, Nishanth Sastry, and Edward Wood. 2019. Tweeting MPs: Digital Engagement between Citizens and Members of Parliament in the UK. Proceedings of the International AAAI Conference on Web and Social Media 13, 01. [3] Arshia Arya, Soham De, Dibyendu Mishra, Gazal Shekhawat, Ankur Sharma, Anmol Panda, Faisal M Lalani, Parantak Singh, Ramaravind Kommiya Mothilal, Rynaa Grover, Sachita Nishal, Saloni Dash, Shehla Rashid Shora, Syeda Zainab Akbar, and Joyojeet Pal. 2022. DISMISS: Database of Indian Social Media Influencers on Twitter. [4] Nichole M. Bauer. 2015. Emotional, Sensitive, and Unfit for Office? Gender Stereotype Activation and Support Female Candidates. Political Psychology 36, 6 (2015), 691–708. [5] Aditi Bhowmick, Paul Novosad, Sam Asher, Elliott Ash, Bilal Siddiqi, Christoph Goessmann, Daniel Chen, and Tanaya Devi. 2021. In-Group Bias in the Indian Judiciary: Evidence from 5.5 Million Criminal Cases. In Proceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS ’21). 47. https://doi.org/10.1145/3460112.3471943 [6] Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa-Anke, Fangyu Liu, Eugenio Martínez-Cámara, et al. 2022. TweetNLP: Cutting-Edge Natural Language Processing for Social Media. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. [7] Tessa M. Ditonto, Allison J. Hamilton, and David P. Redlawsk. 2014. Gender Stereotypes, Information Search, and Voting Behavior in Political Campaigns. Political Behavior 36, 2 (2014), 335–358. https://doi.org/10.1007/s11109-013-9232-6 [8] Kathleen Dolan and Timothy Lynch. 2014. It Takes a Survey: Understanding Gender Stereotypes, Abstract Attitudes, and Voting for Women Candidates. American Politics Research 42, 4 (2014), 656–576. https://doi.org/10.1177/1532673X13503034 [9] Heather Evans. 2016. Do Women Only Talk about ‘Female Issues’? Gender and
Issue Discussion on Twitter. Online Information Review 40, 5 (2016), 660–672.
https://doi.org/10.1108/OIR-10-2015-0338 [10] Heather K. Evans and Jennifer Hayes Clark. 2016. You Tweet Like a Girl!’: How Female Candidates Campaign on Twitter. American Politics Research 44, 2 (2016), 326–352. https://doi.org/10.1177/1532673X15597747 [11] Peter A. Gloor, Joao Marcos, Patrick M. de Boer, Hauke Fuehres, Wei Lo, and Keiichi Nemoto. 2015. Cultural Anthropology through the Lens of Wikipedia: Historical Leader Networks, Gender Bias, and News-based Sentiment. arXiv:1508.00055 [cs.CY] https://doi.org/10.48550/ARXIV.1508.00055 [12] Leonie Huddy and Nayda Terkildsen. 1993. Gender Stereotypes and the Perception of Male and Female Candidates. American Journal of Political Science 37, 1 (1993), 119. https://doi.org/10.2307/2111526 [13] Berrin Beasley John H. Parmelee, Nataliya Roman and Stephynie C. Perkins. 2019. Gender and Generational Differences in Political Reporters’ Interactivity on Twitter. Journalism Studies 20, 2 (2019), 232–247. https://doi.org/10.1080/1461670X.2017.1364140 [14] Ashiqur R. KhudaBukhsh Kunal Khadilkar and Tom M. Mitchell. 2022. Gender Bias, Social Bias, and Representation in Bollywood and Hollywood. Patterns 3, 4 (2022), 100486. https://doi.org/10.1016/j.patter.2022.100486 [15] MingYu (Ethen) Liu. 2023. Topic Modeling. https://ethen8181.github.io/machine-learning/clustering/topic_model/LDA.html. [16] J. Nathan Matias, Sarah Szalavitz, and Ethan Zuckerman. 2017. FollowBias: Supporting Behavior Change toward Gender Equality by Networked Gatekeepers on Social Media (CSCW ’17). 1082–1095. https://doi.org/10.1145/2998181.2998287 [17] Lindsey Meeks. 2016. Gendered Styles, Gendered Differences: Candidates’ Use of Personalization and Interactivity on Twitter. Journal of Information Technology & Politics 13, 4 (2016), 295–310. https://doi.org/10.1080/19331681.2016.1160268 [18] Armin Mertens, Franziska Pradel, Ayjeren Rozyjumayeva, and Jens Wäckerle. 2019. As the Tweet, so the Reply? Gender Bias in Digital Communication with Politicians. In Proceedings of the 10th ACM Conference on Web Science (WebSci ’19). 193–201. [19] Sebo P. 2021. Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference. J Med Libr Assoc. (2021). [20] Anmol Panda, A’ndre Gonawela, Sreangsu Acharyya, Dibyendu Mishra, Mugdha Mohapatra, Ramgopal Chandrasekaran, and Joyojeet Pal. 2020. NivaDuck - A Scalable Pipeline to Build a Database of Political Twitter Handles for India and the United States. In International Conference on Social Media and Society. 200–209. [21] John H. Parmelee. 2013. Political Journalists and Twitter: Influences on Norms and Practices. Journal of Media Practice 14, 4 (2013), 291–305. https://doi.org/10.1386/jmpr.14.4.291_1 [22] Pallavi Prasad. 2020. One in Every Seven Tweets About Indian Women Politicians Is Abusive: Study. https://www.theswaddle.com/women-politicians-india-twitter-trolls. [23] Gazal Shekhawat, Parantak Singh, Rynaa Grover, Saloni Dash, Himani Negi, Ankur Sharma, Somya Sagarika, and Joyojeet Pal. 2021. Twitter Use by Politicians Across States in India. [24] Carole Spary. 2007. Female Political Leadership in India. Commonwealth & Comparative Politics 45, 3 (2007), 253–277. https://doi.org/10.1080/14662040701516821 [25] Genderize team. 2023. Genderize. https://genderize.io/. [26] Jesse Holcomb Usher, Nikki and Justin Littman. 2018. Twitter Makes It Worse: Political Journalists, Gendered Echo Chambers, and the Amplification of Gender Bias. The International Journal of Press/Politics 23, 3 (2018), 324–344. https://doi.org/10.1177/1940161218781254 [27] Jason Gainous Wagner, Kevin M. and Mirya R. Holman. 2017. I Am Woman, Hear Me Tweet! Gender Differences in Twitter Use among Congressional Candidates. Journal of Women, Politics & Policy 38, 4 (2017), 430–455. https://doi.org/10.1080/1554477X.2016.1268871 This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References REFERENCES [1] Toril Aalberg and Anders Todal Jenssen. 2017. Gender Stereotyping of Political Candidates. Nordicom Review 28, 1 (2017), 17–32. https://doi.org/10.1515/nor-2017-0198 [2] Pushkal Agarwal, Nishanth Sastry, and Edward Wood. 2019. Tweeting MPs: Digital Engagement between Citizens and Members of Parliament in the UK. Proceedings of the International AAAI Conference on Web and Social Media 13, 01. [3] Arshia Arya, Soham De, Dibyendu Mishra, Gazal Shekhawat, Ankur Sharma, Anmol Panda, Faisal M Lalani, Parantak Singh, Ramaravind Kommiya Mothilal, Rynaa Grover, Sachita Nishal, Saloni Dash, Shehla Rashid Shora, Syeda Zainab Akbar, and Joyojeet Pal. 2022. DISMISS: Database of Indian Social Media Influencers on Twitter. [4] Nichole M. Bauer. 2015. Emotional, Sensitive, and Unfit for Office? Gender Stereotype Activation and Support Female Candidates. Political Psychology 36, 6 (2015), 691–708. [5] Aditi Bhowmick, Paul Novosad, Sam Asher, Elliott Ash, Bilal Siddiqi, Christoph Goessmann, Daniel Chen, and Tanaya Devi. 2021. In-Group Bias in the Indian Judiciary: Evidence from 5.5 Million Criminal Cases. In Proceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS ’21). 47. https://doi.org/10.1145/3460112.3471943 https://doi.org/10.1145/3460112.3471943 [6] Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa-Anke, Fangyu Liu, Eugenio Martínez-Cámara, et al. 2022. TweetNLP: Cutting-Edge Natural Language Processing for Social Media. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. [7] Tessa M. Ditonto, Allison J. Hamilton, and David P. Redlawsk. 2014. Gender Stereotypes, Information Search, and Voting Behavior in Political Campaigns. Political Behavior 36, 2 (2014), 335–358. https://doi.org/10.1007/s11109-013-9232-6 https://doi.org/10.1007/s11109-013-9232-6 [8] Kathleen Dolan and Timothy Lynch. 2014. It Takes a Survey: Understanding Gender Stereotypes, Abstract Attitudes, and Voting for Women Candidates. American Politics Research 42, 4 (2014), 656–576. https://doi.org/10.1177/1532673X13503034 https://doi.org/10.1177/1532673X13503034 [9] Heather Evans. 2016. Do Women Only Talk about ‘Female Issues’? Gender and
Issue Discussion on Twitter. Online Information Review 40, 5 (2016), 660–672. https://doi.org/10.1108/OIR-10-2015-0338 https://doi.org/10.1108/OIR-10-2015-0338 [10] Heather K. Evans and Jennifer Hayes Clark. 2016. You Tweet Like a Girl!’: How Female Candidates Campaign on Twitter. American Politics Research 44, 2 (2016), 326–352. https://doi.org/10.1177/1532673X15597747 https://doi.org/10.1177/1532673X15597747 [11] Peter A. Gloor, Joao Marcos, Patrick M. de Boer, Hauke Fuehres, Wei Lo, and Keiichi Nemoto. 2015. Cultural Anthropology through the Lens of Wikipedia: Historical Leader Networks, Gender Bias, and News-based Sentiment. arXiv:1508.00055 [ cs.CY ] https://doi.org/10.48550/ARXIV.1508.00055 cs.CY https://doi.org/10.48550/ARXIV.1508.00055 [12] Leonie Huddy and Nayda Terkildsen. 1993. Gender Stereotypes and the Perception of Male and Female Candidates. American Journal of Political Science 37, 1 (1993), 119. https://doi.org/10.2307/2111526 https://doi.org/10.2307/2111526 [13] Berrin Beasley John H. Parmelee, Nataliya Roman and Stephynie C. Perkins. 2019. Gender and Generational Differences in Political Reporters’ Interactivity on Twitter. Journalism Studies 20, 2 (2019), 232–247. https://doi.org/10.1080/1461670X.2017.1364140 https://doi.org/10.1080/1461670X.2017.1364140 [14] Ashiqur R. KhudaBukhsh Kunal Khadilkar and Tom M. Mitchell. 2022. Gender Bias, Social Bias, and Representation in Bollywood and Hollywood. Patterns 3, 4 (2022), 100486. https://doi.org/10.1016/j.patter.2022.100486 https://doi.org/10.1016/j.patter.2022.100486 [15] MingYu (Ethen) Liu. 2023. Topic Modeling. https://ethen8181.github.io/machine-learning/clustering/topic_model/LDA.html . https://ethen8181.github.io/machine-learning/clustering/topic_model/LDA.html [16] J. Nathan Matias, Sarah Szalavitz, and Ethan Zuckerman. 2017. FollowBias: Supporting Behavior Change toward Gender Equality by Networked Gatekeepers on Social Media (CSCW ’17). 1082–1095. https://doi.org/10.1145/2998181.2998287 https://doi.org/10.1145/2998181.2998287 [17] Lindsey Meeks. 2016. Gendered Styles, Gendered Differences: Candidates’ Use of Personalization and Interactivity on Twitter. Journal of Information Technology & Politics 13, 4 (2016), 295–310. https://doi.org/10.1080/19331681.2016.1160268 https://doi.org/10.1080/19331681.2016.1160268 [18] Armin Mertens, Franziska Pradel, Ayjeren Rozyjumayeva, and Jens Wäckerle. 2019. As the Tweet, so the Reply? Gender Bias in Digital Communication with Politicians. In Proceedings of the 10th ACM Conference on Web Science (WebSci ’19). 193–201. [19] Sebo P. 2021. Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference. J Med Libr Assoc. (2021). genderize.io [20] Anmol Panda, A’ndre Gonawela, Sreangsu Acharyya, Dibyendu Mishra, Mugdha Mohapatra, Ramgopal Chandrasekaran, and Joyojeet Pal. 2020. NivaDuck - A Scalable Pipeline to Build a Database of Political Twitter Handles for India and the United States. In International Conference on Social Media and Society. 200–209. [21] John H. Parmelee. 2013. Political Journalists and Twitter: Influences on Norms and Practices. Journal of Media Practice 14, 4 (2013), 291–305. https://doi.org/10.1386/jmpr.14.4.291_1 https://doi.org/10.1386/jmpr.14.4.291_1 [22] Pallavi Prasad. 2020. One in Every Seven Tweets About Indian Women Politicians Is Abusive: Study. https://www.theswaddle.com/women-politicians-india-twitter-trolls . https://www.theswaddle.com/women-politicians-india-twitter-trolls [23] Gazal Shekhawat, Parantak Singh, Rynaa Grover, Saloni Dash, Himani Negi, Ankur Sharma, Somya Sagarika, and Joyojeet Pal. 2021. Twitter Use by Politicians Across States in India. [24] Carole Spary. 2007. Female Political Leadership in India. Commonwealth & Comparative Politics 45, 3 (2007), 253–277. https://doi.org/10.1080/14662040701516821 https://doi.org/10.1080/14662040701516821 [25] Genderize team. 2023. Genderize. https://genderize.io/ . https://genderize.io/ [26] Jesse Holcomb Usher, Nikki and Justin Littman. 2018. Twitter Makes It Worse: Political Journalists, Gendered Echo Chambers, and the Amplification of Gender Bias. The International Journal of Press/Politics 23, 3 (2018), 324–344. https://doi.org/10.1177/1940161218781254 https://doi.org/10.1177/1940161218781254 [27] Jason Gainous Wagner, Kevin M. and Mirya R. Holman. 2017. I Am Woman, Hear Me Tweet! Gender Differences in Twitter Use among Congressional Candidates. Journal of Women, Politics & Policy 38, 4 (2017), 430–455. https://doi.org/10.1080/1554477X.2016.1268871 https://doi.org/10.1080/1554477X.2016.1268871

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: References

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ ইন্টারঅ্যাকশনের মধ্যে জেন্ডার বায়াস উন্মোচন: রেফারেন্স

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern im indischen Twitter: Referenzen

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: referencias

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : références

भारतीय ट्विटर पर पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: संदर्भ

インドのツイッターにおけるジャーナリストと政治家の交流におけるジェンダーバイアスの解明：参考文献

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 참고자료

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: referências

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: ссылки

Hint Twitter'ında Gazeteci-Siyasetçi Etkileşiminde Cinsiyet Önyargısını Ortaya Çıkarmak: Referanslar

Khám phá sự thiên vị giới tính trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Tài liệu tham khảo

揭示印度推特上记者与政客互动中的性别偏见：参考文献

Authors:
(1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de);
(2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de);
(3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de);
(4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Table of Links Abstract and 1 Introduction 2 Background 3 Design and Implementation of Licma and 3.1 Design 3.2 Implementation 4 Methodology and 4.1 Searching and Downloading Python Apps 4.2 Comparison with Previous Studies 5 Evaluation and 5.1 GitHub Python Projects 5.2 MicroPython 6 Comparison with previous studies 7 Threats to Validity 8 Related Work 9 Conclusion, Acknowledgments, and References 9 CONCLUSION In this paper, we presented the first empirical study of crypto misuses in Python. To conduct the study, we implemented the first multi-language analysis tool for crypto misuses with rules to detect common misuses of five different Python libraries as well as the standard Java library. We analyzed 895 popular Python apps from GitHub and 51 MicroPython projects to identify misuses. Our analysis revealed that 52.26 % of the projects using a crypto API, misuse the respective library. Furthermore, we observed that only 7 % of the 1,501 misuses are within the application code. The analysis of embedded applications written in MicroPython revealed the importance of hybrid analysis as the only crypto calls were in C code that got shipped with the projects. To get an impression on the differences between the different domains and languages analyzed in previous studies, we compared our results against the misuses reported for Android apps [4] and C firmware images [13]. Our comparison confirms the impression that an opinionated API design actually helps developers avoiding misuses. While we concentrated on the impact of a user-friendly API design for Python, future work can verify if these results generalize to other languages, like Rust and Go. Thus, extending LICMA with new languages. Further, it may be interesting to extend the currently implemented rules in LICMA by an in-depth analysis of misuses of Python crypto APIs. ACKNOWLEDGMENTS This research work has been co-funded by the Deutsche Forschung gemeinschaft (DFG) – SFB 1119 CROSSING (236615297) and SFB 1053 MAKI (210487104), by the German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE, by the LOEWE initiative (Hesse, Germany) within the emergenCITY center. REFERENCES [1] Yasemin Acar, Michael Backes, Sascha Fahl, Simson Garfinkel, Doowon Kim, Michelle L Mazurek, and Christian Stransky. 2017. Comparing the usability of cryptographic apis. In IEEE Symposium on Security and Privacy (SP). IEEE, 154–171. [2] Yasemin Acar, Christian Stransky, Dominik Wermke, Michelle L Mazurek, and Sascha Fahl. 2017. Security developer studies with github users: Exploring a convenience sample. In Symposium on Usable Privacy and Security (SOUPS). 81– 95. [3] Alexia Chatzikonstantinou, Christoforos Ntantogian, Georgios Karopoulos, and Christos Xenakis. 2016. Evaluation of cryptography usage in android applications. In EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). 83–90. [4] Manuel Egele, David Brumley, Yanick Fratantonio, and Christopher Kruegel. 2013. An empirical study of cryptographic misuse in android applications. In ACM SIGSAC conference on Computer & communications security (CCS), Ahmad-Reza Sadeghi, Virgil Gligor, and Moti Yung (Eds.). ACM, 73–84. [5] Pietro Ferrara, Amit Kr Mandal, Agostino Cortesi, and Fausto Spoto. 2019. Crossprogramming language taint analysis for the iot ecosystem. Electronic Communications of the EASST 77 (2019). [6] Mohammadreza Hazhirpasand, Mohammad Ghafari, and Oscar Nierstrasz. 2020. Java Cryptography Uses in the Wild. In ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). ACM, Article 40. [7] Christopher Kane, Bo Lin, Saksham Chand, and Yanhong A. Liu. 2018. Highlevel Cryptographic Abstractions. CoRR abs/1810.09065 (2018). arXiv:1810.09065 http://arxiv.org/abs/1810.09065 [8] Stefan Krüger, Johannes Späth, Karim Ali, Eric Bodden, and Mira Mezini. 2018. CrySL: An Extensible Approach to Validating the Correct Usage of Cryptographic APIs. In European Conference on Object-Oriented Programming (ECOOP) (Leibniz International Proceedings in Informatics (LIPIcs)), Vol. 109. Schloss Dagstuhl– Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, 10:1–10:27. [9] David Lazar, Haogang Chen, Xi Wang, and Nickolai Zeldovich. 2014. Why does cryptographic software fail? A case study and open problems. In Asia-Pacific Workshop on Systems. 1–7. [10] Zaigham Mushtaq, Ghulam Rasool, and Balawal Shehzad. 2017. Multilingual source code analysis: A systematic literature review. IEEE Access 5 (2017), 11307– 11336. [11] Sarah Nadi, Stefan Krüger, Mira Mezini, and Eric Bodden. 2016. Jumping through hoops: Why do Java developers struggle with cryptography APIs?. In International Conference on Software Engineering (ICSE). 935–946. [12] Sazzadur Rahaman, Ya Xiao, Sharmin Afrose, Fahad Shaon, Ke Tian, Miles Frantz, Murat Kantarcioglu, and Danfeng Yao. 2019. Cryptoguard: High precision detection of cryptographic vulnerabilities in massive-sized java projects. In ACM SIGSAC Conference on Computer and Communications Security. 2455–2472. [13] Li Zhang, Jiongyi Chen, Wenrui Diao, Shanqing Guo, Jian Weng, and Kehuan Zhang. 2019. CryptoREX: Large-scale Analysis of Cryptographic Misuse in IoT Devices. In International Symposium on Research in Attacks, Intrusions and Defenses (RAID). USENIX Association, 151–164. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de); (2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de); (3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de); (4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Authors: Authors: (1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de); (2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de); (3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de); (4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2 Background 3 Design and Implementation of Licma and 3.1 Design 3 Design and Implementation of Licma and 3.1 Design 3.2 Implementation 3.2 Implementation 4 Methodology and 4.1 Searching and Downloading Python Apps 4 Methodology and 4.1 Searching and Downloading Python Apps 4.2 Comparison with Previous Studies 4.2 Comparison with Previous Studies 5 Evaluation and 5.1 GitHub Python Projects 5 Evaluation and 5.1 GitHub Python Projects 5.2 MicroPython 5.2 MicroPython 6 Comparison with previous studies 6 Comparison with previous studies 7 Threats to Validity 7 Threats to Validity 8 Related Work 8 Related Work 9 Conclusion, Acknowledgments, and References 9 Conclusion, Acknowledgments, and References 9 CONCLUSION In this paper, we presented the first empirical study of crypto misuses in Python. To conduct the study, we implemented the first multi-language analysis tool for crypto misuses with rules to detect common misuses of five different Python libraries as well as the standard Java library. We analyzed 895 popular Python apps from GitHub and 51 MicroPython projects to identify misuses. Our analysis revealed that 52.26 % of the projects using a crypto API, misuse the respective library. Furthermore, we observed that only 7 % of the 1,501 misuses are within the application code. The analysis of embedded applications written in MicroPython revealed the importance of hybrid analysis as the only crypto calls were in C code that got shipped with the projects. To get an impression on the differences between the different domains and languages analyzed in previous studies, we compared our results against the misuses reported for Android apps [4] and C firmware images [13]. Our comparison confirms the impression that an opinionated API design actually helps developers avoiding misuses. While we concentrated on the impact of a user-friendly API design for Python, future work can verify if these results generalize to other languages, like Rust and Go. Thus, extending LICMA with new languages. Further, it may be interesting to extend the currently implemented rules in LICMA by an in-depth analysis of misuses of Python crypto APIs. LICMA ACKNOWLEDGMENTS This research work has been co-funded by the Deutsche Forschung gemeinschaft (DFG) – SFB 1119 CROSSING (236615297) and SFB 1053 MAKI (210487104), by the German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE, by the LOEWE initiative (Hesse, Germany) within the emergenCITY center. 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LICMA's Analysis of Crypto API Misuses in Python Apps: Findings and Future Directions 

This paper is under CC 4.0 license. available on arxiv Authors: (1) Zhe Liu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (2) Chunyang Chen, Monash University, Melbourne, Australia; (3) Junjie Wang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China & Corresponding author; (4) Mengzhuo Chen, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (5) Boyu Wu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (6) Zhilin Tian, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (7) Yuekai Huang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (8) Jun Hu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (9) Qing Wang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China & Corresponding author. Table of Links Abstract and Introduction Motivational Study and Background Approach Experiment Design Results and Analysis Discussion and Threats to Validity Related Work Conclusion and References 8 CONCLUSION Automated testing is crucial for helping improve app quality. Despite the dozens of mobile app GUI testing techniques, how to automatically generate the diversified unusual text inputs for fully testing mobile apps remains a challenge. This paper proposes InputBlaster which leverages the LLM to produce the unusual inputs together with the mutation rules which serve as the reasoning chains. It formulates the unusual inputs generation problem as a task of producing a set of test generators, each of which can yield a batch of unusual text inputs under the same mutation rule. The evaluation is conducted for both effectiveness and usefulness, with 136% higher bug detection rate than the best baselines, and uncovering 37 new crashes. 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Leveraging LLMs for Generation of Unusual Text Inputs in Mobile App Tests: Conclusion and References

The decentralized finance (DeFi) ecosystem continues to expand across multiple blockchains, offering new opportunities but also introducing major challenges. One key issue is that of fragmented liquidity - assets and trading activity spread thinly across disconnected platforms. Another threat comes from weaknesses in cross-chain interoperability - bridges that promise to connect blockchains but with security flaws that leave funds at risk. To discuss these problems and potential solutions, I spoke with Boris Povar, a co-founder, and CEO of the project. Eywa Povar brings a strong technical background to tackle these complex topics. With a degree in mathematics and programming, he has worked in crypto since 2017 after a successful career running an IT outsourcing firm. As a portfolio investor and fund manager in the DeFi space, he has direct experience grappling with risks around fragmented capital and insecure bridges. Since 2020, he has focused his efforts on developing a novel architecture for trusted and efficient cross-chain communication. Povar provides insight into the scale and consequences of fragmented liquidity and flaws in today's bridges. The interview sheds light on the roadblocks hindering DeFi's progress as well as innovative solutions in development. Povar lends both technical expertise and real-world perspective to outline a vision for the next stage in DeFi evolution. What are the problems of fragmented liquidity and insecure cross-chain communication? The problem of fragmented liquidity can be formulated as follows: Liquidity located in one blockchain is isolated and not utilized in other blockchains simultaneously. Protocols addressing this issue enable your funds to operate across multiple blockchains/protocols concurrently. For instance, Liquid Staking protocols enable participation in the Ether consensus, earning income, while still utilizing funds in DeFi. Blast operates on a similar principle, where funds in their blockchain function within major DeFi protocols in Ethereum, generating income. However, examining this problem from the perspective of bridges reveals inefficiencies. Traditional lock-mint-burn-unlock bridges are ineffective with liquidity, as they connect assets and blockchains in pairs, requiring liquidity to be added to each link. Liquidity locked in such bridges remains dormant and is utilized ineffectively; L2 bridges serve as examples. Meanwhile, the TVL of bridges is rapidly growing and has reached the total TVL of DEXes (approximately $17 billion), necessitating more efficient utilization. Thus, it can be argued that bridges exacerbate the problem of liquidity fragmentation. The issue of insecure cross-chain communication is exemplified by the Multichain protocol. Projects like Curve and Fantom relied on it for cross-chain communications and liquidity transfer. However, Multichain malfunctioned at one point, resulting in losses for these projects. Their infrastructure was entirely dependent on Multichain, and finding and integrating alternative options took considerable time. Multichain, like many cross-chain protocols, had centralized elements, making its operation heavily reliant on teams. This scenario underscores the risks associated with relying on any single bridge as critical ecosystem infrastructure, which may lead to unforeseen "black swan" events. How big of an issue are these problems currently in DeFi and crypto overall? What are the consequences? High slippages in cross-chain swaps and complex UI/UX pose challenges for the majority of users. Projects are compelled to divide their liquidity across blockchains, incurring high maintenance costs. Furthermore, most bridges exhibit centralized traits. To date, bridge hacks have resulted in losses exceeding $2 billion. At EYWA, we endeavor to consolidate the liquidity of major blockchains, enhancing efficiency without compromising security and decentralization. What initially motivated you to tackle the problems of fragmented liquidity and insecure cross-chain bridges in the crypto industry? I've been actively addressing my own challenges. With involvement in asset management and DeFi capital placement since 2020, and in the industry since 2017, I possess an in-depth understanding of the problems and risks associated with cross-chain fund transfers. Drawing on my technical background, I've meticulously analyzed bridge hacks, resulting in the loss of over $2 billion. Consequently, I grasp what's necessary to ensure the security of cross-chain communications. Moreover, I recognize the imperative of holding liquidity long-term for bridge efficiency. I'm well aware of the inefficiencies in DeFi liquidity utilization, contributing to the dominance of CEXes over DEXes. The first wave of cross-chain bridges, while experimental, merely offered a pricier service without enhancing security or decentralization. I often phrase it as: "It’s still the same CEX, just in a new package." Hence, since late 2020, I've been dedicated to developing a genuinely decentralized cross-chain protocol capable of rivaling CEXes without compromising decentralization and security for development speed and convenience. How exactly does aggregated bridge and consensus architecture provide security for transfers? Most cross-chain bridges currently employ their own messaging protocols, which often fall short in terms of decentralization and entail various risks associated with teams, regulations, and management. It's fair to say that virtually all bridges on the market require trust, leaving the entire ecosystem vulnerable to potential "black swan" events. In response to these challenges, the Eywa team, in collaboration with the Curve team, is developing a trustless bridge that relies on a consensus of the most reliable messaging protocols available. We leverage Axelar, L0, Wormhole, Chainlink CCIP, and other messaging protocols to create a highly secure solution for messaging, custody, and liquidity transfer. By combining Eywa Token Bridge smart contracts with data validation consensus, we establish an Eywa Consensus Bridge that ensures security independently of any team and enables users to retain control over bridge liquidity. When transmitting information, it will be sent in parallel to several messaging protocols based on the user's choice, allowing users to specify preferred bridges and required quorums. The bridge's smart contract in the destination network aggregates transmission results and compares them until a quorum is reached. Users can customize the level of security needed, determining the number of checks. If any protocol returns incorrect data, the transmission will be halted. Imagine initiating a cross-chain transfer of a substantial sum. Initially, your funds are secured in the bridge's smart contract. Subsequently, a cross-chain transaction is initiated, transmitted, and verified concurrently by multiple data transfer protocols. In the destination network, a specialized smart contract receives and compares responses. If consistent information is received from all protocols, the final transaction executes your transfer. However, if any bridge is compromised, resulting in mismatched data, the transaction will be aborted, allowing you to reclaim your funds in the source network. The probability of all transfer protocols being compromised simultaneously approaches zero. How does utilizing Curve’s existing deep liquidity pools enable low-slippage cross-chain trading on CrossCurve? The more liquidity available, the lower the slippages, which is an evident concept. However, to elucidate how utilizes Curve pools, I must delve into its architecture. As mentioned earlier, the issue with bridge liquidity fragmentation lies in the fact that locked liquidity within bridges remains inactive in DeFi. CrossCurve To address this, we utilize Curve's LP tokens as the foundational liquidity. Leveraging Curve's ability to exchange LP tokens within the pool as standard tokens, we generate s-tokens - synthetic collateralized derivatives - by consolidating them on one public blockchain, known as Hubchain (currently Fantom). Subsequently, we establish stable liquidity pools on Curve by connecting s-tokens of similar nature. These pools offer minimal slippage. A typical exchange transaction traverses three blockchains, involving an imbalance operation to add liquidity to the Curve pool in the source network, followed by minting the corresponding s-token to the Hubchain, where it is exchanged for an s-token backed by the Curve LP token from the destination network. The s-token is then burned, releasing the LP token in the destination network, and the final step involves an imbalance withdrawal of liquidity from the Curve pool, transferring it to the user's wallet - adding liquidity, exchanging, and removing liquidity. The greater the liquidity in the pools we utilize, the lower the losses incurred. Additionally, this architecture enables more efficient capital utilization as the liquidity from all blockchains is consolidated in one location simultaneously, rather than in pairs. Consequently, CrossCurve requires substantially less liquidity compared to its competitors to achieve low slippage. What are the main benefits to projects and developers of listing their tokens on CrossCurve? Web3 projects can list their tokens on any chain against crvUSD/Stables/WBTC/WETH/Curve LPs and trade them simultaneously across all chains for any asset. They can also receive yield provided by Eywa, Curve, and Convex on provided liquidity, attracting new users from different blockchains. Fast scaling for the utility of your token in other networks, while also reducing the costs associated with maintaining cross-chain liquidity by keeping a single pool. How does CrossCurve simplify the user experience for cross-chain DeFi operations? We utilize cross-chain ZAP transactions, condensing a large number of transactions for the user. For instance, a typical exchange involves a series of transactions across three blockchains, yet the user only needs to initiate one transaction. How will the CrossCurve yield flywheel attract liquidity providers and projects to the platform? Since all CrossCurve pools are native Curve pools, we have access to various tools for managing incentives. These include direct incentives, veCRV bribes, Convex rewards, and other vote aggregators like StakeDAO, Lobby, and Yearn. Additionally, we can create our own votemarket, accumulate Curve and Convex votes, and participate in CRV Wars. Our plan involves using bonding mechanisms to attract the protocol’s own liquidity to the main pools. The first phase of our flywheel launch will focus on direct incentives for pools, veCRV bribes, and attracting our own liquidity through bonding. Subsequently, we will introduce a DAO and our own vote market. The issuance of the EYWA token will be exclusively available to long-term holders and members of the CrossCurve DAO, thereby strengthening the token's value. Curve liquidity providers will receive enhanced yields on their LPs, and we will provide them with a seamless method to contribute to CrossCurve pools. Projects will have the opportunity to offer incentives on their pools. However, to receive additional incentives from the CrossCurve DAO and partake in their distribution, they must become members.It's worth mentioning that we will assist Curve in distributing their stablecoin crvUSD, as it is their priority. In return, they will support us with incentives by voting for our main pools. How will CrossCurve boost adoption of less liquid layer 1s and layer 2s through liquidity bridges? We are currently constructing a cross-chain liquidity core by combining the largest blockchains by TVL. Subsequently, we will integrate emerging blockchains into our system and link them to this core, simplifying the process of liquidity migration between networks for liquidity providers. What role will the CrossCurve DAO play in solving liquidity fragmentation and boosting chain interoperability? CrossCurve will function as a transport hub for liquidity migration between different chains, offering additional income opportunities for liquidity providers. They will receive a base yield for providing liquidity on the original network, along with a yield for contributing their LPs to CrossCurve pools. Additionally, we intend to serve as a launchpad for Web3 projects seeking to establish multi-chain liquidity. This will enable them to utilize a single liquidity pool that serves all blockchains. How will votemarkets and bribing systems incentivize long term alignment between liquidity providers and the protocol? Long-term liquidity providers will become members of the CrossCurve DAO, engaging in the distribution of issuance among CrossCurve pools. They will also receive platform commission income and incentives from projects seeking augmented rewards. Where do you see the main competition in cross-chain DEX space, and how is CrossCurve positioned against them? My primary competitors include projects such as Stargate, Squid, Synapse, and Hashflow. We position ourselves as a solution primarily catering to liquidity providers and DeFi power users who necessitate seamless movement of substantial liquidity across blockchains. Additionally, we address the challenges faced by Web3 founders, enabling them to reduce the expenses associated with establishing and maintaining multi-chain liquidity while broadening their access to users across various blockchains, thereby diminishing their reliance on specific blockchain ecosystems. Our key advantages include the capacity to facilitate large-volume exchanges with minimal losses, akin to Curve's renowned capability, efficient utilization of liquidity, a seamless user experience, robust security measures safeguarding funds within our bridge, lucrative farming opportunities for liquidity providers, and an attractive proposition for projects seeking to establish their own liquidity pools. What chains and assets will CrossCurve focus on supporting in the initial launch stages? CrossCurve will focus on supporting the Ethereum, Arbitrum, Optimism, Polygon, Avalanche, BNB Chain, and Fantom chains along with the USDT, USDC, DAI, WBTC, ETH, crvUSD, 3crypto Curve liquidity pools, crvUSD-USDT Curve liquidity pools, and 3crv Curve liquidity pools assets. Do you intend to partner up with other major players, DAOs and bridges? If yes, in what capacity? To construct the Consensus Bridge, we are collaborating with Curve and will engage with prominent players in the cross-chain market — L0, Wormhole, Axelar, and Chainlink — as integrators of their solutions. For liquidity building, we have partnered with Curve and other entities within the Curve ecosystem, including Convex, StakeDAO, Yearn, and Lobby, to incentivize their users. We are also collaborating with yield aggregators like Beefy. We are currently in negotiations to integrate all new blockchains that Curve will support. Among those on the list are Mantle, Linea, Scroll, zksync, BNB Chain, and others. Additionally, we plan to identify a new Hubchain, a strategic partner blockchain. To enhance scalability and on-chain performance, we will collaborate with bridge aggregators such as Li.fi, Rubic, and Rango, as well as with wallets like Trust Wallet. In the future, we aim to extend support for liquidity aggregation from other DEXes, such as Balancer and Uniswap.

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