That crazy French guy who has been leading product & new "impossible" ideas in ecommerce and fintech in many places between the Lebanon & Indonesia. Everyone has their favourite angles - to me it's about how you work as an individual, as a team, as a lead or as an organisation which defines success the most. Reach out if you feel you could benefit from a conversation about localisation to new countries, building and scaling a (product) team or improving your product skill set!

My Mentoring Topics

  • Product Management
  • Strategy
  • BNPL
  • E-logistics
  • Working in start-up
  • Remote team set-up
10.January 2024

23.November 2023

Antoine helped me to look on my experiences with a fresh perspective and gave some practical, suggestions to the problem we've discussed. He has shared his experience and perspective on the problem. Overall very positive an helpful session, thank you!

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Antifragile - Things that Gain from Disorder
Nassim Nicholas Taleb

Key Facts and Insights from "Antifragile - Things that Gain from Disorder" Concept of Antifragility: The primary concept introduced by Taleb in this book is 'antifragility'. Unlike fragility and robustness, which are adversely affected or remain unaffected by shocks, stressors, and volatility, antifragile systems actually benefit and grow from such disruptions. Volatility as a Necessity: Instead of avoiding volatility, randomness, and uncertainty, we should embrace them. They play an essential role in the evolution and improvement of systems, be it natural, economic, or individual. Small is Beautiful: The book suggests that smaller, decentralized systems are more antifragile than large, centralized ones. They are more adaptable and less likely to experience catastrophic failures. Non-Predictive Decision Making: Taleb promotes a non-predictive approach to decision-making. Instead of trying to predict the unpredictable future, we should focus on building antifragility into our systems to handle whatever comes. Barbell Strategy: This is a risk-management strategy that involves keeping certain aspects of a system extremely safe and others very risky. This approach allows a system to gain from volatility and uncertainty without being exposed to huge risks. Overcompensation as a Source of Growth: The body and mind tend to overcompensate in response to stressors, leading to growth and improvement. This is an example of antifragility in biological systems. Skin in the Game: Taleb introduces this concept where people should bear the consequences of their actions, which encourages responsible behavior and contributes to overall system antifragility. Role of Time: Something that has been around for a long time is likely to be more antifragile. This is known as the Lindy Effect. Disorder as a Source of Invention: Innovation and discovery often occur in disordered, chaotic environments, suggesting that disorder can actually be beneficial. Iatrogenics: This refers to harm done by the healer or the intervention. Sometimes, doing nothing can be the best strategy. Green Lumber Fallacy: It is the misconception that one must understand the fundamental reasons behind a phenomenon to profit from it. An In-Depth Analysis of "Antifragile - Things that Gain from Disorder" In "Antifragile - Things that Gain from Disorder", Nassim Nicholas Taleb introduces the notion of antifragility, a property beyond mere resilience or robustness. An antifragile system actually benefits from shocks and volatility, in stark contrast to fragile systems that break under stress and robust systems that resist change. Taleb asserts that most systems in life, whether biological, economic, or individual, are antifragile to some extent. This idea of antifragility is a fundamental shift from traditional perspectives that view volatility, randomness, and uncertainty as factors to be avoided. Taleb argues that such disruptions are not only inevitable but also necessary for the growth and evolution of systems. This is a powerful idea that has wide-ranging implications for how we understand and interact with the world. Taleb champions the cause of smaller, decentralized systems. He argues that such systems are more antifragile than large, centralized ones because they can adapt to changes more quickly and are less prone to catastrophic failures. This perspective aligns with ecological principles, where diversity and decentralization contribute to ecosystem resilience. The book also proposes a non-predictive approach to decision-making. Rather than trying to predict the unpredictable future, we should focus on building antifragility into our systems. This is a practical approach that acknowledges the inherent uncertainty of the future and our limited ability to predict it. One of the strategies that Taleb suggests for building antifragility is the Barbell Strategy. This involves keeping some aspects of a system extremely safe while allowing others to be very risky. The safe components provide stability, while the risky ones provide opportunities for growth. Taleb also discusses the concept of overcompensation as a source of growth. When subjected to stressors, the body and mind tend to overcompensate, leading to growth and improvement. This is a clear example of antifragility in biological systems. The idea of having 'skin in the game' is another important concept introduced by Taleb. He argues that people should bear the consequences of their actions, which encourages responsible behavior and contributes to system antifragility. Taleb also explores the role of time in determining antifragility. He suggests that something that has been around for a long time, as per the Lindy Effect, is likely to be more antifragile as it has proven its ability to survive various shocks and stressors. Taleb also positions disorder as a source of invention. He suggests that innovation often arises in chaotic, disordered environments, reinforcing the notion that disorder can be beneficial. The concept of iatrogenics, which refers to harm done by an intervention, is another key idea in the book. Taleb suggests that sometimes, the best strategy is to do nothing, especially when the potential harm of an intervention outweighs its benefits. Finally, Taleb discusses the Green Lumber Fallacy, the misconception that one must understand the fundamental reasons behind a phenomenon to profit from it. This underscores the importance of practical knowledge over theoretical understanding. In conclusion, "Antifragile - Things that Gain from Disorder" offers a radical new perspective on dealing with uncertainty and volatility. It challenges conventional wisdom and provides valuable insights into how we can build antifragility into our systems and our lives. It is a thought-provoking read that encourages us to embrace disorder and uncertainty as drivers of growth and evolution.

The Lean Startup
Eric Ries

Key Insights from "The Lean Startup" Entrepreneurship is Management: A startup is an institution that needs to be managed. The Lean Startup approach fosters companies that are both more capital efficient and that leverage human creativity more effectively. Validated Learning: Startups exist not just to make stuff, make money, or serve customers. They exist to learn how to build a sustainable business. Innovation Accounting: To improve entrepreneurial outcomes and hold innovators accountable, a new kind of accounting is needed, one that focuses on the boring stuff: how to measure progress, how to set up milestones, and how to prioritize work. Build-Measure-Learn: The fundamental activity of a startup is to turn ideas into products, measure how customers respond, and then learn whether to pivot or persevere. Pivot or Persevere: The most successful startups are able to decide when to stick to their guns and when to change direction. Minimum Viable Product (MVP): The Lean Startup method teaches you to drive a startup-how to steer, when to turn, and when to persevere-and grow a business with maximum acceleration. Continuous Deployment and Testing: The Lean Startup methodology promotes continuous rapid prototyping and a "Just do it" mindset. With rapid iterations you can decide if the product vision is viable or not. Adaptive Organization: The Lean Startup proposes an organizational structure that fosters innovation. Small Batches: The Lean Startup process works in small batches to minimize the cost of change and risk. Work smarter not harder: The Lean Startup practice advocates for entrepreneurs to work smarter not harder. Detailed Analysis "The Lean Startup" by Eric Ries is a compelling and innovative approach to launching companies that are faster, smarter, and more successful. At its core, it is about learning what your customers really want and learning it quickly. It's about continuously testing what you think your customers might want and adapting based on the results, and doing this before you run out of money. Entrepreneurship is Management is a fundamental tenet of this book. An entrepreneur, according to Ries, is anyone who creates a new product or service under conditions of extreme uncertainty. This is as true for two people in a garage or a group of seasoned professionals in a Fortune 500 boardroom. Thus, managing a startup is a balancing act between sticking to your core vision and being endlessly adaptable. The concept of Validated Learning is one of the most profound insights in the book. Instead of making elaborate plans based on lots of assumptions, you can make constant adjustments with a steering wheel called the Build-Measure-Learn feedback loop. This concept involves quickly assembling a Minimum Viable Product (MVP) and improving it based on feedback, as opposed to perfecting a product without any customer input. This brings us to the concept of Minimum Viable Product (MVP). The MVP is a version of a new product that allows a team to collect the maximum amount of validated learning about customers with the least effort. This strategy saves time and resources that would have otherwise been wasted on developing products that people don't want. Innovation Accounting is another significant aspect of the Lean Startup methodology. It works in three steps: using an MVP to establish real data on where the company is right now, then tuning the engine (improving the product) from the baseline towards the ideal, and finally making a decision to pivot (changing fundamental aspects of the product) or persevere. The concepts of Pivot or Persevere are central to the Lean Startup methodology. A pivot is a fundamental change in strategy that results from the feedback collected from the MVP. If the MVP isn't achieving the desired results, the startup needs to pivot by making a fundamental change to their product. If the MVP is successful, the startup can persevere and continue improving the product based on customer feedback. Continuous Deployment and Testing, Adaptive Organization, and Small Batches work in tandem to create a responsive and agile startup. Continuous Deployment allows for rapid product iteration, while small batches ensure that a startup can adapt quickly to customer feedback and market changes. The adaptive organization understands that failure is an integral part of innovation and fosters an environment that encourages learning from failure. The Lean Startup methodology encourages entrepreneurs to Work smarter not harder. It's not about the hours you put into your work. It's about the work you put into those hours. The Lean Startup methodology is not just about how to create a more successful entrepreneurial business, it's about what we can learn from those businesses to improve virtually everything we do. In conclusion, "The Lean Startup" provides a scientific approach to creating and managing successful startups in an age of uncertainty. It significantly increases the chances of building a successful venture by learning what customers want quickly and scientifically, as opposed to relying on guesswork or assumptions. This methodology has been adopted by countless startups and corporations around the world and continues to grow in popularity due to its proven success and effectiveness.

Never Split the Difference - Negotiating As If Your Life Depended On It
Chris Voss, Tahl Raz

Key Insights from the Book: The principle of tactical empathy: Understand and recognize the emotions of your counterpart and respond to them in a thoughtful manner. The power of mirroring: Imitate the language and behavior of your counterpart to build rapport and trust. The effectiveness of calibrated questions: Ask questions that allow your counterpart to have control, but steer the conversation towards your desired outcome. The significance of active listening: Listen carefully to what your counterpart is saying and respond accordingly. The role of patience: Give your counterpart time to respond and don’t rush them into making a decision. The importance of a "no": Getting a 'no' is not a failure, but rather an opportunity to understand your counterpart's fears and concerns. The “Ackerman Model”: A strategic bargaining method developed in the FBI, which involves setting a target price, then using a series of calculated offers and conciliatory gestures to reach it. The concept of "Black Swans": Unforeseen events or pieces of information that can dramatically impact the outcome of a negotiation. The value of loss aversion: People are more motivated to avoid losses than to achieve equivalent gains. The utility of "that's right": Getting your counterpart to say "That's right" instead of "You're right," ensures they feel understood and agree with your viewpoint. The "7-38-55 Percent Rule": In communication, 7% of a message is derived from the words, 38% from the tone of voice, and 55% from body language and facial expressions. An In-Depth Analysis of the Book "Never Split the Difference" by Chris Voss and Tahl Raz is a compelling exploration into the art of negotiation. Drawing from his experience as a former FBI hostage negotiator, Voss provides readers with practical techniques to improve their negotiation skills. Understanding and Using Tactical Empathy Tactical empathy is at the heart of successful negotiation. It revolves around understanding and acknowledging the feelings and mindset of your counterpart. By doing so, you can navigate the negotiation process more effectively and achieve favourable outcomes. As a negotiator, it's not enough to understand what the other party wants; you must also comprehend how they feel. This emotional intelligence enables you to build a connection and establish mutual trust, increasing the likelihood of a successful negotiation. Mirroring, Calibrated Questions and Active Listening Voss also highlights the importance of mirroring, calibrated questions, and active listening. Mirroring, which involves imitating your counterpart's language and behaviour, can foster a sense of familiarity and rapport. Calibrated questions, on the other hand, allow you to steer the conversation without appearing aggressive or domineering. These questions typically start with "what" or "how," prompting your counterpart to think deeply and contribute valuable information to the discussion. Active listening is equally crucial. By paying close attention to your counterpart's words, you can identify underlying concerns or interests that may be key to the negotiation. This also signals respect and sincerity, strengthening your relationship with the counterpart. The Value of Patience and the Power of 'No' Patience is a virtue in negotiation. Voss emphasizes the importance of allowing your counterpart sufficient time to respond. A hurried negotiation is unlikely to yield optimal results. Moreover, contrary to common belief, receiving a 'no' from your counterpart is not necessarily a setback. Instead, it can serve as a stepping stone to understanding their fears and concerns better. It gives you the opportunity to address those issues and make a more persuasive case. The Ackerman Model and the Concept of Black Swans The Ackerman model is a bargaining method that involves setting a target price, then using a series of calculated offers and conciliatory gestures to reach it. This method, which requires patience and strategic thinking, can be highly effective in achieving your desired outcome. Voss also introduces the concept of 'Black Swans' – unexpected events or pieces of information that can dramatically alter the negotiation landscape. Identifying potential Black Swans and preparing for them can give you a significant advantage. Loss Aversion, 'That's Right' and the 7-38-55 Percent Rule The book also delves into the psychology of negotiation, discussing concepts like loss aversion and the power of the words 'That's right'. People are typically more motivated to avoid losses than to achieve equivalent gains, and this can be leveraged in negotiation. Getting your counterpart to say 'That's right' instead of 'You're right' ensures they feel understood and agree with your viewpoint. The former indicates genuine agreement, while the latter often signals appeasement. Lastly, Voss presents the "7-38-55 Percent Rule," a principle that underscores the importance of non-verbal communication. It posits that only 7% of a message is derived from words, while 38% comes from the tone of voice, and 55% from body language and facial expressions. In conclusion, "Never Split the Difference" offers a wealth of practical strategies and psychological insights for effective negotiation. It challenges traditional notions, encouraging readers to perceive negotiation through a different lens. Whether it's in a professional context or everyday life, these techniques can undoubtedly enhance your ability to negotiate successfully.

Thinking, Fast and Slow
Daniel Kahneman

Key Insights from 'Thinking, Fast and Slow' Cognitive Ease: The human brain tends to choose the path of least resistance when processing information. System 1 and System 2: Two distinct systems govern our thought processes. System 1 is fast, intuitive, and emotional, while System 2 is slow, deliberate, and logical. Heuristics and Biases: Our brains use mental shortcuts or 'heuristics' to make quick decisions, which can often lead to biases in our thinking. Prospect Theory: People tend to make decisions based on potential losses and gains, not final outcomes. Anchoring Effect: The first piece of information we receive about a subject heavily influences our perception of subsequent information. Availability Heuristic: We tend to judge the probability of events by how easily examples come to mind. Endowment Effect: We value things more when we own them. Hindsight Bias: Our tendency to see events as more predictable than they really are after they have happened. Framing Effect: The way information is presented can drastically affect how we perceive it and make decisions. The Halo Effect: Our overall impression of a person influences how we feel and think about their character. Deeper Analysis of the Book's Concepts 'Thinking, Fast and Slow', a seminal work by Daniel Kahneman, delves into the two systems that drive the way we think—System 1, which is fast and intuitive, and System 2, slow and deliberate. This dual-process theory of cognition is not new, but Kahneman's exploration of how these systems interact, often leading to cognitive biases, is groundbreaking. System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control. It's the part of our brain that responds to a surprising sound in the darkness or decides to swerve to avoid an accident. This system is heavily influenced by our past experiences and emotions, making its responses feel intuitive and automatic. In contrast, System 2 allocates attention to the effortful mental activities that demand it, including complex computations and conscious decision-making. This system is slower and more deliberate, often stepping in to verify and modify the impressions and intuitions from System 1. However, System 2 is lazy and often defaults to the easier, automatic responses of System 1. This is where cognitive biases come in. Heuristics and biases are mental shortcuts that System 1 uses to make quick decisions. While these shortcuts can often be useful, they can also lead to systematic errors in our thinking. For example, the availability heuristic might lead us to overestimate the likelihood of dramatic events (like plane crashes) because they are more memorable and thus more easily available to our minds. Prospect theory, introduced by Kahneman and his colleague Amos Tversky, challenges traditional economic theory, which assumes that humans are rational actors. Instead, prospect theory suggests that people make decisions based on potential gains and losses, not the final outcome. This can lead to seemingly irrational decisions, such as refusing to take a small loss to potentially gain more in the long run. The anchoring effect describes our tendency to rely heavily on the first piece of information we receive (the "anchor") when making decisions. Even when the anchor is arbitrary or irrelevant, it can dramatically influence our judgments and estimates. Similarly, the framing effect reveals that the way information is presented can drastically affect our decisions. For example, people are more likely to opt for a surgical procedure if it’s presented with a 90% survival rate than a 10% mortality rate, even though both statistics convey the same information. In conclusion, 'Thinking, Fast and Slow' highlights how our thought processes—though powerful—are not always as rational, objective, or logical as we might believe. By understanding these biases, we can take steps to mitigate them and make better, more informed decisions.

Daniel Kahneman, Olivier Sibony, Cass R. Sunstein

Key Facts and Insights: Unreliable Judgment: The book emphasizes how human judgments are often filled with "noise" or unwanted variability, leading to inconsistent decisions. It shows that even professionals, like doctors, judges, or hiring managers, often make decisions that are inconsistent and unreliable. System Noise: The authors identify various forms of noise such as system noise (discrepancies in organizational decision-making), level noise (inconsistencies in individual judgments), and pattern noise (variations in how different people interpret the same data). Cost of Noise: The book highlights the significant cost of noise in decision-making, both in terms of economic loss and social injustice. Reducing Noise: Kahneman, Sibony, and Sunstein propose a range of strategies for reducing noise, such as decision hygiene, structured decision-making, and leveraging algorithms. Decision Hygiene: The authors recommend decision hygiene as a method of reducing noise. This involves breaking down decisions into smaller parts, avoiding premature conclusions, and reconsidering initial judgments. Structured Decision-Making: The book recommends structured decision-making, with predefined rules and processes, as another method of reducing noise. This can involve using checklists, guidelines, or scoring systems. Role of Algorithms: The authors suggest that algorithms can often make more consistent and less noisy decisions than humans, even when they are imperfect. Resistance to Algorithmic Decision-Making: Despite the potential benefits of algorithmic decision-making, the authors note that many people and organizations resist it due to a desire for human judgment and perceived fairness. Role of Bias: The book points out that while bias is often seen as the main source of decision error, noise can be just as damaging, if not more so. Organizational Implications: The authors discuss the implications of noise for organizations and suggest that reducing noise can improve fairness, efficiency, and productivity. In-depth Analysis: "Noise" is a groundbreaking exploration of the often-overlooked issue of variability in decision-making. The authors, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein, examine how human judgments are often filled with "noise" or unwanted variability, leading to inconsistent decisions. This is a significant departure from the traditional focus on bias in decision-making literature. The book begins by establishing the concept of noise and exploring its various forms, including system noise, level noise, and pattern noise. Each type of noise contributes to the unreliability and inconsistency of human judgments, even among professionals such as doctors, judges, or hiring managers. The authors highlight the significant cost of noise in decision-making, both in terms of economic loss and social injustice. This cost is often overlooked, but it can be substantial. For example, inconsistent sentencing by judges can lead to gross inequities in the justice system. Similarly, variability in hiring decisions can result in missed opportunities and wasted resources. To address the problem of noise, Kahneman, Sibony, and Sunstein propose several strategies. One is decision hygiene, which involves breaking down decisions into smaller parts, avoiding premature conclusions, and reconsidering initial judgments. This can help to limit the influence of irrelevant factors and reduce noise. Another strategy is structured decision-making, which involves using predefined rules and processes. This can include checklists, guidelines, or scoring systems. Such structured approaches can lead to more consistent decisions by limiting the scope for individual interpretation and discretion. The authors also suggest that algorithms can often make more consistent and less noisy decisions than humans. This is a controversial claim, as many people and organizations resist algorithmic decision-making due to a desire for human judgment and perceived fairness. However, the authors argue that even imperfect algorithms can often outperform humans in terms of consistency. The book is not just about individual decision-making, but also has significant implications for organizations. The authors suggest that reducing noise can improve fairness, efficiency, and productivity. This can involve changes at the individual level, such as using decision hygiene and structured decision-making, but also at the organizational level, such as embracing algorithmic decision-making. In conclusion, "Noise" is a thought-provoking exploration of an underappreciated aspect of decision-making. It challenges our assumptions about the reliability of human judgment and proposes innovative strategies for reducing noise. The insights from this book have the potential to transform not just individual decision-making, but also organizational practices and policies.

The Signal and the Noise - The Art and Science of Prediction
Nate Silver

Key Facts or Insights from "The Signal and the Noise" Importance of Bayesian Thinking: Silver emphasizes the use of Bayesian statistics as a tool to differentiate signal (valuable, meaningful data) from noise (random or irrelevant data). Overconfidence in Predictions: The book discusses the dangers of overconfidence and bias in making predictions. Role of Uncertainty: Recognizing uncertainty is crucial in prediction and should be embraced rather than avoided. Predictive Power of Models: Models are simplifications of reality and have their limits for making precise predictions about the future. False Positives and Negatives: Understanding the difference between false positives and negatives is essential in prediction. Impact of Big Data: The book addresses the misconceptions about the predictive power of big data. Failures of Prediction: Silver analyzes notable prediction failures, such as the 2008 economic crisis, to illustrate his points. Human Intuition and Computers: Despite the rise of machine learning and AI, human intuition still plays a significant role in prediction. Case Studies: Silver uses a variety of case studies from different fields, including baseball, poker, weather forecasting, and politics, to illustrate his concepts. Prediction vs. Forecasting: The book distinguishes between prediction (a definite statement about the future) and forecasting (an estimate of the likelihood of a future event). Statistical Noise: Silver discusses the concept of statistical noise and its impact on data analysis and prediction. Detailed Summary and Analysis In "The Signal and the Noise," Nate Silver delves into the world of prediction, exploring a wide array of case studies from various fields and challenging common misconceptions about the way we understand and interpret data. Silver underlines the importance of Bayesian thinking - a statistical method that involves updating our beliefs about the world based on observed evidence. This iterative process allows us to refine our predictions over time, enhancing their accuracy. He contrasts this with the more traditional, static approach often used in the field of statistics, which can lead to rigid interpretations and, subsequently, errors in prediction. One of the major themes in the book is the danger of overconfidence in making predictions. Silver warns against the tendency to view our models and predictions as absolute truth, reminding us that they are merely simplifications of reality. Overconfidence can lead to ignoring important variables, underestimating uncertainty, and ultimately making poor predictions. Acknowledging uncertainty is a crucial aspect of effective prediction. Silver argues that we should not shy away from uncertainty but rather embrace it as an inherent aspect of the prediction process. This perspective can help us develop more realistic models and make more measured predictions. Silver also delves into the concept of false positives and negatives, a critical aspect of prediction that is often misunderstood. He emphasizes that a false positive (a result that indicates a given condition has been met when it has not) can be just as misleading as a false negative (a result that fails to detect a condition when it is present), leading us to incorrect conclusions and misguided actions. In the era of big data, Silver challenges the notion that more data equates to better predictions. He cautions that while big data can provide more signals, it also introduces more noise. Discerning the signal from the noise is a fundamental challenge in the field of prediction and one that requires careful statistical analysis and interpretation. Throughout the book, Silver provides numerous examples of prediction failures, such as the 2008 economic crisis and the inaccurate predictions in the field of earthquake science. These case studies serve to illustrate his points and underscore the pitfalls of prediction. Despite the growing reliance on machine learning and artificial intelligence, Silver maintains that human intuition still plays a significant role in prediction. He asserts that humans possess a unique ability to recognize patterns and make connections that computers cannot, suggesting a synergistic relationship between human intuition and computational power in making accurate predictions. Silver also distinguishes between prediction and forecasting. While these terms are often used interchangeably, Silver highlights that prediction involves making a definitive statement about a future event, while forecasting involves estimating the probability of a future event. This distinction is crucial for understanding the nature and scope of predictive science. Finally, Silver explores the concept of statistical noise, an often overlooked aspect of data analysis that can significantly impact predictions. He emphasizes the importance of recognizing and accounting for statistical noise in our models and predictions to avoid misleading results. In conclusion, "The Signal and the Noise" is a comprehensive exploration of the art and science of prediction. It provides valuable insights into the complexities of predictive science, highlighting the importance of Bayesian thinking, acknowledging uncertainty, recognizing the limitations of models, and understanding the role of statistical noise. It challenges the reader to think critically about the nature of prediction and offers practical advice for enhancing the accuracy of our predictions. Whether you are a seasoned statistician, a data science enthusiast, or merely a curious reader, this book promises to enrich your understanding of the fascinating world of prediction.