The Courage of Freedom Choosing to become a freelancer not only gives you the opportunity to work for yourself, but it also means embracing a life where you abandon the "security" of a salaried job with its fixed hours, routine, and steady paycheck. It can be intimidating, but it's also a decision that opens the door to immense freedom: Freedom of choice: As a freelancer, you choose your clients, the projects you want to work on, and the hours that suit your lifestyle. Geographic freedom: Depending on your industry, you could work from anywhere in the world, as long as you have an internet connection. It's ideal for digital nomads! Organizational freedom: You are the master of your schedule. You can organize your work around your productivity peaks and balance your professional and personal life as you wish. The Challenges of Freelancing However, this freedom comes at a cost, and that's where courage comes into play. As a freelancer, you face several challenges: Financial instability: Income can be unpredictable and irregular, especially at the beginning of your business. You must learn to plan your finances and manage downtime. Self-discipline: Being self-employed also means being your own boss. This requires rigor, organization, and daily discipline to meet deadlines and remain productive. Multiple management: Beyond your area of ​​expertise, you must also manage a whole host of additional responsibilities: client prospecting, marketing, contract negotiation, administrative and accounting management. Professional solitude: Freelancing can sometimes be isolating. You often work alone, which can lack the human interaction found in a traditional office. Some tips for a successful freelance adventure To take full advantage of your choice and minimize the challenges, here are some tips: Clearly define your goals: Why do you want to become a freelancer? Is it to work on projects more aligned with your passions, to better balance your personal and professional life, or to gain independence? Build a financial buffer: Before you start, set aside an emergency fund that will allow you to cover your expenses for a few months in case of low activity. Develop your network: Word of mouth and professional connections are crucial for finding clients. Don't neglect networking, specialized forums, and even in-person events. Register on platforms like GYFF (https://www.getyourfreefast.ch/), which will facilitate your connection with future clients and also help you quickly find a new project. Promote your skills: We sometimes forget, but your profile is your SHOWCASE! Take the time to clearly define your offering and create an attractive showcase with a portfolio, a website, or concrete examples of your work. Automate and delegate: If certain administrative or technical tasks take up too much of your time, consider delegating or using automation tools to focus on your core business. Join a community: Be active in freelance groups, join coworking spaces, or participate in events. This will help you break the isolation and learn from others. Hapidev (https://www.hapidev.ch/) was designed with this in mind, offering you your own IT community. Choosing courage In short, freelancing is synonymous with freedom, but it requires a strong commitment. This adventure requires believing in your abilities, taking the plunge despite doubts, and being willing to navigate uncertainty. However, for those with the necessary willpower and resilience, it can be immensely rewarding. So yes, getting started requires courage, but this boldness can open the door to a tailor-made career and a lifestyle full of personal and professional rewards. Good luck to all future freelancers who are just getting started! 🚀

Created in 1991 by Linus Torvalds, Linux is an open-source operating system that has revolutionized the world of technology. But how well do you know it? Linux powers 100% of the world's top 500 supercomputers Since 2017, "all" of the Top 500 supercomputers use Linux. It offers unparalleled performance, stability, and flexibility. This monopoly is impressive, particularly in the context of scientific research, artificial intelligence, and advanced simulations.

Have you ever wondered about the sometimes problematic practices in the world of data science and artificial intelligence? I offer an in-depth analysis that sheds light on the underlying issues. The Overuse of the Term "AI": A Marketing and Methodological Trap The term "AI" has become a sort of magic word in the tech world, used to capture the attention of decision-makers or to embellish technical solutions, even when they are not based on actual artificial intelligence techniques. This leads to what is called **AI-washing**: an artificial inflation of the capabilities of a product or project by attributing it a complexity or advancement that does not correspond to reality. The example of the meme below is a humorous nod, but it highlights a real problem: **many so-called "AI" solutions rely only on basic rules, often coded with simple structures (if/else statements, regex, or rudimentary decision trees)**. Of course, this doesn't detract from their usefulness in certain contexts; but it does raise questions about intellectual honesty and clarity in technical choices. The Dangers of Poorly Calibrated Use of AI Indeed, the ill-considered use of AI techniques can lead to three major problems: - Expectations Mismanagement: Decision-makers or end users, influenced by the marketing hype surrounding AI, often expect spectacular results. When their hopes clash with a deterministic solution disguised as AI, it can lead to long-term disappointment and damage trust in data teams. For example, an AI that was supposed to "predict human behavior" but only applies rigid rules risks leading to malfunctions and misunderstandings. - Unnecessary system complexity: A machine learning model or a sophisticated framework is not necessarily the optimal solution when clear business logic would suffice. By opting for over-dimensioning, development becomes tedious, costly, and sometimes less robust than a simple, well-designed solution. - Data team distraction: The "it's AI, so it's necessarily better" attitude often pushes technical teams to overinvest in ML solutions when a traditional system would have been sufficient. Meanwhile, fascinating questions or more complex issues for which AI would have been truly appropriate are sidelined. A Praise for Well-Considered Simplicity A good data scientist or data team—and by extension, a good decision-maker—must be able to: 🔹 Recognize situations where a simple solution effectively meets needs. 🔹 Identify cases where an AI or advanced approach is truly necessary and can create added value. 🔹 Know how to deconstruct the mechanisms behind innovation to remain transparent about the strengths and limitations of the proposed solutions. Real-life example: Consider a chatbot designed to help telecom customers. If 80% of customer queries concern recurring questions like "How do I check my bills?" or "How do I change my SIM card?", a set of simple rules based on keywords (using regex, for example) may be more than sufficient. Before integrating an advanced NLP model like GPT or BERT, the logic can be tested deterministically and the approach can be verified to deliver acceptable customer satisfaction. However, if a more complex analysis is required—such as detecting ambiguous intentions expressed in unconventional sentences ("My network never works properly, I'm considering canceling")—advanced models may become essential. Moral and Best Practices: When to Choose and When to Simplify The key message here is an invitation to **demystify innovation**: the individual or team designing the solution must exercise discernment and honesty in their choices. Before diving into sometimes complex AI frameworks, it's wise to ask the following questions: What is the real user/decision-maker need to solve?Are we primarily looking for speed and robustness, or are we trying to address a non-trivial problem? Am I oversizing the solution to meet vague or marketing expectations? If I choose AI, do I have the resources (time, expertise, data) to guarantee its success? If not, wouldn't deterministic solutions be more appropriate? Is it possible to start with a simple MVP before adding complexity later if necessary and justified? Combining pragmatism and innovation I think it's important to remind all professionals in the data and AI world that sometimes, a good old "if/else" can be your best friend. The main objective remains "to create useful, effective, and tailored solutions" to the real needs of the people or organizations you want to serve. We need to start from the basics: don't try to impress with technical terms or misleading labels, but rather aim for "relevance and simplicity as soon as possible." And if adding artificial intelligence becomes necessary later, only then consider increasing the complexity. What do you think? 😊 Have you encountered situations where a simple system would have been sufficient, but AI was emphasized unnecessarily?

Looking for freelance tech jobs in Switzerland? Hapidev connects top Swiss tech freelancers with companies hiring for high-value projects. Find your dream job here. swiss tech jobs from UX/UI and software development to cloud engineering and cybersecurity, hapidev helps top swiss tech talents find their dream job. show open positions only sign up to apply hard skills compensation model employment type work location contract duration company type reset filters newsletter add your name* email* subscribe Boost Your Career Need help landing your next role or growing in your field? Book a 1-on-1 coaching session and get expert guidance tailored to your goals. book now the swiss job platform for tech talents from UX/UI to remote IT, find your next tech job in switzerland, and grow your career and professional network on hapidev! sign up learn more

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