In a previous article I simulated random portfolios using Monte Carlo methods for the 2023 daily closing prices of the 100 stocks constituting the Nasdaq-100. Here I add the risk-free asset to the portfolio and examine how it affects the coverage in log volatility—return space.

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This post provides a practical Individual Career Plan template you can use to guide self-assessment, prioritize your professional development, and set meaningful goals that align with your vision for the future. By outlining clear objectives and action steps, you create a framework for continuous improvement and more informed career decisions. I use this same template for the data organization I lead, filled it out myself, and shared it with others to inspire them to do the same.

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Hungary’s rushed entry into World War II and its botched attempt to break away from it show how stale leadership trapped in its own fantasy bubble can steer a nation toward disaster. By “political backtesting” these moments — comparing their underlying factors to the present — today’s citizens can weigh future outcomes, draw practical lessons, we can make more informed decisions about where and how to live.

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I run Monte Carlo simulations of content production over random Watts-Strogatz graphs to show various effects relevant to modeling and understanding Randomized Controlled Trials on social networks: the network effect, spillover effect, experiment dampening effect, intrinsic dampening effect, clustering effect, degree distribution effect and the experiment size effect. I will also define some simple metrics to measure their strength. When running experiments these potentially unexpected effects must be understood and controlled for in some manner, such as modeling the underlying graph structure to establish a baseline.

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I examine the coverage of random portfolios in log volatility—return space using Monte Carlo methods with different randomization techniques for the 2023 daily closing prices of the 100 stocks constituting the Nasdaq-100.

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The article illustrates how Monte Carlo simulations serve as a powerful method for exploring statistical concepts in A/B testing, enhancing understanding, improving experimental design and analysis.

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I built a simple Python-based Taboo game server using asyncio to provide a customizable multiplayer experience via Telnet.

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My review of the AI landscape in late 2024, and what the road ahead looks like for those of us not building foundational models.

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Recently, I delivered a techtalk on A/B testing to an audience of non-technical Product Managers and experienced Data Scientists.

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A review and introspect on the second 100 articles written on Bytepawn.

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A few days ago OpenAI CEO Sam Altman published an article titled The Intelligence Age. I used OpenAI's new o1-mini model to critique Sam Altman's writing of its own future.

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I detail my structured approach to managing one-on-one meetings within a 40-person data team, emphasizing people management principles like radical transparency, tailored meeting cadences, and strategies to navigate common managerial challenges to foster a supportive and productive work environment.

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The article explains the Bell inequality using Monte Carlo simulations in Python, and shows how non-local action-at-a-distance can be used to break it with entangled qubits.

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This blog post explores foundational quantum mechanics concepts such as superposition, entanglement, and the challenges posed by locality and determinism, while highlighting the historical efforts of Einstein and Bohm to reconcile these phenomena with hidden variable theories.

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In February 2016, I made a life-changing decision to leave Hungary and seek opportunities abroad, starting in London and later moving to Dubai. This essay explores the multitude of reasons behind my choice and why I have no immediate plans to return to Hungary.

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I discuss Polya's How to Solve it approach to mathematical problem solving and the 2010 paper Teaching general problem-solving skills is not a substitute for teaching math.

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Screening CVs is a critical step in the hiring process, used to determine which candidates progress further in the hiring funnel. While the approach to CV screening can vary by company and industry, the primary goal remains the same: to identify candidates with the potential to succeed in subsequent interview stages.

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In this final post on the toy Javascript async message queue server implementation, I make it compatible with the Python version. I use the library of unit tests developed previously to ensure identical behaviour between the two codebases.

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I write a small library of unit tests for the message queue servers.

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I apply NIST's suite of statistical tests to my randomness extractor implementations from the previous posts.

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