This post shows how to implement the Ricart–Agrawala Distributed Mutual Exclusion algorithm with Lamport's logical clocks using HTTP Flask servers.

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This post shows how to implement Lamport’s Bakery algorithm with Python HTTP Flask servers. The Python code is short and readable, helping newcomers grasp the algorithm’s logic without the syntactic overhead of C++. The asynchronous HTTP flow also generates plenty of overlap between workers, so the need for mutual exclusion is obvious. Exposing the variables through REST endpoints enables observation of ticket picking and critical section entry in real time, potentially making the flow of the algorithm easier to understand.

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I explore Lamport’s Bakery algorithm and show how a naive C++20 implementation breaks on weak memory models, how to fix it with std::atomic, and how to implement bounded tickets.

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Andrej Karpathy says we’re marching from hand-written “Software 1.0” to NN-driven “2.0” and, finally, LLM-powered “3.0.” In my view, neural nets haven’t eaten deterministic code—kernels, browsers, games, and most business logic still run old-school. What has changed is the supply chain: LLM co-pilots (“Software 1.5”) already touch nearly every commit, nudging us towards language and architectures that LLMs are good at. The future belongs to teams that harness those co-pilots while keeping humans firmly on the trigger.

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I summarize thoughts and lessons learned of implementing the same async message queue server in C++, Python and JavaScript over the past 2 years.

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I write the final version of the async message queue server in modern C++20, wire compatible with the Python and Javascript versions.

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A visual and analytical walk from pure white noise to a true random walk.

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The article examines how simple autoregressive stochastic processes behave with the Dickey-Fuller test.

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This post explores how to use the Dickey–Fuller test to check the random‑walk hypothesis for time series using a pure NumPy implementation, and using Monte Carlo simulations to compute critical‑value tables.

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In this follow-up, I analyze 10 years of data on a select group of stocks to show that while higher take-profit and stop-loss thresholds boost win ratios and Sharpe ratios, longer holding times can lower annualized returns.

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In this article, I explore how adopting longer-term, resilient trading strategies with higher take-profit and stop-loss thresholds can deliver more consistent, risk-adjusted performance.

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Recently, I delivered a culture talk on Scalien, my old database startup that never took off. I discussed the business, product and some technical challenges and lessons learned from developing ScalienDB, a Paxos-based distributed NoSQL database.

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In the previous two articles, we explored the coverage of random portfolios in log volatility—return space, both with and without a risk-free asset. We now take the next step, and calculate the Efficient Frontier and Capital Market Line of Markowitz’s theory.

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From an MMT (chartalist) perspective, cryptocurrencies lack sovereign backing and fail to establish the enforced demand necessary to function as true currency. Because they rely solely on speculative sentiment rather than legal mandates (like taxation), their values are unsustainable in the long run and risk ultimately collapsing to zero.

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In 2008, I completed my first half-Ironman — then, later that same year, I took on the full Ironman distance at the traditional ExtremeMan in Hungary, finishing in 13 hours and 38 minutes. By 2013, I had knocked my time down to 11 hours and 36 minutes. Since then, I’ve racked up a total of 56 triathlon finishes. 2024 was one of the best years so far, with a record of 10 triathlon races.

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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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