Recent posts:

• 06 Dec 2025 Inflation Targeting and the Two Percent

  The 2% inflation target of most central banks has become the cornerstone of monetary policy during the last 30 years. How was that number chosen? If price stability is the main variable to optimize, why not target 0%? If the target is 2%, why does inflation regularly exceed it nowadays? Is a return to 2% even possible? Perhaps it's too late and that rate is already a relic of the past that is unattainable without devastating pain and hardship.

  
  
• 21 Nov 2025 Intertemporal Choice

  I want to explore the fundamental topic of intertemporal discounting - the preference toward current, as opposed to delayed, future satisfaction. This preference is innate in humans. We prefer the same amount of goods now, rather than tomorrow. If we are to receive them tomorrow, we demand more of them, so the additional amount compensates for the temporal delay. Intertemporal discounting stands at the foundation of many economic behavioral patterns - consumption, saving, fiscal restraint, discipline. We'll explore the topic from different angles: econ, reinforcement learning, and neuroeconomics.

  
  
• 11 Nov 2025 IS-LM-UIP: Dynamics and Intuition

  This is yet another post on the IS-LM model in macroeconomics, with the twist that this time it's an open economy and I've actually coded it up into a nice dashboard. Needless to say, even though the IS-LM has abysmal predictive power, it captures essential relationships in macroeconomic theory and is a useful educational tool to understand the different market processes. Through a graphical approach, we'll summarize famous economic scenarios and concepts, and we'll explore the role that expectations play within the dynamics.

  
  
• 24 Oct 2025 Impressions From the Far East

  This is the crazy story of my time in China, attending the IROS 2025 conference. I spent ten days across Hangzhou and Shanghai and found this experience very enriching and memorable. Here I'll cover mostly the cultural aspects, of which there are plenty. There will be a separate blog post on the current trends in robotics.

  
  
• 15 Oct 2025 LLM Latent Space Hacking

  From a previous post we've seen that LLMs, in their current form, cannot learn to reason in an end-to-end manner. This is because end-to-end requires a single computation graph in which gradients flow through the generated tokens. Since LLMs sample discrete tokens of variable-length, differentiation is impossible. Supervised finetuning is end-to-end but it only tries to reproduce the reasoning patterns in the expert traces, instead of letting the model discover them on its own. This post explores a related question: can we find those tokens that best condition a given response? We can interpret the response as the correct final answer and the tokens we seek as the best reasoning trace that produces it.