February 2026

Over the last two years, something subtle has shifted in software engineering, and most of us have felt it even if we can’t name it. Teams report being dramatically more productive. Code is written faster, prototypes appear sooner, and the distance from idea to working system has collapsed. And yet, the software we rely on every day doesn’t feel proportionally better. In many cases, it feels harder to understand, harder to change, and more fragile than before. The […]

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Introduction: The Reality of the Build: A Post-Mortem We were building a multi-lens introspection system designed to take a user’s inner state and translate it into structured insights. At a certain point in development, the system just stopped producing outputs. It didn’t crash—but it started timing out, again and again. And when it didn’t time out, it went silent: no insights, no partial responses, no recoverable errors. Just empty. We treated it as an engineering problem. We audited […]

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Technology often arrives with a promise. Faster solutions, smarter systems, better outcomes. When it comes to the environment, that promise feels especially comforting. Artificial intelligence predicts climate patterns, apps track carbon footprints, and startups race to build climate-focused innovations. In a world facing urgent environmental challenges, technology appears as a reassuring answer. Yet beneath this optimism sits a quieter, more uncomfortable question: if technology is always improving, are we slowly handing over our sense of responsibility to it? […]

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When most businesses hear “AI agents,” their first reaction is excitement. Their second reaction is fear. Excitement because AI promises speed, automation, and scalability. Fear because they imagine broken systems, confused employees, angry customers, and that one Monday morning where nothing works and everyone blames “the AI.” Integrating AI agents into your business doesn’t have to feel like open-heart surgery. Done correctly, it feels more like hiring a very capable assistant who quietly makes things easier while your […]

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Graph data often contain noisy and spurious correlations that mask the true causal relationships, which are essential for enabling graph models to make predictions based on the underlying causal structure of the data. Dependence on spurious connections makes it challenging for traditional Graph Neural Networks (GNNs) to generalize effectively across different graphs. Furthermore, traditional aggregation methods tend to amplify these spurious patterns, limiting model robustness under distribution shifts. To address these issues, we propose Causal Neighbourhood Learning with […]

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In environments with sparse or delayed rewards, reinforcement learning (RL) incurs high sample complexity due to the large number of interactions needed for learning. This limitation has motivated the use of large language models (LLMs) for subgoal discovery and trajectory guidance. While LLMs can support exploration, frequent reliance on LLM calls raises concerns about scalability and reliability. We address these challenges by constructing a memory graph that encodes subgoals and trajectories from both LLM guidance and the agent’s […]

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Table of Links Abstract and 1. Introduction 2. Financial Market Model and Worst-Case Optimization Problem 3. Solution to the Post-Crash Problem 4. Solution to the Pre-Crash Problem 5. A BSDE Characterization of Indifferences Strategies 6. The Markovian Case 7. Numerical Experiments Acknowledgments and References Appendix A. Proofs from Section 3 Appendix B. Proofs of BASDE Results from Section 5 Appendix C. Proofs of (CIR) Results from Section 6 Abstract. We study and solve the worst-case optimal portfolio problem […]

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Reinforcement learning (RL) agents often suffer from high sample complexity in sparse or delayed reward settings due to limited prior structure. Large language models (LLMs) can provide subgoal decompositions, plausible trajectories, and abstract priors that facilitate early learning. However, heavy reliance on LLM supervision introduces scalability constraints and dependence on potentially unreliable signals. We propose MIRA (Memory-Integrated Reinforcement Learning Agent), which incorporates a structured, evolving memory graph to guide early training. The graph stores decision-relevant information, including trajectory […]

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Lately, you can’t open YouTube, read the news, or scroll LinkedIn without getting hit by the same narrative. Every day, another CEO confidently declares that software engineers are obsolete and that anyone can prompt their way to the next billion-dollar app. And to be fair, the AI world has completely tanked the barrier to entry for prototyping. With AI integrated into workflows, a lean team of 10 people can ship faster and better than a firm with 200 […]

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