Articles
These days I spend most of my time thinking about the intersection of business strategy, product thinking, and AI systems. I'm particularly interested in how organizations can adopt AI tools practically — not just as demos, but as production systems that solve real problems.
I learn by building. My current focus is on understanding agentic AI systems — what they can actually do, where they break down, and how to evaluate the trade-offs between complexity and value. I write about what I learn so that others navigating the same questions can benefit from the experiments I've already run. I hope to write for people who want to understand AI through the lens of building and decision-making, not just theory.
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I built the same thing three times — each with a different level of "agency." Here's what I learned about what that word actually means, why automatic doesn't equal autonomous, and how to think about the spectrum of agency in AI systems.
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The implementation details: Claude Agent SDK, Exa Search, free vs. paid models, every bug I hit, cost analysis, and the fallback pattern that makes agentic systems production-ready. Full code walkthroughs included.
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A Flask web application that analyses textual input to classify emotions at the sentence level, combining a Logistic Regression model (trained on ~500k tagged observations) with VADER valence scoring and TextBlob phrase extraction. Deployed on Heroku.
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Web scraping posts from two subreddits via the Pushshift API and applying NLP and classification modelling (Logistic Regression, Naive Bayes, Random Forest) to accurately distinguish between communities.
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Using the Ames housing dataset to estimate sale prices and identify features that predict abnormal sales (foreclosures). Covers the full ML project framework: EDA, feature engineering, Lasso/Ridge regression, and model evaluation.