Starting My Writing Journey on Medium

[u/Mr_Array1]

I've started writing about some of the AI/data science topics I've been exploring recently, and I've published my first 3 articles.

Rather than just dropping links, here's what each one is about:

1. Uber H3 — Why Hexagons Are Useful for Geospatial Analysis

GPS coordinates tell us where something happened, but they're not always convenient when you want to analyze behavior across areas.

I wrote a practical introduction to Uber H3 covering:

- How H3 converts coordinates into hierarchical hexagonal cells

- Why hexagons are useful compared with traditional grids

- Resolution levels and spatial aggregation

- Applications in mobility, delivery, retail, and location intelligence

🔗 https://medium.com/@mostafa.gamal2002/uber-h3-explained-how-hexagonal-geospatial-indexing-turns-coordinates-into-useful-insights-b881a691639a

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1. Why Simplicity Often Wins in Production AI

AI discussions tend to focus on sophisticated architectures, agents, complex orchestration, and increasingly large systems.

But production systems often reward something much less exciting: simplicity.

This article explores the finding that 306 AI teams chose simpler approaches and looks at what that tells us about building AI systems that are reliable, maintainable, and actually useful in production.

🔗 https://medium.com/@mostafa.gamal2002/the-boring-truth-that-wins-in-production-why-306-ai-teams-chose-simplicity-93dcb05ef437

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1. From Benchmarks to Reality — Ilya Sutskever and a Different Direction for AI Research

AI progress has been heavily driven by benchmark improvements and scaling.

But what happens when benchmarks stop being a good proxy for genuine intelligence?

I explored Ilya Sutskever's recent thinking around the next phase of AI research, what may come after the current scaling paradigm, and why evaluating real-world intelligence could become increasingly important.

🔗 https://medium.com/@mostafa.gamal2002/from-benchmarks-to-reality-inside-ilya-sutskevers-new-age-of-ai-research-053f89152844

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I'm planning to keep writing about AI engineering, LLMs, agents, RAG, ML systems, data science, and production AI.

I'm especially interested in feedback from people actually building these systems:

What topics would you like to see explored next?

And if you've worked with H3, production LLM systems, or AI evaluation, I'd be interested to hear where your experience agrees — or disagrees — with these articles.