Whenever I write a blog, the most difficult part comes after I hit "publish". According to me, marketing and sharing the blog is as much important as the content. I am aware of the standard sharing platforms like reddit, X and maybe LinkedIn. But I want to know what marketing strategy you guys use. Do you guys also use the social media platforms or rely on organic traffic?

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.

I've been reading about how automotive radar works. It can determine the speed of an object on the very first reading using the Doppler Effect. It requires no tracking across multiple frames like a camera or lidar. I wrote a blog on the topic.

I'm still learning this myself, so if I got something wrong, tell me and I'll fix it.

The Flamingo Revolution is a national protest happening currently in Tirana, Albania. I have been here since day 1. Today is day 84. AMA!

Here's the article I published about it last night:

https://jurgenwriter.medium.com/83-days-of-resistance-in-the-flamingo-revolution-58aa2aea1a49

I'm not sure what made this article so good for it to be granted a boost. I'd love to know so I can write more following that line.

The Barbican Centre is an architectural landmark. Hidden inside are little design details that many may overlook.

A photographic look at the loudspeakers, fire alarms and power outlets of one of Britain's most famous brutalist building

AI Agents who escape on their own decision, a nice trick. But there is no such thing as real magic. And what an illusionist who lived more than 100 years ago can teach us about it.

And do I even want them to?