Most AI personality prompts aim the wrong way. typecast inverts it: your type is the input, and the output is a config for how the assistant should treat you. Satisfaction mode flatters you; growth mode argues with you.

How a timing rule invisible to the code, and to most retirement calculators, changes the math on Roth conversions at 65.

Six silent bugs, none of them obvious. Here’s what actually caught them when building a retirement calculator with AI.

Reflections on GitHub’s new SpecKit toolkit and why spec-driven workflows are essential for scaling agentic AI systems.

Scientific innovation often mirrors patterns found in nature. Neural networks emulate the brain, evolutionary algorithms mimic natural selection, and biomimicry inspires design solutions. Yet the relationship between nature, human creativity, and moral responsibility raises hard questions. Are humans merely imitating creation, or do we risk distorting it? Are we bound by nature’s patterns, or can we transcend them? This post works through those questions from a few angles: philosophical, ethical, and theological.

Repetition has been humanity’s tool for memory and storytelling for millennia. From the rhythmic verses of Homeric epics to modern-day rote learning, repeating information ensures that it is preserved, transmitted, and understood. In recent years, the rise of artificial intelligence (AI) has demonstrated that repetition plays a similarly fundamental role in machine learning. This post explores how repetition shapes both human memory and AI systems, revealing universal principles of learning and information retention.

From Epics to Memes: The Three Ages of Storytelling and the Quixotic Pivot 🔗Storytelling, the mirror of human civilization, has been remade again and again, from the grandiose epics that shaped ancient societies to the introspective novels of modernity, and now, to the fleeting memes and collective digital expressions of today. This evolution reflects not just changing narrative forms, but the shifting identity of humanity itself, moving from communal myth-making to individual exploration and, finally, to a fragmented, participatory digital ethos.

The world of data today feels a bit like wandering through a bustling marketplace in ancient times: a cacophony of voices, new technologies cropping up in every corner, and endless options for every need. Much like how traders would haggle over spices or silk, today’s companies deal in data, moving it across systems, processing it for insights, and storing it for future use. But, just as you wouldn’t use a cart built for hauling grain to transport fine pottery, each tool in the data ecosystem has its purpose.

I’m Pablo Overton, an AI Engineer and Senior Data Engineer at Analog Devices, where I build and scale cloud data platforms (Snowflake, Databricks, dbt, AWS) serving 1,000+ users across digital marketing, sales, and 20+ business domains. My background is unusual: Industrial Engineering in Madrid, Systems Engineering in Tokyo, lean manufacturing on a plant floor, semiconductor supply chain at ADI, and now GTM data platforms: customer signals, lead enrichment, attribution, GDPR-compliant governance.

One shift in mindset changes how a data team works: treat data as a product. It improves how the team operates, and it makes the data itself reliable, usable, and worth trusting across the organization. The Data as a Product Mindset 🔗Picture a baker. Each loaf is a product, built for a customer with a real need. Data deserves the same treatment: a deliberately designed product, built with its users in mind.