Where you live shapes almost everything — your expenses, routines, and long-term options. But when faced with the need or desire to move, how can one best make that decision? For some, the choice may be emotional or circumstantial, if they have the ability to be flexible at all. For others, it’s a problem to solve: plug in housing costs, local salaries, taxes, and utility bills to find the most efficient place to land. (View Highlight)
He factored in the standard rule that housing should cost no more than 30% of your income, but tightened it further by applying the ratio to post-tax earnings, not pre-tax, to maximize saving. Years later, he’s still refining the system: adding live data feeds, adjusting for buyers as well as renters, and building in variables for roommates. In his view, housing isn’t just where you live — it’s the biggest lever for financial freedom. (View Highlight)
Developer Prithwish Nath built an AI agent to tackle a similar question globally: where can a remote worker live well on $2,000 a month? His tool pulled live cost-of-living data from APIs, then ran it through a tightly constrained reasoning loop to evaluate rent, groceries, internet speeds, and overall remote-work infrastructure. Bangkok came out on top, with Bali and Lisbon close behind. But Nath also shows how fragile these rankings are. Choosing what to optimize — cheap rent or fast internet — means deciding upfront what matters and what doesn’t. Building a “perfect” agent, he found, wasn’t just a technical problem. It raised questions about how much weight we give to data over instinct when deciding where to live. (View Highlight)
