My Weird Prompts

The Hidden Billions of Rental Churn

22 min · 4. heinä 2026
jakson The Hidden Billions of Rental Churn kansikuva

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Rental housing debates focus on rent levels and supply—but there's a third cost hiding in plain sight. Every time a tenant is forced to move, they burn thousands in truck rentals, agent fees, painting, cleaning, and dozens of hours of lost time. Multiply that across millions of households moving every two to three years, and the number is staggering: eight to ten billion shekels a year in Israel alone, with zero productive output. This episode builds the first public cost model of rental churn—the deadweight loss that no rent control or zoning reform addresses—and asks why this enormous line item is completely absent from the housing policy conversation.

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jakson Embedding Models vs LLMs: What Actually Connects? kansikuva

Embedding Models vs LLMs: What Actually Connects?

Daniel asks two sharp questions about RAG pipelines: does your embedding model constrain which LLM you can use, and why are new embedding models still releasing if embeddings feel like a solved problem? We break down the architectural decoupling between embedding models and LLMs — they're different neural networks trained for different objectives, and any embedding works with any LLM. But that clean answer makes the second question more urgent: the real innovation in embedding models isn't about general benchmarks — it's about fixing specific failure modes like domain specialization, multilingual alignment, and silent drift that only show up at scale. We also unpack the "silent drift" problem where an auto-embedding model upgrade can quietly break retrieval without anyone noticing until support tickets spike.

4. heinä 202627 min