One thing that seems to be missing in most discussions about "context" is infrastructure.
The dream system for AI agents is probably something like a curated data hub: a place where datasets are continuously ingested, cleaned, structured and documented, so agents can query it to obtain reliable context.
Right now most agents spend a lot of effort stitching context together from random APIs, web scraping, PDFs, etc. The result is brittle and inconsistent.
If models become interchangeable, the real leverage might come from shared context layers that many agents can query.
I put together a small, SQL-backed dashboard comparing the S&P 500 with several "Main Street" indicators: retail sales, construction spending, bank credit, and industrial production (manufacturing, mining, utilities).
The question I wanted to sanity-check was simple: markets seem to be pricing a strong AI-driven growth narrative — do broad macro indicators show a similar acceleration?
The dashboard is fully grounded on public data, and every chart is traceable to the underlying SQL query and datasets.
I’m not pushing a conclusion here — genuinely curious how others interpret the divergence (or lack thereof), and whether there are indicators you’d add or remove.
In 2019, ambient air pollution claimed the lives of young children at alarming rates in several countries. Here's the top 10 list of countries with the highest number of deaths per 100,000 children under 5 due to ambient air pollution:
Nigeria – 18.95
Chad – 18.10
Sierra Leone – 12.02
Mali – 10.56
Guinea – 9.90
Niger – 9.64
Cote d'Ivoire - 9.04
Central African Republic - 8.79
Cameroon - 8.69
Burkina Faso - 8.68
These numbers highlight how air pollution isn't just an urban problem — it's a public health crisis in low-income countries where children are the most vulnerable.
The dream system for AI agents is probably something like a curated data hub: a place where datasets are continuously ingested, cleaned, structured and documented, so agents can query it to obtain reliable context.
Right now most agents spend a lot of effort stitching context together from random APIs, web scraping, PDFs, etc. The result is brittle and inconsistent.
If models become interchangeable, the real leverage might come from shared context layers that many agents can query.