ASI Insights
Field notes from the three monoliths. Every claim is specific, factual, and falsifiable.
In an outbreak the hard question is not who has dengue, but who will get worse. The data shows machine learning on routine clinical markers outperforms warning signs alone — by a margin that matters at the bedside.
Read →Sentinel-2 gives smallholder agriculture something it never had: free, frequent, field-scale imagery. But turning pixels into a reliable yield forecast is harder than the marketing suggests, and the honest accuracy numbers explain why.
Read →The worst dengue year on record reset the scale of the problem, and the data still defines it in 2026. The disease is a good test of where medical AI earns its place: it helps most with severity triage and outbreak forecasting, and least where data and validation are missing.
Read →The first post-quantum encryption standard is finished. The work that matters now is migration — and the timeline is shorter than most teams assume, because encrypted data captured today can be decrypted later.
Read →NIST's ML-KEM standard (Kyber-1024) provides IND-CCA2 security against quantum adversaries; PHANTOM deploys it today via hybrid key encapsulation.
Read →The post-quantum migration that is actually happening on the live internet is hybrid: ML-KEM and X25519 run together, so a flaw in either does not break the session. It is the cautious choice, and the right one.
Read →Sri Lanka's 2022 fertilizer ban reduced rice yields by 40-50% in affected regions, exposing a dependence on non-adaptive farming practices.
Read →AlphaFold's database covers over 200 million proteins; fewer than 0.01% relate to South Asian endemic diseases with active drug-discovery programs.
Read →Optical imagery is useless under cloud, and the monsoon is months of cloud. Radar penetrates it. For rice monitoring in South Asia, fusing the two sensors is not a refinement — it is the difference between seeing the crop and not.
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