BASE 44 ORDER: Build DB schema + nightly MV + prefilter agent for range_ratio — E8 Intelligence Research

Andrew's order (Base 44 console): [SELF-BUILD from enhancement #1160] Add generated column range_ratio with partial index WHERE range_ratio < 0.5. Create nightly materialized view with nested_inside using window functions. Add composite index (nested_inside, range_ratio). Update Python agent to query prefiltered rows before LLM calls. MERLIN: Understood — this is a platform build directive, not a research or trading edge. I'll have the swarm implement the generated column range_ratio with the partial index, create the nightly materialized view using window functions for nested_inside, add the composite index, and update the Python agent to query prefiltered rows before any LLM call. Execution will start immediately and I'll report back on completion. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-01
DOI
https://doi.org/10.5281/zenodo.22225107
Primary Topic
Intelligence, Security, War Strategy
Type
preprint
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preprint

BASE 44 ORDER: Build DB schema + nightly MV + prefilter agent for range_ratio — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Intelligence, Security, War Strategy
preprint

BASE 44 ORDER: Build DB schema + nightly MV + prefilter agent for range_ratio — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

Andrew's order (Base 44 console): [SELF-BUILD from enhancement #1160] Add generated column range_ratio with partial index WHERE range_ratio < 0.5. Create nightly materialized view with nested_inside using window functions. Add composite index (nested_inside, range_ratio). Update Python agent to query prefiltered rows before LLM calls. MERLIN: Understood — this is a platform build directive, not a research or trading edge. I'll have the swarm implement the generated column range_ratio with the partial index, create the nightly materialized view using window functions for nested_inside, add the composite index, and update the Python agent to query prefiltered rows before any LLM call. Execution will start immediately and I'll report back on completion. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Intelligence, Security, War Strategy
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