Spatial field location memory for document processing pipelines. Learns field positions, validates extractions, identifies document types by layout, detects drift, and monitors template health. Zero dependencies, pure Python.
Self-improving LLM document extraction on AWS. Each human QA correction improves future extractions via few-shot self-healing and deterministic rule graduation — no retraining, no redeployment. Costs decrease over time.
Zuva — independent third-party profile of a public API surface, by API Evangelist. Zuva (by Kira Systems) provides a contract and document AI REST API for extracting structured data from unstructured documents. The Zuva DocAI API offers asynchronous OCR, field extraction across 1,400+ pre-built fields, multi-level document classification across 220
YAML-based pattern matching with multi-line capabilities for log normalization using syslog-ng patterndb
End-to-end engineering document parser for detecting title blocks, auto-rotating and cropping drawings, and extracting structured fields with YOLO and Qwen2.5-VL.
Annotated reference of AWK and grep one-liners for structured-text processing: field extraction, FS/OFS/RS separators, BEGIN/END blocks, printf, sub/gsub find-replace, field arithmetic, line/field counting and head/tail/grep/wc emulations, applied to geospatial coordinate data. Personal study notes.
A regex alternative for pulling fields out of XML, JSON, CSV, HTML and key-value text. Survives renamed keys, reformatted values and schema drift, verifies every capture is lossless, and tells you why a field is missing. Pure Python, zero dependencies.
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