Do not use earlier versions of this guide for installation, testing or operation. Replacement client instructions are awaiting Windows validation and release approval. Contact the application owner through your established private support channel for assistance.
Able Varghese AbleVarghese
AbleVarghese
/ PROD-DEPLOYMENT-SET01.md
Last active
October 7, 2026 20:27
SET01 Production Deployment Guide - settlement-pdf-pipeline
AbleVarghese
/ UAT-RUNBOOK-WINDOWS.md
Last active
October 7, 2026 20:26
Windows User Acceptance Testing (UAT) End-to-End Runbook
AbleVarghese
/ UAT-TEST-SCRIPT.md
Last active
October 7, 2026 20:26
Settlement PDF Pipeline - UAT Test Script
AbleVarghese
/ METHODOLOGY.md
Created
August 9, 2026 00:50
Measure your real engineering output from git: audit methodology + script (8,010 commits / 1.27M LOC / COCOMO analysis of a 20-repo agentic-engineering portfolio)
Every number on this profile is computed, not claimed. This document + forensics.py let anyone
reproduce them against the underlying repositories (private repos verifiable on request).
| Metric | Method |
|---|---|
| Commits (8,010) | git log --all --format=%at|%ae|%an across 20 original (non-fork) repositories, Dec 2024 – Aug 2026 |
| Active days (254) | Distinct calendar dates among all commit timestamps |
| Hours (~1,850–2,900) | Session clustering (the git-hours algorithm): commits ≤120 min apart form a session; range = Σ intra-session gaps + per-session start adjustment of 30 min (low) / 120 min (high). An estimate of orchestration wall-clock, labeled as such |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.