Skip to content

Instantly share code, notes, and snippets.

View PandaWhoCodes's full-sized avatar

Thomas Ashish Cherian PandaWhoCodes

View GitHub Profile
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>FaithTech Global Impact & Research Report (2024-2026)</title>
<style>
:root {
--primary: #1A365D;
--secondary: #2B6CB0;
<!DOCTYPE html>
<html lang en>
<head>
<meta charset utf-8>
<meta name=viewport content=width=device-width, initial-scale=1>
<title>FaithTech — Global Impact Report</title>
<script src=https://cdn.tailwindcss.com></script>
<link rel=preconnect href=https://fonts.googleapis.com>
<link href=https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap rel=stylesheet>
<script>
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>FaithTech Impact Report</title>
<style>
body {
font-family: Arial, sans-serif;
line-height: 1.6;
@PandaWhoCodes
PandaWhoCodes / controller-comparison.html
Created April 4, 2026 05:15
Mac M4 Game Controller Comparison - Amazon India (Apr 2026)
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Mac M4 Game Controller Comparison — Amazon India</title>
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; background: #0f0f0f; color: #e0e0e0; padding: 20px; }
h1 { text-align: center; font-size: 1.8rem; margin-bottom: 8px; color: #fff; }
@PandaWhoCodes
PandaWhoCodes / sermon-notes-cross.html
Last active April 3, 2026 06:14
The Offence, the Motifs, and the Response — Sermon Notes on the Cross (Joice, Apr 2026)
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>The Cross — Sermon Notes</title>
<link href="https://fonts.googleapis.com/css2?family=Crimson+Pro:ital,wght@0,300;0,400;0,500;0,600;1,300;1,400&family=Overpass+Mono:wght@300;400;500&family=Nunito+Sans:wght@300;400;600&display=swap" rel="stylesheet">
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
:root {
@PandaWhoCodes
PandaWhoCodes / vertex-ai-ingestion-pipeline.html
Created April 2, 2026 07:08
Vertex AI RAG Ingestion Pipeline — Research & Plan (Saama)
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Vertex AI RAG Ingestion Pipeline — Research & Plan</title>
<link href="https://fonts.googleapis.com/css2?family=Crimson+Pro:ital,wght@0,300;0,400;0,500;0,600;1,300;1,400&family=Overpass+Mono:wght@300;400;500&family=Nunito+Sans:wght@300;400;600;700&display=swap" rel="stylesheet">
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
:root {
@PandaWhoCodes
PandaWhoCodes / index.html
Last active April 2, 2026 06:01
Why Did Jesus Come? — A Topical Analysis of the Four Gospels (29 direct statements from KJV)
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Why Did Jesus Come? — A Topical Analysis of the Four Gospels</title>
<link href="https://fonts.googleapis.com/css2?family=Crimson+Pro:ital,wght@0,300;0,400;0,500;0,600;1,300;1,400&family=Overpass+Mono:wght@300;400;500&family=Nunito+Sans:wght@300;400;600&display=swap" rel="stylesheet">
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
images
@PandaWhoCodes
PandaWhoCodes / llm-token-cost-reduction.md
Created March 16, 2026 15:47
Slashing LLM Token Costs for Insurance PDF Extraction — From /bin/zsh.038 to /bin/zsh.005/run (87% savings)

Slashing LLM Token Costs for Insurance PDF Extraction

Context: A pipeline that fetches emails, triages them via LLM, downloads PDF attachments, extracts insurance policy details via LLM, and saves results. Currently spending ~$0.038/run on Groq Llama 4 Scout.


Current State — Where the Money Goes

Per Run (1 user):
@PandaWhoCodes
PandaWhoCodes / worker-offload-guide.md
Created March 16, 2026 14:08
Offloading FastAPI Pipeline Processing to On-Demand Workers — Cost Analysis & Implementation Guide

Offloading Processing to On-Demand Workers — Complete Guide

Context: A FastAPI app running a sequential email→triage→PDF→LLM→save pipeline (30-60s per run). Goal: cut always-on compute costs and scale individual bottlenecks independently.


Current Architecture — What We're Working With

User Request → FastAPI (always-on server)