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chandana-vs-punith

mcp
Guvenlik Denetimi
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Purpose
This repository is a 30-day educational learning log and collection of tutorials covering AWS and Agentic AI concepts, including Lambda, IAM, RAG, and Model Context Protocol (MCP) implementations.

Security Assessment
Overall risk: Low. Because this is a collection of notes and educational code snippets rather than an installable software package or a standalone MCP server, the attack surface is virtually non-existent. No dangerous permissions were requested, and no supported source files were scanned, meaning there is no executable application code to analyze. The repository itself is meant to document security best practices (like proper IAM scoping and secrets handling), but it does not inherently access sensitive data, execute shell commands, or make network requests on your machine. No hardcoded secrets were detected.

Quality Assessment
The project is very new and actively maintained, with the most recent push occurring today. However, it currently suffers from low community visibility, having only 5 GitHub stars. A significant drawback is the complete lack of a license file. Without an explicit open-source license, the code and snippets technically remain under exclusive copyright by default, which restricts how developers can legally copy, modify, or use the examples in their own projects.

Verdict
Use with caution: the content is highly educational and safe to read, but the absence of a software license means you should avoid directly copying the provided code into your production projects.
SUMMARY

30-day challenge: AWS (Chandana) + Agentic AI (Punith). Daily notes, working code, and the beginner-to-advanced gotchas tutorials skip — Lambda, IAM, RAG, MCP, Bedrock, LangChain, and more.

README.md

chandana-vs-punith

A 30-day learning challenge where two engineers go deep on two of the most in-demand stacks of 2026 — AWS and Agentic AI — and document everything publicly, including the beginner-to-advanced mistakes most tutorials skip.

Punith → Agentic AI track (Claude, Amazon Bedrock, MCP, LangChain, RAG)
Chandana → AWS track (Lambda, IAM, ECS, EKS, OpenSearch, CloudWatch)

Each day we both write up the same topic from our own angle. The goal: by Day 30, both of us are comfortable shipping production-grade work that crosses both domains — agents that run on AWS, AWS systems that integrate with agents.

Why this repo exists

Most "learn AWS" or "learn AI agents" tutorials show you the happy path. They don't tell you that:

  • A psycopg2 wheel built on macOS will silently fail on Lambda
  • A Bedrock Agent's parameters field is a list of {name, value} objects, not a dict
  • A Co-authored-by trailer is the difference between one contributor and two on a GitHub commit
  • An MCP server's tool description is what the model actually reads — your code matters less than that string

This repo is a public, daily learning log of those gotchas. Plain language, working code, real mistakes.

What you'll find here

  • Daily long-form notes under each dayN-<topic>/ folder — written so a beginner can follow along but useful enough that experienced engineers find new gotchas.
  • Two angles per topic — chandana.md (AWS implementation) and punith.md (Agentic AI implementation), so the same concept is shown from both sides.
  • Production-aware examples — every code snippet considers cost, IAM scope, cold starts, idempotency, and security, not just "hello world".
  • Tracking issues — each day has a GitHub issue with the topic plan; closed when the day's notes are merged.

Topics covered (Day 1 → Day 6)

Day Topic Folder Issue
1 AWS Lambda & Python Lambda — handler, event, context, triggers, agent-tool patterns day1-lambda/ #1
2 IAM & Security — policies, AssumeRole, prompt injection, secrets handling day2-iam/ #2
3 RAG end-to-end — embeddings, chunking, OpenSearch, Bedrock Knowledge Bases, reranking day3-rag/ #3
4 MCP (Model Context Protocol) — building servers, hosting on AWS, Claude Desktop integration coming up #4
5 Observability — CloudWatch, X-Ray, LangSmith, OpenTelemetry, agent traces coming up #5
6 Containers — ECS, EKS, Fargate, Dockerized agents, MCP servers in containers coming up #6

Days 7–30 will be planned as we go, based on what gaps show up in our work.

Folder structure

chandana-vs-punith/
├── day1-lambda/
│   ├── README.md     # day's topic + plan
│   ├── chandana.md   # AWS deep-dive
│   └── punith.md     # Agentic AI deep-dive
├── day2-iam/
│   └── ...
└── ...

Folders use the convention day<N>-<short-topic> so the topic is visible from the repo root without having to click in.

Tech stack we're touching

AWS services — AWS Lambda · IAM · S3 · DynamoDB · OpenSearch Serverless · Amazon Bedrock · Bedrock Knowledge Bases · ECS · EKS · Fargate · API Gateway · EventBridge · SQS · CloudWatch · X-Ray · Secrets Manager · KMS · Step Functions

Agentic AI — Claude (Anthropic) · Amazon Bedrock Agents · LangChain · LangGraph · MCP (Model Context Protocol) · RAG (Retrieval-Augmented Generation) · Vector embeddings · Cohere Rerank · Strands Agents · LangSmith · OpenTelemetry

Python — boto3 · langchain · anthropic SDK · mcp · fastapi · pydantic · aws-lambda-powertools · moto

Following along

If you're learning AWS, Agentic AI, or both — this repo is meant to be a useful side-by-side reference, not a course.

  • Star the repo to follow daily updates as Days 7–30 roll out.
  • Spot something wrong? Open an issue or PR — corrections welcome, that's the whole point of learning in public.
  • Want the same structure for your own challenge? Fork it and replace the names. The daily log + dual-angle layout works for any two-track learning.

Common questions

Is this a course?
No. It's a public learning journal. We're learning as we write — not teaching from expertise.

Can I follow only one track?
Yes. Read only chandana.md files for AWS, only punith.md files for Agentic AI. The day's README.md ties them together.

What level is this aimed at?
The notes are written so a beginner can follow with effort, but every day surfaces gotchas an experienced engineer would also learn from.

Why two people?
One person learning two things deeply in 30 days isn't realistic. Two people, one each, comparing notes — is.

Started: April 2026 · Length: 30 days · Tracks: AWS · Agentic AI

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