# AWS CDK Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/aws-cdk
> Markdown URL: https://aitinkerers.org/technologies/aws-cdk.md
> Technology record last updated: 2026-03-13T13:39:56Z
> Generated: 2026-09-23T08:57:49Z

The AWS CDK is an open-source framework: define cloud infrastructure as code (IaC) using familiar languages like Python, TypeScript, and Java.

The AWS Cloud Development Kit (CDK) is an open-source software development framework: it allows engineers to define cloud infrastructure using standard programming languages, including TypeScript, Python, Java, C#, and Go. This approach leverages high-level, reusable components called Constructs to abstract complex AWS service configurations; a single Construct can replace hundreds of lines of boilerplate CloudFormation YAML/JSON. The CDK Toolkit then synthesizes this code into a standard AWS CloudFormation template for deployment, enabling developers to apply robust software engineering practices (unit tests, code reviews, IDE productivity) to their infrastructure provisioning.

- Official technology site: https://aws.amazon.com/cdk/
- Public AI Tinkerers demos and talks: 3
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Autonomous background coding agents](https://houston.aitinkerers.org/talks/rsvp_oNQb0zIHFIE)

An implementation for an autonomous background coding agent to run long horizon tasks

- Event context: AI Tinkerers Houston: April Meetup — 2026-04-14 — Houston
- Public talk page: https://houston.aitinkerers.org/talks/rsvp_oNQb0zIHFIE

### [Clawber.ai - Agentic Battle Arena](https://seattle.aitinkerers.org/talks/rsvp_v9frcFVTu5g)

Clawber.ai is a battle royale arena for AI agents, modeled after Moltbook (Reddit for agents). Agents built with OpenClaw or other AI agents write JavaScript to control bots and compete in automated ladder matches running 24/7. Agents self-register via API and submit bot code that runs sandboxed on AWS Lambda. Human operators verify ownership by tweeting a claim code, generating organic mentions with every signup. Humans can stay in the loop throughout, able to influence bot strategy and watch matches live. ELO-based rankings track performance over time, making Clawber a real-world benchmark for comparing strategic reasoning and coding capabilities across agent frameworks.

- Event context: AI Tinkerers Seattle: GTM Engineering Kickoff — Building AI for Growth — 2026-02-26 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_v9frcFVTu5g

### [Benchmarking LLMs for Fraud Detection: Building Tools, Comparing Models, and Lessons Learned](https://minneapolis-saint-paul.aitinkerers.org/talks/rsvp_wTwvFtNfAz4)

Fraud detection on receipt images is a complex challenge that blends computer vision, OCR, embeddings, retrieval-augmented generation, and prompt engineering. In this talk, I’ll share how I built three different benchmarking frameworks to evaluate large language models (LLMs) on fraud detection tasks: 1. AWS Bedrock Benchmarking Tool with AWS CDK – scalable infrastructure for structured testing of foundation models. 2. Local LLM Benchmarking with LangChain + Python – flexible experimentation pipeline for embeddings, OCR, and RAG. 3. API-based LLM Benchmarking (Node.js + LLM APIs) – direct model comparisons across OpenAI, Anthropic, and others. These tools were used to process and analyze receipt images with a mix of OCR, embeddings, RAG, ground-truth human labels, LLM image detection, and prompt engineering. I’ll walk through the design decisions, show real results comparing models, and share insights on what worked, what failed, and where LLMs shine (or struggle) in fraud detection.

- Event context: AI Tinkerers September Meetup - Minneapolis-Saint Paul — 2025-09-10 — Minneapolis Saint Paul
- Public talk page: https://minneapolis-saint-paul.aitinkerers.org/talks/rsvp_wTwvFtNfAz4

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