# CI/CD Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/ci-cd
> Markdown URL: https://aitinkerers.org/technologies/ci-cd.md
> Technology record last updated: 2026-03-05T09:58:27Z
> Generated: 2026-08-26T14:17:04Z

CI/CD (Continuous Integration/Continuous Delivery) is the automated software pipeline that moves code from commit to production, ensuring frequent, reliable releases.

CI/CD represents the core automation engine for modern DevOps: Continuous Integration (CI) mandates developers frequently merge code, triggering automated builds and unit tests immediately. The Continuous Delivery/Deployment (CD) phase then takes that validated artifact, packages it (often as a Docker container), and deploys it to staging or production environments. Tools like Jenkins, GitLab CI/CD, and GitHub Actions orchestrate this entire flow, drastically reducing the deployment cycle time from days to minutes and cutting manual errors by over 90%.

- Official technology site: https://www.redhat.com/en/topics/devops/what-is-ci-cd
- Public AI Tinkerers demos and talks: 7
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Chunk Sidecars: Inner-Loop Validation for AI Coding Agents](https://sf.aitinkerers.org/talks/rsvp_1SFW5CFN7NA)

Rob Zuber of CircleCI demonstrated Chunk sidecars: automatic, fast, deterministic validation for any agent. He showed how agent hooks can trigger fast remote checks before code reaches the outer CI pipeline, preserving context and shortening the fix-and-verify feedback cycle.

- Event context: AI Tinkerers San Francisco: July GTM Engineering Track w/ Attio — 2026-07-09 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_1SFW5CFN7NA

### [Accurate vibe analytics](https://nashville.aitinkerers.org/talks/rsvp_TEGj2gbUeIQ)

Dbt-skillz is a project I open sourced that "compiles" a dbt project into a claude code compatible skill. We use it in our CI/CD workflow to always stay fresh. We use it to (1) build accurate BI/reporting dashboards, and (2) review them with a review-agent workflow that uses the skill.

- Event context: AI Tinkerers Nashville – Software Development in the Age of AI — 2026-03-25 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_TEGj2gbUeIQ

### [Aysnc AI Coding](https://atlanta.aitinkerers.org/talks/rsvp_nedDEigaRKQ)

AI coding demo, invoking Claude Code via GitHub Actions, and the CI/CD that makes it useful

- Event context: Co-Co-Code & Cocoa: The AI Tinkerers Atlanta Holiday Meetup — 2025-12-16 — Atlanta
- Public talk page: https://atlanta.aitinkerers.org/talks/rsvp_nedDEigaRKQ

### [CRM AI Agent to handle and respond to customer emails](https://hamburg.aitinkerers.org/talks/rsvp_fVm5EXTsiAY)

Businesses receive a high volume of customer emails daily, ranging from product inquiries to technical support requests. Managing and responding to these efficiently requires significant human interaction and effort. This AI agent automates email management by classifying inquiries, retrieving relevant knowledge, and generating personalized responses. It intelligently decides whether to reply instantly, escalate the issue by creating a ticket, or provide troubleshooting guidance—ensuring faster response times and improved customer satisfaction. By leveraging LLMs, vector databases, and automation, this AI agent enhances email handling, reducing manual workload while maintaining high-quality customer interactions. Furthermore, I conducted this project as a learning exercise and proof of concept for an AI-driven CRM automation application.

- Event context: AI Tinkerers Hamburg #2 - February 20 — 2025-02-20 — Hamburg
- Public talk page: https://hamburg.aitinkerers.org/talks/rsvp_fVm5EXTsiAY

### [Building AI Agents for Cancer cure - Optimizing protocol and trial design](https://nyc.aitinkerers.org/talks/rsvp_tK842VNu7zw)

This talk explores the practical applications and strategic integration of AI and GenAI in clinical trials. Clinical trial documents authoring and quality control is still riddled with inefficiency due to the time-consuming, expensive, and manual way of working. AI has the potential to transform this space. I've been developing AI agents to streamline and accelerate the protocol design process, potentially speeding up the pathway from treatment conception to protocol finalization. These agents are constructed using LangGraph and trained on proprietary data labeled by PhD scientists with pharmaceutical industry experience, ensuring accuracy and domain expertise. Firebase serves as the backend for knowledge embedding and agent memory management. The primary technical challenges involve securing high-quality training data and managing memory for agent workflows. outline of challenges including 1.State Management 2.Action Selection 3.Environment Interaction 4.Memory and Knowledge Base 5.AI guardrails. The live demonstration will consist of three parts: I will upload a clinical protocol and query the system about domain-specific topics such as patient demographics and endpoint design. I will showcase a medical writer agent capable of drafting protocols with human in the loop design.Lastly I will demonstrate how I build CICD pipelines on AWS. Finally I will share some technical challenges related to distributed training and model serving for optimized latency and throughput Takeaway : transforming clinical trials, reducing timelines, improving data integrity, and ultimately bringing treatments to patients faster.

- Event context: October Meetup at AI Tinkerers! — 2024-10-17 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_tK842VNu7zw

### [Ellipsis: Fixing build failures on pull requests](https://nyc.aitinkerers.org/talks/rsvp_Amwi91giatU)

You just opened a pull request, but you forgot an import and the linter is failing. What now? Let's see how GPT4 can aid developer productivity but automatically fixing a build issue on a GitHub pull request. Background: In August 2023, I demo'd an "AI junior software engineer" at the first AI Tinkeres x NYC meet up. It made a great demo, but I quickly realized that state-of-the-art LLM's aren't good enough to reliably make nontrivial changes to production-scale codebases. So I scaled down the problem. Today, Ellipsis is a developer tool that reviews pull requests and fixes bugs on them. One of our most requested features is having Ellipsis automatically resolve CICD failures.

- Event context: Omakase Founder Dinner | NY #TechWeek — 2024-06-04 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_Amwi91giatU

### [KushoAI - AI Agent for API Testing](https://bengaluru.aitinkerers.org/talks/rsvp_9mPolBV8Ja8)

Kusho helps you reclaim engineering team bandwidth and ship fast without worrying about unnoticed bugs. Kusho's fine-tuned models are trained to understand API context, write and execute comprehensive tests, and provide analyzed results for debugging, all in sync with your CI/CD process. With Kusho, you get an auto-updating testing set up across hundreds of APIs in hours.

- Event context: AI Tinkerers - Bangalore Inaugural - RSVP REQUIRED — 2024-06-02 — Bengaluru
- Public talk page: https://bengaluru.aitinkerers.org/talks/rsvp_9mPolBV8Ja8

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