# workflow files Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/workflow-files
> Markdown URL: https://aitinkerers.org/technologies/workflow-files.md
> Technology record last updated: 2026-02-24T00:23:04Z
> Generated: 2026-09-22T05:46:05Z

YAML-based configuration files that automate software development lifecycles by defining specific triggers, jobs, and execution environments.

Workflow files function as the declarative blueprint for CI/CD pipelines (typically stored in the .github/workflows directory). These YAML documents orchestrate complex sequences: triggering on push or pull_request events, spinning up virtual runners (like ubuntu-latest), and executing discrete steps such as npm install or docker build. By version-controlling these configurations alongside source code, teams ensure every commit validates against specific linting rules and deployment targets. It is the industry standard for modern automation: defining exactly how code moves from a local branch to a production environment.

- Official technology site: https://docs.github.com/en/actions/using-workflows/about-workflows
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Building a Persistent Memory & Stateful Second Brain AI Agent](https://dhaka.aitinkerers.org/talks/rsvp_kSMaLnvvGGg)

Demonstrating context engineering in practice through Slatekore - an open-source starter kit that gives Gemini CLI persistent memory using Obsidian as the storage layer. 1. State Management Without Infrastructure How to use the file system as persistent state - your Obsidian vault becomes both the agent's memory and its knowledge base. No databases, no vector stores, no MLOps. 2. Context Engineering for Agent Behavior Crafting system prompts (GEMINI.md) and workflow files that define agent capabilities, constraints, and multi-step reasoning patterns. The prompt is the agent's configuration. 3. Tool Use Through Natural Interfaces Connecting the agent to real actions: creating files, managing tasks, building knowledge graphs - all through natural language commands backed by structured templates. Watch a stateful agent capture research, recall context from previous sessions, update its knowledge graph, and execute project workflows - without any model training or fine-tuning.

- Event context: AI Tinkerers Dhaka 2nd Meetup: Dhaka Builds with AI! — 2026-02-07 — Dhaka
- Public talk page: https://dhaka.aitinkerers.org/talks/rsvp_kSMaLnvvGGg

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