applied-ai-lab

A GitHub-ready portfolio lab for Venkat to build small, useful AI products in 3-6 hour weekend sprints.

The goal is to learn modern AI engineering by shipping demoable projects: agents, RAG, structured outputs, evals, MCP-style tools, memory, multimodal workflows, and model routing. Each project should be simple enough to finish quickly, but polished enough to show on a resume.

Portfolio Rules

Each project in this repo should:

• Be demoable in under 2 minutes.
• Teach one or two current AI engineering concepts.
• Include a README.md, architecture notes, example prompts, and a tiny eval.
• Run locally with minimal setup.
• Solve a recognizable user problem instead of feeling like coursework.
• Follow the shared cost and prompt caching standard.

Structure

applied-ai-lab/
  ROADMAP.md
  docs/
    COST_OPTIMIZATION.md
  templates/
    PROJECT_CHECKLIST.md
  projects/
    01-personal-chief-of-staff/
      app/
      docs/
      evals/
      README.md
    02-deep-research-briefing/
      app/
      docs/
      evals/
      README.md

Project Index

#	Project	Concepts	Status
01	Personal Chief of Staff	provider abstraction, structured outputs, tool use, evals	deployed
02	Deep Research Briefing Agent	planning, citations, critique loops	local MVP
03	Ask My Documents	RAG, embeddings, retrieval quality	planned
04	Personal Context MCP Server	tools, resources, reusable context	planned
05	LLM Eval Arena	eval design, model comparison, scoring	planned
06	AI Engineering Manager Copilot	issue summarization, decision support	planned
07	Meeting-to-Decisions Assistant	transcription, extraction, action items	planned
08	Browser Workflow Agent	browser automation, task state	planned
09	Multimodal Product Analyst	image understanding, structured critique	planned
10	AI Data Cleaning Copilot	schema inference, transform suggestions	planned
11	Voice Journal Memory Coach	speech, memory, reflection loops	planned
12	Agent Reliability Dashboard	observability, traces, regression testing	planned
13	Equity Research Lab	source-grounded financial research, provider abstraction, immutable prediction snapshots, retrospective evals	MVP
14	Shorts Studio	text-to-video prompts, per-platform metadata, publish-readiness lint, scheduling	local MVP

Weekend Workflow

1. Pick one project from ROADMAP.md.
2. Keep the first version intentionally small.
3. Add one screenshot or short demo recording after it works.
4. Add 3-5 eval cases before calling it complete.
5. Write a resume bullet that explains the engineering concept and user value.
6. Complete the project shipping checklist, including cache and cost measurements.

Getting Started With Project 01

cd projects/01-personal-chief-of-staff
python3 -m app.server

Then open http://localhost:8000.

The app works in mock mode with no API keys. Add OPENAI_API_KEY or ANTHROPIC_API_KEY to use a real model.

GitHub Profile

Designed for ongoing additions under geeveeyes.

Project 02

cd projects/02-deep-research-briefing
python3 -m app.server

Open http://127.0.0.1:8002 and choose Load example. See the Project 02 README for API key setup and scope.

Project 13

cd projects/13-equity-research-lab
cp .env.example .env.local
npm install
npm run dev

Equity Research Lab runs in demo mode without API keys and is designed to add SEC, analyst, options, persistence, and retrospective-evaluation providers incrementally. See the Project 13 README.

Project 14

cd projects/14-shorts-studio
python3 -m app plan
python3 evals/run_evals.py

Shorts Studio turns episode prompts into Veo prompts, YouTube/TikTok/Instagram metadata, captions and a posting schedule, with no API keys needed. See the Project 14 README.