added hack plan
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Below is a “ready‑to‑hack” checklist that covers everything you’ll need to set up before the hack day starts. It’s broken into three phases – **pre‑planning**, **environment & tooling**, and **project‑specific prep** – so you can tackle each in order.
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---
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## 1️⃣ Pre‑Planning (What we’re building)
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| Item | Why it matters | How to decide |
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|------|----------------|---------------|
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| **Problem statement + success metric** | Keeps the hack focused; avoids “we’ll just keep adding features” trap. | Write a one‑sentence goal and a concrete KPI (e.g., 80 % of user intents correctly handled). |
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| **Scope & MVP** | Defines what you can finish in 24–48 h. | List core features, then prune until only 3–5 “must‑have” items remain. |
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| **User stories / personas** | Helps surface edge cases early. | Draft 2–3 short scenarios (e.g., “As a student, I want the agent to schedule a meeting”). |
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| **Success criteria & demo plan** | Gives you a clear finish line and a demo script. | Write bullet points for each feature that can be shown in the final demo. |
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| **Team roles** | Prevents overlap and gaps (e.g., one person on NLP, another on UI). | Assign: Lead dev, AI/ML engineer, frontend/UI, documentation & testing. |
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---
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## 2️⃣ Environment & Tooling
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### a) Code Repository
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- **Git + GitHub/GitLab/Bitbucket** – set up the repo now; use a `main` branch and an `dev` branch.
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- Add a **`.gitignore`** for Python (or use the template).
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- Create a simple README with project name, goal, and how to run.
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### b) Virtual Environment
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```bash
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python -m venv .venv
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source .venv/bin/activate # Linux/macOS
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.venv\Scripts\activate # Windows
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```
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### c) Dependency Management
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- **`requirements.txt`** or **`pyproject.toml`** (Poetry / Pip‑env).
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- Pin major packages (e.g., `langchain==0.1.*`, `openai==0.*`).
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### d) Language Model & API Keys
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| Service | What you need | Notes |
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|---------|---------------|-------|
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| OpenAI GPT‑4o or GPT‑4 Turbo | API key, set in env var (`OPENAI_API_KEY`) | Make sure quota is enough; consider a free tier for quick tests. |
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| Anthropic Claude | `ANTHROPIC_API_KEY` | Good for “multimodal” prompts if you need images. |
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| Hugging Face models (optional) | Token for private repos | For local inference if you want to avoid API calls. |
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Store keys in a **`.env.example`** file and load them with `python-dotenv`.
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### e) Agent Framework
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- **LangChain/Agentic** or **OpenAI’s new `ChatCompletion` agents**.
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- Install: `pip install langchain openai python-dotenv`
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- If you want modularity, consider the **MCP (Modular Conversational Protocol)** library if it exists; otherwise use a custom message‑passing protocol.
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### f) Development Tools
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| Tool | Purpose |
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|------|---------|
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| VS Code / PyCharm | IDE with Python support. |
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| `black`, `ruff` | Auto‑formatting & linting. |
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| `pytest` or `unittest` | Quick unit tests for critical functions. |
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| Docker (optional) | Snapshot environment for later deployment. |
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### g) Data / Knowledge Base
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- **Static docs**: Markdown/HTML files you want the agent to read.
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- **Vector store**: Use FAISS, Pinecone, or Chroma for embeddings if you’ll do RAG.
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- Pre‑compute embeddings before hack day (script in repo).
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- **Sample user intents**: Create a JSON file with example queries.
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---
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## 3️⃣ Project‑Specific Prep
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### 1. Architecture Sketch
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```
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┌─────────────────────┐ ┌───────────────┐
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│ Frontend UI / CLI │ │ API Layer │
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├─────────────────────┤ ├───────────────┤
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│ Request handler │◄─►│ Agent core │
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├─────────────────────┤ │ (MCP) │
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│ State manager │ └───────────────┘
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└─────────────────────┘
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```
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- Decide: CLI vs web app? Quick prototype → Flask or FastAPI + `uvicorn`.
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### 2. Prompt Templates
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- Draft **system**, **user**, and **assistant** messages.
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- Keep them generic; add placeholders for dynamic content (e.g., `{user_name}`).
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### 3. Agent Workflow
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| Step | Action | Notes |
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|------|--------|-------|
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| 1 | Parse user input | Use regex or simple NLP to detect intent. |
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| 2 | Retrieve context | Query vector store if needed. |
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| 3 | Formulate LLM prompt | Inject retrieved docs + system instructions. |
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| 4 | Call LLM | Get response, optionally chain multiple calls (e.g., “verify facts”). |
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| 5 | Post‑process & output | Format as JSON or plain text for UI. |
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### 4. Testing Strategy
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- **Unit tests**: Prompt generation, vector lookup.
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- **Integration test**: End‑to‑end request → response cycle.
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- Run tests nightly in a CI (GitHub Actions) if time permits.
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### 5. Security & Rate‑Limiting
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- Add simple `sleep(1)` between API calls to avoid hitting rate limits during the hack.
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- Sanitize user input before sending to LLM (avoid injection attacks).
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---
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## 4️⃣ Day‑of‑Hack Checklist
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| Time | Task |
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|------|------|
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| **0–30 min** | Finalise repo, install deps, load env vars. |
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| **30–60 min** | Build minimal UI (CLI or web). |
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| **1–2 h** | Implement core agent loop & prompt templates. |
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| **2–3 h** | Wire vector store / RAG if chosen. |
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| **3–4 h** | Write unit tests for the most critical parts. |
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| **4–5 h** | Run integration test, debug. |
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| **5–6 h** | Prepare demo script; record or capture demo. |
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| **6+ h** | Polish README, add usage instructions, commit final changes. |
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---
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## 5️⃣ Post‑Hack
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1. **Documentation** – add a `docs/` folder with architecture and prompt design.
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2. **License** – choose MIT / Apache-2.0 if you want to share.
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3. **Deploy (optional)** – push to Render, Fly.io, or a Docker container on Heroku.
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4. **Feedback loop** – ask friends for usability testing; iterate.
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---
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### Quick Starter Code Skeleton
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```python
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# main.py
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import os
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from dotenv import load_dotenv
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from fastapi import FastAPI, Request
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts import PromptTemplate
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load_dotenv()
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app = FastAPI()
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llm = ChatOpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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system_prompt = "You are a helpful assistant."
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prompt_template = PromptTemplate(
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input_variables=["user_input"],
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template="{system}\nUser: {user_input}\nAssistant:",
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)
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@app.post("/chat")
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async def chat(req: Request):
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data = await req.json()
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user_text = data.get("message", "")
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prompt = prompt_template.format(system=system_prompt, user_input=user_text)
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response = llm([prompt])
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return {"response": response[0].content}
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```
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Run with:
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```bash
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uvicorn main:app --reload
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curl -X POST http://localhost:8000/chat -H "Content-Type: application/json" -d '{"message":"Hello"}'
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```
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---
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## 🎉 Final Tips
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- **Keep it simple** – you can always extend later.
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- **Version control** – commit often; tag a “ready‑to‑demo” commit.
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- **Timeboxing** – set 15‑min timers for each feature to avoid over‑engineering.
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- **Have fun** – the best hacks happen when the team is laughing and learning.
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Good luck with your hack day! 🚀
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