**1. Spot the problem you really solve** Many students spend hours re‑reading notes, missing key concepts, or juggling multiple assignments. Instead of building a generic tutor, focus on the specific pain point that you or your teammates experience daily. For example, if you often forget to review spaced‑repetition material, design an MVP that sends concise reminders based on the topics you struggle with. **2. Validate before you code** Before writing any code, talk to potential users. Create a simple survey or a landing‑page mockup and ask: "Would you pay $5‑$10 a month for an AI assistant that highlights the hardest topics for you each week?" Collect at least 30‑40 responses. If the interest is low, pivot to another daily struggle – maybe scheduling study sessions or auto‑grading quizzes. **3. Sketch the core value proposition** Define what makes your MVP different from existing apps like Quizlet or Notion. A practical core could be: - **Automatic topic tagging**: Scan uploaded notes and tag each concept. - **Personalized micro‑quizzes**: Generate 5‑10 questions on poorly tagged topics. - **AI‑driven study schedule**: Suggest daily 15‑minute review slots based on weak areas. Keep the feature set to one or two of these to stay minimal. **4. Choose a low‑cost tech stack** - **Backend**: Use a serverless platform (AWS Lambda or Vercel Functions) to avoid server maintenance. - **AI model**: Leverage free tiers of OpenAI’s GPT‑3.5 or a lightweight open‑source model like LLaMA 2 via Hugging Face. - **Data storage**: Store user data in a free tier of Firebase Realtime Database or a personal Google Sheet. - **Frontend**: A simple web app using React with a responsive UI; you can host it on GitHub Pages for free. **5. Build the MVP incrementally** Start with the topic tagging pipeline: 1. Write a script that extracts text from PDFs/uploads. 2. Prompt GPT to list key concepts and assign tags. 3. Save tags to your database. Next, add a simple quiz generator that pulls from the tags and presents multiple‑choice questions. **6. Test with real classmates** Invite 10‑20 classmates to beta test. Provide a feedback form asking: "What works? What feels clunky?" Encourage them to upload a study material and see if the tagging is accurate. Iterate based on their suggestions – maybe refine the prompt engineering or adjust the UI. **7. Reflect and plan for iteration** After the first week, review usage metrics (if you have any). Even without analytics, note which feature got the most positive feedback. Consider adding a scheduler next, using the same AI model to recommend optimal review times based on calendar events. **Actionable takeaway** Start with a single, tangible student pain point, validate quickly, and build a lean MVP using free cloud services. The key is to iterate based on real feedback rather than building a full‑blown product from day one. **Final tip** Document every step in a shared Google Doc. This becomes your startup’s knowledge base and helps new teammates join the project without losing momentum.