The latest articles on DEV Community by SHUBHAM upadhyay (@shubhupadhyay23). https://dev.to/shubhupadhyay23 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4155070%2F3d856826-e9a3-4f95-b4fb-ad750128de65.png https://dev.to/shubhupadhyay23 en SHUBHAM upadhyay Sun, 04 Oct 2026 09:12:09 +0000 https://dev.to/shubhupadhyay23/mitra-152g https://dev.to/shubhupadhyay23/mitra-152g <h1> MITRA — Remember What Matters </h1> <blockquote> <p>This is a submission for the <a href="https://dev.to/challenges/hacktoberfest-weekend-2026-10-01">Hacktoberfest Weekend Challenge: Build for a Friend</a></p> </blockquote> <h2> What I Built </h2> <p>I built <strong>MITRA</strong>, a personal AI memory companion for a friend who kept losing important information inside scattered conversations, voice notes, screenshots, and notes.</p> <p>The problem wasn't that they couldn't remember.</p> <p>The problem was that important information was scattered everywhere.</p> <p>MITRA lets them tell it something naturally, such as:</p> <blockquote> <p>"I talked to Rahul today. His internship application closes Friday and I promised to review his resume tonight."</p> </blockquote> <p>Instead of simply saving that sentence, MITRA uses <strong>Gemma</strong> to understand it and extract structured information:</p> <ul> <li>Person: Rahul</li> <li>Topic: Internship</li> <li>Deadline: Friday</li> <li>Action: Review resume</li> <li>Source: Text or voice</li> </ul> <p>The user reviews the extracted memory before saving it.</p> <p>Later, they can ask:</p> <blockquote> <p>"What did Rahul tell me about his internship?"</p> </blockquote> <p>MITRA retrieves the relevant memory and responds:</p> <blockquote> <p><strong>You told me this before.</strong></p> </blockquote> <p>It then shows the source memory used to support the answer.</p> <p>That was the experience I wanted to build: not another chatbot that talks confidently, but a system that can remember something useful and show me where that memory came from.</p> <p>If MITRA doesn't have a supporting memory, it refuses to invent one.</p> <h2> Demo </h2> <p><strong>Live Demo:</strong> mitra-lilac-seven.vercel.app</p> <p>The public demo includes clearly labelled <strong>DEMO DATA</strong>, so judges can experience the complete product without installing a local AI model.</p> <p>The main flow is:</p> <p><strong>Tell → Understand → Review → Remember → Ask → Retrieve → Source → Connect</strong></p> <p>Try the 60-second demo to see the complete experience.</p> <h2> Code </h2> <p><strong>GitHub:</strong> [<a href="https://github.com/Shubhupadhyay23/mitra-memory.git" rel="noopener noreferrer">https://github.com/Shubhupadhyay23/mitra-memory.git</a>]</p> <p>The repository contains the Next.js frontend, FastAPI backend, memory pipeline, Gemma/Ollama integration, database layer, and documentation.</p> <h2> How I Built It </h2> <p>The core architecture is:</p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code> text Human thought ↓ MITRA UI ↓ Gemma ↓ Structured memory ↓ Persistent storage ↓ Retrieval ↓ Gemma grounded response ↓ Source memories ### Stack - **Next.js + TypeScript** — frontend - **FastAPI + Python** — backend - **SQLAlchemy + SQLite** — persistent memory - **Ollama + Gemma** — local AI reasoning and memory extraction - **Tailwind CSS** — interface - **Antigravity** — agent-assisted development Gemma isn't just an AI button sitting beside the application. It is part of the memory pipeline. When a user gives MITRA information, Gemma helps turn unstructured human language into structured memory. When the user asks a question later, MITRA retrieves relevant stored memories and uses that context to produce a grounded answer. The important part is the boundary between **memory and generation**. If there is no supporting memory, MITRA should not confidently manufacture an answer. --- ## Why Does Open Innovation Matter? For MITRA, open-source AI changed what I could build. A conventional closed API would make it easy to send a prompt somewhere and receive a response. But MITRA is fundamentally about **personal memory**. I wanted the AI responsible for interpreting those memories to be replaceable, inspectable, and capable of running locally. Using **Gemma through Ollama** allowed me to build the core intelligence around a locally runnable model instead of making the entire product dependent on a proprietary hosted model. That gives MITRA an important property: **The user's memories don't have to belong to an AI provider.** The architecture can evolve as better open models become available without rebuilding the entire application around one closed API. Open innovation also made experimentation much easier. I could change the model, adjust the extraction process, test the retrieval behaviour, and keep the surrounding application under my control. For a product dealing with personal memories, that control matters. --- ## What I Learned The hardest part wasn't making an AI chatbot. It was making the AI **not say things it doesn't know**. Memory systems create a different problem from normal chat applications. A convincing wrong answer is worse than saying: &gt; "I don't have a memory supporting that." That led me to build MITRA around three principles: 1. **Extract before storing** 2. **Retrieve before answering** 3. **Show the memory behind the answer** The result is less about having a clever conversation and more about building a trustworthy memory layer. --- ## Limitations MITRA is still a prototype. The strongest local-AI experience currently comes from running Gemma through Ollama, while the public demo uses labelled demo data so judges can experience the application without configuring a local model. The memory system also needs much more real-world testing. In particular, I want to measure: - memory extraction accuracy - retrieval accuracy - incorrect-memory resistance - refusal behaviour - long-term memory usefulness I don't want to call MITRA a solved memory system yet. I want to make it one. --- ## What's Next? The next version would focus on real-world use rather than adding more features. I would like to test MITRA with actual daily memories, measure which memories are useful later, improve retrieval, and make the system increasingly reliable at distinguishing between something I actually told it and something it merely thinks might be true. The goal is simple: **Remember less for me. Remember the right things.** --- ## Prize Categories **Best Use of Gemma** Gemma is used as a core part of MITRA's memory-understanding pipeline rather than being included as a superficial chatbot feature. </code></pre> </div> devchallenge weekendchallenge hf26challenge