Long Notice? Take It Slow. 🦥 Building NoticeNap with Open-Weight AI

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

Built for Hacktoberfest 2026 — Week 3: Slow and Steady

Have you ever opened a recruitment notification, scholarship announcement, internship circular, or examination notice and discovered that the information you need is scattered across dozens of pages?

The deadline is somewhere in the middle. Eligibility criteria are buried in another section. Required documents, application fees, exceptions, and instructions are spread throughout the PDF.

You end up spending more time searching through paperwork than preparing for the opportunity itself.

What if one PDF upload could turn that overwhelming notice into a clear summary, a list of important dates, and an actionable checklist?

That is the idea behind NoticeNap — a local-first AI assistant designed to make reading long, information-heavy PDF notices less tedious.

🔗 Live frontend: https://noticenap.vercel.app 💻 Source code: https://github.com/AnshMeshram/NoticeNap

I built NoticeNap around the spirit of this week's Hacktoberfest theme: Slow and Steady — build open-source AI that rewards laziness.

Not laziness as an excuse to ignore important details, but as a reason to automate repetitive work and spend our energy where it actually matters.

Official notices often contain everything an applicant needs to know, but finding those details manually can be frustrating.

NoticeNap is designed to turn a PDF notice into structured information that is easier to understand and act on.

Here is what the workflow aims to provide:

The goal is simple: instead of repeatedly asking, "Where was that deadline mentioned?", you can review the important information in one place and return to the original page whenever you need to verify something.

Photo: Sloth photo on Unsplash. Unsplash images are free to use under the Unsplash License; credit is appreciated.

NoticeNap is an aid for understanding and organizing notices. It is not a replacement for the official document.

Consider a student applying for multiple scholarships or a job seeker tracking several recruitment notifications.

Each notice requires a similar sequence of work:

NoticeNap aims to make that repeated workflow easier by organizing the information into one reviewable workspace.

The user still makes the important decisions. They decide whether they qualify, confirm the official deadline, and choose what to do next.

The AI handles the tedious first pass. The human stays in control of the final decision.

That is the kind of laziness I wanted to build for: less repetitive searching, less mental clutter, and more time for meaningful work.

NoticeNap combines PDF text extraction, a Python backend, and an open-weight language model served locally through Ollama.

The intended local processing pipeline is:

PDF upload → Text extraction → AI analysis → Structured results → Verification → Action checklist

The application uses PyMuPDF ( fitz ) to extract readable text page by page.

Preserving page boundaries matters because a summary without a way to find its original source can be difficult to verify.

The extraction process maintains page references so that users can return to the relevant section of the notice when references are available.

NoticeNap uses Gemma 2 through Ollama for local, open-weight inference when the configured model service is available.

Instead of requiring every document to be sent to a closed, hosted-model inference API, the intended local setup sends extracted text to the configured Ollama service running in the user's environment.

The model is used to organize the notice into useful categories, including deadlines, eligibility, fees, documents, instructions, exceptions, and missing information.

The backend uses Pydantic to validate structured data returned through the analysis workflow.

The important design principle is that missing information must not become an invented fact.

If a deadline, eligibility condition, or fee cannot be established from the available document content, the appropriate result should be marked “Not found” or “Needs verification”, rather than presented as certain.

Finding information is only half the problem. The next step is remembering what to do with it.

NoticeNap organizes findings into a checklist, supports tracking task progress, and provides Markdown and calendar export functionality where implemented.

The intention is to connect document understanding with an actual workflow, rather than stop at a chatbot response.

Here is how the main components fit together.

The application is organized around a workspace with document analysis, action-list, and document-library experiences. The design uses a calm, paper-inspired visual style and a sloth mascot to reinforce the idea of making tedious work feel lighter.

For a project that processes recruitment notices, scholarship applications, university circulars, and similar documents, where the document is processed matters almost as much as what the model produces.

A notice may contain personal application details or information a user would rather not send to an external inference service.

With the intended local configuration, NoticeNap can run the document-processing and model-inference path using components installed in the user's own environment:

In this configuration, the extracted PDF text does not need to be sent to a third-party model inference API.

Open-weight AI also gives developers more control over model selection and the execution environment. The project can evolve without making a single closed-model API the foundation of its design.

There are trade-offs, of course. Local inference requires suitable hardware, installation, and configuration. Downloading a model initially requires internet access, and model performance depends on the available resources and the document being processed.

I do not consider a local model automatically better at every task. The advantage here is control over the processing environment and the freedom to build around an open-weight model.

The public Vercel URL is a frontend preview, not proof that the complete AI workflow is available as a hosted service.

A browser visiting that frontend cannot automatically connect to Ollama running on the developer's or visitor's computer. End-to-end processing requires the frontend, FastAPI backend, and model runtime to be configured and reachable in the intended environment.

For the local-first workflow, those components need to be set up together.

I think this distinction is important: a project should be transparent about what its interface demonstrates and what its underlying infrastructure actually supports.

An incorrect deadline or eligibility condition can have real consequences for an applicant.

That makes reliability an important part of NoticeNap's design.

The project emphasizes several safeguards:

These safeguards do not eliminate mistakes. Language models can misinterpret content, and source references do not guarantee that an interpretation is correct.

Scanned, image-only PDFs may also require OCR because ordinary text extraction cannot reliably read text that exists only as an image.

That is why the original PDF remains the authoritative source.

A useful document assistant needs to handle more than the ideal case.

The project includes a functional test plan covering scenarios such as:

These are important because a polished summary is not enough if the underlying details cannot be trusted.

The next improvements I would prioritize are stronger extraction evaluations, better OCR support, more comprehensive multilingual testing, improved evidence traceability, and a securely configured end-to-end deployment.

The project setup uses Node.js 20 or newer, Python 3.11 or newer, npm, and Ollama, along with hardware capable of running the selected model.

git clone https://github.com/AnshMeshram/NoticeNap.git cd NoticeNap Enter fullscreen mode Exit fullscreen mode 2. Install and start Gemma 2 with Ollama ollama pull gemma2 ollama serve Enter fullscreen mode Exit fullscreen mode Keep Ollama running in its own terminal.

From the repository root, create and activate a Python virtual environment.

python -m venv. venv. \.venv\Scripts\Activate.ps1 Enter fullscreen mode Exit fullscreen mode On macOS or Linux:

python -m venv.venv source.venv/bin/activate Enter fullscreen mode Exit fullscreen mode Then install the dependencies and start FastAPI:

pip install -r backend/requirements.txt python -m uvicorn backend.main:app \ --host 127.0.0.1 \ --port 8000 \ --reload Enter fullscreen mode Exit fullscreen mode 4. Start the frontend Open another terminal:

cd frontend npm install npm run dev Enter fullscreen mode Exit fullscreen mode Open the application at:

Configuration matters: Check the repository's environment examples and current source code to ensure the frontend points to the local FastAPI backend and the backend uses the intended Ollama endpoint and model. The commands above follow the project's documented setup; verify the current repository if any paths or configuration names have changed.

Source code: https://github.com/AnshMeshram/NoticeNap

I want NoticeNap to help people spend less time hunting through paperwork and more time acting on opportunities.

A long recruitment notice should become easier to review. A scholarship announcement should become easier to turn into a list of tasks. A university circular should be easier to understand without losing sight of its original requirements.

There is still work to do, particularly around OCR, extraction reliability, multilingual accuracy, evidence validation, and deployment.

But I believe this is a useful place for open-source AI: a real, repetitive problem where local inference, structured extraction, and a thoughtfully designed workflow can work together to reduce unnecessary effort.

The best automation is not necessarily the one that does everything without supervision. Sometimes it is the one that removes the frustrating parts while keeping the important decisions in your hands.

Let the notice be long. Your to-do list shouldn't. 🦥

I'd especially welcome ideas and contributions around PDF extraction accuracy, OCR for scanned notices, evidence-backed summaries, multilingual evaluation, accessibility, and reliable local-first inference.

If you were building a local AI tool to automate one repetitive task in your daily life, what would you build?

Created for Hacktoberfest 2026 — Open-Source AI Challenge, Week 3: Slow and Steady.