The digital landscape in Pakistan, much like many other places, is riddled with a pervasive and frustrating issue: suspicious messages. These seemingly official communications, often disguised as alerts from banks, couriers, tax authorities, or government departments, can be incredibly difficult to decipher. Some are legitimate, while many are cunning scams designed to trick recipients into clicking malicious links, making payments, or divulging sensitive information. The real challenge isn’t just reading the message; it’s knowing how to safely respond.
That’s where the idea for Pakistan Notice Helper was born. Developed as part of the Hugging Face Build Small Hackathon, this AI-powered tool tackles a very specific, local safety problem. It’s designed to act as a crucial first line of defense, helping people in Pakistan understand the potential risks of a suspicious message before they take any action.
Introducing Pakistan Notice Helper: Your Local Safety Assistant
Pakistan Notice Helper is not an authenticity checker in the traditional sense. It doesn’t definitively label a message as genuine or fraudulent. Instead, it functions as a smart triage system. Users can submit text or even a screenshot of a suspicious message, and the tool provides a risk label, a concise explanation of potential dangers, highlights visible red flags, and, most importantly, offers clear, safe next steps.
This project perfectly embodies the spirit of “Backyard AI” because it addresses a highly localized problem with a focused solution. Rather than aiming for a sprawling general-purpose assistant, the goal was to demonstrate how effective a small AI model could be when its scope is clear, its behavior well-defined, and its interface tailored to real user needs.
A key feature of Pakistan Notice Helper is its dual-language support for English and Urdu. This was a critical product decision, as scam messages in Pakistan frequently blend English, Urdu, and Roman Urdu. The Urdu mode is not merely a translation; it completely adapts the interface, including right-to-left layout, translated headings, labels, and results, with the model generating its assessment directly in clear Urdu script. This ensures that safety advice is delivered in the language people are most comfortable with, making it easier to understand and act upon.
The Quest for the “Goldilocks” Model
Building Pakistan Notice Helper was a journey of balancing quality, speed, and cost. The initial exploration involved larger models like Qwen3.6 27B, which offered excellent quality but proved too expensive and resource-intensive for a small, focused application. The challenge was to find a model that was “just right.”
- Starting Big: While Qwen3.6 27B delivered exceptional quality, its high VRAM requirements, larger GPU needs, and slower cold-start times made it impractical for a hackathon demo. The quality was top-notch (around 95/100), but deployment costs and responsiveness were significant concerns.
- Trying Small: An attempt with MiniCPM-V 4.6 Q8, a much smaller vision-language model, faced deployment instability and insufficient model quality. It struggled with detecting suspicious messages and failed too many critical test cases.
- Finding the Sweet Spot: The breakthrough came with Qwen3.5 4B Q8, deployed via llama.cpp. This model struck the perfect balance. It was small enough to fit the “Build Small” ethos, fast enough for a responsive user experience, and capable enough to handle high-risk scam cases and screenshot analysis, scoring around 80/100 compared to the larger model’s 95/100. This combination of quality, speed, and cost-effectiveness made it the ideal choice for Pakistan Notice Helper.
This iterative process underscored a crucial lesson: the best model isn’t always the biggest. For a clearly defined task like this, a smaller, more efficient model that meets the product’s safety and performance requirements can be far more effective.
Key Learnings and Future Directions
The development of Pakistan Notice Helper offered several valuable insights. One significant takeaway was that small models thrive when the scope is clear and bounded. The tool doesn’t need to be a general-purpose AI; it needs to identify risk signals, avoid overclaiming, and provide actionable, safe next steps. This precise scope, coupled with careful prompt design and strict output contracts (e.g., forbidding invented URLs or facts), was as vital as the model itself in ensuring reliable and safe behavior.
Another learning involved the intricacies of designing for the Urdu user experience. Direct translations often felt unnatural, and specific UI adjustments, like right-to-left layouts and appropriate font choices, were essential for making the app feel clear, trustworthy, and usable. These weren’t just design details; they were fundamental to the tool’s effectiveness in its target demographic.
Looking ahead, the next major feature for Pakistan Notice Helper will be an agentic verification workflow. Currently, the tool triages messages, identifies risks, and suggests safe next steps. The planned enhancement will allow the app to go further, actively searching the web (using tools like Olostep for web search and scraping) to verify if a notice is original, if similar messages have been reported as scams, and to compare claims against independently discovered official sources. This will transform the tool from a triage assistant into a proactive verification agent.
Throughout the development, AI assistants like Codex played a crucial role, acting as an engineering collaborator. It significantly accelerated progress on the custom frontend, Gradio backend, Modal-hosted llama.cpp server, and various UI refinements, enabling rapid iteration and ensuring the entire system worked cohesively. The project also prioritizes user privacy, implementing a public trace feature that logs only limited, redacted metadata, never sensitive user content, allowing for transparent usage analysis without compromising personal data.
Source: Hugging Face Blog
