I am Curtly Critchlow

Lokademy was born from both personal and practical aspirations. Inspired by my wife's unwavering dedication as a teacher, I envisioned a platform that could replicate the invaluable support and commitment she offers her students on their journey to CSEC success.

Beyond its sentimental origins, Lokademy aims to address practical challenges in education:

• Provide students with limited access to qualified teachers with an alternative avenue to excel in their CSEC studies.
• Deliver personalized feedback on quizzes, addressing the common hurdle of student-to-teacher ratios that hinder individual attention.
• Offer a resilient learning solution for students facing disruptions due to personal or national emergencies, ensuring educational continuity in challenging circumstances.

Developing Lokademy has been an incredibly rewarding experience. This project has deepened my understanding of Generative AI and its vast potential within the education space, particularly how to seamlessly integrate it into a user-friendly Flutter app.

Key technical features of the app include:

• Comprehensive CSEC Syllabus Content for core science subjects: Biology, Chemistry, Physics, and Integrated Science.
• An AI-powered chatbot designed to provide insightful answers to students' questions across various CSEC subjects.
• An interactive chatbot that allows students to test their knowledge through quizzes and receive personalized feedback, enhancing their learning experience and understanding.

Lokademy is currently under active development and undergoing rigorous testing to ensure a seamless and valuable learning experience for students.

Lokademy has been one of the most engaging and enriching projects I've undertaken. Building my first AI-based app from the ground up has significantly expanded my knowledge and practical experience with AI technologies.

Key learnings from this project include:

• Harnessing the power of cloud functions to run sophisticated AI models efficiently and effectively.
• Mastering the art of prompt design to elicit accurate and contextually relevant responses from AI models.
• Gaining proficiency with the Dart `google_generative_ai` package, seamlessly integrating Gemini's capabilities into the Flutter app.
• Implementing efficient data handling techniques to fetch and receive chat history from Cloud Firestore and seamlessly pass it to the Gemini model for contextual understanding and responses.
• Developing effective strategies to provide system instructions to the Gemini model, precisely constraining its behavior to align with Lokademy's educational goals and purpose.
• Successfully grounding the AI model in the CSEC syllabus content, ensuring that responses are relevant, accurate, and aligned with the curriculum.

SSH No Ports Desktop was developed with the core objective of empowering network administrators with enhanced security and flexibility when managing remote devices.

The application addresses key challenges faced by administrators, enabling them to:

• Securely access devices from any location, whether behind routers, NAT, firewalls, VPNs, private/1918 addresses, or even on the move, eliminating traditional barriers to remote access.
• Connect to devices without the need for complex network reconfigurations, such as setting up port forwarding on routers or relying on VPNs, simplifying the connection process and reducing administrative burden.
• Significantly strengthen network security by eliminating the requirement for open listening ports, including the commonly targeted port 22, effectively reducing the attack surface and mitigating potential security vulnerabilities.

Working on SSH No Ports Desktop was a fantastic opportunity to collaborate with talented backend engineers and gain a deeper understanding of the core SSH No Ports package. The process of integrating this package into the Flutter desktop app, along with providing constructive feedback on bugs and feature requests, was both challenging and rewarding.

Key technical highlights of the app include:

• A streamlined connection profile system that allows users to save and manage device information, including names, addresses, ports, and other relevant details, facilitating quick and easy connections.
• Robust support for private key authentication, providing an additional layer of security to prevent unauthorized access to remote devices.
• A built-in terminal emulator that provides users with full access to the device's file system and command-line interface, enabling efficient remote management and troubleshooting.

SSH No Ports Desktop is publicly available on GitHub, Microsoft Store, and App Store, making it accessible to a broad audience of network administrators and developers across different platforms.

This project was instrumental in expanding my understanding of SSH and its complexities. Before developing SSH No Ports Desktop, my knowledge of SSH was limited; however, through this project, I gained valuable insights into its inner workings and security implications.

Key learnings from this project include:

• Collaborating effectively with backend engineers to integrate and utilize their SSH No Ports core package, providing valuable feedback for continuous improvement.
• Gaining a deep understanding of SSH key management, including the use of private and public keys for user authentication, ensuring secure and reliable access control.
• Mastering the integration of a terminal emulator into a Flutter application, extending the app's functionality and providing users with a powerful tool for remote device management.
• Learning to implement keyboard shortcuts within the Flutter framework, enhancing the user experience and streamlining common actions within the app.

AtDataBrowser was designed with a clear mission: to equip the atsign community with an intuitive and powerful tool for exploring and managing their atData. By providing a user-friendly interface and robust features, AtDataBrowser empowers users to take ownership of their decentralized data and leverage the full potential of the atPlatform.

The most rewarding aspect of developing AtDataBrowser was the process of transforming the initial Figma designs into a fully functional and visually appealing Flutter application. Bringing the design vision to life through code was a fulfilling experience that showcased the versatility and power of Flutter's framework.

Key technical highlights of AtDataBrowser include:

• Comprehensive views that display the atKey, associated metadata, and atValue properties, providing users with a detailed understanding of their atData.
• Robust filtering capabilities that enable users to organize and search their atKeys based on various criteria, including date created, date modified, namespaces, atsign, key type, shared with, and shared by. This feature facilitates efficient data management and retrieval.
• Support for applying multiple filters simultaneously, allowing users to refine their searches and quickly pinpoint the specific atData they need.
• Secure and controlled deletion of atKeys, empowering users to manage their data privacy and remove unwanted or outdated information.

AtDataBrowser has undergone rigorous testing, with the second round completed in July 2023. Additional development is underway, incorporating valuable user feedback to enhance the application's functionality and user experience.

AtDataBrowser proved to be an exceptional learning opportunity, deepening my expertise in both Flutter and the atPlatform. Through this project, I acquired valuable knowledge and practical experience in several areas:

• Mastering the use of goRouter for efficient and flexible navigation between screens, enhancing the application's user experience.
• Leveraging the power of Riverpod for effective state management, ensuring a clean and maintainable codebase.
• Implementing advanced state manipulation techniques, including filtering and resetting state without relying on network calls, optimizing performance and user responsiveness.

The Livestock Information System serves as a practical demonstration to the GLDA and Guyana's Ministry of Agriculture (MOA) of the transformative potential of tailored agricultural extension services.

By utilizing a custom-built app to monitor individual farm performance, LIS highlights the benefits of data-driven insights for both farmers and agricultural authorities. Additionally, the app showcases the potential for streamlining extension officer workflows by reducing redundant data collection processes.

LIS was meticulously designed based on feedback from agricultural experts within GLDA, incorporating insights from my personal professional experience working within the agriculture sector.

The most engaging aspect of developing LIS was the implementation of robust service calls for creating, reading, and updating documents within the Firebase backend. This process involved designing efficient data structures and ensuring seamless synchronization between the app and the database, providing a reliable and responsive user experience.

Key technical highlights of the application include:

• Seamless online and offline functionality, ensuring data can be accessed and updated even in areas with limited internet connectivity.
• A comprehensive Farmer module that allows users to register new farmers, view a list of registered farmers, and access detailed information about each farmer's profile and associated farms.
• A detailed Farm module that enables users to register new farms, view a list of registered farms, and access specific farm details, including location, livestock populations, and historical data.
• An Animal Population Survey feature that accurately records livestock populations by species and growth stage, providing valuable insights into livestock demographics and trends.
• An Animal Disaster Survey functionality that tracks livestock losses due to natural disasters, categorized by species and growth stage, facilitating informed responses and support efforts in emergency situations.
• Integration of device GPS capabilities to automatically collect and store the precise location of farms, enhancing data accuracy and facilitating location-based services.
• Utilization of the device camera to capture and save farmer and farm profile pictures within Firebase storage, providing a visual representation of the data and enhancing user engagement.

The Livestock Information System is currently in its Minimum Viable Product (MVP) stage, Since its initial deployment, LIS has facilitated the registration of over 500 farmers as of December 2021, demonstrating its practical value and potential for growth.

Developing LIS has been an invaluable learning journey, significantly advancing my skills in Flutter and Firebase. Key learnings from this project include:

• The critical importance of defensive coding practices, especially when handling network calls, to ensure application robustness and graceful error handling.
• Implementing secure user authentication using Firebase Authentication, safeguarding user data and protecting application access.
• Accessing and utilizing the device camera to capture user-provided images and securely storing them within Firebase Storage, enhancing the application's functionality and user experience.
• Integrating device GPS functionality to retrieve accurate location data and incorporating it into the application's core features.
• Efficiently saving and retrieving data using Cloud Firestore, leveraging its scalability and real-time capabilities.
• Implementing real-time data updates using Firebase Streams, ensuring that users have access to the most current information without the need for manual refresh actions.
• Effectively utilizing Streams in conjunction with the Provider package for state management, enabling dynamic UI updates and a responsive user interface.
• Structuring the codebase using the gSkinner MVC+S architecture, promoting code organization, maintainability, and scalability.

The primary objective of the Nabi-KCL SSHE application was to elevate the health and safety culture on the Nabi-KCL project.

The app achieved this goal by providing a centralized system for recording and monitoring safety observations and incidents, empowering the project team to proactively identify potential hazards, track safety trends, and implement effective preventive measures.

Developing Nabi-KCL SSHE provided a valuable opportunity to explore data analytics and visualization tools. A key highlight of this project was integrating BigQuery to extract and query data from Firebase, followed by visualizing the analyzed data in an interactive Google Data Studio dashboard.

This process demonstrated the power of combining data analysis with mobile application development to provide actionable insights and improve safety outcomes.

Key features of the application include:

• An interactive Google Data Studio dashboard that provided comprehensive analysis of safety observations, enabling the project team to identify trends, potential hazards, and areas for improvement.
• Seamless offline and online functionality, allowing users to record observations and incidents even in areas with limited internet connectivity, ensuring uninterrupted data collection.

While the Nabi-KCL SSHE app is no longer actively maintained, it served as an important project for enhancing my Flutter and Firebase skills. It was developed during my time as a Safety & Security officer on the Nabi-KCL project, showcasing my initiative to leverage technology for improving workplace safety.

The Nabi-KCL SSHE project provided valuable insights and expanded my technical skill set in several key areas:

• Establishing a connection between BigQuery and a Firebase project, enabling seamless data extraction and analysis.
• Mastering the use of SQL commands within BigQuery to effectively query and analyze data stored in Firebase.
• Utilizing Google Data Studio to create interactive and insightful dashboards for visualizing data extracted from BigQuery.
• Seamlessly embedding a Google Data Studio dashboard into a Flutter application, enhancing the app's functionality and providing users with direct access to data-driven insights.

The Layer Bird Profitability Model originated from a client's request for assistance in creating a comprehensive business plan for a new Layer Bird Production Agribusiness.

After successfully completing the initial business plan, I recognized an opportunity to leverage my programming expertise and agricultural knowledge to create a tool that could benefit a wider audience of poultry farmers. This led to the development of the LBPM web application, making the profitability model accessible to farmers beyond the initial client engagement.

The project journey involved building the model initially in Excel for the client's specific requirements and then converting it into a dynamic and interactive web application for broader use and accessibility.

A particularly rewarding aspect of developing LBPM was the challenge of rebuilding the Excel-based model from the ground up using Dart, leveraging Flutter's web capabilities to create an interactive and visually engaging user interface.

The key technical challenge was effectively utilizing the Provider package to dynamically update the UI whenever a user adjusted any model parameter. This involved creating a robust state management system that ensured seamless synchronization between user input and model outputs.

Overcoming these challenges through extensive research, Flutter documentation, and community support (Stack Overflow) deepened my understanding of Flutter's reactive framework and the Provider package's role in building efficient and responsive applications.

The project's success is a testament to my ability to translate my Flutter expertise into practical solutions for real-world problems, particularly within the agricultural domain.

I am committed to the ongoing development and enhancement of LBPM. Future iterations will incorporate additional variables that influence profitability within layer bird production systems, providing farmers with even greater depth and accuracy in their financial analysis.

The positive feedback received from my former boss, an agricultural expert, has further motivated me to expand this project. His request to build similar models for other agricultural production systems highlights the potential for LBPM to serve as a foundation for a suite of agricultural decision-support tools.

As I continue to develop LBPM, I am eager to collaborate with agricultural experts and companies to fine-tune the model and ensure its alignment with real-world industry practices and challenges.

The LBPM project provided valuable hands-on experience with key Flutter concepts and packages, enhancing my skill set in several areas:

• Mastering the implementation of getters and setters in Dart, ensuring proper data encapsulation and controlled access to model parameters.
• Gaining proficiency in using the Provider package for effective state management, allowing for efficient UI updates and a more responsive user experience.
• Integrating the Syncfusion Flutter Chart package to create visually appealing and informative charts, enhancing the dashboard's ability to convey complex data in an easily understandable format.