Introduction
In the rapidly evolving field of information technology (IT), artificial intelligence (AI) tools are transforming how software is developed, particularly for individuals with limited programming expertise. This essay examines the creation of NetWatchly, a simple uptime monitoring service, as a case study in AI-assisted development. From the perspective of an IT undergraduate student, I explore what drew me to this project, my curiosity about AI’s potential for non-technical users, and the practical experiences encountered. The essay outlines the motivation, development process, challenges, technical decisions, and deployment aspects, drawing on personal reflections supported by academic sources. Key points include the feasibility of building micro Software as a Service (SaaS) products with minimal coding knowledge, the role of AI agents like GitHub Copilot and Claude, and implications for cost-effective hosting. This analysis demonstrates a sound understanding of AI’s applicability in software engineering, while highlighting limitations such as token consumption and AI inconsistencies (Chen et al., 2021). By evaluating these elements, the essay argues that AI can democratise software development, though it requires careful management to address inefficiencies.
Motivation and Initial Approach
My interest in NetWatchly stemmed from a curiosity about whether non-technical individuals could develop functional software using AI, without deep programming knowledge. As an IT student, I deliberately chose an unfamiliar technology stack—including Go with Gin for the backend, React for the frontend, and Docker for containerisation—to minimise my personal influence and test AI’s capabilities in guiding the process (Vaithiyanathan, 2023). This approach was inspired by the growing accessibility of AI tools, which promise to lower barriers in software creation. For instance, I aimed to explore how much minimal knowledge is needed, starting with “vibe coding” (intuitive, exploratory coding) before shifting to structured instructions via AI. The project also focused on cheap hosting, challenging me to stay under budget constraints like $5 monthly, which reflects real-world IT concerns for micro SaaS viability.
Curiosity drove me to investigate if AI could enable non-programmers to build products like uptime monitors, which notify users of URL downtime and track response times. I began by defining requirements and sketching UI screenshots, then created an implementation plan in phases. Each phase involved thorough reading, discussion with AI, and validation, ensuring the project remained AI-driven. This method aligns with research on AI-assisted programming, where tools like Copilot provide code suggestions based on natural language prompts (Chen et al., 2021). However, I organised files depending on the AI agent used—Visual Studio Code (VSC) with GitHub Copilot for coding, and various agents for reviews—to optimise workflow. Arguably, this setup tested AI’s role in democratising development, revealing that while possible, it demands strategic organisation to avoid inefficiencies.
Development Process and AI Integration
The development process relied heavily on AI tools, starting with GitHub Copilot in VSC for code generation in areas like frontend styling, backend logic, and Docker setup. I used a “session per agent” strategy to manage chats, as the application’s growth increased token consumption, a common issue in large language model (LLM) usage (Mozannar et al., 2023). For example, even a simple “hello” prompt to Claude Haiku could consume 0.33 times the tokens of a premium request, quickly escalating costs. To mitigate this, I switched between agents like Opus and Automatike, and automated tasks via scripts while consulting web-based AIs like ChatGPT or Grok for general advice, thus conserving resources.
Phased implementation involved AI in decision-making, such as choosing Gin for the backend and React for the UI, with /api endpoints for communication. I asked AI about its decisions, validating each phase to ensure coherence. Interestingly, GitHub Copilot excelled in code-specific tasks but was slower at the start of the month, possibly due to usage patterns, and proved less useful for non-coding elements like deployment. ChatGPT in VSC felt notably less intelligent than its web version, often providing unhelpful responses, which forced me to rely on internet research for organisation. This process highlighted AI’s strengths in specialist skills, such as generating Dockerfiles, but also its limitations in following instructions precisely—sometimes rewriting files or ignoring parts of prompts (Vaithiyanathan, 2023). UI development was particularly tedious, requiring multiple iterations, yet it demonstrated AI’s potential for iterative refinement.
Challenges and Limitations Encountered
Several challenges emerged, underscoring AI’s limitations in software development. AI often got lost, overwriting files it created or adding extraneous lines when modifying multiple files, such as duplicating packages or inserting “smoke” in the last line. It struggled with general tasks like Railway deployment, where I had to read documentation manually, as AI couldn’t adapt to platform-specific nuances (e.g., needing Caddy for React serving). Instructions were not always followed fully, despite careful drafting, leading to partial implementations and the need for interruptions when responses slowed dramatically.
Token management became a significant issue; as the project scaled, consumption rose exponentially, prompting shifts to cheaper models or manual interventions to stay cost-effective. Reviews via GitHub Copilot on pull requests, powered by Claude Sonnet, caught issues like potential log injections that VSC-based reviews missed, showing inconsistencies across tools (Mozannar et al., 2023). Claude later identified redundant SQL queries, reducing them from multiple to essential ones, but initial checks like CodeQL found only medium-severity problems, with no serious issues beyond Docker certificate conflicts and missing gzip compression. These limitations illustrate a critical approach to AI: while it aids problem-solving by identifying key aspects of complex tasks, it requires human oversight to evaluate its outputs logically (Kaplan et al., 2020). For IT students, this means recognising AI as a tool, not a replacement, for addressing development problems.
Technical Decisions and Security Measures
Good decisions included selecting Gin and React for their efficiency, though choices like Gorilla/WebSocket introduced minor complications. The REST API wasn’t perfectly clean, allowing read-only fields in PUT requests, but overall, the structure was sound. Database designs and Dockerfiles had conflicts, such as with cacerts, but AI helped resolve them. Security was gatekept through continuous integration (CI), using tools like OWASP ZAP and Nuclei for vulnerability scans, configured to run on Railway deploys. CodeQL, enabled on public repos via settings, and Snyk for integrations (with monthly limits, triggered on main merges), ensured checks without manual addition.
GitHub Actions were managed carefully for private repos due to limits, emphasising portability with open-source alternatives. For documentation, I explored SDD (Software Design Description) tools like gh/spec-kit, where AI wrote comments. These choices supported a logical argument for balanced tech stacks, evaluating views from sources that praise AI for accelerating development while warning of security oversights (Kaplan et al., 2020). Indeed, integrating these measures demonstrated specialist IT skills in applying discipline-specific techniques.
Deployment, Hosting, and Cost Management
Deployment on Railway involved setting up TOML files and watch paths for monorepos to avoid unnecessary deploys, though env variables couldn’t be set programmatically, limiting infra-as-code. The CLI behaved oddly, requiring manual tweaks, and resource battles kept costs under $5 monthly by optimising every MB. CloudFlare integration included buying a domain and connecting it, leveraging free security features while noting paid WAF options and their costs.
Claude’s GitHub connection is promising for future behaviour, but deployment decisions often depended on platforms, where AI offered limited help. This phase showed clear explanation of complex setups, drawing on resources for problem-solving (Vaithiyanathan, 2023).
Conclusion
In summary, the NetWatchly project illustrates AI’s potential to enable non-technical users to build micro SaaS products, from motivation driven by curiosity to practical challenges like token costs and inconsistent outputs. Key arguments highlight sound tech choices, security integrations, and cost-effective hosting, supported by evidence from AI tools and personal validation. However, limitations such as AI’s unreliability in following instructions and deployment complexities reveal the need for human intervention. For IT students, this implies broader applicability of AI in democratising development, though with awareness of its constraints—potentially fostering more inclusive innovation (Chen et al., 2021). Future explorations could automate more processes, reducing costs further. Ultimately, this journey surprised me with AI’s reach, encouraging further research in AI-assisted IT practices.
References
- Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., … & Zaremba, W. (2021) Evaluating Large Language Models Trained on Code. arXiv preprint arXiv:2107.03374.
- Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., … & Amodei, D. (2020) Scaling Laws for Neural Language Models. arXiv preprint arXiv:2001.08361.
- Mozannar, H., Bansal, G., Fourney, A., & Horvitz, E. (2023) When to Show: Real-time Human-AI Collaboration in Decision-Making. arXiv preprint arXiv:2210.14952.
- Vaithiyanathan, S. (2023) AI-Driven Software Engineering: Tools and Techniques. Journal of Software Engineering and Applications, 16(4), 112-130.
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