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When we talk about generative AI, most people think of text generation or image creation. Over the past weeks, I explored a different side: how these tools can lower barriers to software development. I experimented with Lovable, a platform that helps build web apps, to create a landing page and a sales page for a product that generates automatic outreach messages for LinkedIn ICPs.

Interestingly, my process began not in Lovable but in ChatGPT, since it was cheaper to draft the right prompts there before using Lovable’s more limited credits. In the backend, I connected Lovable to n8n workflows, which allowed customer inputs to trigger an AI agent that scanned LinkedIn profiles and generated personalized outreach messages.

Working with these tools came with clear advantages. Lovable felt intuitive and easy to use, even for someone without a coding background. The integration with n8n also worked more smoothly than expected, making it possible to put together something functional in a short time. It really gave me the sense that prototyping no longer needs to be reserved for professional developers.

At the same time, the difficulties were hard to miss. Setting up n8n workflows required understanding nodes and connections, which was a challenge without coding experience. The language model in Lovable also felt weaker compared to ChatGPT, and the credits were costly. Another drawback was latency: generating a personalized outreach message took up to 40 seconds, which would likely frustrate end users.

Reflecting on this, I see both the promise and the limits. On the one hand, tools like Lovable make it possible for almost anyone to create digital products. On the other, they reveal how important it still is to understand the underlying complexity. It makes me wonder: if building apps becomes accessible to everyone, will the role of professional developers fade, or will they remain crucial as the architects behind these ecosystems?

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Projects like the CitiVerse initiative in Rotterdam, Flanders, and Tampere (CitiVerse, n.d.) show how cities are no longer only physical infrastructures but also evolving into digital platforms. By combining extended reality with urban data, these pilots aim to expand citizen participation and make urban management more transparent and efficient.

Other cities provide interesting comparisons. Barcelona’s Sentilo uses an open-source system to manage sensor data for traffic, energy, and water while ensuring citizen data ownership (Bakıcı et al., 2013). Virtual Singapore applies a comprehensive digital twin that integrates environmental and social data for simulations (Chourabi et al., 2012). Seoul’s Metaverse Seoul offers municipal services in a virtual environment where citizens interact with civil servants as avatars (Seoul Metropolitan Government, 2023).

Academic debates emphasize both promise and caution. Qanazi et al. (2025) argue that most digital twins overlook the social dimensions of urban life and propose “social digital twins” to capture citizen attitudes and interactions more effectively, as seen in Virtual Singapore (Chourabi et al., 2012). Adade et al. (2023) show that digital twins can enhance citizen participation in land-use planning, but only when designed with accessibility, interactivity, and privacy safeguards. Ntanda and Carolissen (2025) highlight how smart city technologies risk reinforcing inequalities if access and digital literacy are not prioritized. Together, these studies underline that the real challenge is not the technology itself, but whether it is deployed inclusively.

The advantages of city-wide platforms are clear. They break down silos between departments, enable real-time responses, and test infrastructure or climate policies virtually before costly rollout, as used in Singapore’s digital twin approach (Chourabi et al., 2012). They also create new ways for citizens to engage with policy-making by visualizing and debating urban changes in accessible formats.

Yet risks remain. Unequal access could deepen exclusion, and if platforms are driven mainly by commercial incentives, public value may be sidelined.

So the key question is: Should smart city platforms like the CitiVerse be developed as public utilities, guaranteeing universal access, or as competitive marketplaces, driving innovation but risking exclusion?

References

Adade, D., de Vries, W. T., Weidner, S., & Kuffer, M. (2023). Digital twin for active stakeholder participation in land-use planning. Land, 12(3), 538. https://doi.org/10.3390/land12030538

Bakıcı, T., Almirall, E., & Wareham, J. (2013). A smart city initiative: The case of Barcelona. Journal of the Knowledge Economy, 4(2), 135–148. https://doi.org/10.1007/s13132-012-0084-9

Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., & Scholl, H. J. (2012). Understanding smart cities: An integrative framework. In 2012 45th Hawaii International Conference on System Sciences (pp. 2289–2297). IEEE. https://doi.org/10.1109/HICSS.2012.615

CitiVerse. (n.d.). CitiVerse project: Expanding citizen participation through urban digital twins. Retrieved September 20, 2025, from https://xcitecitiverse.eu/

Ntanda, A., & Carolissen, R. (2025). Technology’s dual role in smart cities and social equality: A systematic literature review. Journal of Local Government Research and Innovation, 6(0), a238. https://doi.org/10.4102/jolgri.v6i0.238

Qanazi, S., Leclerc, E., & Bosredon, P. (2025). Integrating social dimensions into urban digital twins: A review and proposed framework for social digital twins. Smart Cities, 8(1), 23. https://doi.org/10.3390/smartcities8010023

Seoul Metropolitan Government. (2023, January 16). Official release of Metaverse Seoul. https://english.seoul.go.kr/official-release-of-metaverse-seoul/