Xano is a no-code backend platform designed for building systems with visual logic, APIs, and modular AI components. Tutorials for the platform are available on
https://www.youtube.com/@nocodebackend
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The discussion began by addressing the challenges posed by companies aiming for universal frontend interfaces, particularly their impact on backend systems. Prakash Chandran, CEO and Co-Founder of Xano, shared insights on this topic.
Chandran’s journey in technology started with early computing, including building PCs and adjusting modem bit rates. He spent nearly a decade at Google, leading UX product design for Google Calendar and then design and research for Google for Business and Education. His background is primarily in frontend and UX. After Google, he founded a startup where he served as a frontend engineer, relying on resources like Stack Overflow. His co-founder and current CTO, who worked on Google Photos and Borg (pre-Kubernetes), provided his backend exposure.
Chandran admitted to frequently “writing checks the backend couldn’t cash” during his frontend career. He noted a common misconception among frontend engineers that all processing can be handled on the frontend, leading to performance issues under load. This often results from excessive client-side JavaScript.
While Server-Side Rendering (SSR) is often seen as a solution, it also faces challenges. If heavy workloads and business logic are not appropriately delegated to the backend, even SSR applications can encounter problems, especially at scale in production environments.
The concept of “ephemeral interfaces” or temporary, AI-generated UIs, as seen in demos like Gemini, is exciting. However, Chandran emphasized that these interfaces are only as powerful as the backend APIs supporting them. It is a misconception to think that frontend developers can create these without considering backend complexities. Issues include the need for robust backend components, permissioning, and the orchestration of various tools and APIs.
User expectations for “time to value” have significantly shrunk. Simply displaying a loading indicator while an API processes is often insufficient. Integrating third-party apps into platforms like ChatGPT requires careful consideration of response times, as users expect instant feedback. The reliance on multiple backends (generative AI for UI, third-party APIs for data) creates a complex challenge for managing latency and user experience.
A more practical approach involves starting at an “atomic” or componentized level. Instead of generating entire web experiences, AI could focus on rendering specific, valuable components, like a booking card. This modular building allows for better control and performance, especially regarding caching. The transition to fully on-the-fly generated interfaces is complex and requires significant problem-solving.
Even without AI, challenges persist in frontend-backend connections. Understanding the full scope of backend capabilities (via SDKs or Swagger specs) is one thing, but prioritizing and ordering APIs for specific frontend components is another. This discovery problem, common in microservices, will only worsen with the introduction of AI agents.
Backend tools need to become more “spec-driven.” Platforms should enable developers to define how APIs are intended to be used by agents, including order and scope. The traditional CRUD operations might not be sufficient in a context-aware, agentic world. Backend creators must consider how AI agents will process and interact with their APIs, leading to more refined and scoped API designs.
While AI can generate backend code, it comes with significant risks. Unless an engineer has extensive experience and trusts the model, relying solely on AI for backend generation is risky. If one does not understand the generated code, they do not truly “own” it. Backend systems are critical for business value, governance, security, reliability, and scalability. AI-generated code, often not trained on production-grade standards, requires careful inspection and supervision, especially for mission-critical applications.
Frontend AI risks are often related to user frustration and brand perception, while backend AI risks involve data security and system integrity. The rapid generation of code by AI can overwhelm human engineers, leading to a reliance on AI code review tools. There’s a concern that companies might push AI adoption too quickly, prioritizing speed over understanding and skill development.
A balanced approach to AI adoption is crucial. While AI can accelerate proof-of-concept development and handle boilerplate tasks, human engineers, especially junior ones, still need to write code to develop their skills and understand the underlying logic. Senior engineers can co-create with AI more effectively, guiding its output. The principle remains: if you don’t understand it, you don’t own it.
Frontend engineers benefit greatly from an intimate understanding of how the backend works, what to expect from it, and how to collaborate with backend engineers. This holistic view ensures a snappy and performant user experience, which is the ultimate goal.
To better understand backend needs, frontend developers are encouraged to: use low-code backend tools (like Xano) to visualize business logic and understand primitives (variables, loops, conditionals); build backend components themselves to gain familiarity; engage with backend engineers to learn about production-grade building, scalability, caching, and indexing; and understand the full lifecycle of a request, not just the frontend widget.
The future IDE for backend creation is envisioned as a “mixed” canvas, combining prompt-response (copilot), traditional code (like Xano script), and visual components for better observability. This aims to provide a more accessible control plane for the next generation of developers, bridging the gap between low-code and complex cloud consoles. The evolving nature of frontend development and user consumption patterns necessitates a re-architecture of frontend systems to be performant in new modalities, always mindful of how the backend serves data.
