DEV Community: OneEntry

Vibe Coding in 2026: What Happens After the First Week

OneEntry — Mon, 10 Aug 2026 14:42:01 +0000

A guide to AI app builders, AI coding editors, and the inevitable ecosystem of services that grows around them

Vibe coding is genuinely a new way to build products. The term emerged in February 2025, and in just a year and a half it has grown into an industry generating hundreds of millions of dollars in revenue. Lovable alone reported $500M in ARR. Cursor became the second most popular editor among developers, while GitHub Copilot is now integrated into nearly every second repository on GitHub.

The first week with almost any vibe-coding tool is usually exciting. You can build an MVP in an hour and a half, a working online store in an evening, or a SaaS product with subscriptions and payments over a weekend.

From the second week onward, however, the problems begin to surface and become more noticeable with each passing week. As they accumulate, one question becomes increasingly relevant — a question that rarely appears in marketing materials: what is everything else built from once it can no longer be generated with a single prompt?

That is exactly what this article is about.

AI App Builders: What They Really Do Well

One of the most prominent categories of vibe-coding tools today is AI app builders. The idea behind them is straightforward: the user describes a product in natural language, and the system turns that description into a working frontend with basic logic.

Their biggest advantage is speed. Where building an initial MVP once required weeks of work and a budget of $3,000–5,000, it is now possible to produce a working prototype in 30–90 minutes. And it is not just something that looks like a product; it is functional enough to test the idea in practice.

By 2026, this market had already expanded significantly, with tools developing different areas of specialization. Lovable has become one of the most popular options and can generate complete Next.js projects with Supabase as the backend. Bolt.new by StackBlitz runs directly in the browser through WebContainers and can spin up a project in seconds. v0 by Vercel is particularly strong at generating React and Next.js components and fits naturally into the Vercel ecosystem.

Replit Agent offers a fully cloud-based workflow that includes deployment, a database, authentication, and file handling alongside the code itself. Base44 by Wix is focused primarily on internal tools and admin panels. Manus operates as an AI agent for generating full-stack applications, Softgen focuses on Next.js, and Rork on React Native mobile apps. Create.xyz / Anything follows the model of a closed full-stack builder, while Figma Make can generate code directly from a Figma design.

Despite their differences, all of these tools share one major strength: they are especially effective when the goal is to turn an idea into something tangible as quickly as possible. They reduce the path from concept to a working result from weeks to hours. And that is exactly what they were built to do.

AI Code Editors: How They Are Changing Development

The second major category of vibe-coding tools is AI integrated directly into the development environment. Here, developers still write the code themselves, but AI takes over a significant share of the supporting work: suggesting and completing code, refactoring, helping identify errors, and preparing documentation.

For a professional developer working with a familiar stack, this can increase development speed by 3–5x.

The main tools on the market in 2026 include:

Cursor — effectively the de facto standard among AI code editors, with particularly strong context handling.
Claude Code (Anthropic) — a CLI and IDE plugins with a deep understanding of code architecture.
Windsurf / Devin Desktop (Cognition) — includes the autonomous Cascade agent.
GitHub Copilot (Microsoft) — has the largest user base and is tightly integrated with GitHub.
Devin (cloud) — an autonomous agent that operates in its own cloud sandboxes.
Cline — an open-source extension for VS Code.
Aider — an open-source CLI tool for the terminal.
Amazon Q Developer — deeply integrated into the AWS ecosystem.

These tools are particularly effective in production development, including refactoring, generating supporting code, testing, debugging, and preparing documentation.

They do not replace developers, but they can significantly accelerate their work.

What Everything Else Is Built From

Both categories of AI tools share the same limitation: they primarily work with code. But a real product is not limited to frontend and business logic.

Behind them sit data, content, payments, files, email, analytics, search, real-time functionality, background jobs, error monitoring, infrastructure, and many other components. And each of these components also has to be implemented somewhere.

In practice, it looks like this:

Almost every layer has several alternatives. And each comes with its own API, documentation, operating logic, pricing model, and update cycle.

As a result, a developer’s job is no longer just to write code. They also need to choose the right service for each layer, connect those services correctly, and then keep the entire system working over time.

What This Looks Like in Real-World Projects

In practice, a stack rarely stops at two or three external services. As long as the product remains simple, that may be enough. But as soon as new requirements appear, the number of tools that need to be connected starts to grow.

Stack A. Minimal SaaS — 5 services

Lovable + Supabase + Stripe + Resend + Vercel

For example, a solo founder launches a habit-tracking SaaS. Lovable handles the frontend, Supabase the database and Auth, Stripe the subscription, Resend the welcome email, and Vercel the hosting.

This is close to the minimum viable setup. In most cases, it is difficult to reduce the stack much further.

Stack B. Indie developer with a production SaaS — 9 services

Cursor + Supabase + Stripe + Clerk + Cloudinary + Resend + PostHog + Sentry + Vercel

Once the product moves beyond the first MVP, the stack begins to grow. A solo founder adds analytics, a separate Auth service because Supabase Auth is no longer sufficient, error monitoring, and image optimization.

The result is already a fully functional production SaaS. A significant share of real-world projects remain at roughly this level.

Stack C. Small business: localized online store — 12 services

Lovable + Supabase + Stripe + Midtrans + Resend + Cloudinary + Sanity + Crisp + PostHog + Algolia + Retool + Vercel

When the product serves a real business, the requirements become broader. For example, the owner of a ceramics studio may need more than just an online store: local payment methods, a blog, customer support chat, analytics, search, and a separate admin panel for an assistant.

Each new business requirement adds another service to the stack. As a result, the number of tools grows almost in proportion to the complexity of the product itself.

Stack D. E-commerce on headless Shopify — 8 services

Bolt + Shopify Storefront API + Sanity + Cloudinary + Klaviyo + PostHog + Sentry + Vercel

In another scenario, a brand uses Shopify as the backend but builds the frontend independently to achieve a unique design. Shopify handles products and payments, Sanity the blog, Klaviyo email marketing, while the remaining responsibilities are distributed across additional services.

This is a common setup for brand-first stores.

Across all of these examples, the same pattern repeats: a real product quickly grows into a stack of 5–12 services. None of them covers the product end to end, and all of the connections between them remain the developer’s responsibility.

Limitations of the Services Themselves

The problem is not limited to integration complexity. As long as the product remains simple, many limitations of individual services are barely noticeable. But as the product evolves into a full-fledged business tool, those limitations become increasingly apparent.

1. Data models are not built for e-commerce

Supabase, Firebase, and Neon provide developers with a database, but not with a ready-made e-commerce data model.

Entities such as orders, order_items, discounts, coupons, subscriptions, refunds, bonus_transactions, and loyalty_tiers still have to be designed from scratch.

As a result, every founder ends up solving many of the same problems again. And business rules quickly become more complex. Even a condition like “a coupon cannot be combined with a seasonal discount unless the order total exceeds $50” can eventually turn into dozens or even hundreds of lines of business logic.

2. There is no production-ready admin panel for non-technical staff

Supabase Studio and Firebase Console are designed primarily for developers.

Giving a content manager or operator access to them can be risky, because they may gain access not only to the data they need, but also to internal system data.

That is when another tool, such as Retool or Forest Admin, gets added to the stack.

It is possible to generate an admin panel with Lovable, but then roles, audit logs, version history, and other production requirements still have to be handled separately.

3. Multilingual support remains DIY

There is no unified approach to i18n across this kind of stack.

In Supabase, translations may be stored in separate columns or translation tables. Sanity has its own configuration. In Stripe, locale is set through the Checkout Session. In Resend, separate email templates have to be maintained.

So adding a second language is rarely a matter of changing a single setting. It affects the data structure, UI strings, CMS, email templates, and part of the business logic.

As a result, localization can easily turn into a separate project lasting several weeks.

4. A mobile app effectively creates a second backend scenario

The issue is not mobile itself, but the fact that APIs and integrations often behave differently from web.

Web OAuth does not work the same way in a mobile app, so a native SDK is required. Stripe Checkout does not work inside an app in the same way it does in a browser and requires PaymentIntents, Apple Pay, or Google Pay. Push notifications introduce FCM or APNs and separate subscription logic. Sessions between web and mobile also need to be synchronized.

So a mobile version is usually not simply a port of an existing web product. In practice, it becomes a second product that uses the same database but requires its own integration logic.

5. Roles and permissions are rarely granular enough

Most BaaS platforms provide basic access levels such as admin and authenticated user.

For a real business, that is usually not enough.

You may need:

a content manager to see only products;
a support operator to work only with orders;
a marketer to access only discounts and campaigns;
a finance employee to see only transactions.

This kind of granular RBAC has to be built manually using Row Level Security in Postgres, custom claims in Firebase, or custom middleware.

6. Bulk operations remain a secondary use case

As long as the system works with small amounts of data, this is barely noticeable. But once thousands of products or records are involved, the limitations become obvious.

Importing 5,000 products through the Airtable API runs into rate limits, batching, and long processing times.

With Supabase, developers have to implement bulk inserts and monitor memory usage.

Sanity provides a CLI with a progress bar, but request limits still remain.

None of these tools was originally designed around bulk operations as a first-class use case. As a result, queues, batching, retry logic, and error handling once again become the developer’s responsibility.

The Final Cost of the “Service Zoo”

As the stack grows, its cost is no longer measured only by the number of subscriptions. It begins to show up in three different ways: money, time, and hidden costs that often become apparent only once the product is already in production.

A. Money

If we take the stacks from the previous section, the approximate monthly costs look like this:

Stack A, 5 services: $50–100 per month;
Stack B, 9 services: $150–300 per month;
Stack C, 12 services: $300–700 per month;
Stack D, headless Shopify, 8 services: $500–1,500 per month.

And these are only the base costs.

Most of these services use usage-based billing, which means the final bill depends directly on load. Firebase charges for reads. Supabase charges for bandwidth and compute. PostHog charges for events. Cloudinary charges for transformations and bandwidth. Sanity charges for API calls.

As traffic grows, costs therefore increase across several services at the same time. And that growth is far from always linear.

B. Time

In practice, time often becomes a more expensive resource than the subscriptions themselves.

On average:

setting up a new service from scratch takes 2–8 hours;
learning a third-party SDK and its specifics takes 4–16 hours;
a single breaking API change can cost 1–3 days of work;
a new business feature that touches 5–7 services can require 2–5 days of integration work, even if AI generated the code itself in an hour.

As a result, in a project built around a large number of external services, a significant share of development time is no longer spent on improving the product itself, but on maintaining the connections between its individual components.

In some cases, this can account for 50–70% of a developer’s working time.

C. Hidden Costs

There are also costs that are much harder to see on an invoice, yet often become the most painful over time.

Vendor lock-in. Migrating from Firebase to Postgres can take weeks. Replacing Sanity with another CMS means rewriting frontend queries and reworking the content. Moving from Stripe to another payment processor means migrating saved cards and subscriptions.

System fragility. If a product depends on twelve external services, there are twelve independent points in the chain where something can stop working.

Cognitive load. A new developer joining the project is no longer learning only the codebase. They also have to understand the surrounding infrastructure, the connections between services, and the rules governing how those services interact.

In the end, the cost of the “service zoo” is not limited to monthly subscription fees. It gradually shifts into maintenance, integrations, migrations, team onboarding, and the constant effort required to keep the entire system running.

Conclusion

Vibe coding genuinely works. Lovable and Bolt can build a working frontend in an hour, while Cursor and Claude Code can significantly accelerate professional development. This is no longer an experiment or short-term hype.

AI can write a function in 15 minutes, but a real product consists of much more than functions. Data, payments, emails, files, content, analytics, search, localization, mobile, admin tools, and other layers are still distributed across 8–13 separate SaaS services.

Each of them:

has its own data model;
uses its own SDK and terminology;
updates independently and can break existing integrations;
knows nothing about the services around it;
has functional limitations that the team must address manually.

Every month, smarter AI builders appear. Every quarter, more autonomous AI agents are released. But the infrastructure layer beneath them barely changes. The same zoo of 8–13 services, connected through a web of separate integrations, remains in place.

So the key question is no longer how quickly AI can write code. The real question is what needs to change beneath that code so that the promised shipping velocity stops being an effect of the first two weeks and can be sustained as the product grows.

And for now, in our view, the industry still does not treat this as a distinct systemic challenge. But without solving it, even the smartest AI agent of 2027 may generate code faster and faster, while a complete product will still take weeks to assemble.

Transforming a Restaurant Website into an Operational Ecosystem

OneEntry — Tue, 07 Jul 2026 11:58:06 +0000

There was a time when it was enough for a restaurant to have a simple website with a menu and a “Call” button. Today, guests want more: to place an order online, schedule a pickup time, book a table, leave a review, earn and spend points. All of this happens on a mobile phone and takes just a few seconds. At the same time, the restaurant owner faces many tasks: sales tracking, inventory management, interaction with delivery providers, bonus accrual, marketing.

The market offers hundreds of ready-made solutions: POS terminals, CRM systems, delivery apps, loyalty programs. But at the same time, each service lives its own life. As a result, restaurants use dozens of tools that are often not connected to each other: some for schedules, others for inventory, and others for orders. Data has to be transferred manually, which leads to errors and wasted time. And adding a new service turns into yet another expensive integration project.

For developers, the problem usually looks different. Most modern restaurant applications are not about a complex user interface, but about a large number of integrations with external systems, each of which evolves independently and has its own limitations. Over time, it is the architecture of these integrations that becomes the main source of complexity.

Where the Complexity Lies

The main challenge is connecting all operational systems in such a way that customer-facing applications remain simple. If an application knows how to communicate with every POS system and delivery aggregator, any new service or API change risks bringing everything to a halt.

In practice, this means that the frontend begins to take responsibility for processes that have nothing to do with the user interface: handling various integration scenarios, dealing with the specifics of individual POS systems, accommodating differences between vendor APIs, and managing numerous edge cases. This approach quickly leads to increased code complexity and makes the application more difficult to maintain and evolve.

We believe that the frontend should remain lightweight, while all the complexity should be hidden in the backend. This makes it possible to develop the interface and functionality independently of integrations.

Our Approach: The Backend as a Synchronization Layer

We built a platform that acts as a unified synchronization layer between the point of sale, delivery services, payment systems, loyalty programs, and analytics. The purpose of this layer is to collect data from different sources, transform it into a unified format, and deliver it wherever it is needed.

The idea is to ensure that client applications never communicate directly with external services. Instead, they interact with a single API, while all integration logic, data transformation, and error handling are centralized in one place.

This simplifies operations:

Orders from different channels are automatically sent to the POS system and the kitchen.
Menus, promotions, and prices are updated in one place and instantly reflected across all channels.
Sales, inventory, and customer data are consolidated into a single reporting system.

We use proven architectural patterns: middleware for data exchange, message queues, and reliable APIs.

This approach makes it possible to add new services or replace existing ones with minimal impact on client applications. Changes remain isolated within the integration layer instead of propagating throughout the entire application.

Even if a service is temporarily unavailable, the platform continues to operate, and the data will be delivered once the connection is restored.

Our Experience: A Demo Project Built with Next.js

To validate our ideas, we built a demo project using Next.js. It was important for us not just to build another restaurant application template, but to validate whether the chosen architecture could withstand the growing complexity of real-world business logic.

Our goal was to answer several questions:

Can the frontend remain as flexible as possible, even as business logic becomes more complex?
How can we prevent the backend from turning into a collection of disconnected services?
How can we ensure that integrations do not "leak" into the user interface?
And how can we move away from a rigidly defined data structure without having to rewrite the code every time a new attribute is added?

As part of the experiment, we built a demo application, which you can explore here:

https://oneentry-nextjs-restaurant-demo.vercel.app

The project landing page explains the purpose of the template—to demonstrate how OneEntry works in a real application and highlight its customizable design, flexible functionality, and centralized data management:

https://oneentry-free-template-restaurant-nextjs.vercel.app

The source code is available on GitHub: it is a reference restaurant storefront built with Next.js and React, fully integrated with OneEntry:

https://github.com/ONEENTRY-PLATFORM/nextjs-restaurant-demo

The repository demonstrates how the menu, product catalog, shopping cart, table reservations, and promotional pages are built from data managed in the OneEntry admin panel.

It’s important to note that this project wasn’t built to showcase a user interface. Instead, it was created to validate architectural decisions in an environment as close as possible to a real-world production project.

While working on the demo, we realized that the problem is not the user interface. A simple Dish model with the fields title, description, and price quickly becomes insufficient: attributes such as spiciness, allergens, calories, preparation time, meal sets, availability flags, service tags, delivery settings, promotional badges, and regional pricing begin to appear.

At the same time, the attributes themselves are constantly evolving. One project may introduce new delivery parameters, another may require additional dish properties, while a third may impose integration-specific requirements. If the data model is rigidly defined, changes like these quickly begin to affect the entire application.

In traditional applications, this leads to constant changes to the schema, APIs, and validators until the code becomes filled with special cases and exceptions.

We came to the conclusion that the frontend should not be aware of operational logic, specific POS providers and courier service APIs, hard-code entity structures, or store application-specific workflows. It should remain the application layer responsible for the user experience. All operational complexity should reside in the backend. This is exactly the architecture that OneEntry was originally designed for.

This experiment ultimately confirmed our belief that the scalability of systems like these is determined not by the number of components, but by how clearly responsibilities are separated between application layers and how effectively business logic is isolated from the user interface.

Flexible Menu Management and Dynamic Attributes

One of the biggest challenges restaurants face is managing menus across multiple platforms. In many cases, every change—whether it is a new price, a seasonal dish, or a promotion—requires numerous manual updates. This is because many POS systems have a static structure: changing the context requires creating duplicate menu items.

We offer a different approach. Dynamic menu elements make it possible to assign an unlimited number of attributes to dishes: time of day, sales channel, delivery type, price, image, and more. As a result, the same dish can be displayed differently depending on the context.

From an architectural perspective, this means that the application's structure is no longer tied to a predefined data model. Instead of modifying the database schema or API, developers can extend the model by adding new attributes without changing the existing application code.

Adding or modifying attributes requires neither database migrations nor system downtime.

In addition, our system allows dynamic attributes to be added to any object (menu items, orders, users). For example, dishes can be marked as vegan, assigned regional pricing, or tagged for analytics. All of this is done through the administrative interface without the involvement of developers.

This approach proved especially valuable when working with integrations, where different external systems impose different data structure requirements. Instead of creating separate models for each provider, it is enough to define the required attributes at the configuration level.

A Unified Management Center

The platform enables you to manage everything related to a restaurant’s digital presence from a single interface:

Menu and Products. Create and update dishes, assign attributes, and launch promotions.
Orders and Payments. Receive orders from all channels in the POS system, track their status, and manage deliveries.
Loyalty and Marketing. Configure loyalty programs, segment customers, and launch personalized offers.
Reports. Access up-to-date data on sales, inventory, and channel performance.

All information is consolidated into a single repository, making it possible to make decisions quickly and maintain full control over the business. At the same time, our approach preserves flexibility: you do not have to abandon your existing services, we can integrate with them through a standardized layer.

For developers, this means that adding a new integration does not require reworking the user interface or the application's business logic. Most changes remain isolated within the integration layer.

Conclusion

The restaurant industry is evolving rapidly, and customer expectations are rising along with it. Today, success depends not only on the quality of the food or service, but also on how effectively a business manages its digital processes. OneEntry allows businesses to focus on the customer experience by moving technical complexity into a reliable backend, where all elements of the business model work together as a unified ecosystem.

Working on this project reinforced our belief that the scalability of systems like these is determined not by the number of integrations, but by an architecture that allows those integrations to evolve independently. That is why, from the very beginning, we designed OneEntry around a clear separation of responsibilities between the frontend, business logic, and external services.

System First Architecture for the AI Era

OneEntry — Fri, 22 May 2026 11:56:27 +0000

Introduction: A New Era in Search and Digital Architecture

On May 19, 2026, Google announced at I/O that search is entering an era of AI Mode, where users can create and manage search agents that continuously gather information and act on tasks in the background https://search.google . In this model, the familiar chain “person → Google → list of links” is giving way to AI-driven discovery: agents search, compare, analyze data, and even perform actions for the user . This shift suggests that digital products must expose their data, processes and actions in structured, machine readable ways to be visible to AI agents.

At OneEntry, we recognised early that page centric architectures would not meet these requirements. Our platform was built around a system first approach, focusing on well-defined entities, workflows, statuses, and integrations rather than pages and buttons. We believe that products and services built with these principles — whether on OneEntry or elsewhere — are better prepared for this new world: their backends expose clear processes that AI agents can interact with, helping ensure they remain discoverable and automatable in an AI driven search environment.

Below we provide a technical deep dive into how we structure systems for AI readability, using OneEntry as a concrete example.

How to Make a System Understandable to AI: Schemas, Events and Integrations in OneEntry

In the first article we found that the emergence of agentic AI changes not only search mechanics but also the approach to designing digital products. A page, a button, a form and even a single API method are no longer sufficient when an intelligent agent interacts with the system instead of a person. This article continues the first one. We move from general principles to the technical side: how to describe entities, statuses, actions and events so that AI agents can work with the system without guesswork or chaos.

Backend Is Not Just a Database

During the development of OneEntry we saw that the backend is no longer a passive storage of data. It becomes the operational layer of the system: it manages statuses, orchestrates workflows and connects integrations. The structure of a system is not only table fields but also processes, relationships and events. The task of the backend is to provide a single description of this structure in a form understandable to both people and machines. Such structured, machine readable documentation helps people and AI agents stay in sync and productive. That is why in the modern spec driven approach API contracts (OpenAPI, AsyncAPI) are considered “first class citizens”: they should be versioned and used as the single source of truth, with compatibility verified through contract testing.

How It Looks Technically

1. Entities and Attributes: The Structure Lives in the Backend

In OneEntry the frontend does not describe all fields manually. The model of an entity is stored in the backend: a list of attributes, their types and rules. For example, at first the model of a dish might look like this:

// Entity: Dish
type Dish = {
  title: string;
  description: string;
  price: number;
};

But in real life new characteristics appear: sizes, spiciness, allergens, availability, regional prices, kitchenStatus, deliverySettings. The structure of the entity in OneEntry is stored centrally, so if you need to add a new field, for example kitchenPreparationTime, you do not need to rewrite the entire frontend: you add the attribute to the entity description in the backend, and the form automatically picks up this parameter. This model makes work easier for developers and AI agents, which can dynamically discover the structure of entities via schemas.

2. Forms and Workflows: The Backend Knows Not Only Fields but Also Processes

Not only data are important but also the behaviour of the system. In OneEntry forms (for example, a reservation form) are stored not just as UI templates. The system knows:

what fields the form contains;
what validations apply to each field;
what happens after the form is submitted;
what events are triggered.

This means the backend stores the workflow of the form. When an application is submitted, checks, calls to external services and generation of events may occur. All this is described structurally. An AI agent can obtain this information from a schema and understand which steps need to be executed.

3. Events: The Event Model Helps AI Understand Processes

Every change of state in OneEntry is recorded as an event. For example, the order process can be described by events:

OrderCreated
↓
PaymentInitialized
↓
KitchenNotified
↓
CourierRequested
↓
CustomerNotified

An event is an immutable record with a name, timestamp and payload structure. Agents subscribe to these events and react to them. Event driven architecture (EDA) reduces latency by 70–90 % compared with polling because agents subscribe to events instead of constantly checking for changes . Confluent notes that replacing RPC calls with event driven architecture eliminates rigid dependencies and provides flexibility and scalability .

Events have schemas: the structure specifies the event name, timestamp, source, payload fields and version. Well defined schemas allow producers and consumers to evolve independently without coordination . The event bus receives events from producers and delivers them to all subscribed consumers. This decoupling gives EDA its scalability: producers do not need to know which agents will consume their events, and consumers do not need to know which systems produce the events they care about . New agents can be added to a topic without modifying existing components.

4. Integrations: Part of the System, Not External Black Boxes

OneEntry knows which integrations are connected (payment gateways, delivery, notifications), what actions they have and which events they process. This is described in the structure of the system, so an AI agent does not need to “guess” where payment logic is or where delivery logic is. It sees integrations as part of the workflows described in the schemas.

5. AI Schema, OpenAPI and MCP: How AI Learns the Structure

AI cannot simply “look at a site” and understand how the backend is arranged. Therefore the system must provide a machine readable description of its structure. In OneEntry this is done through several layers:

OpenAPI/Swagger for REST endpoints: AI can load /openapi.json and see the full API contract (methods, parameters, data models).
MCP (Model Context Protocol) or AI schema: these are extended schemas describing entities, their fields, relationships, statuses, allowed actions and related workflows. Confluent notes that MCP provides a universal standard for integrating AI systems with external tools, data sources and applications, ensuring secure and seamless access to up to date information .
Event schema registry: stores event schemas so that agents can validate and evolve message consumption.

Example: a restaurant application on OneEntry contains entities Order, Reservation, Payment, Delivery, Statuses, Events. Based on these data the system automatically generates an AI schema. When an agent requests /ai-schema, it receives a structured representation, for example:

{
  "entity": "Order",
  "actions": [
    "create",
    "cancel",
    "track"
  ],
  "statuses": [
    "created",
    "paid",
    "delivering",
    "completed"
  ]
}

The agent now understands that there is an Order entity, what statuses it has, what actions are available and what transitions are possible. It can correctly call API methods, subscribe to events OrderCreated, PaymentInitialized, DeliveryRequested and orchestrate subsequent steps.

Why This Matters for OneEntry and the AI First Era

The main advantage of OneEntry is that the system stores the structure of the project within itself. It knows its entities, attributes, forms, statuses, relationships, events and workflows. Business logic is not scattered across the frontend, middleware, scripts and separate APIs; it is centralised. Thanks to this, OneEntry is much easier to adapt to the AI first era than classic systems where logic lives in various layers.

In OneEntry the basis is not pages but entities and processes: users, orders, forms, statuses, events, integrations, workflows. The frontend remains important because people still need a convenient UX. But its role changes and it becomes a layer of interaction and visualisation. Business logic, orchestration and integrations remain in the backend, which provides understandable actions for people and AI agents.

The clearer a system describes its entities, statuses, workflows, events and relationships, the easier it is for AI to perform actions, automate processes and make decisions. Systems with clear models, predictable structures, workflows, event mechanisms and described integrations are much better prepared for an AI first internet, where agents will become the new “users,” backend systems will be more important than HTML pages and workflows will be more important than individual endpoints.

Conclusion

The rise of AI driven search and autonomous agents means our systems can no longer be “black boxes” filled with custom logic. To be discoverable and operable in this new landscape, backends need to be system first: centred on clear data models, explicit workflows, machine readable APIs and events. That isn’t about buzzwords - it’s about creating software that other systems (including AI agents) can understand and work with reliably.

We’ve shown how OneEntry approaches this: entities live in the backend rather than the UI, workflows and forms are defined alongside data structures, and events provide a decoupled way for agents to react to changes. These patterns aren’t proprietary; they’re emerging best practices for any team building modern APIs. Whether you use OneEntry or roll your own, focusing on structured schemas, strong event contracts and clear workflows is the practical foundation for AI ready applications.

Sources and Materials

The Future of AI Agents Is Event Driven — Confluent https://www.confluent.io/blog/the-future-of-ai-agents-is-event-driven/
Event Driven Architecture for AI Agents: Patterns and Benefits — Atlan https://atlan.com/know/event-driven-architecture-for-ai-agents/
Spec Driven Development for AI Enabled Enterprise Systems — Microsoft Community Hub https://techcommunity.microsoft.com/blog/educatordeveloperblog/spec-driven-development-for-ai-enabled-enterprise-systems/4520807
Google Search’s I/O 2026 updates: AI agents and more https://blog.google/products-and-platforms/products/search/search-io-2026/
Google Search Goes Agentic—and Doesn’t Need You Anymore — WIRED https://www.wired.com/story/google-search-goes-agentic-and-doesnt-need-you-anymore/

How to use MCP with OneEntry to accelerate frontend development

OneEntry — Wed, 08 Apr 2026 10:38:37 +0000

AI tools are becoming increasingly capable at writing code, explaining APIs, and suggesting implementation approaches. However, when it comes to real projects, they often run into the same limitation: they do not understand the structure of the specific platform they are working with.

This is where MCP (Model Context Protocol) becomes useful — a way to provide an AI tool with access to the actual project structure, including entities, relationships, and available actions within the platform.

When used with OneEntry, MCP can help AI tools interact with platform entities, workflows, and the overall project structure in a more practical way. However, it is important to clarify from the start that MCP is a supporting layer in the development process and does not replace a frontend developer. It can reduce routine work, speed up project onboarding, and assist with repetitive implementation tasks, but architecture, interface decisions, business logic, and the final product quality remain the responsibility of the developer.

What MCP changes in practice

A standard AI assistant can already help with code examples or explain certain concepts. However, without access to the actual project structure, it still mostly operates in a guess-based mode.

With MCP, the assistant can work in a more structured way. Instead of providing only general advice, it begins to understand what entities exist within the platform, how they are connected, and what actions are available.

For a platform like OneEntry, this is important because real projects are built around specific entities, such as authorization and registration, pages, blocks, products, attributes, forms, orders, and integrations.

When AI can take this structure into account, its assistance becomes significantly more useful and accurate.

Why this is useful for frontend development

Frontend teams do not need an AI that replaces them, but one that removes unnecessary friction from their workflow. A frontend developer still needs to design the component tree, define interface behavior, handle performance considerations, and ensure that the product is actually usable for end users. MCP does not do any of this, but it can help with the tasks that tend to slow developers down (using OneEntry as an example):

Understand how OneEntry entities are structured within a project;
Map data structures to frontend components;
Generate boilerplate for working with API responses;
Suggest how to render pages, products, or content blocks;
Help identify which fields and attributes are available;
Speed up debugging when data is missing or incorrectly mapped.

Practical examples

Here are several practical examples where MCP can simplify a developer’s work with OneEntry.

1. Faster understanding of project structure

A developer joins a project and needs to understand how product data is organized. Instead of navigating through different sections of the admin panel, checking documentation, and manually tracing relationships, the assistant can help answer questions such as:

What attributes are attached to this product template?
What fields are used on this page?
What types of blocks are used on the homepage?

2. Generating starter code for components

Imagine a developer needs to build a product card or a dynamic page block in Next.js. MCP can help the assistant understand the expected data structure from OneEntry and generate a useful starting boilerplate.

3. Assistance with content-driven pages

In headless projects, frontend developers often spend time aligning content models with rendering logic. If the assistant understands what pages, blocks, and attributes exist in OneEntry, it can help suggest how to structure the rendering process.

For example:

Which template should be used for the page?
Which attributes need to be passed into the component?
How should optional or nested content be handled?

4. Debugging field mapping issues

One of the common issues in headless development is that the frontend expects one field while the backend returns another. Or a value exists in the admin panel but is attached to a different entity than expected.

This is exactly the type of routine complexity where MCP can be especially useful. It helps reveal how data is actually structured in OneEntry and guides the developer in the right direction more quickly.

5. Supporting onboarding for new team members

For teams working on OneEntry-based projects, onboarding can take time. New developers need to understand modules, templates, relationships, and how content is structured.

MCP can simplify this stage by acting as an assistant that understands the project. Not as a decision-maker, not as an architect, but as a faster guide through the system.

Why this works well with OneEntry

OneEntry fits well with MCP-like scenarios because the platform itself is built as a structured, API-first system.

Projects within it are organized around clearly defined entities and the relationships between them. This makes it easier to provide AI tools with meaningful actions and project context in a controlled way.

This is especially important for frontend teams, as OneEntry is often used as the backend layer for websites, storefronts, and applications, where the frontend remains fully custom.

This separation is critical. It means that MCP can help developers work more efficiently with the backend structure, while the frontend team remains fully responsible for the user experience.

MCP is not a replacement for a frontend developer

At the end of this article, we would like to emphasize this point once again.

There is a lot of discussion today about AI replacing developers, but for real product development, this is an oversimplified view.

In practice, frontend development involves choosing approaches and trade-offs, making UX decisions, working with state and performance, maintaining consistency, understanding business context, and handling edge cases that do not fit into standard patterns.

It is important to understand that MCP does not replace a developer’s work, but rather helps with specific tasks. The most accurate way to look at it is this: MCP provides AI with more useful context, while responsibility for the product still remains with the developer.

Conclusion

For teams using OneEntry, MCP can make AI tools more practical in day-to-day development. It can accelerate project onboarding, reduce the amount of repetitive work, and help bridge the gap between the platform structure and frontend implementation.

How to Build User Authentication and Account Management in a Headless Architecture

OneEntry — Fri, 13 Mar 2026 13:46:01 +0000

Almost every developer has implemented an authentication system at least once: login forms, OAuth providers, user profiles, roles, and session management. While at first glance this may seem like a small piece of functionality, in practice it quickly turns into a separate subsystem of the application. As the product evolves, the requirements for this part of the system continue to grow.

There is a need to support social logins, implement registration via email and password, store user profile data, manage access through roles, and ensure secure handling of tokens and sessions.

In traditional architectures, all of this logic is usually implemented inside the backend application. However, when using a headless approach, authentication becomes part of the platform infrastructure rather than a separate module inside the application. This allows responsibilities to be separated: the frontend remains responsible for the interface and user experience, while the backend platform manages user identification, access rights, and security.

In this article, we will examine how to implement a user account system based on OneEntry, a backend platform built on the principles of headless architecture and cover:

connecting OAuth providers (Google, GitHub, etc.)
configuring authentication in the platform admin panel
implementing login via SDK
managing user profiles and custom attributes
configuring roles and access permissions
ensuring secure handling of user sessions

Configuring Authentication Providers in OneEntry

Once the authentication architecture has been defined, the next step is to configure the login mechanisms in the system. In the headless approach, this configuration is performed not in the application code but at the backend platform level.

In OneEntry, authentication management is handled through the administrative panel. Here you can define which login methods will be available to users, for example login via OAuth providers or local authentication using email and password.

Each such mechanism is implemented through an authentication provider — a separate configuration that defines the method of user authentication and the parameters of interaction with the system.

Creating and configuring a provider is done in the Users → Authentication Providers section of the administrative panel. Below is an example of creating and configuring an authentication provider in the OneEntry interface.

Video: Creating an authentication provider

Video: Editing an authentication provider

Connecting a Third-Party Provider (Google OAuth Example)

One of the most common authentication methods in modern applications is logging in through third-party services such as Google, GitHub, or other OAuth providers. This approach allows users to quickly authenticate without the need to create a separate account.

To use Google OAuth, you must first create an OAuth client in the Google Cloud Console. In the settings, you specify the redirect address to which the user will be returned after successful authentication.

For example: https://your-app.com/oauth/callback

After creating the client, Google provides two key parameters: Client ID and Client Secret. These values are used to configure the authentication provider on the platform side.

Documentation: https://doc.oneentry.cloud/docs/users/examples/google

After obtaining the OAuth client parameters, the provider is added in the OneEntry administrative panel. In the Authentication Providers section, a new OAuth provider is created where the Client ID, Client Secret, and redirect address are specified.

Once the provider is activated, the platform begins handling the entire OAuth process: redirecting the user to the provider, receiving the authorization code, and exchanging it for a token.

Implementing Login via SDK

After configuring the provider at the platform level, authentication can be integrated into the application using the OneEntry JavaScript SDK.

First, install the SDK:

npm install oneentry

Next, you need to initialize the connection to the project:

import { defineOneEntry } from "oneentry";

const { AuthProvider, Users } = defineOneEntry('your-project-url', {
  token: 'your-api-token',
});

Starting OAuth Authentication

After the user completes authentication with the external provider, the application receives an authorization code, which must be passed to the platform.

const body = {
  code: "...",
  redirect_uri: "http://localhost:3000"
};

const response = await AuthProvider.oauth('marker_auth_provider', body);

The platform exchanges the code for a token and creates a user session. After that, the user is returned to the application.

Retrieving the Current User

After successful authentication, user data can be retrieved through the SDK:

const user = await Users.getUser(langCode);
console.log(user);

SDK Documentation:

https://js-sdk.oneentry.cloud/docs/category/authprovider
https://js-sdk.oneentry.cloud/docs/users/

Local Authentication (Email / Password)

In addition to OAuth providers, the platform also supports traditional login scenarios using email and password.

This can be useful in cases where the application requires separate user registration.

const { AuthProvider } = defineOneEntry(PROJECT_URL, {
  token: APP_TOKEN,
});

const body = {
  authData: [
    {
      marker: "login",
      value: "example@oneentry.cloud"
    },
    {
      marker: "password",
      value: "12345"
    }
  ]
};

const response = await AuthProvider.auth('email', body);

Documentation: https://js-sdk.oneentry.cloud/docs/auth-provider/auth

User Profile Management

After authentication, the application gains access to user data and can work with the user’s profile.

For example, retrieving the current user data:

const profile = await auth.getCurrentUser();

Updating profile data is performed using the updateUser method:

const body = {
  formIdentifier: "reg",
  authData: [
    {
      marker: "password",
      value: "yourPassword"
    }
  ],
  formData: [
    {
      marker: "last_name",
      type: "string",
      value: "Username"
    }
  ],
  notificationData: {
    email: "example@oneentry.cloud",
    phonePush: ["+1234567890"],
    phoneSMS: "+1234567890"
  },
  state: {}
};

const response = await Users.updateUser(body);

Documentation: https://js-sdk.oneentry.cloud/docs/users/updateUser

Extending the User Model

The structure of the user profile in OneEntry is not fixed. It can be extended directly from the administrative panel.

In the User Attributes section, additional fields can be added, for example: avatar, phone number, date of birth, or any other profile attributes. Each field can have its own data type and validation rules. This makes it possible to extend the user model without modifying backend code.

Role-Based Access Control (RBAC)

Many applications require restricting access to different parts of the system. A simple role check may look like this:

const user = await auth.getCurrentUser();

if (user.role !== "admin") {
  throw new Error("Access denied");
}

Roles and access permissions are configured in the platform’s administrative panel. Below is an example of configuring roles and access permissions in the OneEntry interface.

Video: Configuring roles and permissions

Documentation: https://doc.oneentry.cloud/docs/users/permissions

Logout and Session Management

Ending a user session is performed through the SDK:

AuthProvider.logout('marker_auth_provider', 'token');

Token refresh and session lifecycle management are handled automatically by the platform when the SDK is configured correctly.

Conclusion

In this article, we schematically showed how our team implemented the authentication and user account system within the platform. Our goal when designing this architecture was to solve a typical problem that teams face as digital products evolve — the increasing complexity and fragmentation of the logic for managing users, roles, and sessions within the application.

In most projects, user, role, and session management gradually turns into a complex set of disconnected solutions that have to be maintained alongside the application. In OneEntry, this functionality is moved to the platform level, allowing developers to focus on product logic and the user interface.

For businesses, this approach results in a more stable application architecture. The user account system and user management become not a separate module within the project, but part of the platform infrastructure that can grow and scale together with the product.

We hope this article was helpful and helped clarify the approach to implementing authentication and user account systems in a headless architecture. We will be glad to hear your questions and feedback.

Managing Data Quality in a Headless Architecture

OneEntry — Tue, 24 Feb 2026 14:47:54 +0000

In the previous article, we explored how OneEntry allows you to manage data structure without changing frontend code: adding attributes, grouping them using markers, and building a dynamic interface driven by the model. This provides flexibility and makes it possible to evolve the product without constant deployments. However, once data becomes dynamic, the focus shifts to ensuring correctness and maintaining predictable system behavior.

If fields can be freely added and modified, who guarantees that invalid values will not end up in them? Where should rules for required fields, formats, and value ranges live? How can UI behavior be controlled through the data model without reintroducing duplicated logic into frontend code?

In a mature headless architecture, responsibility for data integrity cannot reside solely in the interface. It must be embedded in the model itself. In OneEntry, this role is fulfilled by validators and additional attribute fields — mechanisms that allow you to manage data quality and UI behavior at the architectural level rather than at the level of individual components.

Validators as Part of the Data Model

In most projects, validation logic is implemented on the frontend: required fields, email format checks, minimum string length, numeric ranges. This works well at the beginning because the logic sits close to the form, errors are immediately visible to the user, and developers do not have to think about deeper levels of control.

In practice, the problem appears later. If validation exists only in the interface, the data remains fundamentally unprotected. The API can be called directly, bypassing the UI. The frontend itself may change — a mobile application might be introduced, another client added, or an integration with an external system implemented. As a result, validation logic starts to be duplicated across different codebases, diverges over time, and gradually loses consistency. In such a model, responsibility for data integrity becomes fragmented, and the architecture grows dependent on a specific interface implementation.

In OneEntry, validators are defined at the data model level. Validation rules become part of the structure itself rather than an additional layer placed on top of it. This means correctness is enforced regardless of which client interacts with the API. This is where the architectural shift occurs: the model stops being a passive container of fields and begins to define the rules under which data can exist within the system.

Form Validators: Retrieving Configuration from the API

Let us look at a practical example. Suppose the system contains a form called contact_us. On the frontend, we do not define its structure manually. Instead, we retrieve its configuration from the API through the SDK:

const form = await api.Forms.getFormByMarker("contact_us", "en_US");

The response contains an attributes array, and each attribute already includes its validators as part of the model:

{
  "identifier": "contact_us",
  "attributes": [
    {
      "type": "string",
      "marker": "email",
      "validators": {
        "requiredValidator": { "strict": true },
        "emailInspectionValidator": true
      }
    },
    {
      "type": "string",
      "marker": "name",
      "validators": {
        "requiredValidator": { "strict": true },
        "stringInspectionValidator": {
          "minLength": 2,
          "maxLength": 100
        }
      }
    }
  ]
}

This means the frontend receives not just a list of fields, but a fully declarative model with embedded rules. Based on this model, the form can be generated automatically, a validation schema can be assembled dynamically, required fields can be highlighted, and regular expressions or constraints can be applied for client-side validation. The interface becomes an interpreter of configuration rather than a source of business logic.

However, the key point is not the convenience of form generation. Even if the frontend fails to validate the data for any reason or is only partially implemented, OneEntry will still enforce the validators when the data is saved. Validation is embedded in the model and executed at the system level, not at the level of a specific client. This is what distinguishes a declarative architecture from a merely dynamic UI.

Form Submission and Server-Side Validation

Form data is submitted via FormData.postFormsData:

const body = {
  formIdentifier: "contact_us",
  formModuleConfigId: 2,
  moduleEntityIdentifier: "blog",
  replayTo: null,
  status: "sent",
  formData: [
    { marker: "email", type: "string", value: "wrong-email" },
    { marker: "name", type: "string", value: "J" }
  ]
};

const result = await api.FormData.postFormsData(body);

In this example, invalid values are intentionally passed: an email without a valid format and a name string that does not meet the minimum length requirement. If the data violates the rules defined in the model, the record is simply not accepted. The API returns an error, and the object is not stored in the system.

It is important to understand the boundary of responsibility here. The frontend may perform preliminary validation — this improves UX and allows errors to be displayed before submission. But the final decision always belongs to the data model, because it defines which values are acceptable and ultimately guarantees the integrity of the storage layer.

As a result, the API remains protected regardless of the client. It does not matter whether the request comes from a web interface, a mobile application, or a direct API call. Validators function not as a supporting layer for the UI, but as part of the persistence mechanism itself. This transforms the model from a passive description of structure into an active mechanism for enforcing data quality.

An Attribute Is More Than Just a Field

So far, we have discussed validation as a mechanism for controlling values. In OneEntry, however, an attribute is structured much more deeply than a typical “form field” or a database column. It is not limited to storing a value.

An attribute includes a marker that defines its identifier within the model, a type that specifies the data format, a position that affects display order, additionalFields for extending behavior, and metadata required for client-side interpretation. This makes it more than a simple data container, turning it into a structural unit with its own rules and context.

This approach changes how the model is perceived. An attribute becomes an independent entity capable of influencing UI behavior and rendering logic, not just holding a value. To see how this works in practice, let us look at a concrete example below.

Example: Image Attribute with Fallback Logic

Let us look at a more illustrative scenario. A product has an attribute called card_image of type image. The requirement is simple: if the main image is missing, a fallback image should be used; if that is also unavailable, a text message should be displayed instead. In a traditional architecture, this is typically handled in the frontend through conditional checks and hardcoded values. As a result, rendering logic ends up living in the code rather than in the data model.

In OneEntry, this problem is approached differently. Additional fields such as fallback_image and fallback_text can be attached to the attribute. They become part of the model and are delivered together with the primary value through the API.

Below is an example of a response (simplified, but close to the actual SDK format):

{
  "attributeValues": {
    "card_image": {
      "type": "image",
      "value": [
        {
          "filename": "files/project/product/77/image/main.png",
          "downloadLink": "https://project.oneentry.cloud/cloud-static/files/project/product/77/image/main.png",
          "size": 392585
        }
      ],
      "position": 0,
      "additionalFields": [
        {
          "marker": "fallback_image",
          "value": [
            {
              "filename": "files/project/system/fallbacks/no-image.png",
              "downloadLink": "https://project.oneentry.cloud/cloud-static/files/project/system/fallbacks/no-image.png",
              "size": 10240
            }
          ]
        },
        {
          "marker": "fallback_text",
          "value": "Image temporarily unavailable"
        }
      ]
    }
  }
}

What matters here is not the existence of a fallback itself, but the fact that the behavior is defined at the data level. The attribute contains not only the primary value, but also additional elements that determine how the system should respond in different states. Rendering logic becomes part of the model rather than a set of conditional statements embedded in the interface.

Using This in the Frontend

On the frontend, we fetch the product data through a standard SDK request:

const response = await api.Products.getProductsByPageUrl(
  null,
  "en_US",
  { pageUrls: [slug], limit: 1, offset: 0 }
);

const product = response.items[0];

Next, we extract the required attribute and interpret its structure. The main image is stored in value, while the fallback configuration comes through additionalFields:

const imageAttr = product.attributeValues?.card_image;

const mainUrl = imageAttr?.value?.[0]?.downloadLink;

const fallbackUrl =
  imageAttr?.additionalFields
    ?.find(f => f.marker === "fallback_image")
    ?.value?.[0]?.downloadLink;

const fallbackText =
  imageAttr?.additionalFields
    ?.find(f => f.marker === "fallback_text")
    ?.value ?? product.title;

Rendering remains straightforward. We simply select the available source, and use onError as a safeguard in case the primary image fails to load:

<img
  src={mainUrl || fallbackUrl}
  alt={fallbackText}
  onError={(e) => {
    if (fallbackUrl) {
      e.currentTarget.src = fallbackUrl;
    }
  }}
/>

From an architectural perspective, something else matters here. The frontend does not know in advance whether a fallback exists or how it is structured, because it simply reads the model. Rendering logic is defined by the data itself, which means it can change without modifying the code or triggering a deployment. UI behavior is configured through the admin panel, while the interface remains stable and predictable, interpreting configuration rather than embedding it internally.

Architectural Implications: Control, Predictability, and Separation of Responsibility

When validators and additional fields are viewed not as isolated features but as architectural elements, it becomes clear that they solve a systemic problem. The data model begins to govern not only structure, but also data quality, UI behavior, and fallback scenarios. Rules are no longer scattered across components and services; they are centralized and declaratively defined at the model level.

This changes the role of the frontend. It stops being the place where data is validated, patched, and safeguarded with dozens of conditional statements. Instead, the interface becomes an interpreter of the model: it receives configuration, reads the rules, and applies them correctly. Responsibility for integrity shifts to the architectural layer rather than remaining at the implementation level.

For the business, this translates into reduced operational risk. There are fewer form errors, fewer lost submissions caused by invalid data, and fewer incidents resulting from human error or inconsistencies in logic across different clients. Changes can be implemented faster because rules can be adjusted within the model without modifying the codebase or issuing new releases.

For developers, the impact is just as significant. The amount of defensive code decreases, the need to duplicate validation logic across multiple interfaces disappears, and the architecture becomes cleaner and more predictable. Instead of constantly “adding one more validation check,” there is a single point of control for rules. As a result, the number of minor tasks related to validation and logic alignment is reduced, allowing the team to focus on meaningful product development.

Core Idea: The Model as the Center of Architecture

The headless approach is often perceived in a simplified way — as an API replacing templates and the freedom to choose frontend technologies. But its architectural value does not lie in separating rendering; it lies in separating responsibility. The real shift happens when data, its structure, validation rules, and UI behavior can evolve independently of frontend code.

In OneEntry, an attribute is not just a field with a type and a value. It is a managed entity that includes validators, additional fields, and metadata, and therefore can influence how data is interpreted and rendered. The model stops being a static schema description and becomes an active participant in the architecture.

This is what makes it possible to evolve data independently of the interface. The UI can change, be rewritten, or scale across new channels, while rules and logic remain centralized. As a result, headless stops being merely a technological choice and becomes a system design principle in which the data model serves as the point of control rather than a byproduct of implementation.

Thank you for taking the time to read this article. If it resonated with you, we would appreciate your like or comment. 🙌💬

How to Manage Website Data Without Touching Frontend Code

OneEntry — Tue, 10 Feb 2026 10:39:27 +0000

A Practical Headless Approach Using OneEntry

In most modern projects, frontend and backend are already architecturally separated. We use headless CMS platforms, API-first approaches, SDKs, and frameworks such as Next.js or Nuxt, and this architecture is expected to simplify product development. In practice, however, one persistent problem remains.

Almost any change to the data structure still leads to frontend updates, code changes, and a new deployment. In many cases, these are not changes to business logic, but simple content-related updates: adding a new product attribute, renaming a field, or displaying an additional parameter on a page. From a business perspective, these are minor adjustments. From a development perspective, they become a full-fledged task that affects frontend code and the delivery process.

In this article, we will examine why this tight coupling between data and frontend remains typical for most headless solutions, how alternative architectural approaches are usually implemented, and why OneEntry allows data to be managed without any changes to frontend code.

A Typical Scenario: Data Is Tightly Coupled to the Frontend

Let’s consider a common scenario. A project already has a product page, a working frontend, and a properly implemented API integration. At some point, a business request comes in: add a material field, introduce a “Delivery time” attribute, or display a few additional parameters for a category.

From a business perspective, these are purely content changes. No new logic is required, user flows remain the same, and the system’s behavior does not change. In practice, however, in most solutions such updates require changes to data schemas, type definitions, and frontend components, followed by a new deployment.

As a result, the frontend becomes a bottleneck even for simple changes that do not affect business logic.

Why This Happens in Most Headless CMS Platforms

If we look at the problem more closely, the reason becomes clear. Historically, headless CMS platforms were designed around strict data models. Content types are defined upfront, schemas are fixed, and fields are explicitly tied to how they are used in the frontend.

In this kind of architecture, the frontend knows the data structure in advance, directly consumes specific fields in components, and relies on static types or predefined GraphQL queries. As long as the data structure remains unchanged, this approach is convenient and predictable. However, once requirements begin to evolve, the tight coupling between data and interface quickly becomes a limitation.

Instead of simplifying product evolution, the architecture starts to resist change and increasingly requires frontend developer involvement even in cases where only the data itself needs to be updated.

The OneEntry Approach: Data First, Code Second

From the outset, OneEntry is built on a different architectural principle: the frontend should not need to know in advance which attributes exist for a given entity. Instead of rigid, predefined schemas, OneEntry uses a unified attribute model in which the data structure can evolve independently of frontend code.

Attributes are created and configured in the administrative interface and then assigned to the relevant entities—products, pages, users, orders, and other system objects. The API and SDK automatically return all associated attributes, while the frontend works with a universal data structure that does not depend on a fixed set of fields.

Adding a new attribute changes the data but does not require any modifications to the frontend application code.

In Practice: Adding a Product Attribute

Let’s walk through this approach using a practical example.

In the first step, a new attribute is created in the OneEntry administrative panel. The attribute type can be anything—string, number, boolean, list of values, or file.
Once created, the attribute is assigned to the Product entity and saved. At this stage, no frontend developer involvement is required.
Immediately after saving, the attribute becomes available in both the API and the SDK. No additional configuration or query changes are needed, as the OneEntry API and JavaScript SDK automatically start returning the new attribute together with the product data.
At the next stage, the frontend receives the updated data without any code changes. Let’s see how this looks in a Next.js application.

Example: Fetching a Product in Next.js Using the OneEntry SDK

Installing the SDK

npm install oneentry

SDK Configuration

// lib/oneentry.ts
import { defineOneEntry } from "oneentry";

export const oneentryApi = defineOneEntry(
  "https://<your-project>.oneentry.cloud",
  {
    token: process.env.ONEENTRY_API_TOKEN,
    langCode: "en_US",
  }
);

Product Page Using the App Router

// app/products/[slug]/page.tsx
import { oneentryApi } from "@/lib/oneentry";

interface ProductPageProps {
  params: { slug: string };
}

export default async function ProductPage({ params }: ProductPageProps) {
  const { slug } = params;

  const response = await oneentryApi.Products.getProductsByPageUrl(
    null,
    "en_US",
    {
      pageUrls: [slug],
      limit: 1,
      offset: 0,
    }
  );

  const product = response.items[0];

  return (
    <main>
      <h1>{product.title}</h1>

      <ul>
        {product.attributeValues.map((attr) => (
          <li key={attr.id}>
            <strong>{attr.title}:</strong> {String(attr.value)}
          </li>
        ))}
      </ul>
    </main>
  );
}

What’s Important

In this example, the frontend does not know in advance which attributes exist for a product. It simply processes the attributeValues array returned by the API.
A new attribute appears automatically, without any changes to components and without a redeployment.
Each attribute has its own identifier.

At this point, the key architectural advantage becomes clear. If the frontend application is designed around a universal data structure from the beginning, attribute identifiers can be used as control signals for the UI. In this model, attributes become part of a declarative UI definition.

Through conventions and rules implemented on the frontend side, it is possible to define where and how specific attributes should be displayed: in the product card, in a technical section, inside tabs or accordions, or in auxiliary sections of the page.

As a result, when a new attribute is added in the administrative panel, its role in the interface can be defined immediately. The frontend code remains unchanged, and no deployment is required.

It is important to note that OneEntry does not enforce a specific model for working with attributes. Different approaches are possible, including naming conventions, grouping by type, the use of dedicated flag attributes, or custom rules defined by the team. The platform provides the data and metadata, while the decision on how to interpret and render them in the UI remains with the developers.

Dynamic UI Management Through Attributes

Because each attribute has a unique identifier, it can be used not only as a key for rendering a value, but also as part of the interface logic. In this approach, attributes act simultaneously as data and as instructions for the UI.

The simplest and most commonly used option is a prefix-based convention. For example, a team can agree that attributes with the card_ prefix are displayed in the main product card, tech_ attributes appear in a technical section, tab_ attributes are rendered inside tabs, and meta_ attributes are used for SEO or internal purposes.

An administrator adds attributes with the appropriate identifiers, such as card_material, tech_weight, or tab_delivery_time. The frontend remains unchanged.

On the frontend side, the logic remains simple and transparent:

const cardAttributes = product.attributeValues.filter(attr =>
  attr.identifier.startsWith("card_")
);

const techAttributes = product.attributeValues.filter(attr =>
  attr.identifier.startsWith("tech_")
);

const tabAttributes = product.attributeValues.filter(attr =>
  attr.identifier.startsWith("tab_")
);

This is followed by standard component rendering:

<section>
  <h2>Key Specifications</h2>
  <ul>
    {cardAttributes.map(attr => (
      <li key={attr.id}>
        <strong>{attr.title}:</strong> {String(attr.value)}
      </li>
    ))}
  </ul>
</section>

When a new attribute is added with the appropriate prefix, it is automatically rendered in the correct part of the interface. No code changes or redeployments are required.

Prefixes Are Not the Only Option

Using prefixes is just one possible way to organize rendering logic. The OneEntry architecture does not restrict frontend teams to this approach and does not enforce any specific rules. In practice, teams use different patterns, including grouping by identifiers, naming conventions, dedicated flag attributes, configuration attributes for UI control, or combinations of multiple rules.

The key architectural principle of OneEntry is a clear separation of responsibilities. The logic for interpreting data and rendering the interface lives on the frontend side, while the data itself and its structure are managed in the administrative panel. This allows content and interface to evolve independently of each other.

Why This Matters

In this model, the content team manages data directly without involving developers. The frontend remains stable, while the interface stays extensible. The system does not dictate how the UI should be structured, but instead provides the tools needed to build a custom architecture.

OneEntry does not impose rigid rendering rules. The platform delivers all necessary data and metadata, while decisions about how to translate them into an interface are made at the level of the project’s frontend architecture.

Why This Works

The approach is based on treating attributes as independent entities. The API and SDK return a stable data structure that includes both attribute values and metadata. This allows the frontend application to work with a data model rather than a fixed schema, and to remain independent of changes in content structure.

What This Means for Business

Changes can be introduced faster, the number of development tasks is reduced, content teams can work independently, and the risks associated with unnecessary deployments and regressions are significantly lower.

What This Means for Developers

Frontend code becomes more stable, the stream of small “just update a field” tasks disappears, components become more reusable, and project maintenance and scaling are simplified.

When This Approach Is Especially Effective

This approach works particularly well for projects with large catalogs, marketplaces, corporate websites, MVPs and pilot projects, as well as systems where data and interface requirements change frequently.

Conclusion

In closing, it is important to emphasize one key idea: the headless approach is not just about separating frontend and backend. Its core purpose is to separate code from the evolution of data. If any content change still requires frontend modifications, it is a clear sign that the architecture remains too tightly coupled.
OneEntry was designed from the ground up to allow data to evolve freely without requiring changes to frontend code.

We hope this article was useful to you, and we would be glad to see you among OneEntry users.

Why it is beneficial for businesses to have multiple frontends on a single backend

OneEntry — Thu, 22 Jan 2026 14:20:04 +0000

Just a few years ago, one website was enough for most businesses. It served as the main point of contact with customers, acting as a storefront, a checkout, and an office at the same time, with all roads leading there. Today, this situation is rather the exception. In most cases, customers interact with businesses through dozens of different channels. Some visit the website, some use a mobile application, some first discover the brand on social media and continue engaging with it there. Others find the company through marketplace pages, while some come via B2B portals or internal corporate systems, and so on.

In practice, a single business ends up with several channels for generating customer traffic. As a result, each customer forms their own journey and their own expectations of the product or service. It is at this point that a key question arises for the company: should all of this be built as separate systems, each with its own logic, teams, and budgets, or is it possible to design the architecture in a different way?

Based on our observations, the market is still dominated by the classic approach to building digital products. It looks as follows: the website lives its own life, the mobile application evolves separately, the B2B dashboard exists on its own, and the internal admin panel turns into yet another standalone system. Data starts to get duplicated, business logic diverges, the number of errors increases, and maintaining this entire setup becomes more expensive and problematic. As a result, the business ends up not with a single manageable ecosystem, but with a set of fragmented solutions that are highly nuanced, poorly connected to each other, and require significant resources to maintain.

How an architecture with multiple frontends works

This alternative approach starts with a simple but fundamental idea: data and business logic should be located in one place. A single backend stores products, prices, inventory levels, users, orders, and sales rules. This becomes a single source of truth for the entire business, while different interfaces simply access this data and use the shared logic without duplicating it or creating parallel versions of the same processes.

When businesses talk about having multiple frontends, they usually mean a set of separate projects with a single point of entry into the same business. This can include a marketing website, an online store, a mobile application, a B2B portal for partners, a POS system, or internal tools for the team, and so on. For external users, these are different products with different user experiences, while for the business, it is one management system operating under unified rules and using the same data.

Business benefits of this architecture

The main business benefits of this architecture are speed and cost efficiency. A company can launch new customer acquisition channels without rewriting the backend and freely experiment with formats and products. At the same time, teams work in parallel without interfering with each other, maintaining a high level of efficiency and flexibility. When all business logic is concentrated in one place, there is no need to duplicate the same rules across different systems. The number of bugs decreases because changes are made once and immediately applied to all interfaces. Maintenance becomes cheaper since instead of supporting multiple complex systems, the business only needs to maintain a single architectural foundation.

With this architecture, the business also gains much stronger control over its processes. It operates with unified rules, unified data, and a single administrative panel. As a result, management sees the full picture of what is happening, and decisions are made based on one consistent version of data rather than fragmented reports from different systems.

Why traditional CMS platforms limit business growth

Traditional CMS platforms were originally designed as a backend for a website. Their architecture is tightly connected to templates and a specific interface, so as long as a business had only one website, the system handled its tasks well. However, with the emergence of mobile applications, B2B dashboards, marketplaces, and internal tools, these limitations become obvious. Supporting multiple interfaces is difficult, every extension makes the system more complex, and each modification starts to trigger more and more side effects.

At a certain point, the business is forced to use the CMS beyond its original purpose, adding custom logic and temporary workarounds to cover new requirements. As a result, the system turns into a complex structure that is difficult to develop, scale, and maintain without constant costs.

What really happens when a business grows

Business growth in the online environment almost always follows the same pattern. First, a website is launched, then a mobile application appears, partners are onboarded, the company enters marketplaces, and internal tools for the team are developed in parallel. The number of customer acquisition channels increases, and as mentioned earlier in this article, the complexity of management grows at the same time.

In a classic architecture, each new channel brings its own backend. This means separate databases and separate rules for handling orders, prices, inventory, and other business processes. As a result, information starts to diverge across different systems. The price on the website may not match the price in the mobile app. Inventory levels in the warehouse may differ from what users see in the application, and so on.

With a headless architecture built around a single backend, these problems do not occur. All channels work with the same data. When any information is updated, changes are instantly reflected across all sales channels. It is clear that with this approach, the business owner gains a much higher level of control over business processes.

Finally, we would like to note that OneEntry was designed from the ground up in a headless paradigm and fully meets modern requirements for digital product architecture. This approach reduces a wide range of risks, helps businesses save resources, and accelerates growth both in terms of time and budget. We hope this article has been useful to you. If you have any questions, we will be happy to discuss them and share our experience.

Why most businesses don’t actually need a “unique” backend

OneEntry — Fri, 09 Jan 2026 15:01:46 +0000

Myths of custom development and the role of templates and repeatable scenarios

You can hear the phrase “our business is different, we need a unique backend” in almost every second digital project. It is usually said with confidence, sometimes even with pride, as if it were a badge of quality, a marker of seriousness and ambition. And we understand where this comes from. Behind these words is often a sincere desire to build a product professionally: reliable, scalable, ready for growth and resilient to external risks.

If you also factor in how much money has been invested and how much time has been spent, it becomes clear why entrepreneurs associate these investments so directly with the importance of the result and the fulfillment of their ambitions. The more resources poured into a project, the stronger the belief that it simply has to be “really great” and meaningful.

And indeed, back in the 2010s, custom development looked like a symbol of maturity and seriousness, almost the only “right” path. Having your own backend meant you hadn’t used a ready-made template or website builder. It meant real engineering, tailored to the nuances and actual needs of your business.

But in 2026, this logic increasingly works against businesses. Instead of the expected advantage, companies face longer launch timelines, growing budgets, and technical debt that accumulates faster than real business results. And the uniqueness they fought so hard for suddenly turns from a growth engine into a burden, with little tangible business value.

What businesses actually mean by “uniqueness”

When entrepreneurs talk about uniqueness, they most often do not mean the backend at all. In almost every case, they are referring to what the customer sees and what truly differentiates the product in the market.

This could be a distinctive interface and a well-thought-out user experience, an unusual presentation, a unique interaction logic, or a recognizable brand style.
Or it could be unique marketing: a non-standard sales funnel, a specific tone of communication, a particular way of attracting and retaining customers.
Sometimes uniqueness lies in the niche itself or in a rare combination of services that no one else offers in the same way.

But if we are honest, none of these differences actually require a unique server-side architecture. Market rules can change many times a year, execution scenarios may shift, and internal business processes evolve, but the mechanics behind them remain the same. In simple terms, what is truly unique is the set of rules, not the engine that executes them. The backend, in this case, should remain a reliable, standardized, and universal tool that quietly does its job in the background.

80% backend tasks repeat from project to project

If you set industry specifics aside and look at e-commerce and digital services as a whole, the picture turns out to be surprisingly similar. The same basic building blocks migrate from one project to another, like pieces of a constructor that are simply assembled in a different order each time. Almost every product has users, roles and access levels, catalogs of products or services, attributes, filters and categories. There are orders, statuses, cancellations and returns, payments, partial charges and refunds (because you can’t avoid them), promotions, discounts and coupons. Content pages and SEO structures, integrations with CRM, ERP and payment systems are also part of this standard set.

The composition of these blocks hardly ever changes. What does change is their configuration: the rules, connections, scenarios and priorities. The foundation itself remains the same. That is why, in practice, what turns out to be truly unique is not the backend, but how these standard elements are combined and what business scenarios are built on top of them.

The main myths of custom backend development

Myth 1. “Our own backend means maximum flexibility”

It sounds logical: if the system is built specifically for you, then you can do anything. In practice, however, this “flexibility” very quickly runs into reality. It is limited by budget, by the speed of the team, and by how many changes the business is actually willing to pay for. Every new idea stops being a simple configuration task and turns into a full development job. It has to go into the backlog, be estimated, wait in line, and get a budget approved. As a result, flexibility exists only as long as there are enough resources to pay for it.

Myth 2. “Ready-made solutions will definitely limit us”

Limitations do not appear because a solution is ready-made. They arise when a platform has a rigid architecture, cannot be extended, or lacks a proper API. Modern backend platforms moved away from fixed logic a long time ago. They are built around configuration, rules, and scenarios. Not “the way developers designed it,” but “the way the business configures it.” And this is a fundamental difference.

Myth 3. “Custom is cheaper in the long run”

In reality, the opposite happens more and more often. Every year, maintenance becomes more expensive. Dependency on specific developers grows, especially on those who know the system from the inside. Any scaling requires architectural changes. After two or three years, a custom backend rarely looks “cheap.” More often, it turns into one of the biggest expense items in the budget.

Myth 4. “It’s safer this way”

Security is not about hand-written code at all. It is about processes, regular audits, updates, and reliable infrastructure. Platforms that serve hundreds or thousands of clients invest in security systematically. Not “when there is spare time,” but as a core part of the product. That is why their level of protection is often higher than in isolated custom projects.

The price of “uniqueness” that businesses usually don’t account for

When the decision is made to build everything from scratch, the focus is most often on opportunities rather than consequences. How long it will actually take to get to market is usually considered later, just like the real cost of maintenance, endless improvements, and refactoring. Few people plan in advance for the risk of team changes. Today one group of developers is working on the project, tomorrow another. Meanwhile, the system remains shaped by the decisions of the first team, their compromises, and their technical debt, which keeps growing like a snowball.

What initially seemed like a simple scaling task suddenly requires architectural redesign, new budgets, and even more time. As a result, a few years down the line, the “unique” backend stops being an advantage and turns into an anchor that holds the business back.

When a custom backend is actually justified

It is important to be honest with ourselves. Custom development is действительно needed, but far from in every case. It is justified when the product itself is built around a unique algorithm or intellectual property, when the backend is not just infrastructure but the very heart of the business. Custom solutions make sense if the system operates in real time under extreme loads, where every millisecond matters. They are also necessary when a product is based on complex financial, mathematical, or scientific logic that simply cannot be expressed through standard scenarios. Or when the backend itself is the core value of the product, the very thing customers are paying for.

But the truth is that most e-commerce and digital projects do not fall into this category. Their real value lies in the service, the product itself, and the user experience, not in a unique server-side architecture.

The modern alternative to custom development

The market has already provided an answer to this problem. Instead of endlessly building everything from scratch, backend platforms, API-first solutions, composable architectures, and systems with configurable business logic have emerged. The idea is simple and pragmatic: instead of rebuilding the foundation every time, use a ready-made one and adapt it to your specific needs.

This approach allows businesses to focus on what really matters: the product, the customers, and growth, rather than on maintaining yet another “unique” piece of infrastructure.

Where does OneEntry fit into all of this

OneEntry is an example of a platform-based approach that covers typical backend scenarios without the need to build everything from scratch. It is not a store template and not a website builder, but a full-fledged infrastructure platform.

In essence, it is a ready-made backend for e-commerce and content-driven projects, a set of modules with built-in business logic, and an API-first architecture on top of which you can build any frontend. Instead of rewriting code, businesses configure rules and processes. Instead of endless custom development, they manage logic through configurations. Developers gain full freedom on the frontend side, while the company gets a predictable and scalable growth system without unpleasant surprises.

Questions to ask before choosing custom development

Before investing in a “unique backend,” it is worth pausing and honestly answering a few questions.

What exactly is truly unique about our product?
Can this uniqueness be expressed through rules and configurations rather than through tons of code?
How much will system maintenance cost in two years?
What happens if the current development team changes?
Can we start with a platform and refine it gradually as we grow?

Sometimes these questions turn out to be more important than any technical arguments. They help distinguish a real need for custom development from the привычка of doing things “the old way.”

Conclusion

We are convinced that most businesses do not actually need a unique backend. What they really need is a reliable, scalable, and manageable foundation that allows them to grow their product and team with confidence. True uniqueness should live in the product itself, in the customer experience, and in the speed of decision-making. That is where real competitive advantage is created, not in infrastructure that has to be rewritten every few years.

We hope this article was useful for you and helped you look at backend architecture from a different perspective. If you decide to try our product or simply want to discuss your project, we will be happy to answer your questions, share our experience, and help you figure out which approach will work best for your business.

What Did 2025 Bring Us, and What Trends Lie Ahead?

OneEntry — Thu, 25 Dec 2025 12:24:50 +0000

As 2025 comes to an end, one thing has become clear: this was not a year of loud revolutions. Unlike previous years, when artificial intelligence burst rapidly into our reality, and not a year defined by new hype-driven trends dominating online discussions. Instead, 2025 deepened and noticeably reshaped how product teams rethink application architecture, infrastructure, and cost optimization under ongoing market pressure.

The first thing that stands out is a clear shift in priorities. Speed has taken center stage alongside the need to reduce costs. In the familiar triangle of “fast, cheap, high quality,” it is “fast” and “cheap” that have moved to the top, shaping development approaches and architectural decisions. We also tried to examine how quality begins to suffer within this model and what consequences this has for business metrics.

Summing up the outgoing year, we want to share our key observations on how the market is likely to evolve, especially as we ourselves are working on adapting to new trends and the challenges of the future.

What Stands Behind “Fast”: More Tools Did Not Make Teams Faster

The frontend ecosystem continued to expand: new frameworks, plugins, wrappers, and micro-libraries emerged, all promising flexibility and faster development. In practice, however, this increasingly translated into additional overhead—complex coordination, growing dependency chains, and subtle but constant maintenance costs. In the pursuit of “fast,” teams, as we observed, were in fact starting to slow down. Systems became harder to manage: maintaining tool compatibility, updating dependencies, and fixing regressions consumed precisely the time that was supposed to be freed up for product growth and more meaningful work.

Our first conclusion is simple, but not a pleasant one: adding more tools does not necessarily make teams faster.

Centralization Is Returning in a New Form

After many years of decentralizing data, logic, and infrastructure, teams have started to look for balance again. Distributed systems and microservices brought flexibility, but they also led to fragmentation. Data, logic, configurations, and even design rules ended up scattered across different tools and repositories. In 2025, many teams began to bring back centralized control—not by returning to monoliths, but by clearly defining sources of truth. Centralized configurations, shared design tokens, unified backend logic, and aligned integration layers help reduce chaos without sacrificing modularity.

Backend as the Foundation of the New Search and Omnichannel Sales

Another key shift of the past year is related to how search itself has changed. It has stopped being just a user’s entry point to a website and is increasingly becoming an independent intermediary between businesses and consumers. Modern AI aggregators and search engines no longer “lead” users to websites. Instead, they analyze data, compare offerings, and generate ready-made answers and recommendations without the direct involvement of interfaces. In this model, search works not with storefronts, but with meaning. What matters are data structures, product attributes, availability rules, constraints, context, and decision-making logic. When data is fragmented, inconsistent, or spread across multiple systems, a product simply loses visibility and controllability in these new search channels.

At the same time, omnichannel sales continue to grow. Today, the same product exists simultaneously across multiple environments: the web, mobile applications, marketplaces, internal systems, social platforms, and external aggregators. Each channel relies on its own search, filtering, and ranking algorithms, but all of them must be grounded in a single source of truth. This is where the backend stops being a technical detail and becomes a business foundation. It is responsible not only for storing data, but also for:

consistency of catalogs, pricing, and sales rules across channels
controlled search and recommendation logic
the ability to quickly change algorithms without rewriting frontends
data readiness for machine consumption, not just for display in interfaces

As search increasingly “makes decisions on behalf of the user,” the backend becomes the layer that determines whether a product will be found, correctly presented, and ultimately chosen. An architecture that is not prepared for this reality begins to slow business growth, regardless of the quality of interfaces, marketing, or the products being sold.

The Catalog Has Become More Complex Than the Product

One of the key shifts in modern commerce is the evolution of the catalog from a simple list of products into a complex business model with multiple relationships and layers of logic. Today, a catalog is no longer just a set of SKUs and price tags. It is a multidimensional structure that includes hierarchical categories, filters and attributes, availability rules by channel, region, and audience segment, dynamic parameters such as pricing, inventory, and promotions, as well as dependencies—for example, product groups, bundles, and variants.

The catalog no longer lives only in the frontend. It requires coordinated backend logic that can:

manage category structures and relationships between entities
store and version attributes and their values
ensure data consistency across sales channels
distribute rule sets for visibility, filtering, and sorting conditions

In this context, the frontend becomes just one of many data consumers, while the critical element is a single source of truth capable of serving multiple channels simultaneously. When such a source does not exist, or is fragmented, every catalog change turns into a costly project rather than a manageable business operation.

Search as a Channel, Not an Interface Feature

Alongside the growing complexity of catalogs, the very nature of search has changed as well. It has moved beyond being a supporting feature on a website and has become a decision-making channel, especially with the rise of AI aggregators and automated recommendation systems.

Modern search mechanisms are not oriented toward visual interface elements, but toward data structures and the rules used to interpret them. What matters to them is:

which attributes are available in the catalog and in what formats
which conditions are applied for visibility and filtering
how data correlates across entities and how quickly it is updated

AI-driven search analyzes information as a model, not as a list of links. It compares data from different sources, evaluates conditions, and generates optimal results without direct user involvement. That is why modern search systems require websites to provide clear data semantics, consistent attribute structures, and the ability to quickly adapt rules without modifying client applications.

Looking Ahead

The year 2025 showed us one important thing: in an environment of constant change, resilience becomes just as valuable as speed. As search evolves, channels multiply, and products grow more complex, it is architecture that determines whether this becomes a point of growth or a source of endless problems. We see how, for businesses, the foundation of data and logic is turning into a real tool for control and growth, and for developers, in turn, into an opportunity to work with stable systems. Where this balance can be achieved, a rare sense of control emerges, even in a complex and rapidly changing environment. That is why in our work we focus not on individual features, but on architectural integrity. Much of this is already working in practice, and the rest naturally follows the direction we have chosen.

At the end of the year, we would like to thank everyone who continues to design, question, improve, and search for simpler and more effective solutions. May the coming year bring you enough resilience, courage, and engineering ingenuity to turn complexity into advantage, and challenging shifts in the market landscape into an opportunity to build something new—something we may not always be ready for in the moment, but whose results stay with us for a long time.

Wishing you a Happy New Year, and success and good fortune to your projects and your loved ones🎄

80% of the IT Budget Goes to Maintenance, Not Growth: How to Break Out of This Trap

OneEntry — Fri, 19 Dec 2025 10:22:57 +0000

In 2025, companies still continue to spend their technology budgets inefficiently. Industry research shows that 70–80% of all IT expenses are consumed by infrastructure maintenance, fixes, plugin updates and servicing outdated systems. At the same time, the most important part, product development that creates value and drives revenue, receives far fewer resources than it should.

Why does this happen? Why do companies remain stuck in operational spending, and what can be done to change this situation?

In this article, we examine the main reasons that pull budgets down. We also show which approaches help businesses redirect money back into growth instead of spending it on maintaining the past.

The main reason: infrastructure debt grows faster than the business itself

Any company building a digital product eventually faces a snowball effect. The number of integrations increases, hosting and security expenses grow, the codebase becomes more complex and the technical decisions accumulated over the years begin to slow down development. At some point, the team is no longer working on new capabilities but is instead occupied with routine tasks. Developers maintain servers, CI/CD, VPN, SSL and mTLS. They keep dependencies up to date, deal with legacy code, resolve incidents and service outdated databases.

This is how the infrastructure trap takes shape. The more technical legacy a company accumulates, the more expensive it becomes and the less room remains for real growth and meaningful product evolution.

Plugin dependency. A hidden budget drain

Most popular CMS and e-commerce platforms have evolved through plugins for decades. In the beginning, this seems like a quick and convenient solution, but after a year or two such an architecture turns into a serious business risk. What exactly creates these risks?

The plugin model introduces five major problems.

1. Opaque code and lack of control

Plugins are created by third-party authors. Each one has its own style, quality and update schedule. Some plugins conflict with others, some stop being supported entirely and the business becomes dependent on someone else’s code.

2. Rising maintenance costs

Updating the platform almost always carries a risk of breaking functionality. The team has to spend money again on fixes and repeated adjustments. Maintenance becomes an endless cycle.

3. Security vulnerabilities

Global statistics show that up to 90 percent of WordPress hacks occur because of plugins. Each attack is costly. A company loses time, data and faces the risk of penalties.

4. Data fragmentation

Every plugin uses its own tables, formats and logic. Over time, integrations turn into chaos and any change requires disproportionate effort.

5. Scalability issues

When a system consists of many plugins, any growth becomes unpredictable in cost and timeline. The business loses control and stability.

Plugin architecture can indeed be convenient at the prototype stage. But for a scaling business it becomes one of the most expensive ways to maintain a product.

Legacy systems. When maintenance consumes innovation

Many companies continue to rely on outdated monoliths, custom-built CMS solutions, decade-old e-commerce platforms, old security protocols and business logic that is tightly embedded in the code. These systems may feel familiar and predictable, but they create one of the most serious barriers to growth. Let’s look at why this becomes a problem.

The cost of specialists who can maintain such a stack keeps rising. There are fewer experts with these skills and their services are increasingly expensive.
New features are introduced slowly or turn out to be technically impossible. Any improvement runs into the limitations of the architecture.
Every integration becomes a lengthy project. Connecting a CRM, payment system or external service requires months of coordination and development.
Scaling requires deep and costly refactoring. Older systems simply were not designed for modern workloads.

Legacy does more than slow a business down. It pulls money away from innovation and turns progress into a struggle with the past.

How companies can escape the trap of operational spending

To free the budget from routine tasks and return it to product development, a company needs to rethink its approach to infrastructure and architecture.

Step 1. Moving away from a custom backend and self-managed infrastructure

Maintaining a self-built server architecture almost always turns out to be more expensive than it seems at first. In practice this means ongoing costs for a DevOps team, monitoring, ensuring high availability, updates, security, backups, integrations and managing tokens and access rights. Most companies end up paying three to five times more than necessary simply because they handle everything themselves.

Step 2. Centralizing data and business logic

Over time, scattered plugins and microservices turn into a chaotic ecosystem. To manage a system predictably, data and logic need to be consolidated into a single model. Unified catalogs, unified payments, a unified user scheme and unified access rules bring transparency, reduce errors and simplify integrations.

Step 3. Using a platform with built-in functionality instead of plugins

Rather than relying on a set of unrelated modules, companies should use a platform that already provides core features. In this case the business gets a single API, a unified authentication mechanism, built-in business logic, standardized modules for products, orders, users and forms and a predictable cost of ownership. Maintenance stops being a lottery and becomes an organized process.

Step 4. Splitting responsibilities. The frontend focuses on innovation while the backend moves to the cloud

A modern product should evolve where the user sees and feels it. Interfaces, visual design, speed, mobile experience and UX experiments are what people choose a service for. In this model, the backend does not hold back progress. It runs in the cloud, providing stability, security and scalability without slowing down the evolution of the user experience.

How OneEntry helps reduce operational costs without a promotional tone

OneEntry is built on a different architectural philosophy. There are no plugins. Instead, it provides ready-to-use modules and SDKs that work reliably and do not break during updates. This allows companies to significantly reduce operational expenses and minimize technical debt. The overall economic efficiency of the platform is based on this approach, and the following points illustrate it clearly.

First. Eliminating infrastructure costs

The platform includes security mechanisms such as mTLS and tokens, CI/CD, hosting, automatic scaling, logging, monitoring and a fully managed backend. Companies no longer need their own DevOps team or server maintenance. This removes one of the largest cost drivers.

Second. No plugins and no plugin-related risks

Plugin-based architectures often cause conflicts, compatibility issues and security vulnerabilities. In OneEntry these risks do not exist. All modules are connected systematically, operate within a unified logic and are updated simultaneously. This ensures predictability and reduces the likelihood of incidents.

Third. Availability of ready-made business modules

The platform provides built-in functionality for core product scenarios. Products, categories, orders, payments, users, forms, pages, authorization mechanisms and templates are available immediately. The team does not spend resources developing these components from scratch, which reduces development costs and speeds up delivery.

Fourth. Fast launch of new products

The platform enables teams to assemble a working application in days rather than months. Ready-made templates for stores and services help launch faster, while the team can focus on the frontend, interfaces and UX instead of infrastructure.

Fifth. Reduction of technical debt

The architecture remains controlled and flexible. Legacy does not accumulate and the system evolves consistently, preventing the issues that older solutions typically create.

Our conclusion: companies should invest in growth, not maintenance

Every dollar spent on updating a plugin or fixing outdated code is a dollar that could have been directed toward product development and new opportunities. If a business aims to accelerate time to market, control its budget, reduce risks, move away from legacy and scale without chaos, it needs to rethink its approach to infrastructure and the way internal systems are built.
An IT model should support growth rather than limit it. When the backend becomes a reliable service, the product develops faster, the team works with more confidence and resources are used more effectively. We created OneEntry with this philosophy in mind, although the idea itself is much broader and applies to any modern architecture. Because in our view, the businesses that win are those that stop fighting with legacy and instead create room for growth.

Accelerating Frontend Time-to-Market in 2025: How to Ship Products 5–10x Faster

OneEntry — Fri, 05 Dec 2025 12:44:06 +0000

In 2025, the companies that win are the ones that release their products faster than everyone else. While some teams are still assembling developers and aligning on backend requirements, others are already onboarding their first users and capturing market share. And it is the frontend teams who now determine how quickly the first version of a product will appear and how easily it can be scaled afterward.

The good news is that modern tools finally make it possible to shorten the path from idea to MVP from months to just a few days. In this article, we will break down what truly accelerates frontend development and show in practice how OneEntry helps teams ship applications 5–10 times faster, without writing a backend, setting up infrastructure or building an admin panel manually.

Why Speed Has Become the Decisive Factor

Today, a quality product is no longer enough businesses expect fast results. The pace has increased across the board: hypotheses are tested more frequently, marketing needs new experiments, and competitors launch new features almost every week.

Speed to market has become a direct KPI. What used to be considered normal (an MVP in 2–3 months) now feels expensive, slow and completely off-market in 2025. To stay competitive, companies must be able to bring products to market much faster.

What Actually Slows Down Frontend Development

There are more tools than ever, but the core problems remain the same:

Lengthy planning. Before development even starts, the team has to align on everything: screen structure, data models, prototypes. This phase often drags on, even if the implementation itself later moves quickly.
Backend and infrastructure. Building the API, database, authorization, roles and admin panel becomes the most time-consuming part of the project. Up to 60% of the total development time is spent here and most importantly, this is work frontend developers shouldn’t have to do at all.
No unified UI approach. Without a design system and ready-made components, even a simple button turns into a mini-project. This slows teams down and breaks interface consistency.
Weak DevX. Without hot reload, Storybook, mocks and a proper CI/CD pipeline, every feature takes longer than it should. The process becomes a fight with tools rather than real development.

Rapid Prototyping: MVP in Days, Not Months

Rapid prototyping has become the standard for modern product teams. A prototype is first created in Figma, then transformed into a real UI, and after a few quick iterations, hypotheses can already be tested with real users.

In 2025, this stage is accelerated even further thanks to more mature tools:

ready-made design systems and UI kits
templates for Next.js and React
AI-generated interfaces
live previews and instant deployments on Vercel

As a result, teams can now build an MVP in 2–7 days, whereas previously this process took months.

Templates, Components, and Design Systems as a Source of Speed

Teams that rely on ready-made UI components and a unified visual language accelerate development by three to five times. The visual layer of the project becomes predictable, and routine work is reduced dramatically.

Why this works:

components are already tested against common scenarios
interface consistency is maintained automatically
everything can be easily extended to match project requirements
designers and developers operate within the same shared context

Key tools of 2025:

Shadcn/UI, MUI, Chakra UI — libraries of ready-to-use components
Figma Tokens — centralized management of design tokens
AI-driven components in VS Code / JetBrains— on-the-fly interface generation
UI templates for Next.js and React — a fast and structured project start

Low-Code and Cloud Functions: When the Backend Stops Being Mandatory

By 2025, low-code is no longer a trend it has become a standard acceleration tool. It removes from developers the tasks that shouldn’t be done manually: authorization, integrations, form handling, webhooks, notifications, simple CRUD operations. All of this is easily automated.

At the same time, the serverless approach covers up to 80% of technical needs without involving DevOps. The most popular options include Vercel Functions, Cloudflare Workers, AWS Lambda and Firebase Functions.

As a result, infrastructure stops being a bottleneck, and releases move as fast as the frontend itself is ready.

DevX Tools That Reduce Development Time by 5–10x

Modern DevX has become a direct driver of speed. The tools a team relies on determine how quickly new features reach production.

Hot Reload / Fast Refresh — instant UI updates without restarting the application

Storybook — isolated component testing without the influence of the full project

AI assistants — automatic generation of pages, API clients, mocks and repetitive code fragments

CI/CD and edge auto-deploy — no manual configurations, everything builds and deploys automatically

API and SDK generators:

OpenAPI → ready-to-use client

GraphQL → autogenerated queries

These tools cut the feature lifecycle dramatically, and together they deliver a 5–10x acceleration effect.

Where Up to 60% of Time Is Lost: Backend and Infrastructure

The biggest delays in frontend development occur where the frontend doesn’t yet exist on the backend side.

Under the classic approach, the team must go through a long sequence of steps:

design the database
create the API
set up authorization and roles
build the admin panel
configure CI/CD
set up hosting
integrate payment services

Until this infrastructure is ready, the frontend team is forced to wait. This is exactly where up to 60% of the time is lost. The time, that could have been spent building the product.

How OneEntry Reduces Time-to-Market by 5–10x

OneEntry transforms the entire development workflow. Instead of spending weeks preparing infrastructure, the team immediately gets a ready-to-use headless backend, a modular architecture and frontend SDKs. Nothing needs to be set up manually, everything is preconfigured and works out of the box.

What’s included: Headless CMS & Headless E-Commerce

authorization and user management
products and catalogs
forms, pages and blocks
multilingual content
payments and external integrations
webhooks
flexible roles and permissions
instant admin panel
ready-made APIs and SDKs

What this means for a frontend developer:

❌ no need to write a backend

❌ no need to design a database

❌ no need to configure auth

❌ no need to build an admin panel

❌ no need to maintain DevOps

Ready-to-use examples: Next.js Shop Demo & React Native Shop Demo

A developer simply connects oneentry-js-sdk and can immediately start building the interface, skipping the entire infrastructure layer.

A Real Example: How the Development Process Changes

With the classic approach, preparing the foundation takes one to three months. The team sequentially creates the API, designs the database, configures authorization, builds the admin panel, sets roles and permissions, integrates payments and external services and only after that can move on to the user interface.

With OneEntry, the process looks completely different and takes only 3–5 days:

create a project
configure entities and content in the admin panel
connect the SDK
immediately move to the UI

Everything that normally takes weeks such as infrastructure, data models, authorization, the admin panel and integrations is ready from the very first day. OneEntry covers the entire foundation of the project, so the developer does not need to deal with servers, database schemas or role configuration. Instead, they can focus right away on what truly matters for the product: the interface, the logic and the user experience.

Practical Recommendations for Accelerating Time-to-Market

Use ready-made templates

When a project starts from scratch, too much time is spent on tasks that do not create value such as folder structure, basic configuration, build setup, routing and styles. Ready-made templates for Next.js, React Native, Remix or Astro allow you to skip this stage entirely and move straight to what the product is actually created for which is functionality and user experience.

Keep your design system in one place

When the team has a single source of truth, the entire workflow becomes faster. Misalignments between design and development disappear, the number of revisions decreases, and visual decisions take minutes rather than hours. Figma Tokens and Storybook help maintain UI consistency and remove endless rounds of corrections.

Use the headless approach

When the interface is separated from the data, the product becomes more flexible and easier to scale. The team can update the frontend, try new technologies or extend functionality without touching the backend part and without adding unnecessary dependencies.

Automate everything possible

Mock generators, automatic API client creation, CI/CD and component testing remove repetitive tasks and significantly reduce the time needed to release new features.

Conclusion

In 2025, the teams that win are the ones that know how to move fast. The market does not wait. It is no longer about building everything from scratch but about quickly testing hypotheses, releasing the first features and starting to scale. OneEntry makes this possible. The platform provides a ready backend, data structures, authorization, payments, multilingual content and a set of SDKs so that a frontend developer can focus on the product rather than the infrastructure.

If speed is your priority, now is the right time to try tools that help you achieve results faster.