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APIs are supposed to simplify things. But most of the ones we see in the wild do the opposite.
At TLVTech, we’ve worked on backend systems across dozens of startups—from quick MVPs to enterprise-scale platforms. And if there’s one thing we consistently fix, it’s overcomplicated, unclear, or inconsistent API design.
A good API doesn’t just “work.” It’s obvious. It’s boring. It’s predictable.
Here’s what a clean API looks like—and why so many teams get it wrong.
You don’t need to read a spec to understand what POST /users/123/reset-password does. But POST /core/actions/trigger? That’s a guessing game.
Good APIs:
/users, /projects, /sessions)/users/123/reset-password)If you need a wiki to explain your endpoints, your naming is broken.
Too many teams try to outsmart HTTP. Don’t.
Use the standard verbs:
GET to fetchPOST to createPUT or PATCH to updateDELETE to removeNo need for POST /getData or GET /createThing. HTTP already gives you semantics—use them.
GET /users/123/projects/456/comments/789/tasks
This kind of nesting looks structured—but quickly becomes unreadable, hard to test, and painful to maintain.
Unless the hierarchy truly matters, flatten it:
A common mistake: designing APIs like direct database wrappers.
Yes, the data model matters—but the API should reflect how the frontend or client uses the data.
Example:
Instead of forcing 3 requests to get user info, preferences, and subscriptions—offer a GET /users/123/dashboard endpoint that returns everything the UI needs in one go.
Backend is for composition. API is for usability.
Don't break clients with unannounced changes.
/v1/users)If your API changes weekly, no one will trust it long-term.
The best APIs feel invisible. They just make sense. No surprise responses, no guessing game with endpoints, no need to dig through docs just to get started.
At TLVTech, we treat APIs like products—because every time someone calls your API, it is part of the product.
If you're building something and want your backend to scale without chaos, let’s talk.
- The Backend for Frontend (BFF) pattern helps create seamless user experiences by having a unique backend for each user-facing frontend. - BFF optimizes data transfer, preventing irrelevant data from being sent to each frontend and thus improving efficiency. - To implement BFF, design an API layer for each user experience, which results in codebases tailored to specific interface needs, simplifying development, and boosting performance. - BFF offers segregation of concerns, potential for code reuse, and flexibility in making user interface changes. - Drawbacks include redundancy risk and requirement for a new BFF for each user interface. - Suitable languages for implementing BFF include Python, JavaScript, C#, Java, Node.js, and TypeScript. - BFF vs. alternatives like API Gateway or Load Balancers depends on the need for custom user interface vs simplicity and performance optimization. - BFF is beneficial when dealing with multiple frontends needing tailored experiences, while API Gateway is better for unified APIs and scalability.
In the ever-evolving landscape of technology, it can be challenging to keep up with the latest buzzwords. One term you might have heard a lot lately is "serverless." But fear not, because in this blog post, we're going to break down serverless computing in a way that even your grandma would understand. We'll use everyday analogies and simple language to explain how serverless works, what makes it different from traditional computing, and why it matters in today's digital world. So, if you've ever been curious about serverless but didn't know where to start, join us as we embark on this journey to demystify serverless technology.
- Data science consulting empowers businesses by equipping them with the right data tools and strategies, enhancing business performance and enabling data-driven decision making. - These services can revolutionize business strategies, such as optimizing pricing based on customer data, and impact various industries (e.g., e-commerce, healthcare, finance). - When hiring data consulting firms, consider their experience, range of services, client satisfaction rates, and transparency in their fee structure, which can be hourly or project-based. - Data science consulting is a lucrative field with an average salary of $120,000 in the US and high job opportunities due to the increasing importance of data in business decision-making. - Machine learning consulting similarly offers growth opportunities by predicting customer behavior, improving decision-making, and tailoring business solutions for efficiency and accuracy. - Best practices in data science consulting involve clean, accurate data, the right tools for the project, objective analysis, and the ethical handling of data.
