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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.
- Google Vision API is a machine learning tool capable of analyzing images, and can identify objects, texts, faces, and landmarks. - The API can be integrated by creating a project on Google Cloud Console, enabling the API for the project, and making REST API calls. - Key functionalities include optical character recognition with translation capability, object and face detection, image analysis, and detection of explicit content. - To get started, install Google Vision API using Python and 'pip install', then setup for image recognition by: creating a Google Cloud Project, enabling Vision API, downloading a private key, and pointing the `GOOGLE_APPLICATION_CREDENTIALS` variable to that key. - Google Vision API operates with a tiered pricing structure; it isn't free, and cost increases with use. - AutoML, integrated in Google Vision API, simplifies model training by automating the process. It works both online and offline, categorizes images, and detects objects. - To code with Google Vision API in Python, libraries have to be imported, followed by creating an instance for image analysis, and then calling the API operations.
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- Domain-Specific Languages (DSLs) are designed to manage a defined set of tasks effectively in the tech world, like Markdown for formatting, MySQL for managing databases, and CSS for styling web pages. - Domain-Specific Modelling (DSM) uses DSLs to speed up software production. - Tools such as Antlr, Xtext, and Xtend help in crafting and implementing DSLs. - DSLs enhance productivity, better communication among teams, and consistency in software development. However, they require time to learn and limit the flexibility to carry out an extensive range of tasks due to their specific nature. - DSLs are used in app development and offer specific advantages like SQL for interacting with databases and regex for text operations. - There is a balance between DSLs and General-Purpose Languages: DSLs are specialized for specific tasks, while general-purpose languages offer more flexibility. - The future of DSLs includes increased use in AI, data science, Internet of Things, and the growth of visual DSLs.
• Java microservices break down a large application into small, self-contained units that perform a single function, thereby improving system reliability and manageability. • Microservices, smaller than a Lego piece, can function independently but collaborate via APIs and HTTP protocols to deliver a complex application. • Java's reliable, scalable, and secure nature makes it a choice platform for microservices, with support for robust API development and portable across diverse platforms. • Java's frameworks like Spring Boot streamline microservice development, together with containerization tools like Docker, which provides an independent environment for running Java microservices. • Microservices involve breaking down tasks into small, manageable parts, with popular development tools like Maven and principles like decoupled services, and service discovery. • A CV for a specialist in Java microservices should highlight coding and testing skills, along with experience of real-world projects. • Building a Java microservice involves defining its task, using Java tools like Spring Boot, coding and testing the service before deploying it. • Examples of practical Java microservices applications include those used by Netflix and Uber.
