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When you build backends for dozens of startups, patterns emerge—not just in code, but in what actually works at scale, under pressure, and with small teams.
At TLVTech, we’ve refined a set of backend design patterns we use across most projects—regardless of stack, industry, or company size. These patterns help us deliver faster, onboard developers quickly, and avoid messy rewrites later on.
Here’s what we use, and why it works.
We don’t start with microservices. We start with a modular monolith.
Why:
Once there’s real scale or organizational need, we extract services with clear boundaries. Premature microservices = wasted time and complexity.
We keep backend layers clean and predictable:
Why:
This structure works across NestJS, Express, Django, Spring—doesn’t matter. Clean separation always wins.
We use Data Transfer Objects (DTOs) to define inputs and outputs between layers.
Why:
Especially useful in TypeScript and Python with tools like class-validator or Pydantic.
We use async event patterns (pub/sub or message queues) selectively—mainly for:
Why:
We prefer lightweight solutions like Redis streams or AWS SNS/SQS before going full Kafka.
Every project has:
Why:
Observability starts with consistent logging.
We don’t rely on magic. We centralize config using .env files, secrets managers, or config services—so nothing is hardcoded.
Why:
Backend systems don’t win because they’re clever. They win because they’re predictable, understandable, and built to grow.
The design patterns we use are boring on purpose—because boring is what makes products stable, scalable, and easy to maintain.
If you’re building something and want backend that won’t crumble under growth, let’s talk.
- A Minimum Viable Product (MVP) in software development is the simplest version of a product that fulfills its essential purpose. - An MVP is defined as the most basic offering providing enough features to satisfy early users while enabling developers to gather feedback for future development. - The MVP approach saves time and resources by enabling developers to test basic features, gather feedback, and iterate improvements based on real user response. - MVPs play a critical role in agile development, facilitating rapid iterations based on user feedback. - Examples of successful MVPs include Facebook, Twitter, and Amazon, which started with basic functionality and grew based on user response. - Finally, an MVP differs from a full product or a prototype in that it is a usable product with minimal features aimed at early customers, allowing for market testing and feedback for further enhancements.
- Google Vision API is a machine learning tool capable of identifying objects in images for automation purposes. - This API can scan thousands of images quickly, label objects, detect faces, and determine emotions. - It uses OCR for text extraction from images and requires an API key for project deployment. - Google Vision API integrates with Python through the Google Cloud Vision client library. - Key features include text recognition via Optical Character Recognition, product detection, and facial recognition. - Pricing is pay-as-you-go; a free tier is available with limitations for light usage. - To implement in projects, enable the Vision API on Google Cloud, get the API key, install the client library and write your API requests. Python users will need to install AutoML libraries and setup project and model IDs. - A detailed walkthrough guide is available for more complex adjustments to the API.
- Machine learning includes three types of algorithm: supervised, semi-supervised, and unsupervised learning. Supervised is guided learning using labeled data, unsupervised finds patterns in unlabeled data without guidance, and semi-supervised uses both to learn and train. - Four groups of machine learning algorithms are: classification and regression (predictive sorters), and clustering and association (find patterns and associations). - Benefits of machine learning algorithms include decoding patterns, solving problems with minimal human intervention, uncovering unknown insights, predicting trends, automating tasks, and improving security. - To implement machine learning models, we need to gather and clean data, understand the data, select a model, train and test the model, tweak the model, and integrate it into existing systems. - Machine learning models include neural networks, regression techniques, decision trees, and support vector machines. - Future trends in machine learning involves advanced algorithms, improved cybersecurity, scaling of algorithms, and continuous research and development.
