Our ongoing work on the Inmotech-Backend project, a core component of Ryuu-no-Mi's property platform, recently focused on significantly improving its property search capabilities. With a catalog exceeding 30,000 properties, the challenge was to offer highly accurate geographical filtering and a responsive user experience through dynamic search facets.

The Challenge

Previously, our search

In the Ryuu-no-Mi Inmotech-Backend project, our goal is to provide a robust platform for property management. A critical component of any such platform is an intuitive and powerful search capability that allows users to find properties based on highly specific criteria.

The Problem

Traditional basic search often falls short when users need to refine their results based on multiple criteria,

Searching for properties can often be a frustrating experience. Stiff search interfaces that don't dynamically adapt to your choices or provide relevant groupings can quickly turn a hopeful search into a chore. In the Ryuu-no-Mi/Inmotech-Backend project, we've recently rolled out a significant feature to transform this experience, particularly for 'venta' (sale) and 'alquiler' (rental)

Introduction

Ever run into a seemingly simple database operation, like lowercasing a text field, only for it to throw a wrench in your application? This is a common pitfall when dealing with large text data types like CLOB (Character Large Object) in databases, especially within Java applications.

Imagine deploying a new environment or resetting development data, only to find critical fields consistently null or empty where they shouldn't be. This seemingly minor oversight can lead to cascading issues, from UI errors to incorrect business logic. Such was the challenge we recently addressed in our Ryuu-no-Mi/Inmotech-Backend project.

Our Ryuu-no-Mi/Inmotech-Backend handles a variety

Introduction

Working with stale or insufficient test data can significantly hinder development and compromise the reliability of testing. Developers often find themselves wrestling with limited datasets that don't reflect real-world scenarios, leading to missed edge cases and a less robust application.

At Ryuu-no-Mi's Inmotech-Backend project, we faced similar challenges.

Imagine a gym membership system where every family member needs their own access, but the family's main plan dictates how many members can join and when their access expires. This challenge is similar to what we tackled in Inmotech-Backend, enhancing our subscription management to support more flexible agency and agent-level plans.

The Challenge: From Static to Dynamic

Previously, our

In the ongoing development of LearnHub, a key area of focus is establishing a robust and maintainable authentication system. A critical step in achieving this involves implementing core User and Token entities, meticulously designed with a clean architecture approach that leverages ports and interfaces.

Introduction

Imagine building a job platform where the perfect candidate meets the ideal job. The smart-job-matcher project aims to achieve just that. This post dives into recent enhancements focused on integrating job offer data and providing intelligent skill recommendations to streamline the matching process.

Integrating Job Offers

A key update involves bolstering the MatchService