Code Wizards
MockShawk is an AI-powered mock interview platform that provides real-time feedback, expert questions, and personalized insights to boost confidence and performance. Practice anytime and ace your next
Created on 6th February 2025
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Code Wizards
MockShawk is an AI-powered mock interview platform that provides real-time feedback, expert questions, and personalized insights to boost confidence and performance. Practice anytime and ace your next
The problem Code Wizards solves
In today’s competitive job market, acing an interview is crucial for landing the right job. However, many job seekers struggle with interview preparation, leading to anxiety, uncertainty, and missed opportunities. Traditional preparation methods, such as reading interview guides or rehearsing alone, often lack real-time feedback, making it difficult for candidates to assess their strengths and weaknesses effectively. MockShawk addresses these challenges by providing an AI-powered mock interview platform that helps users practice, improve, and gain confidence before facing real-world interviews.
Most people prepare for interviews by reading common questions and attempting to craft responses. However, without structured mock interviews , it is difficult to simulate real-world pressure and get meaningful feedback. Traditional mock interviews with career coaches or mentors can be expensive and hard to access, leaving many candidates unprepared for the real experience.
Most job seekers do not have a way to track their interview performance over time. Without data-driven insights, they remain unaware of their progress, strengths, and areas needing improvement. This lack of structured feedback can slow down improvement and hinder long-term career success.
Most job seekers do not have a way to track their interview performance over time. Without data-driven insights, they remain unaware of their progress, strengths, and areas needing improvement. This lack of structured feedback can slow down improvement and hinder long-term career success.
MockShawk is a game-changer for job seekers looking to improve their interview performance. By offering AI-powered mock interviews, real-time feedback, and personalized improvement insights, it solves the major challenges associated with interview preparation . Whether users struggle with confidence, response structure, or adapting to different interview formats.
This makes it accessible to job seekers from all backgrounds.
Challenges we ran into
A critical hurdle in the early stages of development was the integration of PostgreSQL for managing interview questions, user profiles, and mock interview sessions. Initially, we faced problems establishing a connection between the backend and the database, which resulted in failed attempts to read and write data to the PostgreSQL server. The frustration of seeing error messages like "Connection Refused" or "Authentication Failed" became an early roadblock.
Problem
Our PostgreSQL database connection failed because of several configuration mistakes. The first issue was with the connection string in the backend configuration file. We had either forgotten to update certain parameters or entered incorrect values for the database user, password, host, and database name. Additionally, the environment variables that we intended to use for setting these values were not correctly defined, leading to a misconfiguration when the backend tried to establish a connection. This misconfiguration prevented the application from fetching and storing the interview data in the database, rendering the app incomplete at that stage.
Solution
To solve the database connection issue, we started by reviewing the error logs generated by the backend server. These logs gave us a clear indication that the issue was related to authentication failure. We double-checked the database credentials—username, password, and the database URL—and found discrepancies between the local development environment settings and those on the production server. It became evident that we had neglected to set the correct environment variables that contained the database credentials.
We fixed the issue by first correcting the database credentials in the configuration file, making sure the parameters aligned with those of the PostgreSQL setup. Additionally, we created an .env file to store sensitive data such as database credentials securely. Once the credentials were fixed, ensured that the necessary environment.
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