Founder of OpenCrater — the attention network for the terminal. Building tools that pay developers.

github

Ope Olatunji • Peerlist

<h3>OpenCrater offers a unique sponsorship infrastructure for terminal tooling, enabling open-source maintainers of CLI tools and MCP servers to earn revenue through 'Blips' rendered directly in the terminal. This innovative approach functions similarly to AdSense but is specifically designed for the command-line interface, providing a way for developers to monetize their contributions.</h3><ul><li><strong>For Maintainers:</strong> Earn revenue from impressions and clicks in USDC. The setup is quick, taking only about five minutes, and the SDK is designed to be non-intrusive, with fail-silent behavior and zero dependencies. If no ad campaigns are available, your GitHub Sponsors card can be displayed as a fallback.</li><li><strong>For Advertisers:</strong> Reach developers directly within their active engineering sessions in CLIs like Claude Code and Codex. Payment is structured on a pay-per-click model with a small per-impression fee, featuring precise targeting options based on package type, category, language, placement, and country. The ads are designed with a developer-grade aesthetic, using plain text cards without tracking pixels or popups.</li><li><strong>For Developers:</strong> Earn compute credits by viewing Blips while coding. These credits can be used to continue coding sessions when your weekly plan limit is reached, offering a card-free, cash-free way to access computing resources. The system is designed for effortless opt-out, with a simple environment variable (`OPENCRATER_DISABLE=1`) or a command (`npx opencrater off`) to disable Blips permanently. Blips are also automatically suppressed in CI environments and non-TTY pipes.</li></ul><p>The process for maintainers is straightforward, involving claiming a package, obtaining a key, pasting a hook into their tool, and then earning from impressions and clicks.</p>

OpenCrater

This project analyze the volatility and correlation between Apple and Google stocks, alongside comparing their performance with other S&P 500 stocks. The goals is to facilitate informed investment decisions through data-driven insights. The steps involved are: - Gathering historical stock price data from Yahoo Finance for Apple and Google. - Cleaning and preprocessing the data to ensure accuracy. - Creating relevant variables for deeper insights into stock performance. - Aligning the data temporally for fair comparison. - Utilizing data visualization techniques to present comparisons effectively. - Applying statistical methods to identify patterns and relationships between the stocks. - Comparing the performance of Apple and Google stocks against benchmarks like the S&P 500 to understand their relative performance in the market.

Apple & Google Stock Price Comparison Analysi

In this project, I automate the backup process for core server files at Company XYZ. Currently, Data is being manually access using encrypted password files on core servers each day, leading to human errors, compromised security, and excessive workload. To address this bottleneck, I develop an automated backup script called backup.sh. This script will identify files updated within the last 24 hours and create a compressed archive of these files. The archive will then be moved to a specified destination directory. This automated backup solution streamlines the process of backing up core server files at XYZ. By automating the backup process, I reduce human errors, enhance security, and minimize the workload associated with manual backup tasks. The project will improve operational efficiency and ensure the integrity and availability of critical data.

Automated ETL for Data Enterprise Backup usin

I create a function to log the progress of the project at different stages and extract the tabular information from the given webpage. I transform the dataframe by adding columns for Market Capitalization in GBP, EUR, INR and rounded to 2 decimal places, based on the current exchange rate. And I load the transformed dataframe to an SQL database server as a table and run multiple queries on the database table. Lastly i verify that the log entries have been completed at all stages by checking the contents of the log.txt file.

Automated system for Web Scrapping process Us

I develop a comprehensive data warehouse solution for a solid waste management company operating across major cities in Brazil. Tasked with optimizing data-driven decision-making processes, I designed and implemented a robust data infrastructure capable of handling vast amounts of information regarding waste collection and recycling activities. Steps Taken: 1. Requirement Analysis 2. Data Modeling 3. Data Loading 4. Query Optimization 5. Materialized Query Tables (MQTs) 6. Dashboard Creation: Use IBM Cognos Analytics to visualize key performance indicators (KPIs) and metrics derived from the data warehouse. Project Goal: The primary objective of this project was to empower the solid waste management company with a data-driven approach to optimize waste collection and recycling operations. By establishing a robust data warehouse infrastructure and implementing advanced analytics capabilities, the project aimed to enable the company to: - Gain actionable insights into waste collection trends and patterns across different cities and truck types. - Enhance resource allocation and scheduling strategies for improved operational efficiency. - Track progress towards sustainability goals and identify opportunities for environmental impact reduction. - Facilitate evidence-based decision-making at various levels of the organization to drive continuous improvement initiatives.

Building a Data Warehouse for a Brazil Waste 

Designed and implemented a data pipeline to handle data ingestion, transformation, and querying across multiple database systems, including MongoDB and Cassandra. The project involved importing data from various formats, executing complex queries, and ensuring seamless data migration between databases. Key Details: Imported and managed JSON data in a MongoDB database. Queried and analyzed data within MongoDB using complex aggregation pipelines. Exported data from MongoDB to a CSV file. Imported and configured CSV data into a Cassandra database. Designed and executed CQL queries to analyze data within Cassandra. Created and optimized indexes to enhance query performance in Cassandra. Ensured data integrity and consistency across database systems. Tools Used: MongoDB: Data import, aggregation, and querying. Cassandra: Data import, CQL querying, and indexing. Tools: MongoDB CLI, Cassandra CLI, cqlsh.

Data Base Integration and Query Optimization 

I design ERD Database for company XYZ. XYZ database was stored in several systems and in different format, which include : accounting software data, supplier databases, point of sales (POS) systems data, and spreadsheets. I firstly review the data in all of these systems and then proceed to design a central database to house all of the data using PostgreSQL. Afterwards, I create database attributes and load them with the data from each sources. Finally, I create a data mart that Company XYZ marketing partners require, export them, and load them into staging databases using PhpMyAdmin and a RDBMS. Tools Used in this project: PostgreSQL, MysQL and PhpMyAdmin

Data Integration, Architecture and Distributi

This project involved designing and implementing a data pipeline using Apache Spark components to process employee data in CSV format. As a data engineer, responsibilities included creating a Spark DataFrame from CSV data, defining schemas, and leveraging Spark SQL for data analysis and transformation tasks. The project aimed to extract valuable insights such as average salaries by department, maximum salary by age, and employee demographic statistics. Key tasks included SQL queries, data filtering, self-joins, and calculating aggregate metrics. Through this project, I demonstrated proficiency in Apache Spark SQL and data engineering, contributing to enhanced data-driven decision-making capabilities within the organization.

Data Pipeline for Employee Analytics Using Ap

I work on a project as a Database Administrator with focused on optimizing the data platform comprising various on-premises and cloud-based relational databases including MySQL, PostgreSQL, and IBM DB2. Key responsibilities included configuring, tuning, securing, backing up, and monitoring these databases to ensure peak performance. This project involved three main parts: Part 1: PostgreSQL Server Optimization - Configured PostgreSQL server and performed user management tasks. - Handled database backups efficiently. Part 2: MySQL Server Optimization - Conducted configuration checks and data recovery tasks on MySQL server. - Utilized indexing techniques to enhance database performance. - Identified supported storage engines and optimized backup tasks. Part 3: IBM DB2 Cloud Instance Optimization - Restored data and created indexes to improve query performance on IBM DB2. - Implemented views for simplified query writing. - Connected to IBM DB2 cloud instance from the command line. Throughout the project, I demonstrated proficiency in database management, performance optimization, and troubleshooting, resulting in a robust and efficient data platform.

Database Performance Optimization as a DBA Us

Generated thousands of financial statements data in raw using the ‘RAND’ and ‘RANDBETWEEN’ function in Excel. Created  Financial Statement, Forecast Model, Free Cash Flow Model and Cost Benefit Analysis model with the data using MS Excel.

Elevate 360 Financial Models Using Microsoft 

Developed an end-to-end machine learning pipeline to predict airfoil noise levels using a modified version of the NASA Airfoil Self-Noise dataset. The project involved data extraction, transformation, and loading (ETL) to clean and preprocess the dataset, followed by the creation of a robust machine learning model to predict sound levels. Key tasks included data cleaning, model training and evaluation, and model persistence for future use. This project improved data processing efficiency and predictive accuracy.

NASA Airfoil Noise Prediction ML Pipeline Usi

The project aims is to alleviate congestion on national highways by leveraging data analytics techniques. It involves collecting and analyzing road traffic data from various toll plazas across the country. The data includes vehicle information such as vehicle ID, type, toll plaza ID, and timestamp. I also develop efficient data pipelines and integration processes that streamline the collection, transformation, and loading of road traffic data from diverse sources into a centralized database. By achieving this goal, I provide actionable insights to transportation authorities for better traffic management and congestion reduction strategies on national highways.

National Highway Traffic Data Integration & A

I started by designing a relational database schema for a banking system, carefully modeling entities like users, accounts, transactions, and user roles. This laid the foundation for generating realistic synthetic financial data. Using Python, I created a data generation script to populate the database with synthetic banking records - simulating customer profiles, account balances, transaction histories and more. This synthetic data was then loaded into a PostgreSQL database. To enable cloud-based analytics, I set up a pipeline to sync the PostgreSQL data to Google BigQuery. This allowed me to leverage BigQuery's powerful querying capabilities for large-scale data analysis. Finally, I connected BigQuery to Power BI to create an interactive dashboard visualizing key banking metrics. The resulting report includes account type distributions, historical balance trends, and geographic analysis of customer data. This end-to-end project showcased the power of combining robust data modeling, synthetic data generation, cloud data warehousing, and business intelligence tools to derive actionable insights from complex financial datasets.

Project FinSynth: From Database Design with P

This project involves building an automated data processing pipeline using Apache Airflow to extract, transform, and load (ETL) data from web logs. It demonstrates how to efficiently handle web log data for extracting IP addresses, transforming the data by filtering out specific IP addresses, and then packaging the results for further use or analysis.