The latest articles on DEV Community by Jie Hong (@helexdev). https://dev.to/helexdev https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4212782%2F42703f45-4bde-4cd7-8b4b-64380e6283e4.png https://dev.to/helexdev en Jie Hong Wed, 28 Oct 2026 21:53:19 +0000 https://dev.to/helexdev/38-real-world-ai-evaluation-data-annotation-tasks-open-source-github-projects-for-ai-engineers-l45 https://dev.to/helexdev/38-real-world-ai-evaluation-data-annotation-tasks-open-source-github-projects-for-ai-engineers-l45 <p>Hello DEV Community! 👋</p> <p>I'm <strong>Helex</strong>, a Senior AI Engineer and Full-Stack Developer with experience working with <strong>AfterQuery Experts</strong>.</p> <p>I want to share some of my public GitHub repositories with fellow AI trainers, data annotation specialists, and software engineers working on AI training and evaluation.</p> <p>Over time, I've built a collection of <strong>38 benchmark-style tasks</strong> covering different technical and professional domains, including software engineering, machine learning, debugging, data processing, and evaluation.</p> <p>I'm making these repositories easy to discover so other developers can explore the technical approaches, learn from the implementations, and improve their own AI evaluation skills.</p> <h2> 🚀 My GitHub Repositories </h2> <p><strong>1. AI Data Annotation &amp; Evaluation — 38 Tasks</strong></p> <p><a href="https://github.com/helexdev1004/afterquery-data-annotation" rel="noopener noreferrer">https://github.com/helexdev1004/afterquery-data-annotation</a></p> <p>A central collection of benchmark-style tasks covering multiple domains of AI evaluation and data annotation.</p> <p><strong>2. ML Training Analysis</strong></p> <p><a href="https://github.com/helexdev1004/afterquery-reckon-sweep-cost" rel="noopener noreferrer">https://github.com/helexdev1004/afterquery-reckon-sweep-cost</a></p> <p>An example involving training logs, batch-size experiments, and compute-cost analysis.</p> <p><strong>3. Software Engineering &amp; Database Concurrency</strong></p> <p><a href="https://github.com/helexdev1004/afterquery-ledger-rule-races" rel="noopener noreferrer">https://github.com/helexdev1004/afterquery-ledger-rule-races</a></p> <p>A benchmark-style project exploring concurrency problems and correctness in database systems.</p> <p><strong>4. Site Reliability Engineering</strong></p> <p><a href="https://github.com/helexdev1004/afterquery-reckon-error-budgets" rel="noopener noreferrer">https://github.com/helexdev1004/afterquery-reckon-error-budgets</a></p> <p>A technical evaluation task focused on calculating and understanding service error budgets.</p> <h2> 💡 Why I'm Sharing These </h2> <p>AI training and evaluation require more than simply generating answers.</p> <p>They involve:</p> <ul> <li>Understanding complex technical requirements</li> <li>Writing reliable and maintainable code</li> <li>Developing reproducible test environments</li> <li>Evaluating AI-generated solutions</li> <li>Identifying edge cases and hidden failure modes</li> <li>Applying logical reasoning and sound engineering practices</li> </ul> <p>I hope these public repositories can serve as useful learning references for developers interested in these areas.</p> <h2> 🤝 Let's Connect on GitHub! </h2> <p>I'm continuing to explore AI engineering, benchmark development, full-stack development, and technical evaluation.</p> <p>If you're an AI trainer, software engineer, or developer interested in these topics, I'd love to connect.</p> <p><strong>👉 Follow my GitHub for more repositories, technical examples, and AI engineering resources:</strong></p> <h3> ⭐ <a href="https://github.com/helexdev1004" rel="noopener noreferrer">https://github.com/helexdev1004</a> </h3> <p>Let's keep learning and building together!</p> <p><em>These are personal GitHub resources, not official AfterQuery documentation or training instructions.</em></p> ai machinelearning python Jie Hong Wed, 28 Oct 2026 21:32:20 +0000 https://dev.to/helexdev/38-real-world-ai-evaluation-data-annotation-tasks-open-source-github-projects-for-ai-engineers-4i62 https://dev.to/helexdev/38-real-world-ai-evaluation-data-annotation-tasks-open-source-github-projects-for-ai-engineers-4i62 <p>Hello DEV Community! 👋</p> <p>I'm <strong>Helex</strong>, a Senior AI Engineer and Full-Stack Developer with experience working with <strong>AfterQuery Experts</strong>.</p> <p>I want to share some of my public GitHub repositories with fellow AI trainers, data annotation specialists, and software engineers working on AI training and evaluation.</p> <p>Over time, I've built a collection of <strong>38 benchmark-style tasks</strong> covering different technical and professional domains, including software engineering, machine learning, debugging, data processing, and evaluation.</p> <p>I'm making these repositories easy to discover so other developers can explore the technical approaches, learn from the implementations, and improve their own AI evaluation skills.</p> <h2> 🚀 My GitHub Repositories </h2> <p><strong>1. AI Data Annotation &amp; Evaluation — 38 Tasks</strong></p> <p><a href="https://github.com/helexdev1004/afterquery-data-annotation" rel="noopener noreferrer">https://github.com/helexdev1004/afterquery-data-annotation</a></p> <p>A central collection of benchmark-style tasks covering multiple domains of AI evaluation and data annotation.</p> <p><strong>2. ML Training Analysis</strong></p> <p><a href="https://github.com/helexdev1004/afterquery-reckon-sweep-cost" rel="noopener noreferrer">https://github.com/helexdev1004/afterquery-reckon-sweep-cost</a></p> <p>An example involving training logs, batch-size experiments, and compute-cost analysis.</p> <p><strong>3. Software Engineering &amp; Database Concurrency</strong></p> <p><a href="https://github.com/helexdev1004/afterquery-ledger-rule-races" rel="noopener noreferrer">https://github.com/helexdev1004/afterquery-ledger-rule-races</a></p> <p>A benchmark-style project exploring concurrency problems and correctness in database systems.</p> <p><strong>4. Site Reliability Engineering</strong></p> <p><a href="https://github.com/helexdev1004/afterquery-reckon-error-budgets" rel="noopener noreferrer">https://github.com/helexdev1004/afterquery-reckon-error-budgets</a></p> <p>A technical evaluation task focused on calculating and understanding service error budgets.</p> <h2> 💡 Why I'm Sharing These </h2> <p>AI training and evaluation require more than simply generating answers.</p> <p>They involve:</p> <ul> <li>Understanding complex technical requirements</li> <li>Writing reliable and maintainable code</li> <li>Developing reproducible test environments</li> <li>Evaluating AI-generated solutions</li> <li>Identifying edge cases and hidden failure modes</li> <li>Applying logical reasoning and sound engineering practices</li> </ul> <p>I hope these public repositories can serve as useful learning references for developers interested in these areas.</p> <h2> 🤝 Let's Connect on GitHub! </h2> <p>I'm continuing to explore AI engineering, benchmark development, full-stack development, and technical evaluation.</p> <p>If you're an AI trainer, software engineer, or developer interested in these topics, I'd love to connect.</p> <p><strong>👉 Follow my GitHub for more repositories, technical examples, and AI engineering resources:</strong></p> <h3> ⭐ <a href="https://github.com/helexdev1004" rel="noopener noreferrer">https://github.com/helexdev1004</a> </h3> <p>Let's keep learning and building together!</p> <p><em>These are personal GitHub resources, not official AfterQuery documentation or training instructions.</em></p> github opensource ai machinelearning