empirical-org/Empirical-Core
Jun 2025 – Feb 2026
9 Months active
Languages Used
CSSHTMLJavaScriptRubySQLYAMLShellSCSS
Technical Skills
AI Model EvaluationAPI DevelopmentAPI IntegrationAWSBackend DevelopmentBackground Jobs
brendanshean
PROFILE
Brendanshean
Brendan contributed to the empirical-org/Empirical-Core repository by building and enhancing core features for AI-driven dataset management, trial evaluation, and platform reliability. He developed robust dataset editors and clustering tools, modernized AI backend integrations, and improved trial analysis workflows using Ruby on Rails, React, and TypeScript. His work included implementing secure API endpoints, optimizing job processing with Sidekiq, and refining UI/UX for data editing and reporting. Brendan addressed data integrity, error handling, and scalability through thoughtful refactoring and test-driven development. His engineering approach emphasized maintainability and business value, resulting in a stable, extensible platform for scalable AI experimentation.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
96Total
Bugs
17
Commits
96
Features
42
Lines of code
147,125
Activity Months9
Your Network
5 peopleSame Organization
@quill.org1
Shared Repositories
4
Work History
February 2026
10 Commits • 6 Features
Feb 1, 2026Concise February 2026 monthly summary for Empirical-Core focusing on delivered features, major fixes, impact, and technical capabilities. Emphasizes business value, reliability, and scalable improvements across clustering, API, data editing, and job processing.
10 Commits • 6 Features
Feb 1, 2026Concise February 2026 monthly summary for Empirical-Core focusing on delivered features, major fixes, impact, and technical capabilities. Emphasizes business value, reliability, and scalable improvements across clustering, API, data editing, and job processing.
February 2026
January 2026
11 Commits • 7 Features
Jan 1, 2026Month: 2026-01 — This period delivered measurable business value in Empirical-Core through pricing modernization, AI backend modernization, and reliability improvements, while also reducing maintenance overhead and improving user experience. Notable outcomes include revenue-protecting pricing and renewal logic, expanded AI config capabilities, and more robust rostering and error handling. Key features delivered: - Teacher Premium Pricing Update and Renewal Handling: updated pricing from $80 to $115/year, reintroduced legacy pricing, and refined renewal/upgrade logic to protect revenue and improve customer renewal experience. Commit: 11efd4a86f1310f9177f73224bb160a13b462b9c - Gemini 3 Flash Configuration Support and UI Update: added Gemini-3-Flash config, schema, tests, and UI to support thinking modes and budget parameters. Commit: 6f56d1a2232c7eccdd4be37ddf69260db71f4b80 - Migrate AI Backend to Vertex AI and Include User Roles: migrated AI backend from AI Studio to Vertex AI and added user roles in API requests for better security and integration. Commit: a9bb559e4dce84b609de578e8df86b017fa1901a - Rostering Integration: Remove Username as Unique Identifier: switched to email and external ID for uniqueness, improving robustness of the Clever rostering integration. Commit: c908ffce72e1e5faf79a68aef54cd549607fc961 - AutoML Deprecation Cleanup and Improved Error Handling: removed AutoML code and tests, cleaned lint issues, and enhanced error notifications by passing model names. Commit: a4e21e4c77950b44b85a367ea9cf65b665c13f56 Major bugs fixed: - Regex Metacharacter Bug Fix in Text Highlighting: fixed a bug with regex metacharacters in string formatting functions to improve accuracy of spelling/grammar highlighting. Commit: 4dd62f729e6aa27978673f3f5d178fd964fe7c87 - Quill LMS: Remove Broken Links (connect_tool and grammar_tool): removed broken links to improve navigability. Commit: a9563069f756bb19ba42de2875bf27ed861016de Overall impact and accomplishments: - Revenue protection and potential upsell uplift through pricing modernization and robust renewal handling for Teacher Premium. - Expanded AI capabilities and cloud alignment via Vertex AI migration with role-based access control. - Increased system robustness and maintainability through refactors, deprecations, and lint/test improvements. - Improved user experience and navigation across the platform with UI updates, link hygiene, and roster robustness. Technologies/skills demonstrated: - Ruby on Rails ecosystem, Thor tasks, Zeitwerk, and RuboCop linting upgrades. - AI/ML tooling migration to Vertex AI and role-based API access. - Test-driven improvements and UI work, with a focus on security, maintainability, and performance.
January 2026
11 Commits • 7 Features
Jan 1, 2026Month: 2026-01 — This period delivered measurable business value in Empirical-Core through pricing modernization, AI backend modernization, and reliability improvements, while also reducing maintenance overhead and improving user experience. Notable outcomes include revenue-protecting pricing and renewal logic, expanded AI config capabilities, and more robust rostering and error handling. Key features delivered: - Teacher Premium Pricing Update and Renewal Handling: updated pricing from $80 to $115/year, reintroduced legacy pricing, and refined renewal/upgrade logic to protect revenue and improve customer renewal experience. Commit: 11efd4a86f1310f9177f73224bb160a13b462b9c - Gemini 3 Flash Configuration Support and UI Update: added Gemini-3-Flash config, schema, tests, and UI to support thinking modes and budget parameters. Commit: 6f56d1a2232c7eccdd4be37ddf69260db71f4b80 - Migrate AI Backend to Vertex AI and Include User Roles: migrated AI backend from AI Studio to Vertex AI and added user roles in API requests for better security and integration. Commit: a9bb559e4dce84b609de578e8df86b017fa1901a - Rostering Integration: Remove Username as Unique Identifier: switched to email and external ID for uniqueness, improving robustness of the Clever rostering integration. Commit: c908ffce72e1e5faf79a68aef54cd549607fc961 - AutoML Deprecation Cleanup and Improved Error Handling: removed AutoML code and tests, cleaned lint issues, and enhanced error notifications by passing model names. Commit: a4e21e4c77950b44b85a367ea9cf65b665c13f56 Major bugs fixed: - Regex Metacharacter Bug Fix in Text Highlighting: fixed a bug with regex metacharacters in string formatting functions to improve accuracy of spelling/grammar highlighting. Commit: 4dd62f729e6aa27978673f3f5d178fd964fe7c87 - Quill LMS: Remove Broken Links (connect_tool and grammar_tool): removed broken links to improve navigability. Commit: a9563069f756bb19ba42de2875bf27ed861016de Overall impact and accomplishments: - Revenue protection and potential upsell uplift through pricing modernization and robust renewal handling for Teacher Premium. - Expanded AI capabilities and cloud alignment via Vertex AI migration with role-based access control. - Increased system robustness and maintainability through refactors, deprecations, and lint/test improvements. - Improved user experience and navigation across the platform with UI updates, link hygiene, and roster robustness. Technologies/skills demonstrated: - Ruby on Rails ecosystem, Thor tasks, Zeitwerk, and RuboCop linting upgrades. - AI/ML tooling migration to Vertex AI and role-based API access. - Test-driven improvements and UI work, with a focus on security, maintainability, and performance.
December 2025
16 Commits • 5 Features
Dec 1, 2025December 2025 monthly summary for empirical-org/Empirical-Core: Delivered core data quality and cold-start tooling enhancements, robust trial analysis and reporting improvements, and resource management updates, alongside governance and lifecycle enhancements for LLM prompts and templates. A critical bug fix in cluster tag handling during a major version update closes the month, improving upgrade reliability. The work enabled faster, safer experimentation, better data integrity, and stronger model governance across the platform.
16 Commits • 5 Features
Dec 1, 2025December 2025 monthly summary for empirical-org/Empirical-Core: Delivered core data quality and cold-start tooling enhancements, robust trial analysis and reporting improvements, and resource management updates, alongside governance and lifecycle enhancements for LLM prompts and templates. A critical bug fix in cluster tag handling during a major version update closes the month, improving upgrade reliability. The work enabled faster, safer experimentation, better data integrity, and stronger model governance across the platform.
December 2025
November 2025
14 Commits • 7 Features
Nov 1, 2025November 2025 (Empirical-Core) delivered meaningful features, stabilized dependencies, and improved user experience across the platform. Key features delivered include STI example improvements with subclass validation, accessibility and readability enhancements in the UI, Building AI Knowledge hub enhancements, and a performance optimization for saving user pack sequence items. Additional quality-focused work included a diagnostic improvement to lesson name display and related UX refinements.
November 2025
14 Commits • 7 Features
Nov 1, 2025November 2025 (Empirical-Core) delivered meaningful features, stabilized dependencies, and improved user experience across the platform. Key features delivered include STI example improvements with subclass validation, accessibility and readability enhancements in the UI, Building AI Knowledge hub enhancements, and a performance optimization for saving user pack sequence items. Additional quality-focused work included a diagnostic improvement to lesson name display and related UX refinements.
October 2025
17 Commits • 5 Features
Oct 1, 2025October 2025 delivered key platform enhancements and reliability improvements across Dataset Editor, Trials, and CI/DevOps, enabling faster experimentation and more proactive model health management. The team delivered a complete Dataset Editor v3 with UI overhaul and enhanced trial metrics, streamlined trial creation/retry, proactive LLM health monitoring with Slack alerts, and strengthened test data tooling and CI stability. These changes reduce toil, improve data quality, and accelerate business-ready experimentation.
17 Commits • 5 Features
Oct 1, 2025October 2025 delivered key platform enhancements and reliability improvements across Dataset Editor, Trials, and CI/DevOps, enabling faster experimentation and more proactive model health management. The team delivered a complete Dataset Editor v3 with UI overhaul and enhanced trial metrics, streamlined trial creation/retry, proactive LLM health monitoring with Slack alerts, and strengthened test data tooling and CI stability. These changes reduce toil, improve data quality, and accelerate business-ready experimentation.
October 2025
September 2025
13 Commits • 8 Features
Sep 1, 2025Concise monthly summary for Empirical-Core (2025-09) highlighting security enhancements, content expansions, analytics improvements, and platform reliability. Focused on delivering business value and technical excellence, with concrete deliverables, impact, and learned skills.
September 2025
13 Commits • 8 Features
Sep 1, 2025Concise monthly summary for Empirical-Core (2025-09) highlighting security enhancements, content expansions, analytics improvements, and platform reliability. Focused on delivering business value and technical excellence, with concrete deliverables, impact, and learned skills.
August 2025
2 Commits • 1 Features
Aug 1, 2025Month: August 2025 — Empirical Core team Focus: robust dataset management and reliable LLM interaction through Dataset Editor, with targeted stability fixes to ensure a predictable editing experience and data versioning integrity. Key features delivered: - Dataset Editor v1.1 (Part 1 & 2) for Empirical-Core, delivering a comprehensive dataset editor with enhanced capabilities for LLM configuration, guidelines, clusters, and examples. Significant refactoring and new components were introduced to improve dataset management and LLM interaction. Commit: be19350d95e1172a0ba5b66f63da418294ab9fd1 (Dataset Editor v1.1 - Part 1 & 2, #13134). Major bugs fixed: - Dataset Editor stability fixes: resolved issues around handling of examples when changing usage type, ensured selected guidelines persist after saving, and implemented logic for updating test examples and versioning for dataset changes. Commit: d9c74c42ad0f040edc2c61a3dad4d9b8ebe8a8f7 (#13192). Overall impact and accomplishments: - Accelerated dataset management and experimentation by delivering a robust, user-friendly editor with reliable LLM configuration flow, reducing manual workflow overhead and error-prone steps. Enhanced data integrity through versioning-aware changes and persistence of user-selected guidelines, contributing to faster, more reliable model evaluation and iteration. - Strengthened product quality and maintainability via targeted refactors and clear component boundaries, setting up the codebase for scalable feature delivery in subsequent releases. Technologies/skills demonstrated: - Large-scale refactoring and componentization for dataset management and LLM interaction. - State management and persistence concerns (guidelines, examples, and versioning) in a complex editing workflow. - Versioned data changes and stability-focused debugging.
2 Commits • 1 Features
Aug 1, 2025Month: August 2025 — Empirical Core team Focus: robust dataset management and reliable LLM interaction through Dataset Editor, with targeted stability fixes to ensure a predictable editing experience and data versioning integrity. Key features delivered: - Dataset Editor v1.1 (Part 1 & 2) for Empirical-Core, delivering a comprehensive dataset editor with enhanced capabilities for LLM configuration, guidelines, clusters, and examples. Significant refactoring and new components were introduced to improve dataset management and LLM interaction. Commit: be19350d95e1172a0ba5b66f63da418294ab9fd1 (Dataset Editor v1.1 - Part 1 & 2, #13134). Major bugs fixed: - Dataset Editor stability fixes: resolved issues around handling of examples when changing usage type, ensured selected guidelines persist after saving, and implemented logic for updating test examples and versioning for dataset changes. Commit: d9c74c42ad0f040edc2c61a3dad4d9b8ebe8a8f7 (#13192). Overall impact and accomplishments: - Accelerated dataset management and experimentation by delivering a robust, user-friendly editor with reliable LLM configuration flow, reducing manual workflow overhead and error-prone steps. Enhanced data integrity through versioning-aware changes and persistence of user-selected guidelines, contributing to faster, more reliable model evaluation and iteration. - Strengthened product quality and maintainability via targeted refactors and clear component boundaries, setting up the codebase for scalable feature delivery in subsequent releases. Technologies/skills demonstrated: - Large-scale refactoring and componentization for dataset management and LLM interaction. - State management and persistence concerns (guidelines, examples, and versioning) in a complex editing workflow. - Versioned data changes and stability-focused debugging.
August 2025
July 2025
9 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) monthly summary for empirical-org/Empirical-Core: Stabilized core data workflows, hardened feedback moderation, ensured unique unit naming, and expanded AI data management with Gemini integration. Delivered data integrity improvements for genAI histories, added AI research seed data and versioned datasets, and implemented configurable Gemini retry behavior. These changes reduce moderation errors, prevent unit name collisions, and enable scalable AI experimentation with reliable external API interactions, aligning with business goals of trustworthy moderation, reproducible AI research data, and robust data pipelines.
July 2025
9 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) monthly summary for empirical-org/Empirical-Core: Stabilized core data workflows, hardened feedback moderation, ensured unique unit naming, and expanded AI data management with Gemini integration. Delivered data integrity improvements for genAI histories, added AI research seed data and versioned datasets, and implemented configurable Gemini retry behavior. These changes reduce moderation errors, prevent unit name collisions, and enable scalable AI experimentation with reliable external API interactions, aligning with business goals of trustworthy moderation, reproducible AI research data, and robust data pipelines.
June 2025
4 Commits • 2 Features
Jun 1, 2025Month: 2025-06. Focused on advancing the cold-start experimentation backbone and synthetic data generation, while tightening security and reliability through targeted bug fixes. Delivered backend models, migrations, and full UI layers for ideas and dataset_drafts, and improved evaluation integrity.
4 Commits • 2 Features
Jun 1, 2025Month: 2025-06. Focused on advancing the cold-start experimentation backbone and synthetic data generation, while tightening security and reliability through targeted bug fixes. Delivered backend models, migrations, and full UI layers for ideas and dataset_drafts, and improved evaluation integrity.
June 2025
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Quality Metrics
Correctness89.8%
Maintainability85.4%
Architecture83.4%
Performance82.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
CSSHTMLJSXJavaScriptMarkdownRSpecRubySCSSSQLShell
Technical Skills
AI Model EvaluationAI integrationAPI DevelopmentAPI IntegrationAPI SecurityAPI developmentAWSBack End DevelopmentBackend DevelopmentBackground JobsBug FixingBuild AutomationBuild ToolsCI/CDCSS
Repositories Contributed To
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