Clarity over cleverness
Systems should remain understandable to the humans who build, maintain, and debug them. Obscurity increases operational risk.
⚧️ Pronouns: He/Him
🦕 Hi, I’m Doby Baxter - a transgender & neurodivergent Software Systems Consultant focused on designing clarity within complexity.
Python-focused Software Engineer with experience contributing to scientific simulation infrastructure (ESA Pyxel) and large-scale open-source systems (GitLab ecosystem). I specialize in configuration validation, structured YAML/JSON workflows, developer tooling, CI/CD integration, and reliability improvements.
I work in distributed, Git-based engineering environments and focus on building systems that are maintainable, well-documented, and production-aware. My engineering approach prioritizes clarity, structural integrity, and reducing configuration and input-related risk.
| Technical Competencies | |
|---|---|
| Programming Languages | Python • TypeScript • JavaScript • Rust • Go • PHP • Bash • PowerShell |
| Frameworks | Laravel • Astro • Flutter • Hugo • Node.js • Electron • Tauri • Capacitor |
| Web / Data Formats | HTML • CSS • JSON • YAML |
| Data & Persistence | PostgreSQL • MySQL • SQLite (SQL) |
| DevOps & Cloud |
Git • GitLab • GitLab CI/CD • Docker • AWS • Azure Gradle • Composer • SourceHut • PyPI |
| Identity & Security |
Microsoft Entra ID (RBAC, MFA, policy config) Wireshark • Nmap • Shodan • Metasploit • Netcat • Nikto • Cisco Packet Tracer |
| Research & Applied AI |
ESA Pyxel • NASA DONKI API • GPT4All • ORCA Mini (GGUF) Jupyter • Selenium • SSH (PuTTY) |
| Interactive / Game Dev | Godot (GDScript) • PICO-8 (Lua) • Twine • Android Studio • Pixel-art workflow |
| Governance & Practices |
Agile methodologies • DevOps & DevSecOps • GDPR compliance ISO/IEC 27001 alignment • WCAG accessibility • Automated testing • Technical documentation |
| Engineering Experience |
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Core principles that guide my work in scientific simulation tooling, developer infrastructure, validation systems, and deterministic AI workflows.
Systems should remain understandable to the humans who build, maintain, and debug them. Obscurity increases operational risk.
Input validation, schema enforcement, and configuration safeguards are architectural responsibilities, not optional features.
Predictable execution paths improve auditability, debugging, and reproducibility in complex systems.
Reliable systems do not merely fail less often. They fail in ways that humans can interpret and recover from.
"Doby has demonstrated strong technical skills, thoughtful system-level thinking, and a clear focus on usability and maintainability. He played a key role in developing innovative tooling driven by real user and community needs. He is proactive, reliable, and communicates clearly, particularly when working on complex or cross-cutting features."
Deterministic middleware for enforcing explicit workflow topology in LLM-powered systems. Designed for infrastructure-level AI applications where structural integrity, bounded transitions, and predictable execution paths are required.
Usage Model
The repository is publicly available for inspection and non-commercial use.
Commercial or production deployment requires a license.
Commercial Licensing
Starting from £249 for single-project commercial usage.
Team and enterprise licensing available upon request.
What’s Included
• Commercial license grant
• Packaged Python wheel (.whl) distribution
• Usage and integration instructions
A schema-aware YAML configuration builder for ESA Pyxel simulation modes. Implements guided configuration flows, structured validation logic, and contextual error handling to reduce misconfiguration in scientific detector simulation workflows.
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A structured documentation and contribution index detailing code, validation improvements, and developer-experience enhancements made to the ESA Pyxel scientific simulation framework. Highlights system-level problem analysis and clarity-driven improvements in configuration handling.
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A technical portfolio documenting contributions to the GitLab ecosystem, including backend validation improvements, JSON parsing safeguards, documentation restructuring, and reliability enhancements in AI-adjacent services. Emphasizes cross-functional collaboration and production-aware development practices.
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A Raspberry Pi Zero 2 W–based CLI cyberlab environment designed for systems monitoring, network analysis, and security tooling experimentation. Includes service configuration, resource optimization, and command-line automation within a constrained hardware environment.
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A developer-focused project scanner that analyzes repository structure and configuration patterns to surface potential risks and structural inconsistencies. Designed to provide explainable output and reduce cognitive overload during early-stage code review.
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An AI systems architecture project exploring bounded, human-centered artificial intelligence design. Focuses on transparency, structural safeguards, and accountability mechanisms to preserve user agency within AI-assisted workflows.
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