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About Me
Been enrolled on the Apple iOS Developer Program since 2009, having successfully submitted and continue to update a variety of my own apps on the Apple Store.
https://apps.apple.com/gb/developer/zonk-technology/id726984885
Web Apps:
https://webapps.llmtraining.dev/
LinkedIn Skill Badges:
https://linkedin.skill.assessments.franz.ayestaran.dev
Franbo Cube:
https://franbo.ayestaran.dev/
Playstation Gamer Profile:
https://psnprofiles.com/Raspberry-Pi-SBC
XBOX Gamer Profile:
https://xboxgamertag.com/search/RaspberryPi-SBC
An accomplished and experienced Senior Analyst Programmer with a range of skills encompassing Development, Installation and Configuration of IT Hardware & Software, with a history of employment as an Application, Web, Mobile, Wearable and AI Developer using the latest mainstream technology within an AWS Linux and Windows Azure Environment.
As an AI‑Augmented Software Engineer, I integrate advanced AI tools directly into my software engineering workflow — not to replace engineering judgment, but to amplify it. I combine deep traditional engineering expertise with AI‑driven capabilities such as code generation, refactoring, testing, documentation, architectural exploration, and system analysis to deliver faster, higher‑quality solutions.
I first started computer programming in the 1980s, when home computers used 8 bit processors and user defined programs were stored on magnetic tape.
Key Strengths
• X-Code, Android Studio, Visual Code / Studio, Swift / UI,
• Kotlin, React Native, Watch OS, Wear OS, Objective-C, iOS,
• Windows Mobile, MongoDB, IIS, RESTful APIs, JSON,
• DOT NET Framework, Dreamweaver, Vanilla JavaScript,
• jQuery Mobile, PhoneGap, Cordova, NodeJS, Parse, SSL,
• Windows Server, Windows Azure, MonoDevelop, Firebase,
• Amazon Web Services, Database Hosting, Terraform, Docker,
• MS SQL Server / Reporting Services, MySQL, SQLite,
• AI-Augmented Software Engineering,
• Python, PyTorch, Transformers, GGUF, Ollama, Quantisation,
• LLM Fine-tuning, FlaskAI, LLM, SLM, TinyLlama, Llama.cpp,
• Model Context Protocol (MCP),
• Retrieval-Augmented Generation (RAG).
Bridging symbolic reasoning and scientific discovery by building AI tooling with training modes such as:
• LLM Training text‑based fine‑tuning and interpretability analysis.
• Molecular AI Training – graph, SMILES and multimodal property
prediction for scientific datasets.
• Scientific Graph Training – generalized graph and multimodal
learning for materials, proteins, circuits and 3D structures.
Top skills:
Xcode • Swift
Android Studio • Kotlin
Visual Code • React Native
Quote: The person who says it can’t be done is usually interrupted by the person who has achieved it.