Working with AI

How I use AI

I use AI in many ways. AI deployment, training and content generation (images, video, audio) using node-based pipelines; as well as generative coding when appropriate.

I cover both use cases below.

AI Content Generation

I work with generative AI across model training and content production:

• Model training

  Preparing datasets, pseudoanonymization, fine-tuning, and LoRA creation for character and environmental consistency, all on the fastest available GPUs. The assistant on this site is one example: a fine-tuned Qwen 2.5 trained on years of my own anonymized emails, served on RunPod and gated by Cloudflare.

• GPU provisioning and deployment

  Large-scale image and video work needs more than a chatbot. I provision cutting-edge GPUs, install models, manage checkpoints and LoRAs, and set up the right CUDA environment. Access to the fastest, most cutting-edge GPUs is included in my hourly rate.

• Generative AI workflows

  Node-based pipelines (ComfyUI and similar tools) that get the most out of every GPU cycle: automating repetitive tasks, generating from a dataset, and creating subtle variations in each batch. These workflows are work-for-hire, so you own the copyright.

• Multimodal output

  Images, audio, and video with commercial rights, edited into a finished professional result. Custom music with human-written lyrics in any genre, mastered to a quality suitable for cinema or streaming platforms.

• Adding AI features to apps

  Summarizing documents, classifying records, extracting structured data from messy text, and most things you might want automated. I keep API costs low with rate limiting and careful prompts. My own app, CalorieGoals, uses AI to estimate portion sizes, exercise expenditure, and calorie counts from meal photos.

AI for Software

AI has changed how quickly software can be built, but only in the right hands. I use it where it genuinely saves time, and I skip it where it would cause problems. Either way, every line of code that reaches you is code I have read and understood myself.

When does AI help?

• Repetitive tasks

  Renaming things, converting data formats, updating an old library. AI does this kind of work in minutes, and it is easy for me to verify.

• Learning new systems quickly

  When a project involves a tool I have not used before, AI shortens the research phase considerably.

• Obscure error messages and logs

  AI is good at parsing dense information like debug logs, crash reports, terminal output, and raw API data.

• Well defined functions

  The input and output are known and constrained, AI is exceptional at write the code to provide the proper output.

When do I avoid it?

I do not let AI make decisions about security, architecture, or what your project should do. Those calls require understanding your business, and they are the reason you hire an experienced developer in the first place. I also never ship code I cannot read and explain. AI output is a draft for me to review, not a finished product.

On smaller projects, or in well-understood open-source languages, I can give the AI more room to write the software itself. But once a project grows to medium or large, the AI tends to lose focus, burn far too many tokens just keeping track of the codebase, and slip into the occasional hallucination.

To use generative coding on larger projects without those problems, I rely on a few techniques:

• Phased planning

  The work is split into phases, each with a clear checkpoint.

• Automated testing

  Tests run after each phase, so nothing silently breaks along the way.

• Sub-agents and memory

  Code-review agents and persistent memory keep the AI focused on a large codebase.

• Central API

  One backend that the mobile, web, and desktop versions all reuse.

This is the difference between what people call "vibe coding" and using AI to ship a focused, production-ready app.