Knowledge
worth keeping.

I wrote why I left EVGA because my experience was worth examining honestly. The second essay returned to the people whose judgment, resourcefulness and care made the company meaningful to me. These are my recollections, with the limits that any personal account carries.

I also had a professional question: could my strongest real story reach people who had never encountered my work? Developing and publishing the essays became part of a wider experiment with AI-assisted work. I remain responsible for what I publish, including checking claims and correcting mistakes. The tools do not turn recollection into independent evidence.

I am optimistic about AI and about what NVIDIA and others are helping make possible. I want that work to succeed. From what I see publicly, I believe Jensen Huang has grown since my EVGA years; that is my personal assessment, alongside the experiences I have recorded. Holding a company to high standards is part of wanting its technology to serve people well. My contribution is in digital media: make useful work, improve the process behind it, and test where new tools help.

My work in computer graphics shapes how I judge AI. An unconvincing effect can pull me out of a film; a carefully integrated effect can serve the story so well that I scarcely notice the technique. Poor output deserves criticism. The tool deserves to be judged across its uses, against the quality of the result and the benefit to the person using it.

For me, that includes whether a tool helps someone access knowledge, communicate or do work that was previously difficult to reach. Experience, care, testing and accountability are how I want to make that promise useful.

A product video, a 3D animation or a catalog image carries decisions that the finished file cannot explain. Which specification is authoritative? What does the audience need to understand? Which camera angle or lighting choice reveals the feature? Which source asset, render setting or export belongs in this version?

In production teams, those decisions can become “tribal knowledge”: understood by the person doing the work, difficult for a colleague to recover. A checklist might say “export the video” without explaining the delivery requirements, the approved revision or who needs to review it.

I want to make more of that reasoning usable. Keep examples of an acceptable result, record the important exceptions, and connect instructions to the actual files and decisions. That helps a team today and makes a future handover less dependent on memory.

NASA’s knowledge-management resources provide useful context for learning from experience. My practical focus is the media work I know: capturing production decisions and keeping that guidance useful as assets, briefs and tools change.

At EVGA, I co-produced more than 200 videos with a production partner. My work included planning, sets, camera, lighting, audio, presenting, editing and publishing. Technical support and product responsibilities helped me understand what audiences needed explained.

At ViewSonic, I moved between product management and technical content: demonstrations, 3D visuals, launch materials, technical writing and a standardized multimedia launch workflow. The work required translating product details into something a customer could understand.

At Halo / AddOn Networks, I worked on product animation, filming, post-production and a manufacturing-process film alongside image-production automation. My corporate production contract covered executive communications, location filming, presentations and workflows combining live action, motion design and reusable 3D assets.

That is the experience behind my consulting: digital media production and the workflows that connect the brief, the subject, the creative work and delivery. Product-marketing, communications and creative teams are where I can contribute with confidence.

At AddOn Networks, my Image Creation Tool grew from understanding a repetitive production process. Excel, catalog data, Photoshop actions and the graphics team’s work became parts of a connected system. I supplied workflow knowledge, design and testing; ChatGPT helped generate and revise code.

The lesson was to understand the work before choosing what to replace. Familiar tools can have substantial unused potential. A new interface is valuable when it makes the process clearer and more dependable.

ICT is a specific example of improving a media workflow. It does not establish a universal speed-up for creative work. My production standards remain the same: inspect the details, check the output and make the result usable by the people who depend on it.

My Digital Media Workflow Review is a fixed-fee assessment of one recurring deliverable or production process. That might be product imagery, a demonstration series, a recurring executive video or launch content that takes too much effort to coordinate.

1. Review real work. Follow representative examples from the brief and source material through production, review and delivery. Identify where people repeat work, lose context or struggle to maintain quality.
2. Make the standard clear. Record the decisions, approved examples, responsibilities and exceptions that matter. Separate creative judgment from steps that can be made more repeatable.
3. Choose a practical improvement. Deliver prioritized recommendations and a defined pilot proposal. The answer might be a better template, asset structure, production method, script or AI-assisted step.
4. Test a separately scoped pilot. Agree on representative inputs and acceptance criteria before implementation. Compare quality, production time, rework and usability; document what the team needs to operate and maintain it.

AI earns a place where the output can be inspected and the benefit demonstrated. A faster draft still needs a correct product claim, an appropriate visual treatment and an accountable reviewer.

As an autistic person, I know how much effort a person can spend navigating a system that was not designed around how they work. That experience makes clear expectations, usable interfaces and access to context personal concerns for me.

In a creative team, a clear brief, an example of the required result and an understandable review process can reduce dependence on knowing the right person or feeling comfortable asking the same question again. My ambition is to give ability more room to show itself. Software cannot guarantee a fair workplace; management decisions, accessibility and how people are treated still matter.

You do not have to share my optimism about AI to expect evidence. I want to earn trust by showing what improved, what failed and what still requires a person’s judgment. My wider view of technology begins with the same purpose: help people participate more fully and choose more of how they spend their lives.

Keep the production judgment. Make the next good piece of work easier to deliver.