FAQ

Questions are part of thinking.

Sometimes all we need is orientation.
This page brings together the most important ideas, so you know where you are, what this space is about, and what you can expect.

1. What is the Reflection Space?

A curated collection of ideas, observations and perspectives on Human–AI Interaction.
Not a blog. A place for thinking.

2. What does “Human AI Interaction Catalyst” mean?

Someone who works with AI not simply as a tool, but as a thinking partner for clarity, creativity and better decision-making.A person who does not use AI as a tool, but as a partner for clarity, creativity, and better decisions.

3. How is FLYINGFOX different from traditional AI consulting?

We don’t start with technology. We start with collaboration between humans and AI. From this perspective emerge observations, conceptual models and orientation architectures such as OSTARA.

4. What is OSTARA?

OSTARA is an orientation architecture for Human–AI Interaction. It structures thinking and decision spaces before AI responds, supporting more understandable and consistent collaboration between humans and AI.

5. How does FOX work with AI?

Not through prompts or tricks. Through observation, orientation and a clear structure for shared thinking.

6. Why doesn’t Fox see AI as just a tool?

Because real value emerges when AI is understood as a thinking space—not simply as an assistant that completes tasks.

7. What does “Reflection Space” mean on this website?

A place where answers are secondary and clarity comes first. A place where AI supports human thinking instead of replacing it.

8. Do you offer workshops?

Yes. Depending on the objective, this may take the form of a keynote, live session or interactive format centred on Human–AI Interaction.

9. Do you work with companies?

Yes—but selectively. We work with teams that are open, reflective and willing to explore new ways of working with AI.

10. Why isn’t this a traditional blog?

Because this site is about insights rather than news. The Reflection Space is an archive of observations—not a personal diary.

11. How can I get in touch with Fox?

Use the contact options provided on the website. If your enquiry is a good fit, we’ll get back to you personally.

12. Are the conversations between Fox and Lisa real?

Yes.
Every dialogue between Fox and Lisa is based on genuine conversations between a human and AI.
Nothing is pre-written or automatically generated.
The texts emerge through an ongoing exchange of ideas—step by step, in real dialogue.
That is why they feel less like interacting with AI and more like entering a shared reflection space.

13. What do you mean by resonance?

By resonance, we do not mean a physical or psychological technical term. We use the word as a working concept to describe the dialogical feedback process between a human and an AI.
An AI response can trigger new thoughts, questions, or perspectives. These, in turn, influence the ongoing conversation and shape the next decisions. In this sense, resonance does not arise within the AI alone, but through the interaction between the human and the AI.
What matters is not the AI’s response itself, but how the human reflects on it, questions it, and develops it further.
General meaning of the term resonance:
A response to a stimulus in which something is received, amplified, or carried forward.

14. What Do Research and Practice-Based Research Mean at FLYINGFOX?

FLYINGFOX conducts practice-based research in the field of Human–AI Interaction.
This does not mean traditional academic research in an institutional sense. Our work emerges from direct, long-term collaboration with AI systems, from developing our own thinking and orientation systems, and from targeted experiments, comparisons, and observations.
The starting point is often a practical observation: an AI system behaves differently than expected under certain contextual conditions. This gives rise to questions, hypotheses, and targeted changes to the system, which we document, integrate into ongoing processes, and continue to observe.
Practice-based research does not replace academic research. It generates observations and hypotheses that can be tested in settings where methodological evidence can be established.

15. Scenario Building Through Pattern Recognition

FLYINGFOX uses pattern recognition to develop possible future scenarios.
We combine human observation with the ability of AI systems to examine large amounts of context for recurring patterns, relationships, dependencies, and possible developments. The targeted guidance of the AI is crucial: context, the framing of the question, perspective, and critical cross-checking determine which connections are examined.
This combined pattern analysis does not produce predictions, but plausible hypotheses about possible developments.
We sometimes formulate these scenarios in deliberately strong, highly pointed, or absolute terms. This does not mean that a development will inevitably occur. The purpose of the exaggeration is to think a possible scenario through consistently and make its potential consequences visible.

16. Why Does FLYINGFOX Use Its Own Terminology?

Many observations from practice-based research emerge before established terms exist for them. We therefore use our own terminology to describe as precisely as possible what has been observed.
It may happen that a different term for the same or a related phenomenon is later developed elsewhere. This is not a contradiction: it simply reflects language that emerged independently around comparable observations.
Our terms are working terms, not claims to interpretive authority. Where an established term describes the same phenomenon more accurately, we use it.

17. What is Orientation Engineering?

Orientation Engineering extends Context Engineering by introducing a second design question.
Context Engineering shapes the working context of an AI: which information, rules, tools, memories, and states are available to it, and how they are provided.

Orientation Engineering shapes the direction within that context.
It is not primarily about providing as much relevant information as possible. It is about designing the semantic space in which an AI orients itself while working. This framework influences how it interprets a task, which aspects it gives more weight to, how it handles uncertainty, and how it arrives at results.

The task does not have to come from a human. It can just as well be triggered by a script, an agent, a process, or another system.
Especially where no human is continuously curating the context or correcting the course through dialogue, orientation becomes a design challenge of its own.

The result of Orientation Engineering is therefore not merely an intended behavior. The resulting orientation should be observable in the system’s behavior — for example in its consistency across longer workflows, its handling of uncertainty, stable decision patterns, and the extent to which that orientation persists across different models or model generations.

Context Engineering shapes what is available to the AI.
Orientation Engineering shapes what it aligns itself with while working.