There are three reasons I wanted to meet. The first is a personal connection through cognition and the experience of building something beyond a conventional career path. The second is practical: I would like to understand whether there is a digital initiative or recurring workflow where engineering support would help. The third is the possibility of being useful to people in your wider network over time. Those goals do not require us to decide on a large partnership today. I suggest we spend a few minutes on context, give most of the time to a real example from your work, and finish with a concrete next step if one emerges.

I spent five years in business development at Emotiv in Vietnam, working around EEG applications and consumer research. Your pilot study caught my attention because it brings several ways of observing an experience together. The study involved seventy-two students and combined EEG with eye-tracking and other measures. It found differences in some signals, while learning outcomes and working-memory capacity did not differ. That distinction matters when moving from research to product design. My experience is commercial and practical, while yours includes academic research; they offer different perspectives. I would be interested in which findings you consider useful for designing a better learning experience, and which still need more investigation.

These three threads help us ask better product questions. The hypercuriosity paper is a theoretical proposal, so I would not present it as established evolutionary fact. The later survey of five hundred and twenty-one adults reports associations between hyperactivity–impulsivity traits and self-reported curiosity, without establishing causation. The GenAI study, published in October 2026, adds qualitative accounts of support, authenticity, and dependence: twenty-four people took part in tasks, and fourteen in focus groups. For a prototype, we can ask whether users understand the output, whether they can check its basis, and whether they retain control of the next action. A small usability study would answer those practical questions within its scope.

I see this timeline as context for a conversation about choices. Your public work spans Google Fit, the Lysa project, writing and building Ness Labs, and academic research at KCL. My own starting point was business development at Emotiv before building CTC. I would not claim that our experiences or turning points were the same. What interests me is the practical question beneath both journeys: when a project teaches us something unexpected, how do we decide whether to continue, change direction, or stop? I can share one short example from a project I actually worked on, and would enjoy hearing which experiment changed the way you build.

This map shows the ecosystem around a potential collaboration. On the Ness Labs side are distinct activities: newsletters and essays, Hypercurious, community and Fellowship, Featured Tools, and Anne-Laure’s academic work. The published audience numbers are context, not a forecast of users for a new product. In the middle is the proposed place to work together: one selected experiment with a named owner. On the CTC side are AI Studio, engineering through PowerIT, and a possible venture track. The arrows represent routes we could explore. They do not imply that the systems are connected today or that a university collaboration has been agreed. Which part of this map contains work that matters now?

Your essay gives us a specific place to start. In February, you described an app idea for the community and a dashboard you had begun building for the team, alongside literature reviews, in a world where tools such as Claude Cowork and OpenClaw make a first draft easier. What struck me was the decision problem: choosing what deserves further attention when many things are possible. I would first ask what has happened since then. Are either of those initiatives still important? If so, what is the actual obstacle today: deciding the scope, testing the value, integrating systems, or finding time to maintain it? Those answers determine whether our engineering support would help.

The three circles describe a working relationship we could test. Research contributes disciplined questions and attention to the limits of a finding. An experimental mindset contributes a willingness to try something small, observe it, and change course. Engineering contributes a functioning workflow, reliable handoffs, and a practical understanding of what maintaining the tool will involve. The centre only becomes meaningful when those perspectives meet around an actual task. It does not follow from shared vocabulary alone. I would like us to identify the part that your team already does well and the part where outside help would add something specific. Then we can judge the fit through a small piece of work.

CTC can contribute through three routes. AI Studio is relevant when the work includes an AI-assisted task whose quality and review process need to be designed. PowerIT is the engineering route for implementing a defined workflow, connecting approved systems, testing it, and handing it over. Venture exploration becomes relevant only when there is a product opportunity that both sides want to investigate beyond a single delivery task. We do not need all three for a first pilot. If one simple workflow answers the question, that is enough. I would also distinguish the capabilities we can discuss from any specific promise: team composition, availability, implementation detail, and delivery dates come from the scoped brief.

For the team dashboard, I would begin with one recurring handoff rather than a universal reporting system. Suppose an operations owner prepares a weekly update from an agreed status sheet. A small prototype could organize that input into a summary, unresolved decisions, and next actions with owners, while linking every item back to its source. We would compare the current process with the prototype using the same input. How much preparation and correction are required? Are important items missing? Does someone actually use the result to decide what happens next? This is an example to test with your team, not a claim about how the team works today.

The community companion is a second hypothesis. I would start with one activity that a facilitator already runs and understand where participants or organizers lose the thread. A small companion could support the sequence from choosing an action, to recording what happened, to writing a Plus–Minus–Next reflection. The facilitator should receive only the information participants have agreed to share, and only if it helps with a real decision or conversation. We would assess the quality of the reflection and the work required to support it, not just how many forms were submitted. Existing Circle capabilities or a simple worksheet should be considered before building a separate application.

For evidence-grounded AI, a useful first task might be comparing a small set of approved documents. The output would be a draft table whose claims link to the relevant passages, with missing information and uncertainty kept visible. A human reviewer remains responsible for the interpretation and final use. We would evaluate whether the tool makes that review easier and more accurate, including the time spent finding and correcting mistakes. Your writing about the illusion of clarity and the qualitative GenAI study make that question especially relevant. We should not assume that adding citations makes an answer correct, or that a small prototype can perform an entire academic review.

This is the design principle I would like to explore across the examples. Before showing an AI-generated explanation, we might invite the person to make a brief prediction or explain the idea in their own words. Then the interface can make sources and uncertainty easy to inspect, followed by a deliberate choice about the next action. That sequence draws a practical question from your essay about the illusion of clarity. It is not a validated intervention simply because the theory is appealing. We would observe whether people understand the task and can judge the result, and whether the extra step earns its place or merely adds friction.

We built TinyExp as an independent prototype inspired by the book, to make our interpretation visible and invite correction. If it is useful, I would spend sixty seconds on one small journey: choose an action and a period, record an observation, then reflect through Plus–Minus–Next. The design intention is to support learning without making a pause feel like a failure. I would value your reaction to whether this rhythm supports learning and where our interpretation could be improved. For example, does a short reflection invite a useful observation, or does it become another task to complete? This prototype offers something concrete to discuss before considering a community trial.

The loop brings the earlier pieces together. We begin with a useful signal from a conversation, an essay, or a recurring task. We shape an idea, using AI where it helps, and select a bounded test. Engineering makes that test usable. People try it in the intended context, and a Plus–Minus–Next review helps us decide what to retain, change, or stop. Only then do we consider sharing the result more widely. This is a proposed collaboration process, not a claim that such a system already operates across Ness Labs and CTC. The loop is healthy when it creates a useful decision, including a decision to stop adding software.

PowerIT’s proposed contribution is the work between an interesting draft and a workflow someone can inspect and use. We can begin with notes, a short walkthrough, or code that you have permission to share. First we check what exists and whether it is useful to retain. Then we agree on one user journey, the dependencies, and the acceptance checks before implementing it. Verification should cover the actual task and the relevant failure cases, rather than relying on a polished demo alone. Finally, we document what was built, what remains limited, and who owns operation and support. The depth of each step depends on whether we are delivering a prototype or preparing a production release.

If the selected task is small enough, fourteen days could be a useful container. We would confirm the owner, baseline, workflow, and success measure at the start. The first build uses synthetic data so the core interaction can be reviewed without waiting for broad access. The next stage is observation through agreed checks and, where permitted, a small trial with the intended users. At the end, we review what worked, what did not, and what should happen next. This schedule is a proposal to confirm after scoping. It is not a promise to ship a full production product in two weeks, and the commercial terms are agreed before the work starts.

I would define useful before building. For a dashboard task, we can record how long the current process takes, what gets missed, and how much checking is needed. Then the same synthetic input can be used with the prototype. We should count the entire task, including correction and any work moved to another person. Quality needs an agreed check as well: a fast answer that invents a status is not an improvement. Finally, the owner should be able to explain why they would keep using the result and what it costs to operate. These observations support a practical software decision; they do not establish a cognitive or clinical benefit.

The third goal is to be useful beyond a single project, if the fit develops. Joe’s partnerships role and the Featured Tools interviews suggest a context where founders discuss how their products work. Examples such as Flowing, Littlebird, and Linkflare give us topics to understand, not an assumed referral channel. Fellows who are building something might also have a specific technical question. Separately, researchers may need software for a defined task, but any academic work would follow its institutional, ethics, data, and procurement process. There is no need to arrange introductions today. A relevant connection should arise from a clear need and the willingness of the people involved.

I also want the commercial path to be clear. A scoped pilot should specify the outputs, timing, price, ownership, acceptance, and handover before either side commits resources. If the result is useful and there is continuing work, we can then discuss the most suitable arrangement: a fixed-scope project, an engineering pod with clear responsibilities, or a retainer for ongoing support. Co-building a product would need a separate business case and agreement. Academic procurement would follow the relevant institution’s process. We would choose any ongoing model together after reviewing the pilot and the actual work ahead. The question for today is which kind of engagement would fit how your team normally works with external partners.

If one of the possibilities is relevant, these four fields let us capture a useful brief together. First, who needs the result and what would improve for them? Second, what already exists: notes, a workflow, a wireframe, code, or a working prototype? Third, what is the one essential user journey we should preserve in a first version? Fourth, which integrations and data are genuinely necessary? Alongside those answers, we need to understand the current obstacle, name the person who can make decisions, and agree how success would be judged. I would read the brief back to you before suggesting a scope, so you can correct anything we misunderstood.

If there is a useful task and an owner who wants to explore it, I suggest a small sequence. We could agree to share permitted notes or a short walkthrough within twenty-four hours. Once we have enough context, CTC could aim to return a one-page scope within forty-eight hours, showing the proposed output, assumptions, open questions, and commercial options. Then a twenty-minute review would let us confirm whether the scope and timing are realistic before starting anything. Let’s choose timings that work for your team and name an owner for each step. If there is no suitable task now, we can leave the proposal open and stay in touch.