What the EdTech?

I am not using AI like a chatbot anymore

Rob Dickson

Use Left/Right to seek, Home/End to jump to start or end. Hold shift to jump forward or backward.

0:00 | 4:48
Rob Dickson explains why AI gets more useful when it becomes an operating layer across memory, tools, documents, scheduled research, and human-approved workflows instead of just another chatbot tab.
SPEAKER_00

Most people are still using AI like a search box. They open a tab, ask a question, copy the answer, paste it somewhere else, then the context disappears. Useful, yes, but it is also the least interesting version of what this technology can become. The real shift happens when AI stops being a place I visit and starts becoming a layer across my work, memory, tools, documents, scheduled research, messaging, search across past conversations, reusable workflows, a system that helps me stop losing the thread. That is what I have been building with Frank and Henry. Henry runs on OpenClaw, Frank runs on Hermes. They sit on two small Mac systems in my home lab, not on the district network, and not as a district managed AI system. That matters. This is my environment. I am experimenting so I can understand the workflow, the risks, and the design patterns before making claims about what any of this should mean for schools. Henry is my thought partner. He is where I test ideas, push on framing, work through research questions, and let messy thinking get more organized before it turns into something public. Frank is my orchestrator. He helps preserve context, monitor signals, manage scheduled research, search across memory, work through documents, and connect different parts of the system. Henry helps me think. Frank helps me move. Together, they point to something bigger than a better chatbot. A chatbot answers the prompt in front of it. An operating layer preserves context. Roots work schedules attention and reduces the cost of starting over. Most knowledge work does not fail because people are not smart enough. It fails because context leaks. The meeting happened, but the follow-up died. The article was useful, but the idea never made it into the system. The email mattered, but it got buried. The same report gets rebuilt every month because no one can find the last version. That is the problem I am trying to solve. Not how do I make AI write more words? We have plenty of words. The internet is choking on them. The better question is this: how do I build a system that helps me notice what matters, remember what matters, and act on what matters? The model is not the system. The workflow around the model is the system. If the output dies in the chat window, I do not have an operating layer, I have a very articulate scratch pad. The system starts to matter when the assistant can remember context, load workflows, search prior conversations, work through documents, run scheduled research, and ask for approval before taking external actions. That last part matters. External actions need approval. Drafts are safe. Private context stays private. Publishing, posting, replying, sending, deleting, or sharing require a human decision. That is not distrust. That is trust with architecture. The most practical part of this setup is scheduled research. Frank can run recurring jobs that bring me briefings, trends, writing ideas, and weak signals before I remember to go look for them. Not everything needs an AI model. Sometimes the right answer is a script, sometimes it is an agent, sometimes it is both. This connects directly to education. Most AI conversations in schools still sound like tool conversations. Which chatbot should we use? What should the policy say? How do we stop cheating? What prompt should teachers try? Those are real questions. They are not enough. Agents push on the structure of work itself. If an AI assistant can remember, search, schedule, draft, verify, use tools, and work through documents, the question changes. Where does context get lost? Which workflows depend on one person remembering everything? Which decisions stall because the information is scattered? That is why I keep coming back to intentional design. AI should not be bolted onto broken workflows and called transformation. That is just digitizing compliance with better auto-complete. The opportunity is redesigning the work, not replacing human judgment, not outsourcing relationships, not automating responsibility, redesigning the system so people spend less time reconstructing context and more time making better decisions. Frank and Henry are not replacing my thinking. They are helping me stop losing the thread. And that is a much better goal. The point should not be to make everything more automated. The point should be to make the work more intentional.