u/nmamizerov

Assemblix lets you build an AI chat assistant by dragging blocks onto a canvas and connecting them with lines.

The blocks do simple jobs. One answers the student. One decides where to send the question. One reads your uploaded course materials. One calls your own service and brings back an answer.

Wire them together, press run, and you have a chat your users can talk to. It remembers the conversation.

We built it for our own edtech work, and that is where it runs today. Two flows we use:

• A tutor chat that answers from the course materials and hands hard questions to a live teacher
• A checker that reads a student's homework, gives feedback on it, and logs the result for the curator

Every message shows you its path through the canvas. When an answer comes out wrong, you open the run and see the block that caused it, with what went in and what came out. That mattered for us, because a wrong answer to a student costs more than a wrong answer in a demo.

You run the whole thing on your own server with your own model keys.

Try the demo without installing anything: https://app.assmblx.com

Repo: https://github.com/nmamizerov/assemblix

A star helps other people find it.

I run tech at an online school for communication skills, and I want to share how we ended up building AI-driven practice. The interesting part was a learning-design question more than a technical one.

You can only really learn communication by doing it with someone and getting feedback. That is one-on-one work, and it does not scale. One trainer can sit through maybe a dozen roleplays a week, and our students needed far more reps than that.

Automating the whole trainer felt like the obvious move, but we did not go that way. Instead we went through what a trainer does during a single exercise, step by step: what they pay attention to first, how they judge whether a student handled a moment well, what makes them pick one piece of feedback over another. Once it was written down as separate steps, each one was small enough to hand to an AI: one step evaluates a single skill, another decides whether the scenario moves forward, another generates the counterpart's next line. Today students practice against an AI counterpart and get structured feedback, and we run around 2,000 of these exercises a month.

What I am curious about from this community: how far do you go when you break down a trainer's or an SME's judgment into steps? Is there a point where making expert intuition explicit strips out the thing that made it work?

I run tech at an online school for communication skills, and I want to share how we ended up building AI-driven practice. The interesting part was a learning-design question more than a technical one.

You can only really learn communication by doing it with someone and getting feedback. That is one-on-one work, and it does not scale. One trainer can sit through maybe a dozen roleplays a week, and our students needed far more reps than that.

Automating the whole trainer felt like the obvious move, but we did not go that way. Instead we went through what a trainer does during a single exercise, step by step: what they pay attention to first, how they judge whether a student handled a moment well, what makes them pick one piece of feedback over another. Once it was written down as separate steps, each one was small enough to hand to an AI: one step evaluates a single skill, another decides whether the scenario moves forward, another generates the counterpart's next line. Today students practice against an AI counterpart and get structured feedback, and we run around 2,000 of these exercises a month.

What I am curious about from this community: how far do you go when you break down a trainer's or an SME's judgment into steps? Is there a point where making expert intuition explicit strips out the thing that made it work?

I run tech at an online school for communication skills, and I want to share how we ended up building AI-driven practice. The interesting part was a learning-design question more than a technical one.

You can only really learn communication by doing it with someone and getting feedback. That is one-on-one work, and it does not scale. One trainer can sit through maybe a dozen roleplays a week, and our students needed far more reps than that.

Automating the whole trainer felt like the obvious move, but we did not go that way. Instead we went through what a trainer does during a single exercise, step by step: what they pay attention to first, how they judge whether a student handled a moment well, what makes them pick one piece of feedback over another. Once it was written down as separate steps, each one was small enough to hand to an AI: one step evaluates a single skill, another decides whether the scenario moves forward, another generates the counterpart's next line. Today students practice against an AI counterpart and get structured feedback, and we run around 2,000 of these exercises a month.

What I am curious about from this community: how far do you go when you break down a trainer's or an SME's judgment into steps? Is there a point where making expert intuition explicit strips out the thing that made it work?

I'm launching my own AI agency focused on process automation, and the first thing I want to do is break into the niche I come from - education. For the past few years I've been the CTO of a school for management-through-communication, where we launched AI-powered training simulators, built automated grading of assessments, generated supplementary educational materials for courses, and so on.

I'd love to learn how others are moving in this space and pick up some best practices for myself. Maybe there's something you've been thinking about but haven't figured out how to pull off technically? I'd be glad to help.