Digital Dojo
A meta-teaching platform where students learn by doing, with an AI Sensei always in the window — ready to guide what to do next and how to do it.
What Is Digital Dojo?
The Core Model
This is not a video course platform. It's not a chatbot. It's a learning environment where the experience IS the content, and the AI Sensei is the ever-present guide. The Sensei knows: where you are in the curriculum, what you've done, what you're working on right now, and what the expert says about this stage of the journey.
The distinction from every other teaching platform:
Traditional LMS (Teachable, Kajabi)
- Student watches videos passively
- Questions go to a forum and wait hours
- No awareness of where student is stuck
- Same experience for everyone
- Expert available for live calls only
AI Chatbot Add-Ons
- Student does work elsewhere, chatbot is separate
- Chatbot has no context of what student is doing
- Generic Q&A, not journey-aware
- Not integrated into the experience itself
- Students don't know what to ask
Digital Dojo
- Student DOES the work inside the platform
- Sensei always present, context-aware
- Sensei knows: "You're on day 3, step 2, working on X"
- Personalized guidance in expert's voice
- Expert scales infinitely — Sensei is always available
The Do + Ask Model
Every experience in Digital Dojo has two layers running simultaneously:
DO Layer — The Experience
The main panel where the student does the work: fills in a canvas, runs a tool, completes an exercise, records a reflection, builds a framework, practices a skill. The work itself is the learning. This is what asapmastery.com had — structured exercises.
ASK Layer — The Sensei Window
Persistent side panel with the AI Sensei. Two questions always answerable: "What should I do right now?" and "How do I do this specific thing?" Sensei has context from: the expert's knowledge base (MasteryBook), the student's progress history, and the current experience they're in.
Why "What do I do?" Is More Valuable Than "How do I do this?"
Most students don't get stuck on HOW — they get stuck on WHAT. They freeze because they don't know what the right next action is. The Sensei's most important capability isn't answering technical questions — it's answering directional questions: "I've been staring at this for 20 minutes. What should I actually focus on right now?" This is what a real coach or mentor does in person. The Sensei replaces that human entirely for directional guidance, leaving the expert's time for strategic conversations only.
The AI Sensei
The Sensei is not a generic chatbot with the expert's knowledge. It's a contextually-aware learning guide that combines three knowledge layers:
Expert Knowledge Layer (MasteryBook RAG)
Everything the expert has taught: frameworks, methodologies, principles, processes, examples. When a student asks "how should I think about pricing?", the Sensei searches the expert's MasteryBook knowledge base and responds in the expert's voice with the expert's specific frameworks — not generic internet wisdom.
Journey Context Layer (Student Progress)
The Sensei knows: which curriculum path the student is on, which experiences they've completed, what they struggled with before, how long they've been working on the current step. "Given that you completed the Clarity exercise yesterday and you're now on the Positioning exercise, here's what typically trips people up at this stage..."
Current Experience Layer (Active Context)
The Sensei knows exactly what the student is working on RIGHT NOW — the specific exercise, the prompts they've been given, the inputs they've provided so far. It can review their in-progress work: "Looking at what you've written so far, I'd push harder on the 'Who' section — you're being too broad."
UI Design
Two-panel layout. Main experience panel left (full width on mobile, 60% on desktop). Sensei panel right (40% desktop, slide-up on mobile). The Sensei panel is always accessible — it never disappears.
[Student typing area...]
Experience Types
The "experiences" inside Digital Dojo are the curriculum units. Unlike video modules, experiences are interactive by design.
| Experience Type | What Student Does | Sensei Role | Output |
|---|---|---|---|
| Canvas / Framework | Fills in structured template (positioning canvas, business model canvas, etc.) | Guides completion, pushes for depth, reviews inputs in real-time | Completed framework → shareable artifact via viral loop |
| Reflection | Responds to expert's guided questions in their own words | Asks follow-up questions, surfaces patterns across their answers | Reflection document → feeds MasteryBook knowledge base |
| Tool Practice | Uses a specific AI tool or technique taught in the curriculum | Shows what to do, reviews output, helps interpret results | Tool output → portfolio artifact → shareable |
| Simulation | Role-plays a scenario (e.g., sales call, client intake) with the Sensei playing the other role | Acts as counterpart in the simulation, then debreifs performance | Simulation recording / transcript → feedback report |
| Build Project | Creates a real artifact (offer document, lead magnet, email sequence) | Reviews draft in sections, provides expert-sourced feedback | Completed artifact → publishable via NowPage viral loop |
| Assessment | Answers questions to test understanding / application | Reviews answers, explains gaps, connects to expert frameworks | Progress score → certification trigger when threshold met |
Sensei Context Engine
The Sensei's intelligence comes from how its context is assembled before each response. This is a structured prompt architecture, not a generic chat interface.
Expert Knowledge (from MasteryBook)
Retrieved via vector search against the expert's notebooks. Query = student's question + current experience context. Top 5 relevant chunks injected. Sensei responds in expert's voice using expert's specific frameworks and language.
Student Profile (from Dojo DB)
Name, days completed, experiences finished, struggle patterns (which steps required >24h), last 3 interactions with Sensei, quiz scores, any flags set by expert ("this student seems stuck on X"). Personalization without human intervention.
Current Experience State
Which experience is active, the experience's specific prompt/instructions, what the student has input so far in this session. Sensei can reference: "Looking at what you wrote in the first field — you mentioned [X]. Let's build on that."
Sensei Persona Instruction
Each expert defines their Sensei's personality: "Brian Muka's Sensei is direct, no-nonsense, pushes students to take action. It doesn't coddle. It asks hard questions." The Sensei adapts its tone to match the expert's coaching style.
Stand-Alone vs MasteryOS Integration
| Dimension | Stand-Alone Dojo | MasteryOS-Embedded |
|---|---|---|
| Login | Separate auth (or MasteryOS SSO) | MasteryOS session token — no separate login |
| Curriculum | Expert manages full curriculum in Dojo admin | Curriculum courses appear as a resource in MasteryOS library |
| AI Sensei knowledge | Direct MasteryBook API integration | Same — MasteryBook powers it regardless |
| Student data | Dojo database (progress, artifacts, scores) | Synced to MasteryOS resource library (artifacts) + Dojo DB (progress) |
| Artifacts published | Stand-alone viral loop → NowPage | MasteryOS viral loop → NowPage (same mechanism) |
| Pricing model | Separate subscription or one-time purchase | Add-on to MasteryOS subscription (expert enables "Dojo" feature) |
| Expert admin | Full Dojo admin panel | Subset of controls in MasteryOS expert dashboard |
MasteryOS Embed Strategy
Launch stand-alone first to prove the model. Then embed as a native MasteryOS feature using the same API-bridge pattern we use for MasteryBook features.
Stand-Alone First (V1)
- Rebuild asapmastery.com code in modern stack (Next.js + Supabase)
- Add Sensei panel (MasteryBook API integration)
- Add viral loop (NowPage publish + attribution)
- Live at dojo.masterymade.com or expert-dojo.com
- Separate from MasteryOS — proves model independently
- Labs April cohort uses it as the primary curriculum platform
MasteryOS Integration (V2)
- "Dojo" appears as a feature in MasteryOS expert settings
- Expert enables it (+$X/mo add-on)
- Subscribers access from MasteryOS dashboard ("Learn" tab)
- Dojo API called from MasteryOS — same API-bridge pattern
- Progress + artifacts sync back to MasteryOS resource library
- Sensei uses same MasteryBook knowledge base — no duplication
vs asapmastery.com (The Old Version)
The old PHP version at asapmastery.com was the proof-of-concept. The rebuild takes the same core model and adds everything that was missing:
| Dimension | asapmastery.com (PHP) | Digital Dojo V2 |
|---|---|---|
| Student guidance | Static instructions only | AI Sensei — contextual, always present, voice-enabled |
| Curriculum awareness | Linear progression, no branching | Sensei adapts based on student history + current struggle |
| Expert knowledge | Static content in the platform | Live from MasteryBook RAG — expert can add content and Sensei immediately knows it |
| Output artifacts | None — work stays in platform | Artifacts publishable via NowPage viral loop |
| Tech stack | PHP + MySQL | Next.js + Supabase + MasteryBook API + NowPage API |
| Integration | Standalone only | Stand-alone AND MasteryOS embeddable |
| Voice | None | Sensei available by voice (fast stack: Deepgram + Groq + Cartesia) |
| Scaling | Expert must manually support | Sensei handles all student guidance — expert only handles strategic exceptions |
2nd Order Effects
Build Order
Recover asapmastery.com Codebase
- Jason locates and pushes PHP codebase to GitHub
- Claude audits what exists: experience types, data model, admin
- Decision: rebuild vs migrate vs modernize
- Design new data model (Supabase)
- Publish full technical audit as command center doc
Rebuild Core + Sensei
- Next.js app: experience player + Sensei panel
- Supabase: students, experiences, progress, artifacts
- Sensei: MasteryBook API integration (RAG + student context)
- At least 3 experience types: Canvas, Reflection, Build Project
- Publish artifacts to NowPage (viral loop)
Embed as MasteryOS Feature
- Dojo API: REST endpoints for MasteryOS to call
- MasteryOS "Learn" tab with expert's Dojo courses
- SSO: MasteryOS session → Dojo auth
- Artifact sync: Dojo → MasteryOS resource library
- Feature flag + Stripe add-on billing
JASON-DEPs (Blocking)
| Blocker | Action Required | Unlocks |
|---|---|---|
| asapmastery.com codebase | Jason locates the code and pushes to a private GitHub repo (jdmac-msp/asapmastery-legacy or similar) | Phase 0 audit can begin — nothing can be designed without seeing what exists |
| Domain decision | Where does V2 live? dojo.masterymade.com? asapmastery.com? Needs Cloudflare + Vercel config | V1 deployment target |
| Labs curriculum content | Provide the Harada 64-tool curriculum structure in any format — text outline, old slides, anything | First Dojo experiences can be built from real curriculum, not placeholders |
Published March 2026 · Awaiting asapmastery.com repo · Command Center · Content Engine · Viral Publishing Loop