Japonics / SELF-DEVELOPMENT
SENSEI
A private self-development operating system connecting daily planning, habits, goals, learning, retrospection, mentoring, travel, and relationships.
A private self-development operating system
Where the project started
Sensei connects goals, habits, learning, and retrospection through cross-module signals (habit → goal, learning → goal, retrospection → next plan), with privacy as the default mode and a non-punitive approach to broken streaks.
Sensei connects productivity, goals, learning, and retrospection through cross-module signals. The key architecture decision is separating behavioral facts from interpretation and insights, with privacy as the default mode.
Business / Product
Problems and solutions
Each tile connects a real product tension with the decision that clarified the domain, UX, or operations.
Users stitch self-development together from Notion, habit trackers, task managers, journals, and learning apps.
Design an integrated, non-judgmental system where daily data turns into reflection and better next decisions.
The scope could easily become a feature list without one product, data, and ownership model.
Sensei connects productivity, goals, learning, and retrospection through cross-module signals. The key architecture decision is separating behavioral facts from interpretation and insights, with privacy as the default mode.
The project needed decisions that would stay readable after the first version: for users, the team, and further delivery.
Cross-module signals: habit -> goal -> retrospection. Learning and book reviews connected to goals.
Roles and competencies
The competencies this build required
Competencies are shown as ownership roles: CTO, Tech Lead, engineering, DevOps, cloud, security, and AI where they were part of the work.
Fractional CTO
Connecting the product thesis, domain risk, and priorities so technology supports business decisions instead of becoming a separate workstream.
Open competency- Product category and positioning
- Scope priorities and risk
- Decisions ready for founder or CTO review
- Product thesis translated into technical priorities.
- Risks framed in a language stakeholders can review.
Tech Lead
Shaping responsibility boundaries, domain modeling, and architecture decisions so the project can grow without drifting into an accidental monolith.
Open competency- Bounded contexts and ownership
- Readable technical decisions
- Roadmap without implementation chaos
- Domain boundaries and decisions that remain reviewable after MVP.
- Ownership model readable for later delivery stages.
Software Engineer
Turning the domain into screens, APIs, flows, and maintainable implementation with focus on clarity and post-MVP evolution.
Open competency- Backend and application contracts
- UX surfaces ready for iteration
- Code and maintenance model
- Implementation tied to the real product workflow.
- Contracts and code prepared for post-launch iteration.
DevOps Engineer
Designing delivery, observability, and operations so release, diagnostics, and recovery are part of the product.
Open competency- CI/CD and release readiness
- Observability and production signals
- Operations without manual rituals
- Release, diagnostics, and recovery designed as product capabilities.
- Production signals ready for maintenance without guessing.
Security Engineer
Behavioral data and the private journal are private by default, and the non-punitive habit model is also a data-security decision: the system never profiles a user around 'failures'.
Open competency- Privacy as the default contract for behavioral and journal data
- No sharing of retrospection or journal entries beyond the account owner
- Non-punitive data model: gaps are a signal, not a risk flag
- Privacy, roles, and auditability built into the product model.
- Security visible in domain decisions, not only around the UI.
AI/ML Engineer
Treating AI as a product capability: with context, constraints, versioned signals, and a clear boundary between facts and interpretation.
Open competency- LLM as a controlled capability
- Signal and projection quality
- Responsible product constraints
- AI constrained by context, signals, and product responsibility.
- Boundary between facts and interpretation preserved in architecture.
Stack by competency
Tech stack
The stack is grouped by competency so it shows both technology and responsibility: software, leadership, DevOps, cloud, security, and AI.
- .NET / ASP.NET Core
- .NET MAUI mobile client
- Angular web client
- Domain logic engines
- PostgreSQL
- Domain modeling
- AI-assisted domain model
- OpenTelemetry
- Cloud Logging
- Privacy architecture
- Non-punitive data model
- Owner-only data sharing
- AI feedback loops
Product preview
Gallery
Selected screens and materials showing the product surface, UX decisions, and work outcome.