01 / CASE STUDY FRAMEWORKMetups Zimbabwe
A local second-hand marketplace designed around the realities of mobile commerce in Zimbabwe.
RoleFounder · Solo developer
StackHTML, CSS, JavaScript, Supabase
FocusTrust, discovery, mobile use
Context & challenge
Add the market insight, user problem, and the constraint that made this worth solving.
Goal & success signals
Define the intended outcome and the measures that demonstrate progress.
Approach
Explain research, product decisions, build phases, and your reasoning.
Key decisions
Show 2–3 decisions with trade-offs: local payments, moderation, listing flow, or PWA choices.
Outcome & next steps
Add launch evidence, feedback, metrics, what changed for users, and what you would improve next.
Suggested evidence: marketplace screens, the listing journey, architecture diagram, launch metrics, and a short reflection.
02 / CASE STUDY FRAMEWORKMDUMENI AI Agronomist
AI crop guidance and market intelligence for Zimbabwean smallholder farmers.
RoleLead developer · Team of 5
StackReact Native, FastAPI, Supabase
FocusPractical agricultural guidance
Context & challenge
Describe the farming decisions users face, their information gaps, and access constraints.
Goal & success signals
Clarify the user outcome, team objective, and how usefulness or trust is measured.
Approach
Outline discovery, team collaboration, technical plan, and the end-to-end user journey.
Key decisions
Document AI boundaries, language/accessibility choices, data sources, and implementation trade-offs.
Outcome & next steps
Add prototype or rollout evidence, farmer feedback, delivery results, and the next product milestone.
Suggested evidence: a farmer journey map, key app screens, system flow, team responsibilities, and validation notes.
03 / CASE STUDY FRAMEWORKEloqui Android Tutor
An offline-first English vocabulary companion with multi-AI support.
RoleSolo developer
StackKotlin, Compose, Room, MVVM
FocusReliable personal learning
Context & challenge
Introduce the learning problem and why an offline-first experience matters.
Goal & success signals
State the intended learning behaviour and how a useful tutor experience is evaluated.
Approach
Describe the learning loop, Android architecture, data model, and AI integration plan.
Key decisions
Show the reasoning behind offline storage, MVVM, background work, and multi-AI support.
Outcome & next steps
Add build status, testing insights, learning outcomes, and the roadmap for the next release.
Suggested evidence: vocabulary flow, Compose screens, data/architecture diagram, test results, and a product reflection.