Using Vibe Coding to Build Products That Make a Social Impact

There are moments when technology meets purpose, and people feel the change. This article opens with a clear case: Better.sg gathered eight volunteer developers and two testers to deliver a platform that helps trainees run home-based cafés. The team moved from prompts to a working MVP in six weeks.

The result combined natural language guidance, disciplined orchestration, and practical tools: a Lovable prototype, Supabase integration, authentication, RBAC, Resend notifications, and a payment flow. Volunteers cleared 27 UAT bugs in 1 hour 43 minutes using about 40–45 prompts.

The narrative shows how a prompt-to-production approach compresses cycles and keeps governance intact. Readers will see how short work streams, strict naming, and small PR discipline enabled secure creation and scale with minimal cost.

Key Takeaways

• Prompt-driven work streams can deliver an MVP in six weeks.
• Natural language guidance helps align developers and product goals.
• Disciplined naming and PR rules preserve auditability and quality.
• Low operating costs and clear metrics make pilot scale predictable.
• Orchestration replaces hand-scale code without losing control.

Executive Summary: From Prompt to Product with Real-World Social Outcomes

A compact, conversational pipeline converted sketches into a secure, usable MVP in six weeks. Volunteers used natural language prompts and integrated tools to deliver a working platform with authentication, RBAC, order management, and Resend notifications.

Phase 1 focused on critical delivery: data model, auth, role-based access, order lifecycle, and notifications. Phase 2 covered the design system and user-facing enhancements. The team invested about 50–55 volunteer hours and relied on Lovable plus Supabase and Resend to move quickly.

• Fast validation: MVP in six weeks with real users onboarded.
• Rapid QA: 27 UAT bugs (11 critical) fixed in 1h43 using ~40–45 targeted prompts.
• Low cost: free tiers for hosting and $20 for the IDE layer; scale estimated near $70/month at ~50,000 customers.

The approach emphasized disciplined prompts—one page or feature at a time—with explicit access rules and dependency graphs. This reduced rework and improved developer productivity: AI handled boilerplate while humans curated policies and secure templates.

Strong governance—strict naming, small PRs, and controlled rollbacks—kept previews stable and merges safe. The result translated technical velocity into real inclusion: home-based cafés could manage menus, accept orders, and notify customers reliably.

Context and Objectives: Why We Chose Vibe Coding for Social Impact

The team selected a prompt-driven workflow to meet a tight timeline while keeping policy controls explicit. Better.sg partnered with The Majurity Trust to fund a platform that helps trainees from disadvantaged backgrounds run home-based cafés. The aim was operational inclusion: fast setup, few friction points, and secure transactions.

User intent and goals: process, results, and applicability

Users needed a simple path to take orders, manage menus, and receive confirmations. Owners, customers, and administrators required clear roles and access rules so transactions stayed reliable and safe.

The approach used natural language prompts to scope features tightly. This enabled iterative validation with the partner and kept risk low while delivering an MVP in six weeks.

Target beneficiaries and integration outcomes

The platform targeted trainees and small operators who lacked tools on existing marketplaces. Off-the-shelf systems had fragmented features and weak RBAC. A custom build with Lovable’s Supabase integration gave the data model flexibility and secure-by-policy access control.

• Operational goal: empower owners with minimal setup.
• Technical goal: seamless ordering, secure roles, and automated notifications.
• Developer role: orchestrate components and policies instead of hand-writing every line.

Background: The Partners, the Mission, and the Constraints

A 2,800-member community and a foundation fund aligned around one measurable operational goal. Better.sg led a volunteer build under The Majurity Trust’s Foundry Forward Fund to close a platform gap for home-based cafés.

The matched partner is a training enterprise that supports aspiring F&B workers from disadvantaged backgrounds. The program reinvests revenue to help trainees move from training to steady income.

Better.sg and The Majurity Trust

Better.sg supplied community developers and project coordination. The Majurity Trust provided grant oversight and outcome targets. This pairing kept the build focused on measurable user results.

The training enterprise and beneficiaries

Participants received role-based access to manage menus and orders. The goal was real economic inclusion—not just a demo but live transactions that generate income.

Time, team, and budget constraints

The delivery team was ten volunteers: eight developers and two testers, each at most four hours per week. A strict six-week timeline shaped a phased process.

“We chose a pragmatic toolchain to protect guardrails and speed delivery.”

• Core accelerator: Lovable with Supabase integration.
• Phase 1: data model, auth, RBAC, orders, notifications.
• Controls: small PRs, naming rules, targeted prompts.

Problem Definition: No Platform Fit for Home-Based Cafés

Existing marketplaces left a gap between simple browsing and the operational needs of home-based cafés. The partner’s prototype demonstrated registration and browsing, but it did not support full operations necessary for live commerce.

Missing features vs. real requirements

The immediate shortfalls were clear: order management, payment flow, authentication, and a robust data model. These gaps meant the demo could not handle end-to-end order lifecycles or enforce multi-role permissions.

Role-based access was essential—public visitors, customers, owners, and admins needed distinct, enforceable permissions so local control and trust remain intact.

• Integrated email notifications were mission-critical for pickups and account events.
• A consistent design system mattered for mobile-first users and first-time owners.
• Security-by-policy (Supabase) would govern who can read or write records.

The team cataloged non-negotiable MVP requirements versus nice-to-haves. This clarified integration patterns, reduced scope creep, and preserved future extensibility for reviews, loyalty, dashboards, and supplies.

Our Approach: Staged Delivery, Scoped Prompts, and Rapid Validation

Delivery split into tight stages let the team push useful features ahead of optional polish. Phase 1 concentrated on must-have functionality: authentication, RBAC, order lifecycle, and notifications. Phase 2 reserved design system work and UX enhancements until core behavior was stable.

Phase 1 MVP vs. Phase 2 enhancements

Phase 1 produced a shippable MVP that handled live orders and permissions. Phase 2 added visual consistency and non-essential features without risking regressions.

Limiting scope per prompt to improve accuracy and control

The team used page-by-page prompts with Lovable and Supabase. Each prompt named the page, tables, joins, and explicit role rules. This template reduced hallucinations and kept outputs predictable.

• Predictable artifacts: React hooks, Supabase policies, and transform functions.
• Fast validation: review the hook, inspect policy logic, test with data.
• Security-first: RBAC guardrails existed before new features.

“Small prompts, small PRs — steady progress with clear audit trails.”

Result: higher productivity through orchestration, disciplined control, and a repeatable implementation process that scales.

Toolchain and Architecture: Lovable, Supabase, and Resend Integration

A small, well-integrated stack turned conversational prompts into enforceable application behavior. The team treated the stack as a single implementation system where each piece had a focused role.

Natural language prompts and prompt engineering expertise

Lovable acted as the conversational IDE. It translated natural language prompts into frontend hooks, schema suggestions, and Supabase policies. Initial schemas were ~85% accurate; developers fixed foreign keys and added missing fields.

RBAC, data model, and security-by-policy

Supabase provided the managed Postgres backbone and the policy engine that enforced role-based access. Seeing the ERD click into place made dependencies explicit and reduced risky joins.

Design system, email notifications, and payment flow

Resend was integrated via Supabase functions to automate account, order, and reset emails with dynamic content. A consistent design system supplied predictable components and accessible patterns.

• Payment flow was treated as first-class: policies guarded sensitive mutations.
• Prompt engineering expertise shortened correction cycles and kept implementation predictable.
• Each architectural decision favored maintainability so volunteers could onboard quickly.

“Tools complemented one another—reducing boilerplate while preserving control and auditability.”

From Prototype to Production Data Model

Screens and interactions became the source of truth for the underlying tables and relationships.

The UI mapped directly to entities: cafés, opening hours, menu items, orders, and customers. That mapping gave the team a clear model that matched user flows.

Lovable produced roughly 85% of the schema. Developers corrected missing foreign keys and added feature fields. These targeted fixes sped implementation while preserving accuracy.

Deriving entities and the ERD “wow” moment

The Supabase ERD made relationships visible. The team validated cardinality, spotted missing links, and closed gaps fast. Seeing joins and ownership lines triggered immediate fixes.

Policies and functions: control, validation, and transformations

Role-based policies restricted reads and writes at table and row levels. Supabase functions handled joins, computed fields, and validation logic as controlled gateways.

• Integrity first: constraints prevented silent data drift.
• Layered control: policies plus functions standardized behavior across pages.
• Natural language prompts stayed precise, naming tables and fields to reduce ambiguity.

Result: a production-grade model that balanced speed and correctness—an essential trade-off in rapid software development.

Implementation Journey: Page-by-Page Replacement of Mock Data

Each view became a targeted integration task: replace mocks, link tables, and lock down access.

Real-time Supabase integration happened across 14 pages. The team integrated pages one at a time to avoid context overload. Lovable generated hooks and policy templates. Developers reviewed and refined each artifact before merge.

Real-time Supabase integration with role-based access

Role rules stayed consistent: public and customers had read-only access. Owners could write only when user_id = owner_id. Admins had elevated policies. Supabase functions handled joins and complex logic to centralize validation.

Typical prompt structure: access rules and dependency graphs

Prompts named page path, target tables, related tables, and access rules. The dependency graph guided sequencing: core tables first, then joins and derived views. This cut rework and kept policies least-privilege.

Unlike traditional programming: conversational orchestration over boilerplate

Unlike traditional programming, the team specified intent, verified generated code, and tested. This process minimized boilerplate and let developers focus on UX and correctness.

“A page-by-page approach turned a large migration into repeatable, reviewable steps.”

Authentication, RBAC, and Notifications: Fast to Set Up, Heavy on Testing

Getting users to sign in proved easy; keeping authorization correct required discipline. Authentication came online quickly, but early tests revealed inconsistent role names across the database, frontend logic, and TypeScript interfaces. That mismatch created subtle authorization bugs that only appeared under specific flows.

Naming consistency across DB, frontend, and TypeScript

The team enforced strict naming conventions and shared type definitions. They updated schema, hooks, and TypeScript interfaces in tandem to remove ambiguity.

Result: authorization checks became predictable. RBAC rules no longer failed due to simple casing or pluralization errors.

Resend middleware via Supabase functions and secure email templates

Notifications used Supabase functions as middleware to orchestrate Resend calls. Functions injected secure links and context into templates and handled retries.

Early dummy emails spiked bounce metrics; the team moved to controlled test addresses and versioned templates. That made iterative changes safe and auditable.

• Authentication surfaced naming costs early and led to a naming-first policy.
• RBAC was audited for least privilege, especially around order updates and owner mutations.
• Notifications relied on the system middleware to keep secrets out of client code.
• Natural language prompts specified roles and allowed generated code to align with policy expectations.
• Tight control loops—test, adjust, re-test—kept the security surface predictable under rapid change.

“Test depth mattered more than test quantity—edge cases proved decisive.”

User Acceptance Testing, Bug Fixing, and Refactoring at Speed

User tests revealed practical edge cases that only real usage could surface. UAT ran across two days and produced a precise list: 27 issues, 11 marked critical.

Results were swift: volunteers closed all defects in 1 hour 43 minutes using roughly 40–45 targeted prompts. That cadence proved the prompt-driven process could resolve regressions without broad rewrites.

27 bugs cleared in under two hours with ~40–45 prompts

The team used short, failing-scenario prompts that named tables, hooks, and expected outcomes. This kept fixes focused and reviewable.

Order lifecycle and payment refactors with minimal code churn

Clarifying the order lifecycle with the partner enabled surgical refactors. Centralizing payment transformations and validations inside Supabase functions limited the blast radius of changes.

• UAT validated the decision to freeze scope and test the MVP in production-like conditions.
• Separation of concerns—hooks, policies, functions—made reviews fast and safe.
• Forgot-password and a super admin dashboard shipped by week eight using existing hooks.
• The team tracked challenges with checklists and small diffs to keep productivity high.

“Targeted prompts plus modular code let the team stabilize the app rapidly without rewriting large swaths.”

Overall, this phase converted rapid feedback into durable fixes. The approach sustained development momentum while keeping governance and quality intact—an outcome that improved both team productivity and confidence in the codebase.

Working Across GitHub and Lovable: Processes, PR Discipline, and Rollbacks

Teams synchronized two platforms so every preview could become a trusted commit in minutes. This clarity made working across Lovable and GitHub predictable and low-risk.

Small, incremental PRs prevented merge conflicts. Contributors opened focused branches, kept diffs tiny, and waited for preview commits to land before merging. That habit reduced churn and sped reviews.

Small, incremental PRs to prevent merge conflicts

When a preview failed, the team rolled it back in Lovable. They split the change into two or three smaller PRs and re-applied increments until the preview was stable.

Library installs and preview stability

Library additions required an explicit prompt to Lovable to install. That simple step avoided partial installs and broken builds in CI.

• Sync rules: previews = commits; merges reflect back in seconds.
• Code reviews emphasized intent, policy, and naming consistency.
• Integration health checks validated Supabase contracts before merge.
• Prompt-pattern docs in the repo standardized requests and reduced onboarding friction.

“AI-accelerated delivery still depends on basic engineering hygiene.”

Result: disciplined processes amplified the tools’ benefits. The approach preserved control while letting volunteers focus on meaningful development and safe integration.

Results and Impact: Cost, Time, Scale, and Social Inclusion

The project converted constrained volunteer time into a dependable, production-ready system. In six weeks the team reached pilot readiness while preserving governance and quality.

Volunteer hours and pilot readiness

The MVP landed on roughly 50–55 volunteer hours. That work supported a pilot with seven cafés—real operators using live order flows and notifications.

Operating costs and scale economics

During the MVP the stack ran on free Supabase and Resend tiers plus a $20 Lovable layer. Estimated monthly cost to serve ~50,000 customers is about $70/month, showing the system’s efficiency and suitability for nonprofits and small enterprises.

Productivity, developer experience, and community outcomes

• Prompt orchestration reduced boilerplate and sped iteration; developers focused on data integrity and UX.
• Volunteers reported improved productivity and faster feedback loops.
• Users—owners and customers—benefited from predictable notifications and role-based access.

“Lean resources, clear prompts, and strict controls turned short time into lasting value for the community.”

Vibe Programming in Practice: Psychology, Roles, and Team Dynamics

Teams reported that conversational prompts smoothed task handovers and kept attention on outcomes rather than syntax. This reduced extraneous cognitive load and made it easier to enter flow. Quick previews and tiny diffs kept interruptions brief and motivation high.

Flow, reduced cognitive load, and instant feedback loops

Frequent feedback reinforced momentum. Short cycles let developers validate assumptions fast and fix only what mattered.

Psychological benefits: clearer goals, fewer context switches, and a steady sense of progress.

From coding to orchestrating: future software development roles

Work shifted away from hand-writing boilerplate toward orchestration. New core skills included prompt design, oversight, and architectural thinking.

Traditional programming remained valuable; it was paired with meta-skills that verify and govern outputs.

Democratization, access, and evolving professional identities

Natural language interfaces widened access and welcomed contributors with varied backgrounds. Roles evolved: some focused on policy and data contracts, others on user outcomes.

“Status moved from craft alone to system-level impact and shared knowledge.”

• Developers aligned around intent and review cycles rather than syntax debates.
• Knowledge work emphasized observability, policy, and reliable access control.
• The future of software development values collaboration across new roles and shared verification.

Risks and Mitigations: Technical Debt, Security, and Model Dependence

Teams that move fast with model-generated code face new forms of technical debt and governance gaps. Rapid tooling reduces boilerplate but can introduce brittle assumptions in schemas, policies, and integrations.

Validation, audits, and governance for AI-generated code

Mitigations begin with repeated validation. Policy audits, naming standards, and review checklists catch drift early.

Test with real data safely — dummy addresses skewed email metrics during the pilot and taught the team to stage tests before wide release.

“Small PRs, naming rules, and explicit install prompts turned rapid changes into safe commits.”

Project management controls to prevent drift and scope creep

Project controls keep speed from becoming technical debt. Use phase gates, rollback plans, and small merges to contain scope.

• Processes: checklists for prompts, installs, and policy changes.
• Integration reviews: verify RBAC and data contracts before merge.
• Traditional programming instincts—defensive design and clear interfaces—remain critical.

These controls let teams harness velocity without compounding risk. With disciplined reviews and practical guardrails, rapid software creation stays sustainable.

Translating the Model to the United States: Playbooks for Nonprofits and Startups

Translating a pilot into a US-ready product means pairing ERD-first design with local privacy and testing practices. This playbook adapts scoped prompts, RBAC-first policies, and small PR discipline to American nonprofits and early-stage startups.

Requirements capture, integration patterns, and testing processes

Start with clear requirements as user journeys and RBAC matrices. Convert those into an ERD and prioritize critical flows: orders, payments, and notifications.

Reuse the page-by-page integration pattern: replace mocks with explicit prompts that codify access rules and data joins. Testing should focus on role scenarios before UI polish.

Training teams in prompt engineering and system oversight

Train volunteers and staff in prompt engineering and system oversight so they can audit generated artifacts and preserve data integrity. Build a short curriculum that covers naming, policies, and monitoring.

• Costs: early stages can run on free/low-tier services; budget modestly for scale.
• Compliance: align privacy and data controls with US regulations from day one.
• Monitoring: track emails, errors, and bounces to protect deliverability and reputation.

Result: a practical playbook that lets nonprofits and startups balance speed, governance, and lasting value in the field.

vibe coding for social impact: Key Takeaways and Repeatable Processes

Prioritize data design: map roles and entities before writing a single hook. This ERD-first view reduced rework and clarified policies early. Teams found that modeling the domain saved time and kept requirements testable.

Start with a secure model and iterate with constrained prompts

Begin with the model derived from UI and user roles. That made access rules explicit and kept policies aligned across DB, front end, and types.

Enforce least-privilege RBAC by default and route complex logic through database functions. Use small, focused prompts that name pages, tables, and roles; then review generated code before merging.

Adopt incremental merges, test with real data, and plan refactors

Keep merges small and reversible. Previews should be routine checkpoints; rollbacks must feel ordinary, not dramatic.

Test with realistic data—especially emails and payments—to reveal operational nuances. Organize hooks and functions so you can refactor orders or payment flows with minimal churn.

• Document prompt patterns, naming rules, and access contracts.
• Measure experience, not only speed; stable flows reduce support time.
• Train the team in prompt design and oversight to grow orchestration ability.

“ERD-first design, scoped prompts, and small PRs created a repeatable library of processes for future builds.”

Conclusion

, This case shows that orchestration beats reinvention when timelines are tight. A model-first approach and scoped prompts turned volunteer effort into a live MVP in six weeks.

Volunteers combined ERD-first design, policy-driven RBAC, and Resend notifications to create a low-cost platform ready for a seven-café pilot. The work stayed sustainable and auditable with small PRs, audits, and clear prompt templates.

Developers expanded roles toward architecture and oversight, aligning practical engineering judgment with prompt craft. As the article argues, this process scales to US nonprofits and startups when paired with regulatory awareness and disciplined reviews.

Start small, secure by default, and iterate with scoped prompts—that path lets software development deliver measurable value while protecting quality and mission.