Mbwira

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Inspiration

Rwanda has thirteen psychiatrists. For thirteen million people.

That ratio is among the worst in the world, and it is not the only ratio that matters. Twenty-seven percent of Rwandan youth aged fourteen to twenty-five live with a diagnosable psychological disorder. In 2023, the Rwanda Biomedical Center documented two thousand eight hundred and seventy-nine suicide attempts.

The shortage is one half of the problem. The other half is older. Researchers interviewing Rwandan health workers found that the local language used to conceptualize mental illness is itself dismissive — even where care exists, people will not reach for it. Rwanda's mental-health crisis is not only a shortage. It is a silence.

I am a student at ALU. I have watched friends, classmates, and family members sit inside that silence. The Machines of Loving Grace essay describes a future in which AI helps people find care and meaning. Mbwira — Kinyarwanda for "speak to me" — is one careful attempt to be honest about what AI can and cannot do in that direction, and to build a first door for the twenty-seven percent who today have no door at all.

What it does

A user opens the app on their phone. The first thing they see is a disclosure: I am Mbwira. I am an AI — not a therapist, not a doctor. Your words go to Anthropic to write a reply; I keep nothing once you close this tab. Two real helpline numbers are visible from the start. They acknowledge, and begin.

The conversation runs on Claude Sonnet 4.6 with a system prompt grounded in the Resilience-Oriented Therapy framework developed in Rwanda by Interpeace — not in cognitive-behavioral therapy translated into Kinyarwanda. The model reflects, names what it hears, and asks one quiet question at a time. It refuses to diagnose. It refuses to give medical advice. When the moment fits, it can offer one small, evidence-based self-help tool — a grounding exercise, a longer-exhale breath, a sentence to write down — offered, never prescribed.

It speaks in English or French. When a user writes in Kinyarwanda, it reads it, but switches into English to reply and acknowledges the gap honestly — because faking Kinyarwanda fluency would end trust in the first message.

Behind every message, a silent classifier — Claude Haiku 4.5 — assesses risk across five levels. When risk crosses the threshold, the conversation interrupts and a full-screen card surfaces with two verified Rwandan lines: the suicide-prevention hotline (8015) and the Rwanda Biomedical Center (114). A second tap opens the Find a human drawer — a curated directory of real Rwandan support: MindSky for youth, CARAES Ndera, Isange One Stop Centre, community health workers, campus wellness.

The ethics are not a footer. They are a drawer the user can open at any time, listing the five lines Mbwira will not cross.

How I built it

Solo, full time, in nine days.

Stack: Next.js 16 (App Router, Turbopack), React 19, TypeScript, Tailwind CSS v4. Deployed on Vercel in the Frankfurt region — the closest edge to Kigali on the platform.

Two models in parallel:

• Claude Sonnet 4.6 carries the conversation — empathy, cultural reasoning, the long thread of the session. The system prompt is grounded in the three ROT commitments (trauma is collective and inherited; reflection before advice; cultural rootedness over imported clinical language), with explicit identity, anti-diagnosis, crisis-handoff, and Kinyarwanda-honesty rails. Cached with cache_control: ephemeral — after the first request the prompt costs ~10% of input pricing.
• Claude Haiku 4.5 is the silent crisis classifier, called on every user message. Returns one of five risk levels as strict JSON, validated by a zod schema. On any parse failure the route fails safe and defaults to low — never none. Two-layer safety: even if the classifier misses, the system prompt also instructs Sonnet to escalate in its own reply.

Privacy: no database, no accounts. Conversations live only in component memory and vanish when the tab closes. The user's words are sent to Anthropic to generate the reply; I keep nothing.

Streaming: Server-Sent Events through a custom ReadableStream route handler with X-Accel-Buffering: no so no proxy buffers chunks. On the client, a smooth typewriter component paces the uneven network deltas into an even character-by-character reveal — the same calm typing feel as Claude.ai.

Safety perimeter on the public endpoint: per-IP rate limiting (in-memory) as a first speed bump, plus a hard spend cap on the Anthropic account as the absolute ceiling.

Verification: I wrote a 40-message adversarial test suite across eight failure-mode categories (oblique distress, metaphor, sarcasm, false positives, abstract questions, Kinyarwanda input, French input, imminent crisis, evasion). After one round of prompt tuning, the classifier passes 40 of 40 with zero safety-critical under-grades. The suite is committed and re-runs in under a minute.

Engineering hygiene: 35+ commits with clear scopes, TypeScript strict, lint clean, production build clean, WCAG AA contrast verified across all UI states.

Challenges I ran into

Kinyarwanda is a low-resource language and Claude's Kinyarwanda is not reliable. A native-speaker test (me) confirmed the model produced sentences that sounded grammatically wrong and culturally off. Many builders would have shipped it anyway. I made the opposite call: the system prompt now forbids Claude from generating Kinyarwanda sentences. When a user writes in Kinyarwanda, Claude reads it, switches to English, and acknowledges the gap in one honest sentence. The honesty about the limit became a feature.

Crisis classification surfaced three under-grades on the first adversarial run — self-erasure framing without the word suicide ("the world would be lighter without me"), calm family-loss disclosures, and hypothetical method queries ("just curious, what would happen if someone took all their meds"). I added three explicit rules. Re-running confirmed 40 of 40.

Mental-health AI is contested ethical territory. Every decision — the explicit identity disclosure, the no-diagnosis rail, the hard crisis handoff, the no-persistence default, the what this will not do drawer — was made with the question "if this were misused, what would happen, and what stops it?" in mind.

Accomplishments I'm proud of

• 40 of 40 adversarial classifier tests passing, zero safety-critical under-grades. Adversarially tested before demo day — a rare thing in a hackathon.
• The honesty about Kinyarwanda's limits, encoded in the system prompt — a refusal to fake fluency, framed as a feature.
• Two-layer crisis detection: a silent Haiku classifier and an in-prompt Sonnet rail. Both have to fail to miss a crisis.
• Verified helpline integration: every number in the product was sourced from official channels (RBC, national news) — no guesses.
• Working production deploy with prompt caching, SSE streaming, smooth typewriter reveal, branded favicon and OG image, WCAG AA contrast.
• The conversational tone. When tested with "I have not slept properly in three weeks. My mother survived 1994 and my father has never spoken about it," Mbwira responded with "this weight that lives in the house but has no name." That is the bar I wanted, and it cleared it.

What I learned

That the right ethical stance for an AI product is not a privacy policy. It is a perimeter the user can see, encoded in the UI and the system prompt and the test suite, all at once.

That Resilience-Oriented Therapy is a better grounding for a Rwandan mental-health product than cognitive-behavioral therapy translated into Kinyarwanda — because grief in Rwanda is often collective and inherited, and the therapeutic framework should match that.

That redundancy is the point in safety — a silent classifier and a conversational model are stronger together than either alone.

That building honestly about limitations is more credible than building marketing.

What's next for Mbwira

The next ninety days, in order of leverage:

1. Real user testing with ten to twenty ALU students under a research protocol developed with the ALU faculty of psychology.
2. Partnership conversation with the Rwanda Biomedical Center and the national suicide-prevention line to verify escalation protocols and explore whether anonymised, aggregate crisis-card data could inform public-health planners.
3. Kinyarwanda surface widening via a small fine-tune on Mbaza NLP open data, with native-speaker review of every generated line before it ships.
4. An IRB-style ethics review with the ALU faculty before any wider release.
5. Move the deployment region into Rwanda once user volume justifies it.

The right metric for this product is not engagement. The right metric is whether users we never see find a human after talking to us. That is hard to measure without violating the privacy promise. I do not yet know how to do this well; the open question is part of what v2 must answer.

Built With

• anthropic
• claude
• claude-haiku
• claude-sonnet
• next.js
• react
• server-sent-events
• tailwindcss
• typescript
• vercel
• zod