Featured project
Public
Glyph
AI adaptive learning that fits the learner and the subject.
Source checked October 21, 2026. Safeguards describe the implementation, not a new hosted security audit.
Public learning entry point. Account features require sign-in.

Background
One-size-fits-all study tools don't adapt to the learner or the subject.
Concept
A learner enters a topic (or uploads a PDF), and Glyph generates a concept map, a curriculum, and an interactive learning "expedition" — cinema-style visual explainers, tutor annotations, and, for STEM topics, derivation walks, sandboxes, simulations, and challenge arenas. A domain classifier gates features so a narrative topic and a calculus topic each get the right components, and difficulty is set from the learner profile.
Build story
The source implementation treats pasted study text as untrusted: concept generation calls a shared sanitizer that strips selected invisible characters and redacts known instruction-override patterns. URL imports reject listed local/private hosts and refuse redirects; PDF extraction checks the file signature and applies a 10 MB upload limit. On sign-in, a claim path attempts to attach anonymous study records to the account, with each table updated separately. Co-built with Audrey; Ben led the agent-driven audit-and-fix waves across the app.
Outcomes
The public home offers the generate-learn workflow, while pricing clearly says online checkout is not switched on. The source includes regression tests that reject unsupported publisher claims, including spaced repetition and concept-level teacher analytics. Account carryover is implemented, but partial failures are possible; this source review does not establish a lossless migration or successful hosted generation.
Explore the public app
Safety patterns, adapted per app
The “Fleet Kit” describes recurring safety and growth patterns, not a shared package. Each app implements its own version. These examples are visible in the source; they are not a new audit of the hosted services.
In Glyph: Concept generation sanitizes study text. Its shared limiter uses user, session or IP-based request buckets, but allows requests through if the limiter service fails.
- Absorby →Untrusted text is sanitized and fenced before selected AI calls; feature-bucket usage checks compare successful calls with daily limits, without an atomic spending guarantee.
- Soundpetal →The music assistant filters instruction-like text and wraps user messages as data; message, token and burst limits bound usage.
Tech / stack
- React · TypeScript · Tailwind
- Supabase edge functions · Gemini through AI gateway
- ElevenLabs narration + music generation
- In-house PDF text extraction · Leaflet · three.js
- Vitest · product-copy regression checks
Public learning entry point. Account features require sign-in.
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