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Project Info

bomti

Devpost

Inspiration

Writing a strong self-introduction or interview answer is difficult because people often know what they did, but struggle to explain the context, evidence, and impact clearly. We wanted to create a career-writing tool that helps applicants improve their own stories without replacing their voice with generic AI text. Bomti was inspired by the idea of an AI career editor: a tool that points out vague claims, missing evidence, and weak connections between actions and results, then gives practical directions for revision.

What it does

Bomti analyzes self-introduction and interview answers against five dimensions: Context fit Specificity Evidence and credibility Generic or cliché expressions Tone and readability Users enter a question, their answer, target role, and optional company context. Bomti returns an overall score, dimension-level feedback, supporting sentence evidence, and concrete improvement directions. Guest users can try the diagnostic flow with limited usage. Logged-in users can save, review, and delete their evaluation history.

How we built it

We built Bomti with Next.js, React, and TypeScript. The interface is designed as a calm editorial workspace, with a dedicated landing page, diagnostic form, results dashboard, history page, and account settings page. For persistence and privacy-sensitive data handling, we designed the project around Supabase and PostgreSQL with row-level security policies. The project includes database migrations, generated database types, fixtures, and integration checks for tenant isolation and account deletion behavior. We also built automated checks for: Input validation and consent requirements Guest limits and provider failure states XSS-safe evidence rendering Saved evaluation history and deletion flows Account deletion lifecycle behavior Responsive layouts and accessibility Score boundaries from 0 to 100

Challenges we ran into

The biggest challenge was balancing useful feedback with privacy and user control. Career answers can contain personal experiences, employer information, and sensitive context, so we designed the flow to avoid treating the user’s original answer as disposable training data. Another challenge was making the interface feel helpful rather than judgmental. A score alone is not actionable, so we paired every result with dimension-level explanations, evidence references, and revision directions. We also had to keep the UI redesign aligned with the product’s existing behavioral contracts. As the experience evolved from a single evaluation form into separate landing, diagnosis, result, history, and account screens, we updated browser tests to verify submissions, cancellations, XSS protection, deletion, and responsive behavior end to end.

What we learned

We learned that trustworthy AI writing tools need more than a polished result screen. They need clear consent, transparent limits, safe failure states, and a way for users to control their data. We also learned how valuable automated visual and accessibility checks are during UI iteration. They helped us preserve keyboard access, score-meter semantics, responsive layouts, and user-facing flows while making large design changes.

What's next

for Bomti Next, we want to add more role-specific feedback, richer revision workflows, and a production deployment with a fully configured authentication and Supabase environment. Our goal is to make career writing feel less like guessing what recruiters want and more like clearly communicating real experience.

Analysis

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CSSIn code
  • Next.jsIn code
  • ReactIn code
  • SQLIn code
  • SupabaseIn code
  • TypeScriptIn code
  • VercelIn code
  • HTMLClaimed
  • Node.jsClaimed
  • PostgreSQLClaimed

7 of 10 appear in the indexed code. 3 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

  • CodexConfig

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

684 KB

Source files

138

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

0 stars