OpenAI 2026 hackathon

LF Patient

LF Patient helps health sciences students find patients for supervised care, while giving everyone a clearer, safer platform to discover services and express interest.

Solo project by John Paul Gonia · 0 likes · 0 comments

Archive position — measured, not model output

0 likes on Devpost

2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #4,975 place in the like-ranked listing is a tie-break inside that group, not a ranking.

Projects (log scale)

1
10
100
1k
10k
05,592
11,758
2285
3–4132
5–975
10+14

Likes on Devpost. ▲ marks this project's group.

Show the figures
LikesProjectsShare of archive
05,59271.2%
11,75822.4%
22853.6%
3–41321.7%
5–9751.0%
10+140.2%
Devpost like counts for all 7,856 archived projects, captured when this archive was built.

Executive Summary

What the company appears to be

LF Patient is a Philippines-focused marketplace for supervised care offered through Dental Hygiene and Dental Medicine students. The platform allows students to publish structured listings with care details, photos, location, schedule, and costs, while enabling patients to browse and express interest without creating an account. It includes AI-assisted listing creation but does not automatically publish or apply suggestions.

What changed

The project was built as a hackathon submission (Devpost entry) using AI tools like Codex and GPT-5.6 for development, testing, and documentation. It includes features such as student profile verification via school email, private patient response management, and server-side image processing.

Single most important open question

Is there evidence of any traction or user adoption beyond the single founder's personal use and development?

Back to contents

What The Product Actually Is

The description states that LF Patient is a Philippines-focused marketplace for supervised care offered through Dental Hygiene and Dental Medicine students. It allows students to:

  • Create and verify their student profile
  • Publish clear listings with care details, photos, location, schedule, and costs
  • Preview and share listings across social media, group chats, and direct messages
  • Privately manage patient responses for each listing

Patients can:

  • Browse and search listings without creating an account
  • Compare clear care details and listing photos
  • Share listings with someone who may need them
  • Express interest privately through their preferred contact methods

The platform does not request diagnoses, symptoms, medication details, insurance information, or medical history. Patient contact details are visible only to the student who owns the listing.

Key technical elements include

  • Frontend built with React, TypeScript, and Vinext on Cloudflare Pages
  • API and database run on Graylabs behind nginx and Cloudflare
  • Secure features such as HttpOnly sessions, authenticated encryption for private contact details, strict Origin and CORS validation, persistent rate limits, owner-only access to patient responses, server-side publication checks, and one narrow public data model shared by marketplace cards, listing pages, social metadata, and sharing copy

AI integration

  • Uses Codex and GPT-5.6 for product exploration, implementation, security review, testing, deployment preparation, documentation
  • Includes an optional "Fill with AI" workflow that turns existing material (text or image) into structured field suggestions
  • AI suggestions are never applied automatically; students must explicitly review and apply them

Not evidenced No revenue, customer data, or adoption metrics are provided.

Back to contents

Positioning & Claim Evolution

The description states that LF Patient sits at the "awkward gap between an informal 'looking for patients' post and a trustworthy public platform: clearer facts, visible care boundaries, and one page and link that is easy to share."

It aims to keep the reach of social sharing while giving students and patients a clearer, safer platform to connect.

Key claims from the description

  • It helps health sciences students find patients for supervised care
  • Gives everyone a clearer, safer platform to discover services and express interest
  • Keeps the reach of social sharing but adds clarity and safety
  • Designed to be easy to share via one page/link

Not evidenced No evidence of how this positioning compares to existing solutions or whether it has evolved from an earlier version. The description does not indicate any prior versions or iterations.

Back to contents

Target Customer & ICP

The description states that LF Patient targets:

  • Students: Dental Hygiene and Dental Medicine students in the Philippines
  • Patients: Individuals seeking supervised care, who can browse listings without creating an account

It also notes that the platform is piloted in the Philippines and can expand to other countries and medical fields.

ICP (Ideal Customer Profile)

  • Dental Hygiene and Dental Medicine students in the Philippines
  • Patients looking for supervised care from these students

Not evidenced No evidence of customer segmentation beyond this basic grouping. No data on how many students or patients are currently using it, or whether there is a defined market need.

Back to contents

Business Model & Pricing Evidence

The description does not provide any information about pricing or business model.

It states that:

  • Students can publish listings with care details, photos, location, schedule, and costs
  • Patients can browse and express interest without creating an account
  • Patient contact details are visible only to the student who owns the listing

Not evidenced No mention of fees, monetization strategies, or how the platform intends to generate revenue.

Back to contents

Technical & Delivery Signals

The description provides some technical details:

  • Built with React, TypeScript, and Vinext on Cloudflare Pages
  • Node.js API and PostgreSQL database run on Graylabs behind nginx and Cloudflare
  • Secure features include:
    • HttpOnly sessions
    • Authenticated encryption for private contact details
    • Strict Origin and CORS validation
    • Persistent rate limits
    • Owner-only access to patient responses
    • Server-side publication checks
    • One narrow public data model shared by marketplace cards, listing pages, social metadata, and sharing copy
  • API checks listing images, removes their metadata, resizes them, and converts them to WebP before storage
  • Database and private development email service have no public listener
  • Cloudflare protects the API with hostname-scoped Strict TLS

AI tools used

  • Codex as primary environment for product exploration, implementation, security review, testing, deployment preparation, and documentation
  • GPT-5.6 through Codex helped in comparing ideas, studying patterns, defining boundaries, coordinating bounded work, finding problems, validating behavior, aligning materials, and generating assets

Not evidenced No evidence of scalability, infrastructure capacity, or production readiness beyond the single developer's build.

Back to contents

Traction & Maturity Signals

The description states that this is a hackathon submission, built by one person (John Paul Gonia), and includes:

  • A working student-to-patient marketplace backed by PostgreSQL
  • Responsive patient and student experiences
  • Searchable service and Philippine location discovery
  • Shareable listing pages with dynamic social metadata
  • Public listing photos with server-side image processing
  • Protected patient contact details with encryption and owner-only access
  • Structured student profiles and school-email verification
  • Validated through automated tests and desktop/mobile browser QA
  • Deployed the frontend and API behind Cloudflare

Not evidenced No evidence of user adoption, revenue, or customer engagement beyond the single developer’s own use.

Back to contents

Competitive Context

The description does not provide any information about competitive landscape or existing solutions in the market.

It mentions that students often rely on Facebook groups, group chats, and personal networks to find patients for supervised care, but does not name or describe competing platforms.

Not evidenced No evidence of competitors, their features, pricing, or market share.

Back to contents

Key Risks & Red Flags

  • Single-founder project: The entire product was built by one person (John Paul Gonia), which raises concerns about scalability and long-term maintenance.
  • No revenue or traction data: There is no evidence of any monetization strategy or user adoption beyond the developer’s own use.
  • Limited scope: The platform is currently focused only on Dental Hygiene and Dental Medicine students in the Philippines, with plans to expand — but no indication of how that expansion will be executed.
  • AI dependency: Heavy reliance on AI tools (Codex, GPT-5.6) for development may pose risks if those services change or become unavailable.
  • Trust and verification issues: The description notes challenges in building trust without implying endorsement, and protecting patients without adding friction — indicating potential regulatory or ethical concerns.

Back to contents

Diligence Questions To Ask The Founders

  1. What is the actual market demand for this type of platform among dental hygiene and medicine students in the Philippines?
  2. How many students and patients are currently using the platform?
  3. Are there any partnerships with colleges or universities that could support adoption?
  4. What is the plan for monetization, and how will the platform sustain itself long-term?
  5. Has the founder considered legal or regulatory implications of connecting students with patients in healthcare settings?
  6. How does the platform handle disputes or issues between students and patients?
  7. What are the plans for scaling beyond the Philippines and into other health disciplines?
  8. Is there any intention to integrate with existing academic systems or student databases?

Back to contents

Investment/Partnership Verdict

The description indicates that LF Patient is a hackathon submission built by one person, with no evidence of revenue, customer adoption, or traction.

It is described as a working prototype with secure features and AI-assisted development, but lacks any indication of commercial viability or market validation.

Verdict Not ready for investment or partnership at this stage. The project shows technical capability and some thoughtfulness around privacy and usability, but there is no evidence of product-market fit, revenue, or customer engagement.

Back to contents

Source

Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.

The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.