Gunnar Hostetler

Why: I wanted to see what would happen if I applied the same OpenIntelligence engine to clinical information. OpenClinic is the prototype where I tested it against chart-shaped data (not production medical software).

Clinical Data Sync: I integrated the SMART on FHIR standard using secure OAuth flows (ASWebAuthenticationSession). This lets the app connect directly to hospital sandbox endpoints and securely download structured Patient, Condition, and Medication resources.

Local Storage: Instead of sending patient histories to a cloud database, the app parses and maps the incoming FHIR clinical resources directly into local SwiftData schemas, keeping health records private and offline.

Current State: I reused the same agentic reasoning loop from OpenIntelligence, wired up SMART on FHIR discovery and OAuth (ASWebAuthenticationSession) to pull demo Patient, Condition, and MedicationRequest resources into a local model. It's surprisingly good at finding relevant information in the chart and answering questions with citations, but the current data is too limited to be more than a demo of the retrieval and reasoning patterns. The next step would be to connect it to real FHIR endpoints and see how it handles the messiness of real clinical data, which comes with a multitude of complexities.

Why: OpenAI announced they were deprecating the Completions API, and my immediate thought was "they're killing my first app!" - OpenAssistant - so I opened VSCode with OpenAssistant in one window and OpenResponses in another, using LLMs to help swap out all the endpoints and rebuild the flow on the new Responses endpoints.

Computer Use: I built a custom streaming interface that translates browser tool-calls (clicking, typing, scrolling, screenshots) into a native Swift environment, letting the model navigate web pages in real time.

Connected Tools: To give the model access to the real world, I integrated Model Context Protocol (MCP) connectors and Apple EventKit, allowing it to securely read and update Gmail, Google Calendar, Dropbox, Notion, and Apple reminders.

What I learned: Because of all the App Store Connect rejections on my first two apps, I learned exactly what reviewers looked for. I got this one passed through review without a single rejection on the first pass.

Why: When I started getting into deeper development with OpenAssistant, the app would start losing context during large document queries. I looked up vector databases, found Pinecone was the standard, and realized they didn't have an iOS app, so I decided to build one. I wanted to query multiple indexes and namespaces in a single chat thread, which was something the OpenAI Assistants playground was limited in doing.

On-Device File Reader: To feed documents into Pinecone, I built a local parser using Apple's PDFKit and Vision OCR. It extracts text from PDFs, DOCX files, and raw photos, automatically chunking and converting them into embeddings on the fly.

Network Safety: To prevent the app from lagging or getting stuck when a server fails, I built a network circuit breaker. If Pinecone fails twice in a row, the app automatically pauses requests and lets the user know immediately.

What I learned: I took the backend from OpenAssistant and swapped out the storage layer for Pinecone. Honestly, in retrospect, the initial interface looked awful, but I later rebuilt it with the Responses API and clean embeddings. Getting this on the App Store was a personal milestone.

Why: When ChatGPT-4 first came out, it BLEW my mind. I was working with tons of medical documentation at Stryker and using it to learn fast, but their official app was slow, crashed, and limited me to 5 files at a time. I got curious about their API, saw people making wrappers, and thought, why would I pay for someone else's app when I can just build my own?

File Formatting: OpenAI, at the time, would reject standard iPhone files (like live HEIC photos or RTF documents) when uploaded. To fix this, I set up a local conversion system that automatically turns these files into standard JPEGs and plain text on the device before sending them off.

"Is it done yet?": Since OpenAI's Assistants API didn't respond instantly, I built a timer system that checks in with OpenAI every 2 seconds to see if the assistant is done thinking, so the app can display the answer the second it's ready.

What I learned: Making an iOS app isn't easy. I built it by copying OpenAI API references into text files, uploading them to a playground vector store for context, and copying and pasting the red errors back in to get corrected code. I repeated this until it worked, spent $99 on a developer account, and finally got it on the store after being rejected upwards of 30 times because they kept telling me everything that was wrong with my app.