I'm a Type 1 diabetic and software engineer. Last year I went months between endocrinologists with no clinician reviewing my data. I'm an engineer, so I built the tool I needed — and now I'm open sourcing it. GlycemicGPT is a self-hosted platform that connects continuous glucose monitors, insulin pumps, and existing Nightscout instances to an AI analysis layer running on your own infrastructure. Data sources:

Dexcom G7 (cloud API) Tandem t:slim X2 and Mobi pumps (direct BLE) Nightscout (point it at your existing instance and you're running in minutes)

What the AI layer does:

Daily briefs summarizing overnight and 24-hour patterns Meal response analysis Conversational chat with RAG-backed clinical knowledge Predictive alerting with configurable thresholds and caregiver escalation

Important: this is monitoring and analysis only. GlycemicGPT does not deliver insulin, does not control your pump, and is not a closed-loop system. It reads your data and gives you insight on top of it. Your clinical decisions stay between you and your care team. Architecture:

Self-hosted via Docker or K8S — the GlycemicGPT stack runs entirely on your hardware BYOAI — bring your own AI provider. Use Ollama for fully local operation (no data leaves your hardware), or point it at Claude, OpenAI, or any OpenAI-compatible endpoint if you prefer a hosted model. Data flows directly from your instance to the provider you choose; nothing is routed through any centralized service operated by the project. GPL-3.0, no subscriptions, no vendor lock-in

Stack:

Backend API: FastAPI, Python 3.12, PostgreSQL 16, Redis 7 Web Dashboard: Next.js 15, React 19, Tailwind CSS, shadcn/ui AI Sidecar: TypeScript, Express, multi-provider proxy Android App: Kotlin, Jetpack Compose, BLE Wear OS: Kotlin, Wear Compose, Watch Face Push API Plugin SDK: Kotlin interfaces, capability-based, sandboxed

Looking for contributors — especially folks with BLE/Android experience or anyone in the diabetes tech space. Plugin SDK is documented if you want to add support for new devices. GitHub: https://github.com/GlycemicGPT/GlycemicGPT

The risk to benefits ratio of introducing a language model to interpret so clear signals is nowhere near justified.

Monitoring and analytics is important, but it is a solved problem. A language model will only be able to hallucinate about the relationship between meals and glycemic response. At best it does no harm, at worst it can directly misinform.

Looks interesting, being a Whoop user for the last few years, I have seen for myself that their AI Coach/AI based suggestions are a hit or miss 3 out of 10 times, slightly concerned about how accurate this will. Not a diabetic patient, but I do monitor my levels with a CGM from time to time, will definitely check it out!

I'm a T1D who has an insulin pump looping with AndroidAPS and NightScout, what does this give you that Nightscout and Autotune doesn't give you?

And how do you deal with AI hallucinations?

I've done this with the Libre 2 sensor. I added Gemini to it. It gets like 2 weeks of readings at once, and the user can "chat to their data". I added a meals tool as well, where the user can photo their meal, and the ai estimates the impact on the readings.

It's so helpful to offload some the thinking about the condition to ai, all these people moaning about 'muh safety' don't get it. T1D suffers have to think about it all day all the time. A person doesn't have their own blood glucose data in their head.

Went through pregnancy with the mother having recently-diagnosed T1 diabetes – just barely not killed by grave neglect on behalf of healthcare due to how badly they missed the diagnosis to begin with.

On your work:

this is legit

it is appreciated

Hats off, I salute this, thank you

I'm a T1D and tbh it's not that hard to manage, I just wouldn't need that. But for kids or the elderly, I see a use case.

The hardest to learn was that an unhealthy lifestyle resulted in a diabetes that was harder to manage. Too much carbs, not enough exercise, etc. After adjusting my lifestyle, it became quite easy.

The most pain, in my experience, comes from the discrepancy between the CGM - measured value and the prick-test value, even when accounting for time lag. I've used several CGMs and they've all been wildly off sometimes. I have a few T1D acquaintances who relied on their CGM alone and have significantly improved their HbA1c after accounting for that.

Maybe that information is useful to you.

What’s the limit on badges in a README

"This will all end in tears, I just know it"

Marvin

I'm just happy to see a GPL project.

This is THE ONE domain where you would want to use classical machine learning and not unreliable LLMs. Unless you want to kill yourself, that is.

Life imitates comedy...

The alerts system and sharing with caregivers is a solved problem already (e.g. Dexcom's Follow, Abbot's LibreLinkUp).

Do you find the analytics actually helps? I.e. a lot of this will depend on what you ate and whether or not you logged it?

FDA approved?