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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 escalationImportant: 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-inStack: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, sandboxedLooking 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
Show HN: GlycemicGPT – Open-source AI-powered diabetes management
A privacy-focused, self-hosted, open-source alternative for diabetes data analysis, offering insights and predictive alerts without vendor lock-in or subscription fees.
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AI Executive Synthesis
A privacy-focused, self-hosted, open-source alternative for diabetes data analysis, offering insights and predictive alerts without vendor lock-in or subscription fees.
GlycemicGPT addresses a critical gap in chronic disease management: personalized, privacy-preserving data analysis. The self-hosted, open-source model, combined with BYOAI flexibility, directly counters vendor lock-in and data privacy concerns prevalent in health tech. This product highlights a growing market demand for user control over sensitive health data and the ability to leverage AI insights without relinquishing ownership. For SaaS in healthcare, this signals a need for transparent, auditable, and highly customizable solutions. The "monitoring and analysis only" disclaimer is crucial for regulatory compliance, yet the predictive alerting and RAG-backed chat offer significant value. This project demonstrates the power of community-driven, open-source initiatives to disrupt established markets by prioritizing user autonomy and technical transparency over proprietary, subscription-based models.
Related Ecosystem & Alternatives
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Deep-Dive FAQs
What is GlycemicGPT – Open-source AI-powered diabetes management?
GlycemicGPT – Open-source AI-powered diabetes management is analyzed by our AI as: A privacy-focused, self-hosted, open-source alternative for diabetes data analysis, offering insights and predictive alerts without vendor lock-in or subscription fees.. It focuses on GlycemicGPT addresses a critical gap in chronic disease management: personalized, privacy-preserving data analysis. The self-hosted, open-source mo...
Where did GlycemicGPT – Open-source AI-powered diabetes management originate?
Data for GlycemicGPT – Open-source AI-powered diabetes management was aggregated directly from the Hacker News community ecosystem, representing raw developer and early-adopter sentiment.
When was GlycemicGPT – Open-source AI-powered diabetes management publicly launched?
The initial public indexing or launch date for GlycemicGPT – Open-source AI-powered diabetes management within our tracked developer communities was recorded on May 15, 2026.
How popular is GlycemicGPT – Open-source AI-powered diabetes management?
GlycemicGPT – Open-source AI-powered diabetes management has achieved measurable traction, logging over 62 traction score and facilitating 49 recorded discussions or engagements.
Which technical categories define GlycemicGPT – Open-source AI-powered diabetes management?
Based on metadata extraction, GlycemicGPT – Open-source AI-powered diabetes management is categorized under topics such as: self-hosted platform, AI analysis layer, continuous glucose monitors, insulin pumps.
What are some commercial alternatives to GlycemicGPT – Open-source AI-powered diabetes management?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as Blood Sugar Journal, which offers overlapping value propositions.
How does the creator describe GlycemicGPT – Open-source AI-powered diabetes management?
The original author or development team describes the product as follows: "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 s..."
Community Voice & Feedback
I truly appreciate your work (and I’ll absolutely take a look at it).But let me say one thing: I’ve been diabetic for more than 20 years. Ten years of management with finger pricks, three measurements a day, and insulin pens (thinking about it now, it feels completely insane that anyone could imagine managing this madness in such a primitive way). Then came years of CGM systems (I’m on my third one now, with different types of sensors, but that’s not the point).I tinkered, automated, hacked things. But in the end I came to one conclusion: you need a competent specialist (someone who also understands that we tend to be a bit tech-obsessed) who, besides listening to you, actually imposes a strategy. We are the ones who need to adapt to the mainstream approach so we can speak the same language and have methods that are compatible with “everyone else.” No doctor will ever fully understand your custom system, and meanwhile the key to proper management is not in what you built.Precise carb counting (without cheating yourself), correct boluses given at the right times, marking exercise, boredom and repetition, and being lucid about the effects of changes (agreed upon beforehand!) to CGM settings — changes that should only be made when you’re certain you’ve been a “good and precise patient.”I’m saying this from the perspective of my own devastated situation. I now have an HbA1c of 5.8, but only after 20 years of smashing my head against the wall (and suffering incredible damage, many mistakes, and the classic “I’ve figured it all out myself” approach).Stay strong.
Interesting.
I can see the utility if you're going to see a nurse practitioner. But if your physician doesn't pull the actual charts for your device and visually inspect them.... try finding someone else.
I can see the utility if you're going to see a nurse practitioner. But if your physician doesn't pull the actual charts for your device and visually inspect them.... try finding someone else.
Does it prompt logging? For example, when I was trying to monitor my BG after diagnosis, I tried to log my meals to correlate later, but 1) would forget and 2) wouldn’t have the energy to time align the stats. So a tool that even saw changes in BG and shot me a text or message (did you eat/exercise do something @ [time]?) and used the LLM or something else to capture and enrich the metadata. Paired with boring things like med reminders (I just realized I forgot my metformin while typing this) and giving me an easy visualizer with these meta points would be useful. If I’m tracking sleep on a device etc.As others have said, the analysis might be risky. I don’t want to trust interpretation to anyone but myself (bear my own risk) or my clinician. But just remembering to capture the data and making it easily time alignable and possible augmentable in the future would be useful.
As a urologist who built and runs his own clinic management software, I'd encourage thinking about this question early: what does the system do when the LLM refuses to answer, returns malformed JSON, or hallucinates a glycemic value? In medical contexts, a 'silent failure' (system continues despite bad data) is much worse than a noisy failure (system stops and asks the user). The 'happy path' for an LLM-powered medical tool is usually well-designed. The failure paths are where the project lives or dies. Curious how you handle that.
I don't think that LLMs are trustworthy companions in managing a complex metabolic disease like diabetes - especially if you deviate (ever so slightly) from the norm (very lean, very active, strict diet, etc.)!I'm a T1D myself and like to experiment with ChatGPT (or Opus). My experiences are mixedLLMs are overly cautious when it comes to correcting with insulin. They regularly advise against correcting before going to bed, even if this means that my blood glucose remains above 140 mg/dl for the whole night.I am following a low to medium carb diet (
Really nice of you to share this, well done!About the risks, managing type 1 diabetes is exhausting, and most people will still sanitycheck the output alongside the hundreds of treatment decisions they make every day. That doesn’t change the fact that tools like this can nudge you to notice and look into patterns or things that needs attention.
This is quite possibly a horrible idea. Personal anecdote: ChatGPT once read a blood work report value as 40, when the actual report said 4.
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.
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.
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?
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