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4,157 ideas

GlycemicGPT – Open-source AI-powered diabetes management

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 spa

Hacker News4mo agoToolAI

85FL score
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Needle: We Distilled Gemini Tool Calling into a 26M Model

Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices.We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention is the right primitive for this, and FFN parameters are wasted at this scale.Simple Attention Networks: the entire model is just attention and gating, no MLPs anywhere. Needle is an experimental run for single-shot function calling for consumer devices (phones, watches, glasses...).Training: - Pretrained on 200B tokens across 16 TPU v6e (27 hours) - Post-trained on 2B tokens of synthesized function-calling data (45 minutes) - Dataset synthesized via Gemini with 15 tool categories (timers, messaging, navigation, smart home, etc.)You can test it right now and finetune on your Mac/PC: https://github.com/cactus-compute/needleThe full writeup on the architecture is here: https://github.com/cactus-compute/needle/blob/main/docs/simp...We found that the "no FFN" finding generalizes beyond function calling to any task where the model has access to external structured knowledge (RAG, tool use, retrieval-augmented generation). The model doesn't need to memorize facts in FFN weights if the facts are provided in the input. Experimental results to published.While it beats FunctionGemma-270M, Qwen-0.6B, Granite-350M, LFM2.5-350M on single-shot function calling, those models have more scope/capacity and excel in conversational settings. We encourage you to test on your own tools via the playground and finetune acc

Hacker News4mo agoToolAI

75FL score
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GridTravel – A community based travel app for users to share routes

Hey HN,My co-founders and I have been building GridTravel, a free iOS app for planning and sharing travel routes with turn-by-turn GPS nav. We just launched yesterday after App Store approval.We're three 21-year-old cofounders and best friends since middle school. We built GridTravel after years of frustration navigating new cities on every trip we took together.The idea: most people either search Google for "top 10 places to visit in…" lists or go on social media to get inspiration on where to go. GridTravel is built around user-generated routes — actual paths someone walked, that you can follow, save, download, and discover from other travelers. Users also have the ability to create private routes and collaborate with their friends.Tech stack: Mapbox (Nav SDK + maps), Supabase (auth, DB, storage), and Swift. Native iOS for now, Android coming soon.Our two real cost drivers are Mapbox Search (hit when users create routes) and Mapbox Navigation (hit when users use live navigation). Both have free tiers, then scale with MAU. We launched fully free to remove the barrier to entry. Revisiting pricing in Year 2 once nav costs start burning a hole in our pocket.Current state: we're in the UGC cold-start hole. The app's value scales with route density in a given city, but route density requires users, who require routes. Classic chicken and egg. Our current plan: 1. Manually seed 25–30 routes per city, starting with 5-10 priority cities where we have personal networks rather than spreading ourselves thin. 2. Short-form content as the primary social channel (TikTok, reels, shorts). Doing A/B testing: whether route walkthroughs convert better than informational/skit videos. 3. Partnering with micro-influencers in those cities (5k-50k following) for in-app routes plus cross-posts on their channelsCurious what HN thinks. Especially anyone who's shipped a UGC product. What worked for you on cold start? What do you wish you'd done dif

Hacker News4mo agoToolDesign & Creative

70FL score
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