AI

AI One Health – Connecting technologists with health professionals

Hacker News

AI One Health – Connecting technologists with health professionals

We built AI One Health as an open community where technologists can connect with researchers, veterinarians, doctors, climate scientists, and anyone curious about how AI can support the health of people, animals, and the environment. - What is One Health? One Health is the idea that human, animal, and environmental health are deeply interconnected. Think pandemic preparedness, zoonotic disease monitoring, antimicrobial resistance, or climate-health modelling; challenges that can't be solved by any single discipline working alone. - What is it? AI One Health isn't a startup or research project; it's a meeting point for bridging the gap between AI practitioners and One Health professionals. We noticed that plenty of people work on AI, and plenty work on interconnected health challenges, but they rarely talk to each other. The platform brings together researchers, developers, artists, students, conservationists, and everyday citizens to exchange ideas about using AI wisely to improve life on Earth. - Why should developers care? These interdisciplinary spaces are where interesting collaborations emerge. When technologists connect with domain experts working on complex health challenges, both sides benefit from perspectives they wouldn't encounter otherwise. Beyond the satisfaction of working on problems that matter, these kinds of cross-disciplinary connections often lead somewhere unexpected. You might discover a research collaboration that becomes your next project, meet a domain expert who needs exactly your skillset, or stumble into insights that spark something entirely new. The goal is simply to connect people from different worlds so they can think, learn, and act together. We're planning to host an online conference in the coming year to bring the community together and showcase what happens when AI meets One Health. Check it out: https://ai-onehealth.com

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, single · Missing: mac, agents, macos
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TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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