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Lizzy Sleep – AI-powered sleep training

Hacker News

Lizzy Sleep – AI-powered sleep training

Hey HN I’m excited to show Lizzy Sleep - an app designed to help you sleep train your child (3 mths - 2 years). It’s free to try on the App Store ( https://apps.apple.com/us/app/lizzy-sleep/id6478873750?ign-i... ). Loom if you don't want to download: https://www.loom.com/share/69ecca3afd094ec7be420d809ccbb6bf?... Backstory: My wife and I had our first child in 2020. We knew we didn’t want to co-sleep and needed our personal sleep and boundaries. As our son got older and heavier, rocking him to sleep was getting unmanageable. It hurt our backs and we had no free time. We looked up tons of information about sleep training all over the internet and even bought some expensive PDFs teaching us stuff… but it all led to us being more confused in the end. There was conflicting information out there on the internet, and most of all there was no feedback loop and help when you’re unsure of what you’re doing. We eventually discovered sleep consulting through https://sleepbabyconsulting.com . A consultant gives you 1:1 help based on your family and preferences, puts together a personalized sleep plan and information for you, and then gives you troubleshooting support over text/calls for a few weeks. In a matter of a week our son went from needing to be rocked for every single nap/bedtime, to being a completely independent sleeper. That kid’s now 4.5 and he’s still a great sleeper! I recommended sleep training to every friend who had kids, but recognized that any 1:1 professional service comes with a high price tag. I felt that technology could help with accessibility to that information and support. Well last year LLMs came roaring and it felt like a great domain to technology match. So I teamed up with the owner of Sleep Baby Consulting to build Lizzy Sleep. Product/Tech: The app is fairly simple - it basically mimics in person sleep consulting but adapts it to a mobile form factor. 1. Intake Questionnaire - It takes in your preferences through a quick questionnaire. About you, your child, and learns how you’re doing things today. Most sleep training methods fail not because the method is bad, but because parents aren’t committed to the consistency required. So having a plan be personalized to you is crucial for adherence. 2. Sleep plan - We generate a personalized sleep plan. It’s designed to be read in under 30 mins to buck the trend of a lot of sleep information out there that’s extremely long and cumbersome. The thing I learned was sleep training is more simple than it seems and the foundational knowledge is relatively small in size. It’s also hopefully easier to read and digestible than a book, PDFs, combing through Reddit/FB groups. 3. AI chat - Similar to how I was able to text my sleep consultant troubleshooting questions, you can chat with Lizzy to ask all your sleep questions. Whether general ‘Google-able’ questions, or more personal troubleshooting scenarios about your child, you can ask Lizzy and you’ll get a response in a few seconds. This feedback loop and ability to adapt to your situation is crucial to sticking with the plan. Behind the scenes, the tech is relatively simple in terms of some RAG + prompt engineering, but the key here was just lots of iterations on how to get the types of responses and approach we wanted. Any LLM off the shelf can answer questions, but the hard part was working on the approach we wanted the LLM to take. We also supervise these chats and can interject as expert humans. So the app is really a combination of key insights about the actual content of sleep training, and then how people succeed/fail with it. We have a few early users and customers who have seen success in sleep training and other sleep habits. If you have young kids, give it a try. Thanks!

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Indie HackersFits the IH revenue-focused audience · Strong signals: wife, ios · Missing: supports, reddit linkedin, podcasting
97%97% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
66%66% 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: users, calls · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, personal, apps · Missing: mobile apps, entrepreneurs, video
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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