Original on Medium

In my previous post, I shared how to deploy OpenClaw with Ollama on a local machine. This time, I want to dive into a real-world use case — analyzing baby tracking data 100% privately using AI.

Recently, I developed an app called OnlyBaby using vibe coding. The app is entirely AI-generated and aims to help parents keep track of their baby and mother’s health data. But the big question was: How can I analyze this sensitive data without sharing it with public cloud-based AI tools?

Here’s how I made it work — flaws, fixes, and all.

The Problem: Private Baby Data Needs Private AI

OnlyBaby tracks a lot of personal information — sleep cycles, feeding times, diaper changes, mood, and more. I wanted to use the power of AI to analyze this data for early signs of health issues, irregularities, or just useful parenting insights.

But I had a major concern:

I didn’t want to send this data to any third-party cloud AI.

That’s when I realized Openclaw could be the perfect fit.

My Setup: Ollama + Openclaw + WhatsApp

Here’s the architecture I used:

• OnlyBaby app collects baby & mother tracking data.
• I send the data via WhatsApp to my own Mac Studio.
• Ollama runs a local large language model (LLM) on Mac Studio.
• Openclaw, deployed on the same machine, processes the incoming data.
• The result: AI-powered insights — 100% private, no cloud needed.

Two Problems I Faced (And How I Solved Them)

1. LLMs Kept Forgetting the Context

I noticed that my model forgot previous messages, making it impossible to maintain a meaningful conversation about the baby’s ongoing data.

✅ Solution: Increase Context Length

Use this command to boost the model’s memory:

OLLAMA_CONTEXT_LENGTH=131072 ollama serve

This simple tweak drastically improved context retention.

2. LLMs Seemed Dumb with Raw JSON Data

Initially, when I sent the raw tracking data, the AI didn’t know what to do with it. It lacked domain-specific knowledge about baby care.

✅ Solution: Write a Custom Skillset

I created a skill module specifically for OnlyBaby:

• GitHub: OnlyBabySkills
• It tells OpenClaw how to interpret the JSON structure, and what insights to extract.

Once I integrated this skill into OpenClaw’s processing flow and told OpenClaw must use the skill for these specific json files, the system worked like a charm.

Results: AI Insights, On My Terms

Now, when I send tracking data via WhatsApp:

• My local Mac Studio handles everything.
• No data ever leaves my network.
• OpenClaw uses the skills to analyze the baby’s health trends.
• I receive actionable insights in near real-time.
• OpenClaw can collect more information about baby and give smart suggestions.

It’s like having a pediatric assistant that lives on my desk — but one that respects my data privacy.

Why This Matters

• Privacy-first parenting: Sensitive health data stays local.
• Developer flexibility: Write your own domain-specific skills for analysis.
• Edge AI in action: OpenClaw + Ollama = Personal AI Assistant.

Final Thoughts: Trust AI, But On Your Terms

This experiment proves that you can harness the power of AI without compromising your privacy. Open-source tools like OpenClaw, combined with clever system design and a bit of problem-solving, make it all possible.

If you’re building apps like OnlyBaby — or anything involving private data — this architecture might inspire your next move.

Let me know if you want a deeper dive into the OnlyBabySkills or how to structure the JSON data for better analysis!

✍️ Written with AI, run by AI, secured by me.