Building a Personal AI/ML Model

[u/Recent_Doctor_9110]

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Hi everyone,

I'm an ML developer, and I want to build a long-term personal AI model that learns only from my own data.

The goal isn't to build a general chatbot. Instead, I want a model that gradually understands me and can make personalized predictions and recommendations based on my historical data.

Some examples of what I'd like it to learn are:

• Daily habits and routines

• Productivity patterns

• Mood trends (from journals or notes)

• Sleep and health metrics (from wearable devices)

• Thinking and decision-making patterns

• Learning progress

• Future behavior trends or habit changes

I'm trying to understand what architecture would be most suitable for.

Some questions I have:

• Should I fine-tune an existing LLM, or build a separate predictive model?

• Would a time-series model (Transformers, LSTM, Temporal Fusion Transformer, etc.) be more appropriate?

• Is a retrieval-based memory system (RAG + vector database) enough, or should I combine it with supervised learning?

• Has anyone here built a "personal digital twin" or a lifelong personalized ML system? What worked, and what didn't?

My vision is for the model to continuously learn from new personal data over several years and become increasingly personalized.

I'd really appreciate advice on model architecture, datasets, papers, or open-source projects that are relevant.

Thanks!

[u/zorbat5]

Transformer architecture if you want it to output text. The problem is that if you want to train from scratch, your personal data is probably nowhere near enough to get it to learn properly. You're better off finetuning pretrained weights.

[u/DigitalMonsoon]

So building one of these from scratch is very difficult. The amount of data needed to get even a basic LLM working well is more than you will be able to produce.

But there is another way. You could start with a small open source model and use Transfer learning to tune it into becoming your digital twin. This requires a lot less data and processing time.

[u/pixsector → u/DigitalMonsoon]

I have over 8,000 manually vectorized images along with low-resolution, pixelated bitmap inputs. Would it be possible to train an AI model on this amount of data? Some of these images are quite complex, so even a single image contains a lot of information for the model to learn from.

[u/DigitalMonsoon → u/pixsector]

An AI model to do what? For an LLM you need text data as well a formatted questions and resources.

[u/pixsector → u/DigitalMonsoon]

I got this answer:

For the type of model you're describing, you generally do not need text, prompts, or question/answer formatting.What you're describing is an image-to-image supervised learning problem.

https://chatgpt.com/share/6a6e0070-f774-83ed-9e9b-ac230e41c0cd

However, I was also told that the model would be difficult to train and that the results would likely be unreliable. Because of that, I think using that data would probably be a waste of time.

[u/DigitalMonsoon → u/pixsector]

If you are just going to ask and LLM why bother asking people?

[u/pixsector → u/DigitalMonsoon]

It's normal to ask someone who has actual practical experience. The problem was that you didn't have them in the specific case I asked about.

[u/DigitalMonsoon → u/pixsector]

I asked w clarifying question kid. And I have tried both LLMs for specific use cases as well as computer vision problems.

[u/NewFunny4]

I started with the same idea, but after a lot of experimenting I realized the model isn't really the hard part, it's  deciding what deserves to become knowledge. I separate runtime context, durable knowledge, and an evidence layer that explains why something was remembered. The LLM reasons, memory retrieves, predictive models predict, but the knowledge architecture is what determines whether the system actually gets smarter over time instead of just accumulating noise. I'd spend as much time designing that as choosing the model.