In 2025, AI models learned to effectively search and process vast amounts of information to take actions. This has shown its colors the most in coding, eg through harnesses like claude code that have had a sizable impact on programmers’ workflows.
But this year of progress doesn’t seem to have had that much of an effect on the personalization of our interactions with models, ie whether models understand the user’s context, what they care about, and their intentions, in a way that allows them to answer better. Most chatbot users’ personalization is still limited to a system prompt. Memory features don’t seem that effective at actually learning things about the user.
But the reason machine learning has not succeeded at personalization is lack of data (as it often is!). We do not have any validated ways of grading ML methods (training or outer-loop) for personalization. Getting good eval signals for personalization is hard, because grading a model’s personalization is intrinsically subjective, and requires feedback at the level of the life and interactions that the model is being personalized to. There is no verified signal, and building a generic rubric seems hard. These facts do not mesh well with the current direction of machine learning, which is just now starting to go beyond verifiable rewards into rubrics, and is fueled by narratives of human replacement that make personalization not key (If I am building the recursively improving autonomous agi, why do I need to make it personalized).1
Start by giving models more information
But how do we obtain data for personalization? I think the first step to answering this question is having consumers of AI curate their personal data and then share it to enrich their interactions with AI systems.
Instead of just a system prompt, giving models a searchable artifact of our writing, notes, and reading history. Something agents can explore when your questions might benefit from context—and write to, to remember things for later.
whorl is a local server that holds any text you give it—journal entries, website posts, documents, reading notes, etc…—and exposes an MCP that lets models search and query it. Point it at a folder or upload files.
I gave it my journals, website, and miscellaneous docs, and started using Claude Code with the whorl MCP. Its responses were much more personalized to my actual context and experiences.
Examples
First I asked it:
do a deep investigation of this personal knowledge base, and make a text representation of the user. this is a text that another model could be prompted with, and would lead them to interacting with the user in a way the user would enjoy more
It ran a bunch of bash and search calls, thought for a bit, and then made a detailed profile of me, and my guess is that its quality beats many low effort system prompts, linked here.
I’m an ML researcher, so I then asked it to recommend papers and explain the motivation for various recs. Many of these I’ve already read, but it has some interesting suggestions, quite above my usual experience with these kinds of prompts. See here.
These prompts are those where the effect of personalization is most clear, but this is also useful in general chat convos, allowing the model to query and search for details that might be relevant.
It can also use the MCP to modify and correct the artifact provided it, to optimize for later interactions – especially if you host a “user guide” there like the one I linked. Intentionally sharing personal data artifacts is the first step to having agents that understand you.
Conclusion
Personalization requires data. People need to invest into seeing what models can do with their current data, and figuring out what flows and kinds of interactions this data is useful for, towards building technology that can empower humans towards their goals. whorl is a simple tool that makes that first step easy. People who have already created a bunch of content should use that content to enhance their interactions with AIs.
Why do I want personalization, though, beyond a brief custom prompt directly telling the model how I want it to behave? Just sounds like context pollution and an invitation to sycophancy.
In 2025, AI models learned to effectively search and process vast amounts of information to take actions. This has shown its colors the most in coding, eg through harnesses like claude code that have had a sizable impact on programmers’ workflows.
But this year of progress doesn’t seem to have had that much of an effect on the personalization of our interactions with models, ie whether models understand the user’s context, what they care about, and their intentions, in a way that allows them to answer better. Most chatbot users’ personalization is still limited to a system prompt. Memory features don’t seem that effective at actually learning things about the user.
But the reason machine learning has not succeeded at personalization is lack of data (as it often is!). We do not have any validated ways of grading ML methods (training or outer-loop) for personalization. Getting good eval signals for personalization is hard, because grading a model’s personalization is intrinsically subjective, and requires feedback at the level of the life and interactions that the model is being personalized to. There is no verified signal, and building a generic rubric seems hard. These facts do not mesh well with the current direction of machine learning, which is just now starting to go beyond verifiable rewards into rubrics, and is fueled by narratives of human replacement that make personalization not key (If I am building the recursively improving autonomous agi, why do I need to make it personalized).1
Start by giving models more information
But how do we obtain data for personalization? I think the first step to answering this question is having consumers of AI curate their personal data and then share it to enrich their interactions with AI systems.
Instead of just a system prompt, giving models a searchable artifact of our writing, notes, and reading history. Something agents can explore when your questions might benefit from context—and write to, to remember things for later.
whorl - My first guess
Over the break, I built a very simple software tool to do this. It’s called whorl, and you can install it here.
whorl is a local server that holds any text you give it—journal entries, website posts, documents, reading notes, etc…—and exposes an MCP that lets models search and query it. Point it at a folder or upload files.
I gave it my journals, website, and miscellaneous docs, and started using Claude Code with the whorl MCP. Its responses were much more personalized to my actual context and experiences.
Examples
First I asked it:
do a deep investigation of this personal knowledge base, and make a text representation of the user. this is a text that another model could be prompted with, and would lead them to interacting with the user in a way the user would enjoy more
It ran a bunch of bash and search calls, thought for a bit, and then made a detailed profile of me, and my guess is that its quality beats many low effort system prompts, linked here.
I’m an ML researcher, so I then asked it to recommend papers and explain the motivation for various recs. Many of these I’ve already read, but it has some interesting suggestions, quite above my usual experience with these kinds of prompts. See here.
These prompts are those where the effect of personalization is most clear, but this is also useful in general chat convos, allowing the model to query and search for details that might be relevant.
It can also use the MCP to modify and correct the artifact provided it, to optimize for later interactions – especially if you host a “user guide” there like the one I linked. Intentionally sharing personal data artifacts is the first step to having agents that understand you.
Conclusion
Personalization requires data. People need to invest into seeing what models can do with their current data, and figuring out what flows and kinds of interactions this data is useful for, towards building technology that can empower humans towards their goals. whorl is a simple tool that makes that first step easy. People who have already created a bunch of content should use that content to enhance their interactions with AIs.