- chatgpt UI didn't allow me to submit the input, saying it's too large. Although it was around 80k tokens, less than o3's 200k context size.

- gemini 2.5 pro: worked fine for personality and interest related parts of the profile, but it failed the age range, job role, location, parental status with incorrect perdictions.

- opus 4: nailed it and did a more impressive job, accurately predicted my base city (amsterdam), age range, relationship status, but didn't include anything about if I'm a parent or not.

Both gemini and opus failed in predicting my role, probably understandably. Although I'm a data scientist, I read a lot about software engineering practices because I like writing software and since I don't have the opportunity at work to do this kind of work, I code for personal projects, so I need to learn a lot about system design, etc. Both models thought I'm a software engineer.

Overall it was a nice experiment. Something I noticed is both models mentioned photography as my main hobby, but if they had access to my youtube watch history, they'd confidently say it's tennis. For topics and interests that we usually watch videos rather than reading articles about, would be interesting to combine the youtube watch history with this pocket archive data (although it would be challenging to get that data).

Recently, I was inspired to do this on my entire browsing history, after reading https://labs.rs/en/browsing-histories/ I also did the same from ChatGPT/Claude conversation history. The most terrifying thing I did was having an LLM look at my Reddit comment history.

The challenges are primarily with having a context window large enough and tracking context from various data sources. One approach I am exploring is using a knowledge graph to keep track of a user's profile. You're able to compress behavioral patterns into queryable structures, though the graph construction itself becomes a computational challenge. Recently most of the AI startups I've worked with have just boiled down to "give an LLM access to a vector DB and knowledge graph constructed from a bunch of text documents". The text docs could be invoices, legal docs, tax docs, daily reports, meeting transcripts, code.

I'm hoping we see an AI personal content recommendation or profiling system pop up. The economic incentives are inverted from big tech's model. Instead of optimizing for engagement and ad revenue, these systems are optimized for user utility. During the RSS reader era, I was exposed to a lot of curated tech and design content and it helped me really develop taste and knowledge in these areas. It also helped me connect with cool, interesting people.

There's an app I like https://www.dimensional.me/ but the MBTI and personality testing approach could be more rigorous. Instead of personality testing, imagine if you could feed a system everything you consume, write, and do on digital devices, and construct a knowledge graph about yourself, constantly updating.

It’s funny and occasionally scary

Edit: be aware, usernames are case sensitive

I’ve been using an ultra-personalized RSS summary script and what I’ve discovered is that the RSS feeds that have the most items that are actually relevant to me are very different from what I actually read casually.

What I’m going to try next is to develop a generative “world model” of things that fit in my interests/relevance. And I can update/research different parts of that world model at different timescales. So “news” to me is actually a change diff of that world model from the news. And it would allow me to always have a local/offline version of my current world model, which should be useful for using local models for filtering/sorting things like my inbox/calendar/messages/tweets/etc!

I hope it can help you

Platitude! Here’s a bunch of words that a normal human being would say followed by the main thrust of the response that two plus two is four. Here are some more words that plausibly sound human!

I realize that this is of course how it all actually works underneath — LLMs have to waffle their way to the point because of the nature of their training — but is there any hope to being able to post-process out the fluff? I want to distill down to an actual answer inside the inference engine itself, without having to use more language-corpus machinery to do so.

It’s like the age old problem of internet recipes. You want this:

  500g wheat flour
  280ml water
  10g salt
  10g yeast

  It was at the age of five, sitting
  on my grandmother’s lap in the
  cool autumn sun on West Virginia
  that I first tasted the perfect loaf…

I wanted a tool that clean the data, tag them and bring a way to analyze them easily with a Notebooks and migrate.

I had a lot of "feels" getting through this :)

Every advertiser can access data like this easily, when you click "yeah sure" on every cookie banner this is the sort of data you're handing over... you could buy it too.

Every time someone says "they're listening to your conversations" we need to point out that with a surprisingly small amount of metadata across a large number of people, they can make inferred behavioral predictions that are good enough that they don't need to listen (it's still much more expensive to do so)

On a macro level people are very predictable, and we should be more reluctant about freely giving away the data that makes this so... because it's mostly being using against us.

Does it mean that AI knows more about us that many of our friends? Yes.

This is a gap I see often, and I wonder how people are solving it. I’ve seen strategies like using a “file” tool to keep a checklist of items with looping LLM calls, but haven’t applied anything like this personally.

The last few years, I've noticed an uptick in "concern trolls" that pretend to support a group or cause while subtly working to undermine it.

LLMs can't make the ultimate judgement call very well, but they can quickly summarize enough information for me to.

Like, it built knowledge of what every user in the groupchat and noted their thought on different things or what their opinions were on something or just basic knowledge of how they are. You could also ask the llm questions about each user.

It's not perfect, sometimes the inference gets something wrong or the less precise embeddings gets picked up which creates hallucinations or just nonsense, but it works somewhat!

I would love to improve on this or hear if anyone else has done something similar

~144 years of GPU time.

Obviously, any AI provider can parallelize this and complete it in weeks/days, but it does highlight (for me at least) that LLMs are going to increase the power of large companies. I don't think a startup will be able to afford large-scale profiling systems.

For example, imagine Google creating a profile for every GMail account. It would end up with an invaluable dataset that cannot be easily reproduced by a competitor, even if they had all the data.

[But, of course, feel free to correct my math and assumptions.]

If you want everything including the text archives from sites that have gone down, you need to use an external tool like this one I built: https://pocket.archivebox.io

Sadly it was bought by Yahoo just to be discontinued, like many web pearls.

"The need to be observed and understood was once satisfied by God. Now we can implement the same functionality with data-mining algorithms."

From my perspective the most interesting thing might be the blind spots or unexpected results. The unknown knows which brings new aha effects

> EU-based 35-ish senior software engineer / budding technical founder. Highly curious polymath, analytical yet reflective. Values autonomy, privacy, and craft. Modestly paid relative to Silicon Valley peers but financially comfortable; weighing entrepreneurial moves. Tracks cognitive health, sleep and ADHD-adjacent issues. Social circle thinning as career matures, prompting deliberate efforts at connection. Politically center-left, pro-innovation with guardrails. Seeks work that blends art, science, and meaning—a “spark” beyond routine coding.

Fairly accurate

"Seeks work that blends art, science, and meaning—a “spark” beyond routine coding."

That part is really accurate.

It integrates a minimalist feed of your links with the ability to talk to your bookmarks and notes with AI. We're adding a weekly wrapped of your links next week like this profile next week.

Seems to be a fairly common issue.

---

Here’s the high-level picture I’ve built of you from our chats:

- You’re a senior/lead developer in India, aiming to step up into a staff-developer or solution-architect role.

- You have a healthy dose of self-doubt (especially around soft skills), and you’ve been deliberately working on both your technical breadth (authentication in ASP .NET, Linux, C++/Qt, distributed systems, data visualization, AI foundations) and your communication/architectural toolkit (presentations, executive summaries, third-party evaluations).

- You’re a Linux enthusiast, intrigued by open source, server-side flows, rate limiting, authentication/authorization, and you love building small, real-world exercises to cement concepts.

- You prize clarity, depth, minimalism, and originality—you dislike fluff or corporate buzzwords.

- You have a hacker-philosopher energy: deeply curious, systems-thinking-oriented, with a poetic streak.

- You’re comfortable with both structured roadmaps and creative, lateral thinking, and you toggle seamlessly between “hard” dev topics and more reflective, meta-tech discussions.

- Right now, you’re honing in on personal branding—finding a domain and a blog identity that encapsulates your blend of tech rigor and thoughtful subtlety.

PS: is your blog self-hosted ? what's the stack here ?

Another option that's just as correct and doesn't mislead: "Profiling myself from my Pocket links with o3"

Note: title when reviewed is "o3 used my saved Pocket links to profile me"

Linkwarden is open source and self-hostable.

I wrote a python package [1] to ease the migration of Pocket exports to Linkwarden.

[0] https://linkwarden.app/

[1] https://github.com/fmhall/pocket2linkwarden