The Folder I Never Opened I fed 15 years of my private conversations to an AI and asked it who I am – part 1
I wasn’t looking for them. I was cleaning up Proton Drive on an afternoon in March 2026, a sugar-loaded Highlands coffee going cold next to the keyboard – it hadn’t been good warm either – and there it was, in a backup inside a backup: a folder named Trillian. 122.8 megabytes. Carried across four machine migrations and two continents by a guy who never once opened it.
Four years of my private messages. December 2009 through December 2013, age 21 to 25, logged by a chat client that logged everything by default because that’s what software did back then. Nobody archived this. It simply failed to get deleted for sixteen years.
I’d been playing with Claude Opus 4.6 for weeks at that point, mostly on work things, mostly impressed. So the question arrived fully formed, the way the stupid ones do: what would a machine see in there that I can’t?
There was no crisis behind it, no anniversary, no dark night of the soul. A folder, a model, and an afternoon with nothing better in it.
This post is the engineering half – the pile, the pipeline, the parts where I nearly published a confident and entirely fictional psychological profile without noticing. Part 2 is what it found. Part 2 is where it stops being fun.
The pile
8,936 files on disk, 4,634 of them log-shaped. Five distinct formats, because Trillian spoke ICQ, ASTRA, Skype, Facebook Chat and MSN, and every protocol got its own dialect: Skype XML, Trillian’s native XML, plaintext .log, and a second plaintext variant with different escaping. Plus -assets.xml files full of thumbnail metadata, which are not messages and got thrown out. Half the partners existed twice, as partner.xml next to partner.log, same conversation at two fidelities.
The tree had two branches. PLATFORM/Query/ held the consolidated per-partner masters. _CLOUD/YYYY/WW/ held weekly chunks per partner per protocol, synced to Trillian’s cloud service. Same conversations, different granularity, and – remember this, it’s the entire plot of the next section – no shared naming convention between them.
German, overwhelmingly. English creeping in year over year, which I noticed at the time and thought meant nothing.
So I wrote a normalizer. normalize_chatlogs.py walks the tree, sniffs each file’s actual format instead of trusting its extension or its path, and emits one chronologically sorted CSV: timestamp, source_file, platform, direction, subject_handle, partner_handle, partner_id, text, is_system. Flat CSV on purpose. The thing consuming this was a language model reading slices of text, not a query engine, and a proper database would have been me performing engineering at an audience of nobody.
The fiddly bit was working out which messages were mine. Past-me changed handles the way other people change haircuts, so the script carries a SUBJECT_IDENTIFIERS set with partial matching – kronos, kronyboy, luitpold, zollorsch, mjamblubmiau, a couple of numeric ICQ IDs. Sender matches, flag it outgoing. Everything else is incoming. Crude. Held up fine.
Forty-one percent of me was a copy
First full run: 720,205 messages across 677 conversation partners.
Both numbers were wrong, and the part that still bothers me is that they looked completely fine. Plausible volume. Plausible partner count. A number that big carries its own authority – you don’t audit a figure that flatters you.
The _CLOUD weekly chunks were near-total duplicates of the Query masters. File-level dedup never saw a thing, because the two trees named the same person differently: skype-br0k3nstyl3 over here, br0k3nstyl3 over there. Group by filename stem, find zero matches, feel great about yourself.
I could have excluded _CLOUD from the walk. Simpler, faster, and resting on an assumption I had never verified – that the cloud branch was a strict subset of the master branch. If it wasn’t, I’d have deleted real conversations and never known which ones. So instead: hash every message, drop exact repeats, don’t care where they came from.
The key is (partner_id, timestamp, direction, md5(text)). Hashing rather than comparing keeps the in-memory set survivable across hundreds of thousands of rows, and carrying the partner and direction fields stops ok, lol and ja from colliding across four years of unrelated conversations, which they otherwise absolutely would.
720,205 in. 425,491 out. Forty-one percent of the corpus was duplication.
That isn’t a tidiness problem. _CLOUD coverage wasn’t uniform across the timeline – some periods were duplicated hard, others hardly at all. Which means every ratio in Part 2 – who I talked to more, when I got louder, the quarter a friendship fell off a cliff – would have been skewed by an uneven multiplier that varied by year. Not wrong in a way anybody could see. Wrong in a way that produces a beautifully evidenced, quarterly-broken-down, fully cited psychological profile of a person who does not exist.
And I’d have published it. That’s the thing I sat with for a while. It would have read exactly as convincing.
The summary that ate the post
Before slicing anything, split_corpus.py emits corpus_summary.txt: partner rankings by volume, messages per quarter, outgoing and incoming ratios per partner and per period, subject handle evolution, and the ratio extremes – who I talked at, and who talked at me.
Pure counting. No content, no model, nothing had read a single message yet.
It was already the most interesting artifact in the project.
Here are my own handles, chronologically, with volumes. xXKronosXx, 12,000 messages, ICQ, gamer era, the xX-blob-Xx format that was peak late-MySpace shit and yes we all knew it. KronyBoy, roughly 28,000, transitional. luitpold, then luitpold.zollorsch, 41,000 – my actual name, the boring legally-issued one. And finally www.mjamblubmiau.de, 55,000. A Skype handle that is, and I want to be precise about this, a URL. At some point I stopped picking a name and started picking a domain.
Nobody coded that. No framework produced it, no model interpreted it. A script counted strings and handed me the shape of my own identity in four rows of a text file – and every one of those transitions maps onto something Part 2 spends thousands of words unpacking.
The ratios pulled the same trick. Outgoing share of messages: about 45% in 2010, 48% in 2011, 55.3% in 2012, 51.3% in 2013. Four years from listener to loudest voice in the room, visible in nothing but message counts. No sentiment analysis. No language model. Counting.
I keep coming back to this, because it’s the least AI thing in a post about AI. The heaviest structural finding in the project fell out of wc -l with extra steps.
Seventeen quarters
The normalized CSV landed around 73 MB. You can’t hand that to a model. You can’t hand it a tenth of that.
So: slice by year and quarter, 2009-Q4.csv through 2013-Q4.csv, seventeen files. Quarterly rather than per-partner, and that choice isn’t housekeeping – it’s the thesis wearing a filename. A per-partner slice gives you one clean file per relationship and destroys the only thing I actually wanted to look at, which is the moment one relationship displaces another. Slice along the axis your analysis runs on. Mine ran on time.
Then every quarter went through the same loop.
First a throwaway Python script profiled the slice – message and system-message counts, direction split, platform distribution, active days, which handles were in use, a top-20 partner ranking with per-partner ratios, and what share of the quarter went to the top three people. Numbers only.
Then, and only then, reading. The scan decided what got opened: the high-volume partners, the sharp ratio anomalies, whoever appeared out of nowhere or stopped existing. Statistics said where to look. Messages said what was there. That order is the whole method, and reversing it leaves you two options, both bad – read 425,491 messages (impossible) or read a convenient sample and call it evidence (worse).
Findings got coded against an eight-dimension framework I’d written before touching the data: attachment dynamics, personality structure, cognitive style, shadow material, identity development, and so on, each grounded in a published model so the coding stayed consistent instead of vibing. I’m not listing all eight with citations. The first version of this post did, and it read like a syllabus someone was hoping you’d be impressed by. The part that matters is the ordering – rubric first, data second, so I couldn’t quietly move the goalposts somewhere more flattering once I saw what was coming.
A cumulative pattern tracker carried forward across every pass: outgoing ratio, primary relationship, active behavioral patterns, self-presentation mode. Each new quarter got read against the trajectory instead of in isolation. Without it, seventeen snapshots. With it, a line.
Nerdy Rudi is a real person
Partway through, a handful of Facebook display names started turning up in the subject_handle field. Which is to say: the pipeline had decided they were me. Alternate accounts. More masks.
They were my contacts. Actual human beings from my actual contact list, dropped into the wrong column by a Trillian quirk where Facebook’s sender field doesn’t reliably identify the account owner.
No statistical check on earth would have caught that. An alias and a mislabeled contact look identical from inside the data – same volume shape, same ratio behavior, same everything. The only instrument in the room capable of noticing was a person who remembered Rudi.
Which is the argument for the phase I nearly skipped.
After the quarterly passes I ran a structured Q&A round – every open question the analysis had accumulated, handed back to me. The rule going in: anything that would change what got observed gets asked immediately, anything that would change how it gets interpreted waits for the batch.
Essentially all of them were interpretive. That was the finding. The data could see what happened at perfect resolution and had no access whatsoever to why. Was this handle a person or a mask? Was that sentence literal, or someone being dramatic at 3 AM? Which parts of a story I told somebody in 2010 were true, and which were furniture? Message volume said one relationship was the defining one of that period; emotional register pointed at a quieter one. Both readings were defensible from the text. Only one was right, and the corpus does not know which.
Several findings changed materially in that round. One of them inverted an entire year’s interpretation, and it belongs in Part 2 rather than in a methodology section, because it involves a person who died.
Data hands you the skeleton. Somebody who was there has to say where it hurt.
Knowing when not to fix it
The obvious move with the Facebook artifact was to patch the parser and re-run.
I didn’t. Re-running meant re-slicing, which meant invalidating seventeen completed quarterly passes, which meant redoing the single most expensive part of the project – for a few hundred misattributed messages against quarterly volumes north of twenty thousand. Sub-one-percent. So I documented the error, accounted for it during synthesis, and left it in the data.
That felt wrong for about a day. It was correct. Every engineer eventually learns that a known, quantified, documented bug beats an unknown clean re-run, and most of us learn it the expensive way first.
The same principle sits under the one design decision I’d defend in court: the raw normalized CSV stayed immutable. Identity consolidation, filtering, slicing – all downstream, all editable, none of it requiring another pass over 4,500 files. Which is why the partner ID fix cost me nothing.
That one’s funny, briefly. Facebook contacts were appearing twice, once as foo@chat.facebook.com and once as foo@chat.facebook.com.log, because a file extension was leaking into the partner_id field. Strip the extension and the unique partner count drops from 557 to 441 – and the corrected rankings changed which relationships looked significant. Sixteen percent of my social graph was a string-handling bug.
Six further merges were manual, because no heuristic I trust links an ICQ number to a Skype handle to a Facebook ID. You build a map, a human confirms it, done. One of those I only caught because a first Skype account went quiet almost exactly when a second started talking. No shared identifier anywhere. Just two date ranges that refused to overlap, and me sitting there going: oh. That’s you.
The other half of the mirror
The chat logs stop dead at the end of 2013. So the second dataset was this blog – 691 posts, 2008 to now, exported as a 3.6 MB CSV. 93 of them sit in Personal Lore, 42 in Love, Sex & Identity, which is a suspiciously low number given, um, the source material.
It does two jobs. It bridges 2014 to 2026, including the entire Vietnam era, about which the chat logs know precisely nothing. And across the overlap window, 2010 through 2013, both corpora exist at once: the same weeks, in private and in public, written by the same person for two audiences who were never going to compare notes.
The lens had to change between them. Chat is dyadic – ratios, latency, who initiates, who fades. Blog is monologue – self-narrative, topic drift, emotional register, what I chose to say out loud and to whom. One shows what I did. The other shows what I said I did.
The gap between those two is one of the more uncomfortable things in Part 2.
What was actually doing the reading
Claude Opus 4.6. Not a therapist – a language model, applying a rubric I wrote, to text I handed it, with no access to my face, my voice, or anything I didn’t type. If that makes you skeptical: good. Hold onto that.
What it’s extraordinarily good at is the one thing no human could have done here: holding a framework steady across seventeen passes over several days, without fatigue, without drifting toward last session’s conclusions, without getting uncomfortable when the material turned ugly and quietly softening the coding. Everything in Part 2 is phrased as patterns consistent with rather than as a diagnosis, because a diagnosis requires a license and this had none.
Roughly 1.8 million tokens across the whole project – about thirteen novels’ worth of text, all of it about me, processed by something that has never met me. Around 1.2 million of that went into the seventeen quarterly passes alone; the rest split across the blog corpus, the Q&A round, and assembling two documents, an intermediate findings file and the formal profile. Both were generated as .docx through Node and the docx npm library, which is a deeply unglamorous ending for a project about the soul.
Those documents ended up doing a second job I hadn’t planned for. When a working session ran out of context, the intermediate findings file let a fresh session pick up with the entire foundation instead of fragments. Deliverables as handoff artifacts. It’s the most boring thing I learned here and it’s the one I’ve reused most since.
A note on everybody else in the file
This is my data, about me, at my request. But a chat log is dyadic by definition. It’s full of other people’s words, other people’s lives, other people’s bad nights.
Everyone in the findings is anonymized, with two exceptions. Jayden, who knows about this project and consented to it. And Phương, who died in November 2024, whose name I keep because he deserves to be remembered and because I’ve already written our story in public.
If you recognize yourself in Part 2: thank you for being there, whatever role you played. And the analysis is about me, not you. You were the context. I was the variable.
Part 2 has the findings – a seven-phase arc, the patterns that keep showing up regardless of which chapter they turn up in, a diagnosis, and one number I still haven’t been able to put down.
But the thing I keep circling back to is that text file. Four handles, four volumes, no model, no framework, no interpretation. xXKronosXx. KronyBoy. luitpold.zollorsch. www.mjamblubmiau.de.
Sixteen years later I’m still doing the last one. You’re reading it.
