Puan
28,728
Çözümler
0
- Konum
- Freanchey
- Mesajlar
- 3,047
- Katılım
- 2 Eki 2026
- Tepkime puanı
- 3,565
- Puan
- 28,728
- Web sitesi
- glomaxgpt.com
How I used AI to investigate millions of historical records and surfaced a forgotten meteorite report, three lost rhinos, and unrecorded volcano eruptions.
Last week, on October 1st, 2026, the historian Benjamin Breen published a post called
Using Opus 5.5 to discover a new eyewitness account of the dodo.
He used AI to search digitized records from the Dutch East India Company. The Company dominated the spice trade and ran a string of ports across Asia from 1602 until it collapsed under its debts and was dissolved in 1799.
A project called GLOBALISE has turned millions of the Company’s handwritten pages into searchable text. Searching those transcriptions with AI, Breen found a 1615 ship’s journal in which sailors on Mauritius “caught many tortoises, dodos”. A new eyewitness record of the extinct dodo, sitting in plain sight for four hundred years.
I read the article and immediately thought, I can do this.
Not because I’m a historian, but because I’m a software engineer who happens to be very adept at piloting AI agents and creating agentic workflows. Breen’s post was the
inspiration, and I wanted to extend the approach: I could search for many different historical mysteries in parallel, and I could
chain together a few different AI models to filter results and automate a few of the cumbersome, time consuming manual steps.
But the appeal went beyond the technical challenge. I think there’s something honorable about adding to what we know. Adding even one new data point to a couple of niche fields felt like a small but worthwhile contribution to make. I thought it would be really cool if I could do that.
I powered on my small but capable home AI lab and got to work.
The Plan
I started by asking an AI “Deep Research” assistant a question: which open historical questions are most likely to be solvable with data
that already exists online? It came back with thirteen candidates, ranked by how complete and accessible the data was, how much AI could
help, whether anyone had already done it, and, most importantly, whether an answer could be checked against an original page. That last one matters more than it sounds. AI
models can be confidently wrong, so every claim had to end at a real document: a scan of the actual handwritten letter or the actual
printed newspaper, with an archive reference that anyone can look up and read for themselves. The list
included animals in the Dutch East India Company archives, a giant volcanic eruption from 1808 that nobody has ever located, felt
earthquakes in old Dutch newspapers, unrecorded meteorite falls, and ships that completely vanished.
Then I teamed up with Claude Code, Anthropic’s AI tool, and we invented this research pipeline together.
First came the reading material: the GLOBALISE transcriptions of the Dutch East India Company archive (4.35 million pages,
from the 1600s to the 1790s), the Dutch national library’s digitized newspapers, two centuries of American newspapers, and a few ship
logbooks for good measure.
If I sat down to read just the Dutch East India Company pages myself, at two minutes a page, eight hours a day, five days a week, it
would take me about 70 years. And that’s before the newspapers. My homebrew AI lab got through the entire archive in a single
twelve-hour overnight run.
The trouble with old documents is that nobody spelled anything the same way twice. Handwriting and old print come out of text
recognition full of errors, and 17th-century Dutch spells “rhinoceros” about fifteen different ways. A plain keyword search would miss
most of what I was looking for. That overnight run was my graphics card turning every passage into a mathematical fingerprint of its meaning, 5.7
million passages from the Dutch East India Company archive alone. That let me search for what a passage was
about, not just which words it happened to use.
Even then, a single search could return tens of thousands of hits, far too many for a person, or even a big AI model, to read
affordably. Instead, the first read went to a tiny, fast “System One” decision model called Jev, which
answers only narrow questions: Is this a real animal? Is it wild? Where is it? It costs a few cents per million words. Having it read
59,000 mentions of elephants cost me about three dollars.
That’s the part of this project I’m proudest of, and it was my idea, not the AI’s. When I proposed using Jev as a filtering mechanism, Claude Code’s Fable model didn’t yet know what a System One type decision model was; I had to explain the idea before we could build the pipeline around it. In most research like this, the bottleneck is a person reading candidate passages one by one and deciding which are worth a closer look. Putting a cheap, fast judge in that seat automated the initial screening and freed me to focus on the strongest candidates and the direction of the investigation. The pages I examined closely had already survived two rounds of machine reading.
Only the passages Jev flagged moved on. Claude Haiku, a bigger model, read those few dozen closely, translated them and pulled out the
dates and places. Then the Claude Code agent opened the scan of each original handwritten page to check the transcription against the original. Before calling anything new, I checked it against the catalogues the specialists themselves use.
A quick note on how AI was used in this project: I stayed actively involved in the process throughout and piloted the AI through the course of the entire project. I steered the investigation toward new questions and sources, decided which leads were worth pursuing, and recognized when I was grasping at straws and needed to move on. The systems could search and read at a scale I couldn’t, but deciding where to go next, or when to stop, still took my judgment. This was AI-assisted research, not a completely autonomous investigation.
There was one more rule, and it mattered most. Before you trust a search that finds nothing, you have to prove it can find something you
a
Last week, on October 1st, 2026, the historian Benjamin Breen published a post called
Using Opus 5.5 to discover a new eyewitness account of the dodo.
He used AI to search digitized records from the Dutch East India Company. The Company dominated the spice trade and ran a string of ports across Asia from 1602 until it collapsed under its debts and was dissolved in 1799.
A project called GLOBALISE has turned millions of the Company’s handwritten pages into searchable text. Searching those transcriptions with AI, Breen found a 1615 ship’s journal in which sailors on Mauritius “caught many tortoises, dodos”. A new eyewitness record of the extinct dodo, sitting in plain sight for four hundred years.
I read the article and immediately thought, I can do this.
Not because I’m a historian, but because I’m a software engineer who happens to be very adept at piloting AI agents and creating agentic workflows. Breen’s post was the
inspiration, and I wanted to extend the approach: I could search for many different historical mysteries in parallel, and I could
chain together a few different AI models to filter results and automate a few of the cumbersome, time consuming manual steps.
But the appeal went beyond the technical challenge. I think there’s something honorable about adding to what we know. Adding even one new data point to a couple of niche fields felt like a small but worthwhile contribution to make. I thought it would be really cool if I could do that.
I powered on my small but capable home AI lab and got to work.
The Plan
I started by asking an AI “Deep Research” assistant a question: which open historical questions are most likely to be solvable with data
that already exists online? It came back with thirteen candidates, ranked by how complete and accessible the data was, how much AI could
help, whether anyone had already done it, and, most importantly, whether an answer could be checked against an original page. That last one matters more than it sounds. AI
models can be confidently wrong, so every claim had to end at a real document: a scan of the actual handwritten letter or the actual
printed newspaper, with an archive reference that anyone can look up and read for themselves. The list
included animals in the Dutch East India Company archives, a giant volcanic eruption from 1808 that nobody has ever located, felt
earthquakes in old Dutch newspapers, unrecorded meteorite falls, and ships that completely vanished.
Then I teamed up with Claude Code, Anthropic’s AI tool, and we invented this research pipeline together.
First came the reading material: the GLOBALISE transcriptions of the Dutch East India Company archive (4.35 million pages,
from the 1600s to the 1790s), the Dutch national library’s digitized newspapers, two centuries of American newspapers, and a few ship
logbooks for good measure.
If I sat down to read just the Dutch East India Company pages myself, at two minutes a page, eight hours a day, five days a week, it
would take me about 70 years. And that’s before the newspapers. My homebrew AI lab got through the entire archive in a single
twelve-hour overnight run.
The trouble with old documents is that nobody spelled anything the same way twice. Handwriting and old print come out of text
recognition full of errors, and 17th-century Dutch spells “rhinoceros” about fifteen different ways. A plain keyword search would miss
most of what I was looking for. That overnight run was my graphics card turning every passage into a mathematical fingerprint of its meaning, 5.7
million passages from the Dutch East India Company archive alone. That let me search for what a passage was
about, not just which words it happened to use.
Even then, a single search could return tens of thousands of hits, far too many for a person, or even a big AI model, to read
affordably. Instead, the first read went to a tiny, fast “System One” decision model called Jev, which
answers only narrow questions: Is this a real animal? Is it wild? Where is it? It costs a few cents per million words. Having it read
59,000 mentions of elephants cost me about three dollars.
That’s the part of this project I’m proudest of, and it was my idea, not the AI’s. When I proposed using Jev as a filtering mechanism, Claude Code’s Fable model didn’t yet know what a System One type decision model was; I had to explain the idea before we could build the pipeline around it. In most research like this, the bottleneck is a person reading candidate passages one by one and deciding which are worth a closer look. Putting a cheap, fast judge in that seat automated the initial screening and freed me to focus on the strongest candidates and the direction of the investigation. The pages I examined closely had already survived two rounds of machine reading.
Only the passages Jev flagged moved on. Claude Haiku, a bigger model, read those few dozen closely, translated them and pulled out the
dates and places. Then the Claude Code agent opened the scan of each original handwritten page to check the transcription against the original. Before calling anything new, I checked it against the catalogues the specialists themselves use.
A quick note on how AI was used in this project: I stayed actively involved in the process throughout and piloted the AI through the course of the entire project. I steered the investigation toward new questions and sources, decided which leads were worth pursuing, and recognized when I was grasping at straws and needed to move on. The systems could search and read at a scale I couldn’t, but deciding where to go next, or when to stop, still took my judgment. This was AI-assisted research, not a completely autonomous investigation.
There was one more rule, and it mattered most. Before you trust a search that finds nothing, you have to prove it can find something you
a