The APIs are pretty interchangeable too. Just ask to convert from one to the other if you need to.

LLM's ability to take 7400+ handwritten entries and try to make a narrative out them is amazing. With all of the AI experiments on HN lately, we're figuring out the power of LLMs, but it most cases, it still needs a human refining touch, and we need to remember that. Or else it just looks like AI slop.

Digitizing history in different ways, with different resources that are unique or only known to small groups, might be a new development area, and that's exciting. As I've shown, and how other people have shared, using AI tools to digitize things which haven't previously been done before is now possible. Are there ways to make this easier for everybody? New techniques to discuss? I don't know, and I'd love to talk about it.

Concerning OCR: I used Mistral because of a posting here describing advancements with handwriting recognition a month or so ago. I didn't actually compare them. And I've got my setup that I can rerun everything again later if there are advancements in the area. Again, another area to keep track of and discuss.

These diary pages come largely from Stirling City, just north of Chico, and later from the Hat Creek district, on Hwy 89 north of Mt. Lassen. Nearby, many historical records were lost in the Paradise Camp Fire, and digitizing some of the records in some of the local museums is something this is a test run for.

  —Lance (CSU Chico ‘93 Computer Engineering)

"Fix up my packs. Load the 2 mules with 225# each. Take the 2 loads to trail camp at Lake Everett, Unload. Have lunch with the Trail cook. Haze mules & ride to 7 1/2 PM."

Horses are mentioned 2586 times. That'd be a whole study on how they're used in the back country. (Edit: horse number is inflated since part of the diary form at one point asks for "Horse Mileage". Will have to refine search).

I've never had one of my sites with this much traffic. With everything as static files, website is still holding. Thank you all.

"mistral-ocr-latest" did really good handwriting transcription, considering how tight and small some of the handwriting is. Then back to Claude API calls to summarize by month and collect people and places from all of the entires.

Claude then created static html pages from what started as a Flask app. Published on Dreamhost.

https://forestrydiary.com/

This is one of those projects I've sat on for years, but with Claude and Mistral helping with the handwriting recognition, and even helping me write a custom scanning app that would auto scan each page and put it into a database as I assembled everything.

As far as I know, this is the only US Forestry Diary that has been fully scanned in and published. I understand that there are other diaries in some collections, but none have been scanned in. I hope this helps somebody. Please let me know if it does.

This is the sort of project Claude and AI can help with - A personal project that sits on the shelf forever, but now a reasonable project that can be published in my spare time. I'm not trying to earn money on this, but just improving our knowledge and history just a little bit.

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Claude is making it easier to have bespoke data and dashboards for anything. We're going to make a lot of them, for all reasons. I've also made apps with Django interfaces, but quick, global interfaces are going to become in demand.

I didn’t realize how close LLMs are to the old magazines. Let it give you a seed, then use that springboard to learn everything else.

What problem does normalization solve? You don't have to parse and run through a tree every time you're looking for data. You would, however, need to rebuild the tree through self joins or other references in other cases, I suppose. It depends how far you break down your data. I understand that we all see data structures a bit differently, however.