c_moscardi

LADW saved me in undergrad, but I was pretty much exactly the target audience in an honors-level freshman math course:

"[per Treil, LADW is for] a student who, while not yet very familiar with abstract reasoning, is willing to study more rigorous mathematics than what is presented in a “cookbook style” calculus type course."

But yeah it's really attempting to introduce you to higher mathematics rather than get you comfortable doing linear algebra per se.

Riding in the family rail car like it’s 1895 (and you’re a robber baron)

We chatted a few months back -- congrats on launch! Looks like a great UX.

Amazing! GitHub actions to compute a giant skim matrix is an incredible hack.

I pretty regularly work with social science researchers who have a need for something like this... will keep it in mind. For a bit we thought of setting something like this up within the Census Bureau, in fact. I have some stories about routing engines from my time there...

Worth noting that there already exists an ecosystem of these sorts of contracting firms (nava, skylight, truss I believe, forgetting even more) that, basically, pitch themselves as the antidote to beltway banditry

People from the federal civic tech nexus started them up over the past decade as they termed out of 18F, USDS, and PIF

Came to post this — it’s the same underlying technology, just a lot more compute now.

Hi HN! I've spent a couple of months fiddling with OCR and wanted to share some of my findings.

The approach I share here (fine-tuning recent deep learning models) is the first one that's gotten me anything resembling high-quality OCR on these particular noisy historical documents. OCRing these has been something of a white whale for me for several years (except, a white whale that I have spent comparatively little time on).

At this point I think I am reasonably competent in OCR, but no expert... Curious for your thoughts.

Figures, too! Yeah you could write some logic essentially on top of a library like this, and tune based on optimizing for some notion of recall (grab more surrounding context) and precision (direct context around the word, e.g. only the paragraph or 5 surrounding table rows) for your specific application needs.

Using the models underlying a library like this, there's maybe room for fine-tuning as well if you have a set of documents with specific semantic boundaries that current approaches don't capture. (And you spend an hour drawing bounding boxes to make that happen).

Funnily enough, this is another great tactic for getting emails returned (looping in someone with more leverage than you or asking them to follow up for you)!