You have this flipped. Expense management systems with embedded company cards are an order of magnitude easier to managed and reconcile than dealing with thousands of employees' personal expenses and payouts
In an all cash deal the Vendor (buyer) will purchase all shares of the Target (seller) for cash and cancel those shares. A substantial amount of the cash will be held back in escrow subject to a number of clauses and released at a future date.
This will protect the buyer against misrepresentations.
There are often also targets that have to be met to achieve the full purchase price but not always disclosed
I have the opposite problem, we have just finished a massive amount of AWS credits from our VC and unfortunately cannot stack these. Any advice on how to get more credits?
Because at least then then you don't have to deal with upselling, inefficiently speaking, etc. Some people like self-checkout. However, I don't see any advantage to a computer listening to my order and arguing with me (upselling) instead of clicking buttons in 1/10 the time.
Every drive-thru that has the monitor where you get to check your order for accuracy eventually starts displaying ads instead -- lowering accuracy and offering something you don't want.
One of the McDonald's near me has been using the order taking software in one of the drive thru lanes for a while now. It's such a better experience compared to probably 90% of the employees that have been taking orders there.
It doesn't have someone whisper/shout through the speaker because they don't know how loud they need to speak to be heard outside. It speaks clearly and can be understood which is not always the case with the bored and disinterested high school workers the store tends to hire. And my personal favorite is that I'm not greeted with "McDonalds! Thank you! Order please!" which seems to be the standard greeting they're training employees with at that location.
I've only had it get an order wrong once (though the food does still get made incorrectly about 10% of the time) at which point it quickly switched over to an actual person to complete the order. You can tell it's more efficient too because at busier times, the non-human order taker lane moves cars through at a two or even three to one ratio.
Overall, I'm surprised that more locations haven't switched to the software already and especially that other drive thru businesses haven't begun testing the software out here.
I’ve noticed they’re mostly staffed in the back cooking though, which is arguably a much better task for a human than recording basic information into a computer system. I trust a LLM to get my order right more often than someone who’s soul has been completely crushed by the banality of their task in life. At least making the food is actually doing something.
this is absolutely not true, I know engineering managers with around 5-7 years of total experience ( aka people under 30 ) making $400,000 CAD in cash compensation at shop. They recently did a cash/equity split where employees could choose their split and not a huge surprise many chose the maximum cash
there are no government engineering positions in Canada paying anywhere close to this
I've worked extensively in this space. For those looking for just an OCR solution MSFT's offering "read" is by and far the most accurate. Key-value, table and other information extraction is a much harder problem. Anything that can go wrong in production will. Documents with extra pages, rotated, blacked out, fuzzy. There are many steps that go into making document extraction really e2e.
The biggest enterprise users are doing thousand+ of pages a minute and also turn document extraction into a scaling distributed systems problem
A few days ago, IBM announced a new OCR system[1]. Have you by chance compared it to Microsoft's offering? I'm currently looking for the best-in-class OCR solution for scanned PDF documents.
Call me biased, but I've learned over time that anything that comes out of the Waston team looks good only in PR statements but sucks at production - especially at tasks like OCR. YMMV.
We currently develop solutions in this area and I believe that isolated OCR is not the solution to go. Things are moving rapidly towards end-to-end processing of documents with huge transformer models and I also believe that multi-modal GPT models will quickly win all usecases.
If you guys are interested to work in that topic and are located in the northern Germany region, pop me a message.
I would like to extract text from approximately 2000 PDF files (machine generated, not scanned) in which the layout can be different on a file basis. Some have normal paragraphs, others two columns and even three columns. All contain tables, but I am not interested in them. Do you know a good (semi-)automatic solution for this?
this is a hard problem and will require an enterprise solution unfortunately. If its only 2000 pdfs you might be better outsourcing to an off-shore consulting agency to do it manually
Do you have any recommendations for OCR of receipts and grocery bills? I’ve dreamt of having a little app to analyse grocery spending and distribute bills among multiple people, but every time I checked, the state of receipt OCR was surprisingly too bad for this…
Last I checked I saw a grocery bill example using https://github.com/mindee/doctr and was fairly accurate. Bear in mind that was last year, hopefully it got even better or there are other libraries
