I know; I thought they'd have a handy parts list on their new site. But you are right; I should have looked in their Google Drive docs. There's a section - "Bill of materials and cost breakdown", but details are buried somewhere. Thanks, though.
> Their last success was acquiring Instagram in 2012.
WhatsApp can be dubbed a success as well, and Oculus wasn't a flop. And, what does that tell you about the company? They can only acquire and integrate products. Why? Because Leetcode (LC). Fk LC, Hard!!!
> I formerly used that cost hundreds or thousands of dollars for a license
Azure Doc Intelligence charges $1.50 for 1000 pages. Was that an annual/recurring license?
Would you mind sharing your OCR model? I'm using Azure for now, as I want to focus on building the functionality first, but would later opt for a local model.
I took a long break from document processing after working on it heavily 20 years ago. The tools I used before were ABBYY FineReader and PrimeOCR. I haven't tried any of the commercial cloud based solutions. I'm currently using GLM-OCR, Chandra OCR, and Apple's LiveText in conjunction with each other (plus custom code for glue functionality and downstream processing).
Try just GLM-OCR if you want to get started quickly. It has good layout recognition quality, good text recognition quality, and they actually tested it on Apple Silicon laptops. It works easily out-of-the-box without the yak shaving I encountered with some other models. Chandra is even more accurate on text but its layout bounding boxes are worse and it runs very slowly unless you can set up batched inference with vLLM on CUDA. (I tried to get batching to run with vllm-mlx so it could work entirely on macOS, but a day spent shaving the yak with Claude Opus's help went nowhere.)
If you just want to transcribe documents, you can also try end-to-end models like olmOCR 2. I need pipeline models that expose inner details of document layout because I need to segment and restructure page contents for further processing. The end-to-end models just "magically" turn page scans into complete Markdown or HTML documents, which is more convenient for some uses but not mine.
Qwen 3 and 3.5 models are quite capable. Perhaps the greatest benefit of GLM-OCR is speed: it's only a 0.9 billion parameter model, so it's fast enough to run on large volumes of complicated scans even if all you have for inference is an entry level MacBook or a low end Nvidia card. Even CPU based inference on basic laptops is probably tolerable with it for small page volumes.
They were told that the election was stolen and the system was rigged. Under the pretense that you were told no more fair and free elections would you not go do exactly what the j6ers did, I think you would be foolish to say no.
The only core issue is that everything that was said that day was a whole a total lie and the responsibility hangs with the liars
Now, Someone has to review tests! Just shifting ownership! Claude has just released 'Code Review'. But I don't think you can leave either one on autopilot.
Appreciate your work! Healthcare is a regulated industry. Everything (Research, proposals, FDA submissions, Compliance docs, Accreditation Standards, etc.) is documented and follows a process, which means there's a lot of thesis. You can't sneak in anything unverified or unreliable. Why does healthcare need a JEPA\World model?
Regulation is quickly catching up to modern AI techniques; for the most part, the approach is to verify outputs rather than process. For example, Utah's pilot to let AI prescribe medications has doctors check the first N prescriptions of each medication. Medicare is starting to pay for AI-enabled care, but tying payment to objective biomarkers like cholesterol or blood pressure actually got better.
There are many ways to circumvent that, even while claiming to offer it for free.
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