The intro is brilliant, the tech and the math is average(you can probably find way better math explanations here on HN). Check out the interview around 22 min mark for something REALLY cool!
No affiliation, I just liked the way they have covered the tech.
I'm not sure about the rebranding of products, but some of the features like the learning path creation and coding challenges definitely look interesting.
If this leads to SO regaining some of its popularity, I'm all for it!
I've bought a bunch of Philips products over the years, and always hated the fact that a simple thing like a charger breaking would lead to throwing away the entire thing- especially grooming products like trimmers. This release of 3D printable models definitely feels like a step in the right direction.
I think being snarky about little things isn't really promoted on HN- it's the first guideline for commenting here. Be kind and try to answer the question if you do have an answer.
Cognitive Search is nowhere as good as a 'pure' vector DB. Behind the scenes, it's a managed elasticsearch/opensearch with some vector search capabilities. The 'AI' implementations I've done with Cognitive Search always boil down to hybrid(vector+fts) text search.
You can pretty much solve this using modern DL models. There are options depending on how accurate you want your model and how much compute you have.
There is an entire spectrum of models, from something like Mask-RCNN, U-Net family upto something like Meta's SAM, which you can use without even training.
I'll recommend the Spotlight paper by Google[1]. There are very interesting datasets they created for this purpose. They mention they have a screen-action-screen dataset that is in-house and it doesn't look like they'll open it. Maybe owning Android has its advantages.
There's a recent paper by Huggingface called IDEFICS[2] that claims to be an open source implementation of Flamingo(an older paper about few-shot multi-modal task understanding) and I think this space will be heating up soon.
Based on the constraints and some of the comments, a different avenue would be to reduce your upfront ask.
Try and assess if you really need all the full-time employees you mention. You can get very far with part-time employees, freelancers and outsourced development till the point where you have an MVP built for which investors can put up money. If you really need FTEs, you can (partially)pay them in ESOPs of the company against future revenue.
This is somewhat possible. I've created a way to chat to our company's material publicly. We used a lot of prompt engineering and custom guardrails to achieve this. However, it severely limited the length of the conversation that a user can have.
I'd like to recommend ekatra.one, a GPT-powered education platform designed for under-served learners, especially in India. Helps with personalized learning experiences and uses WhatsApp for course delivery.
This is a fantastic resource for someone who's just getting interested in Photogrammetry, via Neural Radiance Fields(NeRF). I'm curious if those should be included here.
As an aside, perhaps this repo should be renamed to start with `awesome`, since it helps with SEO for github.
Be kind. Don't be snarky. Converse curiously; don't cross-examine. Edit out swipes.