one point the author seems to be missing as to why we need full self driving everywhere is in the logistics industry. We are talking about interstate driving, from point A to point B, in this case it might involve industria environments such as a truck stop, weighing, loading/unloading etc, something that requires decision making across different environments (urban, highway, dense residential etc). Tesla FSD has the real potential for this unlock, more than just a ride hailing use case of getting people from point A to point B.
i have been your user since the early days, I want to say congrats you guys, I have been recommending your framework to everyone. I appreciate the responsive support you gave me on discord (though in the end, my questions are already on the docs lol)
on top of http level caching, you can do any type of caching (redis / fs / etc) just like a regular rest but at a granular level, for ex: user {comments(threadId: abc, page: 1, limit: 20) { body, postedAt} is requested and then cached, another request can come in thread(id: abc) {comments(page: 1, limit: 20) {body, postedAt} you can share the cache.
but of course, there is always the classic dataloader as well.
I am not saying that use graphql and all the problems will be solved, but i am saying that the problem that OP proposed has been solved in an arguably "better" way, as it does not tie the presentation (HTML) with the data for cases of multiplatform apps like web, or native apps.
N+1 is a solved problem at the framework level
If GraphQL actually affects your performance, congratulations, your application is EXTREMELY popular, more so than Facebook, and they use graphql. There are also persisted queries etc.
Not sure about caching, if anything, graphql offers a more granular level of caching so it can be reused even more?
The only issue I see with graphql is the tooling makes it much harder to get it started on a new project, but the recent projects such as gql.tada makes it much easier, though still could be easier.
i have been looking for something like this, the closest I could find by googling was celery workflow, i think you should do better marketing, I didn't even realize that hatchet existed!
I 100% agree with this, I think you can even extend this to any AI, in the end, IMO, as the llm is more commoditized, the surface of which the value is delivered will matter more
gemini flash is notorious for hallucinating the output of the OCR, be careful with it. For straight forward, semi-structured, low page count (under 5) it should perform well, but the more the context window is stretched the more the output gets more unreliable
It's a weird timing because I just launched https://dochq.io - ai document extraction where you can define what you need to get out your documents in plain English, I legitimately thought that this was going to be such a niche product but hell, there has been a very rapid rise for AI-based OCR lately, an article/tweet even went viral 2 weeks ago I think? About using Gemini to do OCR, fun times.