141–150 of 177 posts
I'm starting to think that LLM's are a solution in need of a problem like how crypto and the blockchain was. Have we not already solved web scraping?
"I'm starting to think computers are a solution in the need of a problem. Have we not already solved doing math?"
Earlier quoted context omitted.
You must get a lot of test emails to that FANTASTIC gmail address. Funny how it might even be worth some decent money.
That's not literally his e-mail :D. He means that you have to replace it with his HN username. It would have been better to write it like this: [HN username]@gmail.com
GPT-4 (and Claude) are definitely the top models out there, but: Llama, even the 8b, is more than capable of handling extraction like this. I've pumped absurd batches through it via vLLM. With serverless GPUs, the cost has been basically nothing.
Can you explain a bit more about what "serverless GPUs" are exactly? Is there a specific cloud provider you're thinking of, e.g. is there a GPU product with AWS? Google gives me SageMaker, which is perhaps what you are referring to?
You ship your code as a container within a library they provide that allows them to execute it, and then you're billed per-second for execution time.
Like most FaaS, if your load is steady-state it's more expensive than just spinning up a GPU instance.
If your use-case is more on-demand, with a lot of peaks and troughs, it's dramatically cheaper. Particularly if your trough frequently goes to zero. Think small-scale chatbots and the like.
Runpod, for example, would cost $3.29/hr or ~$2400/mo for a single H100. I can use their serverless offering instead for $0.00155/second. I get the same H100 performance, but it's not sitting around idle (read: costing me money) all the time.
Is there a "html reducer" out there? I've been considering writing one. If you take a page's source it's going to be 90% garbage tokens -- random JS, ads, unnecessary properties, aggressive nesting for layout rendering, etc. I feel like if you used a dom parser to walk and only keep nodes with text, the html structure and the necessary tag properties (class/id only maybe?) you'd have significant savings. Perhaps the…
I wrote an in-house one for Ribbon. If there’s interest, will open source this. It’s amazing how much better our LLM outputs are with the reducer.
Is there a "html reducer" out there? I've been considering writing one. If you take a page's source it's going to be 90% garbage tokens -- random JS, ads, unnecessary properties, aggressive nesting for layout rendering, etc. I feel like if you used a dom parser to walk and only keep nodes with text, the html structure and the necessary tag properties (class/id only maybe?) you'd have significant savings. Perhaps the…
I wrote an in-house one for Ribbon. If there’s interest, will open source this. It’s amazing how much better our LLM outputs are with the reducer.
Is there a "html reducer" out there? I've been considering writing one. If you take a page's source it's going to be 90% garbage tokens -- random JS, ads, unnecessary properties, aggressive nesting for layout rendering, etc. I feel like if you used a dom parser to walk and only keep nodes with text, the html structure and the necessary tag properties (class/id only maybe?) you'd have significant savings. Perhaps the…
I wrote an in-house one for Ribbon. If there’s interest, will open source this. It’s amazing how much better our LLM outputs are with the reducer.
Is there a "html reducer" out there? I've been considering writing one. If you take a page's source it's going to be 90% garbage tokens -- random JS, ads, unnecessary properties, aggressive nesting for layout rendering, etc. I feel like if you used a dom parser to walk and only keep nodes with text, the html structure and the necessary tag properties (class/id only maybe?) you'd have significant savings. Perhaps the…
Jina.ai offer a really neat (currently free) API for this - you add https://r.jina.ai/ on the beginning of any API and it gives you back a Markdown version of the main content of that page, suitable for piping into an LLM. Here's an example: https://r.jina.ai/https://simonwillison.net/2024/Sep/2/anato... - for this page: https://simonwillison.net/2024/Sep/2/anatomy-of-a-textual-us... Their code is open source so you…
We've had the best success by first converting the HTML to a simpler format (i.e. markdown) before passing it to the LLM. There are a few ways to do this that we've tried, namely Extractus[0] and dom-to-semantic-markdown[1]. Internally we use Apify[2] and Firecrawl[3] for Magic Loops[4] that run in the cloud, both of which have options for simplifying pages built-in, but for our Chrome Extension we use dom-to-semanti…
We've had the best success by first converting the HTML to a simpler format (i.e. markdown) before passing it to the LLM. There are a few ways to do this that we've tried, namely Extractus[0] and dom-to-semantic-markdown[1]. Internally we use Apify[2] and Firecrawl[3] for Magic Loops[4] that run in the cloud, both of which have options for simplifying pages built-in, but for our Chrome Extension we use dom-to-semanti…
Have you compared markdown to just stripping the HTML down (strip tag attributes, unwrap links, remove obvious non-displaying elements)? My experience has been that performance is pretty similar to markdown, and it’s an easier transformation with fewer edge cases.