jeffreyw128 | 8 months ago | on: How to make almost anything (2019)
jeffreyw128's comments
jeffreyw128 | 1 year ago | on: Local Deep Research – ArXiv, wiki and other searches included
If you want to add embeddings over internet as a source, you should try out exa.ai. Includes: wikipedia, tens of thousands of news feeds, Github, 70M+ papers including all of arxiv, etc.
disclaimer: I am one of the founders (:
jeffreyw128 | 1 year ago | on: Show HN: Exa (YC S21) – embeddings search agent with >20x recall than Google
Cofounder Jeff here. Both!
jeffreyw128 | 1 year ago | on: Ask HN: Who is hiring? (November 2024)
Jeff, cofounder of Exa.ai here. LLMs represent a brand new opportunity to organize humanity's knowledge, in a way that hasn't been done before. We're an AI research lab focused on AI-powered search algorithms (using embeddings), currently applied to vast swaths of the web (we make our money as a search API).
A little about us: - Raised series A a few months ago. https://techcrunch.com/2024/07/16/exa-raises-17m-lightspeed-... - 15 people, fully in person in SF. Our team - https://exa.ai/team - Our mission: https://exa.ai/blog/superknowledge
We're hiring pretty broadly across engineering - AI research, high performance Rust (e.g., we build an in-house vector DB), and full stack. If the mission of organizing the Internet motivates you, it's a good fit :)
jeffreyw128 | 1 year ago | on: Ask HN: Who is hiring? (October 2024)
Jeff, cofounder of Exa.ai here. LLMs represent a brand new opportunity to organize humanity's knowledge. We're an AI research lab focused on AI-powered search algorithms (using embeddings), currently applied to vast swaths of the web (we make our money as a search API).
A little about us:
- Raised series A a few months ago. https://techcrunch.com/2024/07/16/exa-raises-17m-lightspeed-...
- 15 people, fully in person in SF. Our team - https://exa.ai/team
- Our mission: https://exa.ai/blog/superknowledge
We're hiring pretty broadly across engineering - AI research, high performance Rust (e.g., we build an in-house vector DB), and full stack. If the mission of organizing the Internet motivates you, it's a good fit :)
jeffreyw128 | 1 year ago | on: Ask HN: Who is hiring? (July 2024)
Jeff, cofounder of Exa here. LLMs represent a brand new opportunity to organize humanity's knowledge, in a way that hasn't been done before. We're an AI research lab focused on AI-powered search algorithms (using embeddings), currently applied to vast swaths of the web (we make our money as a search API).
We're hiring pretty broadly across engineering - AI research, high performance Rust (e.g., we build an in-house vector DB), and full stack. If the mission of organizing the Internet motivates you, it's a good fit :)
jeffreyw128 | 1 year ago | on: A look at search engines with their own indexes (2021)
jeffreyw128 | 1 year ago | on: Ask HN: Who is hiring? (June 2024)
Jeff, cofounder of Exa here. LLMs represent a brand new opportunity to organize humanity's knowledge, in a way that hasn't been done before. We're an AI research lab focused on AI-powered search algorithms (using embeddings), currently applied to vast swaths of the web (we make our money as a search API).
We're hiring pretty broadly across engineering - AI research, high performance Rust (e.g., we build an in-house vector DB), and full stack. If the mission of organizing the Internet motivates you, it's a good fit :)
jeffreyw128 | 2 years ago | on: Compare Google, Bing, Marginalia, Kagi, Mwmbl, and ChatGPT
jeffreyw128 | 2 years ago | on: Compare Google, Bing, Marginalia, Kagi, Mwmbl, and ChatGPT
For paywalls/login - we play pretty straight, always obey robots.txt, etc.
jeffreyw128 | 2 years ago | on: Compare Google, Bing, Marginalia, Kagi, Mwmbl, and ChatGPT
For your search - I would recommend turning autoprompt off and searching something like "Here is a great summary of the best computer mice to use:".
Our embeddings model is trained on how links are talked about on the Internet, if that helps with querying. So you have to query like how someone would refer to a link before sharing it
jeffreyw128 | 2 years ago | on: Compare Google, Bing, Marginalia, Kagi, Mwmbl, and ChatGPT
Try https://search.metaphor.systems - it's fully neural embeddings-based search. No keywords, only an embedding of what the actual content of a webpage is.
So in the mentioned example of searching for Youtube downloaders, with Metaphor you'll get only Youtube downloaders (https://search.metaphor.systems/search?q=This%20is%20the%20b...)
Full disclosure - I work there :p
jeffreyw128 | 2 years ago | on: Most AI startups are doomed
jeffreyw128 | 2 years ago | on: The Small Website Discoverability Crisis (2021)
Really good for finding personal pages, niche blog posts, etc.. Algorithm doesn't at all weigh website popularity explicitly.
(Disclaimer: I'm one of the cofounders)
jeffreyw128 | 2 years ago | on: Generative AI could make search harder to trust
(shameless plug) At Metaphor (https://platform.metaphor.systems/), we’re building a search engine that avoids SEO content by relying on human curation + neural embeddings for our index + retrieval algorithm. Our mission is to ensure that the information we receive is as high quality and truthful as possible as AI adoption marches onwards. You (or your LLM) can feel free to give it a try :)
jeffreyw128 | 2 years ago | on: Building search for the post-ChatGPT world
We wrote a blog post about our adventures in building a (neural) search engine in the post-LLM world. We hope it gives a perspective on how we're thinking about the future of search and the role that tools like Metaphor could have. In brief, we think that LLMs will do more searches than humans, using tools like Metaphor.
You can play with our search here (http://metaphor.systems/) or check out our API here (https://platform.metaphor.systems/). The API is free to use up to 1000 requests/month, and if you're a student or nonprofit, just reach out and we can probably do more.
Cheers!