future VS researcher

For the record, Microsoft stole this idea from me, and I implemented it first, although as a parody: https://future.attejuvonen.fi

A few weeks go someone posted "WeChatGPT", which used the API and pretended to be a dystopian, CCP-controlled search engine of the future. Every prompt ends with an advertising tie-in. Unfortunately it only now supports the pre-recorded queries, but I was impressed at how consistently on-point it was for the brands being shilled.

WeChatGPT+ now with 8% less censorship!

Why imagine when you can experience the future now: https://future.attejuvonen.fi (from a recent thread here).

The website: https://future.attejuvonen.fi/

In this particular case (summarizing search results), it seems to work well. I think anchoring to known information helps avoid a lot of the issues (hallucinations, boring language, etc).

You can check out an open source demo I made if you want to play around with how search + GPT work - https://github.com/VikParuchuri/researcher .

I've had the exact same problem, and have experimented with Kagi, DDG, etc, to try to find better results.

ChatGPT, in my opinion, is great for "how do I code X" type questions, but isn't so good at the types of queries you mentioned, due to the lack of a search engine.

My weekend project was an open source combination of Google + GPT that returns pretty good results for these types of queries. You can check it out here - https://github.com/VikParuchuri/researcher

Example - the response to "what are the best current smartphones" is:

`...According to Search Result [2], the best phones have been thoroughly reviewed and tested, and include the Apple iPhone 14 and 14 Pro, the Pixel 7 Pro and the Samsung Galaxy S22 Ultra. Search Result [5] also states that there are strong options available at all price levels, so you don't have to spend a lot to get something great...`

This is very cool! It's nice to see other implementations outside of ChatGPT.

I noticed that it doesn't always cite sources, so I assume that some results are coming straight from an LLM, and some are web search + LLM.

If you're always looking for sources to be cited, I built a project this weekend that will summarize a topic and cite sources - https://github.com/VikParuchuri/researcher . It's definitely slower than this implementation, but it is self-hosted and hackable.

I don't think it's a huge lift to restrict a language model to "known" good facts from search results. And to have it cite sources.

I made a proof of concept this weekend - https://github.com/VikParuchuri/researcher . There are some issues, but it's very useful.

I built a side project recently to help with this. It searches Google, then feeds the relevant results from the pages into GPT-3 to get a summary. It seems to be accurate so far in my testing - https://github.com/VikParuchuri/researcher .