Show HN: I made the slowest, most expensive GPT (ithy.com)
This doesn't really apply to math/coding (where o1 or Gemini can probably one-shot an excellent response), but more to online search, where information is more fluid and there's no "right" search engine + text restructuring + model combination every time. Even o1 doesn't have online search, so it's obviously a hard problem to solve.
An example is something like "best ski resorts in the US", which will get a different response from every GPT, but most of their rankings won't reflect actual skiers' consensus - say, on Reddit https://www.reddit.com/r/skiing/comments/sew297/updated_us_s... - because there's so many opinions floating around, a one-shot RAG search + LLM isn't going to have enough context to find how everyone thinks. And obviously, offline GPTs like o1 and Sonnet/Haiku aren't going to have the latest updates if a resort closes for example.
So I’ve spent the last few months experimenting with a new project that's basically the most expensive GPT I’ll ever run. It runs search queries through ChatGPT, Claude, Grok, Perplexity, Gemini, etc., then aggregates the responses. For added financial tragedy, in-between it also uses multiple embedding models and performs iterative RAG searches through different search engines. This all functions as sort of like one giant AI brain. So I pay for every search, then every embedding, then every intermediary LLM input/output, then the final LLM input/output. On average it costs about 10 to 30 cents per search. It's also extremely slow.
I know that sounds absurdly overkill, but that’s kind of the point. The goal is to get the most accurate and comprehensive answer possible, because it's been vetted by a bunch of different AIs, each sourcing from different buckets of websites. Context limits today are just large enough that this type of search and cross-model iteration is possible, where we can determine the "overlap" between a diverse set of text to determine some sort of consensus. The idea is to get online answers that aren't attainable from any single AI. If you end up trying this out, I'd recommend comparing Ithy's output against the other GPTs to see the difference.
It's going to cost me a fortune to run this project (I'll probably keep it online for a month or two), but I see it as an exploration of what’s possible with today’s model APIs, rather than something that’s immediately practical. Think of it as an online o1 (without the $200/month price tag, though I'm offering a $29/month Pro plan to help subsidize). If nothing else, it’s a fun (and pricey) thought experiment.
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[ 3.0 ms ] story [ 120 ms ] threadi asked a subjective history question about England and Ithy's analysis was great, and did indeed add to other GPTs!
i did find the UI a bit confusing at first, that's my only nitpick. i signed in (nice easy flow) and will definitely continue to use!
looks like anthropic is slowing down ithy analysis/responses, at least today during my tests just now anyway.
great app! well done o7
o7
Cheers!
Parts of this are borrowed from https://github.com/assafelovic/gpt-researcher
It's actively being developed (I pitch in where I can; I added the xAI integration this week), so I'd recommend starting here! The creator of the project, Assaf, has been nothing but friendly.
To determine which of the items could be used to make an actual fire, we need to analyze the definitions provided:
1. *Glaarg*: A wooden item.
2. *Bliirg*: A non-wooden item.
3. *Neerg*: A non-existent thing.
4. *Eeerg*: A thing that actually exists.
Now, let's look at the specific terms:
- *Bipk*: A glaarg (wooden item) that is also a neerg (non-existent thing). Since it is non-existent, it cannot be used to make a fire.
- *Vokp*: A glaarg (wooden item) that is also an eeerg (existent thing). Since it is a wooden item that exists, it can be used to make a fire.
- *Jokp*: A bliirg (non-wooden item) that is also an eeerg (existent thing). While it exists, it is non-wooden, so it may not be suitable for making a fire depending on its material.
- *Fhup*: A bliirg (non-wooden item) that is also a neerg (non-existent thing). Since it is non-existent, it cannot be used to make a fire.
Based on this analysis, the only item that can be used to make an actual fire is a *vokp*, as it is a wooden item that exists.
It's more of just a better search engine. Use it for stuff you'd Google. Offline LLMs are always going to have a better price-performance ratios than RAGs like this or Perplexity.
Weird that your query triggered the filter. Maybe GPT is just that afraid of keto diets...
(I'll look into it; thanks for the note!)
Filtering malicious (not controversial) usage is ideal as allowing users to flood all of the AI services with jailbreak/against-ToS query attempts can be bad news for your API keys (as well as a likely waste of money given the failure rate of such queries).
https://ithy.com/article/b848ebc9a32140ffa766a1520113846c
https://ithy.com/article/4b116d2032e54c03862db84e71bcfc8f
https://big-agi.com/ has this "BEAM" concept as well where you can put your message through as many models as you have configured then run fuse/guided/compare/custom to merge them all together into one comprehensive. more expensive response.
https://files.catbox.moe/tr82vs.png
https://files.catbox.moe/beuyfx.png
However Ithy does produce something much much better! This is really cool! I wonder if you could cache questions and answers, and start creating your own "reddit" knowledgebase to RAG from and avoid having to dive deep again $$$.
I have a feeling that Perplexity and ChatGPT are doing something similar [caching], since common questions I'd ask like "top movies this year" will be answered nearly-instantaneously, way faster than GPT-4o could have done on its own.
The only explanation for this is that so many users ask certain questions, they cache the response and return the cached answer.
I'd love to do this for Ithy, but it'll be a while before I get the scale of ChatGPT/Perplexity that's needed for this...
I've found that very generic queries like "best ski resorts in the US" seem woefully pouted by top 10 spam sites. LLMs do not want to give any useful info about that no matter how much prompting I seem to give.
I was looking for an app that does X,Y,Z recently and no amount of prompting for open source would get me anything but a handful of stock answers I would get from a random spam site.
Like I'll try to find the top burgers in midtown. Perplexity or ChatGPT online searching will always find "Top 10 Burgers in Midtown" by https://nycreviewed.com/ (their SSL certificate isn't even valid). But this itself is a GPT-generated list, and their list isn't even in midtown.
So now I end up with a list of 10 burger places that aren't in midtown.
Ithy kinda solves this by utilizing multiple search engines and LLMs, so spam sites like this are more likely to get filtered out somewhere in the pipeline.
I'm just applying this theory to online search.
I degoogled a while ago…
Update 2:30 PM ET: Back up (for now). Still waiting for Anthropic and Gemini quota increase requests, so those have been migrated to GPT-4o for now. Running on 2 VPCs, in the process of launching 2 more. Confident that I can increase the daily limits by EOD once everything's more stable.
Update 1:30 PM ET: HN blew up Ithy and it's 99% down right now, congrats ._.
1. I've exceeded my weekly Anthropic API limits; I've gotten in touch with their sales team and I've temporarily disabled the Anthropic model.
2. Blew past my Google API limits as well. I was using Gemini for prompting and aggregation, and I'm waiting for their quota increase response. In the meantime, I've switched to GPT-4o for the prompting/aggregation.
3. My VPC is at 100% CPU load. Launching more right now with some load balancing.
4. Limits were previously 5 anon / 20 per logged in user. Reduced this to 1 anon / 3 logged in while I deal with the load issues. Planning bring these back up as soon as everything's working again.
Hope to get this all back online within an hour or two. Sorry for the crappy launch. To think I was an SRE in a past life...
https://news.ycombinator.com/item?id=42403006 ("Ask HN: Ideas for spending $8k in Anthropic credits?")
thank you. just seeing this and playing with this has expanded how i think about these type of systems
Confident that I can increase the daily limits by EOD once everything's more stable.
Thought: there is a marquee of example queries but it doesn't seem like there is a way to see what an answer looks like without individually consuming a search as a user. Maybe if these were clickable to a cached version it'd be easier to get an idea of the outputs without costing so much?
The only explanation for this is that so many users ask certain questions, they cache the response and return the cached answer.
I'd love to do this for Ithy, but it'll be a while before I get the scale of ChatGPT/Perplexity that's needed for this...
IF you want free tokens and IF your visitors agree for all their input and output to be used by all AIs involved for research and training or whatever.. you might be able to strike up the same kind of dealio that LMSYS has with lmarena - > github.com/lm-sys/FastChat - > lmarena .ai
to use the site you have a big obvious popup explaining the data use quickly and efficiently