I like that this relies on generating SQL rather than just being a black-box chat bot. It feels like the right way to use LLMs for research: as a translator from natural language to a rigid query language, rather than as the database itself. Very cool project!
Hopefully your API doesn't get exploited and you are doing timeouts/sandboxing -- it'd be easy to do a massive join on this.
I also have a question mostly stemming from me being not knowledgeable in the area -- have you noticed any semantic bleeding when research is done between your datasets? e.g., "optimization" probably means different things under ArXiv, LessWrong, and HN. Wondering if vector searches account for this given a more specific question.
I was thinking about it a fair bit lately. We have all sorts of benchmarks that describe a lot of factors in detail, but all those are very abstract and yet, those do not seem to map clearly to well observed behaviors. I think we need to think of a different way to list those.
I think a prompt + an external dataset is a very simple distribution channel right now to explore anything quickly with low friction. The curl | bash of 2026
Really useful currently working on a autonomous academic research system [1] and thinking about integrating this. Currently using custom prompt + Edison Scientific API. Any plans of making this open source?
I think you misunderstood. The API key is for their API, not Anthropic.
If you take a look at the prompt you'll find that they have a static API key that they have created for this demo ("exopriors_public_readonly_v1_2025")
The use case could vary from person to person. When you think about it, hacker news has large enough data set ( and one that is widely accessible ) to allow all sorts of fun analyses. In a sense, the appeal is:
> I can embed everything and all the other sources for cheap, I just literally don't have the money.
How much do you need for the various leaks, like the paradise papers, the panama papers, the offshore leajay, the Bahamas leaks, the fincen files, the Uber files, etc. and what's your Venmo?
Just comes down to your own view of what AGI is, as it's not particularly well defined.
While a bit 'time-machiney' - I think if you took an LLM of today and showed it to someone 20 years ago, most people would probably say AGI has been achieved. If someone wrote a definition of AGI 20 years ago, we would probably have met that.
We have certainly blasted past some science-fiction examples of AI like Agnes from The Twilight Zone, which 20 years ago looked a bit silly, and now looks like a remarkable prediction of LLMs.
By todays definition of AGI we haven't met it yet, but eventually it comes down to 'I know it if I see it' - the problem with this definition is that it is polluted by what people have already seen.
> If someone wrote a definition of AGI 20 years ago, we would probably have met that.
No, as long as people can do work that a robot cannot do, we don't have AGI. That was always, if not the definition, at least implied by the definition.
I don't know why the meme of AGI being not well defined has had such success over the past few years.
Completely disagree - Your definition (in my opinion) is more aligned to the concept of Artificial Super Intelligence.
Surely the 'General Intelligence' definition has to be consistent between 'Artificial General Intelligence' and 'Human General Intelligence', and humans can be generally intelligent even if they can't solve calculus equations or protein folding problems. My definition of general intelligence is much lower than most - I think a dog is probably generally intelligent, although obviously in a different way (dogs are obviously better at learning how to run and catch a ball, and worse at programming python).
Hopefully your API doesn't get exploited and you are doing timeouts/sandboxing -- it'd be easy to do a massive join on this.
I also have a question mostly stemming from me being not knowledgeable in the area -- have you noticed any semantic bleeding when research is done between your datasets? e.g., "optimization" probably means different things under ArXiv, LessWrong, and HN. Wondering if vector searches account for this given a more specific question.
Larger, more capable embedding models are better able to separate the different uses of a given word in the embedding space, smaller models are not.
what makes this state of the art?
[1] https://github.com/giatenica/gia-agentic-short
If you take a look at the prompt you'll find that they have a static API key that they have created for this demo ("exopriors_public_readonly_v1_2025")
who knows what kind of fun patterns could emerge
How much do you need for the various leaks, like the paradise papers, the panama papers, the offshore leajay, the Bahamas leaks, the fincen files, the Uber files, etc. and what's your Venmo?
Okaaaaaaay....
While a bit 'time-machiney' - I think if you took an LLM of today and showed it to someone 20 years ago, most people would probably say AGI has been achieved. If someone wrote a definition of AGI 20 years ago, we would probably have met that.
We have certainly blasted past some science-fiction examples of AI like Agnes from The Twilight Zone, which 20 years ago looked a bit silly, and now looks like a remarkable prediction of LLMs.
By todays definition of AGI we haven't met it yet, but eventually it comes down to 'I know it if I see it' - the problem with this definition is that it is polluted by what people have already seen.
No, as long as people can do work that a robot cannot do, we don't have AGI. That was always, if not the definition, at least implied by the definition.
I don't know why the meme of AGI being not well defined has had such success over the past few years.
Surely the 'General Intelligence' definition has to be consistent between 'Artificial General Intelligence' and 'Human General Intelligence', and humans can be generally intelligent even if they can't solve calculus equations or protein folding problems. My definition of general intelligence is much lower than most - I think a dog is probably generally intelligent, although obviously in a different way (dogs are obviously better at learning how to run and catch a ball, and worse at programming python).