emerging-trajectories
open-interpreter
emerging-trajectories | open-interpreter | |
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6 | 24 | |
57 | 48,820 | |
- | 7.3% | |
9.1 | 9.9 | |
13 days ago | 4 days ago | |
Python | Python | |
MIT License | GNU Affero General Public License v3.0 |
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emerging-trajectories
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Large language models (e.g., ChatGPT) as research assistants
I think LLMs can do a lot more than people assume, but they need to be given the proper frameworks.
When was the last time a researcher, economist, etc. was given 10,000 papers and simply told "do some original work"? That's not how it works. Daniel (the author) provides some good examples where _streamlined_ work can happen, but again, this is pretty basic stuff.
To push this further, though, imagine LLMs that fill in frameworks... A few steps here: (1) do a lit review, (2) fill in the framework, (3) discuss what might be missing, and maybe even try and fill in the missing information.
I'm doing something like this with politics and economics (see: https://emergingtrajectories.com/) and it works generally well. I think with a ton more engineering, curating of knowledge bases, etc., one can get these LLMs to actually find some new "nuggets" of information.
Admittedly, it's very hard, but I think there's something there.
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Ask HN: Is RAG the Future of LLMs?
RAG will have a place in the LLM world, since it's a way to obtain data/facts/info for relevant queries.
Since you asked about alternatives...
(a) "World models" where LLMs structure information into code, structured data, etc. and query those models will likely be a thing. AlphaGeometry uses this[1], and people have tried to abstract this in different ways[2].
(b) Depending on how you define RAG, knowledge graphs could be a form of RAG or alternatively an alternative to them. Companies like Elemental Cognition[3] are building distinct alternatives to RAG that use such graphs and give LLMs the ability to run queries on said graphs. Another approach here is to build "fact databases" where, you structure observations about the world into standalone concepts/ideas/observations and reference those[4]. Again, similar to RAG but not quite RAG as we know it today.
[1] https://deepmind.google/discover/blog/alphageometry-an-olymp...
[2] https://arxiv.org/abs/2306.12672
[3] https://ec.ai/
[4] https://emergingtrajectories.com/
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Long-form factuality in large language models
For those interested in using search-augmented "reasoning", I implemented something similar in Emerging Trajectories[1], an open source package that forecasts geopolitical and economic events. We extract facts[2] from various websites (Google searches, news articles, RSS feeds) and have the LLM generate a hypothesis on a metric.
We're tracking the info forecasts to see how well this does for future events. For example, we're pitting the LLMs against each other to predict March 2024 CPI[3].
[1] https://emergingtrajectories.com/
[2] Sample code: https://github.com/wgryc/emerging-trajectories/blob/main/eme...
[3] https://emergingtrajectories.com/a/statement/28
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Ask HN: What are some actual use cases of AI Agents?
I'm working on research agents to help with economic, financial, and political research. These agents are open source (see: https://github.com/wgryc/emerging-trajectories).
The use cases are pretty straight forward and low risk:
1. Run a Google web search.
2. Query a news API.
3. Write a document based on the above, while citing sources.
Here's an example of something written yesterday, where I'm forecasting whether July 2024 will be the hottest on record: https://emergingtrajectories.com/a/forecast/74
This is working well in that the writeups are great and there are some "aha" moments, like the agent finding and referencing the The National Snow and Ice Data Center (NSIDC)... Very cool! I wouldn't have thought of it.
Then there's the part where the agent also tells me that the Oregon Department of Transportation has holidays during the summer, which doesn't matter at all.
So, YMMV, as they say... But I am more productive with these agents. I wouldn't publish anything formally without confirming and reviewing the content, though.
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Ask HN: What have you built with LLMs?
LLM agents to forecast geopolitical and economic events.
- Site: https://emergingtrajectories.com/
- GitHub repo: https://github.com/wgryc/emerging-trajectories
I've helped a number of companies build various sorts of LLM-powered apps (chatbots mainly) and found it interesting but not incredibly inspiring. The above is my attempt to build something no one else is working on.
It's been a lot of fun. Not sure if it'll be a "thing" ever, but I enjoy it.
open-interpreter
- OpenInterpreter – Natural language interface to your computer
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LaVague: Open-source Large Action Model to automate Selenium browsing
I think openinterpreter [1] were one of the first teams in this space along with shroominic code interpreter api and afaik they started with selenium but have expanded to do a lot more os level work but wonder if having a more narrow specialization could help these newer projects be better at the one thing they are focused on.
[1] https://openinterpreter.com/
- The Next Generation of Claude (Claude 3)
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Ask HN: What are some actual use cases of AI Agents?
I taught https://github.com/KillianLucas/open-interpreter how to use https://github.com/ferrislucas/promptr
Then I asked it to add a test suite to a rails side project. It created missing factories, corrected a broken test database configuration, and wrote tests for the classes and controllers that I asked it to.
I didn't have to get involved with mundane details. I did have to intervene here and there, but not much. The tests aren't the best in the world, but IMO they're adding value by at least covering the happy path. They're not as good as an experienced person would write.
I did spend a non-trivial amount of time fiddling with the prompts I used to teach OI about Promptr as well as the prompts I used to get it to successfully create the test suite.
The total cost was around $11 using GPT4 turbo.
I think in this case it was a fun experiment. I think in the future, this type of tooling will be ubiquitous.
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Show HN: Shelly: Write Terminal Commands in English
My understanding is that ShellGPT aims to be a complete OS assistant. It's similar to Open Interpreter (https://github.com/KillianLucas/open-interpreter).
Shelly is a mini tool at the moment that only generates and executes commands for you.
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ollama local - smart file manager?
https://github.com/KillianLucas/open-interpreter Both OpenAI and Local
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Why would you use the code interpreter?
Yeah there's a program called openinterpreter, It works beautifully. https://openinterpreter.com/
- What is the MOST useful GPT powered tool you've used?
- Open-interpreter: OpenAI's Code Interpreter in your terminal, running locally
What are some alternatives?
zsh_codex - This is a ZSH plugin that enables you to use OpenAI's Codex AI in the command line.
flink-cdc - Flink CDC is a streaming data integration tool
dspy - DSPy: The framework for programming—not prompting—foundation models
FLaNK-HuggingFace-BLOOM-LLM - https://huggingface.co/bigscience/bloom into NiFi
RecipeUI - Discover, test, and share APIs in seconds
rivet - The open-source visual AI programming environment and TypeScript library
cligpt - Terminal autocomplete integation with GPT
sqllineage - SQL Lineage Analysis Tool powered by Python
FastMJPG - FastMJPG is a command line tool for capturing, sending, receiving, rendering, piping, and recording MJPG video with extremely low latency. It is optimized for running on constrained hardware and battery powered devices.
FLaNK-SaoPauloBrazil - FLaNK-SaoPauloBrazil
chatgpt_macro_for_texstudio - The ChatGPT Macro for TeXstudio is a user-friendly integration that connects TeXstudio with OpenAI's API.
FableForge - Generate a picture book from a single prompt using OpenAI function calling, replicate, and Deep Lake