Ask HN: What are some actual use cases of AI Agents?

1. emerging-trajectories

   1 8 72 6.1 Python

   Open source framework for using LLMs to forecast political, economic, and social events.

   

   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.

2. InfluxDB

   www.influxdata.com featured

   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. hn-search

   2 2,098 559 2.9 TypeScript

   Hacker News Search

   

   My RSS reader is an A.I. agent, I have written a huge number of comments mentioning it

   https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...

4. open-interpreter

   3 36 59,963 9.0 Python

   A natural language interface for computers

   

   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.

5. promptr

   4 16 931 5.5 JavaScript

   Promptr is a CLI tool that applies plain language instructions to the filesystem. Instructions can utilize a liquidjs based templating system. Use cases include refactoring, code generation, and experimentation.

   

   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.

6. outlines

   5 44 12,085 9.0 Python

   Structured Outputs

   

   It's pretty easy to force a locally running model to always output valid JSON: when it gives you probabilities for the next tokens, discard all tokens that would result in invalid JSON at that point (basically reverse parsing), and then apply the usual techniques to pick the completion only from the remaining tokens. You can even validate against a JSON schema that way, so long as it is simple enough.

   There are a bunch of libraries for this already, e.g.: https://github.com/outlines-dev/outlines

7. open-interpreter

   6 1 0 9.9

   This is a proof of concept experiment that enables Open Interpreter to use the Promptr CLI tool - see https://github.com/ferrislucas/open-interpreter/pull/1 (by ferrislucas)

   

   Here's the fork of Open Interpreter that I was experimenting with: https://github.com/ferrislucas/open-interpreter/pull/1/files

   The system prompt that adds the Promptr CLI tool is here: https://github.com/ferrislucas/open-interpreter/pull/1/files...

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