open-interpreter
dspy
open-interpreter | dspy | |
---|---|---|
24 | 22 | |
48,820 | 11,228 | |
7.3% | 20.5% | |
9.9 | 9.9 | |
4 days ago | 1 day ago | |
Python | Python | |
GNU Affero General Public License v3.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
dspy
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Computer Vision Meetup: Develop a Legal Search Application from Scratch using Milvus and DSPy!
Legal practitioners often need to find specific cases and clauses across thousands of dense documents. While traditional keyword-based search techniques are useful, they fail to fully capture semantic content of queries and case files. Vector search engines and large language models provide an intriguing alternative. In this talk, I will show you how to build a legal search application using the DSPy framework and the Milvus vector search engine.
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Pydantic Logfire
I’ve observed that Pydantic - which we’ve used for years in our API stack - has become very popular in LLM applications, for its type-adjacent features. It serves as a foundational technology for prompting libraries like [DSPy](https://github.com/stanfordnlp/dspy) which are abstracting “up the stack” of LLM apps. (some opinions there)
Operating AI apps reveals a big challenge, in that debugging probabilistic code paths requires more than the usual introspective abilities, and in an environment where function calls can have very real monetary impact we have to be able to see what’s happening in the runtime. See LangChain’s hosted solution (can’t recall the name) that allows an operator to see prompts and responses “on the wire”. (It just occurred to me that Langchain and Pydantic have a lot in common here, in approach.)
Having a coupling between Pydantic - which is *just about* the data layer itself - and an observability tool seems very interesting to me, and having this come from the folks who built it does not seem unreasonable. WRT open source and monetization, I would be lying if I said I wasn’t a little worried - given the recent few months - but I am choosing to see this in a positive light, given this team’s “believability weight” (to overuse Dalio) and history of delivering solid and really useful tooling.
- Ask HN: Most efficient way to fine-tune an LLM in 2024?
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Princeton group open sources "SWE-agent", with 12.3% fix rate for GitHub issues
DSPy is the best tool for optimizing prompts [0]: https://github.com/stanfordnlp/dspy
Think of it as a meta-prompt optimizer, it uses a LLM to optimize your prompts, to optimize your LLM.
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Winner of the SF Mistral AI Hackathon: Automated Test Driven Prompting
Isn’t this just a very naive implementation of what DsPY does?
https://github.com/stanfordnlp/dspy
I don’t understand what is exceptional here.
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Show HN: Fructose, LLM calls as strongly typed functions
Have you done any comparison with DSPy ? (https://github.com/stanfordnlp/dspy)
Feels very similiar to DSPy except you dont have optimizations yet. But I like your API and the programming model your are enforcing through this.
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AI Prompt Engineering Is Dead
I'm interested in hearing if anyone has used DSPy (https://github.com/stanfordnlp/dspy) just for prompt optimization for GPT-3.5 or GPT-4. Was it worth the effort and much better than manual prompt iteration? Was the optimized prompt some weird incantation? Any other insights?
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Ask HN: Are you using a GPT to prompt-engineer another GPT?
You should check out x.com/lateinteraction's DSPy — which is like an optimizer for prompts — https://github.com/stanfordnlp/dspy
- SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
- FLaNK Stack Weekly for 12 September 2023
What are some alternatives?
zsh_codex - This is a ZSH plugin that enables you to use OpenAI's Codex AI in the command line.
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
flink-cdc - Flink CDC is a streaming data integration tool
playground - Play with neural networks!
FLaNK-HuggingFace-BLOOM-LLM - https://huggingface.co/bigscience/bloom into NiFi
MLflow - Open source platform for the machine learning lifecycle
RecipeUI - Discover, test, and share APIs in seconds
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.
rivet - The open-source visual AI programming environment and TypeScript library
prompt-engine-py - A utility library for creating and maintaining prompts for Large Language Models
cligpt - Terminal autocomplete integation with GPT
AgentOoba - An autonomous AI agent extension for Oobabooga's web ui