aipl
ContainerSSH
| aipl | ContainerSSH | |
|---|---|---|
| 4 | 12 | |
| 119 | 2,574 | |
| - | 0.8% | |
| 9.2 | 5.8 | |
| 6 months ago | about 1 month ago | |
| Python | Go | |
| MIT License | Apache License 2.0 |
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.
aipl
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Ask HN: Tell us about your project that's not done yet but you want feedback on
AIPL is an "Array-Inspired Pipeline Language", a tiny DSL in Python to make it easier to explore and experiment with AI pipelines.
https://github.com/saulpw/aipl
When you want to run some prompts through an LLM over a dataset, with some preprocessing and/or chaining prompts together, AIPL makes it much easier than writing a Python script.
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The Problem with LangChain
Yes! This is why I started working on AIPL. The scripts are much more like recipes (linear, contained in a single-file, self-evident even to people who don't know the language). For instance, here's a multi-level summarizer of a webpage: https://github.com/saulpw/aipl/blob/develop/examples/summari...
The goal is to capture all that knowledge that langchain has, into consistent legos that you can combine and parameterize with the prompts, without all the complexity and boilerplate of langchain, nor having to learn all the Python libraries and their APIs. Perfect for prototypes and experiments (like a notebook, as you suggest), and then if you find something that really works, you can hand-off a single text file to an engineer and they can make it work in a production environment.
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Langchain Is Pointless
I agree, and that's why I've been working on AIPL[0]. Our first v0.1 release should be in the next few days. https://github.com/saulpw/aipl
It's basically just a simple scripting language with array semantics and inline prompt construction, and you can drop into Python any time you like.
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Re-implementing LangChain in 100 lines of code
I also was underwhelmed by langchain, and started implementing my own "AIPL" (Array-Inspired Pipeline Language) which turns these "chains" into straightforward, linear scripts. It's very early days but already it feels like the right direction for experimenting with this stuff. (I'm looking for collaborators if anyone is interested!)
https://github.com/saulpw/aipl
ContainerSSH
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Ask HN: Tell us about your project that's not done yet but you want feedback on
- Build your own honeypot with ContainerSSH (DevConf CZ 2021) [4]
[1]: https://containerssh.io
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Unique submission node per user
What about https://containerssh.io/ ?
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One user per pod, SSH inbound.
Take a look at ContainerSSH.
- ContainerSSH: Launch containers on demand.
- ContainerSSH: Launch containers on demand
- Container + SSH as a development environment
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Container + SSH = a good development environment
Hey folks, one of the authors here. The website contains a lot more information than the GitHub page: https://containerssh.io
If you have any questions, I'd be happy to answer.
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Horizon view but can deliver SSH instead of gui
Threw some keywords into Google and this popped out: https://containerssh.io/
What are some alternatives?
modelfusion - The TypeScript library for building AI applications.
docker4ssh - 🐋 Docker containers and more via ssh
hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
ssh2docker - :whale: standalone SSH server that connects you to your Docker containers
multi-gpt - A Clojure interface into the GPT API with advanced tools like conversational memory, task management, and more
Netmaker - Netmaker makes networks with WireGuard. Netmaker automates fast, secure, and distributed virtual networks.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
rosboard - ROS node that turns your robot into a web server to visualize ROS topics
llm - Access large language models from the command-line
envd - 🏕️ Reproducible development environment
llm-gpt4all - Plugin for LLM adding support for the GPT4All collection of models
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