terraform-github-actions
AutoPR
| terraform-github-actions | AutoPR | |
|---|---|---|
| 1 | 12 | |
| 690 | 1,192 | |
| - | - | |
| 8.5 | 9.5 | |
| 19 days ago | 2 months ago | |
| Python | Python | |
| - | 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.
terraform-github-actions
-
Is Atlantis workflow possible without a backend?
Nice work! I also kinda dislike the atlantis approach with the always running instance. I want to spin up a little something when I need it. So what made you go with atlantis? Also, what would you say are the key differences to something like dflook/terraform-github-actions?
AutoPR
- AutoPR, autonomously write pull requests in response to issues via ChatGPT
-
I made AutoPR, an open source, github action that makes pull requests in response to issues
The github is : https://github.com/irgolic/AutoPR
- AutoPR: GPT-4 Powered GitHub Issue Resolver
- GitHub - irgolic/AutoPR: Fix issues with AI-generated pull requests, powered by ChatGPT
- Automatically generate code/PR from an issue using AI
-
I made an automatic pull request generator, using issues as prompts
Check it out, and let me know if you have any ideas on how you'd like to use it or make it better.https://github.com/irgolic/AutoPR
-
AutoPR: Generate pull requests from issues with GPT-4/GPT-3.5
Added a manual trigger, here's the PR your issue generated: https://github.com/irgolic/AutoPR/pull/27
-
This is BONKERS. I am trying to find the limits of ChatGPT in practical programming help. This is an example transpiler. It's written maybe 10,000 LOC for me at this point, and I am absolutely certain AI will replace all but the most creative and thoughtful programmers.
Agreed. Would love to see this project take off: https://github.com/irgolic/AutoPR
-
AutoPR: GitHub Action that automatically creates pull requests from issues
- Opens PRs for the generated code, making it easy to review and merge changes.
By using this GitHub Action, you can skip the manual steps of prompt design, and let the AI handle code generation and PR creation for you.
Here's how I used AutoPR to make the license for itself: https://github.com/irgolic/AutoPR/pull/4
Feel free to give it a try, and I would love to hear your feedback! It's still in alpha and it works for straightforward tasks, but I have a plan to scale it up. Let's see how far we can push GPT-4 and create a more efficient development process together.
Happy coding!
-
Show HN: GPT Repo Loader – load entire code repos into GPT prompts
So I have been toying on an automated PR GitHub Action for a bit but seeing this spurred me to actually put it into a format for y'all to try. It uses GPT-4 to automatically generate code based on issues in your repo and opens pull requests for those changes.
You can find my solution here: https://github.com/irgolic/AutoPR/
What I can do:
- Automatically generates code based on issues in your repo.
What are some alternatives?
tfcloud-alt - terraform cloud alternative
llmflows - LLMFlows - Simple, Explicit and Transparent LLM Apps
digger - Digger is an open source IaC orchestration tool. Digger allows you to run IaC in your existing CI pipeline ⚡️
gpt-repository-loader - Convert code repos into an LLM prompt-friendly format. Mostly built by GPT-4.
PyPDFMerger - A PDF merger tool using Python TK by GPT-4
aidev - AI developer: ask GPT-4 to modify an entire folder full of files
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
llama_index - LlamaIndex is a data framework for your LLM applications
E2B - Secure cloud runtime for AI apps & AI agents. Fully open-source.
aide - LLM shell and document interogator
1filellm - Specify a github or local repo, github pull request, arXiv or Sci-Hub paper, Youtube transcript or documentation URL on the web and scrape into a text file and clipboard for easier LLM ingestion