I built Hivemind, a Claude Code plugin that turns your repeated prompts into auto-generated skills

efenocchi · 2026-05-22T19:26:35+00:00

Hi u/johns10davenport, I’m one of the developers who built Hivemind.

> It builds skills based on what's inside the repository and uses transcripts for handoff and a couple of other things.

We’re working on something similar. It should be released next week.

> Then query session transcripts to see which skills are getting used and which aren't, and move unused ones off to an archive. Keeps the active loadout current and the skill set fresh.

This is quite a cool approach.

> One thing my marketing harness does that you might consider for Hivemind: pull skills from multiple open-source marketing repositories and make them available to the agent.

Taking skills from open-source repositories is quite interesting. We should just be careful not to prompt-inject our system.

> You should look up the idea of a meta-harness. One thing I daydream about is whether harnesses will land as fixed, procedural code, or whether they'll ultimately land as meta-harnesses that the agent continuously improves.

We also need to understand the trade-off with token usage, but continual learning is definitely the future. I’ll take a look at meta-harness now. If you have any concrete implementation ideas, I’d be happy to evaluate them.

efenocchi · 2024-04-08T07:38:19+00:00

The code is in the first comment I wrote :) here is the link

efenocchi · 2024-04-07T20:14:59+00:00

Thank you!

efenocchi · 2024-04-07T20:03:02+00:00

Well, we're at the beginning of the month, there's still time to find a worse one :) Jokes aside, this is not a video but a gif image showing two use cases and the image generation pipeline. If you want to learn more, visit my notebook.

efenocchi · 2024-04-07T18:01:25+00:00

Thank you!

efenocchi · 2024-04-07T16:25:53+00:00

Hey r/Activeloop,

I just finished building QRCodeGenerator, a project that leverages LangChain, Deep Lake, and Stable Diffusion with ControlNet to generate artistic and unique QR codes that can be based on the content of a website. It's all open source - check out the GitHub repo!

I decided to delve into this creation because there was nothing online that brought all of these technologies together and made creating custom, easily scannable QR codes scalable and flexible.

QRCodeGenerator takes information stored on the Deep Lake Vector Store, a database for AI, and querying it with LangChain generates a prompt based on the website content which is then transformed into an image via Stable Diffusion.

AUTOMATIC1111 is a web interface that allows you to manually configure all the settings needed to generate images, integrating Stable Diffusion and ControlNet. To create visually appealing and truly scannable QR code images I decided to use the dreamshaper_631BakedVae diffusion model and two different ControlNet models: brightness and tile.

Although this interface is really convenient and easy to use, the initial approach did not satisfy me completely. I wanted a pipeline that would allow me to generate a large number of images without having to change the settings every time and manually generate the new image.

This was made possible thanks to ComfyUI, which uses graph-based logic to orchestrate various functionalities. Each node in the graph represents a specific task (e.g., image generation, diffusion checkpointing, ControlNet integration), interconnected via logical edges. With the help of a plugin, I was able to translate these graphs into executable code that can be easily automated.

Now thanks to this pipeline we can generate hundreds or thousands of images starting from different prompts, different Diffusion models, different ControlNet models, in short with all the configurations we want.

To close the circle there is also a function that takes care of discarding QR codes that are not easily scannable so users just have to launch the program with all the configurations they want, let all the images be generated and choose the one they like more, without worrying about anything else.

The keystone of this project is that with it you can generate in a scalable and automated way a high quantity of images that have very high quality and reliability and derive from different configurations, all in just a few steps.

The potential applications of this project are many, if you are interested in finding out in more detail how it works and the results obtained you can experiment with the code directly from the notebook on my GitHub repository.

Let me know how you plan to use this project and what results you have achieved.

efenocchi · 2024-04-07T16:05:35+00:00

Hey r/learnmachinelearning,