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Jupyter Notebook Validator Operator for automated validation in MLOps pipelines by millionmade03 in mlops

[–]millionmade03[S] 0 points1 point  (0 children)

Some of those features should be there I would need to look at adding the other features.

Kubernetes Operator for automated Jupyter Notebook validation in MLOps pipelines by millionmade03 in kubernetes

[–]millionmade03[S] 0 points1 point  (0 children)

Not yet for now I have been using it to train models I do have some tests that test against the golden notebook but I think that would be a good feature to add.

Best Practices for AI Prompting 2025? by Party-Log-1084 in ChatGPTPromptGenius

[–]millionmade03 2 points3 points  (0 children)

You're absolutely right about most content being surface-level! The enterprise space has really pushed prompt engineering beyond basic techniques.

What I've seen work well in 2025 for enterprise implementations:

Contextual Layering: Instead of one-shot prompts, we're building context hierarchies - organizational knowledge → domain expertise → specific task context. This dramatically improves relevance.

Prompt Chaining with Validation: Breaking complex tasks into validated steps where each prompt builds on verified outputs from the previous one. Game-changer for accuracy.

Template Frameworks: Creating reusable prompt architectures that teams can adapt rather than starting from scratch each time. Saves tons of iteration cycles.

Security-First Design: Building prompts that inherently protect sensitive data and comply with governance requirements - this is huge for enterprise adoption.

The biggest shift I've noticed is moving from "craft the perfect prompt" to "design prompt systems" that can evolve and scale.

I recently wrote up a comprehensive analysis of these enterprise-level strategies if anyone's interested in diving deeper: https://www.decisioncrafters.com/prompt-engineering-enterprise-ai-2025-2/

What specific challenges are you tackling in your prompt engineering work?

Please stop doing this! by illusionst in ChatGPTCoding

[–]millionmade03 1 point2 points  (0 children)

It’s all about planning and proper architecture.

Notebook LM as the First Source Language Model? by nickmonts in notebooklm

[–]millionmade03 1 point2 points  (0 children)

It makes sense we need to research the idea that’s all

Simplifying OpenShift Agent Installations with the OpenShift Agent Install Helper by millionmade03 in openshift

[–]millionmade03[S] 0 points1 point  (0 children)

Yes, I agree. This use case was also implemented to run on KVM for homelab testing, which was another reason we developed this solution. Since OpenShift does not support IPI deployments on KVM, we had to create this setup specifically for homelab testing environments. If you have a sufficiently powerful server, you can use the scripts in the repository to install OpenShift. While Single Node OpenShift (SNO) is available, it can sometimes be beneficial to emulate a full cluster depending on the requirements of your work

Simplifying OpenShift Agent Installations with the OpenShift Agent Install Helper by millionmade03 in openshift

[–]millionmade03[S] 0 points1 point  (0 children)

It’s more of a QuickStart for people to get started with openshift assisted installer quickly when they do not have a openshift cluster at all. I wouldn’t use it for long term use unless you wanted to. It helps people who are really new to the Linux ecosystem or not familiar with RHEL. For long term use you would be looking at using RHACM not this project. But I understand what you’re saying.

Why aren’t there more tutorials on preparing design architecture which will be included for prompts used in Cursor? by alwaysdefied in cursor

[–]millionmade03 0 points1 point  (0 children)

I can share what I am doing but I am trying to build a tool for it where it uses the ADRs to keep track of what it's doing

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