Abhishek Veeramalla iam-veeramalla

Goal: Build an automated AI workflow that performs security analysis on every GitHub Pull Request and posts a summarized report back as a PR comment.

Workflow Steps:

Setup Trigger:

Configure GitHub Pull Request trigger

Events:

Run LLMs Locally Using llama.cpp

This tutorial shows how to run Large Language Models locally on your laptop using llama.cpp and GGUF models.

It works on:

curl -fsSL https://ollama.com/install.sh | sh

ollama pull glm-4.7-flash # or gpt-oss:20b (for better performance)

curl -fsSL https://openclaw.ai/install.sh | bash

curl -fsSL https://ollama.com/install.sh | sh

In this tutorial, You will learn:

The best guide to install Kubeflow or any of its components is the official documentation.

In this tutorial, we will learn how to Install, Configure and Use the most popular component of Kubeflow that is KFP (Kubeflow Pipelines).

apiVersion: v1
kind: Service
metadata:
  name: my-db-headless-service
spec:

	import argparse
	import requests
	from dotenv import load_dotenv

	from langchain_core.tools import tool
	from langchain_core.messages import ToolMessage
	from langchain_google_genai import ChatGoogleGenerativeAI

	from langgraph.graph import StateGraph, END

	import redis
	import time
	import random
	import string
	import statistics

	# Redis connection details
	REDIS_HOST = "redisval-bu8xv7.serverless.use1.cache.amazonaws.com"
	REDIS_PORT = 6379 # TLS port (verify if TLS supported on this port!)
	REDIS_PASSWORD = None