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Tutorials, Errors and Exceptions
Its a journey to understand things better. It will have tutorials, any error/exceptions encountered, its resolutions and lots of learning.
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Stop Guessing, Find Perfect Local LLM with OllamaAdvisor
Ollama Advisor Walkthrough Have you ever excitedly run ollama run llama3:70b on your 16GB MacBook Air, only to watch your system grind to an absolute halt? The fan spins up like a jet engine, your cursor freezes, and your swap memory immediately maxes out. The biggest bottleneck in local AI isn't the models themselves—it's hardware-model mismatch. Engineers are blindly downloading quantized models without calculating the overhead of the OS, background applications, model weig
Ankit Agrahari
4 days ago4 min read
Built a Webhook Inspector from Scratch and Shipped It — Here's Everything That Went Wrong
Live at: https://hookspy.in HookSpy - Walk Through Every developer integrating Stripe, Razorpay, or GitHub has been there. You set up a webhook, fire a test event, and... nothing. The endpoint didn't respond. Or it did but your handler crashed silently. Or you just want to see the exact payload the service sends before writing a single line of handler code. Tools like RequestBin and Webhook.site exist. But I wanted to build my own — one I understood end to end, could deploy m
Ankit Agrahari
May 127 min read
CodeForgeAI: Building a 5-Agent Multi-LLM Pipeline That Writes, Reviews, Tests, and Deploys Java Code — Entirely Locally
Multi Agent Framework TL;DR — CodeForgeAI is a Spring Boot + Vaadin application that orchestrates five specialised AI agents (Business Analyst → Code Generator → Code Reviewer → Test Generator → Test Executor) to transform a PDF requirements document into reviewed, tested, and deployed Java code — all running on-premise on a developer laptop, with no cloud LLM calls, no data leaving the machine. Table of Contents Motivation & Goals Tech Stack End-to-End Pipeline Architecture
Ankit Agrahari
Apr 1216 min read
Building DevOps Intelligence using MCP Server with Spring AI: Tools, Challenges & Solutions
Devops Intelligence Today, I successfully built and deployed a Model Context Protocol (MCP) server using Spring AI that exposes real DevOps infrastructure through intelligent tools. But the journey? Let's just say it involved more debugging than coding. In this post, I'll walk you through: What we built (the DevOps Intelligence Platform) The tools we created (K8s, Prometheus, Logs, Deployments) Every challenge we faced (and how we solved them) Why Spring AI 2.0.0-M2 is the s
Ankit Agrahari
Mar 147 min read
Production Monitoring: You Can't Fix What You Can't See
Previous parts: Part 1: Kafka Producer | Part 2: Consumer + DLQ | Part 3: Real-Time Aggregations | Part 4: Docker + Kubernetes Infographics - NotebookLM You know that feeling when your app is running in production and someone asks "Is everything okay?" and you respond with "...I think so?" Yeah, that's not good enough. After deploying StreamMetrics to Kubernetes with a 3-node KRaft Kafka cluster, dockerized microservices, and validated 10K events/sec throughput, I realized
Ankit Agrahari
Mar 78 min read
From Localhost to Kubernetes: Deploying StreamMetrics at Scale
Part 4 of the StreamMetrics Series Previous parts: Part 1: Kafka Producer | Part 2: Consumer + DLQ | Part 3: Real-Time Aggregations Streammetric Bottleneck Explainer by NotebookLM Love how after building something locally, you think " okay, it works on my machine! " and then reality hits when you try to deploy it. Docker says " works on my machine " is not an excuse anymore, and Kubernetes says " hold my beer, let's make it production-ready. " This is the tale of taking Str
Ankit Agrahari
Mar 28 min read
Real-Time Aggregations with Kafka Streams at 10K Events/Sec
Part 3 of the StreamMetrics Series Previous parts: Part 1: Kafka Producer | Part 2: Consumer + DLQ | Part 4: From Localhost to Kubernetes Building production-grade streaming analytics: windows, state stores, and performance validation Overview In Parts 1 and 2, we built a Kafka producer and consumer that process individual events reliably. But processing 10,000 raw events per second creates a new problem: How do you extract insights from that fire hose of data? Enter Kafka
Ankit Agrahari
Feb 217 min read
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