Developing Smart Applications with AI Function Calling: A 3-Step Guide
Learn how LLMs interact with external tools. A step-by-step guide to developing smart applications with AI Function Calling.
11 posts found.
Learn how LLMs interact with external tools. A step-by-step guide to developing smart applications with AI Function Calling.
I examine the cost-benefit balance of fine-tuning LLMs in enterprise applications, explaining when it makes sense based on my practical experience.
I analyze the performance of different LLM models based on their workloads. Comparing GPT-5.5, Claude, Gemini, and DeepSeek to help you choose the right.
Sharing my experience building self-hosted AI automations using n8n. Creating no-code agent flows, RAG, and multi-LLM integration steps.
I examine the cost increases brought by GitHub Copilot's new token-based pricing model and the strategies I've developed to counter it.
A real-world hardware guide for running local LLMs. I explain the effects of VRAM, quantization, CPU, and disk speed based on my own experiences. Budget and…
In this guide, I'll walk you through setting up and running your own Large Language Model (LLM) on your local machine using Ollama. We'll do it in 5 simple.
Ensure your data privacy by setting up your own local LLM with Ollama and Open WebUI. A comprehensive guide.
Mustafa Erbay's pragmatic take on whether using a vector database is truly necessary for your AI projects, exploring trade-offs and alternative approaches.
I examined the impact of large language models (LLMs) on retrieval quality in Retrieval-Augmented Generation (RAG) systems. Real-world scenarios and concrete.
We explore when and why to stretch the tool usage limits of AI agents, with practical examples and technical analyses. We'll delve into trade-offs and...