Author(s): Rashmi Originally published on Towards AI. Getting started with workflow Automation n8n is an open-source workflow automation tool that allows you to connect different apps and services together to automate tasks and processes. Think of it as a way to make your various tools “talk to each other” without writing code. AI Automation with n8nThe article discusses the open-source workflow automation tool n8n, detailing its key features, integrations, and practical use cases for aut...| Towards AI
Author(s): EzInsights AI Originally published on Towards AI. Vector databases like Pinecone, Weaviate, Milvus, and FAISS are the backbone of modern AI applications — from RAG (Retrieval-Augmented Generation) to semantic search and recommendation systems. Optimizing them is critical for speed, cost, and accuracy. Here’s a detailed breakdown of 14 key optimization techniques every AI/ML engineer should master: 1. Choose the Right Index Type Why it matters: Different index types balance spee...| Towards AI
Author(s): EzInsights AI Originally published on Towards AI. Artificial Intelligence (AI) has reached a fascinating stage of growth. Large Language Models (LLMs) such as GPT-4, Claude, and LLaMA can generate text, answer questions, write code, and assist with research. Yet, despite their power, most of these models remain passive responders. They are great at conversation but struggle when asked to perform real-world tasks that require reasoning across multiple steps or interacting with exter...| Towards AI
Author(s): Aditya Gupta Originally published on Towards AI. Introduction What would be easier: teaching someone to play the guitar who has already learned the piano, or teaching someone who has never touched a musical instrument? Most of us would agree that the person with piano experience would pick up guitar much faster. They already understand concepts like reading music, rhythm, and finger coordination, which transfer easily to the new instrument. Transfer learning in artificial intellige...| Towards AI
Last Updated on September 30, 2025 by Editorial Team Author(s): Tanmoy Das Originally published on Towards AI. $1,250 followed right after my first AI Gig. This story is about how I became one of the very early adopters of ChatGPT and used it to earn my first $100 without much prior knowledge or expertise on AI. That gig eventually got me $1,350, but the point is… Image created by Google Gemini’s Nano BananaIn the article, the author recounts how he discovered ChatGPT during a layover and...| Towards AI
Last Updated on September 30, 2025 by Editorial Team Author(s): Talib Originally published on Towards AI. Okay, welcome to your third graph. What are we going to do this time? Well, enough processing multiple values and everything. Let’s actually get the graph more complicated. That’s why we’re going to be building a sequential graph. All it all that basically means is we’re going to be creating and handling multiple nodes that can sequentially process and update different parts of th...| Towards AI
Last Updated on September 30, 2025 by Editorial Team Author(s): Devi Originally published on Towards AI. Navigation: Why SLMs on CPUs are Trending When CPUs Make Sense SLMs vs LLMs: A Hybrid Strategy The CPU Inference Tech Stack Hands-On Exercise: Serving a Translation SLM on CPU with llama.cpp + EC2 Why SLMs on CPUs are Trending Traditionally, LLM inference required expensive GPUs. But with recent advancements, CPUs are back in the game for cost-efficient, small-scale inference. Three big sh...| Towards AI
Last Updated on September 30, 2025 by Editorial Team Author(s): Harsh Chandekar Originally published on Towards AI. If you’ve ever asked a large language model (LLM) like GPT or Gemini a question and received a response that sounded too smooth to be wrong — but was completely made up — you’ve met the phenomenon of hallucination. These aren’t hallucinations in the psychedelic sense, but in the sense of confidently fabricated details. Think of your overly confident friend who will inv...| Towards AI
Last Updated on September 30, 2025 by Editorial Team Author(s): Kuriko Iwai Originally published on Towards AI. A step-by-step guide to achieving continuous data integration and delivery in production ML systems Building robust Machine Learning (ML) applications demands meticulous version control for all components: code, models, and the data that powers them. Photo by Mohamed Nohassi on UnsplashThe article explores the complexities of establishing a Data CI/CD pipeline tailored for scalable ...| Towards AI
Author(s): Ali Oraji Originally published on Towards AI. Overfitting is when a neural network (or any ML model) captures noise and characteristics of the tr ...| towardsai.net
Author(s): MKWriteshere Originally published on Towards AI. How Apple eliminated the need for separate visual AI systems with one tokenizer that handles all ...| towardsai.net
Author(s): Vlad Johnson Originally published on Towards AI. In the rapidly evolving field of Artificial Intelligence, multi-agent systems have emerged as a ...| towardsai.net
Author(s): Shauvik Kumar Originally published on Towards AI. “The goal is to turn data into information, and information into insight.” — Carly Fiorina, for ...| towardsai.net
Author(s): Padmaja Kulkarni Originally published on Towards AI. When most teams talk about “AI or data risk,” the conversation always drifts toward accuracy ...| towardsai.net
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Author(s): Shubham Saboo Natural Language ProcessingGPT-3 for Corporates — Is Data Privacy an Issue?GPT-3 is transforming the way how businesses can leverag ...| towardsai.net
Author(s): Towards AI Editorial Team Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are buildi ...| towardsai.net