10 Simple AI Tools That Will Save You Hours Every Week
Author(s): Anna Jey Originally published on Towards AI. 10 Simple AI Tools That Will Save You Hours Every Week I remember the exact moment I hit “peak burnout.” It wasn’t dramatic. There was no screaming match or coffee thrown against a wall. …
Retrieval Augmented Generation (RAG) Explained: Why AI Needs It
Author(s): Abinaya Subramaniam Originally published on Towards AI. Large Language Models (LLMs) have rapidly become the engine behind intelligent applications, from chatbots to document assistants to sophisticated automation tools. Their ability to understand context, reason through text, and generate human-like responses often …
LangGraph & Redis: Build Smarter AI Agents With Memory & Persistence
Author(s): Kushal Banda Originally published on Towards AI. Redis Redis and LangGraph now work together. You can build AI agents that remember conversations across sessions using LangGraph framework paired with Redis’ persistent memory layer. Before this, agents started fresh each conversation. Now …
The Great LLM Convergence: When Everyone’s Best Becomes Nobody’s Advantage
Author(s): Ali Khalilvandi Originally published on Towards AI. The AI landscape in late 2025: where differentiation goes to die. Image credit: Gemini Nano Banana Last year I had an answer when people asked me what LLM to use. GPT-4. Done. This year? …
🚀 The Ranking Revolution: Why Your RAG System Needs Learning to Rank (And How to Build It Right)
Author(s): MahendraMedapati Originally published on Towards AI. The hidden layer that makes or breaks your AI application — and why almost nobody talks about it You’ve built a beautiful RAG pipeline. Your embeddings are state-of-the-art. Your LLM is fine-tuned. You’ve spent weeks …
More than Chat: Fixing Multilingual Search Queries with LoRA finetuning on Apple Silicon
Author(s): Sreejith Sreejayan Originally published on Towards AI. I recently read a fantastic engineering article from Zepto (one of India’s fastest-growing quick-commerce apps) about a difficult search problem. In India, people don’t just search in English. They search in a mix of …
Autonomous vs Semi-Autonomous Agents
Author(s): Rashmi Originally published on Towards AI. Autonomous vs Semi-Autonomous Agents As AI systems evolve from single-step LLM calls to multi-step, goal-driven workflows, a major architectural decision emerges: This guide provides a deep technical explanation, including architecture diagrams, workflows, components, pros/cons, examples, …
Autonomy Loops: Reflection → Evaluation → Correction → Execution
Author(s): Rashmi Originally published on Towards AI. Autonomy Loops: Reflection → Evaluation → Correction → Execution Autonomy loops represent self-improving AI systems that iteratively refine their outputs through a four-stage cycle: Reflection, Evaluation, Correction, and Execution. This pattern enables agents to self-correct, …
Why Most RAG Systems Fail in Production and the Simple Fix That Improves Accuracy Fast
Author(s): Divy Yadav Originally published on Towards AI. Source: By the author You spent two weeks building a RAG application. It retrieves documents. It generates answers. You tested it with a few questions. It looked good. Then you put it in production …
🚀 The David vs. Goliath Revolution: How Small AI Models Are Crushing the Giants in 2025
Author(s): MahendraMedapati Originally published on Towards AI. When the Underdog Becomes the Champion Remember when everyone said you needed massive computing power and billions of dollars to compete in AI? Yeah, that just got flipped on its head. 🎯 Picture this: It’s …
DOCKER’S MCP TOOL KIT: The App Store for Real-World AI Agents
Author(s): Baivab Mukhopadhyay Originally published on Towards AI. DOCKER’S MCP TOOL KIT: The App Store for Real-World AI Agents AI agents are finally good enough to be useful, but most teams hit the same wall: connecting those agents to real tools is …
Running Unsloth On a Jetson AGX Orin Device With Jetson-Containers
Author(s): Stephen Cow Chau Originally published on Towards AI. Background I have been for a long time container first, mainly because of the ease of environment isolation (including but not limited to system package, programming library package…). For Jetson it’s a little …
Data Exploration with Python: A Hands-On Demo in EDA (and Why It’s Essential for Model Building)
Author(s): Faizulkhan Originally published on Towards AI. Exploratory Data Analysis (EDA) is the bridge between raw data and reliable machine-learning models. In this post, we will learn the “why” and the “how” through a complete, runnable example in Python using NumPy, Pandas, …
The Builder’s Notes: I Tested 5 De-Identification Tools on 10,000 Clinical Notes. Most Failed on the Same 3 Edge Cases
Author(s): Piyoosh Rai Originally published on Towards AI. Presidio caught 94% of patient names. The 6% it missed included the only patient who could actually be re-identified. Here’s how to benchmark de-identification tools before they break in production. 99% accuracy on benchmarks. …
Understanding Gradient Boosted Trees: The Foundation of XGBoost
Author(s): Utkarsh Mittal Originally published on Towards AI. Understanding Gradient Boosted Trees: The Foundation of XGBoost Gradient Boosted Trees have revolutionized machine learning, powering some of the most successful algorithms in data science. Before diving into the complexities of XGBoost, it’s essential …
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