Claude Sonnet 4.5: The AI That Builds Software as You Speak
Author(s): Gaurav Shrivastav Originally published on Towards AI. From fixing real-world code to imagining apps on the fly, Sonnet 4.5 turns software creation into a live collaboration. What if you could build software differently? Image Generated by Gemini Nano BananaAnthropic’s release of …
Transfer Learning in AI: Reusing Knowledge to Solve New Problems
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 …
Automating Data CI/CD for Scalable MLOps Pipelines
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 …
How Soft Tokens Are Making AI Models 94% More Diverse at Reasoning
Author(s): MKWriteshere Originally published on Towards AI. Meta’s breakthrough lets language models think in continuous concepts instead of discrete words with zero computational overhead Current AI models think by choosing words. One word at a time. Like you’re navigating a maze by …
Building Python Automation Systems That Saved Me Months of Work
Author(s): Code with Margaret Originally published on Towards AI. How I streamlined data, reports, and workflows into efficient pipelines When I first started automating with Python, I underestimated just how much time I could save. At first, it was small scripts — …
Beyond ML Loss Function: Cost Functions and Hypothesis Testing in Supply Chain
Author(s): Siddharth Mahato Originally published on Towards AI. Understand how Cost Functions and Statistics can help you go beyond accuracy with visuals and examples. Source: Image on Unsplash Manufacturing is a large and crucial part of the economy. It involves processing and …
Do AI Agents Really Use the Tools You Build for Them? I Tested It.
Author(s): Marie Humbert-Droz, PhD Originally published on Towards AI. Testing tool coverage in local agents and how to improve compliance. I thought my healthcare AI agent would call my lab-checking tool every time it encountered lab values. Instead? Only 1 out of …
Understanding Neural Networks — and Building One!
Author(s): Aditya Gupta Originally published on Towards AI. Why Do We Need Neural Networks? Imagine trying to teach a computer to do something humans find easy like recognizing a face in a photo, understanding someone’s accent, or predicting which movie you’ll enjoy …
In-Context Learning Explained: Why LLMs Need 100 Examples, Not 5
Author(s): MKWriteshere Originally published on Towards AI. New research reveals the truth about few-shot learning and what it means for your AI applications What happens when you feed ChatGPT examples in your prompts isn’t what you think Image Generated by Author Using …
I Built a Clinical AI Agent — and It Skipped the Tools I Gave It
Author(s): Marie Humbert-Droz, PhD Originally published on Towards AI. An evaluation of tool coverage in local healthcare agents, with a simple fix. I thought my healthcare AI agent would call my lab-checking tool every time it encountered lab values. Instead? Only 1 …
CSV Plot Agent with LangChain & Streamlit: Your Introduction to Data Agents
Author(s): Sarah Lea Originally published on Towards AI. How you can learn the basics of tool-based agents with LangChain, GPT-4o-mini and Streamlit. If you work with data a lot, you’re probably familiar with this. You open a new CSV file and always …
ATOKEN: A Unified Tokenizer for Vision Finally Solves AI’s Biggest Problem
Author(s): MKWriteshere Originally published on Towards AI. How Apple eliminated the need for separate visual AI systems with one tokenizer that handles all content types While competitors grabbed headlines with flashy AI demos, Apple’s researchers were quietly solving visual AI’s most fundamental …
From A/B Testing to DoubleML: A Data Scientist’s Guide to Causal Inference:
Author(s): Rohit Yadav Originally published on Towards AI. Image by Author This article is a comprehensive guide to the most common causal inference techniques, complete with practical examples and code. While the scenarios are inspired by real-world use cases I have worked …
Traditional RAG vs Context Engineering vs Corrective vs Contextual: A Decision Guide
Author(s): Vikram Bhat Originally published on Towards AI. A practical framework for developers to choose the optimal RAG architecture based on accuracy, cost, and complexity requirements In my previous blog, I introduced Context Engineering as a method for optimizing how LLMs consume …
Risk-Adjusted Returns with Python (Part 2): Sharpe Ratio versus Treynor Ratio (Friends or Foes)
Author(s): Siddharth Mahato Originally published on Towards AI. Two legendary metrics for measuring risk-adjusted performance. Which one should you trust for your portfolio? Introduction In Part 1, we analyzed and understood about the Treynor Ratio, a metric to measure investment performance based …
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