Harness Engineering with LangChain DeepAgents and LangSmith
Struggling to make AI systems reliable and consistent? Many teams face the same problem. A powerful LLM gives great results, but a cheaper model often fails on the same task. This makes production systems hard to scale. Harness engineering offers a solution. Instead of changing the model, you build ...
You already think like a Bayesian. Your stats class just taught the formula before the intuition. Here's a 5-step framework to apply it at work.
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The Causal Inference Playbook: Advanced Methods Every Data Scientist Should Master
Master six advanced causal inference methods with Python: doubly robust estimation, instrumental variables, regression discontinuity, modern difference-in-differences, heterogeneous treatment effects and sensitivity analysis. Includes code and a practical decision framework.
The post The Causal Infe...
Google DeepMind found multi-agent networks amplify errors 17x. Learn 3 architecture patterns that separate $60M wins from the 40% that get canceled.
The post The Multi-Agent Trap appeared first on Towards Data Science.
Excel 101: IF, AND, OR Functions and Conditional Logic Explained
You reading this tells me you wish to learn more about Excel. This article continues our Excel series, where we explored the VLOOKUP function in the last iteration. The complete VLOOKUP guide demonstrated how the function works and how best to use it. This time, we shall bring the same focus to cond...
How Vision Language Models Are Trained from “Scratch”
A deep dive into exactly how text-only language models are finetuned to *see* images
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Personalized Restaurant Ranking with a Two-Tower Embedding Variant
How a lightweight two-tower model improved restaurant discovery when popularity ranking failed
The post Personalized Restaurant Ranking with a Two-Tower Embedding Variant appeared first on Towards Data Science.
Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction
Navigating the performance cliff: How pairing MRL with int8 and binary quantization balances infrastructure costs with retrieval accuracy.
The post Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction appeared first on Towards Data Science.
Building a Real Image Matching Project with Gemini Embedding 2
Google recently introduced Gemini Embedding 2, its first natively multimodal embedding model. This is an important step forward because it brings text, images, video, audio, and documents into a single shared embedding space. Instead of working with separate models for each type of data, developers ...
I Finally Built My First AI App (And It Wasn’t What I Expected)
A beginner-friendly walkthrough of API calls, environment variables, and real-world AI infrastructure
The post I Finally Built My First AI App (And It Wasn’t What I Expected) appeared first on Towards Data Science.
In this guide, you learn how to install and run PersonaPlex locally step by step, so you can experience real time, interruptible speech to speech AI directly on your own machine.
How the Fourier Transform Converts Sound Into Frequencies
A visual, intuition-first guide to understanding what the math is really doing — from winding machines to spectrograms
The post How the Fourier Transform Converts Sound Into Frequencies appeared first on Towards Data Science.
Setting Up a Google Colab AI-Assisted Coding Environment That Actually Works
This article focuses on Google Colab , an increasingly popular, free, and accessible, cloud-based Python environment that is well-suited for prototyping data analysis workflows and experimental code before moving to production systems.
Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules
I really thought I was onto something big: add a couple of simple domain rules to the loss function, and watch fraud detection just skyrocket on super-imbalanced data. The first run looked amazing… until I fixed a sneaky threshold bug and ran the whole thing across five different random seeds. Sudde...
Building a Like-for-Like solution for Stores in Power BI
Like-for-Like (L4L) solutions are essential for comparing elements. It's about comparing only comparable elements, in this case, comparing stores over time. Let's see a solution built in a Semantic model.
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How to design and implement agent skills for custom agents outside the Claude ecosystem
The post What Are Agent Skills Beyond Claude? appeared first on Towards Data Science.
For different learning styles, goals, and comfort levels, finding a SQL course that matches how you learn is hard. Some learners want theory first. Others want to run queries immediately. And many learners just want proof of effort at the end in the form of a certificate. This list is built with tha...
Claude Flow: The AI Orchestration Framework Redefining Multi-Agent Automation
Claude Flow is an open-source orchestration framework designed to run multiple Claude agents in coordinated workflows. Instead of relying on a single LLM prompt chain, it allows developers to build systems where specialized agents collaborate, share memory, and divide complex tasks into manageable s...