Using Gemma 4, Ollama, OpenAI Agents SDK, and Tavily MCP to build a lightweight research agent
The post From Local LLM to Tool-Using Agent appeared first on Towards Data Science.
Amplify the Expert: A Philosophy for Building Enterprise RAG
Enterprise Document Intelligence [Vol.1 #M1] - The thesis behind every architectural choice in this series
The post Amplify the Expert: A Philosophy for Building Enterprise RAG appeared first on Towards Data Science.
You’ve heard the warnings! Don’t tell ChatGPT your secrets. The robots are reading everything. Your data is the product. And yet here you are: using them as a subscriber. Because AI is genuinely useful! The good news: that distrust is healthy, and you don’t have to choose between using AI and protec...
Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory
I benchmarked raw chat history, vector-only RAG, and a context graph on the same multi-agent conversations. The results exposed a surprising weakness in relational retrieval.
The post Vector RAG Isn’t Enough — I Built a Context Graph Layer for Multi-Agent Memory appeared first on Towards Data Scienc...
The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark
A reproducible benchmark on latency, cost, and reproducibility, and where agents actually earn their keep.
The post The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark appeared first on Towards Data Science.
In this tutorial, we will show you how to use Gemini to create Google Sheets, build a useful table, generate formulas, analyze data, and improve the spreadsheet with follow-up prompts.
3 Agents. 3 LLMs. 1 Aging GPU: Engineering Parallel Inference on Bare Metal
Beat the 8GB VRAM limit. Learn how to run three different LLMs on a single 8GB GPU using C++ layer multiplexing and admission control.
The post 3 Agents. 3 LLMs. 1 Aging GPU: Engineering Parallel Inference on Bare Metal appeared first on Towards Data Science.
5 Open Source Omni AI Models That Handle Text, Images, Audio, and Video
Take a practical look at multimodal, any-to-any systems for vision-language reasoning, speech interaction, document intelligence, real-time assistants, local deployment.
Letting an LLM Pick the Right RAG Page: The Arbiter Pattern at the End of Retrieval
Enterprise Document Intelligence [Vol.1 #7C] - One LLM call ranks the candidates with reasons. The output is one typed object your auditor can defend
The post Letting an LLM Pick the Right RAG Page: The Arbiter Pattern at the End of Retrieval appeared first on Towards Data Science.
The Self-Improving Loop in AI Agents: Architecture, Benefits, and How it Outperforms Traditional Agent Workflows
Most AI agents today follow fixed instructions and never get smarter on their own. They finish a task, forget what happened, and repeat the same mistakes tomorrow. A new design called the self-improving loop changes this. It lets agents learn from every result and improve over time. This guide expla...
A Three-Phase Factual Recall Circuit in Gemma-2B and Gemma-12B-IT
Activation patching reveals how facts are stored, routed, and read out across transformer layers, and why the residual stream does most of the work
The post A Three-Phase Factual Recall Circuit in Gemma-2B and Gemma-12B-IT appeared first on Towards Data Science.
Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End
Enterprise Document Intelligence [Vol.1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last
The post Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End appeared first on Towards Data Science.
Harness-1: The 20B Retrieval Subagent That Beats GPT-5.4 at Search
Most search agents try to handle too many jobs at once. They generate new queries, remember what they have already explored, collect evidence, and decide what is relevant as the search keeps expanding. That can make the whole process messy, expensive, and hard to control. Harness-1 takes a simpler a...
Explore the best local coding models for private AI coding, fast GGUF inference, agentic workflows, multimodal development, and running powerful open models on your own GPU.
Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG
Enterprise Document Intelligence [Vol.1 #7A] - Stop searching strings. Filter line_df and toc_df. Pick anchors small, expand context large
The post Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG appeared first on Towards Data Science.
For years, AI progress has centered on scaling individual foundation models: larger parameters, longer context windows, stronger reasoning, and better tool use. Sakana AI’s Fugu points elsewhere, behaving like one model from the outside while coordinating multiple expert agents internally. A single ...
Clustering Unstructured Text with LLM Embeddings and HDBSCAN
The current era of Generative AI seems to primarily focus on chat interfaces and prompts, but the range of applications of large language models , or LLMs for short, is not limited to just that.
5 Essential Approaches to Robust Outlier Detection
Outliers can easily ruin the performance of any predictive analysis models you build: robustly detecting and handling them is crucial in any data project. This article lists and compares five essential approaches for detecting them.
Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode
From installing Ollama to launching OpenCode with a local model, step by step.
The post Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode appeared first on Towards Data Science.
Agentic AI is not failing because the technology is bad. It is failing because of five specific misconceptions that teams carry into their first deployments and each one is correctable.