Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python.
The post You Probably Don’t Need an Agent Framework appeared first on Towards Data Science.
OpenAI Just Launched 3 Free AI Courses with Certificates
Having the right certificate can make all the difference. But with so many out there, getting the right one isn’t easy. That’s where OpenAI Academy comes in. OpenAI, the company behind the ChatGPT models, has introduced a learning platform through its OpenAI academy that offers AI courses for upskil...
Tired of your monthly API bill? Follow this tested guide to set up a high-performance local LLM on your Mac Mini without the headaches.
The post Run a Local LLM with OpenClaw on Your Mac Mini appeared first on Towards Data Science.
LLM Fallbacks Break Agent Pipelines — I Built the Missing Recovery Layer
LLM rate limits don't just interrupt agent pipelines—they can silently corrupt structured outputs when fallback models receive incompatible payloads. I built a recovery layer that classifies failures, adapts payloads across model tiers, preserves execution state, and maintains schema integrity durin...
Building an End-to-End Sentiment Analysis Pipeline with Scikit-LLM
Traditional machine learning pipelines for predictive tasks like text classification usually rely on extracting structured, numerical features from raw text — for instance, TF-IDF frequencies or token embeddings — to feed into classical models such as logistic regression, ensembles, or support vecto...
Autoregressive Models: Predicting the Future Using the Past
Autoregressive models are one of the most important ideas in time series forecasting and sequence modeling. The name may sound technical at first, but the concept is surprisingly intuitive. An autoregressive model predicts the next value by looking at previous values. That is the core idea. For exam...
The Protocol That Cleaned Up Our Agent Architecture
A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server
The post The Protocol That Cleaned Up Our Agent Architecture appeared first on Towards Data Science.
Building Time-Series Machine Learning Models with sktime in Python
In this article, we’ll build time-series machine learning models in Python using sktime and explore its core data structures for forecasting workflows.
I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions.
A single model hands you a single answer and no sense of how much it hinges on the dozens of choices buried inside it.
The post I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions. appeared first on Towards Data Science.
Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG
Enterprise Document Intelligence [Vol.1 #5quater] - The other parsers read the words on a page. A vision model also reads the pictures
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GPU Time-Slicing for Concurrent LLM Agents on Kubernetes
A systems-level deep dive into the hidden microarchitectural costs of Kubernetes GPU time-slicing, and what it actually costs to co-locate Agentic AI workloads.
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Larger Context Windows Don’t Fix RAG — So I Built a System That Does
Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows and show why computation queries must be routed away from RAG en...
Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload
Enterprise Document Intelligence [Vol.1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine. No key, no per-page bill, nothing leaves the building
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A Harness for Every Task: Putting a Team of Claudes on One Job
Claude can now write its own harness on the fly, custom-built for the task at hand.
The post A Harness for Every Task: Putting a Team of Claudes on One Job appeared first on Towards Data Science.
Local models in 2026 are good enough. For the tasks Claude Code handles daily: code completion, refactoring, debugging, codebase explanation; a well-chosen quantized model running locally covers the vast majority of real use cases at zero per-token cost and with no rate limits.
Gemini models have always kept up with AI advancements. From text-based chatbots in 2023, Gemini has evolved into a multimodal system capable of understanding and generating text, audio, images… and now videos. AI video generation is no longer a standalone tool. With Gemini Omni, video creation bec...
Take the next step to building real workflows with Spark on your laptop
The post PySpark for Beginners: Beyond the Basics appeared first on Towards Data Science.
DiffusionGemma: Google’s Diffusion-Based Open Model for Faster Text Generation
Large language models usually generate text one token at a time. While this autoregressive approach delivers strong quality and instruction following, it can be inefficient for local users because GPUs often spend more time moving weights from memory than doing parallel compute. Google DeepMind’s Di...