AI Tools & Frameworks Directory

Most AI automation content sounds useful, but then leaves you with one big question: where to start? Instead of only talking about automation, you probably want to create real-world automation workflows with minimal coding. That’s where the power of low-code platforms like n8n comes into play. Here ...

A beginner's honest walkthrough of Extract, Transform, Load using the GitHub API The post I Built My First ETL Pipeline as a Complete Beginner. Here’s How. appeared first on Towards Data Science.

Learn how to use Mimesis library to generate a balanced, counterfactual dataset that helps analyze potential bias in your models.

How did semantic search evolve from simple keyword matching into modern transformer-based language understanding? This hands-on article builds four generations of semantic search systems step by step using Python. The post From TF-IDF to Transformers: Implementing Four Generations of Semantic Search...

Let's take a look at five more fundamental concepts that every Python developer should have in their toolkit.

Implementing hybrid search strategies is a critical step in building modern RAG (Retrieval-Augmented Generation) systems , especially when shifting from prototype to production-ready solutions.

It’s like having your own personal expert AWS solutions architect and data engineer rolled into one. The post Introducing the Agent Toolkit for Amazon Web Services appeared first on Towards Data Science.

Claude Cowork shifts AI from chat-based assistance to task delegation. Instead of giving users instructions, it performs actions directly on the user’s computer, files, applications, and browser workflows. Combined with Playwright MCP, Claude Desktop can open pages, click buttons, fill forms, extrac...

Engineer token-efficient, self-adapting workflows for production The post From Prototype to Profit: Solving the Agentic Token-Burn Problem appeared first on Towards Data Science.

How AI architecture prevents plausible but wrong analytics The post Hybrid AI: Combining Deterministic Analytics with LLM Reasoning appeared first on Towards Data Science.

Alibaba’s Qwen team has unveiled Qwen3.7-Max, a flagship model built for the agent era. Unlike conventional chatbot-focused LLMs, it is designed as a foundation for autonomous AI agents that can code, debug, use tools, manage workflows, and execute long-running enterprise tasks. Alibaba claims the m...

For AI engineers who want to understand every step, not just call the library The post Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Corpus scale appeared first on Towards Data Science.

Quantum Machine Learning promises access to exponentially large representational spaces, but before any computation can happen, classical data must first be embedded into quantum systems. This article explores one of the most overlooked bottlenecks in QML: getting data into a quantum computer effici...

Keyword search breaks the moment a user types something a document doesn't literally say.

In this tutorial, we will give Gemma 4 two new tools and watch the model decide, on its own, when to look around and when to compute.

A practitioner's warning about generated variables in causal analysis The post LLM Themes Are Not Observations appeared first on Towards Data Science.

Google Search just went from being an encyclopedia to an assistant. That’s the crux of everything Google announced in its recent I/O conference for 2026. The buzzword is “AI agents”, which now enter Google Search, its coding platforms, and even get a whole new standalone app for themselves. The idea...

Most LLM failures in production aren’t random — they’re predictable. I kept hitting broken JSON, silent failures, and outages that froze my entire app. Prompt engineering didn’t fix it. So I built a control layer above the model — and took structured output reliability from 0% to 100% without changi...

How unlearning fixes mode collapse in synthetic survey replies The post Can LLMs Replace Survey Respondents? appeared first on Towards Data Science.

AI agents can quickly become expensive without a clear strategy for planning, skill coverage, and budgets. This article shows how to use operations research and data science to optimize AI agent cost and resource allocation. You will learn how to frame common agent problems—skill coverage, project a...

Learn how to utilize Python's Mimesis library for anonymizing sensitive production data, based on a step-by-step example to try yourself.

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The real challenge in building reliable AI The post From Possible to Probable AI Models appeared first on Towards Data Science.

Google Gemini’s next-generation family offering: Gemini 3.5 is here!  Gemini 3.5 Flash combines frontier intelligence with real-world action and supports high-speed agentic workflows, coding, and multimodal reasoning while maintaining the low latency expected from the Flash series. With Gemini 3.5 P...