AI Tools & Frameworks Directory

In the world of data science, SQL still remains the powerful tool for defining the data, data manipulation, data aggregation and data analysis. While basic SQL commands are very fundamental, and everyone knows about it. If you want to be the unique in the crowd then you should know advanced features...

I have been running local models as part of my daily workflow for some time, and what surprised me most is how often local turned out to be the better choice, not a compromise.

In this article, we will explore five fundamental concepts that every Python developer should have in their toolkit.

Learn how to use Python itertools to build efficient and scalable time series features.

Here are 5 small language models that hare one important trait: they all support structured tool calling in a compact, open-weight package.

There are some obvious signs that can instantly differentiate between regular and advanced AI users. One, for instance, is the use of voice AI for daily tasks. While majority users still toil away on their keyboard for the perfect prompt, a person proficient in the use of AI now simply speaks to it....

Discover how AI agents will revolutionize data science in 2026, and learn why they won't replace you but will make you a faster, smarter analyst.

This article discusses how to implement an infrastructure for measuring and controlling overly verbose LLM responses.

Learn how to build an AI-powered Learning Management System from scratch using Ollama, FastAPI, and React. A step-by-step guide for beginner and intermediate developers.

Learn FastAPI through templates, examples, guides, auth tools, microservices, full-stack starters, and machine learning projects.

Memory shapes how humans think and how AI agents act. Without it, an agent only responds to the current input; with it, it can keep context, recall past actions, and reuse useful knowledge. AI memory spans short-term, episodic, semantic, and long-term memory, each with different design trade-offs ar...

If you are feeding structured data into an LLM, there is a good chance you are paying a JSON tax.

If you’re an aspiring AI engineer looking to sharpen your skills, building AI agents is one of the most effective ways to get hands-on experience. AI agents represent practical applications of AI across domains, from personal assistants and recommendation systems to financial traders. Here are 10 AI...

Learn how to build a holistic pipeline for rigorous, statistical EDA, validating several important data properties.

Feature engineering is the foundation of strong machine learning systems, but the traditional process is often manual, time-consuming, and dependent on domain expertise. While effective, it can miss deeper signals hidden in unstructured data such as text, logs, and user interactions. Large Language ...

This article is a quick, everyday tour of seven distributions you'll actually recognize once you know what to look for. No heavy math. No gatekeeping.

The headline may sound extreme here. Of course, Claude is not replacing CFOs tomorrow morning. But with the debut of Claude’s new Financial Services Solution by Anthropic, it has clearly moved to a new direction in the world of finance, one where AI does way more than crunch numbers or explain stuff...

Building a RAG system just got much easier. Google’s File Search tool for the Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, indexing are all managed for you. And with the latest update, it’s gone multimodal. You can now search through both text and im...

Learn which seven OpenCode plugins add memory, search, Gemini, terminal control, analytics, and reusable skills to make AI coding workflows stronger.

One is genuine curiosity. The other is someone who already knows what they want and went looking for a number to back it up.

Turn Claude Code into your AI coding partner with these 5 hands-on projects, from beginner-friendly builds to advanced agent workflows.

Most ML projects do not fail because of model choice. They fail in the messy middle: finding the right dataset, checking usability, writing training code, fixing errors, reading logs, debugging weak results, evaluating outputs, and packaging the model for others. This is where ML Intern fits. It is ...

Projects are the bridge between understanding AI and actually building with it. While the last couple of years were dominated by generative models, the shift now is toward systems that can think in steps, use tools, and act with a clear objective. This guide brings together over 15 solved agentic AI...

AI chatbots are the new norm. What earlier was “ask Google” has now largely become “ask Claude”. And that is not just a change of platforms. The new form of conversational guidance goes a whole lot deeper than trying to find the best car for you or looking for an upskilling course. It now spills […]...