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n8n has set out itself as one of the best low-code AI development platforms. The characteristic drag-and-drop interface of n8n has won the hearts of many coders and non-coders alike. The low entry barrier and high skill ceiling makes it the perfect tool for executing ideas on the go.  But there is something missing… It’s hard to come by challenging projects on n8n! This makes it difficult to test proficiency on the platform.  The following projects are here to solve that. Curated to test the depth of your understanding of the n8n platform, the projects range from automation to generation…

Image by Author   # Introduction  When you start letting AI agents write and run code, the first critical question is: where can that code execute safely? Running LLM‑generated code directly on your application servers is risky. It can leak secrets, consume too many resources, or even break important systems, whether by accident or intent. That’s why agent‑native code sandboxes have quickly become essential parts of modern AI architecture. With a sandbox, your agent can build, test, and debug code in a fully isolated environment. Once everything works, the agent can generate a pull request for you to review and merge.…

AI Agents are being widely adopted across industries, but how many agents are needed for an Agentic AI system? The answer can be 1 or more. What really matters is that we pick the right number of Agents for the task at hand. Here, we will try to look at the cases where we can deploy Single-Agent systems and Multi-Agent systems, and weigh the positives and negatives. This blog assumes you already have a basic understanding of AI agents and are familiar with the langgraph agentic framework. Without any further ado, let’s dive in. Single-Agent vs Multi-Agent If we are…

Image by Editor   # Introduction  Machine learning practitioners encounter three persistent challenges that can undermine model performance: overfitting, class imbalance, and feature scaling issues. These problems appear across domains and model types, yet effective solutions exist when practitioners understand the underlying mechanics and apply targeted interventions.   # Avoiding Overfitting  Overfitting occurs when models learn training data patterns too well, capturing noise rather than generalizable relationships. The result — impressive training accuracy paired with disappointing real-world performance. Cross-validation (CV) provides the foundation for detecting overfitting. K-fold CV splits data into K subsets, training on K-1 folds while validating on the remaining…

LLMs like ChatGPT, Claude, and Gemini, are often considered intelligent because they seem to recall past conversations. The model acts as if it got the point, even after you made a follow-up question. This is where LLM memory comes in handy. It allows a chatbot to go back to the point of what “it” or “that” means. Most LLMs are stateless by default. Therefore, each new user query is treated independently, with no knowledge of past exchanges. However, LLM memory works very differently from human memory. This memory illusion is one of the main factors that determine how modern AI…

Image by Author   # Introduction  Most Python developers treat logging as an afterthought. They throw around print() statements during development, maybe switch to basic logging later, and assume that is enough. But when issues arise in production, they learn they are missing the context needed to diagnose problems efficiently. Proper logging techniques give you visibility into application behavior, performance patterns, and error conditions. With the right approach, you can trace user actions, identify bottlenecks, and debug issues without reproducing them locally. Good logging turns debugging from guesswork into systematic problem-solving. This article covers the essential logging patterns that Python developers…

If you are searching for free LLM APIs, chances are you already want to build something with AI. A chatbot. A coding assistant. A data analysis workflow. Or a quick prototype without burning money on infrastructure. The good news is that you no longer need paid subscriptions or complex model hosting to get started. Many leading AI providers now offer free access to powerful LLMs through APIs, with generous rate limits and OpenAI-compatible interfaces. This guide brings together the best free LLM APIs available right now, including their model options, request limits, token caps, and real code examples. Understanding LLM APIs LLM APIs operate…

Image by Author   # Introduction  Hugging Face Datasets provides one of the most straightforward methods to load datasets using a single line of code. These datasets are frequently available in formats such as CSV, Parquet, and Arrow. While all three are designed to store tabular data, they operate differently at the backend. The choice of each format determines how data is stored, how quickly it can be loaded, how much storage space is required, and how efficiently the data types are preserved. These differences become increasingly significant as datasets grow larger and models more complex. In this article, we will look at how Hugging Face Datasets works with CSV, Parquet, and Arrow, what…

Prompt engineering isn’t about creating elaborate prompts. It’s about developing the judgment to choose the right structure, logic, and level of control for a given task. This article gives you 40 scenario-based questions and answers that reflect real decisions you make when working with LLMs in production. Try answering each question before revealing the solution. The explanations focus on why one approach works better than the others in the given scenario. Q1. A customer support team needs to automatically route incoming tickets into one of four fixed categories: Billing, Technical, Account, or Other. High accuracy and consistency are critical.  Which solution is…

Image by Author   # The Setup  You’re about to train a model when you notice 20% of your values are missing. Do you drop those rows? Fill them in with averages? Use something fancier? The answer matters more than you’d think. If you Google it, you’ll find dozens of imputation methods, from the dead-simple (just use the mean) to the sophisticated (iterative machine learning models). You might think that fancy methods are better. KNN considers similar rows. MICE builds predictive models. They must outperform just slapping on the average, right? We thought so too. We were wrong.   # The Experiment…