Welcome to M5BMachine 5-Minute Briefing

Dates and times shouldn’t break your code, but they often do. These five DIY Python functions help turn real-world dates and times into clean, usable data.

Modern AI applications rely on intelligent agents that think, cooperate, and execute complex workflows, while single-agent systems struggle with scalability, coordination, and long-term context. AgentScope AI addresses this by offering a modular, extensible framework for building structured multi-ag...

I just downloaded the latest 4 Billion parameter model. I hit ‘Run‘. After a while, the Google Colab instance crashes. Sounds familiar? Well this is bound to happen if we don’t pay attention to the required VRAM and what VRAM we are providing to the model. Quantization is something that can help you...

At its core, ML involves algorithms that analyze data, recognize patterns, and make predictions. These models “learn” from past data to improve their performance over time. For example, an ML model trained on user purchase history can predict which products a customer might buy next. Artificial Inte...

Job descriptions of Data Engineering roles have changed drastically over the years. In 2026, these read less like data plumbing and more like production engineering. You are expected to ship pipelines that don’t break at 2 AM, scale cleanly, and stay compliant while they do it. So, no – “I know Pyth...

Python remains at the forefront data science, it is still very popular and useful till date. But on the other hand strengthens the foundation underneath. It becomes necessary where performance, memory control, and predictability become important.

In the past couple of years, we have seen vibe coding evolve from just an idea that sounded “fancy” to a full-time practice for many budding developers. What used to sound like a gimmick is now a must-have skill, even in the professional world. Proof? The recent round of funding secured by one such ...

Are you following the trend or genuinely interested in Machine Learning? Either way, you will need the right resources to TRUST, LEARN and SUCCEED. If you are unable to find the right Machine Learning resource in 2026? We are here to help. Let’s reiterate the definition of Machine Learning… Machine ...

Understanding data starts with statistics. These 7 statistics concepts give you the foundation to analyze and interpret with confidence.

Through the lens of a serial entrepreneur, this article explores how the AI revolution is shifting from infrastructure to the application layer, where the greatest opportunities lie in solving specialized, data-heavy industry problems rather than perfecting raw technology.

These five options make long-running jobs easier, faster, and less frustrating than Colab.

Talking to software feels natural now, until you need real business data. That’s where things usually break. MCPToolbox to Databases fixes this by giving AI agents safe, reliable access to production databases through a standardized MCP interface. Databases become first-class tools that agents can i...

AI can whip up Python code in no time. The challenge, however, is keeping the code clean, readable, and maintainable.

We tuned four classifiers on student performance data with proper nested cross-validation and statistical testing. The result? Tuning changed nothing.

AI and ML developers often work with local datasets while preprocessing data. Engineering features, and building prototypes make this easy without the overhead of a full server. The most common comparison is between SQLite, a serverless database released in 2000 and widely used for lightweight trans...

The most trusted GitHub repositories to help you master coding interviews, system design, backend engineering, scalability, data structures and algorithms, and machine learning interviews with confidence.

Cyber threats aren’t slowing down, and data teams are now some of the most attractive targets in the small business world.

Stop wrestling with messy JSON. These five Python functions help you parse, validate, and transform JSON data efficiently.

We interact with LLMs every day. We write prompts, paste documents, continue long conversations, and expect the model to remember what we said earlier. When it does, we move on. When it doesn’t, we repeat ourselves or assume something went wrong. What most people rarely think about is that every res...

Imagine stepping into your first data science interview—your palms are sweaty, your mind racing, and then… you get a question you actually know the answer to. That’s the power of preparation. With data science reshaping how businesses make decisions, the race to hire skilled data scientists is more ...

It is 2026, and in the era of Large Language Models (LLMs) surrounding our workflow, prompt engineering is something you must master. Prompt engineering represents the art and science of crafting effective instructions for LLMs to generate desired outputs with precision and reliability. Unlike tradi...

The yearly GATE exam is right around the corner. For some this was a long time coming—for others, a last minute priority. Whichever group you belong to, preparation would be the only focus for you now.  This article is here to assist with those efforts. A curated list of GATE DA learning material th...

Among the most valuable roles in the corporate world today, data science enjoys a pole position with just a handful of alternatives that can even match its scale. Record-breaking salaries are being paid to data scientists across the globe for their highly unique skill set, as well as the massive imp...

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 […] The post Single-Age...