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Generative AI Security Scoping Matrix

2025-09-03T02:56:19+00:00

Generative AI Security Scoping Matrix Cheat Sheet The Generative AI Security Scoping Matrix is a framework to classify generative AI (GenAI) use cases by the level of ownership and control over the models and data. It helps organizations assess and prioritize security requirements based on their generative AI deployment approach. The matrix defines 5 scopes from least to most ownership and control: Governance & Compliance Legal & Privacy Risk Management Controls Resilience Scopes of Generative AI Use Cases Buying Generative AI (Low Ownership) Scope 1: Consumer app Uses free or paid public third-party services like ChatGPT and Midjourney. No ownership or [...]

Generative AI Security Scoping Matrix2025-09-03T02:56:19+00:00

What is Chain of Thought Prompting?

2025-08-31T16:52:13+00:00

A prompting technique in Large Language Models (LLMs) where the model is guided to show intermediate reasoning steps before arriving at the final answer. Inspired by how humans solve problems step by step. Helps LLMs handle complex reasoning tasks such as math, logic, and multi-step decision-making. Key Concepts Step-by-Step Reasoning: Instead of jumping to an answer, the model explains its thought process. Intermediate Steps: Similar to “showing work” in math problems. Better Accuracy: Effective in arithmetic, logical reasoning, and multi-hop questions. Prompt Example: “Let’s think step by step.” Benefits Improves reasoning accuracy. Makes the model’s output more interpretable. Reduces errors [...]

What is Chain of Thought Prompting?2025-08-31T16:52:13+00:00

Getting Started with n8n + A Quick Workflow Example

2025-09-04T15:10:12+00:00

In a world full of repetitive tasks, automation is not a new thing. For years, humanity designed machines and systems to take over the monotonous or large-scale work from humans. We've come a long way from industrial automation, it now uses the algorithms to create system that can automatically check, send, or even sort your emails and documents, manage your store's inventory, and even drive (would I still need a license in this case?). With all the power automation gave us, we still crave more. We thought 'what if automation could learn, adapt, and create?' a thought that is coming into reality [...]

Getting Started with n8n + A Quick Workflow Example2025-09-04T15:10:12+00:00

Agentic AI in Applications: Turning Ideas into Autonomous Workflows

2025-08-29T13:53:44+00:00

AI Agents in Applications: Turning Ideas into Autonomous Workflows Artificial Intelligence has quickly advanced from basic chatbots to sophisticated generative models. But in 2025, a new era is unfolding: AI agents, also known as Agentic AI, that don’t just generate responses but act as autonomous digital teammates—capable of planning, executing, and completing tasks end-to-end. For students, developers, and IT professionals, understanding how Agentic AI works, and where it’s being applied, is key to preparing for the future of technology. What are AI Agents (Agentic AI)? Through its advanced design, Agentic AI is able to: Plan workflows independently. Seamlessly connect with [...]

Agentic AI in Applications: Turning Ideas into Autonomous Workflows2025-08-29T13:53:44+00:00

What is Model Context Protocol (MCP)?

2025-08-28T09:19:36+00:00

An open, model‑agnostic protocol introduced by Anthropic in November 2024, designed to standardize how AI systems (huge language models, LLMs) connect with external data sources and tools via a JSON‑RPC interface. Often likened to a “USB‑C port for AI,” offering a universal interface rather than bespoke integrations per system. Key Benefits of MCP Provides a standardized interface so LLMs can easily connect to multiple tools and data sources without custom adapters. Solves the “N×M” problem, removing the need to build a unique connector for every AI–tool combination. Ensures structured and validated exchanges, supporting better debugging, version control, and reliability in [...]

What is Model Context Protocol (MCP)?2025-08-28T09:19:36+00:00

Turning AI Prompts into Real-World Results

2025-08-29T10:51:27+00:00

It all starts with a simple AI prompt. When you have an idea that you want to explore further, one of the easiest ways to keep the ball rolling is to turn that idea into a prompt for an AI tool. This helps clarify your vision, spark new directions, and provide a starting point you can build from. AI doesn’t magically finish your work for you, but it can set you up with a solid foundation that saves time and keeps your momentum going. As an active volunteer in the Philippines’ tech community, I’ve been to numerous events and noticed [...]

Turning AI Prompts into Real-World Results2025-08-29T10:51:27+00:00

From Concept to Code: Build Your First MCP Server

2025-08-27T16:27:09+00:00

For a long time now, LLMs or large language models have been going around the tech industry with its massive knowledge on languages. They can comprehend and even generate human language with bull's-eye fluency. Nevertheless, despite their strengths, powerful as they are, these intelligent models act in isolation. They have no hands to implement and interact with the world; they lack the ability to connect to databases, fetch data, or even trigger an action in a completely different application. Consequently, this lack of ability incapacitates the LLM's high potential when communicating with machines. This is why the introduction of the [...]

From Concept to Code: Build Your First MCP Server2025-08-27T16:27:09+00:00

What is Federated Learning?

2025-08-26T16:51:20+00:00

A machine learning technique where multiple devices or servers collaboratively train a shared model without sharing raw data. Instead of sending data to a central server, only the model updates (gradients/parameters) are sent, keeping sensitive information local. Key Concepts Decentralized Training: Data stays on local devices (e.g., smartphones, IoT, edge devices). Model Aggregation: A central server collects and averages model updates to improve the global model. Privacy-Preserving: Minimizes risk of exposing personal or sensitive data. Communication Efficiency: Reduces the need for large-scale raw data transfer. Edge AI Integration: Often paired with edge computing for real-time AI. How Federated Learning Works [...]

What is Federated Learning?2025-08-26T16:51:20+00:00

What are Clustering Algorithms in Machine Learning?

2025-08-25T06:43:36+00:00

Clustering is an unsupervised learning technique that groups similar data points without predefined labels. It helps discover hidden patterns, segment data, and reduce dimensionality in datasets. Key Concepts Clustering: Grouping data points based on similarity or distance metrics. Unsupervised Learning: No labeled data; the model identifies structure independently. Distance Metrics: Commonly used metrics include Euclidean, Manhattan, and Cosine similarity. Popular Clustering Algorithms 1. K-Means Clustering Divides data into K clusters by minimizing the variance within each cluster. Fast, easy to implement, and works well with large datasets. It requires predefining K and is sensitive to outliers. Customer segmentation, image compression. [...]

What are Clustering Algorithms in Machine Learning?2025-08-25T06:43:36+00:00

All About Vibe Coding: The Laid-Back Future of Programming

2025-09-03T14:22:18+00:00

Coding no longer has to mean endless lines of text, late-night bug hunts, and constant fixes. A growing number of developers are turning to vibe coding,  a style that feels more like a creative conversation than a technical grind. Instead of wrestling with syntax, developers describe what they want and let AI handle the heavy lifting. As a result, coding shifts from typing code to shaping ideas. This article covers what vibe coding is, where it came from, why it matters, and how it’s influencing the future of software development. Origin of the Term The term vibe coding was popularized [...]

All About Vibe Coding: The Laid-Back Future of Programming2025-09-03T14:22:18+00:00

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