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Agent Skills: On-the-fly capabilities for your AI Agents

2026-01-23T14:25:43+00:00

Agent Skills are the new boom. If you’ve been following the agentic AI space since late 2025, you’ve likely noticed a shift. Previously, we spent most of 2024 and 2025 obsessing over tools—giving LLMs calculators, database access, and API keys. But giving a new employee a laptop and a login doesn’t make them productive. You still have to train them. For example, you have to show them how your organization works. That is exactly what Claude Agent Skills are. Released by Anthropic in October 2025, Agent Skills have quietly become one of the most powerful features in the Claude ecosystem, [...]

Agent Skills: On-the-fly capabilities for your AI Agents2026-01-23T14:25:43+00:00

Amazon Bedrock’s LLM-as-a-Judge: Automate AI Evaluation with Nova Lite + Claude

2026-01-23T15:25:15+00:00

Evaluating your LLM’s quality should not cost you too much money or even weeks of your time.  You’re probably  stuck in a limbo of choosing between two options that have their own drawbacks: Automated metrics like BLEU, ROUGE and accuracy scores? Sure, they are quite fast and cheap, but they ultimately fall short in judging real conversations. Since they simply match word patterns, they're not a good fit for open ended responses simply because they can't tell  if they actually understood the question or its tone. As for human reviewers, they do get it right. They captured the context/subtlety and [...]

Amazon Bedrock’s LLM-as-a-Judge: Automate AI Evaluation with Nova Lite + Claude2026-01-23T15:25:15+00:00

A Beginner’s Guide to the Machine Learning Pipeline on GCP

2026-01-08T13:02:44+00:00

When people hear the term "machine learning," they often imagine complex math, advanced algorithms, or mysterious "AI magic" happening behind the scenes. In reality, machine learning on the cloud is far more practical and structured than it may sound. At its core, an ML pipeline is a series of steps that transform raw data into useful predictions. Think of it like a typical software workflow: You prepare your code You build the application You deploy it Users interact with it An ML pipeline follows the same idea, just with different building blocks. Instead of starting with source code, you begin [...]

A Beginner’s Guide to the Machine Learning Pipeline on GCP2026-01-08T13:02:44+00:00

Building a Cost-Aware RAG Application with Amazon Bedrock

2026-01-06T08:43:56+00:00

What if your Client can have a Chatbot that throws a highly accurate responses based on your documents? Without having a guilt of the monthly expenses. Without even subscribing to any costly,  AI-support subscriptions. Only pay per inquiry requests to your model provider,  and of course only costs you cents, and it lessens when your client is already satisfied with the answer, as it returns a fully verified response based from the documents you have in your database? Consider this as well, it is cost-aware, making sure that it will notify you once it exceeds your budget limits for you [...]

Building a Cost-Aware RAG Application with Amazon Bedrock2026-01-06T08:43:56+00:00

Zero-Sweat: A Comprehensive Guide to IAM Policy Autopilot

2025-12-28T16:02:53+00:00

Picture this: your application works perfectly on your local machine. You deploy it to AWS, then immediately hit an “Access Denied” error. If you’ve worked with AWS for any length of time, you’ve experienced this. What follows is usually a frustrating dive into IAM documentation, trial-and-error permission updates, and lost development momentum. AWS Labs created IAM Policy Autopilot to solve exactly this problem. IAM Policy Autopilot analyzes your application code and generates AWS IAM policies based on the actual SDK calls your code makes. No guessing. No hallucinated permissions. Just deterministic, repeatable policy generation. What Is IAM Policy Autopilot? IAM [...]

Zero-Sweat: A Comprehensive Guide to IAM Policy Autopilot2025-12-28T16:02:53+00:00

The Year of the Agent: Anthropic’s Claude AI Models and Agents

2025-12-26T17:20:34+00:00

  Looking back on the past year of 2025 coding with the help of artificial intelligence, we can safely say that it was the year of agents, especially pioneered by Anthropic with Claude Code. AI products have matured to offer highly reliable agents that can understand, navigate, and work seamlessly on large codebases. It is the end of the old ways: of manually copying and pasting code into web AI applications. Agents are now actually part of the codebase, navigating around like a real developer. And they can now be left with long-running tasks on their own,opening and working on [...]

The Year of the Agent: Anthropic’s Claude AI Models and Agents2025-12-26T17:20:34+00:00

AI-Driven Cloud Security at AWS re:Invent 2025

2025-12-26T11:28:51+00:00

Cloud computing continues to accelerate at a pace that traditional security models were never designed to support. Development teams now provision infrastructure in minutes, deploy services continuously, and scale applications automatically. However, security processes often lag behind this speed. In many organizations, security still enters the workflow after key architectural decisions are already finalized. As a result, teams spend more time fixing problems than preventing them. Although many organizations attempt to shift security earlier in development, the results are often disappointing. Security tools may run during build or deployment stages, yet they frequently lack the context required to provide meaningful [...]

AI-Driven Cloud Security at AWS re:Invent 20252025-12-26T11:28:51+00:00

Amazon Q in Practice: How AWS’s AI Assistant Actually Works for Businesses and Developers

2025-12-21T16:15:25+00:00

Amazon Q is often introduced as AWS's generative AI assistant, but that description doesn't really explain why it exists or how it behaves once you start using it. If you treat Amazon Q like a general chatbot, it can feel restrictive or underwhelming. If you treat it as an AWS-native system designed around identity, permissions, and retrieval, it becomes much easier to understand. And much more useful. I've spent a lot of time working with Amazon Q while creating video content for Tutorials Dojo courses, and most of what I'll share here comes from that hands-on experience. My goal is [...]

Amazon Q in Practice: How AWS’s AI Assistant Actually Works for Businesses and Developers2025-12-21T16:15:25+00:00

AWS Security Agent: Context-Aware Application Security

2025-12-23T07:20:43+00:00

The Problem: Security Can't Keep Up In the current engineering landscape of our industry, modern software teams are supposed to be built to be able to move fast. Continuous integration, automated deployments, and agile workflows have seen the rise of weekly and even daily releases to be the norm. With rising business and customer demands in the time of rapid advancements in technology, new features, fixes, and changes are constantly pushed to production. However, we all know that security is not meant to be fast and shouldn't be, lest we leave behind vulnerabilities and loopholes in the pursuit of speed. [...]

AWS Security Agent: Context-Aware Application Security2025-12-23T07:20:43+00:00

Automating PII Detection and Redaction with Amazon Comprehend

2026-01-08T08:21:54+00:00

Organizations today are entrusted with enormous amounts of sensitive information. Customer support logs, healthcare records, financial transactions, and even training datasets often contain Personally Identifiable Information (PII) such as names, phone numbers, email addresses, or credit card numbers. Protecting this information is not just a matter of compliance with regulations like GDPR, HIPAA, or PCI DSS. It is also central to maintaining customer trust and reducing the risk of data breaches. Amazon Comprehend, a managed natural language processing (NLP) service, provides a powerful way to automate the detection and redaction of PII. Instead of relying on manual review or custom [...]

Automating PII Detection and Redaction with Amazon Comprehend2026-01-08T08:21:54+00:00

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