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Effectively building AI agents on AWS Serverless

AWS just dropped support for buildingserverless agentic AI systems. You’ll need the Strands Agents SDK, Bedrock AgentCore (preview), plus trusty tools like Lambda and ECS. What’s new? Agentic AI flips the script. Instead of dumb prompt-in, response-out bots, you getgoal-driven loopswith memory, too.. read more  

Effectively building AI agents on AWS Serverless
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Introducing AWS Cloud Control API MCP Server: Natural Language Infrastructure Management on AWS

AWS dropped theCloud Control API MCP Server, a mouthful of a name for a tool that makes 1,200+ AWS resources manageable through a standard CRUDL API—using natural language. Think: describe what you want, and tools like Amazon Q Developer turn it into actual infra code. It doesn’t stop there. It val.. read more  

Introducing AWS Cloud Control API MCP Server: Natural Language Infrastructure Management on AWS
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Some thoughts on LLMs and Software Development

Most LLMs still play autocomplete sidekick. But seasoned devs? They get better results when the model reads and rewrites actual source files. That gap—between how LLMs are designed to work and how prosactuallyuse them—messes with survey data and muddies the picture on real gains in code quality and.. read more  

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The Most Important Machine Learning Equations: A Comprehensive Guide

A new reference rounds up the core ML equations—Bayes’ Theorem, cross-entropy, eigen decomposition, attention—and shows how they plug into real Python code using NumPy, TensorFlow, and scikit-learn. It hits the big four: probability, linear algebra, optimization, and generative modeling. Stuff that.. read more  

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Combining GenAI & Agentic AI to build scalable, autonomous systems

Agentic AI doesn’t just crank out content—it takes the wheel. Where GenAI reacts, Agentic AI plans, perceives, and acts. Think less autocomplete, more autonomous ops. Hook them together, and you get a full-stack brain: content creation, real-time decisions, adaptive workflows, all learning as they .. read more  

Combining GenAI & Agentic AI to build scalable, autonomous systems
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ECScape: Understanding IAM Privilege Boundaries in Amazon ECS

A new ECS security mess—ECScape—lets low-privileged tasks on EC2 act like the ECS agent. That’s bad. Real bad. Why? Because it opens the door to stealing IAM credentials from other ECS tasks sharing the same host. Here’s the trick: The attacker hits the instance metadata service (IMDS) and fakes a .. read more  

ECScape: Understanding IAM Privilege Boundaries in Amazon ECS
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How to prepare for the Bitnami Changes coming soon

The Bitnami team has delayed the deletion of the Bitnami public catalog until September 29th. They will conduct a series of brownouts to prepare users for the upcoming changes, with the affected applications list being published on the day of each brownout. Users are advised to switch to Bitnami Sec.. read more  

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Availability Models: Because “Highly Available” Isn’t Saying Much

Antithesis and Jepsen want to kill hand-wavy "high availability" talk. Instead, they push for clearavailability models—majority,total,sticky, etc.—that spell out when an operationactuallyworks during failures. It's about precision, not platitudes. Why it matters:This reframes availability from a va.. read more  

Availability Models: Because “Highly Available” Isn’t Saying Much
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Observability in Go: What Real Engineers Are Saying in 2025

Go observability still feels like pulling teeth. Manual instrumentation? Tedious. Span coverage? Spotty. Telemetry volume? Totally out of hand. Even with OpenTelemetry gaining traction, Go lags behind Java and Python when it comes to auto-instrumentation and clean context propagation. Devs are hunt.. read more  

Observability in Go: What Real Engineers Are Saying in 2025
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Google Develops KFuzzTest For Fuzzing Internal Linux Kernel Functions

Google droppedKFuzzTest, a lean fuzzing tool built to hit Linux kernel internals—way past just syscalls. It brings a clean API, docs, and sample targets to get fuzzing fast. Why it matters:KFuzzTest marks a shift. Kernel fuzzing’s no longer just about hammering syscalls—it’s going deeper into the g.. read more  

OpenAI is an independent artificial intelligence research organization that was founded in 2015 with the goal of promoting the development and safe use of advanced AI technologies. The organization's research focuses on a wide range of AI applications, including natural language processing, computer vision, and robotics. OpenAI's research is driven by a team of world-class researchers and engineers who work to develop cutting-edge AI technologies that are both powerful and safe.

One of the key goals of OpenAI is to promote responsible AI development. To this end, the organization works closely with policymakers, industry leaders, and other stakeholders to ensure that AI is developed in a way that is ethical and beneficial for society. OpenAI also provides resources and training to help people better understand AI and its potential impacts.

OpenAI's research has led to numerous breakthroughs in the field of AI, including the development of advanced language models like GPT-3, which can generate coherent and human-like text. The organization has also developed AI technologies that are used in a variety of applications, from self-driving cars to medical diagnostics.

Overall, OpenAI is at the forefront of AI research and development, and is working to ensure that AI is developed in a way that benefits everyone.