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Jun 8th 2026, 14:30 by Eric D. Schabell
This series is a general-purpose getting-started guide for those of us wanting to learn about the Cloud Native Computing Foundation (CNCF) project Fluent Bit.
Each article in this series addresses a single topic by providing insights into what the topic is, why we are interested in exploring that topic, where to get started with the topic, and how to get hands-on with learning about the topic as it relates to the Fluent Bit project.
Jun 8th 2026, 14:00 by Abhishek Trehan
With a plethora of pre-trained LLM models available off the shelf, integrating them into applications is becoming increasingly accessible. But getting them into production is a different story. In this article, I'll walk through the pattern I followed to deploy an agentic AI solution to production, one that leverages OpenAI models and LangGraph, hosted on AWS ECS Fargate.
Why ECS Fargate
The primary reason to choose ECS Fargate over SageMaker is simplicity. Fargate is fully managed and on-demand, meaning there is no idle infrastructure cost. You pay for what you run, and you don't have to worry about managing the underlying EC2 instances. For a team that wants production-grade hosting without the operational overhead, Fargate hits a practical sweet spot. Lambda was off the table for obvious reasons, such as cold start penalties and memory caps, making it a poor fit for any LLM workload.
Jun 8th 2026, 13:00 by Ninaad Rao
Most AI Agent frameworks treat the model as a black box: you register tools, the model picks one, the tool runs, and the cycle repeats. This pattern is perfect for demos, but for a production system, it requires more complex systems. We need to manage context windows, cache API calls, filter sensitive tools by role, and compact the information history within models to avoid token limits.
I landed on middleware while reviewing issues for deepagents and understanding their codebase. This is when I started to wonder what middleware really is in the context of AI agents and its significance. This got me thinking: how do other frameworks handle this problem? So I went ahead and installed Pydantic AI, read the CrewAI source, and checked Langchain and Autogen.
Jun 8th 2026, 12:00 by Nicholas Volkhin
When PHP developers compare XML approaches, the comparison often starts in the wrong place.
It usually becomes a vague question like this: "What is the best XML library for PHP?" That is too broad to be useful.
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