cms VS aws-lambda-java-libs

So, I recently set up a comment system in a Next.js app using BCMS, and I wrote about the whole process.

Speaking of taking action, try out BCMS as a headless CMS for AI-driven content management.

Try BCMS and check out how this powerful combination can transform your content management strategies.

Well, we don’t to brag, but this year we are coming to present all the news and new features related to our product BCMS-headless CMS.

p.s. self-hosted BCMS is still open-source and available at github.com/bcms/cms

Ready to try? Let BCMS be your modular asset management platform.

Choosing BCMS headless CMS for managing structured content is smart due to its inherent flexibility, scalability, and efficiency. By decoupling the backend from the front end, a headless CMS allows for seamless content delivery across various platforms, ensuring that your content remains consistent and personalized for users, whether they are accessing it via a website or mobile apps.

So, thanks to the BCMS headless CMS, the clouds have parted, and the skies are clear. CaaS and SaaS can now reign as cloud champions, working together to drive digital transformation and operational efficiency in cloud computing services.

Each of these starters uses the same foundational concepts ( and BCMS headless CMS) but applies them in ways that highlight the distinct advantages of the respective frontend frameworks.

So far our high level architecture diagram wasn't very impressive - we only used AWS Amplify service to host our web application. Of course there are many services under the hood like Route 53, CloudFront, Certificate Manager, Lambda and S3, but Amplify provides level of abstraction, so that we don't have to think about it.

Rajesh Pandey outlines key components: "For serverless, AWS Lambda and API Gateway allow you to build low-latency AI APIs without managing servers." Tools like Modal handle GPU deployments.

"Scale to zero" for intermittent workloads: For non-critical internal tools or low-traffic services, consider a serverless approach with AWS Lambda and SageMaker Serverless Inference. While the latency might be slightly higher due to cold starts, you pay absolutely nothing when the service isn't being used. This can result in massive savings over time.

While many cloud providers offer serverless options, my go-to, especially when working with Next.js, is Vercel. Their integration with Next.js API Routes makes developing and deploying serverless functions incredibly intuitive and fast. For more complex, enterprise-level solutions, AWS Lambda is the industry leader, offering immense flexibility and a vast ecosystem of services.

Building an AWS Lambda function to process and update embeddings

In this application, we will create products and retrieve them by their ID and use Amazon DynamoDB as a NoSQL database for the persistence layer. We use Amazon API Gateway which makes it easy for developers to create, publish, maintain, monitor and secure APIs and AWS Lambda to execute code without the need to provision or manage servers. We also use AWS SAM, which provides a short syntax optimised for defining infrastructure as code (hereafter IaC) for serverless applications.

Lambda supports various programming languages, including Node.js, Python, Java, Go, Ruby, .NET, and even custom runtimes via containers. For the most up-to-date list and details, check out the official AWS Lambda documentation.

In this tutorial, you will walk through the process of building, testing, and deploying a multi-agent AI system using LangGraph, Docker, AWS Lambda, and CircleCI. You will develop a research-driven AI workflow where different agents,such as fact-checking, summarization, and search agents, work together seamlessly. You will package this application into a Docker container, deploy it to AWS Lambda, and automate the entire pipeline using CircleCI.

Backend: It handles API functions (e.g. AWS Lambda, Cloudflare Workers, GCP Functions)

Teachers, freelancers, and inbox zero purists rejoice: I built EmailDrop, a one-click AWS deployment that turns incoming emails into automatic Google Drive uploads. With Postmark's new inbound webhooks, AWS Lambda, and a little OAuth wizardry, attachments fly straight from your inbox to your Google Drive. In this post, I’ll walk through how I built it using Postmark, CloudFormation, Google Drive, and serverless tools, and how you can deploy it with zero manual code.