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Having been on the development side of Aurora, I’ve been curious about how customers use Aurora databases. Here’s a quick tutorial on it: Step 1: Create Aurora PostgreSQL Cluster Via AWS Console: Navigate to RDS Console Go to AWS Console → RDS → Databases Click “Create database” Database Creation Method Select “Standard create” Engine Configuration Engine type: Amazon Aurora Edition: Amazon Aurora PostgreSQL-Compatible Edition Version: Aurora PostgreSQL 15.4 (or latest available) Templates ...

As of early 2025, I have worked at AWS for one year and nine months after graduating from college. My experience had grown a lot, and while I seek to catch up with the latest trends, I am starting to lose track of the different tech stacks that are renewing at ever increasing rate. I have been in search for a way to document my experiences. One engineering skill that I definitely learned is that writing, specifically technical writing and documentation, are highly valuable. Hence, I am starting this blog to: ...

First, decide the high-level stack. For frontend, do you want React/Vue or simple HTML/JS/CSS? I have chosen FastAPI (Python) for backend and React (Next.js) for frontend, hosting on EC2 and potentially use Nginx in future Launch an EC2 instance, recommended to use t3.small or above (I faced OOM issues using t2.micro free tier) Choose Ubuntu 22.04 LTS as the OS. Configure default security groups. Also allow all traffic on port 8000 for FastAPI If you want to use EC2 instance connect, be sure to allowlist the ec2 instance connect CIDR on port 22, e.g. for PDX the CIDR is 18.237.140.160/29 Connect to EC2 instance and install dependencies ...

Generative AI text-to-image models have been growing at increasing rate and are fun. As of late 2024/early 2025, Stable diffusion made by Stability.AI is one of the most popular open-source AI image models, along with DALL-E and closed-source models like Midjourney. (Would also note here that I want to explore text-to-video models next) Diffusion models Stable Diffusion and DALL-E are diffusion models, which is one of the generative models and categorized as supervised learning in machine learning. Diffusion models areis also known as latent variable generative models. Without the math details, diffusion models work by destroying training data/images through the successive addition of Gaussian noise (Forward Diffusion or Noising), and then reversing the process to recover the data (Reverse Diffusion or Denoising). After training and learning the diffusion and denoising processes, the diffusion model can generate data by passing randomly sampled noise through the learned denoising process (Sampling procedure). ...

https://www.youtube.com/watch?v=YBQ_ps6e71k&t=667s