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In the age of “vibe coding” – where developers (and even non-developers) rely on AI code generators to rapidly produce software – the traditional steps of careful planning and architecture design are often skipped.

Generative AI (GenAI) models have undergone rapid advancements in the past year, and many of us have already experienced its benefits—often as end users consuming AI-generated content. However, as more companies and developers look to incorporate these models into automated workflows—where outputs are fed directly into other systems rather than displayed to humans—the requirements and constraints become more complex.

Our hands-on first impressions of Claude Sonnet's computer-use feature, tested through a real image-interpretation use case.

How Claude Sonnet 3.5 performed on detailed visual interpretation tasks — accuracy, limitations, and key takeaways from our testing.

At APPGAMBiT, we have been building AWS Serverless-based Cloud applications for many years now. Over the years, we have found that the most critical factors for an MVP are: Faster Time to Market, Cost and Reliability. One of the reasons, why we prefer to use AWS Serverless services, if they are applicable, because Serverless infrastructure and Event-driven architecture can fulfill these effectively and we can build a strong foundation as well.

Here's a comprehensive list of ways startups and early-stage businesses can avail extra AWS credits.

In the ever-evolving healthcare landscape, managing and processing large volumes of health records efficiently and securely is crucial. At APPGAMBiT, we recently developed a robust solution using NodeJS and the HL7 library to tackle this challenge. Here's an in-depth look at our approach and the benefits it brings.

Back in 2022 after the launch of VPC-integration for AWS App Runner, we decided to move one of our customer's NodeJS/Angular Web application to the service for experimental purposes. Before that we were running the application on EC2 and Application Load Balancer with DocumentDB for MongoDB as the primary database.

Following use cases curated across different industries highlight the potential for Machine Learning, Generative AI and LLMs to transform processes, improve efficiency, and enhance decision-making. It is not an exhaustive list and we are just (positively) speculating that some of these will realize soon, while many of the listed use cases are already available for general use.

This blog post delves into three advanced methods for personalizing large language models (LLMs): extended context windows, Retrieval-Augmented Generation (RAG) architecture, and long-term memory capabilities.

In the rapidly evolving field of Software Development, the integration of Artificial Intelligence (AI) and Large Language Models soon will no longer just a futuristic concept — but may evolve to a practical tool to position how we approach project planning and execution.

We recently had a use-case that was a perfect fit to utilize the latest Claude 3 Haiku Model combined with AWS Serverless Services.

As of 2024, Large Language Models (LLMs) have become wildly popular to a myriad of applications, from automating customer service interactions to aiding in creative writing. Understanding how these sophisticated models function can seem challenging at first. However, breaking down their internal working into more digestible components makes it easier to understand.

AWS offers many tools to help secure your account. However, because many of these measures aren't active by default, you must take direct actions to enable the guardrails. In this guide we will highlight some of the best practices to consider when securing your account and its resources.

Step-by-step guide to setting up cost-efficient, long-term backups on Amazon S3 — based on a real-world 30TB infrequent-access use case.

A practical FinOps guide to cutting cloud costs in 2024 — budgeting, cost visibility, and optimization tactics for cloud-native teams.

Debunking common web hosting myths for JS frontend apps — what actually matters when choosing between EC2, S3, Netlify, or Vercel.