Systems | Development | Analytics | API | Testing

How AI will change software engineering: Insights from Bitrise's VP Engineering

Whether you agree with Elon's prediction or not, it's hard to ignore AI's far-ranging impact, especially on how we approach work. Over the last two years, we have seen AI progress rapidly, leaving many of us wondering, "Will AI replace my job?" It's a question that software engineers have also been grappling with. As ironic as it may seem, the people writing the code driving the technology revolution face the same uncertainty about whether AI might replace them in the future.

How Thrivent Uses Real-Time Data for AI-Driven Fraud Detection

In today’s fast-paced financial services landscape, customers have a shorter attention span than ever. To meet clients’ growing demands for real-time access to information and keep innovating in areas like fraud detection and personalized financial advice, Thrivent needed to overhaul its data infrastructure. With data scattered across siloed legacy systems, diverse tech stacks, and multiple cloud environments, the challenge was a bit daunting. But by adopting Confluent Cloud, Thrivent was able to unify its disparate data systems into a single source of truth.

Gen AI for Marketing - From Hype to Implementation

Gen AI has the potential to bring immense value for marketing use cases, from content creation to hyper-personalization to product insights, and many more. But if you’re struggling to scale and operationalize gen AI, you’re not alone. That’s where most enterprises struggle. To date, many companies are still in the excitement and exploitation phase of gen AI. Few have a number of initial pilots deployed and even fewer have simultaneous pilots and are building differentiating use cases.

SQL for data exploration in a multi-Kafka world

Every enterprise is modernizing their business systems and applications to respond to real-time data. Within the next few years, we predict that most of an enterprise's data products will be built using a streaming fabric – a rich tapestry of real-time data, abstracted from the infrastructure it runs on. This streaming fabric spans not just one Apache Kafka cluster, but dozens, hundreds, maybe even thousands of them.

The Defense Can Rest While AI Handles The Legal Documents

What’s one thing all your favorite legal shows have in common? Whether it’s Suits or The Lincoln Lawyer, they rarely show the amount of paperwork lawyers have to handle on a daily basis. Understandably so, paperwork isn’t the most glamorous part of the job but that doesn’t mean it’s not crucial. In fact, lawyers deal with tens, if not hundreds, of documents on a daily basis during most parts of their job, such as discovery, research, or drafting.

Perforce's Approach to Open-Source Communities

Perforce has been contributing and working in open source for decades now. We understand that open source is the linchpin for technology that supports businesses today. Our approach to open source is not unique to the industry at large or a company of our size (we have around 1,700 employees and 800 are on my team), but questions of our approach to open source became much more visible when we acquired Puppet – which has a really dedicated open-source community and a long history with open source.
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Simplifying AWS Testing: A Guide to AWS SDK Mock

Testing AWS services is an essential step in creating robust cloud applications. However, directly interacting with AWS during testing can be complicated, time-consuming, and expensive. The AWS SDK Mock is a JavaScript library designed to simplify this process by allowing developers to mock AWS SDK methods, making it easier to simulate AWS service interactions in a controlled environment. Primarily used with AWS SDK v2, AWS SDK Mock integrates with Sinon.js to mock AWS services like S3, SNS, and DynamoDB.

Episode 11: The future of data lakes: Open table formats, metadata and AI | AWS

Paul Meighan, Director of Product Management at AWS, shares how enterprises are increasingly looking for ways to integrate more data sources in their environment — especially with data lakes. From turning S3 buckets into databases to establishing better metadata layers, Meighan explores the rapid evolution of data lakes alongside data warehouses. He also explains the pivotal role AI, ML and GenAI workloads and applications will play in large metadata environments, driving innovative analytics and business insights.

RAG Application with Kong AI Gateway, AWS Bedrock, Redis and LangChain

For the last couple of years, Retrieval-Augmented Generation (RAG) architectures have become a rising trend for AI-based applications. Generally speaking, RAG offers a solution to some of the limitations in traditional generative AI models, such as accuracy and hallucinations, allowing companies to create more contextually relevant AI applications.