Adding AI to your software development life cycle (SDLC) comes with great opportunities — and great dangers. Is the risk worth the reward? This was the topic of conversation when Sascha Wiswedel, Senior Solutions Engineer at Atlassian, and Simon Maple, Principal Developer Advocate at Snyk, teamed up to discuss security in the (AI-assisted) software development lifecycle.

In the 21st century, the lifeblood of several multi-billion dollar corporations is the data of their users they possess and how they go upon it, whether it be including new policies benefitting the users of their platform, or finding more aspects of data they can take to their advantage to increase their profit margins ever so slightly? All of the above requires data in the baseline.

In a significant stride towards enhancing the capabilities of LangChain, the team introduces LangGraph, a module designed to facilitate the creation of cyclical graphs within the LangChain ecosystem. This novel addition, completely interoperable with LangChain, aims to empower developers in building agent runtimes with greater flexibility and efficiency.

The AI revolution that kicked into overdrive last year has taken the world by storm, and as the development and productization of Large Language Models and other AI technologies continue – the revolution shows no signs of slowing down.

In the ever-evolving landscape of cybersecurity, businesses and individuals find themselves in a relentless battle against the surge of cybercrime, which continues to escalate in complexity and frequency. Despite the significant investments in cutting-edge cybersecurity solutions, the financial toll of cybercrime persists, with costs escalating annually.

With Valentine’s Day just around the corner, it comes as no surprise that Egress’ Threat Intelligence team is starting to see an uptick in romance-based phishing attacks. In particular, they noted a staggering 43% increase in attacks impersonating well-established dating apps including Tinder and Hinge between January 1, 2024, until February 5, 2024, compared to 2023. This is only likely to increase as the day draws closer.

Generative AI (à la OpenAI’s GPT and the likes) is a powerful tool for summarizing information, transformations of text, transformation of code, all while doing so using its highly specialized ability to “speak” in a natural human language. While working with GPT APIs on several engineering projects an interesting idea came up in brainstorming, how well would it work when asked to describe information provided in raw JSON into natural language?

It’s a Sunday morning and things are pretty quiet around the Fusilero household, which is especially noticeable now that the chaos of the holiday season has passed and the kids are back at school. So, I’m catching up on my reading and research – starting with Splunk Predictions 2024.

Artificial Intelligence tools such as Bard, ChatGPT, and Bing Chat are the current big names in the Large Language Model (LLM) category which is on the rise. LLMs are trained on vast data sets to be able to communicate by using everyday human language as a chat prompt. Given the flexibility and potential of LLMs, companies are integrating them into many workflows inside the tech industry to make our lives better and easier.