Systems | Development | Analytics | API | Testing

As a current HubSpot user and veteran of a HubSpot partner agency, I know first-hand how powerful HubSpot is. And they’ve built a lot of great reporting enhancements into the platform over the last few years. And yet – we still hear that even the best HubSpot power users run into limitations (in functionality, pricing, or both).

Three in 4 programmers have tried artificial intelligence (AI). This factoid comes from a recent Wired survey on the habits of engineers with respect to AI tooling like GitHub Copilot. Though Wired used a pool of only around 700 engineers, Gartner’s prediction from a year ago was that 75% of enterprise software engineers would use AI by 2028. To many of us, it’s starting to feel like that’s already happened.

Confluent has established itself as a leader in event streaming, providing not only a robust platform but also a rich portfolio of pre-built connectors. These connectors act as bridges, effortlessly channeling data between a multitude of systems, from databases and applications to cloud services. This extensive portfolio empowers users to weave together their data landscapes with remarkable ease and flexibility.

Fintech had a busy year in 2024. Global companies like HDFC, SBI, Paytm, and Google are eliminating the need for traditional financial intermediaries and utilising the best fintech software development companies to provide customers with almost anything, including banking, insurance, credit, mortgages, and more. The invention that gave rise to financial technology, or Fintech, seemed like science fiction even ten years ago.

In our previous article, we explored the basics of monitoring Ecto with AppSignal, covering everything from initial setup to tracking key metrics such as query execution time and resource consumption. We even set up custom instrumentation for database connection pools to gain deeper insights into our application's performance. However, setting up monitoring is just the first step toward maintaining a healthy, high-performing Elixir application.

As enterprises double down on AI, there’s a hard truth many leaders are starting to face — they’ve invested in the promise of AI, but they can’t always trust the data behind the predictions. Whether you're training a model, building RAG pipelines, or scaling intelligent automation, AI outcomes are only as reliable as the data feeding them. Yet most organizations still can’t answer a critical question with confidence: Is our data truly AI-ready?

If you’re leading a data, analytics, or AI initiative right now, you know the pressure. AI is no longer a future project - it’s a business imperative. Executives want results, boards want differentiation, and the window to deliver is closing fast. That’s why Salesforce’s intent to acquire Informatica should raise serious questions for data leaders. Not just because of what it means for Informatica, but for what it could mean for your AI roadmap.

Fivetran's Quickstart data models transform data for quick and easy consumption by data teams and agents.

Artificial Intelligence(AI) has recently become a hot topic across industries transforming sectors like finance, healthcare, education and research. The two of its subfields are Generative AI and Machine Learning(ML), but both of these terms are often confused for one another. we will explore the difference in purpose, techniques and capabilities and tools like Keploy’s GenAI-powered testing platform makes big difference in software testing.

Customer service chatbots and co-pilots and smart call center analysis applications are prime use cases for AI and generative AI. These AI systems and agents can provide real-time recommendations, support customer service at scale, generate insights that can be used in downstream applications to reduce churn and increase revenue, and more. How can customer service organizations grow and optimize their use of data and AI?