Insights
February 23, 2026
There’s something that confused many people when they first learned about large language models. LLMs are literally called language models, so how do they generate all other kinds of information?
The promise of AI-powered software development is profoundly appealing: describe what you want, and watch the product materialize. However, as AI coding tools matured through 2025, research and real-life production experiences revealed a complex reality. Productivity gains are real, but they are highly conditional, unevenly distributed, and often absorbed by downstream bottlenecks.
As the tech industry’s next big thing, AI agents have become an object of almost comical hype. Gartner recently reported that out of thousands of products marketed as AI agents, only about 130 had genuine agentic capabilities, while the rest were merely rebranded AI assistants and chatbots. This type of
Open-source large language models (LLMs) have improved significantly in the past twelve months in terms of performance, developer experience, and community support. Does this mean they can offer a more cost-efficient, flexible, and secure alternative to commercial cloud platforms? Let’s explore what advantages can make open-source LLMs a viable solution
Introduction Welcome to the second part of rapid application development using LLMs, specifically the LLM from Vertex AI. If you missed the first part where we laid the groundwork, I’d suggest giving it a look for a step-by-step walkthrough. READ PART 1 Now that we have successfully built a PoC
Introduction Large language models (LLMs) are rapidly transforming the software development landscape, offering unprecedented capabilities for generating code, translating languages, composing various creative text formats, and answering your questions in an informative way. However, effectively integrating LLMs into the software development process can be challenging. This article provides a practical guide
