Using AI tools is becoming very expensive. Instead of broader usage, there are pushbacks from companies to limit their AI spending. Uber burned through its entire 2026 AI coding tools budget in just four months – monthly API costs per…
Building your first AI agent in 2026 is not the same as calling an LLM API. While a standard LLM call is a static request-response cycle, an agent has the ability to use tools, search the web, reason through failures,…
In the realm of enterprise AI systems, one often-overlooked yet critical component is AI agent permissions. These permissions are the backbone of operational correctness and security, ensuring that autonomous AI agents operate within their intended scope without causing chaos. Think…
AI-ready backends require three capabilities: streaming response support, vector storage access, and async concurrency. These capabilities work together to create a robust and efficient backend that can handle the demands of large language models. Streaming response support is a game-changer…
The next major AI wave is shifting from public chatbots toward internal enterprise AI tools. For too long, businesses have been experimenting with public-facing chatbots, often with limited success. However, the real potential of AI lies in its ability to…
When it comes to Large Language Models, understanding determinism is crucial for ensuring the reliability and consistency of their outputs. One common misconception is that temperature controls all aspects of an LLM, from floating-point arithmetic to GPU parallelism and batch…
Recommendation engines are one of the most practical real-world applications of graph databases. Whether it is Netflix suggesting movies, Amazon recommending products or LinkedIn proposing new connections, these systems rely heavily on traversing relationships between entities. This is exactly where…
Vector databases have become a crucial component in modern AI systems due to the limitations of traditional SQL search. These databases are now used in various applications such as internal knowledge assistants, semantic search, and long-term AI memory. But what…
AI-generated applications are accelerating the shift from manual coding to automated software creation. This shift is making the no-code/low-code/full-code debate more relevant than ever. Business needs for faster delivery are driving the growth of no code in the age of…
AI coding has shifted from autocomplete to autonomous agents that plan, write, test, and refactor code end-to-end. The key question is not if developers change-it’s which role they evolve into. AI impacts developers in multiple ways. On one hand, it’s…
