Author(s): ML Point Originally published on Towards AI. The Launch-day Failure Nobody Expects Most failures in production are independent of the model’s architecture or output. If we blame the model for all the issues, it will create a false sense of security …

Author(s): ML Point Originally published on Towards AI. A practical guide to the three architecture layers people keep mixing together The confusion is understandable. All three ideas sit around the same model, all three influence reliability, and all three can contain “loops.” …

Author(s): ML Point Originally published on Towards AI. Introduction The widespread adoption of LLMs solidified prompting as the standard interface between users and foundation models. At first, prompts were strictly text-based instructions in natural language used to guide a model’s underlying reasoning. …

Author(s): ML Point Originally published on Towards AI. How Attention Entered Real-Time Object Detection Object detection solves the problem of labelling all the objects that are relevant for a frame and tag their position. A good detector should perform both of the …

Author(s): ML Point Originally published on Towards AI. Measuring What AI Actually Understands About Urdu As large language models increasingly market themselves as multilingual, one critical question often remains unanswered: how do we verify that claim for languages outside the English-centric core? …

Author(s): ML Point Originally published on Towards AI. 7 Agentic AI Trends Redefining the “Year of the Proof” in 2026 In 2024 and 2025, the tech world was riding a massive, intoxicating AI high. We fell for the myth of the ‘general-purpose …