Latest articles on AI, technology, and software development.
Unlike static, rules-based systems, AI agents can learn, adapt, and optimize processes dynamically. As they interact with data, systems, people, and other agents in real time, AI agents can execute entire workflows autonomously. But unlocking their potential requires redesigning
The Anthropic UK expansion story is less about diplomatic courtship and more about what happens when a government punishes a company for having principles. In late February, US Defence Secretary Pete Hegseth gave Anthropic CEO Dario Amodei a stark ultimatum: remove guardrails pre
If you have worked with retrieval-augmented generation (RAG) systems, you have probably seen this problem.
With the launch of KiloClaw, enterprises now have a tool to enforce governance over autonomous agents and manage shadow AI. While businesses spent the last year securing large language models and formalising vendor agreements, developers and knowledge workers started moving on th
A couple of years ago, most machine learning systems sat quietly behind dashboards.
In agentic AI systems , when an agent's execution pipeline is intentionally halted, we have what is known as a state-managed interruption .
This article is divided into three parts; they are: • How Attention Works During Prefill • The Decode Phase of LLM Inference • KV Cache: How to Make Decode More Efficient Consider the prompt: Today’s weather is so .
Feature engineering is where most of the real work in machine learning happens.
Creating an AI agent for tasks like analyzing and processing documents autonomously used to require hours of near-endless configuration, code orchestration, and deployment battles.