From LLMs to AI systems is a long journey. Just like a sponge alone doesn't make a cake, LLMs alone can't create the rich, layered AI experiences we rely on today. This article explores how the AI Cake is formed—how modular systems, orchestrated workflows, and
As Agent Spaces begin to define how humans and AI agents collaborate, a critical design choice emerges: why not build a single Agent Space that does everything? A universal platform might sound efficient, but in practice, specialization beats generalization when solving hard problems. This article makes the case for domain-specific
A single news item—Apple exploring AI search in Safari—yesterday wiped ~$150 billion off Alphabet's market value overnight. Big Tech is racing to embed AI-driven answers directly into browsers, search engines, and smart devices. Read on to see why specialized AI search for vertical domains like DeFi
According to the 2025 Microsoft Work Trend Index, 82% of leaders plan to use digital labor to scale their workforce within the next 12–18 months. With AI now abundant, affordable, and instantly available, it’s no longer bound by headcount or expertise. Intelligence is becoming a scalable resource, like
Artificial Intelligence (AI) agents are rapidly moving beyond simple automation and into complex environments, interacting with the physical world. But for an AI to truly act intelligently, it needs to understand its surroundings. This is where Computer Vision (CV) comes in. Computer Vision is the field of AI that enables
Artificial Intelligence (AI) is rapidly evolving, and at the forefront of this evolution are AI Agents. These aren't just chatbots; they're autonomous entities designed to perceive their environment and take actions to achieve specific goals. But how are these agents built? The answer lies in their
Artificial Intelligence (AI) agents are rapidly moving from science fiction to everyday reality. From customer service chatbots to complex decision-making systems in healthcare and finance, these intelligent entities are poised to reshape our world. But with great power comes great responsibility. Developing AI agents ethically – addressing bias in AI, ensuring
Artificial Intelligence (AI) is rapidly evolving, moving beyond simple task automation to complex reasoning and problem-solving. But to truly think – or at least, to convincingly simulate thinking – AI agents need more than just data. They need understanding. That's where Knowledge Graphs come in. They are becoming a foundational
Artificial Intelligence (AI) agents are rapidly moving beyond simple task automation. They're becoming increasingly sophisticated, capable of understanding, interpreting, and responding to human language in meaningful ways. This leap in capability isn't happening in a vacuum. It's driven by advancements in Natural Language Processing
From enhancing decision-making to running decentralized apps, AI Agents are quietly changing Web3 and the real world. Driven by rapid technological advancements, AI Agents are unlocking several applications, such as AI orchestration - poised to deliver transformative potential shortly.Think of AI orchestration as being the conductor of an orchestra.
Memory blocks are revolutionizing how we create and deploy AI agents. These blocks serve as extended knowledge repositories for agents, dramatically enhancing their capabilities and defining their utility in specific domains.Think of it this way: Your AI brain = Processing (LLM) + Memory (Memory blocks) For creators, memory blocks offer versatile
At Distilled AI, we empower creators to build AI agents with high-level confidentiality while expanding their capabilities beyond standard language models. To achieve this, AI agents need efficient communication standards to interact with external tools and knowledge sources – this is where a protocol like Model Context Protocol (MCP) comes in.