AI & Machine Learning Editor
Arjun covers the frontiers of artificial intelligence and machine learning. With a background in computational research and 8 years of tech journalism, he breaks down complex AI systems for a broad audience.
AI agents are moving from demos to production infrastructure. A clear-eyed explanation of what they are, how they work, and where the architecture gets hard.
As OpenAI prepares its next generation model, we analyze what GPT-5 will likely deliver, what it won't, and what that means for developers, enterprises, and society.
OpenAI's Operator is the most capable consumer browser agent yet. We tested it extensively. Here's what it excels at, where it falls short, and what it tells us about where agents are heading.
Vision-language models, audio-text fusion, and unified multimodal architectures are collapsing the boundaries between modalities. Here's what that means in practice.
Complex tasks require multiple cooperating agents. Here's how leading teams are architecting multi-agent systems, the patterns that work, and the failure modes to avoid.
Inference costs for frontier models have dropped 99% in two years. This isn't just an economic story — it's a product design story.
Anthropic's computer use capability gives Claude direct control of a desktop environment. We analyze the architecture, test the capabilities, and evaluate the safety model.
Two dominant approaches for adapting foundation models to enterprise use cases. We break down when to use each, how to combine them, and the hidden costs decision-makers miss.
Both frameworks have matured substantially. We compare their architectures, strengths, and ideal use cases to help teams make an informed choice.
The EU AI Act is in force, the US approach is evolving, and India is drafting its framework. A clear map of where global AI governance stands and what it means for builders.
What embeddings actually are, why vector similarity search is so powerful, and how to choose and architect a vector database for production workloads. No fluff.
Enterprise AI agents need governance, security, and reliability that consumer showcases don't address. Here's what it actually takes to deploy agents at enterprise scale.
The open-weight model ecosystem has matured dramatically. Here's which models are genuinely competitive with frontier proprietary models and what that means for enterprise strategy.
Prompting an agent is fundamentally different from prompting a chatbot. Here are the techniques that work for reliable, long-horizon agent behavior.
Hallucination is the failure mode everyone knows about and few teams have systematic strategies for. Here's what the research says and what practitioners have learned works in production.
Devin, GitHub Copilot Workspace, Cursor, and Claude Code represent different points on the autonomous coding spectrum. Here's an honest assessment.
The observation that model capability scales predictably with compute, data, and parameters has been one of AI's most consequential discoveries.
Memory is the unsolved frontier of agentic AI. We map the types of memory, current implementation approaches, and the research directions defining the next generation.
From diagnostic imaging to drug discovery to clinical documentation, AI is making real inroads in healthcare. Here's an honest assessment of what's working.
As agents gain more autonomy and take more consequential actions, the safety and alignment challenges multiply. Here's what the research says and what practitioners should be doing.