Latest articles on AI, technology, and software development.
Sparse activation architectures let models scale to hundreds of billions of parameters without proportionally scaling compute. Here is why that changes who can build and run frontier AI.
OpenAI's o3, Claude 3.7 Sonnet's extended thinking, and DeepSeek R1 made "slow, deliberate AI reasoning" mainstream. But running a reasoning model on every task is like hiring a PhD to answer yes/no questions. Here's a practical framework for when extended thinking actually moves the needle — and when it just burns tokens.
INT4 and INT8 quantization have made it possible to run 7B and 13B parameter language models on consumer laptops without a cloud connection. Here is what changed, how it works, and what hardware you need today.
AI agents can now operate software, call APIs, and execute multi-step tasks without human involvement at each step. Here's how the underlying architecture works — and what the real limits are.
The biggest gains in AI capability right now don't come from bigger training runs. They come from giving models more time to think at inference. Here's what that means and why it matters.
MCP went from an Anthropic-specific proposal in November 2024 to the de-facto industry standard for connecting AI agents to tools and data sources. Here is what that means for developers building AI-powered products today.
After two years of enterprise AI agent deployments, clear patterns have emerged. Here is an honest breakdown of which architectures deliver value, where they still fail, and what engineers should build or avoid right now.
After two years of enterprise AI agent deployments, clear patterns have emerged. Here is an honest breakdown of which architectures deliver value, where they still fail, and what engineers should build or avoid right now.
For most enterprise AI deployments in 2026, RAG outperforms fine-tuning on cost, maintainability, and accuracy for knowledge-intensive tasks. Here's the data-backed decision framework engineers and architects need.
OpenAI has quietly restructured how its o3 and o4-mini models allocate compute, introducing a hybrid reasoning mode that reduces inference costs by up to 40 percent while maintaining benchmark scores on most tasks. The change affects API pricing starting June 2026.
Anthropic has introduced a new agentic AI technique called dreaming, which allows autonomous systems to review their past behavior and refine their strategies without human prompting — a significant step toward self-correcting AI.
Speculative decoding has emerged as the most practical technique for making large language models faster at inference time — and it works by exploiting a counterintuitive trick about how autoregressive generation actually behaves.