Latest articles on AI, technology, and software development.
Salesforce Agentforce crossed $800M ARR. Microsoft has 160,000 organizations running custom agents. But deploying AI agents at enterprise scale looks nothing like the demos. Here's what production actually demands.
Speculative decoding lets large language models run 2–3x faster by using a small draft model to propose tokens and a large model to verify them in parallel — no extra training, no quality loss.
Small language models have crossed a threshold: models with fewer than 10 billion parameters are now handling customer support, code generation, document parsing, and real-time inference tasks that demanded GPT-4-class compute in 2023. Here's what changed and what it means for AI deployment.
Reasoning models like OpenAI o3 and Gemini 2.5 Pro spend extra compute at inference time to work through problems step by step — and that difference in architecture produces measurably different results on complex tasks. Here is what actually changes, when it matters, and when it does not.
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.