Woyce
A clear-eyed look at what artificial general intelligence actually means, why researchers disagree about it, and what the disagreement means for anyone building with AI today.
A clear-eyed look at what superintelligence means, why researchers disagree so sharply about how close it is, and what evidence would actually settle the argument.
A plain-language walkthrough of how transformer models work — the architecture behind ChatGPT, Claude, and nearly every modern AI system — with no equations.
A practical explainer on how large language models break text into tokens, why the process shapes cost and accuracy, and what builders need to know about it.
A plain-language breakdown of what a context window is, why large language models lose track of earlier conversation, and what that means for anyone building on top of them.
Large language models confidently state false things because of how they're trained and how they generate text, not because of a bug that a patch can remove. Here's the mechanism, and what actually reduces the problem.
A plain-language breakdown of the Mixture of Experts (MoE) architecture — how sparse activation lets AI labs build enormous models without paying enormous compute bills for every query.
A plain-language explainer on test-time compute — the technique of letting an AI model spend extra computation at inference time to reason through harder problems.
A plain explanation of AI scaling laws — the empirical relationships between compute, data, and model size that predict how much smarter a model gets as you make it bigger.
A plain-language guide to multimodal AI — how models that combine text, images, audio, and video actually work, and what that means for builders and businesses.
A plain-language walkthrough of how diffusion models turn random noise into photorealistic images and coherent video, and what that means for teams building with them.
A clear, jargon-light breakdown of what AI alignment actually means, why it's hard, and why it matters beyond research labs.
A practical explainer on AI interpretability — the field trying to understand what's actually happening inside neural networks, why it's hard, and why it matters for anyone deploying these systems.
Embodied AI explores whether machine intelligence must be grounded in a physical body that senses and acts on the world, rather than trained purely on text or images.
A guide to the AI research milestones — from reasoning and autonomous agents to compute scaling and reliability — that will define progress through 2030.
A plain explanation of test-time training — the technique that lets AI models update their own weights or memory while running, instead of staying frozen after pretraining.
RL environments are the simulated worlds where AI agents practice tasks and get scored, and they've become critical infrastructure for training today's agentic models.
A closer look at reinforcement learning with verifiable rewards (RLVR), the training method behind today's reasoning models, and how it differs from RLHF.
A practical explainer on knowledge distillation for large language models — how a smaller student model learns from a larger teacher model, and why it now lets compact models rival much bigger ones.
A look at why frontier labs are moving past pure transformer stacks toward linear attention, state space models, and hybrid designs that mix both.
A practical look at context engineering — the discipline of deciding what goes into an AI agent's context window, why it matters more than prompt wording, and how teams build it into production systems.
A plain-language look at Agent Skills and the SKILL.md format — what they are, how progressive disclosure works, and why they're becoming a shared standard across AI products.
A look at how AI agent evaluation is shifting from saturated pass/fail benchmarks like SWE-bench toward task-horizon metrics that measure how long an agent can work autonomously.
Background coding agents run in isolated cloud environments, work on tasks asynchronously, and return pull requests for review rather than requiring a developer to sit and watch.
A practical explainer on how AI agents are being given the ability to pay for things, and the three competing protocols — Google's AP2, Coinbase's x402, and OpenAI's ACP — trying to standardize it.
A structured, confidence-tagged set of technology predictions spanning 2027 to 2040, each anchored to a real 2026 development, built to be scored publicly every year.
Tell us about your project. We'll be honest about whether we're the right fit — and if we are, we move fast.