Every letter in the soup

Creating three-dimensional shapes from text, a single image, or multiple views.

Training a huge model by splitting the work three ways at once — across data, across layers, and within layers — over many GPUs.

Comparing two versions by routing traffic to each and measuring which yields better outcomes.

Editing an open-weight model's internals to strip out its ability to refuse requests, without retraining it.

A Hugging Face library that runs the same PyTorch training script across CPUs, GPUs, and Google's AI chips.

Specialized hardware built to run AI math far faster than a general-purpose CPU.

The share of a model's predictions that are correct against known answers.

A human approval step before an AI agent does something consequential.

The intermediate value a neuron or layer computes as data passes through a network.

Saving memory in training by recomputing the model's intermediate working values instead of storing them all.

The nonlinear step that lets stacked network layers learn complex patterns.

Nudging a model's behavior by adjusting its internal signals as it runs, instead of retraining it.

Quantization that protects the few weights most important to a model's outputs.

An adaptive optimizer that tunes a separate learning rate for each parameter.

A version of the Adam training optimizer that handles weight decay (a pull toward smaller numbers) separately.

Small trainable modules inserted into a frozen model so it can learn new tasks cheaply.

An input crafted to make a model produce a wrong or unintended output.

Input crafted to exploit a model's weaknesses and trigger unintended behavior.

An LLM that can plan, call tools, and act in a loop toward a goal.

Evaluations built to measure what AI agents can actually accomplish, not just model knowledge.

A structured profile describing what an AI agent can do and how to connect to it.

Techniques that let an agent retain information across turns or sessions.

A protocol for letting an AI agent make payments on a user's behalf, with limits and an audit trail.

Giving an AI agent only the permissions it needs for the task at hand.

A walled-off environment that limits what an AI agent can reach or change.

Many lightweight AI agents working at once, with useful behavior emerging from the group rather than one boss agent.

An open standard that lets separate AI agents advertise capabilities and work together.

AI systems that plan, call tools, observe results, and act over many steps toward a goal.

A Linux Foundation project set up to steward open, shared standards for AI agents.

When an AI agent handles a whole coding task end to end — planning, writing, running, and fixing code — not just autocompleting lines.

Online shopping where AI agents help search, compare, buy, track, or return products.

A way for an AI agent to get better over time by rewriting its own notes, instead of retraining the model.

The repeating cycle in which an agent reasons, acts, observes, and continues.

Retrieval driven by an agent loop that decides when and what to search.

When an AI agent researches a question across the web on its own — running many searches, reading pages, and assembling an answer.

Agentic coding measured in a live terminal, where the AI runs real shell commands to complete tasks the way a developer would.

A multi-step process where one or more AI agents plan, use tools, and act to complete a goal.

A software toolkit for building and coordinating AI agents.

An open, plain-Markdown file convention for giving coding agents project-specific instructions.

A web browser with an AI agent built in that can read pages and act on them for you.

The worry that AI investment and hype have outpaced real value and may sharply correct.

The software that schedules and manages AI workloads across large GPU clusters.

An AI tool that helps developers write, explain, fix, and review code.

Safeguards built around a deployed AI system so it can't cause serious harm even if it turns out to be misaligned.

An AI assistant embedded in a product to help users get work done within it.

Using AI to defend computer systems — and the new risks AI brings to security.

A 2025 US plan to tier countries' access to AI chips, rescinded before it took effect and replaced by case-by-case deals.

The study of how to build and use AI in ways that are fair, safe, and respectful of people.

The rules, institutions, and processes that shape how AI is built and deployed.

Using generative models to alter existing images, often from text instructions.

A structured review of how an AI system may affect people, rights, and operations.

The practical ability to understand AI systems and use them responsibly.

An organization-wide system for governing AI policies, risks, roles, and controls.

The competition among companies and countries to build the most capable AI fastest.

The broad practice of reducing risks and harms from AI systems.

Low-quality, mass-produced AI-generated content that clutters feeds, search results, and inboxes.

An AI system that teaches and helps a learner one-on-one, adapting to their needs.

A past period when AI funding and interest collapsed after hype outran results.

A hard high-school math competition repurposed to test mathematical reasoning in models.

A finite, well-defined sequence of steps that solves a problem or performs a computation.