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Top AI Content Creators

We follow the best AI creators on YouTube and write a short summary for each video, the takeaways, the audience, and why it is worth a watch. Every summary links straight to the original video so you hear it from the creator. Find your next must-watch.

31 curated creators1,111 videos458 shorts
LatestTopOfficial LLMs118Marketing42Automation97AI/ML Engineering81AI Engineers261Misc11All
Other Topics, 17 more topics
ChatGPT146Codex27Claude & Anthropic155Job Search15Forward Deployed Engineer4Claude Code33Gemini34Open Source68AI Tools35AI Agents89Loops22Startups22Learning & Career89Prompting71Enterprise & Business38Safety & Policy48Chips & Hardware33
Official LLMs creators, 6 creators
OpenAIAnthropicClaudeGoogle DeepMindMistral AIGoogle Cloud Tech

Official LLMs videos

118 videos match. Showing the newest 24.

Thumbnail for Google Antigravity: Building a Real-Time AI Race Coach5:56

Google Antigravity: Building a Real-Time AI Race Coach

Google Developer Experts collaborated at Sonoma Raceway to build an on-device AI race coach capable of delivering low-latency real-time guidance. Because cloud connectivity was unavailable on the racetrack, the team deployed an open-weight Gemma model running directly on an onboard device Tensor Processing Unit. Vehicle telemetry including GPS, accelerometer, pedal position, and steering angle was routed through a CAN to USB converter into the AI model. The engineering team used Google Antigravity to rapidly build, debug, and iterate on the application directly trackside between 20-minute driving sessions. After initial issues with excessive chatter and mismatched track positioning were resolved, the AI coach successfully provided timely throttle prompts that increased the driver lap speed.

Sep 10, 2026Read summary
Thumbnail for Graph Engineering with ADK6:10

Graph Engineering with ADK

Relying on a single large prompt to fetch data and reason in one model call causes hallucinations because ungrounded models lack deterministic API and course data. Graph engineering organizes complex systems by separating predictable operations into deterministic Python functions while reserving large language model calls for reasoning tasks. In Google's Agent Development Kit 2.0 (ADK 2.0), functions and agent nodes operate as peers within the same orchestration graph and wiring structure. Key structural patterns in graph engineering include fan-out for parallel execution, join nodes for aggregating parallel branch outputs without custom aggregator agents, and routing for selecting execution paths. Deterministic routers using code logic are faster, cheaper, and more reliable than LLM routers whenever branching decisions are based on structured data and closed sets of options. Developers should use static graph workflows when a process structure is known beforehand, dynamic workflows when graph shape depends on runtime inputs, and autonomous agents when workflows cannot be predefined.

Sep 10, 2026Read summary
Thumbnail for GPT-6 Astra First Impressions From Businesses3:48

GPT-6 Astra First Impressions From Businesses

Industry partners from Box, Figma, cxo.dev, Ramp, BAM, and Cognition share early impressions of GPT-6 Astra across complex real-world workflows. Box demonstrates how GPT-6 Astra handles nuanced business calculations across spreadsheets, avoiding double-counting errors in complex financial data. Figma shows GPT-6 Astra generating complex vector animations by coordinating moving elements to create optical motion effects. cxo.dev and Ramp highlight the model's autonomous computer and browser use capabilities to construct multi-step workflows and implement code changes directly. BAM and Cognition demonstrate Astra's capacity to navigate ambiguous research trees and optimize lower-level GPU kernels for significant runtime speedups.

Sep 9, 2026Read summary
Thumbnail for Introducing GPT-6 Astra for developers3:24

Introducing GPT-6 Astra for developers

GPT-6 Astra is OpenAI's latest frontier model designed for complex tasks requiring high raw intelligence and minimal hand-holding. The model demonstrates improved accuracy and efficiency in computer use, executing desktop application tasks in the background using screenshots. Astra shows enhanced creative and reasoning performance across code generation, 3D rendering, and text generation. OpenAI introduced asynchronous tool calling in the responses API, allowing the model to continue working while tool executions run in parallel. Steering support in the responses API enables developers to inject context and redirect active model runs without cancelling ongoing processes.

Sep 4, 2026Read summary
Thumbnail for Graph Engineering 1015:56

Graph Engineering 101

Graph engineering is an architecture pattern for complex artificial intelligence systems that structures processes into networks of agent nodes and deterministic function nodes. The core components of agent architectures include the harness for tools, memory, and guardrails, the loop for iterative reasoning cycles, and the graph for multi-step workflow routing. Google Agent Development Kit enables shared state across different nodes and agents throughout an orchestrated graph workflow. A pull request review pipeline illustrates graph engineering through parallel fan-out tasks, a join node that synthesizes findings, and a router that directs code to fixer agents or human approval. Graph engineering focuses on operational behavior and execution control flow, whereas knowledge graphs and GraphRAG focus on data representation. While agent swarms rely on dynamic agent personas to tackle ambiguous tasks, graph engineering offers predictability, debuggability, and control for well-defined workflows.

Sep 3, 2026Read summary
Thumbnail for How to monitor & optimize Google Gemini agents with Grafana Cloud7:22

How to monitor & optimize Google Gemini agents with Grafana Cloud

Manual prompt-by-prompt debugging fails when AI agents move to production environments serving hundreds or thousands of concurrent users. The Grafana Sigil SDK provides OpenTelemetry-native instrumentation that captures Gemini agent tool calls, execution decisions, and response latency without altering underlying business logic. Grafana Cloud AI Observability aggregates metrics including error rates, token consumption breakdown, cost estimations, and latency percentiles across model versions. Built-in AI evaluators and automated Slack assistants can diagnose operational bottlenecks such as rate-limit exhaustion and excessive token usage. Integration with Claude Code inside Slack allows engineering teams to close the remediation loop by generating and applying fixes to agent prompts and source code.

Sep 3, 2026Read summary
Thumbnail for WeatherNext 3: More accurate, timely, and local weather forecasts4:36

WeatherNext 3: More accurate, timely, and local weather forecasts

WeatherNext 3 is an operational global AI weather forecasting model that updates hourly and delivers forecasts down to five-kilometer spatial resolution. Unlike traditional physics-based simulations or earlier AI systems that relied on six-hour reanalysis cycles, the model directly processes raw satellite and weather station data. The model outputs three native spatial resolutions in a single pass, covering 25-kilometer atmospheric fields, 9-kilometer surface variables, and 5-kilometer temperature and humidity data. Specialized outputs are included for renewable energy operators, specifically measuring wind speed and direction at 100-meter turbine heights alongside solar radiation and cloud cover. Google DeepMind is integrating WeatherNext 3 across consumer platforms, including Google Search, Gemini, and Google Maps.

Sep 3, 2026Read summary
Thumbnail for How the Claude Code team uses Claude Code22:23

How the Claude Code team uses Claude Code

Members of the Claude Code technical staff conduct 70 to 80 percent of their daily development work through an agent named Claude Tag inside Slack. Rapid progress in AI model capabilities causes software architecture and tool design to shift substantially every few months. Engineers have shifted from micro-managing individual tool calls and transcripts to delegating broader goals and reviewing autonomous outputs. Automated fan-out workflows use test-time compute to parallelize deep research, bug identification, and adversarial code reviews. Claude Code automates the software verification loop by executing tests, capturing UI screenshots, and compiling feedback directly into Pull Requests.

Sep 2, 2026Read summary
Thumbnail for End-to-end agentic development with Google Gemini, Antigravity, and GitLab8:13

End-to-end agentic development with Google Gemini, Antigravity, and GitLab

GitLab integrates directly with Google Cloud IAM and Google Artifact Registry to manage permissions and container images. Developers can use Gemini inside the Antigravity IDE to create visual UI mockups and translate design images into working application code. Model Context Protocol (MCP) server integration allows the Antigravity IDE to create and update GitLab issues and track planning workflows smoothly. GitLab Duo Agent Platform enables automated code review using specialized agents such as Develop with Gemini before merging code. GitLab CI/CD pipelines run automated security tests, upload build artifacts to Google Artifact Registry, and deploy directly to Cloud Run.

Sep 2, 2026Read summary
Thumbnail for Create Slides, Docs, and Templates3:18

Create Slides, Docs, and Templates

ChatGPT Work connects with external productivity tools like Google Drive through plugins to generate live Google Docs and Google Slides presentations. Raw meeting transcripts can be structured into designated sections including executive summaries, categorized updates, bolded decisions, and action items tables. ChatGPT Work flags missing owners or deadlines explicitly instead of fabricating or guessing missing data. Generated Google Docs can serve as the direct foundation for concise five-slide leadership update decks formatted according to previously created slide decks. The Template Creator feature stores formatting, layouts, and structures for docs and decks, enabling direct reuse for recurring weekly meetings.

Aug 28, 2026Read summary
Thumbnail for Getting Started with ChatGPT Work3:54

Getting Started with ChatGPT Work

ChatGPT Work connects to third-party workplace applications like Gmail and Slack to complete cross-app tasks. Users can access Work mode through the ChatGPT desktop application or directly on chatgpt.com. Third-party integrations require installing plugins and authenticating account permissions once. ChatGPT Work allows users to review, edit, and approve drafts before messages or emails are dispatched. The Wake Pet feature provides a visual notification when background tasks are completed or need attention.

Aug 28, 2026Read summary
Thumbnail for Launch your portfolio website with AI8:30

Launch your portfolio website with AI

Google AI Studio Build Mode allows developers to rapidly prototype full-stack and frontend web applications using natural language prompts. The build interface automatically generates code, provides selectable visual design themes, and displays a live interactive preview sandbox. Generative AI outputs are non-deterministic, meaning identical prompts can yield different code, layouts, and simulated background details. Developers can iterate on generated applications by asking the assistant in natural language to add, modify, or remove specific UI sections and features. Web applications created in Google AI Studio can be deployed directly to Google Cloud Run with a hosted URL ending in ai.studio or mapped to a custom domain. Completing the builder lab awards the Completed Builder Journey learning badge on Google Developer profiles.

Aug 28, 2026Read summary
Thumbnail for Launch your to-do web app with AI7:57

Launch your to-do web app with AI

A full-stack application consists of a front end user interface, a back end server connection, and a database for persistent storage. Google AI Studio Build Mode generates full-stack web applications from natural language prompts using Gemini models. Firebase Firestore integration allows applications generated in Google AI Studio to store data with real-time synchronization. Firebase Authentication provides built-in Google Sign-In support to restrict data access to specific users. Applications can be deployed to Cloud Run with a single click and given custom vanity URLs under the ai.studio domain.

Aug 28, 2026Read summary
Thumbnail for Launch your web-based video game with AI11:39

Launch your web-based video game with AI

Event sourcing records every game turn as a distinct sequential event rather than overwriting existing database entries. Google AI Studio Build Mode generates the full-stack web application, Firestore integration, and Minimax AI opponents from natural language prompts. Firestore security permission errors can be diagnosed and resolved automatically using the Fix feature in Google AI Studio. Firebase Firestore real-time data synchronization allows live multiplayer matches across separate devices without manual socket configuration. Logging event history enables time-travel features such as scrubbing through past turns and rolling back moves.

Aug 28, 2026Read summary
Thumbnail for Generate playable HTML5 games with Mistral Medium and GLM8:13

Generate playable HTML5 games with Mistral Medium and GLM

A playable HTML5 dungeon crawler game was generated entirely through AI agents without writing manual game code. The generation pipeline uses GLM 5.2 via Mistral API completions for large-scale code writing and Mistral Medium as an automated QA code reviewer. A generate-review-fix loop automatically inspects the generated HTML5 code for runtime errors and broken game logic up to five times. Specific engineering constraints in the prompt prevent common LLM game failure modes such as wall clipping, missing health attributes, and dropped keypresses. An edit mode enables targeted prompt modifications to existing game code without regenerating the entire project from scratch. A local Python HTTP server is launched automatically to serve the completed single-file HTML5 game directly in the browser.

Aug 28, 2026Read summary
Thumbnail for 7 AI agent patterns to improve your coding workflow11:29

7 AI agent patterns to improve your coding workflow

Modular agent skills replace long, confusing prompts by allowing coding agents to read short skill descriptions and dynamically load full instructions only when needed. Skill files are plain text files stored either per project in the project root directory or globally in Gemini configuration directories. The seven core skill design patterns categorize capabilities into domain knowledge, tool wrappers, inversion, generators, reviewers, pipelines, and meta skills. Inversion patterns force the AI agent to stop guessing and ask clarifying questions or follow structured root cause analysis before writing code. Pipeline skills orchestrate sequential development workflows such as test-driven development through strict multi-step execution guards. Developers can create custom skills as text files following guided resources like Google codelabs and evaluate them using published whitepapers.

Aug 27, 2026Read summary
Thumbnail for Build a real-time voice AI agent with Gemini Live API7:13

Build a real-time voice AI agent with Gemini Live API

Traditional text-to-speech AI systems operate in one direction, whereas Gemini Live provides bidirectional, simultaneous audio-to-audio streaming. The application architecture consists of three components: a browser frontend, a lightweight backend relay, and the Gemini Live API connected via WebSockets. The core interaction loop follows four simple steps: open the session, send microphone audio, receive model audio, and play the response in the browser. Voice Activity Detection (VAD) is built directly into Gemini Live to detect when speech starts and stops without requiring custom detection code. Barge-in allows users to interrupt the agent mid-sentence, which feels instantaneous when local audio playback is stopped immediately upon detecting mic input. External tools and functions allow the voice agent to execute real actions like controlling music playback, provided the functions return responses quickly.

Aug 26, 2026Read summary
Thumbnail for From notebook to production: Serving JAX at scale3:05

From notebook to production: Serving JAX at scale

Moving a trained JAX model from a notebook into production requires choosing a serving pattern that avoids unexpected just-in-time compilation latency during live user requests. Using jax.jit provides the simplest in-process Python serving path, but models should be warmed up with dummy inputs to absorb initial compilation costs before handling live traffic. Ahead-of-time compilation explicitly lowers Python code to StableHLO intermediate representation and compiles it to a device executable to eliminate first-call latency spikes. Portable deployments can be achieved using jax.export for serialized StableHLO artifacts or jax2tf to export standard TensorFlow SavedModel formats for existing TensorFlow serving infrastructure. Debugging and optimizing JAX on GPUs relies on a four-layer mental model consisting of JAX Python tracing, XLA compilation, CUDA or cuDNN execution, and GPU hardware.

Aug 26, 2026Read summary
Thumbnail for Scale JAX models to multi-GPU systems3:28

Scale JAX models to multi-GPU systems

Scaling JAX workloads across multiple GPUs focuses on array placement and sharding declarations rather than rewriting training routines with low-level communication code. Data parallelism in JAX operates by replicating model parameters across devices, splitting data batches into shards, and performing automatic gradient averaging across GPUs. Four core concepts structure multi-GPU placement in JAX: Mesh, PartitionSpec, NamedSharding, and jax.device_put. JAX supports both declarative automatic sharding and explicit low-level control through shard_map combined with jax.lax.pmean. A Tiny Transformer language model built with Flax NNX demonstrates multi-GPU training, checkpointing with Orbax, and length-padded JIT generation.

Aug 26, 2026Read summary
Thumbnail for Build and optimize JAX training loops3:25

Build and optimize JAX training loops

A clean JAX training step is designed as a pure function that ingests parameters, optimizer state, and a batch, returning updated states and metrics. Using fixed batch sizes enables JAX and XLA to reuse compiled execution steps on the GPU without triggering repeated recompilations. Converting GPU metrics to Python floats on every single step forces CPU-GPU synchronization and severely degrades training loop throughput. Naive attention materializes the full quadratic attention matrix, whereas fused implementations like cuDNN scaled dot-product attention drastically reduce memory and latency. Decoder architectures rely on causal attention masking, where Multi-Head, Grouped-Query, and Multi-Query Attention offer trade-offs in KV cache footprint while keeping the same output shape.

Aug 26, 2026Read summary
Thumbnail for Getting started with JAX on NVIDIA GPUs4:00

Getting started with JAX on NVIDIA GPUs

Verifying GPU execution requires checking hardware visibility with nvidia-smi as well as inspecting jax.devices and jax.default_backend in code. JAX distinguishes between host CPU memory and device GPU memory, meaning operations like converting to NumPy arrays or casting tensors to Python floats can cause synchronization bottlenecks in hot loops. The core functional transformations in JAX are jit for compiling, grad for automatic differentiation, and vmap for vectorization and batching. JAX compiles functions via XLA based on input shape and data type signatures, causing re-compilation whenever array shapes change dynamically unless padded with masks. Python conditional branching fails on traced values inside jit-compiled functions, requiring JAX-compatible control flow primitives like jnp.where, lax.cond, and lax.scan. Accurate benchmarking on GPUs requires calling block_until_ready on array outputs because JAX dispatches GPU kernels asynchronously.

Aug 26, 2026Read summary
Thumbnail for Mistral and Microsoft Expand Global Strategic Partnership to Give Enterprises AI They Can Control

Mistral and Microsoft Expand Global Strategic Partnership to Give Enterprises AI They Can Control

Microsoft and Mistral AI announce a global strategic partnership. The partnership focuses on enhancing compute capacity and AI technology access through Azure. Mistral's models will be available via Copilot Studio and Azure local. The collaboration aims to meet the demands of regulated industries and enhance customer control over AI. The partnership emphasizes shared values and commitment to creating value for customers.

Jul 21, 2026Read summary
Thumbnail for What racing reveals about working with AI - the OpenAI Podcast Ep. 22

What racing reveals about working with AI - the OpenAI Podcast Ep. 22

AI is transforming racing operations and improving logistics and driver performance. Data processing in real-time is essential for gaining racing insights. Collaboration between human and machine is critical for success in competitive racing.

Jul 16, 2026Read summary
Thumbnail for Build data-driven lesson plans with Claude for Teachers

Build data-driven lesson plans with Claude for Teachers

Claude for Teachers helps educators organize classroom data efficiently. Teachers can easily generate differentiated lesson plans based on student performance. The tool prioritizes student privacy and offers customizable options.

Jul 15, 2026Read summary