Загрузка видео...

Не удалось загрузить видео

На главную

Stop burning tokens on documentation. Agent Skills uses progressive disclosure to give your agent the right answer right away, pulling in specialized context only when it's actually needed. See them in action →

GoogleCloudTech's profile picture

Google Cloud Tech

1,312,523 subscribers

11,173 просмотров • 5 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

AI agents work better when they bring in context step by step. With Box Agent skills and progressive disclosure of LangChain skills, developers can quickly build AI agents that load the right skill at the right time, stay grounded in Box content, and keep access tightly scoped. Less context bloat. More control. Faster enterprise AI development.
0:52

AI agents work better when they bring in context step by step. With Box Agent skills and progressive disclosure of LangChain skills, developers can quickly build AI agents that load the right skill at the right time, stay grounded in Box content, and keep access tightly scoped. Less context bloat. More control. Faster enterprise AI development.

Box

627,810 просмотров • 4 месяцев назад

Context-aware, action-ready. ChatGPT agent understands the task, chooses the right tool, and gets to work. It uses connectors, context, and custom instructions to take smarter actions on your behalf.
2:35

Context-aware, action-ready. ChatGPT agent understands the task, chooses the right tool, and gets to work. It uses connectors, context, and custom instructions to take smarter actions on your behalf.

OpenAI

143,170 просмотров • 1 год назад

Introducing the Lifetimely Profit Agent - powered by the $100Bn in GMV. It uses the benchmarks from across all the GMV in Lifetimely, to find the highest leverage Profit opportunities in your business, and can actually take action to drive Profit. It's live in Lifetimely right now!
0:37

Introducing the Lifetimely Profit Agent - powered by the $100Bn in GMV. It uses the benchmarks from across all the GMV in Lifetimely, to find the highest leverage Profit opportunities in your business, and can actually take action to drive Profit. It's live in Lifetimely right now!

Patrick Barnes

14,164 просмотров • 2 месяцев назад

Introducing Skills in SciSpace Agent. Specialized modes built for specific research workflows. Describe the task, and the right workflow activates automatically. Yes, the right Skill gets loaded based on what you need. Built to simplify multi-step research work and reduce friction around execution. Skills are now live. Try it inside SciSpace Agent.
0:40

Introducing Skills in SciSpace Agent. Specialized modes built for specific research workflows. Describe the task, and the right workflow activates automatically. Yes, the right Skill gets loaded based on what you need. Built to simplify multi-step research work and reduce friction around execution. Skills are now live. Try it inside SciSpace Agent.

SciSpace

43,422 просмотров • 5 месяцев назад

You can now use skills in v0. Attach one from the prompt bar, and the agent uses it in every generation. Pull them from your saved skills, or your repo.
0:30

You can now use skills in v0. Attach one from the prompt bar, and the agent uses it in every generation. Pull them from your saved skills, or your repo.

v0

61,190 просмотров • 2 месяцев назад

You can decide how much autonomy you give to the agent in Google Antigravity Drop asynchronous feedback within artifacts and the agent folds it in when the time is right.
0:45

You can decide how much autonomy you give to the agent in Google Antigravity Drop asynchronous feedback within artifacts and the agent folds it in when the time is right.

Google Antigravity

81,962 просмотров • 9 месяцев назад

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below
1:05:19

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below

rari

28,241 просмотров • 27 дней назад

Introducing the context course: a free course on doing ML with agent context. You will learn how to train models, optimize inferences, and build datasets, all by defining harness context with`SKILLS.md`, Plugins, MCP, Subagents, and Hooks. The course includes: - Weekly live AMA on YouTube - Weekly practical projects for ML with context - Instructions in Pi, Codex, Claude, and Opencode - Tutorials and guides on fundamentals - Interactive Quizzes Learn to give AI agents the right knowledge, tools, and structure to actually get work done. Skills, MCP servers, plugins, multi-agent workflows, and building an agent from scratch. Join here:
0:30

Introducing the context course: a free course on doing ML with agent context. You will learn how to train models, optimize inferences, and build datasets, all by defining harness context with`SKILLS.md`, Plugins, MCP, Subagents, and Hooks. The course includes: - Weekly live AMA on YouTube - Weekly practical projects for ML with context - Instructions in Pi, Codex, Claude, and Opencode - Tutorials and guides on fundamentals - Interactive Quizzes Learn to give AI agents the right knowledge, tools, and structure to actually get work done. Skills, MCP servers, plugins, multi-agent workflows, and building an agent from scratch. Join here:

Ben Burtenshaw

16,905 просмотров • 3 месяцев назад

A completely local agent that lives right inside your pocket. 📱 Watch Gemma 4 run 100% locally in the Google AI Edge Gallery app. It converts images into JSON schemas, transcribes audio, and uses agent skills to interact with apps, all entirely offline.
3:20

A completely local agent that lives right inside your pocket. 📱 Watch Gemma 4 run 100% locally in the Google AI Edge Gallery app. It converts images into JSON schemas, transcribes audio, and uses agent skills to interact with apps, all entirely offline.

Google Gemma

88,361 просмотров • 3 месяцев назад

🧠 Want to give your coding agent new capabilities? Use Agent Skills in Visual Studio Code! Agent skills let you package instructions and resources into reusable capabilities for your agent. Skills can be loaded on demand and called directly from chat using slash commands like /skill-name. Learn more:
0:26

🧠 Want to give your coding agent new capabilities? Use Agent Skills in Visual Studio Code! Agent skills let you package instructions and resources into reusable capabilities for your agent. Skills can be loaded on demand and called directly from chat using slash commands like /skill-name. Learn more:

Visual Studio Code

55,133 просмотров • 5 месяцев назад

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.
0:22

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,166 просмотров • 3 месяцев назад

Your AI agent has your API keys. A poisoned document tells it to curl your secrets to an attacker's server. This is credential exfiltration, and it's the #1 risk in agentic AI right now. The fix is removing the secret from the agent entirely. Agent Vault sits between your agent and the APIs it calls. The agent gets dummy credentials, and Agent Vault swaps in the real ones at the network layer. The agent never sees your keys. We just dropped a full video + guide on connecting Hermes Agent to Agent Vault on a VPS!
34:40

Your AI agent has your API keys. A poisoned document tells it to curl your secrets to an attacker's server. This is credential exfiltration, and it's the #1 risk in agentic AI right now. The fix is removing the secret from the agent entirely. Agent Vault sits between your agent and the APIs it calls. The agent gets dummy credentials, and Agent Vault swaps in the real ones at the network layer. The agent never sees your keys. We just dropped a full video + guide on connecting Hermes Agent to Agent Vault on a VPS!

Infisical

86,213 просмотров • 3 месяцев назад

Stop wasting tokens on unit test generation 🙅. Try the new 'Finding-tests' AI agent skill in JetBrains Rider ! Rider’s coverage insights, powered by the built-in JetBrains dotCover tool, help your AI agent spend up to 50% fewer AI credits when generating tests.
0:56

Stop wasting tokens on unit test generation 🙅. Try the new 'Finding-tests' AI agent skill in JetBrains Rider ! Rider’s coverage insights, powered by the built-in JetBrains dotCover tool, help your AI agent spend up to 50% fewer AI credits when generating tests.

JetBrains Rider

11,842 просмотров • 3 месяцев назад

Watch how the agent in Antigravity uses Nano Banana 2 Lite to generate stunning images and then integrates them into your project. It's that easy.
0:35

Watch how the agent in Antigravity uses Nano Banana 2 Lite to generate stunning images and then integrates them into your project. It's that easy.

Google Antigravity

131,843 просмотров • 6 месяцев назад

ANTHROPIC SPENT $2.3M ON A LOOP MANUAL WITH 7 PRINCIPLES THAT TURN AN AGENT INTO A MULTI-TASK ASSISTANT 4 years of development and 1000+ hours of lectures - now anyone can launch an agent that earns instead of falls Simplicity → Tool Design → Evaluation → Disclosure → Orchestration → Stop Conditions start with the simplest solution - one call x1, workflow x4, agent x15 - most tasks never needed an agent at all 60% of failures come from a tool written vaguely - clear parameters complete the task that vague ones stop the agent generates, grades itself and returns with notes if wrong - without this the first answer is always the final one 50 tools in context and the agent gets confused - give it 3 relevant ones and it completes the task Opus 5 plans, Fable 5 executes in parallel - parallelism wins only when subtasks do not depend on each other without a turn limit the agent can burn $10,000 overnight and you find out in the morning when nothing can be changed bookmark & paste into Claude Code today - tomorrow the agent earns instead of falls
0:48

ANTHROPIC SPENT $2.3M ON A LOOP MANUAL WITH 7 PRINCIPLES THAT TURN AN AGENT INTO A MULTI-TASK ASSISTANT 4 years of development and 1000+ hours of lectures - now anyone can launch an agent that earns instead of falls Simplicity → Tool Design → Evaluation → Disclosure → Orchestration → Stop Conditions start with the simplest solution - one call x1, workflow x4, agent x15 - most tasks never needed an agent at all 60% of failures come from a tool written vaguely - clear parameters complete the task that vague ones stop the agent generates, grades itself and returns with notes if wrong - without this the first answer is always the final one 50 tools in context and the agent gets confused - give it 3 relevant ones and it completes the task Opus 5 plans, Fable 5 executes in parallel - parallelism wins only when subtasks do not depend on each other without a turn limit the agent can burn $10,000 overnight and you find out in the morning when nothing can be changed bookmark & paste into Claude Code today - tomorrow the agent earns instead of falls

Sprytix

27,543 просмотров • 1 месяц назад

LM Studio is the most popular way to run open-source LLMs on your own hardware. Your Hermes Agent now runs natively on LM Studio: auto-discovering your models, loading them on demand with the right context size, and using the right reasoning level for each model.
0:25

LM Studio is the most popular way to run open-source LLMs on your own hardware. Your Hermes Agent now runs natively on LM Studio: auto-discovering your models, loading them on demand with the right context size, and using the right reasoning level for each model.

Nous Research

186,253 просмотров • 4 месяцев назад

Introducing Threejs Awesome Graphics Agent Skills npx threejs-awesome-graphics-agent-skills install --agent codex I collected some of the best looking graphic Three.js open source projects on X and distilled them into an agent skills pack, AAA game graphics right out of the box so anyone can create games that don't look like cheap demos These are just examples included in the agent skills. All projects referenced are noted as source materials. I will continuously update this to include new projects with epic graphics
1:24

Introducing Threejs Awesome Graphics Agent Skills npx threejs-awesome-graphics-agent-skills install --agent codex I collected some of the best looking graphic Three.js open source projects on X and distilled them into an agent skills pack, AAA game graphics right out of the box so anyone can create games that don't look like cheap demos These are just examples included in the agent skills. All projects referenced are noted as source materials. I will continuously update this to include new projects with epic graphics

Scott

37,742 просмотров • 2 месяцев назад

Introducing My.AGNT I built a one-click OpenClaw agent — and it's free. The goal of My.AGNT is to make sure everyone can participate in the agent economy. All you have to do is sign up with your email, and you get your own personalized agent on OpenClaw with prebuilt skills: → Soul → Identity → Wallet → Full Grok X Search → Full Internet Search → All AI Agent MegaETH Skills → 8004 Registration via AGNT Coming soon: → Polymarket Skills → Abstract Chain AI Agent Skills → So much more Your agent, without all the hassle.
0:34

Introducing My.AGNT I built a one-click OpenClaw agent — and it's free. The goal of My.AGNT is to make sure everyone can participate in the agent economy. All you have to do is sign up with your email, and you get your own personalized agent on OpenClaw with prebuilt skills: → Soul → Identity → Wallet → Full Grok X Search → Full Internet Search → All AI Agent MegaETH Skills → 8004 Registration via AGNT Coming soon: → Polymarket Skills → Abstract Chain AI Agent Skills → So much more Your agent, without all the hassle.

tut™

39,515 просмотров • 5 месяцев назад

Support for Agent Plugins is live in Cursor (and I made a plugin for searching X!) Agent Plugins combine Skills and MCP servers into a single package Think about them like an Ikea furniture kit: your table comes with instructions, parts, and tools. You use the instructions to understand how things work (which tools on which parts), then use the tools to assemble your furniture Agent Plugins are bundles of tools & resources that allow agents to accomplish a goal: - MCP servers: fixed context and tools - Skills: dynamic instructions, loaded progressively Plugins bundle tools and instructions so your agent is able to use tokens efficiently Many of us already use MCP servers and skills together - Agent Plugins formalize this pattern for more powerful workflows I'm excited to see where the standard takes us!
0:24

Support for Agent Plugins is live in Cursor (and I made a plugin for searching X!) Agent Plugins combine Skills and MCP servers into a single package Think about them like an Ikea furniture kit: your table comes with instructions, parts, and tools. You use the instructions to understand how things work (which tools on which parts), then use the tools to assemble your furniture Agent Plugins are bundles of tools & resources that allow agents to accomplish a goal: - MCP servers: fixed context and tools - Skills: dynamic instructions, loaded progressively Plugins bundle tools and instructions so your agent is able to use tokens efficiently Many of us already use MCP servers and skills together - Agent Plugins formalize this pattern for more powerful workflows I'm excited to see where the standard takes us!

matt palmer

39,554 просмотров • 22 дней назад

There's a built-in agent inside Claude Code you can invoke just by telling Claude to use the claude-code-guide agent Save this... you'll need it It's not a local file in your project. It's a built-in subagent defined in Claude Code's system itself. It answers questions about Claude Code CLI, the Agent SDK, and the Claude API. It uses Glob, Grep, Read, WebFetch, and WebSearch, but can't edit or write files. Its only job is to answer questions.
0:22

There's a built-in agent inside Claude Code you can invoke just by telling Claude to use the claude-code-guide agent Save this... you'll need it It's not a local file in your project. It's a built-in subagent defined in Claude Code's system itself. It answers questions about Claude Code CLI, the Agent SDK, and the Claude API. It uses Glob, Grep, Read, WebFetch, and WebSearch, but can't edit or write files. Its only job is to answer questions.

Daniel San

57,240 просмотров • 5 месяцев назад