Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

Introducing hob - the independent workspace for serious agent work. Run agents in parallel. Switch models without losing context. Trace every output back to the conversation that made it. → Launch offer: 2 months free, then a special price locked in👇

Andrew D. Anderson

1,085 subscribers

37,972 просмотров • 2 месяцев назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 27

Фото профиля Bevan Barton

Bevan Barton2 месяцев назад

@hob_app Hob!

Фото профиля Andrew D. Anderson

Andrew D. Anderson2 месяцев назад

@hob_app Long time, no see, fellow trail blazer! I'd love for you to try hob! Sending a DM now. 🐧

Фото профиля Gianfran.co

Gianfran.co2 месяцев назад

@hob_app Awesome! Hob

Фото профиля Andrew D. Anderson

Andrew D. Anderson2 месяцев назад

@hob_app You got it! Is it even possible for you to juggle more agents? I bet, yes! DMed!

Фото профиля AdamHurwitz.eth

AdamHurwitz.eth2 месяцев назад

@hob_app I like the freedom to choose your own model, it can run locally, and private encryption for server side work. Will keep this in mind as a use @AskVenice more!

Фото профиля Andrew D. Anderson

Andrew D. Anderson2 месяцев назад

@hob_app @AskVenice We are big @AskVenice fans! hob fully supports it via OpenCode! Let us know how it goes.

Фото профиля Jordi Pinyana

Jordi Pinyana2 месяцев назад

@hob_app Hob 🙌

Фото профиля Andrew D. Anderson

Andrew D. Anderson2 месяцев назад

@hob_app DM on the way!!!!

Фото профиля Josep Chetrit

Josep Chetrit2 месяцев назад

@hob_app @levelsio you that are always on the edge should check that.

Фото профиля Andrew D. Anderson

Andrew D. Anderson25 дней назад

Oh my, the two months trial offer expired already. Luckily, the two week trial is standard. Don't let the fear of the unknown stop you, there's a better way to work with agents.🫡

Фото профиля CodePhobic

CodePhobic2 месяцев назад

@hob_app Looks really good!

Фото профиля sue irgens

sue irgens2 месяцев назад

@hob_app Hob

Фото профиля Andrew D. Anderson

Andrew D. Anderson2 месяцев назад

@hob_app What are you going to build with it? Code in your DMs!

Фото профиля Megan S. Irgens

Megan S. Irgens2 месяцев назад

@hob_app Hob

Фото профиля Andrew D. Anderson

Andrew D. Anderson2 месяцев назад

@hob_app We got you. Check your DMs!

Фото профиля Trung Nguyen

Trung Nguyen2 месяцев назад

@hob_app Hob

Фото профиля Andrew D. Anderson

Andrew D. Anderson2 месяцев назад

@hob_app Let us know how it goes! Code in your DMs.

Фото профиля Byte | yoursaudit

Byte | yoursaudit2 месяцев назад

@hob_app @NousResearch mogged them

Фото профиля gregor

gregor2 месяцев назад

@hob_app 🙌🏼🙌🏼

Фото профиля Andrew D. Anderson

Andrew D. Anderson2 месяцев назад

@hob_app I didn't read the magic word though...

Фото профиля Hallyu Website

Hallyu Website2 месяцев назад

@hob_app Hob 😅

Фото профиля Andrew D. Anderson

Andrew D. Anderson2 месяцев назад

@hob_app Oh yeah! You're in, stranger. Check your DMs.

Фото профиля Georgii

Georgii2 месяцев назад

@hob_app Looks like hob!

Фото профиля Samuel 🦤 (🧱,🔥)

Samuel 🦤 (🧱,🔥)2 месяцев назад

@hob_app Hob

Фото профиля Andrew D. Anderson

Andrew D. Anderson2 месяцев назад

@hob_app You got it! DM incoming! Be sure to let us know how it goes!

Фото профиля xMarkitus™

xMarkitus™2 месяцев назад

@hob_app Hob👨🏻‍💻

Фото профиля Andrew D. Anderson

Andrew D. Anderson2 месяцев назад

@hob_app On it! Check your DMs! 🚀

Похожие видео

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph

rewind

27,195 просмотров • 2 месяцев назад

Subagents have arrived in Gemini CLI! 🤖🚀 Create your own custom subagents in Gemini CLI! Subagents are specialized, expert agents that the main agent can delegate work to. 📦- Subagents have their own set of tools, MCP servers, system instructions, and context window. 🏷️- Use Agent to explicitly delegate to a subagent 🧹- Keeps the main context window clean ⚡️- Speed up work by running agents in parallel Read more in the launch blog below 👇

Subagents have arrived in Gemini CLI! 🤖🚀 Create your own custom subagents in Gemini CLI! Subagents are specialized, expert agents that the main agent can delegate work to. 📦- Subagents have their own set of tools, MCP servers, system instructions, and context window. 🏷️- Use Agent to explicitly delegate to a subagent 🧹- Keeps the main context window clean ⚡️- Speed up work by running agents in parallel Read more in the launch blog below 👇

Jack Wotherspoon

222,206 просмотров • 5 месяцев назад

We’re excited to launch the Codex app, a command center for building with agents. It gives you a focused space to manage multiple agents at once, run work in parallel, and collaborate with agents over long-running tasks.

We’re excited to launch the Codex app, a command center for building with agents. It gives you a focused space to manage multiple agents at once, run work in parallel, and collaborate with agents over long-running tasks.

OpenAI Developers

1,306,493 просмотров • 7 месяцев назад

Built a Self Executing Agent Loop that can run 300 agents through 4,000 steps without needing a human to restart the process. - 300 agents. - 4,000 steps per run. - 5 live data feeds. - 3 verification passes. The important part isn't the agent count. It's that the system can execute, verify its own output, update context, and keep running without waiting for the next prompt.

Built a Self Executing Agent Loop that can run 300 agents through 4,000 steps without needing a human to restart the process. - 300 agents. - 4,000 steps per run. - 5 live data feeds. - 3 verification passes. The important part isn't the agent count. It's that the system can execute, verify its own output, update context, and keep running without waiting for the next prompt.

Ricker

54,985 просмотров • 16 дней назад

I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.

I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.

AI Breakfast

78,285 просмотров • 2 месяцев назад

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

codila

61,260 просмотров • 2 месяцев назад

Launching Notebooks on Agent Two: Day 5 of 12 Days of Agent Two. With notebooks, you have complete control over the work you do with individual models. invideo's got ALL the state-of-the-art models across images, video, sound, music, creative tools. So essentially you can work with the Agent in free-flow, switch to the notebook for hands-on granular work on particular gens (both creating and editing gens), and switch back to the Agent as and when you like with no loss of context or output. This is the full range of how you can do creative work. And that's why Agent Two is the frontier intelligence agent out there. 4x faster, at half the price.

Launching Notebooks on Agent Two: Day 5 of 12 Days of Agent Two. With notebooks, you have complete control over the work you do with individual models. invideo's got ALL the state-of-the-art models across images, video, sound, music, creative tools. So essentially you can work with the Agent in free-flow, switch to the notebook for hands-on granular work on particular gens (both creating and editing gens), and switch back to the Agent as and when you like with no loss of context or output. This is the full range of how you can do creative work. And that's why Agent Two is the frontier intelligence agent out there. 4x faster, at half the price.

Invideo

1,091,714 просмотров • 1 месяц назад

Have you tried forking chat sessions in VS Code? Create a new independent session that keeps the conversation history so you can explore alternative approaches without losing your original context. Watch the video to see how it works.

Have you tried forking chat sessions in VS Code? Create a new independent session that keeps the conversation history so you can explore alternative approaches without losing your original context. Watch the video to see how it works.

Visual Studio Code

28,488 просмотров • 6 месяцев назад

$ANTHROPIC JUST QUIETLY SHIPPED A FEATURE THAT LETS CLAUDE SPAWN A WHOLE TEAM OF AGENTS THAT MESSAGE EACH OTHER AND REVIEW EACH OTHER'S WORK. It's a Claude Code feature called agent teams. The team lead spawns multiple agents that share a task list and message each other directly, not subagents reporting back, actual peers. In the demo a QA agent caught three bugs, sent the work back to the front-end and back-end devs, they fixed it, app shipped in one pass. How to run it: 1. Enable it. Needs Claude Code v2.1.32+. Add to settings.json: "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" }. Or paste that to Claude and say "add this to my settings." Restart. 2. Prompt in plain English. Start with a goal (agents wake with zero context), then "create a team of 3 using Sonnet," describe each role, its deliverable, and who it messages when done. 3. The rules: each agent owns its own files, define exact outputs, name who talks to who, keep it to 3-5 agents. Use it for complex work with separate parts running in parallel. Skip it for simple or sequential tasks, teams cost 3-4x the tokens. Bookmark this.$

ANTHROPIC JUST QUIETLY SHIPPED A FEATURE THAT LETS CLAUDE SPAWN A WHOLE TEAM OF AGENTS THAT MESSAGE EACH OTHER AND REVIEW EACH OTHER'S WORK. It's a Claude Code feature called agent teams. The team lead spawns multiple agents that share a task list and message each other directly, not subagents reporting back, actual peers. In the demo a QA agent caught three bugs, sent the work back to the front-end and back-end devs, they fixed it, app shipped in one pass. How to run it: 1. Enable it. Needs Claude Code v2.1.32+. Add to settings.json: "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" }. Or paste that to Claude and say "add this to my settings." Restart. 2. Prompt in plain English. Start with a goal (agents wake with zero context), then "create a team of 3 using Sonnet," describe each role, its deliverable, and who it messages when done. 3. The rules: each agent owns its own files, define exact outputs, name who talks to who, keep it to 3-5 agents. Use it for complex work with separate parts running in parallel. Skip it for simple or sequential tasks, teams cost 3-4x the tokens. Bookmark this.

Yarchi

462,597 просмотров • 3 месяцев назад

Ex-Google engineer built a graph-based workflow that made Claude Code do nearly 10 days of work in about an hour. Here's the exact setup he uses. step 1. create a separate Git worktree for every task. run: git worktree add ../task-1 -b task-1 open a new terminal, cd into it, and launch Claude Code. repeat this for every agent. each one gets its own branch, so nothing conflicts. step 2. give every Claude agent a different task. same repository. different branches. they all work in parallel. step 3. once they're done, review the pull requests. keep the good ones. merge what works. step 4. add a review agent. paste each PR into Claude and ask: "does this implementation match the original specification" every agent now has another agent reviewing its work before it gets merged. step 5. scale it up. 48GB of RAM can comfortably run around 20 Claude Code agents. 128GB can handle roughly 50. most engineers run Claude Code once and wait. he runs dozens of Claude Code agents in parallel, then merges the best results. this 60-minute lecture is one of the best free resources on graph engineering and multi-agent workflows. bookmark it and watch it below.

Ex-Google engineer built a graph-based workflow that made Claude Code do nearly 10 days of work in about an hour. Here's the exact setup he uses. step 1. create a separate Git worktree for every task. run: git worktree add ../task-1 -b task-1 open a new terminal, cd into it, and launch Claude Code. repeat this for every agent. each one gets its own branch, so nothing conflicts. step 2. give every Claude agent a different task. same repository. different branches. they all work in parallel. step 3. once they're done, review the pull requests. keep the good ones. merge what works. step 4. add a review agent. paste each PR into Claude and ask: "does this implementation match the original specification" every agent now has another agent reviewing its work before it gets merged. step 5. scale it up. 48GB of RAM can comfortably run around 20 Claude Code agents. 128GB can handle roughly 50. most engineers run Claude Code once and wait. he runs dozens of Claude Code agents in parallel, then merges the best results. this 60-minute lecture is one of the best free resources on graph engineering and multi-agent workflows. bookmark it and watch it below.

MIKE

32,199 просмотров • 1 месяц назад

Today we’re launching BrowserClaw! AI agents are great at writing code, but are bad at using software. BrowserClaw fixes that: agents that operate a real browser like a human, work inside your logged-in apps, and run multi-step workflows in parallel. Go try it now! It's Free and Open Source. Launch video 👇

Today we’re launching BrowserClaw! AI agents are great at writing code, but are bad at using software. BrowserClaw fixes that: agents that operate a real browser like a human, work inside your logged-in apps, and run multi-step workflows in parallel. Go try it now! It's Free and Open Source. Launch video 👇

Browser OS

14,878 просмотров • 1 месяц назад

Railway's new remote MCP (and the Railway Agent it accesses) are the best way for agents to work with everything in your Railway workspace. We released it last week and Cody De Arkland takes it and the new updates to the Railway CLI for a spin. Check it out 👇

Railway's new remote MCP (and the Railway Agent it accesses) are the best way for agents to work with everything in your Railway workspace. We released it last week and Cody De Arkland takes it and the new updates to the Railway CLI for a spin. Check it out 👇

Railway

10,774 просмотров • 5 месяцев назад

SpaceXAI just dropped full guide on how to run multiple agents "I stopped running Grok Bot as separate chats. now every project gets its own AI team, manager and task board" one Projects Manager agent creates the project, opens the channel and assigns the right agents: • coder • researcher • writer every team gets its own context, tasks and workspace → instead of managing prompts, you manage teams → instead of running one agent, you run an organization SpaceXAI shared the full setup, roles and workflow for running multiple GrokBot teams at once worth more than 100 multi-agent posts on X save now, then read how to build a parallel agent system in the article below

SpaceXAI just dropped full guide on how to run multiple agents "I stopped running Grok Bot as separate chats. now every project gets its own AI team, manager and task board" one Projects Manager agent creates the project, opens the channel and assigns the right agents: • coder • researcher • writer every team gets its own context, tasks and workspace → instead of managing prompts, you manage teams → instead of running one agent, you run an organization SpaceXAI shared the full setup, roles and workflow for running multiple GrokBot teams at once worth more than 100 multi-agent posts on X save now, then read how to build a parallel agent system in the article below

0xRafy

12,230 просмотров • 10 дней назад

How to manage multiple AI agents at once: I run 10 to 15 parallel AI agent threads a day. Some days even more. Hermes, OpenClaw, Claude Code, Codex, all going at once. But the problem is more agents doesn't equal more output. Past a certain number of threads, you lose context. You forget what you told which agent. You duplicate work across two threads doing the same job. This is how we're solving for it:

How to manage multiple AI agents at once: I run 10 to 15 parallel AI agent threads a day. Some days even more. Hermes, OpenClaw, Claude Code, Codex, all going at once. But the problem is more agents doesn't equal more output. Past a certain number of threads, you lose context. You forget what you told which agent. You duplicate work across two threads doing the same job. This is how we're solving for it:

ericosiu

11,868 просмотров • 2 месяцев назад

Introducing the new Firecrawl MCP 🔥 It now uses 50% less context on every /search, /scrape, and /interact call. Plus, instant onboarding with OAuth for humans and a keyless version for agents. Get agent-ready web context in any MCP client today:

Introducing the new Firecrawl MCP 🔥 It now uses 50% less context on every /search, /scrape, and /interact call. Plus, instant onboarding with OAuth for humans and a keyless version for agents. Get agent-ready web context in any MCP client today:

Firecrawl

30,645 просмотров • 1 месяц 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.

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

11,180 просмотров • 3 месяцев назад

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Aaron Levie

103,880 просмотров • 1 год назад

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.

Nikos

19,883 просмотров • 1 месяц назад

Launching 'Expert Agents' on Agent Two. A specialised creative crew of AI agents, trained on real creative expertise. Name the role and it's filled: creative director, DOP, storyboard artist, and more. And they talk to each other. Independently. In parallel. You're no longer briefing each agent one by one: you're running a crew, exactly like on set. Expert agents. Now in conversation. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.

Launching 'Expert Agents' on Agent Two. A specialised creative crew of AI agents, trained on real creative expertise. Name the role and it's filled: creative director, DOP, storyboard artist, and more. And they talk to each other. Independently. In parallel. You're no longer briefing each agent one by one: you're running a crew, exactly like on set. Expert agents. Now in conversation. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.

Invideo

3,411,050 просмотров • 1 месяц назад

Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses. trace every run → judge it with an LLM → diagnose → fix → ship. That loop is how agents self-improve over time. Agent loops + memory + harness + evals - thats the stack. Watch it, then save the framework below.

Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses. trace every run → judge it with an LLM → diagnose → fix → ship. That loop is how agents self-improve over time. Agent loops + memory + harness + evals - thats the stack. Watch it, then save the framework below.

Movez

566,519 просмотров • 2 месяцев назад