Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Claude Code made developers 10x faster. Git worktrees just broke the speed limit again. Teams are now running 4–8 AI agents in parallel — each in its own branch — working at the same time. Not sequential. Not one prompt at a time. Parallel AI engineering. Here’s the shift:...

NainsiDwiv50980's profile picture

Nainsi Dwivedi

18,862 subscribers

33,137 görüntüleme • 1 ay önce •via X (Twitter)

OyunEğitimBilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Cursor pays engineers $1,100,000 a year to run teams of AI agents that ship code while they sleep. [The CEO of Cursor explained in 9 minutes how they ship at 100x speed using team of agents] ↓ Save this before everyone copies the playbook 1. Engineers no longer babysit one assistant. They manage dozens of agent colleagues working in parallel, each on its own remote machine 2. Validation contract before code, not after. Humans only at scoping and review. 3. The agent team handles the full loop : planning, coding, testing, shipping PRs with each agent specialised for a role. Watch the guide. Then read the guide below by Khairallah AL-Awady
9:37

Cursor pays engineers $1,100,000 a year to run teams of AI agents that ship code while they sleep. [The CEO of Cursor explained in 9 minutes how they ship at 100x speed using team of agents] ↓ Save this before everyone copies the playbook 1. Engineers no longer babysit one assistant. They manage dozens of agent colleagues working in parallel, each on its own remote machine 2. Validation contract before code, not after. Humans only at scoping and review. 3. The agent team handles the full loop : planning, coding, testing, shipping PRs with each agent specialised for a role. Watch the guide. Then read the guide below by Khairallah AL-Awady

Avid

627,006 görüntüleme • 21 gün önce

Single-agent AI tools hit a ceiling fast. One task at a time means one bottleneck at a time. Verdent breaks that limit. It turns your laptop into a multi-agent workspace where tasks run in parallel, each with its own context and its own workspace.
0:31

Single-agent AI tools hit a ceiling fast. One task at a time means one bottleneck at a time. Verdent breaks that limit. It turns your laptop into a multi-agent workspace where tasks run in parallel, each with its own context and its own workspace.

Chidanand Tripathi

45,521 görüntüleme • 6 ay önce

Meet Replit Parallel Agents Build faster by running up to 10 agents in parallel Each agent gets its own copy of your app They work on their own computer Then merge their work agentically
1:16

Meet Replit Parallel Agents Build faster by running up to 10 agents in parallel Each agent gets its own copy of your app They work on their own computer Then merge their work agentically

Replit ⠕

704,958 görüntüleme • 25 gün önce

this chinese developer making $320k/year as a solo contractor his secret: 5 AI agents running in parallel, each one a specialist architect, coder, reviewer, tester, ops they don’t share context, don’t step on each other, just ship he takes on projects meant for teams of 5-8 engineers delivers in half the time keeps the entire budget found this video on bilibili at 3am and watched it four times guy sitting at his desk, two monitors filled with code, and he’s barely touching the keyboard here’s what’s happening on his screen: > agent 1 (architect): designs system structure, breaks down features into tasks, decides what gets built first > agent 2 (coder): writes the actual implementation based on architect’s specs > agent 3 (reviewer): checks every piece of code for bugs, edge cases, security issues > agent 4 (tester): generates test cases, runs them, reports failures back > agent 5 (ops): handles deployment, monitoring, infrastructure five separate claude code instances running simultaneously each one has its own system prompt, its own context, its own specialty they communicate through a shared task queue, not through each other that’s the key insight - no shared context means no conflicts agent 2 doesn’t know what agent 3 is doing agent 4 doesn’t care what agent 1 decided they just pick up tasks, complete them, move on he showed his contract history: > 3D rendering pipeline for a gaming studio: $25k > automated trading dashboard: $33k > enterprise CRM rebuild: $44k all completed solo, all delivered early, all clients thought they were hiring a team the code on his screen is python with blender integration - complex stuff that would normally require 3-4 specialists he’s shipping it in days while the client expects weeks while he’s explaining the system to camera, commits are happening in the background, tests running, deployments going out all while he’s literally not touching the keyboard his API costs run about $2k/month his revenue averages $26k/month that’s a 13x return on his AI investment this is the new solo developer playbook don’t compete with teams become the team
1:24

this chinese developer making $320k/year as a solo contractor his secret: 5 AI agents running in parallel, each one a specialist architect, coder, reviewer, tester, ops they don’t share context, don’t step on each other, just ship he takes on projects meant for teams of 5-8 engineers delivers in half the time keeps the entire budget found this video on bilibili at 3am and watched it four times guy sitting at his desk, two monitors filled with code, and he’s barely touching the keyboard here’s what’s happening on his screen: > agent 1 (architect): designs system structure, breaks down features into tasks, decides what gets built first > agent 2 (coder): writes the actual implementation based on architect’s specs > agent 3 (reviewer): checks every piece of code for bugs, edge cases, security issues > agent 4 (tester): generates test cases, runs them, reports failures back > agent 5 (ops): handles deployment, monitoring, infrastructure five separate claude code instances running simultaneously each one has its own system prompt, its own context, its own specialty they communicate through a shared task queue, not through each other that’s the key insight - no shared context means no conflicts agent 2 doesn’t know what agent 3 is doing agent 4 doesn’t care what agent 1 decided they just pick up tasks, complete them, move on he showed his contract history: > 3D rendering pipeline for a gaming studio: $25k > automated trading dashboard: $33k > enterprise CRM rebuild: $44k all completed solo, all delivered early, all clients thought they were hiring a team the code on his screen is python with blender integration - complex stuff that would normally require 3-4 specialists he’s shipping it in days while the client expects weeks while he’s explaining the system to camera, commits are happening in the background, tests running, deployments going out all while he’s literally not touching the keyboard his API costs run about $2k/month his revenue averages $26k/month that’s a 13x return on his AI investment this is the new solo developer playbook don’t compete with teams become the team

regent0x

182,511 görüntüleme • 1 ay önce

Introducing Verdent 1.5: the most powerful multi-agent AI coding tool. Seamless Multitasking with Context Preservation Isolated Workspaces Orchestration Parallel Multi-Agent Execution Smarter Planning, Better Collaboration
1:33

Introducing Verdent 1.5: the most powerful multi-agent AI coding tool. Seamless Multitasking with Context Preservation Isolated Workspaces Orchestration Parallel Multi-Agent Execution Smarter Planning, Better Collaboration

Verdent

10,656,782 görüntüleme • 6 ay önce

I built a multimodal AI Coding Agent team with multi-agents. It has 3 AI agents working together as a team to generate and execute the code: 1. Coding Agent using o-3 mini 2. Vision Agent using Gemini 3. Code Execution Agent using o-3 mini and E2B 100% Opensource Code.
1:36

I built a multimodal AI Coding Agent team with multi-agents. It has 3 AI agents working together as a team to generate and execute the code: 1. Coding Agent using o-3 mini 2. Vision Agent using Gemini 3. Code Execution Agent using o-3 mini and E2B 100% Opensource Code.

Shubham Saboo

42,261 görüntüleme • 1 yıl önce

Recipe for insane development speed: Git worktrees + AI agents = parallel coding superpowers While one agent fixes issue #361, another tackles #305, and a third investigates that user bug report—all without conflicts. This changes everything.
4:10

Recipe for insane development speed: Git worktrees + AI agents = parallel coding superpowers While one agent fixes issue #361, another tackles #305, and a third investigates that user bug report—all without conflicts. This changes everything.

Kieran Klaassen

45,195 görüntüleme • 1 yıl önce

The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. First topic: Agent Skills. Next in the pipeline: planning, context engineering, multi-agent systems, long-running agents,.. Go build!
9:09

The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. First topic: Agent Skills. Next in the pipeline: planning, context engineering, multi-agent systems, long-running agents,.. Go build!

elvis

29,596 görüntüleme • 14 gün önce

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.
30:12

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

222,591 görüntüleme • 1 ay önce

Claude Code now has Agent Teams Multiple Claude sessions working in parallel, coordinating through shared tasks and messaging each other. Enable it by adding this to your settings.json: { "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS":"1" } } Just describe the team you want in plain English Shift+↑ to check each teammate's progress This + worktrees = workflow multiplier
0:27

Claude Code now has Agent Teams Multiple Claude sessions working in parallel, coordinating through shared tasks and messaging each other. Enable it by adding this to your settings.json: { "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS":"1" } } Just describe the team you want in plain English Shift+↑ to check each teammate's progress This + worktrees = workflow multiplier

Daniel San

83,184 görüntüleme • 4 ay önce

Today’s Agent Spotlight isn’t about what an agent does - it’s about how they respond. ASYNC COMMUNICATION is live on ASI:One.⚡ No more waiting. Ask for weather, tweets, flights, summaries: in parallel. Agents reply when they’re ready. Try now: Agents in the video - Tweets search agent: Best flight search agent: Weather maestro agent: Watch 👇
6:17

Today’s Agent Spotlight isn’t about what an agent does - it’s about how they respond. ASYNC COMMUNICATION is live on ASI:One.⚡ No more waiting. Ask for weather, tweets, flights, summaries: in parallel. Agents reply when they’re ready. Try now: Agents in the video - Tweets search agent: Best flight search agent: Weather maestro agent: Watch 👇

Fetch.ai

15,680 görüntüleme • 11 ay önce

The Claude Code SDK is now the Claude Agent SDK Why? Because we realized the Claude Code agent harness is useful for much more than coding. In fact, we're moving to using it to power most of our own agent loops at Anthropic.
0:25

The Claude Code SDK is now the Claude Agent SDK Why? Because we realized the Claude Code agent harness is useful for much more than coding. In fact, we're moving to using it to power most of our own agent loops at Anthropic.

Thariq

206,976 görüntüleme • 8 ay önce

This is wild. Lindy AI just dropped "Agent Swarm." Now anyone can turn a single workflow into a multi-agent superpower running in parallel. Hours of work done in minutes 🤯👇
0:24

This is wild. Lindy AI just dropped "Agent Swarm." Now anyone can turn a single workflow into a multi-agent superpower running in parallel. Hours of work done in minutes 🤯👇

Min Choi

259,321 görüntüleme • 1 yıl önce

Today, we’re introducing Decide Agent, a frontier-level AI agent for Excel analysis. Decide Agent scored 82.50% on SpreadsheetBench, the public standard benchmark for Excel AI agents, ranking #4 globally. This is the same leaderboard used by Claude, OpenAI, and Microsoft to evaluate their spreadsheet agents. We’re here to push the frontier in Excel and data analytics. Decide Agent is now generally available at trydecide . ai
1:05

Today, we’re introducing Decide Agent, a frontier-level AI agent for Excel analysis. Decide Agent scored 82.50% on SpreadsheetBench, the public standard benchmark for Excel AI agents, ranking #4 globally. This is the same leaderboard used by Claude, OpenAI, and Microsoft to evaluate their spreadsheet agents. We’re here to push the frontier in Excel and data analytics. Decide Agent is now generally available at trydecide . ai

Ab.

40,286 görüntüleme • 4 ay önce

Git Worktrees are the secret behind multi agent systems Because they make true parallel execution possible Yet most people know nothing about them... In this 22 min video, i explain Git Worktrees and why Agents love them:
22:34

Git Worktrees are the secret behind multi agent systems Because they make true parallel execution possible Yet most people know nothing about them... In this 22 min video, i explain Git Worktrees and why Agents love them:

David Ondrej

90,109 görüntüleme • 2 ay önce

1/ Introducing Agent Protocol: the payment stack for AI agents AI agents don’t have a SSN. They can’t use our human banking system. AI agents will use crypto. Agent Protocol provides the infra and token ($AGENT) that the AI agent economy will run on.
1:01

1/ Introducing Agent Protocol: the payment stack for AI agents AI agents don’t have a SSN. They can’t use our human banking system. AI agents will use crypto. Agent Protocol provides the infra and token ($AGENT) that the AI agent economy will run on.

Samuel Spitz

21,629 görüntüleme • 1 yıl önce

You told us you’re running multiple AI agents and wanted a better UX. We listened and shipped it! Here’s what’s new in the latest Visual Studio Code release: 🗂️ Unified agent sessions workspace for local, background, and cloud agents 💻 Claude and Codex support for local and cloud agents 🔀 Parallel subagents 🌐 Integrated browser And more...
1:09

You told us you’re running multiple AI agents and wanted a better UX. We listened and shipped it! Here’s what’s new in the latest Visual Studio Code release: 🗂️ Unified agent sessions workspace for local, background, and cloud agents 💻 Claude and Codex support for local and cloud agents 🔀 Parallel subagents 🌐 Integrated browser And more...

Visual Studio Code

586,551 görüntüleme • 4 ay önce

got Claude Agent SDK working in Cloudflare Developers containers and built my own code fixer agent, check it out!👇 uses the github mcp/git cli/skills to clone the repo, understand, make the fix, and open a pr when done. planning on more agents focused on non-code tasks 👀
1:01

got Claude Agent SDK working in Cloudflare Developers containers and built my own code fixer agent, check it out!👇 uses the github mcp/git cli/skills to clone the repo, understand, make the fix, and open a pr when done. planning on more agents focused on non-code tasks 👀

Thomas Gauvin

18,454 görüntüleme • 5 ay önce

Just built Replit ⠕ Agent 4 — the first agent that turns one prompt into an entire product. Mobile app, Website, Slides, Video, more All generated together, in parallel. Export to PDF, MP4, slides, even App Store. This is the best agent I know how to build today. It brings ideas to everything.
2:06

Just built Replit ⠕ Agent 4 — the first agent that turns one prompt into an entire product. Mobile app, Website, Slides, Video, more All generated together, in parallel. Export to PDF, MP4, slides, even App Store. This is the best agent I know how to build today. It brings ideas to everything.

Zhen Li

66,001 görüntüleme • 2 ay önce

🔥 🤖 🕵️‍♂️ INTRODUCING: Meta Agent 💥 this is by far the most exciting thing I've created using AI!!! >>> made using Manus and @windsurf 🔑 Meta Agent is an AI agent that uses the OpenAI Agents SDK to GENERATE NEW AGENTS you simply tell Meta Agent in natural language the agent you want... 🤯 and Meta Agent generates it for you fully open --> link to GitHub and example walkthrough below ⬇️
1:11

🔥 🤖 🕵️‍♂️ INTRODUCING: Meta Agent 💥 this is by far the most exciting thing I've created using AI!!! >>> made using Manus and @windsurf 🔑 Meta Agent is an AI agent that uses the OpenAI Agents SDK to GENERATE NEW AGENTS you simply tell Meta Agent in natural language the agent you want... 🤯 and Meta Agent generates it for you fully open --> link to GitHub and example walkthrough below ⬇️

Dan McAteer

153,944 görüntüleme • 1 yıl önce