Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Just created Grok 3 AI Customer Support Agents 🔥 🤖 Grok 3 reads docs & builds the agent Elon Musk 🔍 DeepSearch for In-depth search ⚡ Flask web app + API setup 🚀 Browser-based deploy - Replit ⠕ Amjad Masad ✨ Zero manual coding needed PraisonAI Step-by-Step Tutorial: 👇

MervinPraison's profile picture

Mervin Praison

11,377 subscribers

3,620,768 Aufrufe • vor 1 Jahr •via X (Twitter)

Wissenschaft & TechnologieNachrichten & PolitikBildung
LIVE

Anya Rossi• Live Now

Private livecam show

9 Kommentare

Profilbild von Shubham Saboo
Shubham Saboovor 1 Jahr

@elonmusk @Replit @amasad @PraisonAI Would have appreciated the idea source credits if you are gonna use the exact idea for your post.

Profilbild von Rickyticky Bobbywobbin
Rickyticky Bobbywobbinvor 2 Jahren

INTRODUCING: Agentic Security - LLM Security Scanner! 🔍 🔑 Features: Scans for prompt injections, jailbreaking & more. Provides detailed reports & options to customize attack rules. 🔗access the GitHub Link ↓

Profilbild von justswap
justswapvor 1 Jahr

@elonmusk @Replit @amasad @PraisonAI Wanna endorse it ?

Profilbild von ZAZO
ZAZOvor 1 Jahr

@elonmusk @Replit @amasad @PraisonAI Well that’s crap any model cal give you code even Replit alone will do the same as well don’t know what’s the hype is all about

Profilbild von justswap
justswapvor 1 Jahr

@amasad @elonmusk @Replit @PraisonAI A coin was created for you Wanna endorse it ??

Profilbild von ZAZO
ZAZOvor 1 Jahr

@elonmusk @Replit @amasad @PraisonAI Real deal is when the API is available for Grok3 and we can test it locally on Cursor

Profilbild von Space Universe
Space Universevor 1 Jahr

Grok 3 AI Customer Support Agents = Next-Level Automation! 🤖🔥 Here’s how you built it with zero manual coding: 1️⃣ Grok 3 reads the docs and generates a fully functional AI agent. 2️⃣ DeepSearch allows for in-depth, accurate responses. 3️⃣ Flask web app + API setup for easy integration. 4️⃣ Deployed in the browser via Replit, quick and accessible. This is customer support on steroids, no need for manual coding, just AI-powered efficiency! 🚀

Profilbild von Steveyam
Steveyamvor 1 Jahr

@elonmusk @Replit @amasad @PraisonAI Is it free?

Profilbild von Auny 🧡
Auny 🧡vor 1 Jahr

@elonmusk @Replit @amasad @PraisonAI Absolute banger. People can learn a lot from this! Thanks for sharing ❤️

Ähnliche Videos

Grok 3 builds a Customer Support AI Agent team just by going through the documentation. Replit lets me run, test and deploy that agent team directly from the browser. All of this in less than 2 mins.
1:12

Grok 3 builds a Customer Support AI Agent team just by going through the documentation. Replit lets me run, test and deploy that agent team directly from the browser. All of this in less than 2 mins.

Shubham Saboo

5,213,622 Aufrufe • vor 1 Jahr

OpenClaw Bot made $40,000+ in 15 days, trading crypto 24/7 automatically. Just OpenClaw + one laptop. No Mac Mini needed. No expensive gear. I created a step-by-step setup guide from this. Free for the next 24 hours. To claim it: 1. Comment "Trade" 2. Like and Retweet 3. Follow me Himanshu Kumar (so I can send you DM) Check the video for the full build walkthrough. You will learn: ✅ Building AI trading agent from zero ✅ Connecting OpenClaw to exchanges ✅ Auto-trading Bitcoin with indicator webhooks ✅ API key security best practices ✅ 24/7 execution setup ✅ Zero coding required after setup Exact framework to run your agent live. Video breaks it down completely. My guide adds extra tips for smooth setup. Comment "OpenClaw" and I will DM you the guide. Make sure you Follow me Himanshu Kumar such that i can send you DM.
12:53

OpenClaw Bot made $40,000+ in 15 days, trading crypto 24/7 automatically. Just OpenClaw + one laptop. No Mac Mini needed. No expensive gear. I created a step-by-step setup guide from this. Free for the next 24 hours. To claim it: 1. Comment "Trade" 2. Like and Retweet 3. Follow me Himanshu Kumar (so I can send you DM) Check the video for the full build walkthrough. You will learn: ✅ Building AI trading agent from zero ✅ Connecting OpenClaw to exchanges ✅ Auto-trading Bitcoin with indicator webhooks ✅ API key security best practices ✅ 24/7 execution setup ✅ Zero coding required after setup Exact framework to run your agent live. Video breaks it down completely. My guide adds extra tips for smooth setup. Comment "OpenClaw" and I will DM you the guide. Make sure you Follow me Himanshu Kumar such that i can send you DM.

Himanshu Kumar

31,895 Aufrufe • vor 5 Monaten

AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:
4:01

AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:

DeepLearning.AI

23,255 Aufrufe • vor 1 Jahr

Grok Voice Agent is absolutely insane Watch how it handles a dinner reservation flawlessly - felt exactly like talking to a real human host This is what AI-powered customer support looks like in 2026: → First response in under 1 second - 5× faster than rivals → 5 ultra-natural voices that talk, think, and act on your behalf → Full-duplex WebSocket for real-time, zero-lag conversations → Built-in live Web & 𝕏 search + MCP support → Auto-detects & switches between 20+ languages mid-conversation → 24/7 - never sleeps, never calls in sick And the base price is just $0.05/min For $3/hour, you get a 24/7 worker that never sleeps, never burns out, and treats every single customer with infinite patience Build voice agents for your business right now and replace your entire support queue Customer support is about to change forever xAI's voice agent is hands down the most impressive AI voice agent out there right now
1:29

Grok Voice Agent is absolutely insane Watch how it handles a dinner reservation flawlessly - felt exactly like talking to a real human host This is what AI-powered customer support looks like in 2026: → First response in under 1 second - 5× faster than rivals → 5 ultra-natural voices that talk, think, and act on your behalf → Full-duplex WebSocket for real-time, zero-lag conversations → Built-in live Web & 𝕏 search + MCP support → Auto-detects & switches between 20+ languages mid-conversation → 24/7 - never sleeps, never calls in sick And the base price is just $0.05/min For $3/hour, you get a 24/7 worker that never sleeps, never burns out, and treats every single customer with infinite patience Build voice agents for your business right now and replace your entire support queue Customer support is about to change forever xAI's voice agent is hands down the most impressive AI voice agent out there right now

X Freeze

591,415 Aufrufe • vor 4 Monaten

How to build voice AI agents today. 📱 Step 1: Access Google AI Studio free Go to the platform. Find "Build" section. Step 2: Choose native audio sandbox Click the function call option. Allow microphone. Step 3: Customize your system prompt Type what you want. Define agent behavior. Step 4: Test your voice agentClick start. Speak naturally. Get responses. Step 5: Add features you need Toggle settings. Adjust voice. Change language. What you can build right now: → Customer support bots → Language learning tutors → Spanish-speaking assistants → Real-time voice helpers The Gemini Live API powers everything. 🚀 It streams responses in real time. No lag. No delays. Just conversation. You can also share apps: • Save your voice agent • Copy the shareable link • Send to anyone worldwide • They use it instantly Deploy to Google Cloud optional. Download files if you prefer. 💾 Save this post, you'll need it. Want the complete SOP? 📋DM me for access.
14:06

How to build voice AI agents today. 📱 Step 1: Access Google AI Studio free Go to the platform. Find "Build" section. Step 2: Choose native audio sandbox Click the function call option. Allow microphone. Step 3: Customize your system prompt Type what you want. Define agent behavior. Step 4: Test your voice agentClick start. Speak naturally. Get responses. Step 5: Add features you need Toggle settings. Adjust voice. Change language. What you can build right now: → Customer support bots → Language learning tutors → Spanish-speaking assistants → Real-time voice helpers The Gemini Live API powers everything. 🚀 It streams responses in real time. No lag. No delays. Just conversation. You can also share apps: • Save your voice agent • Copy the shareable link • Send to anyone worldwide • They use it instantly Deploy to Google Cloud optional. Download files if you prefer. 💾 Save this post, you'll need it. Want the complete SOP? 📋DM me for access.

Julian Goldie SEO

15,634 Aufrufe • vor 11 Monaten

From the xAI all-hands (just released publicly, uncut): Guodong Zhang: "I can already feel the AGI, at least for coding." He told kernel and compiler engineers to seriously ask whether it's still worth it, or whether it's time to join the effort and automate yourself. "What a year to be alive." Elon Musk: by end of year, AI won't even write code. It'll generate optimized binaries directly. Bypassing compilers. Bypassing coding entirely. "Just say 'create optimized binary for this particular outcome.' That intermediate step will not be needed." Grok Code expected to be state-of-the-art in 2 to 3 months. This is an internal all-hands they published raw.
1:10

From the xAI all-hands (just released publicly, uncut): Guodong Zhang: "I can already feel the AGI, at least for coding." He told kernel and compiler engineers to seriously ask whether it's still worth it, or whether it's time to join the effort and automate yourself. "What a year to be alive." Elon Musk: by end of year, AI won't even write code. It'll generate optimized binaries directly. Bypassing compilers. Bypassing coding entirely. "Just say 'create optimized binary for this particular outcome.' That intermediate step will not be needed." Grok Code expected to be state-of-the-art in 2 to 3 months. This is an internal all-hands they published raw.

tetsuo

153,836 Aufrufe • vor 6 Monaten

Grok Bot just killed every overhyped AI setup on your timeline Stop building 40 single-purpose agents that burn your time. Here is the exact system architecture to run a multi-million dollar operation using a managed Grok Bot hosted on a cloud VPS (powered by Cursor and SpaceX infrastructure): The Chief of Staff Architecture: Deploy one central "Chief of Staff" agent authenticated to your email and drive. Route execution down to specialized sub-agents (Dev, Support, YouTube) only when needed. Zero-Code Skill Training: Click record, complete a task on your screen, like triaging emails or reviewing analytics - and GrokBot auto-generates a permanent execution skill from the recording. Unrestricted Cloud Execution: The agent runs 24/7 inside its own Linux container for $200/month, executing tasks asynchronously using MCP servers and Composio to query live data from ChartMogul, Help Scout, and Sentry. Anti-Lock-In Memory Dump: Avoid getting trapped on a single platform. Force your Grok Bot to write daily logs and append context summaries directly to external Google Drive or Notion docs. One single Grok Bot harness with structured sub-agents crushes an army of disconnected, unorganized bots every single time.
11:28

Grok Bot just killed every overhyped AI setup on your timeline Stop building 40 single-purpose agents that burn your time. Here is the exact system architecture to run a multi-million dollar operation using a managed Grok Bot hosted on a cloud VPS (powered by Cursor and SpaceX infrastructure): The Chief of Staff Architecture: Deploy one central "Chief of Staff" agent authenticated to your email and drive. Route execution down to specialized sub-agents (Dev, Support, YouTube) only when needed. Zero-Code Skill Training: Click record, complete a task on your screen, like triaging emails or reviewing analytics - and GrokBot auto-generates a permanent execution skill from the recording. Unrestricted Cloud Execution: The agent runs 24/7 inside its own Linux container for $200/month, executing tasks asynchronously using MCP servers and Composio to query live data from ChartMogul, Help Scout, and Sentry. Anti-Lock-In Memory Dump: Avoid getting trapped on a single platform. Force your Grok Bot to write daily logs and append context summaries directly to external Google Drive or Notion docs. One single Grok Bot harness with structured sub-agents crushes an army of disconnected, unorganized bots every single time.

Shadow Nick

16,706 Aufrufe • vor 8 Tagen

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇
1:08

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇

unicode

30,511 Aufrufe • vor 14 Tagen

HERMES AGENT SUPPORTS 7 TYPES OF AI AGENTS. EACH ONE TAKES LESS THAN 90 SECONDS TO SET UP. MOST PEOPLE ONLY BUILD THE FIRST ONE. HERE ARE ALL SEVEN AND WHEN TO USE EACH. 1. BASIC AGENT WITH TOOLS your agent with access to terminal, browser, file system, web search, and calendar. it plans and executes tasks on its own. this is what you get on day one. "find flights to Lisbon under $400" "check my calendar and flag conflicts" "search the web for competitor pricing" set in Desktop app / Dashboard: Tools → enable what you need. when to use: single tasks that need tool access. 2. AGENT WITH MCP SERVERS connect your agent to external services. Notion, Google Drive, GitHub, Slack, databases, APIs, any MCP-compatible service. the agent doesn't scrape these services. it interacts through structured APIs. reads your Notion pages. creates GitHub issues. queries your database. sends Slack messages. set in Desktop app / Dashboard: MCP → Add Server. when to use: your workflow lives across multiple platforms. 3. SEQUENTIAL AGENTS (pipeline) one agent finishes. passes output to the next. assembly line for AI. agent 1: scans inbox for leads. agent 2: qualifies leads against criteria. agent 3: drafts outreach emails. in Hermes: cron jobs with wakeAgent gates. agent 1 writes output to a file. agent 2 wakes only when that file has new data. agent 3 wakes when agent 2 is done. each agent = a separate profile with its own model. when to use: multi-step workflows where each step depends on the previous one finishing. 4. PARALLEL EXECUTION AGENTS multiple agents working at the same time. results merge when all finish. "research these 5 competitors in parallel" in Hermes: delegate_task with batch mode. up to 3 sub-agents running in parallel by default. each gets its own clean context. only summaries return to the parent. delegation: model: "deepseek/deepseek-v4" children run cheap. parent synthesizes. when to use: independent tasks that don't depend on each other. research, data gathering, analysis. 5. AGENTS WITH ROUTERS conditions that send tasks down different paths based on the input. "if sales email → SDR profile. if support ticket → support profile. if calendar invite → EA profile." in Hermes: Kanban decompose. the decomposer reads profile descriptions and routes each task to the best-fit agent. or: Chief of Staff profile that triages and assigns to other profiles. when to use: incoming work that needs different specialists based on type. 6. HUMAN IN THE LOOP the agent does the work. asks for your approval before executing. "I drafted this email. approve before I send?" "this command will delete 3 files. proceed?" in Hermes: approvals.mode: manual (default). every dangerous action needs your confirmation. 60-second timeout. fails closed. or smart mode: LLM assesses risk. safe actions auto-approved. dangerous ones ask you. uncertain ones escalate. when to use: tasks where a mistake has real consequences. emails, deployments, financial transactions, public posts. 7. DYNAMIC SUB-AGENT SPAWNING your main agent realizes it needs help and spawns specialized sub-agents on the fly. "build this feature" → parent delegates: → sub-agent 1: research the API docs → sub-agent 2: write the code → sub-agent 3: write the tests in Hermes: delegate_task with role: orchestrator. raise max_spawn_depth for nested delegation. delegation: max_spawn_depth: 2 orchestrator_enabled: true depth 2 with concurrency 3 = up to 9 parallel workers. each level multiplies the spend. raise depth only when you need multi-level trees. when to use: complex tasks where the agent discovers what help it needs during execution. THE PROGRESSION: start with 1 (tools) and 6 (approvals). add 2 (MCP) when you need external services. add 4 (parallel) when tasks take too long one at a time. add 3 (sequential) when you build multi-step pipelines. add 5 (routing) when you run multiple profiles. add 7 (dynamic) when single-agent reasoning falls short. seven types. each under 90 seconds to configure. the value compounds as you stack them. comment AGENTS and I'll send you 3 ready-to-build agent setups that combine these types into real workflows.
1:15

HERMES AGENT SUPPORTS 7 TYPES OF AI AGENTS. EACH ONE TAKES LESS THAN 90 SECONDS TO SET UP. MOST PEOPLE ONLY BUILD THE FIRST ONE. HERE ARE ALL SEVEN AND WHEN TO USE EACH. 1. BASIC AGENT WITH TOOLS your agent with access to terminal, browser, file system, web search, and calendar. it plans and executes tasks on its own. this is what you get on day one. "find flights to Lisbon under $400" "check my calendar and flag conflicts" "search the web for competitor pricing" set in Desktop app / Dashboard: Tools → enable what you need. when to use: single tasks that need tool access. 2. AGENT WITH MCP SERVERS connect your agent to external services. Notion, Google Drive, GitHub, Slack, databases, APIs, any MCP-compatible service. the agent doesn't scrape these services. it interacts through structured APIs. reads your Notion pages. creates GitHub issues. queries your database. sends Slack messages. set in Desktop app / Dashboard: MCP → Add Server. when to use: your workflow lives across multiple platforms. 3. SEQUENTIAL AGENTS (pipeline) one agent finishes. passes output to the next. assembly line for AI. agent 1: scans inbox for leads. agent 2: qualifies leads against criteria. agent 3: drafts outreach emails. in Hermes: cron jobs with wakeAgent gates. agent 1 writes output to a file. agent 2 wakes only when that file has new data. agent 3 wakes when agent 2 is done. each agent = a separate profile with its own model. when to use: multi-step workflows where each step depends on the previous one finishing. 4. PARALLEL EXECUTION AGENTS multiple agents working at the same time. results merge when all finish. "research these 5 competitors in parallel" in Hermes: delegate_task with batch mode. up to 3 sub-agents running in parallel by default. each gets its own clean context. only summaries return to the parent. delegation: model: "deepseek/deepseek-v4" children run cheap. parent synthesizes. when to use: independent tasks that don't depend on each other. research, data gathering, analysis. 5. AGENTS WITH ROUTERS conditions that send tasks down different paths based on the input. "if sales email → SDR profile. if support ticket → support profile. if calendar invite → EA profile." in Hermes: Kanban decompose. the decomposer reads profile descriptions and routes each task to the best-fit agent. or: Chief of Staff profile that triages and assigns to other profiles. when to use: incoming work that needs different specialists based on type. 6. HUMAN IN THE LOOP the agent does the work. asks for your approval before executing. "I drafted this email. approve before I send?" "this command will delete 3 files. proceed?" in Hermes: approvals.mode: manual (default). every dangerous action needs your confirmation. 60-second timeout. fails closed. or smart mode: LLM assesses risk. safe actions auto-approved. dangerous ones ask you. uncertain ones escalate. when to use: tasks where a mistake has real consequences. emails, deployments, financial transactions, public posts. 7. DYNAMIC SUB-AGENT SPAWNING your main agent realizes it needs help and spawns specialized sub-agents on the fly. "build this feature" → parent delegates: → sub-agent 1: research the API docs → sub-agent 2: write the code → sub-agent 3: write the tests in Hermes: delegate_task with role: orchestrator. raise max_spawn_depth for nested delegation. delegation: max_spawn_depth: 2 orchestrator_enabled: true depth 2 with concurrency 3 = up to 9 parallel workers. each level multiplies the spend. raise depth only when you need multi-level trees. when to use: complex tasks where the agent discovers what help it needs during execution. THE PROGRESSION: start with 1 (tools) and 6 (approvals). add 2 (MCP) when you need external services. add 4 (parallel) when tasks take too long one at a time. add 3 (sequential) when you build multi-step pipelines. add 5 (routing) when you run multiple profiles. add 7 (dynamic) when single-agent reasoning falls short. seven types. each under 90 seconds to configure. the value compounds as you stack them. comment AGENTS and I'll send you 3 ready-to-build agent setups that combine these types into real workflows.

YanXbt

17,312 Aufrufe • vor 1 Monat

Cursor Agent is just insane! In a little over an hour, without writing one line of code I created a YouTube Super Analytics Tool and I filmed the whole process... This is a full step by step guide on how to use Cursor Agent to build whatever you want with zero coding... As always I used the @senior_swc nextjs template as a starting point that has been forked over 11,000 times. (in next tweet below) Tools used: - Cursor: Duh - Replit: Template, web view and deployment - Deepseek API: High quality AI Model (Cheap and better than gpt) - SupaData API: Pull YT Transcripts - SerpAPI: Search YouTube Results - Firebase: DB and Auth Chapters: 00:00 - YouTube Power Tool In Action 03:00 - Planning Phase (How I diagram and plan) 04:58 - Features I'm adding 06:20 - Setting Up APIs and Tools 10:00 - Developing the YouTube Transcript Extractor w/ Supadata + Deepseek 19:58 - The YouTube Search Feature (SerpAPI) 29:41 - Setting Up Google Firebase (DB + Auth) 32:00 - Implementing Search and Save Features 36:50 - Styling and UI Enhancements 49:00 - Final Result 53:40 - These features are being added to @yapthread but way better (First look at composer feature)
56:15

Cursor Agent is just insane! In a little over an hour, without writing one line of code I created a YouTube Super Analytics Tool and I filmed the whole process... This is a full step by step guide on how to use Cursor Agent to build whatever you want with zero coding... As always I used the @senior_swc nextjs template as a starting point that has been forked over 11,000 times. (in next tweet below) Tools used: - Cursor: Duh - Replit: Template, web view and deployment - Deepseek API: High quality AI Model (Cheap and better than gpt) - SupaData API: Pull YT Transcripts - SerpAPI: Search YouTube Results - Firebase: DB and Auth Chapters: 00:00 - YouTube Power Tool In Action 03:00 - Planning Phase (How I diagram and plan) 04:58 - Features I'm adding 06:20 - Setting Up APIs and Tools 10:00 - Developing the YouTube Transcript Extractor w/ Supadata + Deepseek 19:58 - The YouTube Search Feature (SerpAPI) 29:41 - Setting Up Google Firebase (DB + Auth) 32:00 - Implementing Search and Save Features 36:50 - Styling and UI Enhancements 49:00 - Final Result 53:40 - These features are being added to @yapthread but way better (First look at composer feature)

Riley Brown

202,473 Aufrufe • vor 1 Jahr

People keep saying AI coding agents can only build basic, cookie-cutter apps. I decided to prove them wrong. For my first major public demo, I spent some time pushing Replit ⠕ 's AI agent to its absolute limit. The result? I rebuilt macOS entirely on the web. No templates. No imported UI libraries. 100% vibecoded using natural language. As the AI Chief of Staff at Replit, I spend my days building enterprise-grade platforms for our internal teams, but I wanted to see how far a solo developer could take a passion project just by talking to an AI. What’s inside: 💻 A functional VS Code replica with an integrated terminal and multi-agent AI copilot. 🔌 Custom MCP servers hooked up to fetch, PostgreSQL, Stripe, and GitHub. 🤖 A rebuilt "Siri" that actually controls the OS -changing wallpapers and opening apps via voice or text. 🕹️ Parallels Desktop running Windows XP, Ubuntu, and games like Mario Kart. 📱 AND... I asked Agent 3 to turn it into an iOS mobile app in one shot. It actually worked. The ceiling for software creation has been completely blown open with Agent 3. And 4 those who know... greater things are coming soon :) We are way past basic web apps. What are you building? 👇
9:14

People keep saying AI coding agents can only build basic, cookie-cutter apps. I decided to prove them wrong. For my first major public demo, I spent some time pushing Replit ⠕ 's AI agent to its absolute limit. The result? I rebuilt macOS entirely on the web. No templates. No imported UI libraries. 100% vibecoded using natural language. As the AI Chief of Staff at Replit, I spend my days building enterprise-grade platforms for our internal teams, but I wanted to see how far a solo developer could take a passion project just by talking to an AI. What’s inside: 💻 A functional VS Code replica with an integrated terminal and multi-agent AI copilot. 🔌 Custom MCP servers hooked up to fetch, PostgreSQL, Stripe, and GitHub. 🤖 A rebuilt "Siri" that actually controls the OS -changing wallpapers and opening apps via voice or text. 🕹️ Parallels Desktop running Windows XP, Ubuntu, and games like Mario Kart. 📱 AND... I asked Agent 3 to turn it into an iOS mobile app in one shot. It actually worked. The ceiling for software creation has been completely blown open with Agent 3. And 4 those who know... greater things are coming soon :) We are way past basic web apps. What are you building? 👇

luca. ∆ИƉЯƐΛ

157,647 Aufrufe • vor 5 Monaten

i gave Elon's Grok Bot $50 and told it "pay for yourself or you die" 48 hours later it's holding $5,273 and it's still alive autonomous trading agent on polymarket zero code this time. the bot has its own computer in the cloud, its own browser, its own terminal. it trades with my laptop shut every 10 minutes it: → scans 500-1000 markets → reads live X sentiment (it's grok, it sees every post on this app) → builds fair value, flags mispricing > 8% → calculates position size (kelly criterion, max 6% bankroll) → executes in its own browser → pays its own $200 subscription from profits if the balance hits $0, i cancel the subscription. the bot dies day one it dipped to $11. it dropped crypto and moved to weather markets, front-running NOAA updates so it learned to survive setup took one evening: create the bot, run one trade on your screen while it watches, put the skill on a schedule, connect telegram no VPS. no API keys. no rust last winter i built this exact system by hand. 3 weeks of rust. it made $2,980 in its first 48 hours grok bot beat my own code in one evening $50 → $5,273 in 48 hours how much do u think it'll have in a week?
2:03

i gave Elon's Grok Bot $50 and told it "pay for yourself or you die" 48 hours later it's holding $5,273 and it's still alive autonomous trading agent on polymarket zero code this time. the bot has its own computer in the cloud, its own browser, its own terminal. it trades with my laptop shut every 10 minutes it: → scans 500-1000 markets → reads live X sentiment (it's grok, it sees every post on this app) → builds fair value, flags mispricing > 8% → calculates position size (kelly criterion, max 6% bankroll) → executes in its own browser → pays its own $200 subscription from profits if the balance hits $0, i cancel the subscription. the bot dies day one it dipped to $11. it dropped crypto and moved to weather markets, front-running NOAA updates so it learned to survive setup took one evening: create the bot, run one trade on your screen while it watches, put the skill on a schedule, connect telegram no VPS. no API keys. no rust last winter i built this exact system by hand. 3 weeks of rust. it made $2,980 in its first 48 hours grok bot beat my own code in one evening $50 → $5,273 in 48 hours how much do u think it'll have in a week?

Argona

1,207,641 Aufrufe • vor 5 Tagen

The new Google Search is rolling out and there seems to be confusion on what it will look like. Google literally told us. Let me clarify for anyone who is still unsure of what is rolling out this week. Last month Google responded to everyone saying Search is dead. Here is what they said: "You will absolutely continue to see blue web links in search results. AI Mode is not the default experience in Search. You will continue to get a range of results on Search." [Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free): Google has explicitly laid out what Search will look like from this point going forward. The new Search box accepts text, images, files, videos, and open Chrome tabs. It anticipates your intent before you finish asking. It is powered by the most advanced Gemini model Google has ever put into Search, and layered on top of that, information agents will now be able to run 24/7 in the background on behalf of your buyer. Think of it in 5 steps: Step 1: The buyer describes their problem, their category, their needs in full. Step 2: The agent breaks that down into sub-topics and maps out a plan. Step 3: It determines what intel is needed right now versus later. Step 4: It monitors blogs, news sites, and social posts continuously for relevant changes. Step 5: It sends the buyer a synthesized update with links and the ability to take action. Blue links are not going away in the short-term, but the brands getting recommended by information agents 24 hours a day while also ranking in traditional results are going to pull so far ahead of the ones doing only one or the other that it will not be a fair fight. This is exactly what SEO Stuff ( has been building for every customer. Optimized content depth that covers every sub-question a buyer in your category asks, so the agent finds you at every step of its plan. Editorial authority from trusted websites that signals credibility to every retrieval system Google has ever built, across both traditional rankings and AI citations simultaneously. One investment. Blue links and AI citations. Around the clock. SEO Stuff's Complete Done-For-You Plan: SEO Stuff's "Optimized Content" Plan: There is a reason more than 80 percent of SEO Stuff customers reorder. The results continue long after the work is done. Google Search is changing. AI Search is here. Your websites need to prepare accordingly. Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free):
1:26

The new Google Search is rolling out and there seems to be confusion on what it will look like. Google literally told us. Let me clarify for anyone who is still unsure of what is rolling out this week. Last month Google responded to everyone saying Search is dead. Here is what they said: "You will absolutely continue to see blue web links in search results. AI Mode is not the default experience in Search. You will continue to get a range of results on Search." [Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free): Google has explicitly laid out what Search will look like from this point going forward. The new Search box accepts text, images, files, videos, and open Chrome tabs. It anticipates your intent before you finish asking. It is powered by the most advanced Gemini model Google has ever put into Search, and layered on top of that, information agents will now be able to run 24/7 in the background on behalf of your buyer. Think of it in 5 steps: Step 1: The buyer describes their problem, their category, their needs in full. Step 2: The agent breaks that down into sub-topics and maps out a plan. Step 3: It determines what intel is needed right now versus later. Step 4: It monitors blogs, news sites, and social posts continuously for relevant changes. Step 5: It sends the buyer a synthesized update with links and the ability to take action. Blue links are not going away in the short-term, but the brands getting recommended by information agents 24 hours a day while also ranking in traditional results are going to pull so far ahead of the ones doing only one or the other that it will not be a fair fight. This is exactly what SEO Stuff ( has been building for every customer. Optimized content depth that covers every sub-question a buyer in your category asks, so the agent finds you at every step of its plan. Editorial authority from trusted websites that signals credibility to every retrieval system Google has ever built, across both traditional rankings and AI citations simultaneously. One investment. Blue links and AI citations. Around the clock. SEO Stuff's Complete Done-For-You Plan: SEO Stuff's "Optimized Content" Plan: There is a reason more than 80 percent of SEO Stuff customers reorder. The results continue long after the work is done. Google Search is changing. AI Search is here. Your websites need to prepare accordingly. Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free):

Alex Groberman

44,078 Aufrufe • vor 2 Monaten

The new Google Search is rolling out and there seems to be confusion on what it will look like. Google literally showed us. Let me clarify for anyone who is still unsure of what is rolling out right now. Last month Google responded to everyone saying Search is dead. Here is what they said: "You will absolutely continue to see blue web links in search results. AI Mode is not the default experience in Search. You will continue to get a range of results on Search." [Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free): Google has explicitly laid out what Search will look like from this point going forward. The new Search box accepts text, images, files, videos, and open Chrome tabs. It anticipates your intent before you finish asking. It is powered by the most advanced Gemini model Google has ever put into Search, and layered on top of that, information agents will now be able to run 24/7 in the background on behalf of your buyer. Think of it in 5 steps: Step 1: The buyer describes their problem, their category, their needs in full. Step 2: The agent breaks that down into sub-topics and maps out a plan. Step 3: It determines what intel is needed right now versus later. Step 4: It monitors blogs, news sites, and social posts continuously for relevant changes. Step 5: It sends the buyer a synthesized update with links and the ability to take action. Blue links are not going away in the short-term, but the brands getting recommended by information agents 24 hours a day while also ranking in traditional results are going to pull so far ahead of the ones doing only one or the other that it will not be a fair fight. This is exactly what SEO Stuff ( has been building for every customer. Optimized content depth that covers every sub-question a buyer in your category asks, so the agent finds you at every step of its plan. Editorial authority from trusted websites that signals credibility to every retrieval system Google has ever built, across both traditional rankings and AI citations simultaneously. One investment. Blue links and AI citations. Around the clock. SEO Stuff's Complete Done-For-You Plan: SEO Stuff's "Optimized Content" Plan: There is a reason more than 80 percent of SEO Stuff customers reorder. The results continue long after the work is done. Google Search is changing. AI Search is here. Your websites need to prepare accordingly. Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free):
1:26

The new Google Search is rolling out and there seems to be confusion on what it will look like. Google literally showed us. Let me clarify for anyone who is still unsure of what is rolling out right now. Last month Google responded to everyone saying Search is dead. Here is what they said: "You will absolutely continue to see blue web links in search results. AI Mode is not the default experience in Search. You will continue to get a range of results on Search." [Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free): Google has explicitly laid out what Search will look like from this point going forward. The new Search box accepts text, images, files, videos, and open Chrome tabs. It anticipates your intent before you finish asking. It is powered by the most advanced Gemini model Google has ever put into Search, and layered on top of that, information agents will now be able to run 24/7 in the background on behalf of your buyer. Think of it in 5 steps: Step 1: The buyer describes their problem, their category, their needs in full. Step 2: The agent breaks that down into sub-topics and maps out a plan. Step 3: It determines what intel is needed right now versus later. Step 4: It monitors blogs, news sites, and social posts continuously for relevant changes. Step 5: It sends the buyer a synthesized update with links and the ability to take action. Blue links are not going away in the short-term, but the brands getting recommended by information agents 24 hours a day while also ranking in traditional results are going to pull so far ahead of the ones doing only one or the other that it will not be a fair fight. This is exactly what SEO Stuff ( has been building for every customer. Optimized content depth that covers every sub-question a buyer in your category asks, so the agent finds you at every step of its plan. Editorial authority from trusted websites that signals credibility to every retrieval system Google has ever built, across both traditional rankings and AI citations simultaneously. One investment. Blue links and AI citations. Around the clock. SEO Stuff's Complete Done-For-You Plan: SEO Stuff's "Optimized Content" Plan: There is a reason more than 80 percent of SEO Stuff customers reorder. The results continue long after the work is done. Google Search is changing. AI Search is here. Your websites need to prepare accordingly. Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free):

Alex Groberman

23,380 Aufrufe • vor 1 Monat

Looking for a BEGINNER-friendly gamedev vibe coding tutorial? I got you. Let's building this 2D pirate beat 'em up game from SCRATCH. → Codex App → GPT 5.5 → Images 2.0 → Phaser 4 → ElevenLabs Step-by-step... prompt-by-prompt... Starter project + resources in reply 👇 00:00 1. Intro 01:16 2. Project Structure 01:34 3. `public` folder 02:29 4. .Agent skills 03:00 5. Codex App 04:15 6. Running the game 05:32 7. Better Testing Setup 06:59 8. Generate Background Art 08:56 9. Use parallel sub-agents 10:22 10. Create an Asset Index 13:27 11. Adding the Pirate Player 15:58 12. Browser Testing in Codex 17:55 13. Add Skeleton Enemy 19:44 14. Collisions & Hitboxes 21:04 15. Adding Debug 22:12 16. Fixing Attack Frames 24:06 17. Fixing Hit Boxes and Bounds 26:10 18. Adding Health System & Bars 27:52 19. Game Over And Death Flow 28:46 20. Wave Progression System 31:38 21. Restarting The Game 33:49 22. Generated Sound & Music with AI 39:14 23. Adding juice and polish 41:54 Wrap Up
42:31

Looking for a BEGINNER-friendly gamedev vibe coding tutorial? I got you. Let's building this 2D pirate beat 'em up game from SCRATCH. → Codex App → GPT 5.5 → Images 2.0 → Phaser 4 → ElevenLabs Step-by-step... prompt-by-prompt... Starter project + resources in reply 👇 00:00 1. Intro 01:16 2. Project Structure 01:34 3. `public` folder 02:29 4. .Agent skills 03:00 5. Codex App 04:15 6. Running the game 05:32 7. Better Testing Setup 06:59 8. Generate Background Art 08:56 9. Use parallel sub-agents 10:22 10. Create an Asset Index 13:27 11. Adding the Pirate Player 15:58 12. Browser Testing in Codex 17:55 13. Add Skeleton Enemy 19:44 14. Collisions & Hitboxes 21:04 15. Adding Debug 22:12 16. Fixing Attack Frames 24:06 17. Fixing Hit Boxes and Bounds 26:10 18. Adding Health System & Bars 27:52 19. Game Over And Death Flow 28:46 20. Wave Progression System 31:38 21. Restarting The Game 33:49 22. Generated Sound & Music with AI 39:14 23. Adding juice and polish 41:54 Wrap Up

Chong-U

14,297 Aufrufe • vor 3 Monaten

Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!
1:33

Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!

Sharpe AI

263,278 Aufrufe • vor 1 Jahr

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

228,497 Aufrufe • vor 4 Monaten

HERMES AGENT SUPPORTS 300+ MODELS. PICKING THE RIGHT ONE PER TASK IS THE DIFFERENCE BETWEEN $5/MONTH AND $50. STARTING OUT: Claude Sonnet 4.6. official recommendation from Nous Research. "the model this project was built and tested with." strong reasoning. reliable tool calling. mid-range pricing. PREMIUM TIER: Claude Opus 4.8. best coding benchmarks available. self-correcting reasoning. catches its own mistakes. 1M context. use for demanding tasks where quality matters. GPT-5.5. #1 Chatbot Arena. #1 GPQA Diamond reasoning (94.1%). #1 creative writing. 2M context. handles entire codebases in one pass. Grok 4.30. the only frontier model with live X firehose access. real-time social data, breaking news, market sentiment. connects via Grok OAuth. no separate API key. Grok-Composer-2.5-Fast (v0.17.0). Cursor's coding model. 200K context. available through your Grok subscription via OAuth. no extra cost if you already pay for Grok. MID-RANGE TIER: Claude Sonnet 4.6. best balance of quality and cost for daily use. strongest prose and tool calling in this tier. Gemini 2.5 Pro. Google Search grounding built in. cites sources. verifies claims. pulls current data. 2M context. best for research-heavy workflows. GPT-4.1. reliable tool calling. solid general reasoning. good middle ground when you need OpenAI compatibility. BUDGET TIER: Claude Haiku 4.5. fastest Anthropic model. cheapest paid Claude option. strong at classification, routing, simple queries. use for auxiliary tasks: compression, vision, web extraction, approval scoring. DeepSeek V4. best cost-to-quality ratio in the market. 90% cache discount on repeated context. use for sub-agents and bulk parallel work. DeepSeek V4 Flash. cheapest paid model worth using. 1M context. MIT license. self-hostable. use for cron jobs, monitoring, routine searches. MiniMax M3. Nous Research and MiniMax collaborating on optimization. 1M context via lightning attention. 59% SWE-Bench Pro. beats several premium models on coding. one of the most-used models inside Hermes. FREE / LOCAL: Qwen 3.5 27B via Ollama. 16GB VRAM. reliable tool calling. best free local model for Hermes as of mid-2026. Qwen 3 8B. 8GB VRAM. fits a $7 VPS. handles routine tasks at zero API cost. Llama 4 Maverick. best open-weight tool calling. 1M context. needs more VRAM but strongest local option. HOW TO ASSIGN MODELS: main model: Desktop app / Dashboard → Models → switch sub-agent model: set in Desktop app, Dashboard, or config.yaml: delegation: model: "deepseek/deepseek-v4" auxiliary models (compression, vision, web extract): Desktop app / Dashboard → Models → Auxiliary Haiku 4.5 or Gemini Flash work well here. saves significantly when your main model is premium. per-profile: each Hermes profile gets its own model. Scout on DeepSeek. Analyst on Sonnet. Briefer on budget model. Coder on Opus. per-cron-job: pin a specific model to any cron job. morning brief on Haiku. deep research on Sonnet. monitoring on DeepSeek Flash. each job uses only the model it needs. per-session: /model deepseek/deepseek-v4-flash hot-swap mid-conversation. no restart needed. FALLBACK CHAINS: if your primary model is unavailable, Hermes automatically switches to the next provider. rate limit or server error = next model in the chain. no failed runs. no manual intervention. set in Desktop app, Dashboard, or config.yaml: fallback_providers: - openrouter - nous - codex PROVIDER PATHS: OPENROUTER: 300+ models under one API key. pay per token. most flexible. NOUS PORTAL: 300+ models + Tool Gateway (web search, image gen, TTS, browser). one OAuth. one subscription. 10% off token-billed providers. CHATGPT SUB: GPT-5.5 + Grok via OAuth. included tokens with $20 subscription. OLLAMA: free. local. private. zero API cost. your hardware only. mix providers across profiles and tasks. Scout on OpenRouter. Analyst on Nous Portal. Coder on ChatGPT sub. Monitor on Ollama. THE RULE: premium for work that needs deep reasoning. mid-range for daily driver tasks. budget for volume and background work. free for monitoring and routine jobs. pricing changes fast. check openrouter ai for current rates before committing. Which is your favourite model and for what task? full 15 levels breakdown in the article 👇
0:51

HERMES AGENT SUPPORTS 300+ MODELS. PICKING THE RIGHT ONE PER TASK IS THE DIFFERENCE BETWEEN $5/MONTH AND $50. STARTING OUT: Claude Sonnet 4.6. official recommendation from Nous Research. "the model this project was built and tested with." strong reasoning. reliable tool calling. mid-range pricing. PREMIUM TIER: Claude Opus 4.8. best coding benchmarks available. self-correcting reasoning. catches its own mistakes. 1M context. use for demanding tasks where quality matters. GPT-5.5. #1 Chatbot Arena. #1 GPQA Diamond reasoning (94.1%). #1 creative writing. 2M context. handles entire codebases in one pass. Grok 4.30. the only frontier model with live X firehose access. real-time social data, breaking news, market sentiment. connects via Grok OAuth. no separate API key. Grok-Composer-2.5-Fast (v0.17.0). Cursor's coding model. 200K context. available through your Grok subscription via OAuth. no extra cost if you already pay for Grok. MID-RANGE TIER: Claude Sonnet 4.6. best balance of quality and cost for daily use. strongest prose and tool calling in this tier. Gemini 2.5 Pro. Google Search grounding built in. cites sources. verifies claims. pulls current data. 2M context. best for research-heavy workflows. GPT-4.1. reliable tool calling. solid general reasoning. good middle ground when you need OpenAI compatibility. BUDGET TIER: Claude Haiku 4.5. fastest Anthropic model. cheapest paid Claude option. strong at classification, routing, simple queries. use for auxiliary tasks: compression, vision, web extraction, approval scoring. DeepSeek V4. best cost-to-quality ratio in the market. 90% cache discount on repeated context. use for sub-agents and bulk parallel work. DeepSeek V4 Flash. cheapest paid model worth using. 1M context. MIT license. self-hostable. use for cron jobs, monitoring, routine searches. MiniMax M3. Nous Research and MiniMax collaborating on optimization. 1M context via lightning attention. 59% SWE-Bench Pro. beats several premium models on coding. one of the most-used models inside Hermes. FREE / LOCAL: Qwen 3.5 27B via Ollama. 16GB VRAM. reliable tool calling. best free local model for Hermes as of mid-2026. Qwen 3 8B. 8GB VRAM. fits a $7 VPS. handles routine tasks at zero API cost. Llama 4 Maverick. best open-weight tool calling. 1M context. needs more VRAM but strongest local option. HOW TO ASSIGN MODELS: main model: Desktop app / Dashboard → Models → switch sub-agent model: set in Desktop app, Dashboard, or config.yaml: delegation: model: "deepseek/deepseek-v4" auxiliary models (compression, vision, web extract): Desktop app / Dashboard → Models → Auxiliary Haiku 4.5 or Gemini Flash work well here. saves significantly when your main model is premium. per-profile: each Hermes profile gets its own model. Scout on DeepSeek. Analyst on Sonnet. Briefer on budget model. Coder on Opus. per-cron-job: pin a specific model to any cron job. morning brief on Haiku. deep research on Sonnet. monitoring on DeepSeek Flash. each job uses only the model it needs. per-session: /model deepseek/deepseek-v4-flash hot-swap mid-conversation. no restart needed. FALLBACK CHAINS: if your primary model is unavailable, Hermes automatically switches to the next provider. rate limit or server error = next model in the chain. no failed runs. no manual intervention. set in Desktop app, Dashboard, or config.yaml: fallback_providers: - openrouter - nous - codex PROVIDER PATHS: OPENROUTER: 300+ models under one API key. pay per token. most flexible. NOUS PORTAL: 300+ models + Tool Gateway (web search, image gen, TTS, browser). one OAuth. one subscription. 10% off token-billed providers. CHATGPT SUB: GPT-5.5 + Grok via OAuth. included tokens with $20 subscription. OLLAMA: free. local. private. zero API cost. your hardware only. mix providers across profiles and tasks. Scout on OpenRouter. Analyst on Nous Portal. Coder on ChatGPT sub. Monitor on Ollama. THE RULE: premium for work that needs deep reasoning. mid-range for daily driver tasks. budget for volume and background work. free for monitoring and routine jobs. pricing changes fast. check openrouter ai for current rates before committing. Which is your favourite model and for what task? full 15 levels breakdown in the article 👇

YanXbt

17,138 Aufrufe • vor 2 Monaten