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I built an MVP of thanks to NextJS and Vercel My stack - Next.js (fullstack) - Neon - Serverless Postgres (cloud postgresql) - Bland (voice agent platform) - GPT-4 (AI) Tools + Libraries - VS Code - Continue running Anthropic Claude Opus - shadcn (react component library) - Screen... show more

Gillinghammer

2,949 subscribers

100,825 Aufrufe • vor 2 Jahren •via X (Twitter)

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11 Kommentare

Profilbild von Gillinghammer

Gillinghammervor 2 Jahren

@nextjs @neondatabase @usebland @continuedev @AnthropicAI @shadcn wow more than 160 bookmarks so far 📙 Looking for companies to engage in high-velocity iteration to rapidly refine features and improvements tailored to their specific needs

Profilbild von Nolan Makatche

Nolan Makatchevor 2 Jahren

@vercel @nextjs @neondatabase @usebland @continuedev @AnthropicAI @shadcn This is awesome! Really nice execution on a great idea. Seeing @usebland in action is giving me a bad case of shiny object syndrome. 😂

Profilbild von Gillinghammer

Gillinghammervor 2 Jahren

@vercel @nextjs @neondatabase @usebland @continuedev @AnthropicAI @shadcn I know exactly how you feel

Profilbild von Krishna Singh

Krishna Singhvor 2 Jahren

@vercel @nextjs @neondatabase @usebland @continuedev @AnthropicAI @shadcn Awesome video 👍🏻 Keep up with #good work 🩵

Profilbild von Lee Robinson

Lee Robinsonvor 2 Jahren

@vercel @nextjs @neondatabase @usebland @continuedev @AnthropicAI @shadcn 🔥 Sending you some Next.js swag!

Profilbild von latentsauce

latentsaucevor 2 Jahren

@vercel @nextjs @neondatabase @usebland @continuedev @AnthropicAI @shadcn Super cool!! Have you tried @Vapi_AI ? How do you like bland so far?

Profilbild von Gillinghammer

Gillinghammervor 2 Jahren

@vercel @nextjs @neondatabase @usebland @continuedev @AnthropicAI @shadcn @Vapi_AI just saw their demo yesterday!

Profilbild von Ahmed oubadi

Ahmed oubadivor 2 Jahren

@vercel @nextjs @neondatabase @usebland @continuedev @AnthropicAI @shadcn Bro, you're taking MVP to the next level, this is how MVP should look like, good luck.

Profilbild von Gillinghammer

Gillinghammervor 2 Jahren

@vercel @nextjs @neondatabase @usebland @continuedev @AnthropicAI @shadcn thank you 😊 @shadcn deserves a lot of credit for that!

Profilbild von 𝒟𝑒𝓇𝑒𝓀

𝒟𝑒𝓇𝑒𝓀vor 2 Jahren

@vercel @nextjs @neondatabase @usebland @continuedev @AnthropicAI @shadcn Looks awesome 👏🏻 What’s the next move?

Profilbild von Gillinghammer

Gillinghammervor 2 Jahren

@vercel @nextjs @neondatabase @usebland @continuedev @AnthropicAI @shadcn getting a few customers on board to pressure test. working w a staffing firm right now that currently screen 3,000 candidates per week - goal is to fully automate those by June.

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I built a money printer 🤯 Imagine listing items once and having an AI sales agent handle EVERYTHING else. That's what i built this weekend ✨ 1. Upload a photo of your item 2. AI analyzes brand, model & condition 3. Research prices across Facebook & Amazon 4. AI agent takes over: ◆ Answers buyer questions instantly ◆ Counters lowball offers intelligently ◆ Closes the deals at your target price ◆ You just ship the item You literally just: → Take photo → Ship item → Get paid This This project is 100% free and Open Source. I built this to test out AI agents Tech Stack: Vercel AI SDK shadcn Prisma Postgres #buildinpublic #opensource

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📣 My newest course is out! 📣 This time we will be building an original SaaS Platform for video calls and summaries with AI Agents 🤖 Are you ready to make 1M ARR 😁? 🤖 Real-time video calls with AI agents 📞 Video and chat powered by Stream 🔐 Authentication handled by Better Auth 💳 Subscription payments via Polar 🧠 AI summaries and context chat using OpenAI ⚙️ Background jobs powered by Inngest 🧑‍💻 Code reviews with CodeRabbit 🗄️ Scalable Postgres database via Neon Postgres 🌐 Built with Next.js 15 and React 19 🎨 Tailwind CSS v4 and shadcn UI for styling 📱 Fully responsive and mobile-friendly 🚀 Ready for production deployment on Vercel

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💥 Introducing Vibe, an AI powered app builder similar to Lovable, Replit and v0. No, this isn't my SaaS announcement, this is a product that you will learn how to build in my newest FREE course! 🚀 🚀 Next.js 15 + React 19 🎨 Tailwind v4 + shadcn UI 📡 tRPC.io 🧙‍♂️ for full-stack type safety 🔁 Inngest background jobs 🧠 Inngest agent toolkit 🔐 authentication 💳 billing 🧱 Component and app generation from AI prompts 🗂️ Live project preview with URL access 🖥️ E2B cloud sandboxes for runtime execution 🐳 Docker -based sandbox templating 🧠 AI model support (OpenAI, Anthropic, Grok) 📦 Prisma + Neon Postgres for database integration 🤖 CodeRabbit AI-powered PR reviews 🧾 Built-in credit system with usage tracking 🧪 Preview + code explorer toggle

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Code With Antonio

92,891 Aufrufe • vor 1 Jahr

A voice agent powered by gpt-oss. Running locally on my macBook. Demo recorded in a Waymo with WiFi turned off. I'm still on my space game voice AI kick, obviously. Code link below. For conversational voice AI, you want to set the gpt-oss reasoning behavior to "low". (The default is "medium".) Notes on how to do that and a jinja template you can use are in the repo. The LLM in the demo video is the big, 120B version of gpt-oss. You can use the smaller, 20B model for this, of course. But OpenAI really did a cool thing here designing the 120B model to run in "just" 80GB of VRAM. And the llama.cpp mlx inference is fast: ~250ms TTFT. Running a big model on-device feels like a time warp into the future of AI.

A voice agent powered by gpt-oss. Running locally on my macBook. Demo recorded in a Waymo with WiFi turned off. I'm still on my space game voice AI kick, obviously. Code link below. For conversational voice AI, you want to set the gpt-oss reasoning behavior to "low". (The default is "medium".) Notes on how to do that and a jinja template you can use are in the repo. The LLM in the demo video is the big, 120B version of gpt-oss. You can use the smaller, 20B model for this, of course. But OpenAI really did a cool thing here designing the 120B model to run in "just" 80GB of VRAM. And the llama.cpp mlx inference is fast: ~250ms TTFT. Running a big model on-device feels like a time warp into the future of AI.

kwindla

202,147 Aufrufe • vor 11 Monaten

$I just compared Claude Code vs Codex vs Cursor CLI The task was to build a Next.js app with Tailwind 4 and shadcn components to collect customer feedback and showcase it with a widget. I gave all three the same prompt and let them go for 30 minutes to see what they came up with. Claude Code with Opus 4.1 Even though I told it to set up the app in the existing project folder, it tried to create a directory for it. After I interrupted and told it not to do that, it built a demo form and landing page with no errors. I had to ask it to make the demo interactive so users could submit a testimonial and preview it. The landing page looked like AI and was pretty basic, but it worked and it was done in a fraction of the time of the others. Total tokens used: 33k Codex with GPT-5 At the end of the 30 minutes I just could not get Codex to produce a working app. It got stuck in a loop of not being able to set up Tailwind 4 and despite many, MANY, attempts, I ended up with a "failed to compile" error. Total tokens used: 102k Cursor Agent with GPT-5 This was the slowest agent by far and a couple of times I actually thought it got stuck in a loop and was close to Ctrl+C'ing to cancel it. The TUI is really nice though, especially how it shows diffs and it did eventually build a working app (after one or two slight errors that needed fixing) The demo was interactive and it had a very minimal design that looked bare but also a lot less like an "AI generated" app than the Opus 4.1 design. It also wasn't too chatty and just did what it needed to do! Code quality was on a par with Opus 4.1, but it did use 5.5x as many tokens to get there. Still cheaper than Opus on a direct comparison but not when you factor in a Claude Code Max subscription. Total tokens: 188k I'll be able to do a proper comparison and record some videos when I'm back from holiday but for now, Opus is still the more capable model out of the box and Claude Code is the more complete CLI product. It will be interesting to see how Cursor evolve their CLI though with commands and subagents because I think with GPT-5 they have a real shot at providing competition for Claude Code if they can optimise output to get similar quality with less tokens. Jump to 0:40 in the video to see the two apps. Which do you think is which? ;)$

I just compared Claude Code vs Codex vs Cursor CLI The task was to build a Next.js app with Tailwind 4 and shadcn components to collect customer feedback and showcase it with a widget. I gave all three the same prompt and let them go for 30 minutes to see what they came up with. Claude Code with Opus 4.1 Even though I told it to set up the app in the existing project folder, it tried to create a directory for it. After I interrupted and told it not to do that, it built a demo form and landing page with no errors. I had to ask it to make the demo interactive so users could submit a testimonial and preview it. The landing page looked like AI and was pretty basic, but it worked and it was done in a fraction of the time of the others. Total tokens used: 33k Codex with GPT-5 At the end of the 30 minutes I just could not get Codex to produce a working app. It got stuck in a loop of not being able to set up Tailwind 4 and despite many, MANY, attempts, I ended up with a "failed to compile" error. Total tokens used: 102k Cursor Agent with GPT-5 This was the slowest agent by far and a couple of times I actually thought it got stuck in a loop and was close to Ctrl+C'ing to cancel it. The TUI is really nice though, especially how it shows diffs and it did eventually build a working app (after one or two slight errors that needed fixing) The demo was interactive and it had a very minimal design that looked bare but also a lot less like an "AI generated" app than the Opus 4.1 design. It also wasn't too chatty and just did what it needed to do! Code quality was on a par with Opus 4.1, but it did use 5.5x as many tokens to get there. Still cheaper than Opus on a direct comparison but not when you factor in a Claude Code Max subscription. Total tokens: 188k I'll be able to do a proper comparison and record some videos when I'm back from holiday but for now, Opus is still the more capable model out of the box and Claude Code is the more complete CLI product. It will be interesting to see how Cursor evolve their CLI though with commands and subagents because I think with GPT-5 they have a real shot at providing competition for Claude Code if they can optimise output to get similar quality with less tokens. Jump to 0:40 in the video to see the two apps. Which do you think is which? ;)

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194,949 Aufrufe • vor 11 Monaten

quick demo showing how Market Analyst GPT can analyze any chart from a screenshot try it:

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238,987 Aufrufe • vor 2 Jahren

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luca. ∆ИƉЯƐΛ

156,686 Aufrufe • vor 4 Monaten

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Code With Antonio

11,638 Aufrufe • vor 1 Jahr

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

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66,339 Aufrufe • vor 2 Monaten

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okay Higgsfield Supercomputer is genuinely insane 🤯 it scans your competitors every Monday and drafts 3 ads before you open your laptop it's a cloud-native AI agent built for end-to-end creative production no local setup. runs in your browser or Telegram. here's what it actually does: → orchestrates every major model Claude Opus 4.7, GPT-5.5 Pro, Gemini 3.1 Pro, plus every image and video model → three layers of memory brief it once, it remembers context across every session → 40+ built-in tools with Skills you teach once and trigger with a slash command → scheduled tasks daily ad variations, weekly competitor scans, monthly content calendars → connects to Slack, Drive, Notion, Gmail, Figma and 30+ more powered by enhanced Hermes Agent Higgsfield was an AI video tool, this isn't a video tool anymore it's the whole studio.

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17,975 Aufrufe • vor 2 Monaten

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Matt Turck

11,908 Aufrufe • vor 2 Monaten

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4,745,234 Aufrufe • vor 4 Monaten

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"This PM skill has our language, ideas, and principles. It's like a fully onboarded employee." Here's my new episode with Eno Reyes (co-founder Factory) where he shares: ✅ Live demo: Building an app from a Granola meeting transcript ✅ His PM skill for writing PRDs, prioritizing features, and much more ✅ Real talk about competing in the white hot AI coding space Some quotes from Eno: "AI agents aren't just for engineers. Internally, our number three user is in sales." "Use Opus to plan and GPT to execute. That combo outperforms either alone." "Everyone in your company should have access to read the code base to ask questions." 📌 Watch now: Thanks to our sponsors: Linear: The AI agent platform for modern teams Replit ⠕: From 0 to full stack app in 2 min.

Peter Yang

77,246 Aufrufe • vor 5 Monaten

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!

Avi Chawla

186,931 Aufrufe • vor 7 Monaten

🚨BREAKING: Someone just built the open source Loom killer that turns your screen recordings into AI agents. It's called Bloom and it does something Loom never could. Your recordings don't just sit as files. They become queryable, searchable, agent-ready data the moment you stop recording. Here's what actually happens: You hit record. Bloom captures your screen, mic, and system audio locally. While you're still recording, it uploads chunks to the cloud in real time. The second you stop, it auto-generates a full transcript, creates visual embeddings, and indexes every spoken word. Then the wild part kicks in. You can query your recordings through APIs. You can hook Claude Code directly into your video library and ask it questions. Your screen recording just became a knowledge base. → Local-first, your files never get locked in → Cmd+Shift+R starts and stops from anywhere on your system → One-click shareable link for any recording → Chat directly with your video content through VideoDB Chat → Works with Claude Code and other agent frameworks out of the box Recordings are no longer files. They're inputs for AI. MIT License. 100% Opensource. Link in comments.

🚨BREAKING: Someone just built the open source Loom killer that turns your screen recordings into AI agents. It's called Bloom and it does something Loom never could. Your recordings don't just sit as files. They become queryable, searchable, agent-ready data the moment you stop recording. Here's what actually happens: You hit record. Bloom captures your screen, mic, and system audio locally. While you're still recording, it uploads chunks to the cloud in real time. The second you stop, it auto-generates a full transcript, creates visual embeddings, and indexes every spoken word. Then the wild part kicks in. You can query your recordings through APIs. You can hook Claude Code directly into your video library and ask it questions. Your screen recording just became a knowledge base. → Local-first, your files never get locked in → Cmd+Shift+R starts and stops from anywhere on your system → One-click shareable link for any recording → Chat directly with your video content through VideoDB Chat → Works with Claude Code and other agent frameworks out of the box Recordings are no longer files. They're inputs for AI. MIT License. 100% Opensource. Link in comments.

Rimsha Bhardwaj

128,583 Aufrufe • vor 3 Monaten

(4 DAYS BEFORE SUBMISSIONS CLOSE) I get this question a lot about the Find Evil! hackathon: What does “find evil” actually mean? In this case, the name comes from a real command. I built an autonomous incident response agent I built on the SIFT Workstation. Then I typed “find evil” as a prompt into Claude Code. And it did (watch the demo). I was blown away to watch the autonomous agent run a complete C drive forensic analysis, across 200+ tools via MCP. The agent identified threat actor and context, the attack chain, malware deployment method, persistence mechanisms, code injection analysis, network connections, command-and-control (C2) infrastructure, a complete malicious process tree, and a chronological activity timeline. Two days after I shared initial findings, Anthropic released their report on how threat actors were deploying Claude Code with operational tools and letting it go do evil. (Same thing I was doing.) Find Evil! is the first hackathon dedicated to building autonomous AI agents for incident response. 4,178 defenders are working on final Find Evil! hackathon submits. (This number makes me very happy to see so many diving in. And wishing that the thousands more in our community were experimenting with us.) Your job: teach an AI agent to think like a senior analyst, how to sequence its approach, recognize when something doesn’t add up, and self-correct when it gets it wrong. There are FOUR DAYS left to build with us! (Very few of us are actual AI experts. The rest of us including me are learning.) Register: Apply to judge: We need DFIR, AI, cybersecurity, and open-source reviewers who can separate useful autonomous response tools from polished demos. Apply: I am SO EXCITED to see what comes out of this hackathon and goes back to the community. Sponsored by SANS Institute

(4 DAYS BEFORE SUBMISSIONS CLOSE) I get this question a lot about the Find Evil! hackathon: What does “find evil” actually mean? In this case, the name comes from a real command. I built an autonomous incident response agent I built on the SIFT Workstation. Then I typed “find evil” as a prompt into Claude Code. And it did (watch the demo). I was blown away to watch the autonomous agent run a complete C drive forensic analysis, across 200+ tools via MCP. The agent identified threat actor and context, the attack chain, malware deployment method, persistence mechanisms, code injection analysis, network connections, command-and-control (C2) infrastructure, a complete malicious process tree, and a chronological activity timeline. Two days after I shared initial findings, Anthropic released their report on how threat actors were deploying Claude Code with operational tools and letting it go do evil. (Same thing I was doing.) Find Evil! is the first hackathon dedicated to building autonomous AI agents for incident response. 4,178 defenders are working on final Find Evil! hackathon submits. (This number makes me very happy to see so many diving in. And wishing that the thousands more in our community were experimenting with us.) Your job: teach an AI agent to think like a senior analyst, how to sequence its approach, recognize when something doesn’t add up, and self-correct when it gets it wrong. There are FOUR DAYS left to build with us! (Very few of us are actual AI experts. The rest of us including me are learning.) Register: Apply to judge: We need DFIR, AI, cybersecurity, and open-source reviewers who can separate useful autonomous response tools from polished demos. Apply: I am SO EXCITED to see what comes out of this hackathon and goes back to the community. Sponsored by SANS Institute

Rob T. Lee

14,405 Aufrufe • vor 1 Monat

“We’ve got /loop, /goal, and workflows. They all get the agent to run for long periods of time.” Here’s my new episode with Thariq from the Claude Code team, where he shared how he plans, builds, and runs loops with Claude Code: → How /loop, /goal, and workflows work differently → Live demo: Editing launch videos with Claude → Using HTML artifacts to plan and learn Some quotes from Thariq: “We cut Claude Code’s system prompt by 80%. As models have gotten smarter, they need less direction, fewer constraints, and fewer examples.” “Planning is an iterative process of exploring, investigating, and finding out what you don’t know.” “One failure mode I see is that people glaze over AI’s plans. You want to make sure it’s something you read.” I've been wanting to interview Thariq for a long time and I learned so much from this epsiode. 📌 Watch now: Thanks to our sponsors: Riverside: All-in-one AI studio for podcasts and video Wispr Flow: 4x faster than typing with your voice

“We’ve got /loop, /goal, and workflows. They all get the agent to run for long periods of time.” Here’s my new episode with Thariq from the Claude Code team, where he shared how he plans, builds, and runs loops with Claude Code: → How /loop, /goal, and workflows work differently → Live demo: Editing launch videos with Claude → Using HTML artifacts to plan and learn Some quotes from Thariq: “We cut Claude Code’s system prompt by 80%. As models have gotten smarter, they need less direction, fewer constraints, and fewer examples.” “Planning is an iterative process of exploring, investigating, and finding out what you don’t know.” “One failure mode I see is that people glaze over AI’s plans. You want to make sure it’s something you read.” I've been wanting to interview Thariq for a long time and I learned so much from this epsiode. 📌 Watch now: Thanks to our sponsors: Riverside: All-in-one AI studio for podcasts and video Wispr Flow: 4x faster than typing with your voice

Peter Yang

186,950 Aufrufe • vor 9 Tagen

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.

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

228,113 Aufrufe • vor 3 Monaten

Short every SaaS company on planet earth Today Cursor announced a REALLY sick feature that probably cost them millions of dollars to make Their AI agent records demo videos of itself after it builds things I gave the announcement to my OpenClaw. It built it out in 5 minutes. I pasted in the announcement to Henry. He said on it chief. 5 minutes later he not only built out the entire feature, but recorded a demo video of it too It's now implemented into our entire workflow. Now every time I ask my OpenClaw to build something, a demo video will be attached to every PR At this point how does any SaaS survive? You can take quite literally any feature they build, give it to your personal assistant, and it's built out in 5 minutes What moat is left? When I have superintelligence running locally on my mac studio, and it's able to build out any piece of software I can imagine in minutes, literally what value is left in any software company? This is the most exciting, frightening, awe inspiring time to ever be alive

Short every SaaS company on planet earth Today Cursor announced a REALLY sick feature that probably cost them millions of dollars to make Their AI agent records demo videos of itself after it builds things I gave the announcement to my OpenClaw. It built it out in 5 minutes. I pasted in the announcement to Henry. He said on it chief. 5 minutes later he not only built out the entire feature, but recorded a demo video of it too It's now implemented into our entire workflow. Now every time I ask my OpenClaw to build something, a demo video will be attached to every PR At this point how does any SaaS survive? You can take quite literally any feature they build, give it to your personal assistant, and it's built out in 5 minutes What moat is left? When I have superintelligence running locally on my mac studio, and it's able to build out any piece of software I can imagine in minutes, literally what value is left in any software company? This is the most exciting, frightening, awe inspiring time to ever be alive

Alex Finn

814,042 Aufrufe • vor 5 Monaten

She literally explained how her $100M AI startup runs completely out of an operating system in Claude Code: 2:04 - The Company OS GitHub structure 5:40 - The 1% vs 99% problem 9:00 - 3 steps to build your own Company OS 12:30 - Slack automation demo: feature request triage 14:31 - Playbook to agent pipeline 22:51 - Company culture needed 29:02 - PMs shipping front-end + back-end 29:44 - The captain model explained 32:37 - Continuation to captain model 37:38 - Two-track product reviews 50:08 - The AI Ops team and the Sasha model 57:59 - The screen-share interview 59:01 - The 4 levels of AI maturity

She literally explained how her $100M AI startup runs completely out of an operating system in Claude Code: 2:04 - The Company OS GitHub structure 5:40 - The 1% vs 99% problem 9:00 - 3 steps to build your own Company OS 12:30 - Slack automation demo: feature request triage 14:31 - Playbook to agent pipeline 22:51 - Company culture needed 29:02 - PMs shipping front-end + back-end 29:44 - The captain model explained 32:37 - Continuation to captain model 37:38 - Two-track product reviews 50:08 - The AI Ops team and the Sasha model 57:59 - The screen-share interview 59:01 - The 4 levels of AI maturity

Aakash Gupta

386,296 Aufrufe • vor 1 Monat