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Anthropic pays $785K/year for Forward Deployed Engineers. in 15-minute workshop, Palantir’s senior engineers revealed the actual AI FDE roadmap in a live demo: • 10% → 0:44 - the manual baseline: hundreds of safety incidents triaged by hand every week • 30% → 3:23 - one prompt + ontology... show more

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OpenAI pays $785K/year to developers who know how to apply Forward Deployed Engineering in AI. in 40-minute talk, Head of FDE at OpenAI revealed full roadmap for how they actually use FDE internally: • 10% → 2:46 - why Morgan Stanley was their first FDE case • 30% → 9:05 - FDE eval-driven development explained • 55% → 16:01 - FDE live-demo: LLM rerouting a supply chain • 80% → 24:33 - advice for founders building FDE teams • 100% → 29:06 - the biggest FDE mistake they made this year 40 minutes replaces a $500 enterprise AI deployment course bookmark & watch - then read how to become FDE engineer in article below ↓

OpenAI pays $785K/year to developers who know how to apply Forward Deployed Engineering in AI. in 40-minute talk, Head of FDE at OpenAI revealed full roadmap for how they actually use FDE internally: • 10% → 2:46 - why Morgan Stanley was their first FDE case • 30% → 9:05 - FDE eval-driven development explained • 55% → 16:01 - FDE live-demo: LLM rerouting a supply chain • 80% → 24:33 - advice for founders building FDE teams • 100% → 29:06 - the biggest FDE mistake they made this year 40 minutes replaces a $500 enterprise AI deployment course bookmark & watch - then read how to become FDE engineer in article below ↓

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Creator of Claude Code just dropped a free masterclass on agent engineering with Opus 5 from 0% to 100%: “80% of our engineers are using self-improving loops” 10% → 3:38 - why they deleted 80% of the system prompt 30% →6:55 - delete your CLAUDE.md every 6 months 55% →17:44 - one prompt, 11 days, entire codebase rewritten 80% → 21:59 - the prompt that's been running for 15 days 100% →25:27 - two promts that spawn 1,000 agents most people are still writing longer prompts - he's deleting them and running 1,000 agents instead bookmark & watch - then read the full agent orchestration playbook below ↓

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Anthropic Managed Agents Lead: "At Anthropic, >90% of our engineers are building with self-improving loops. In 4-6 months, it will be 100%. my agentic loops can run for hours without spending hundreds of dollars." in this 40-minute podcast, an Anthropic team lead reveals how to build effective agents from scratch. Agent → harness → loops → memory = modern agent This one video will replace 10 paid courses on vibe-coding. Watch it today, then explore the same setup in the article below.

Anthropic Managed Agents Lead: "At Anthropic, >90% of our engineers are building with self-improving loops. In 4-6 months, it will be 100%. my agentic loops can run for hours without spending hundreds of dollars." in this 40-minute podcast, an Anthropic team lead reveals how to build effective agents from scratch. Agent → harness → loops → memory = modern agent This one video will replace 10 paid courses on vibe-coding. Watch it today, then explore the same setup in the article below.

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Anthropic engineer: "At Anthropic, >90% of our engineers are building with self-improving loops. In 4-6 months, it will be 100%. my agentic loops can run for days without spending hundreds of dollars." in this 40-minute podcast, an Anthropic engineer reveals how to build effective agents from scratch. Agent → harness → loops → memory = modern agent This one video will replace 10 paid courses on vibe-coding. Watch it today, then explore the same setup in the article below.

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

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We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.

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Codez

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Anthropic engineer just released a 2-hour workshop on "Graph Engineering" for agentic systems: “80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.” • 00:00 - Introduction to RAG & Graphs • 06:39 - Core of "Graph Engineering" (state, nodes) • 14:29 - 3 feedback loops of Graph agents • 23:06 - Agent evaluation with Graphs • 36:29 - Agent cycles in graphs • 1:15:22 - Agentic RAG & agent context • 1:41:20 - Evaluation datasets based on Graphs This 2-hour workshop will replace 10 paid courses on agentic engineering. Watch it today, then learn how to become a Graph Engineer in the article below.

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Codez

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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

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

Dan Shipper 📧

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At first, prompting seemed to be a temporary workaround for getting the most out of large language models. But over time, it's become critical to the way we interact with AI. On the Lightcone Podcast, Garry, Harj, Diana, and Jared break down what they've learned from working with hundreds of founders building with LLMs: why prompting still matters, where it breaks down, and how teams are making it more reliable in production. They share real examples of prompts that failed, how companies are testing for quality, and what the best teams are doing to make LLM outputs useful and predictable. 0:58 - Parahelp’s prompt example 4:59 - Different types of prompts 6:51 - Metaprompting 7:58 - Using examples 12:10 - Some tricks for longer prompts 14:18 - Findings on evals 17:25 - Every founder has become a forward-deployed engineer (FDE) 23:18 - Vertical AI agents are closing big deals with the FDE model 26:13 - The personalities of the different LLMs 27:26 - Lessons from rubrics 29:47 - Kaizen and the art of communication

At first, prompting seemed to be a temporary workaround for getting the most out of large language models. But over time, it's become critical to the way we interact with AI. On the Lightcone Podcast, Garry, Harj, Diana, and Jared break down what they've learned from working with hundreds of founders building with LLMs: why prompting still matters, where it breaks down, and how teams are making it more reliable in production. They share real examples of prompts that failed, how companies are testing for quality, and what the best teams are doing to make LLM outputs useful and predictable. 0:58 - Parahelp’s prompt example 4:59 - Different types of prompts 6:51 - Metaprompting 7:58 - Using examples 12:10 - Some tricks for longer prompts 14:18 - Findings on evals 17:25 - Every founder has become a forward-deployed engineer (FDE) 23:18 - Vertical AI agents are closing big deals with the FDE model 26:13 - The personalities of the different LLMs 27:26 - Lessons from rubrics 29:47 - Kaizen and the art of communication

Y Combinator

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A GERMAN DEVELOPER REPLACED HIS ENTIRE DEV TEAM WITH KIMI K2.6, VISUALIZED EVERYTHING IN OBSIDIAN AND NOW MAKES $80,000/MONTH SOLO 1 trillion parameters, 32 billion activated per token and a SWE-Bench score of 65.8 - Kimi K2.6 reads the entire client codebase, understands the architecture, writes production code and ships for $150-300 in API costs while a traditional agency pays developers $4,800 for the exact same project. 300 parallel agents per run deliver 100+ files simultaneously - search, analysis, coding and writing all in parallel - and Obsidian visualizes the entire knowledge graph in real time while the agents work. A traditional agency with 10-15 people keeps 30% margin after salaries. He keeps 90% - $72,000 in monthly profit with $500 in overhead. By month 10 Kimi handles 80% of the technical work and he manages only strategy and client relationships - while Obsidian maps every project, every client and every agent in one graph that updates itself.

Noisy

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Custom Agents become generally available today. What we learned in the alpha: - Over a million custom agents went live! The use cases are SO broad. Seeing what people do with them is easily my favorite part of the job. - Agents are not just for engineers! People from every function are now building their own factories. (watch this space at Developer day next week!) - The combination of custom agents with Notion Workers is basically magic! What you can automate is now limited by your own imagination. - Control matters! Context, permissions, and cost came up a lot, and we shipped major improvements within the alpha. - On cost, people love the ability to experiment with different models, and swap out more expensive models with open-weight ones, where it makes sense. - Usage-based pricing is new for Notion, but becoming the standard for AI. It fits the idea of intelligence as a service. Really grateful for all the feedback. It directly shaped what we shipped. Keep it coming, my DMs are open.

Custom Agents become generally available today. What we learned in the alpha: - Over a million custom agents went live! The use cases are SO broad. Seeing what people do with them is easily my favorite part of the job. - Agents are not just for engineers! People from every function are now building their own factories. (watch this space at Developer day next week!) - The combination of custom agents with Notion Workers is basically magic! What you can automate is now limited by your own imagination. - Control matters! Context, permissions, and cost came up a lot, and we shipped major improvements within the alpha. - On cost, people love the ability to experiment with different models, and swap out more expensive models with open-weight ones, where it makes sense. - Usage-based pricing is new for Notion, but becoming the standard for AI. It fits the idea of intelligence as a service. Really grateful for all the feedback. It directly shaped what we shipped. Keep it coming, my DMs are open.

Akshay Kothari

29,899 görüntüleme • 3 ay önce

New The Peel with Tony Holdstock-Brown • Inngest - the hidden layer of AI infrastructure - why evals today are "batshit insane" - building Inngest's own cloud to get 20x lower cost - growing 35x YoY after AWS and Cloudflare copied them, and - building a company without a personal brand Timestamps: 0:00 The hidden infra layer every AI agent runs on 1:46 Building complex chains of logic 3:31 Why agent SDK's don't go far enough 4:49 Healthcare was the original event-driven nightmare 6:32 Storing traces on your infrastructure enables self-improving loops 14:26 Why Inngest was already in the right place for AI 15:49 Score agents off product events, not LLM's 17:31 The OpenAI copy-paste signal 21:24 Swap in LLMs and cut costs 23:44 How customers pulled the product forward 25:41 Orchestration belongs outside the sandbox 29:48 Building a neocloud to cut costs 20x 32:09 Most neoclouds just resell AWS 32:54 AI infrastructure is all converging 34:49 Why Claude can't just build your backend 36:44 How to build a software factory 39:12 Agents are a lottery you get addicted to 42:44 Loops must always exist 45:38 If models keep getting better, why orchestrate? 48:28 When incumbents steal your features 52:30 You can't vibe code infrastructure 55:54 Why Tony has no personal brand 59:38 Dev tools GTM without Twitter 1:03:20 Lessons from the founder of DuckDuckGo 1:10:39 Truth as a company value 1:13:08 Taking too long adapting to AI 1:15:10 Startups are 100% R&D 1:17:19 Ali from Databricks 1:19:03 Writing his own code, Voice-to-text with local models 1:23:53 Evals are batshit insane

Turner Novak 🍌🧢

46,735 görüntüleme • 17 gün önce

Over the past year, we've been building our own internal agent infrastructure at YC: over 350 tools, self-improving skill loops, and a shared organizational brain that gets smarter overnight. In this episode of the Lightcone Podcast, we sat down with YC General Partner Pete Pete Koomen to talk about how he led the effort from the ground up. We cover how giving agents unrestricted access to one database was the key unlock, the self-improving skill loops that get smarter overnight, and why he thinks we've arrived at the personal computer moment for AI. 00:39 — YC's AI Stack 02:15 — The Finance Team Problem That Started It All 05:07 — SQL Access Changes Everything 07:20 — One Database to Rule Them All 09:14 — Jevons Paradox 10:07 — Denormalizing for Agents 12:15 — The Single-Player Era of Agents 14:16 — 350 Tools and a Shared Registry 16:24 — Skillify, DRY, and MECE Resolvers 18:23 — The Self-Improving Dream Cycle 20:26 — The Two-Sentence Pitch Skill 23:06 — How Super Intelligence Compounds 25:10 — Recording Everything as a Building Layer 27:10 — The Shared Organizational Brain 29:18 — Trust-Default Culture as a Requirement 30:44 — Raising the Floor for New Employees 32:35 — Horseless Carriages 34:24 — Why Chat Is the Best Interface for Agents 38:50 — Just-in-Time Software 40:49 — Centralizing vs. Decentralizing AI 43:32 — The Personal AI Revolution

Over the past year, we've been building our own internal agent infrastructure at YC: over 350 tools, self-improving skill loops, and a shared organizational brain that gets smarter overnight. In this episode of the Lightcone Podcast, we sat down with YC General Partner Pete Pete Koomen to talk about how he led the effort from the ground up. We cover how giving agents unrestricted access to one database was the key unlock, the self-improving skill loops that get smarter overnight, and why he thinks we've arrived at the personal computer moment for AI. 00:39 — YC's AI Stack 02:15 — The Finance Team Problem That Started It All 05:07 — SQL Access Changes Everything 07:20 — One Database to Rule Them All 09:14 — Jevons Paradox 10:07 — Denormalizing for Agents 12:15 — The Single-Player Era of Agents 14:16 — 350 Tools and a Shared Registry 16:24 — Skillify, DRY, and MECE Resolvers 18:23 — The Self-Improving Dream Cycle 20:26 — The Two-Sentence Pitch Skill 23:06 — How Super Intelligence Compounds 25:10 — Recording Everything as a Building Layer 27:10 — The Shared Organizational Brain 29:18 — Trust-Default Culture as a Requirement 30:44 — Raising the Floor for New Employees 32:35 — Horseless Carriages 34:24 — Why Chat Is the Best Interface for Agents 38:50 — Just-in-Time Software 40:49 — Centralizing vs. Decentralizing AI 43:32 — The Personal AI Revolution

Y Combinator

753,647 görüntüleme • 2 ay önce

Agents Already Spent $76 Million: Circle's AI Agent stack, x402, stablecoins and the Agentic Economy Shoal Signal with Corey Cooper, Senior Manager, DevRel at Circle, hosted by Zaddycoin We cover how Circle is building the agent money stack with $USDC, Arc, x402, why EIP-3009 makes payments gasless, who is actually transacting today, whether SaaS is dead, and why agents buying their own services points to a trillion dollar TAM. 0:00 What Circle is building for agents 3:01 Why x402 matters for builders 6:54 USDC, gasless transfers, and facilitators 10:57 The Circle agent stack and nano payments 14:17 Who is using x402 and for what 16:46 Pay-per-use flattens the internet 19:50 A new persona, and $76 million transacted 23:03 What is missing for agent to agent 28:05 Every app has APIs, the TAM is trillions 29:26 Is SaaS dead 31:57 Discovery as the real value layer 36:28 How Circle curated its marketplace 41:23 Why it has to run on stablecoins 46:00 Cash App to agent in one second 49:38 Where the alpha is, trading on Robinhood 52:08 Try it yourself

Agents Already Spent $76 Million: Circle's AI Agent stack, x402, stablecoins and the Agentic Economy Shoal Signal with Corey Cooper, Senior Manager, DevRel at Circle, hosted by Zaddycoin We cover how Circle is building the agent money stack with $USDC, Arc, x402, why EIP-3009 makes payments gasless, who is actually transacting today, whether SaaS is dead, and why agents buying their own services points to a trillion dollar TAM. 0:00 What Circle is building for agents 3:01 Why x402 matters for builders 6:54 USDC, gasless transfers, and facilitators 10:57 The Circle agent stack and nano payments 14:17 Who is using x402 and for what 16:46 Pay-per-use flattens the internet 19:50 A new persona, and $76 million transacted 23:03 What is missing for agent to agent 28:05 Every app has APIs, the TAM is trillions 29:26 Is SaaS dead 31:57 Discovery as the real value layer 36:28 How Circle curated its marketplace 41:23 Why it has to run on stablecoins 46:00 Cash App to agent in one second 49:38 Where the alpha is, trading on Robinhood 52:08 Try it yourself

Shoal Research

20,578 görüntüleme • 1 ay önce

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

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

228,270 görüntüleme • 3 ay önce

Crypto built the root of trust. AI needs it. At Permissionless Wealth (自由财富) IV’s Open Source AI Summit presented by NEAR Protocol, Sreeram Kannan dropped the most important update in Crypto x AI this year. ☁️ Eigencloud Supercharging Verifiable AI. ☁️ Crypto built the foundation for digital property, programmable money, and trustless execution. But it was never built to run AI. EigenCloud changes that. For the first time, you can deploy unstoppable, verifiable AI agents, not behind an API key, but as sovereign entities that own, earn, and act onchain. 🔥 Smart contracts that think. 🔥 AI that can stake. 🔥 Agents that can own. This is not a demo. It’s a new reality. And it’s the most powerful unlock in crypto since Ethereum. EigenCloud Timestamps:⁣ 0:10 Introduction⁣ 0:32 EigenLayer → EigenCloud 1:53 Autonomous Verifiable Services 3:06 Evolving from Infra to Apps⁣ to AI 5:41 EigenCloud Architecture 9:30 Bringing AI Onchain⁣ with EigenVerify 12:01 Supercharge AI 15:44 Sovereign AI Agents 17:48 Making Autonomous Agents Practical⁣ 18:37 AI Inference, Model, and Agents 19:03 Deep stack of Verifiability 21:15 Call to Builders: Build Powerful Expressive Apps with Agents

Crypto built the root of trust. AI needs it. At Permissionless Wealth (自由财富) IV’s Open Source AI Summit presented by NEAR Protocol, Sreeram Kannan dropped the most important update in Crypto x AI this year. ☁️ Eigencloud Supercharging Verifiable AI. ☁️ Crypto built the foundation for digital property, programmable money, and trustless execution. But it was never built to run AI. EigenCloud changes that. For the first time, you can deploy unstoppable, verifiable AI agents, not behind an API key, but as sovereign entities that own, earn, and act onchain. 🔥 Smart contracts that think. 🔥 AI that can stake. 🔥 Agents that can own. This is not a demo. It’s a new reality. And it’s the most powerful unlock in crypto since Ethereum. EigenCloud Timestamps:⁣ 0:10 Introduction⁣ 0:32 EigenLayer → EigenCloud 1:53 Autonomous Verifiable Services 3:06 Evolving from Infra to Apps⁣ to AI 5:41 EigenCloud Architecture 9:30 Bringing AI Onchain⁣ with EigenVerify 12:01 Supercharge AI 15:44 Sovereign AI Agents 17:48 Making Autonomous Agents Practical⁣ 18:37 AI Inference, Model, and Agents 19:03 Deep stack of Verifiability 21:15 Call to Builders: Build Powerful Expressive Apps with Agents

EigenCloud

65,722 görüntüleme • 1 yıl önce

My second interview with Fil Aronshtein (Fil Aronshtein) co-founder of Dirac Dirac just released the ITAR-compliant enterprise version of BuildOS. Dirac supercharges manufacturers by automating their production planning with just a CAD file. Link to interview on YouTube in comment below! 0:19 Moving from El Segundo into the Empire State Building 3:30 BuildOS - the automated work instruction platform 7:50 Designing a product for manufacturing engineers 13:15 Building strong customer relationships 17:25 Bringing production planning context back to the US 20:52 Trends in reshoring 24:27 Biggest challenge in the past year 28:17 Understanding your customers 33:16 Advantages of being in the Empire State Building 36:13 Working with a great co-founder 39:45 Chewing glass 44:55 Focus over the next 12 months 47:45 Having friends you can lean on during hard times 50:09 Evolving as a leader 53:37 Taking design inspiration from video games 56:49 Pride at legacy incumbents

My second interview with Fil Aronshtein (Fil Aronshtein) co-founder of Dirac Dirac just released the ITAR-compliant enterprise version of BuildOS. Dirac supercharges manufacturers by automating their production planning with just a CAD file. Link to interview on YouTube in comment below! 0:19 Moving from El Segundo into the Empire State Building 3:30 BuildOS - the automated work instruction platform 7:50 Designing a product for manufacturing engineers 13:15 Building strong customer relationships 17:25 Bringing production planning context back to the US 20:52 Trends in reshoring 24:27 Biggest challenge in the past year 28:17 Understanding your customers 33:16 Advantages of being in the Empire State Building 36:13 Working with a great co-founder 39:45 Chewing glass 44:55 Focus over the next 12 months 47:45 Having friends you can lean on during hard times 50:09 Evolving as a leader 53:37 Taking design inspiration from video games 56:49 Pride at legacy incumbents

Ti Morse

18,095 görüntüleme • 1 yıl önce