Загрузка видео...

Не удалось загрузить видео

На главную

An engineer at Block (formerly Square) has an AI agent watching his screen all day. The engineer will discuss a feature with a colleague on Slack. A few hours later, the agent has already built the feature and opened a PR. This isn't some distant AI future. It's happening...

lennysan's profile picture

Lenny Rachitsky

430,474 subscribers

503,125 просмотров • 11 месяцев назад •via X (Twitter)

ОбразованиеНаука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

My biggest takeaways from Dhanji Prasanna, CTO of Block: 1. Block’s internal AI agent "Goose" is saving employees on average 8 to 10 hours per week. The company built an open-source tool called Goose that handles tasks from organizing files to writing code. Across the entire company, they’re seeing roughly 20% to 25% of manual work hours saved, and that number keeps climbing. 2. Non-technical teams are getting the biggest productivity boost from AI, not engineers. People in legal, risk management, and operations are now building their own software tools that previously would have required months on an engineering team’s roadmap. What used to take weeks now takes hours, and employees do it themselves without waiting. 3. Changing organizational structure unlocked more productivity than any AI tool. To transform into a truly “technology driven” company, Block reorganized from separate business units (each with their own GM and engineering teams) to a single functional structure where all engineers report to one leader. This “boring” change enabled a unified technology strategy and drove more acceleration than any AI tool. 4. Code quality has almost nothing to do with product success. YouTube became one of Google’s most successful products despite storing videos as blobs in a MySQL database with a slow Python stack. Meanwhile, Google Video had superior technology with more formats and higher resolution but failed completely. The lesson: Focus on solving real problems for people, not on perfect code. 5. AI enables teams to explore multiple paths simultaneously instead of choosing one up front. Previously, limited resources meant teams had to pick their best guess for an experiment. Now AI can build multiple different approaches overnight, allowing teams to compare five or six options and throw away entire features if they don’t feel right—a practice that was unthinkable before. 6. Most successful products start as tiny experiments, not big initiatives. Cash App began as a hack-week idea. Goose started as one engineer’s side project. Block’s Bitcoin product came from a three-person hackathon team. In contrast, Google Wave had 70 to 80 engineers before having real users and failed. Small experiments that prove value beat large up-front investments. 7. Leaders must use AI tools daily to drive real organizational adoption. Block’s CEO Jack Dorsey, the CTO, and the entire executive team use Goose every single day. This hands-on experience teaches them how workflows actually change and drives authentic adoption throughout the organization far more than reading articles or attending conferences about AI. 8. AI excels at new projects but struggles with complex legacy systems. Teams building new applications or working on greenfield platforms see aggressive productivity gains. But in existing codebases with years of accumulated complexity, the gains aren’t there yet. Deploy AI where it works best rather than everywhere at once. 9. Giving away valuable technology for free can be a winning strategy. Block open-sourced Goose even though it could have been a standalone billion-dollar business. Even their competitors actively use it. The philosophy: build things that benefit everyone and outlast your own company. This commitment to open-source technology attracts talent and builds industry goodwill while advancing everyone’s capabilities. 10. Purpose should drive your technology choices, not the other way around. Rather than chasing every AI trend or trying to be at the forefront of every technology, identify what truly matters to your company and customers. Block stays focused on economic empowerment, which guides their technology decisions and keeps them from getting distracted by every new advancement. Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Sinch — Build messaging, email, and calling into your product: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 — A global leader in digital identity verification: A
1:41

My biggest takeaways from Dhanji Prasanna, CTO of Block: 1. Block’s internal AI agent "Goose" is saving employees on average 8 to 10 hours per week. The company built an open-source tool called Goose that handles tasks from organizing files to writing code. Across the entire company, they’re seeing roughly 20% to 25% of manual work hours saved, and that number keeps climbing. 2. Non-technical teams are getting the biggest productivity boost from AI, not engineers. People in legal, risk management, and operations are now building their own software tools that previously would have required months on an engineering team’s roadmap. What used to take weeks now takes hours, and employees do it themselves without waiting. 3. Changing organizational structure unlocked more productivity than any AI tool. To transform into a truly “technology driven” company, Block reorganized from separate business units (each with their own GM and engineering teams) to a single functional structure where all engineers report to one leader. This “boring” change enabled a unified technology strategy and drove more acceleration than any AI tool. 4. Code quality has almost nothing to do with product success. YouTube became one of Google’s most successful products despite storing videos as blobs in a MySQL database with a slow Python stack. Meanwhile, Google Video had superior technology with more formats and higher resolution but failed completely. The lesson: Focus on solving real problems for people, not on perfect code. 5. AI enables teams to explore multiple paths simultaneously instead of choosing one up front. Previously, limited resources meant teams had to pick their best guess for an experiment. Now AI can build multiple different approaches overnight, allowing teams to compare five or six options and throw away entire features if they don’t feel right—a practice that was unthinkable before. 6. Most successful products start as tiny experiments, not big initiatives. Cash App began as a hack-week idea. Goose started as one engineer’s side project. Block’s Bitcoin product came from a three-person hackathon team. In contrast, Google Wave had 70 to 80 engineers before having real users and failed. Small experiments that prove value beat large up-front investments. 7. Leaders must use AI tools daily to drive real organizational adoption. Block’s CEO Jack Dorsey, the CTO, and the entire executive team use Goose every single day. This hands-on experience teaches them how workflows actually change and drives authentic adoption throughout the organization far more than reading articles or attending conferences about AI. 8. AI excels at new projects but struggles with complex legacy systems. Teams building new applications or working on greenfield platforms see aggressive productivity gains. But in existing codebases with years of accumulated complexity, the gains aren’t there yet. Deploy AI where it works best rather than everywhere at once. 9. Giving away valuable technology for free can be a winning strategy. Block open-sourced Goose even though it could have been a standalone billion-dollar business. Even their competitors actively use it. The philosophy: build things that benefit everyone and outlast your own company. This commitment to open-source technology attracts talent and builds industry goodwill while advancing everyone’s capabilities. 10. Purpose should drive your technology choices, not the other way around. Rather than chasing every AI trend or trying to be at the forefront of every technology, identify what truly matters to your company and customers. Block stays focused on economic empowerment, which guides their technology decisions and keeps them from getting distracted by every new advancement. Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Sinch — Build messaging, email, and calling into your product: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 — A global leader in digital identity verification: A

Lenny Rachitsky

812,755 просмотров • 11 месяцев назад

We built a B2B SaaS sales company and here’s what it taught us about B2B SaaS sales 🧵👇 (but actually) Today we’re launching Rox, the first publicly available AI agent swarm for the top sales teams, and in the private beta it already helped reps grow their books 30%. 2025 is going to be a huge year for growth. Enterprises are doubling next year’s revenue goals, but no one is doubling team sizes. Every rep will have to bring in more, and AI can help them do that. But there’s a wrong way and a right way. Much of today’s AI aims to replace low-value work. But sales follows a power law: 90% of revenue comes from the top 15% of enterprise sales reps. The greatest gains will come from supercharging the highest value work — raising the ceiling, not the floor. Rox equips the very best with a swarm of AI agents, acting as an army of analysts to help them plan, prioritize, research, engage, and keep up with their customers. Over 35 of the best-performing enterprise sales teams have adopted Rox virally. For example, Ramp has rolled it out to their AE and AM teams, and we are now integrating their internal data systems with Rox. The Enterprise AE team alone gains 225+ hours per week to boost pipeline execution activities. Rox is now in public beta. No barriers. No need to request a demo. Try it now for free →
3:08

We built a B2B SaaS sales company and here’s what it taught us about B2B SaaS sales 🧵👇 (but actually) Today we’re launching Rox, the first publicly available AI agent swarm for the top sales teams, and in the private beta it already helped reps grow their books 30%. 2025 is going to be a huge year for growth. Enterprises are doubling next year’s revenue goals, but no one is doubling team sizes. Every rep will have to bring in more, and AI can help them do that. But there’s a wrong way and a right way. Much of today’s AI aims to replace low-value work. But sales follows a power law: 90% of revenue comes from the top 15% of enterprise sales reps. The greatest gains will come from supercharging the highest value work — raising the ceiling, not the floor. Rox equips the very best with a swarm of AI agents, acting as an army of analysts to help them plan, prioritize, research, engage, and keep up with their customers. Over 35 of the best-performing enterprise sales teams have adopted Rox virally. For example, Ramp has rolled it out to their AE and AM teams, and we are now integrating their internal data systems with Rox. The Enterprise AE team alone gains 225+ hours per week to boost pipeline execution activities. Rox is now in public beta. No barriers. No need to request a demo. Try it now for free →

Rox

243,136 просмотров • 1 год назад

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
2:49

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 просмотров • 1 год назад

instead of watching 2 hours of Netflix tonight, watch this 40-minute masterclass from the founder of a $20B China AI company it's the clearest explanation I've seen of how Agent Swarms and AI systems actually work at scale useful whether you've never built an agent in your life or have been using Claude every day for the past year I took the key ideas and turned them into a practical guide on how to actually build with Kimi find it below
39:17

instead of watching 2 hours of Netflix tonight, watch this 40-minute masterclass from the founder of a $20B China AI company it's the clearest explanation I've seen of how Agent Swarms and AI systems actually work at scale useful whether you've never built an agent in your life or have been using Claude every day for the past year I took the key ideas and turned them into a practical guide on how to actually build with Kimi find it below

Kirill

13,894,911 просмотров • 4 месяцев назад

ALICE Agent Deployment is live Users can now deploy their own ALICE AI agent to monitor tokens 24/7 with real-time risk detection. >Lifetime access with 1,000,000 $ALICE >Track up to 10 tokens at once >Live ALICE AI risk analysis every 10 minutes >Seamlessly add and remove tokens as needed >Keep an active $ALICE dashboard running while trading >Access the agent deployment feature instantly by typing "agent" or from the home page >This is autonomous AI security, built to work around the clock—analyzing every risk, every move, in real time. Upcoming integrations will include webhook alerts to Telegram, Discord, and email, expanding how $ALICE delivers intelligence. The future of AI-powered crypto security is here, and this is just the beginning.
0:53

ALICE Agent Deployment is live Users can now deploy their own ALICE AI agent to monitor tokens 24/7 with real-time risk detection. >Lifetime access with 1,000,000 $ALICE >Track up to 10 tokens at once >Live ALICE AI risk analysis every 10 minutes >Seamlessly add and remove tokens as needed >Keep an active $ALICE dashboard running while trading >Access the agent deployment feature instantly by typing "agent" or from the home page >This is autonomous AI security, built to work around the clock—analyzing every risk, every move, in real time. Upcoming integrations will include webhook alerts to Telegram, Discord, and email, expanding how $ALICE delivers intelligence. The future of AI-powered crypto security is here, and this is just the beginning.

Alice

18,946 просмотров • 1 год назад

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.
2:14

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

Aaron Levie

24,728 просмотров • 5 месяцев назад

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.
3:38

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,647 просмотров • 1 год назад

"What you do is you prioritize the top few benchmarks and then you probably bullshit the rest." We talk a lot about how AI is making coding easier for non-technical folks, but don't hear much about how the most elite engineers are delegating their most technically complex work. Ankur Goyal (CEO Braintrust) and team has to deliver huge volumes of data at lightning fast speeds, which means they're always looking at ways to optimize their DB stack. The problem? Technical hypotheses are expensive and slow to test when your human team has to manually review, so you look at 1-2 things and then move on with your best guess. With AI, they're able to bring much more rigor to their most technical work. In this episode, he shows us: - how a coding agent can run a full matrix of benchmarks against ever open source column store; something no eng had time to manually do before - how to build evals the same way you'd write a PRD - how he uses his best engineer's "vibe check" as the gold standard, and scales it across the product as scoring functions This is an episode by engineers for engineers, where Ankur and I geek out on CEOs who code, brute force coding agents, and why you should be using AI to solve your hardest engineering problems. Watch now:
1:17

"What you do is you prioritize the top few benchmarks and then you probably bullshit the rest." We talk a lot about how AI is making coding easier for non-technical folks, but don't hear much about how the most elite engineers are delegating their most technically complex work. Ankur Goyal (CEO Braintrust) and team has to deliver huge volumes of data at lightning fast speeds, which means they're always looking at ways to optimize their DB stack. The problem? Technical hypotheses are expensive and slow to test when your human team has to manually review, so you look at 1-2 things and then move on with your best guess. With AI, they're able to bring much more rigor to their most technical work. In this episode, he shows us: - how a coding agent can run a full matrix of benchmarks against ever open source column store; something no eng had time to manually do before - how to build evals the same way you'd write a PRD - how he uses his best engineer's "vibe check" as the gold standard, and scales it across the product as scoring functions This is an episode by engineers for engineers, where Ankur and I geek out on CEOs who code, brute force coding agents, and why you should be using AI to solve your hardest engineering problems. Watch now:

claire vo 🖤

17,764 просмотров • 3 месяцев назад

A founder turned down a $1M job offer from Meta to build a $5M ARR company with 9 people. His CAC is in cents. He has no sales team, no CS org, and no plans to build one (read on for the full playbook). Meet Ethan, the founder of Jobright, and if you are an early-stage lean AI company, this might be the most valuable thing you read this week. While every other AI startup was raising, hiring aggressively, and building org charts before they had customers, Ethan was talking to users (face-to-face). Their first million (revenue) was pure product-market fit mode. It came entirely from word of mouth, with users pulling in their friends because the product worked. The next four million came from clearly defining their ICP, tracking CAC, and obsessing over growth as a data-driven system. They built an experiment pipeline with a strict 7-day ship-or-kill rule. Channels that couldn't hit payback targets were automatically cut. And through all of it, the headcount barely moved. Because every time they spotted a repetitive workflow, their default instinct was to build an agent (when most founders hire). Their first internal AI agent took an enterprise manager from 5 accounts to 50, without any additional hires. Last month alone, they shipped 3 new internal agents: • An inbox agent that reads, classifies, and drafts responses • An ops agent that turns messy client requests into structured tasks • An outreach agent that finds relevant partners, writes personalized first messages, and runs follow-up sequences Each of those consumed hours of manual labor every week, but now they run on autopilot. That is what 9 people running like 50 looks like in practice. It is one of the best 0 to $5M stories I have come across in the lean AI space. So I spent hours going deep on every decision, system, and principle behind how they did this, and turned it into a super actionable playbook for founders who are pre-revenue or going from 0 to 1. Inside, you'll get: • The exact growth OS they used to go from $1M to $5M ARR • How they built internal agents that let 9 people do the work of 50 • The hiring filter that screens for true AI-native operators • The 2-question test every feature must pass before it gets built • How they structured growth after hitting PMF (the full funnel with owner metrics) • What building from $0 → $1M looks like vs. $1M → $5M • The data flywheel they've been compounding since day one that gets harder to replicate, and how to design yours from scratch Originally, I put this together as a resource for founders I work with directly. But the insights here are too actionable to keep internal, so I'm sharing them publicly. It's one of the most detailed operating blueprints I've put together for those aspiring to join the Lean AI Leaderboard. If you are one of them, grab this right away as it will save you months of expensive guesswork. Ethan, Eric, and team, welcome to the Lean AI Leaderboard!🚀 Link to the playbook:
0:29

A founder turned down a $1M job offer from Meta to build a $5M ARR company with 9 people. His CAC is in cents. He has no sales team, no CS org, and no plans to build one (read on for the full playbook). Meet Ethan, the founder of Jobright, and if you are an early-stage lean AI company, this might be the most valuable thing you read this week. While every other AI startup was raising, hiring aggressively, and building org charts before they had customers, Ethan was talking to users (face-to-face). Their first million (revenue) was pure product-market fit mode. It came entirely from word of mouth, with users pulling in their friends because the product worked. The next four million came from clearly defining their ICP, tracking CAC, and obsessing over growth as a data-driven system. They built an experiment pipeline with a strict 7-day ship-or-kill rule. Channels that couldn't hit payback targets were automatically cut. And through all of it, the headcount barely moved. Because every time they spotted a repetitive workflow, their default instinct was to build an agent (when most founders hire). Their first internal AI agent took an enterprise manager from 5 accounts to 50, without any additional hires. Last month alone, they shipped 3 new internal agents: • An inbox agent that reads, classifies, and drafts responses • An ops agent that turns messy client requests into structured tasks • An outreach agent that finds relevant partners, writes personalized first messages, and runs follow-up sequences Each of those consumed hours of manual labor every week, but now they run on autopilot. That is what 9 people running like 50 looks like in practice. It is one of the best 0 to $5M stories I have come across in the lean AI space. So I spent hours going deep on every decision, system, and principle behind how they did this, and turned it into a super actionable playbook for founders who are pre-revenue or going from 0 to 1. Inside, you'll get: • The exact growth OS they used to go from $1M to $5M ARR • How they built internal agents that let 9 people do the work of 50 • The hiring filter that screens for true AI-native operators • The 2-question test every feature must pass before it gets built • How they structured growth after hitting PMF (the full funnel with owner metrics) • What building from $0 → $1M looks like vs. $1M → $5M • The data flywheel they've been compounding since day one that gets harder to replicate, and how to design yours from scratch Originally, I put this together as a resource for founders I work with directly. But the insights here are too actionable to keep internal, so I'm sharing them publicly. It's one of the most detailed operating blueprints I've put together for those aspiring to join the Lean AI Leaderboard. If you are one of them, grab this right away as it will save you months of expensive guesswork. Ethan, Eric, and team, welcome to the Lean AI Leaderboard!🚀 Link to the playbook:

Henry Shi

51,591 просмотров • 6 месяцев назад

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:
49:42

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

Dan Shipper 📧

67,958 просмотров • 5 месяцев назад

Ken Griffin, founder of Citadel, the most profitable hedge fund in history, on the moment that left him "shocked and depressed" about AI: "one of our team members built an agentic AI system to recreate academic papers in finance." "we have a legion of young masters and PhDs doing this work. it takes roughly six to eight weeks to reproduce a paper." "my colleague built an AI system that would read a paper, reproduce it, verify the results, produce the results on out of sample data — in about two to three hours per paper." "this is not just a white collar job. this is a master's or PhD-level job. six weeks of work turned into two to three hours." "and then OpenAI just solved a math problem that no one had solved for eighty years." "competitive moats are being filled in at lightning speed." the founder of Citadel watching PhD-level work get compressed from six weeks to two hours. and his response wasn't to cut headcount, it was "i will take every single productivity gain i can get because with the talented people we have, we just have more to go after."
4:06

Ken Griffin, founder of Citadel, the most profitable hedge fund in history, on the moment that left him "shocked and depressed" about AI: "one of our team members built an agentic AI system to recreate academic papers in finance." "we have a legion of young masters and PhDs doing this work. it takes roughly six to eight weeks to reproduce a paper." "my colleague built an AI system that would read a paper, reproduce it, verify the results, produce the results on out of sample data — in about two to three hours per paper." "this is not just a white collar job. this is a master's or PhD-level job. six weeks of work turned into two to three hours." "and then OpenAI just solved a math problem that no one had solved for eighty years." "competitive moats are being filled in at lightning speed." the founder of Citadel watching PhD-level work get compressed from six weeks to two hours. and his response wasn't to cut headcount, it was "i will take every single productivity gain i can get because with the talented people we have, we just have more to go after."

Goshawk Trades

184,255 просмотров • 2 месяцев назад

We're winning the AI browser wars, and we're not even trying. Our mission isn't to build a browser. We're an agent company, not a browser company. We've seen firsthand how teams struggle to get started with AI. The tools they tried are too complex and lack the context you need. We decided to build a browser since it's already the tool you work in. We all know how to use a browser. You can pick up Strawberry and start using AI agents from day one. By building a browser we are also positioning ourselves to build towards the internet of the future. An internet where trillions of agents will be communicating with each other. This is the biggest opportunity of our lifetime. But someone has to make sure it is safe, effective and accessible to teams across the world. That is our mission.
0:52

We're winning the AI browser wars, and we're not even trying. Our mission isn't to build a browser. We're an agent company, not a browser company. We've seen firsthand how teams struggle to get started with AI. The tools they tried are too complex and lack the context you need. We decided to build a browser since it's already the tool you work in. We all know how to use a browser. You can pick up Strawberry and start using AI agents from day one. By building a browser we are also positioning ourselves to build towards the internet of the future. An internet where trillions of agents will be communicating with each other. This is the biggest opportunity of our lifetime. But someone has to make sure it is safe, effective and accessible to teams across the world. That is our mission.

Charles Maddock

42,381 просмотров • 3 месяцев назад

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.
1:49

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

Nav Toor

394,563 просмотров • 5 месяцев назад

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?
1:33

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Ricardo

2,976,578 просмотров • 4 месяцев назад

Calling all senior developers! Applications are now open for the Andela AI Engineering Bootcamp. This intensive (and free!) program is designed to equip you with all the competencies you need to become a forward-deployed, enterprise-ready, and AI-fluent engineer. This training will combine a series of expert-led sessions with hands-on activities to make you a complete AI engineer over 10 weeks. Duration: 10 Weeks, kicking off on Monday, February 16. This program is open to technologists who: ✦ Have at least 8 years of experience as a software engineer. ✦ Have solid experience working with Python or Node.js, APIs, Databases, and software architecture. ✦ Good knowledge of TypeScript, modern frameworks like React or Next.js, and exposure to AI-related tools or libraries such as LangChain, OpenAI APIs, or vector databases. ✦ Can commit 40 hours per week for the next 10 weeks. ✦ A PC (not a phone) with an internet connection is required. Either Mac (Linux) or Windows. ✦ Are NOT employed full time, either with an Andela Client or externally. Apply here:
1:15

Calling all senior developers! Applications are now open for the Andela AI Engineering Bootcamp. This intensive (and free!) program is designed to equip you with all the competencies you need to become a forward-deployed, enterprise-ready, and AI-fluent engineer. This training will combine a series of expert-led sessions with hands-on activities to make you a complete AI engineer over 10 weeks. Duration: 10 Weeks, kicking off on Monday, February 16. This program is open to technologists who: ✦ Have at least 8 years of experience as a software engineer. ✦ Have solid experience working with Python or Node.js, APIs, Databases, and software architecture. ✦ Good knowledge of TypeScript, modern frameworks like React or Next.js, and exposure to AI-related tools or libraries such as LangChain, OpenAI APIs, or vector databases. ✦ Can commit 40 hours per week for the next 10 weeks. ✦ A PC (not a phone) with an internet connection is required. Either Mac (Linux) or Windows. ✦ Are NOT employed full time, either with an Andela Client or externally. Apply here:

Andela

457,133 просмотров • 7 месяцев назад

What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.
1:02

What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.

Harper Carroll

32,624 просмотров • 1 месяц назад

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.
1:15

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.

MIKE

39,041 просмотров • 2 месяцев назад

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!
2:55

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

fxnction

27,570 просмотров • 3 месяцев назад

The goose team from Spiral joined us last night to introduce the Goose Development Kit (GDK) They walk through what goose is, why it's evolving into a toolkit for building open-source agentic apps, and what’s coming next Full talk ↓ Timestamps: 0:00 - Introducing Presidio Bitcoin and the Open Source AI Summit 1:40 - Why Spiral (Spiral) is expanding into AI with goose (Steve Lee) 4:59 - Introducing the Goose Development Kit (GDK) 10:34 - Goose's path from app to platform (Alex Hancock) 14:40 - Making agents easier to build (Douwe Osinga) 21:10 - Shipping goose as Rust APIs (Jack Amadeo) 25:15 - Building apps on top of goose (Lifei Z) 32:08 - Local models and open weights in goose (Jasper Hugo) 41:12 - Cutting token costs with routing (mic) 43:30 - Mesh-LLM: sharing local AI compute 47:53 - Q&A: Sharing compute across devices 50:20 - Finding hidden token costs (Filip Kujawa) 54:58 - Q&A: What makes goose different 59:17 - Q&A: Why GDK can stay neutral 1:06:52 - Q&A: Benchmarking harnesses without overfitting 1:10:48 - Q&A: Should goose train its own models? 1:15:20 - Q&A: Building specialized apps on goose
1:19:47

The goose team from Spiral joined us last night to introduce the Goose Development Kit (GDK) They walk through what goose is, why it's evolving into a toolkit for building open-source agentic apps, and what’s coming next Full talk ↓ Timestamps: 0:00 - Introducing Presidio Bitcoin and the Open Source AI Summit 1:40 - Why Spiral (Spiral) is expanding into AI with goose (Steve Lee) 4:59 - Introducing the Goose Development Kit (GDK) 10:34 - Goose's path from app to platform (Alex Hancock) 14:40 - Making agents easier to build (Douwe Osinga) 21:10 - Shipping goose as Rust APIs (Jack Amadeo) 25:15 - Building apps on top of goose (Lifei Z) 32:08 - Local models and open weights in goose (Jasper Hugo) 41:12 - Cutting token costs with routing (mic) 43:30 - Mesh-LLM: sharing local AI compute 47:53 - Q&A: Sharing compute across devices 50:20 - Finding hidden token costs (Filip Kujawa) 54:58 - Q&A: What makes goose different 59:17 - Q&A: Why GDK can stay neutral 1:06:52 - Q&A: Benchmarking harnesses without overfitting 1:10:48 - Q&A: Should goose train its own models? 1:15:20 - Q&A: Building specialized apps on goose

Presidio Bitcoin

126,900 просмотров • 2 месяцев назад