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Today, we're making Error Tracking by Better Stack generally available. Sentry-compatible. AI-native. At 1/6th the price. Here's why we built it, and how to get the most out of it. What's wrong with error tracking today? Most teams use Sentry. It's solid! But at scale, the bills get brutal....

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Juraj Masar

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14,920 次观看 • 3 个月前 •via X (Twitter)

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What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch
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What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

GREG ISENBERG

83,387 次观看 • 29 天前

Seer, Sentry's AI debugger, analyzes your issues and finds the root cause. Now you can pass that analysis directly to a @GitHub Copilot agent which picks up the context, generates a fix, and opens a pull request. Learn more in our latest video with Paul Jaffré 👇
3:03

Seer, Sentry's AI debugger, analyzes your issues and finds the root cause. Now you can pass that analysis directly to a @GitHub Copilot agent which picks up the context, generates a fix, and opens a pull request. Learn more in our latest video with Paul Jaffré 👇

Sentry

12,054 次观看 • 7 天前

Your AI is average. Mathematically If you're using the same ChatGPT as 100M other people with zero context about your actual work, you're getting average intelligence. Generic answers to specific problems. I watched my team copy-paste the same project context between Claude, ChatGPT, and Gemini all day. Re-uploading the same docs every session. Re-explaining the same projects on repeat. That's not an AI problem. That's a context problem. ClickUp just shipped Brain² and it flips the whole thing: one AI that already knows every task, doc, chat, and decision in your company — and how they all connect. No briefing. No re-explaining. It just knows. Pick any model (GPT, Claude Opus, Gemini), it routes to the best one for the job, runs with full context of your work, and actually does the work alongside your team. Multiplayer AI that gets smarter every time anyone on your team uses it. The wild part: the more your team uses it, the more it becomes your company's collective brain. 150 years of team expertise on tap instead of one person's memory. Average is a choice now.
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Your AI is average. Mathematically If you're using the same ChatGPT as 100M other people with zero context about your actual work, you're getting average intelligence. Generic answers to specific problems. I watched my team copy-paste the same project context between Claude, ChatGPT, and Gemini all day. Re-uploading the same docs every session. Re-explaining the same projects on repeat. That's not an AI problem. That's a context problem. ClickUp just shipped Brain² and it flips the whole thing: one AI that already knows every task, doc, chat, and decision in your company — and how they all connect. No briefing. No re-explaining. It just knows. Pick any model (GPT, Claude Opus, Gemini), it routes to the best one for the job, runs with full context of your work, and actually does the work alongside your team. Multiplayer AI that gets smarter every time anyone on your team uses it. The wild part: the more your team uses it, the more it becomes your company's collective brain. 150 years of team expertise on tap instead of one person's memory. Average is a choice now.

Suryansh Tiwari

244,145 次观看 • 13 天前

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.
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The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The Startup Ideas Podcast (SIP) 🧃

13,486 次观看 • 25 天前

Travis Kalanick: Tesla is the Google of the Physical AI Era “I like to break down the physical AI stack: (it) includes computation, a physical AI model, all the things you think of.” “What about land? Development? That should be in that stack. What about chemistry? That needs to be in the stack. Manufacturing needs to be a stack.” “When you look at the stack, you're like, ‘Damn, Tesla's got this sh*t.’ They are the Google of this era.” “What I mean by that is, if you were doing a startup in the 2000s, the first question you would get is, ‘Why isn't Google gonna kill you?’ Or, ‘Why isn't Google just gonna do it?’ They’re not even gonna know that they killed you.” “And before that, it was Microsoft in the late 90s. Uber had a time, 2010s.” “But in the physical AI space, that's sort of a Tesla thing.” -------------------------------------------- Thanks to our partner for making this happen!: EY (EY) Austin vibes meet AI innovation. Thanks to EY for co‑hosting with us at #SXSW. Discover what executives are saying about AI transformation in the latest AI Pulse Survey.
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Travis Kalanick: Tesla is the Google of the Physical AI Era “I like to break down the physical AI stack: (it) includes computation, a physical AI model, all the things you think of.” “What about land? Development? That should be in that stack. What about chemistry? That needs to be in the stack. Manufacturing needs to be a stack.” “When you look at the stack, you're like, ‘Damn, Tesla's got this sh*t.’ They are the Google of this era.” “What I mean by that is, if you were doing a startup in the 2000s, the first question you would get is, ‘Why isn't Google gonna kill you?’ Or, ‘Why isn't Google just gonna do it?’ They’re not even gonna know that they killed you.” “And before that, it was Microsoft in the late 90s. Uber had a time, 2010s.” “But in the physical AI space, that's sort of a Tesla thing.” -------------------------------------------- Thanks to our partner for making this happen!: EY (EY) Austin vibes meet AI innovation. Thanks to EY for co‑hosting with us at #SXSW. Discover what executives are saying about AI transformation in the latest AI Pulse Survey.

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Introducing Libra AI, the SOTA super agent that amplifies your work, enabling you to bring your best everyday. Your work life will never be the same starting today. > Libra understands all your data and context like a human teammate > Takes actions on your behalf > Replaces the patchwork of vertical AI tools with one that actually does the work Experience it today, now in early access Libra AI⚡️

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1,922,959 次观看 • 1 个月前

clickup just mass-obsoleted every AI agent platform here's the thing nobody's talking about: AI doesn't fail because the models are bad. it fails because it has no context. you paste in a prompt. it hallucinates. you blame the tool. but the tool was never the problem. the CONTEXT was. clickup just solved this. their new "super agents" live INSIDE your workspace. they see everything: - your tasks - your docs - your meetings - your conversations - your decisions 100% context. always on. always scraping. this isn't bolted-on AI calling APIs. this is AI that already knows your business. i've been running an AI backend for my agency. the architecture works - but you have to BUILD the context layer. clickup just made that automatic. i already use it. have tons of data there. this is about to be unbelievable. if you're building automations and you're not paying attention to this— you're about to get lapped. playing with it hard over the next 2 days. full deep dive coming. drop "SUPER" and i'll send you the breakdown when it's ready.
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clickup just mass-obsoleted every AI agent platform here's the thing nobody's talking about: AI doesn't fail because the models are bad. it fails because it has no context. you paste in a prompt. it hallucinates. you blame the tool. but the tool was never the problem. the CONTEXT was. clickup just solved this. their new "super agents" live INSIDE your workspace. they see everything: - your tasks - your docs - your meetings - your conversations - your decisions 100% context. always on. always scraping. this isn't bolted-on AI calling APIs. this is AI that already knows your business. i've been running an AI backend for my agency. the architecture works - but you have to BUILD the context layer. clickup just made that automatic. i already use it. have tons of data there. this is about to be unbelievable. if you're building automations and you're not paying attention to this— you're about to get lapped. playing with it hard over the next 2 days. full deep dive coming. drop "SUPER" and i'll send you the breakdown when it's ready.

Nozz

20,271 次观看 • 6 个月前

AI agents are failing in production...not a surprise. As you scale your knowledge base, embeddings start creating noise. It’s called ‘semantic collapse’ - when conversations run too long, you have hundreds of PDFs, millions of data points to give to your AI. Your AI can’t flag it because it doesn’t know it’s hallucinating. Similarity gets passed off as relevance. Fix your context. Make your agents work. Build intelligent AI. If your AI is plateauing at 50% accuracy and hallucinations are still a problem, let's talk. Book a 20 minute demo with the link in the next thread. We'll dig into your setup and find out how we can help.
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AI agents are failing in production...not a surprise. As you scale your knowledge base, embeddings start creating noise. It’s called ‘semantic collapse’ - when conversations run too long, you have hundreds of PDFs, millions of data points to give to your AI. Your AI can’t flag it because it doesn’t know it’s hallucinating. Similarity gets passed off as relevance. Fix your context. Make your agents work. Build intelligent AI. If your AI is plateauing at 50% accuracy and hallucinations are still a problem, let's talk. Book a 20 minute demo with the link in the next thread. We'll dig into your setup and find out how we can help.

Nishkarsh

1,297,661 次观看 • 3 个月前

🚨 BREAKING: Google just dropped the most practical AI release of 2025. It handles your emails, data, docs, meetings and does it with context. This is AI that actually saves time. Here’s everything they announced (and why it matters for your team):
1:35

🚨 BREAKING: Google just dropped the most practical AI release of 2025. It handles your emails, data, docs, meetings and does it with context. This is AI that actually saves time. Here’s everything they announced (and why it matters for your team):

Brendan Jowett

1,168,210 次观看 • 1 年前

I built a self-hosted Sentry clone that runs entirely on Cloudflare Workers, and I think it showcases one of the most underrated features in the Cloudflare ecosystem: Service Bindings. Let me explain why this matters. When you have multiple Cloudflare Workers (an API, a webhook handler, a cron job), they all need common things: error tracking, authentication, rate limiting, metrics. The typical solution? External HTTP calls to third-party services. That means: - 50-200ms latency per call - Egress fees - Your data leaving your infrastructure - Another vendor to manage Service bindings let Workers call each other directly inside Cloudflare's network. No HTTP. No internet. Just internal RPC with <5ms latency. With Workers Sentinel, any Worker in my account can just point Sentry-SDK into the Service binding, and have all errors flow into one centralized dashboard, stored in Durable Objects with SQLite. No external calls. No added latency. Service bindings aren't just for error tracking. You can centralize: 🔐 Authentication — One Worker that validates tokens for all your services 📊 Metrics — Centralized collection without external observability costs 🚦 Rate Limiting — Shared counters that actually work across Workers 🚩 Feature Flags — Instant propagation, no deployment needed Think of it as building your own internal microservices mesh, but at the edge, with zero network overhead. Workers Sentinel uses two Durable Objects: - AuthState (singleton) — users, sessions, projects - ProjectState (per-project) — issues, events, stats Events are fingerprinted and grouped intelligently. The dashboard is a Vue.js app served from the same Worker. I could say i built this to learn Durable Objects or that I needed error tracking for side projects, but honestly I just need a way to show my wife why I'm sending $200/month to some guy named Claudio who apparently helps me write code. The whole thing is open source. Deploy it to your Cloudflare account, point your Sentry SDKs at it, and you're done. But more importantly: take a closer look at service bindings. They're the glue that turns a collection of Workers into an actual platform. Most Cloudflare customers I talk to aren't using them, and they're missing out. To the Sentry team: I love your work. Genuinely. Sentry is battle-tested, has incredible features, and is what you should use for anything that matters. This project is a toy. A learning exercise. A weekend hack that got slightly out of hand. Please do not trust your production errors to this dummy clone. If your startup goes down at 3 AM because Workers Sentinel missed an edge case, that's on you. I warned you. Use the real thing. But if you want to learn about Durable Objects, service bindings, and how error tracking works under the hood? Clone away. Your Workers shouldn't be islands. Connect them.
1:53

I built a self-hosted Sentry clone that runs entirely on Cloudflare Workers, and I think it showcases one of the most underrated features in the Cloudflare ecosystem: Service Bindings. Let me explain why this matters. When you have multiple Cloudflare Workers (an API, a webhook handler, a cron job), they all need common things: error tracking, authentication, rate limiting, metrics. The typical solution? External HTTP calls to third-party services. That means: - 50-200ms latency per call - Egress fees - Your data leaving your infrastructure - Another vendor to manage Service bindings let Workers call each other directly inside Cloudflare's network. No HTTP. No internet. Just internal RPC with <5ms latency. With Workers Sentinel, any Worker in my account can just point Sentry-SDK into the Service binding, and have all errors flow into one centralized dashboard, stored in Durable Objects with SQLite. No external calls. No added latency. Service bindings aren't just for error tracking. You can centralize: 🔐 Authentication — One Worker that validates tokens for all your services 📊 Metrics — Centralized collection without external observability costs 🚦 Rate Limiting — Shared counters that actually work across Workers 🚩 Feature Flags — Instant propagation, no deployment needed Think of it as building your own internal microservices mesh, but at the edge, with zero network overhead. Workers Sentinel uses two Durable Objects: - AuthState (singleton) — users, sessions, projects - ProjectState (per-project) — issues, events, stats Events are fingerprinted and grouped intelligently. The dashboard is a Vue.js app served from the same Worker. I could say i built this to learn Durable Objects or that I needed error tracking for side projects, but honestly I just need a way to show my wife why I'm sending $200/month to some guy named Claudio who apparently helps me write code. The whole thing is open source. Deploy it to your Cloudflare account, point your Sentry SDKs at it, and you're done. But more importantly: take a closer look at service bindings. They're the glue that turns a collection of Workers into an actual platform. Most Cloudflare customers I talk to aren't using them, and they're missing out. To the Sentry team: I love your work. Genuinely. Sentry is battle-tested, has incredible features, and is what you should use for anything that matters. This project is a toy. A learning exercise. A weekend hack that got slightly out of hand. Please do not trust your production errors to this dummy clone. If your startup goes down at 3 AM because Workers Sentinel missed an edge case, that's on you. I warned you. Use the real thing. But if you want to learn about Durable Objects, service bindings, and how error tracking works under the hood? Clone away. Your Workers shouldn't be islands. Connect them.

Gabriel Massadas

28,656 次观看 • 5 个月前

.Cohere is defining the future of sovereign AI. Autumn Moulder, VP of Engineering at Cohere, breaks down the requirements for a full AI sovereign stack at #NVIDIAGTC: ✅ Full-Stack Sovereignty: Deploying everything from the model to applications and reasoning traces within a single data center. ✅ The Power of Openness: Why open models like NVIDIA Nemotron are critical for data lineage and navigating evolving regulations.
1:05

.Cohere is defining the future of sovereign AI. Autumn Moulder, VP of Engineering at Cohere, breaks down the requirements for a full AI sovereign stack at #NVIDIAGTC: ✅ Full-Stack Sovereignty: Deploying everything from the model to applications and reasoning traces within a single data center. ✅ The Power of Openness: Why open models like NVIDIA Nemotron are critical for data lineage and navigating evolving regulations.

NVIDIA AI

10,965 次观看 • 3 个月前

Seth Godin gave a masterclass on how to build an unforgettable brand in the age of AI: 1. Marketing is not about spend. It is about creating the conditions for other people to eagerly spread your idea. 2. Authenticity is overrated. What customers actually want is consistency. Show up the same way every single time and that is worth more than any Super Bowl ad. 3. Everything your company does is a marketing decision. How you answer the phone. What you charge. How you design things. Marketing is not a department. It is everything. 4. Trust is simple. Make a promise. Keep it. Especially when it is hard. 5. Successful brands are built with your customers talking about you. Not you talking about you. 6. A brand is not a logo. A brand is a promise. Nike has a brand. Hyatt has a logo. One of them you know exactly what to expect. The other you do not. 7. You are measuring the wrong things. Follower counts. Stock price. Open rates. False proxies will take your business in the wrong direction faster than anything else. 8. Social media followers mean nothing. Godin has 400,000 Instagram followers and says if he posts about a new book maybe 12 people buy it. The number is a distraction. 9. Stop trying to be famous. The goal is not to get more famous. The goal is to get less famous and more trusted. 10. Average marketing reaches average people. Average people will not buy your product. You need the people who will talk about you, challenge you, and eagerly pay more for better. 11. When you pick your customers you pick your future. Stop trying to reach everyone. Start trying to deeply serve someone specific. 12. Better beats louder every time. One guy running a wine email list with 130,000 subscribers does $30 million a year in revenue. No ads. No social media hustle. Just consistently better. 13. The real opportunity with AI is not making things cheaper. It is making things better. The businesses that use AI to deepen relationships will win. The ones using it to cut costs will race to the bottom. 14. Your job is not to do your job. Your job is to solve problems for other people and make things better by making better things. Everything else is just noise. 15. When AI becomes the buyer it will always choose the cheapest option. If your entire business strategy is being the cheapest, AI will destroy you. The only protection is being worth it in ways that cannot be easily measured. 16. The next level of marketing is permission at a depth nobody has achieved before. The brand that knows your tools, your projects, your needs, and shows up to help without being asked will be impossible to replace. 17. Most businesses will use AI to spam more people faster. The businesses that win will use AI to serve fewer people better. That gap is the biggest opportunity in marketing right now. 18. You have a squadron of summer interns available for twenty dollars a month. They are not that good but they are very eager. The businesses learning to be good bosses of AI right now will have an enormous advantage over everyone waiting to figure it out later. 19. The question every business should be asking is not how do I get more attention. It is how do I become the kind of business that people would genuinely miss if it disappeared tomorrow. That answer is your entire marketing strategy.
36:56

Seth Godin gave a masterclass on how to build an unforgettable brand in the age of AI: 1. Marketing is not about spend. It is about creating the conditions for other people to eagerly spread your idea. 2. Authenticity is overrated. What customers actually want is consistency. Show up the same way every single time and that is worth more than any Super Bowl ad. 3. Everything your company does is a marketing decision. How you answer the phone. What you charge. How you design things. Marketing is not a department. It is everything. 4. Trust is simple. Make a promise. Keep it. Especially when it is hard. 5. Successful brands are built with your customers talking about you. Not you talking about you. 6. A brand is not a logo. A brand is a promise. Nike has a brand. Hyatt has a logo. One of them you know exactly what to expect. The other you do not. 7. You are measuring the wrong things. Follower counts. Stock price. Open rates. False proxies will take your business in the wrong direction faster than anything else. 8. Social media followers mean nothing. Godin has 400,000 Instagram followers and says if he posts about a new book maybe 12 people buy it. The number is a distraction. 9. Stop trying to be famous. The goal is not to get more famous. The goal is to get less famous and more trusted. 10. Average marketing reaches average people. Average people will not buy your product. You need the people who will talk about you, challenge you, and eagerly pay more for better. 11. When you pick your customers you pick your future. Stop trying to reach everyone. Start trying to deeply serve someone specific. 12. Better beats louder every time. One guy running a wine email list with 130,000 subscribers does $30 million a year in revenue. No ads. No social media hustle. Just consistently better. 13. The real opportunity with AI is not making things cheaper. It is making things better. The businesses that use AI to deepen relationships will win. The ones using it to cut costs will race to the bottom. 14. Your job is not to do your job. Your job is to solve problems for other people and make things better by making better things. Everything else is just noise. 15. When AI becomes the buyer it will always choose the cheapest option. If your entire business strategy is being the cheapest, AI will destroy you. The only protection is being worth it in ways that cannot be easily measured. 16. The next level of marketing is permission at a depth nobody has achieved before. The brand that knows your tools, your projects, your needs, and shows up to help without being asked will be impossible to replace. 17. Most businesses will use AI to spam more people faster. The businesses that win will use AI to serve fewer people better. That gap is the biggest opportunity in marketing right now. 18. You have a squadron of summer interns available for twenty dollars a month. They are not that good but they are very eager. The businesses learning to be good bosses of AI right now will have an enormous advantage over everyone waiting to figure it out later. 19. The question every business should be asking is not how do I get more attention. It is how do I become the kind of business that people would genuinely miss if it disappeared tomorrow. That answer is your entire marketing strategy.

Yasmine Khosrowshahi

126,501 次观看 • 20 天前

AI has completely changed how code gets written. Models can now generate thousands of lines in minutes. That speed is exciting but it also creates a new problem... reviews are slow. Context is missing. Bugs slip through. The bottleneck isn’t writing code anymore, it’s understanding and shipping it safely. It’s why I’ve been looking closely at tools built AI-first for this stage of the workflow. One that stands out is Graphite Agent. It sits inside your PRs and does what reviews have always struggled with at scale: → Explains logic line-by-line → Surfaces and fixes bugs → Resolves failing checks in context All without leaving the review flow.
0:32

AI has completely changed how code gets written. Models can now generate thousands of lines in minutes. That speed is exciting but it also creates a new problem... reviews are slow. Context is missing. Bugs slip through. The bottleneck isn’t writing code anymore, it’s understanding and shipping it safely. It’s why I’ve been looking closely at tools built AI-first for this stage of the workflow. One that stands out is Graphite Agent. It sits inside your PRs and does what reviews have always struggled with at scale: → Explains logic line-by-line → Surfaces and fixes bugs → Resolves failing checks in context All without leaving the review flow.

Shruti

49,398 次观看 • 9 个月前

Errors!! That's the most stressful part about AI coding We lose 40% credits in debugging code We tried to solve it with CodeGuide Debug Agent Just screenshot your error message and upload/paste it The Agent will write a detailed debug prompt Copy &amp; paste it. Error solved.
0:43

Errors!! That's the most stressful part about AI coding We lose 40% credits in debugging code We tried to solve it with CodeGuide Debug Agent Just screenshot your error message and upload/paste it The Agent will write a detailed debug prompt Copy &amp; paste it. Error solved.

CJ Zafir

12,118 次观看 • 1 年前

DAVID SACKS ON THE AI RACE: "The US is currently in an AI race, and our chief global competition is China, obviously. They're the only other country that has the talent, the resources, and the technology expertise to basically beat us in AI. And I think whoever wins this AI race, that's going to have tremendous ramifications for both our economy and our national security. Clearly, we want the US to be the winner, just like we were with the internet, and every other technology revolution before that […] We know that to win this AI race, we have to be the most innovative. You can't regulate your way just to beating your competitor. You have to out-innovate them. And we know that in the United States, the innovation comes from the private sector, not the government. So we have to do everything we can to help our companies win, to help them be innovative, and that means getting a lot of red tape out of the way… We have to have the most AI infrastructure in the US. It has to be the easiest place to build it. All of the new data centers that are going in, they require tremendous power, so getting ahead of the curve on energy, making sure we stand up all of this new infrastructure we're going to need to basically produce these AI factories… We want the US technology stack to dominate globally. We want to be the partner of choice for the whole world… I think everyone in Silicon Valley understands that the way that you win a technology race is to have the biggest ecosystem […] You just want everybody to be building on top of your technology stack, and that's what we want for the United States." David Sacks w/Marc Benioff Dreamforce
2:15

DAVID SACKS ON THE AI RACE: "The US is currently in an AI race, and our chief global competition is China, obviously. They're the only other country that has the talent, the resources, and the technology expertise to basically beat us in AI. And I think whoever wins this AI race, that's going to have tremendous ramifications for both our economy and our national security. Clearly, we want the US to be the winner, just like we were with the internet, and every other technology revolution before that […] We know that to win this AI race, we have to be the most innovative. You can't regulate your way just to beating your competitor. You have to out-innovate them. And we know that in the United States, the innovation comes from the private sector, not the government. So we have to do everything we can to help our companies win, to help them be innovative, and that means getting a lot of red tape out of the way… We have to have the most AI infrastructure in the US. It has to be the easiest place to build it. All of the new data centers that are going in, they require tremendous power, so getting ahead of the curve on energy, making sure we stand up all of this new infrastructure we're going to need to basically produce these AI factories… We want the US technology stack to dominate globally. We want to be the partner of choice for the whole world… I think everyone in Silicon Valley understands that the way that you win a technology race is to have the biggest ecosystem […] You just want everybody to be building on top of your technology stack, and that's what we want for the United States." David Sacks w/Marc Benioff Dreamforce

Ron Pragides 

231,781 次观看 • 8 个月前

Built an AI maturity assessment. It's designed to answer... • how AI-native your team really is • where the bottlenecks are • what good looks like • and what to do next We turned the AI transformation model into something practical you can actually use. Takes about 3 minutes. Chat with it and it gives back: • a detailed AI maturity assessment • strengths and gaps across your workflows • practical recommendations • a downloadable report you can share internally The goal wasn’t to make a gimmicky quiz (please give me direct feedback on how to improve). It’s meant to be a practical diagnostic for how modern teams are actually adopting AI at work. Give it shot!
0:55

Built an AI maturity assessment. It's designed to answer... • how AI-native your team really is • where the bottlenecks are • what good looks like • and what to do next We turned the AI transformation model into something practical you can actually use. Takes about 3 minutes. Chat with it and it gives back: • a detailed AI maturity assessment • strengths and gaps across your workflows • practical recommendations • a downloadable report you can share internally The goal wasn’t to make a gimmicky quiz (please give me direct feedback on how to improve). It’s meant to be a practical diagnostic for how modern teams are actually adopting AI at work. Give it shot!

Hurley

33,186 次观看 • 2 个月前

3 months in, today is fun because we start to collaborate with builders. We have been hard at work. 🔥 Our goal by the end of year is to make the entire AI stack adaptable. Data is the foundation all AI progress has been built on. So, it is our natural starting point.
0:24

3 months in, today is fun because we start to collaborate with builders. We have been hard at work. 🔥 Our goal by the end of year is to make the entire AI stack adaptable. Data is the foundation all AI progress has been built on. So, it is our natural starting point.

Sara Hooker

42,742 次观看 • 4 个月前