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Introducing: Sazabi (Sazabi). The AI-native observability platform for fast moving engineering teams. Backed by engineering leaders from the world's top AI and dev tool companies: Graphite, Vercel, Browserbase, LangChain, Browserbase, and more. Sazabi is taking a radically different approach to observability, centered on three core principles: 1. LESS IS...

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Sherwood

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117,804 次观看 • 6 个月前 •via X (Twitter)

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Most underrated skill today: Observability. I feel you can build a career just on this and have 2 decades of guaranteed money if you learn the skill well, and I'll tell you how below. Reality: Most teams I help look at me with dead eyes when I ask them about observability. That's sad, but it's the current state of the industry: • No tracing • No structured data • No way to understand what's happening when things break Outside of the most sophisticated companies, that's still where most teams are. AI is now writing a ton of code, and people are shipping this code to production. However important observability was before, it is 10x more important today. Believe me, you don't want to be the one pushing slop into production and then guessing how it works, with no way to understand what happens. Here is your first step: Honeycomb is running a FREE, 3-day virtual event focused entirely on observability. The event is running May 12-14. Did I mention it's completely free? Everything will revolve around observability for the agent era. Register here: Thanks to the Honeycomb.io System Status Updates team for partnering with me on this post.
1:33

Most underrated skill today: Observability. I feel you can build a career just on this and have 2 decades of guaranteed money if you learn the skill well, and I'll tell you how below. Reality: Most teams I help look at me with dead eyes when I ask them about observability. That's sad, but it's the current state of the industry: • No tracing • No structured data • No way to understand what's happening when things break Outside of the most sophisticated companies, that's still where most teams are. AI is now writing a ton of code, and people are shipping this code to production. However important observability was before, it is 10x more important today. Believe me, you don't want to be the one pushing slop into production and then guessing how it works, with no way to understand what happens. Here is your first step: Honeycomb is running a FREE, 3-day virtual event focused entirely on observability. The event is running May 12-14. Did I mention it's completely free? Everything will revolve around observability for the agent era. Register here: Thanks to the Honeycomb.io System Status Updates team for partnering with me on this post.

Santiago

19,983 次观看 • 3 个月前

I miss building simple, working software. At some point, we decided to complicate everything for no reason. Today, people can't build anything without using three frameworks, 17 libraries, and a swarm of microservices. And here is a funny paradox: To understand how these complex systems work, we've had to build systems and tools that generate data we can later analyze. But the more data we produce, the harder it is to process and make sense of it. We are in the middle of an observability crisis. The tools we have are inefficient, and we don't have enough people to keep systems running. A few weeks ago, I met the team Resolve AI, and they have built a fundamentally new approach to observability and incident management: Instead of depending on humans to run a system, Resolve built a Production Software Engineer who runs the system using AI while letting people supervise. And it's not only crazy, but I think this will fundamentally change how we monitor and maintain systems in production for years to come. I recorded a quick video to showcase a simple example of how Resolve works behind the scenes. There are two main things I'd like you to notice: 1. The tool can correlate data across logs, metrics, and traces coming from different systems. You don't have to do any work to get the information that matters right in front of you. 2. (This is the big one!) The tool can diagnose what's happening and give you instructions on how to solve it. It can produce causal relationships across the entire system stack. Resolve is backed by investors like Replit's founder Amjad Masad, Reid Hoffman, Jeff Dean, Fei Fei Li, Andy Price, among others. They are currently working with a select number of companies and want to onboard a few more. If you are interested in trying them out, go to this link: Honestly, this is one of the most impressive uses of AI I've seen.
5:18

I miss building simple, working software. At some point, we decided to complicate everything for no reason. Today, people can't build anything without using three frameworks, 17 libraries, and a swarm of microservices. And here is a funny paradox: To understand how these complex systems work, we've had to build systems and tools that generate data we can later analyze. But the more data we produce, the harder it is to process and make sense of it. We are in the middle of an observability crisis. The tools we have are inefficient, and we don't have enough people to keep systems running. A few weeks ago, I met the team Resolve AI, and they have built a fundamentally new approach to observability and incident management: Instead of depending on humans to run a system, Resolve built a Production Software Engineer who runs the system using AI while letting people supervise. And it's not only crazy, but I think this will fundamentally change how we monitor and maintain systems in production for years to come. I recorded a quick video to showcase a simple example of how Resolve works behind the scenes. There are two main things I'd like you to notice: 1. The tool can correlate data across logs, metrics, and traces coming from different systems. You don't have to do any work to get the information that matters right in front of you. 2. (This is the big one!) The tool can diagnose what's happening and give you instructions on how to solve it. It can produce causal relationships across the entire system stack. Resolve is backed by investors like Replit's founder Amjad Masad, Reid Hoffman, Jeff Dean, Fei Fei Li, Andy Price, among others. They are currently working with a select number of companies and want to onboard a few more. If you are interested in trying them out, go to this link: Honestly, this is one of the most impressive uses of AI I've seen.

Santiago

82,074 次观看 • 1 年前

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. Just 100M exceptions with 90 day lookback? ~$30,000 on Sentry. We charge ~$5,000 for the exact same thing. The math isn't subtle. And so most teams still end up sampling. Which means missing the exact exception that caused the outage. The bigger problem: errors are orphaned data. Your exception lands in Sentry. Your logs are in Datadog. Your traces are somewhere else. Root cause analysis becomes a multi-tab archaeology project at 3 am. We built error tracking natively inside Better Stack: the same platform where your logs, traces, metrics, uptime checks, and on-call schedules already live. Errors are just another signal. They belong together. The part that changes how your team works: Our AI SRE doesn't just surface errors. It fixes them. See a new exception? One click. The AI SRE analyzes the full context, from stack traces, environment variables, browser sessions, related logs and recent deploys, and opens a pull request. Not a ticket. Not a summary. A pull request with the fix. This is what happens when error tracking is fully integrated with the rest of your observability stack instead of bolted on separately. The AI has everything it needs to actually act. The migration is trivial: 1. Keep your existing Sentry SDK. Don't touch a single line of instrumentation code. 2. Point the DSN at Better Stack. 3. Done. Errors flow in. Your dashboards work. Your alerts work. 4. New exception appears. Click "Fix with AI SRE." Pull request lands in your repo. 5. Review, merge, close. That's the whole workflow. The AI angle is real, not a marketing badge. LLMs are genuinely good at fixing bugs if they have full context. The reason AI coding assistants sometimes frustrate engineers is incomplete information, not the model. We solve that by giving the AI SRE your entire telemetry stack as context. Stack traces, logs, traces, service maps, previous incidents and much more. All of it, in one place, at the moment it matters. Observability tools are only useful if you actually ingest all your data. At current prices of other tools, most teams can't afford to. Now you can, and your AI SRE can actually do something about it.
0:20

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. Just 100M exceptions with 90 day lookback? ~$30,000 on Sentry. We charge ~$5,000 for the exact same thing. The math isn't subtle. And so most teams still end up sampling. Which means missing the exact exception that caused the outage. The bigger problem: errors are orphaned data. Your exception lands in Sentry. Your logs are in Datadog. Your traces are somewhere else. Root cause analysis becomes a multi-tab archaeology project at 3 am. We built error tracking natively inside Better Stack: the same platform where your logs, traces, metrics, uptime checks, and on-call schedules already live. Errors are just another signal. They belong together. The part that changes how your team works: Our AI SRE doesn't just surface errors. It fixes them. See a new exception? One click. The AI SRE analyzes the full context, from stack traces, environment variables, browser sessions, related logs and recent deploys, and opens a pull request. Not a ticket. Not a summary. A pull request with the fix. This is what happens when error tracking is fully integrated with the rest of your observability stack instead of bolted on separately. The AI has everything it needs to actually act. The migration is trivial: 1. Keep your existing Sentry SDK. Don't touch a single line of instrumentation code. 2. Point the DSN at Better Stack. 3. Done. Errors flow in. Your dashboards work. Your alerts work. 4. New exception appears. Click "Fix with AI SRE." Pull request lands in your repo. 5. Review, merge, close. That's the whole workflow. The AI angle is real, not a marketing badge. LLMs are genuinely good at fixing bugs if they have full context. The reason AI coding assistants sometimes frustrate engineers is incomplete information, not the model. We solve that by giving the AI SRE your entire telemetry stack as context. Stack traces, logs, traces, service maps, previous incidents and much more. All of it, in one place, at the moment it matters. Observability tools are only useful if you actually ingest all your data. At current prices of other tools, most teams can't afford to. Now you can, and your AI SRE can actually do something about it.

Juraj Masar

15,063 次观看 • 4 个月前

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."
3:06

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."

Patrick OShaughnessy

393,820 次观看 • 6 个月前

Do Trainers Take Too Much Credit?? The older I get, the more I understand that training is a piece of the puzzle for players or teams. Early on in my career I thought it was everything, and that a players success was my success. A players success is their success, and all the different people in their lives are there to hopefully help guide them. When I realized that, I became a better trainer because I realized I was serving them and not my own ego.
0:51

Do Trainers Take Too Much Credit?? The older I get, the more I understand that training is a piece of the puzzle for players or teams. Early on in my career I thought it was everything, and that a players success was my success. A players success is their success, and all the different people in their lives are there to hopefully help guide them. When I realized that, I became a better trainer because I realized I was serving them and not my own ego.

Steve Dagostino

36,658 次观看 • 2 年前

AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.
0:32

AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.

Cristiano R. Amon

15,783 次观看 • 2 个月前

AI Mocap tools are evolving at breakneck speed... and I could find more than 10 all compiled and compared in a list you can get by subscribing to the XR AI Spotlight newsletter (link on top of my profile). This is Cartwheel and the stepping on that box was one of the most solid I saw. There is a bit of floatiness in the feet and they are not perfectly grounded, but since I tested the tool, the team has pushed massive improvement. What AI mocap tool do you use in your workflow? Let me know in the comments
0:24

AI Mocap tools are evolving at breakneck speed... and I could find more than 10 all compiled and compared in a list you can get by subscribing to the XR AI Spotlight newsletter (link on top of my profile). This is Cartwheel and the stepping on that box was one of the most solid I saw. There is a bit of floatiness in the feet and they are not perfectly grounded, but since I tested the tool, the team has pushed massive improvement. What AI mocap tool do you use in your workflow? Let me know in the comments

Gabriele Romagnoli

34,907 次观看 • 9 个月前

Multi-agent systems offer incredible potential and unprecedented risks. How do you solve for observability, failure mode analysis, and guardrailing in the era of agents? Today, we’re announcing our Agent Reliability platform to observe, evaluate, guardrail, and improve agents at scale. You can get started with the complete platform for trustworthy agentic AI today for free, and here’s how we’re solving some of the biggest challenges in agent reliability: - Observability redesigned for agents Trace views collapse under complex workflows, so we created the Graph View, Timeline View, and Conversation View to offer rich, intuitive visualizations of agent decisions, tool calls, and conversation flows. This multi-dimensional approach enables teams to pinpoint exactly where and why agents deviate or fail. - Automated Failure Mode Analysis with our new Insights Engine Our Insights Engine ingests your logs, metrics, and agent code to automatically surface nuanced failure modes and their root causes. But knowing the problem is not enough; you need to know how to fix it. Insights Engine delivers actionable fixes and can even apply them automatically. With adaptive learning, your insights become smarter and more relevant as your agents evolve. - Evaluating Agents Across Multiple Dimensions Agentic systems interact across complex pathways, and evaluating their performance requires new metrics that reflect this increasing complexity. To deliver comprehensive agentic measurements, we’ve added more out-of-the-box agent metrics like flow adherence, agent flow, agent efficiency, and more. For specialized domains and unique workflows, custom metrics powered by our new Luna-2 small language models can be rapidly designed and fine-tuned for your specific use case. - Real-Time Guardrails Powered by Luna-2 As AI agents become more autonomous and complex, failures like hallucinations or unsafe actions increase dramatically. Without real-time guardrails, these errors will hurt your user experience and brand reputation. Our Luna-2 family of small language models is purpose-built to provide low-latency, cost-effective guardrails that actively stop agent errors before they happen. With support for out-of-the-box and custom metrics, Luna-2 enables enterprises to enforce safety, compliance, and reliability at scale. Enterprises running hundreds of agents and processing hundreds of millions of queries daily already rely on Galileo’s Agent Reliability platform to protect their users, safeguard brand trust, and accelerate innovation. Agent Reliability is available starting today. Try it for free and experience the new standard in AI reliability. Learn more below 👇
2:43

Multi-agent systems offer incredible potential and unprecedented risks. How do you solve for observability, failure mode analysis, and guardrailing in the era of agents? Today, we’re announcing our Agent Reliability platform to observe, evaluate, guardrail, and improve agents at scale. You can get started with the complete platform for trustworthy agentic AI today for free, and here’s how we’re solving some of the biggest challenges in agent reliability: - Observability redesigned for agents Trace views collapse under complex workflows, so we created the Graph View, Timeline View, and Conversation View to offer rich, intuitive visualizations of agent decisions, tool calls, and conversation flows. This multi-dimensional approach enables teams to pinpoint exactly where and why agents deviate or fail. - Automated Failure Mode Analysis with our new Insights Engine Our Insights Engine ingests your logs, metrics, and agent code to automatically surface nuanced failure modes and their root causes. But knowing the problem is not enough; you need to know how to fix it. Insights Engine delivers actionable fixes and can even apply them automatically. With adaptive learning, your insights become smarter and more relevant as your agents evolve. - Evaluating Agents Across Multiple Dimensions Agentic systems interact across complex pathways, and evaluating their performance requires new metrics that reflect this increasing complexity. To deliver comprehensive agentic measurements, we’ve added more out-of-the-box agent metrics like flow adherence, agent flow, agent efficiency, and more. For specialized domains and unique workflows, custom metrics powered by our new Luna-2 small language models can be rapidly designed and fine-tuned for your specific use case. - Real-Time Guardrails Powered by Luna-2 As AI agents become more autonomous and complex, failures like hallucinations or unsafe actions increase dramatically. Without real-time guardrails, these errors will hurt your user experience and brand reputation. Our Luna-2 family of small language models is purpose-built to provide low-latency, cost-effective guardrails that actively stop agent errors before they happen. With support for out-of-the-box and custom metrics, Luna-2 enables enterprises to enforce safety, compliance, and reliability at scale. Enterprises running hundreds of agents and processing hundreds of millions of queries daily already rely on Galileo’s Agent Reliability platform to protect their users, safeguard brand trust, and accelerate innovation. Agent Reliability is available starting today. Try it for free and experience the new standard in AI reliability. Learn more below 👇

Galileo

1,276,298 次观看 • 1 年前

.cova on why AI made creativity more valuable: "The rise of AI has given way to a massive flooding of the internet. As a creator, I've noticed a lot of audiences have realized this, and the work that really cuts through the noise is work that is really resonant. You can tell it comes from a place the creator is deeply passionate about, and it feels very authentic. It's made for connection, but it's also very true to the person making it." "With AI producing this flood of content, people have felt like the media they're consuming feels more rootless. And therefore the high-taste, high-creativity stuff that's actually crafted by the person, people are starting to value way more." "I use so much AI, and a lot of it's invisible, a lot of it's very visible. I've found a way to automate away the logistical side of things, so I can focus on the creativity. I built a bunch of custom tools for video-making, and I built them all with AI. Even though I prefer to edit myself and I script everything myself, I still use AI to bounce ideas off of." "The way I initially blew up was I used AI to transform my dorm room into different crazy sets, and people thought I was a professional. I was by no means a professional. It was my tiny dorm room, and I added a piano there, a vinyl record player, fairy lights, a moving robot in the background, cool lighting. It was all AI. I really accredit AI to a lot of my success."
2:26

.cova on why AI made creativity more valuable: "The rise of AI has given way to a massive flooding of the internet. As a creator, I've noticed a lot of audiences have realized this, and the work that really cuts through the noise is work that is really resonant. You can tell it comes from a place the creator is deeply passionate about, and it feels very authentic. It's made for connection, but it's also very true to the person making it." "With AI producing this flood of content, people have felt like the media they're consuming feels more rootless. And therefore the high-taste, high-creativity stuff that's actually crafted by the person, people are starting to value way more." "I use so much AI, and a lot of it's invisible, a lot of it's very visible. I've found a way to automate away the logistical side of things, so I can focus on the creativity. I built a bunch of custom tools for video-making, and I built them all with AI. Even though I prefer to edit myself and I script everything myself, I still use AI to bounce ideas off of." "The way I initially blew up was I used AI to transform my dorm room into different crazy sets, and people thought I was a professional. I was by no means a professional. It was my tiny dorm room, and I added a piano there, a vinyl record player, fairy lights, a moving robot in the background, cool lighting. It was all AI. I really accredit AI to a lot of my success."

MTS

10,699 次观看 • 23 天前

This might be the best "AI Engineer" I've tried so far. ​ I'm an old school developer who started 30 years ago. I feel very uncomfortable letting AI take control of my code, but for the sake of science, I spent two hours building an application that took me weeks to build a couple of years ago. ​ I used Pythagora, a brand new tool backed by Y Combinator. They just released to the public. ​ Keep in mind that I use AI every day to write code, but Pythagora is something different: it's a tool that leads, and uses you—the human—as the copilot. ​ I go into more details in the video, but here is the TLDR; ​ 1. Holy molly! We've made a ton of progress on this front! This is way better than Devin when I tested it a few months back. ​ 2. Love the approach of generating a plan with sub-tasks before writing any code. ​ 3. The tools never tries to do too much: it tackles every small task one at a time, and gives you instructions so you test everything. ​ 4. It does exactly what you'd do when it gets stuck: writes a bunch of logs and uses those to correct itself. Pretty neat! ​ 5. It's fast. It runs locally. It's an extension to Visual Studio Code. ​ I'm impressed, but I don't think this tool is for me. ​ I'm not the type of developer who's ready to relinquish control. I felt I had no connection with the code because I didn't write it. It was not my code. ​ I know many people who don't care about this. I know many people who will get tremendous value out of Pythagora. I hope they keep pushing the limits, providing feedback, and helping this get to a point where old folks like me feel more comfortable using it. ​ Don't take my word for it. The best thing you can do is to give it a try and see how you feel using it. ​ Thanks to the team who built this, for all of the explanations and support, and especially, for sitting and listening to my dumb questions for 2 hours while I tested this.
8:37

This might be the best "AI Engineer" I've tried so far. ​ I'm an old school developer who started 30 years ago. I feel very uncomfortable letting AI take control of my code, but for the sake of science, I spent two hours building an application that took me weeks to build a couple of years ago. ​ I used Pythagora, a brand new tool backed by Y Combinator. They just released to the public. ​ Keep in mind that I use AI every day to write code, but Pythagora is something different: it's a tool that leads, and uses you—the human—as the copilot. ​ I go into more details in the video, but here is the TLDR; ​ 1. Holy molly! We've made a ton of progress on this front! This is way better than Devin when I tested it a few months back. ​ 2. Love the approach of generating a plan with sub-tasks before writing any code. ​ 3. The tools never tries to do too much: it tackles every small task one at a time, and gives you instructions so you test everything. ​ 4. It does exactly what you'd do when it gets stuck: writes a bunch of logs and uses those to correct itself. Pretty neat! ​ 5. It's fast. It runs locally. It's an extension to Visual Studio Code. ​ I'm impressed, but I don't think this tool is for me. ​ I'm not the type of developer who's ready to relinquish control. I felt I had no connection with the code because I didn't write it. It was not my code. ​ I know many people who don't care about this. I know many people who will get tremendous value out of Pythagora. I hope they keep pushing the limits, providing feedback, and helping this get to a point where old folks like me feel more comfortable using it. ​ Don't take my word for it. The best thing you can do is to give it a try and see how you feel using it. ​ Thanks to the team who built this, for all of the explanations and support, and especially, for sitting and listening to my dumb questions for 2 hours while I tested this.

Santiago

212,056 次观看 • 1 年前

David Friedberg: The AI Jobs Panic Is a Crock of Sh*t Why? The revenue potential outweighs the cost savings by 100x. “There is no job loss with AI. I've said it a thousand times, and I will say it again, and again, and again. What I see on the ground, and what I've seen at dozens of companies, including my company that I run, there are two sides to a business. There is revenue and there’s costs. On the cost side of the equation, AI can be used to reduce humans doing things that cost money, to some extent. The effect there, I would argue, is nominal. The real opportunity with AI is on the revenue side, where suddenly one engineer can do 100x or 1000x what they used to be able to do, meaning you can make more products at your company, whether those are agricultural seed products, or boats and ships, or software for companies, or clothing, or what have you. Because of AI, everyone has the ability to expand their revenue base to create more products, and that is the foundation of good economic prosperity. It is called productivity. We can grow productivity in this country with AI. So where I see AI being used is on the revenue side 100x more than the cost side. And in that equation, people are hiring like crazy. We cannot hire enough people. I just had a review meeting with my product and engineering team two days ago, and they're like, ‘We want to add an extra 15 headcount to our engineering squads because we have all this opportunity to do stuff that we couldn't otherwise do.’ So we are going to hire more people. And to Sacks' point, we are seeing that show up in the jobs numbers. The idea that AI is going to destroy jobs is a Luddite idea that is being disproven every single day, and I see it on the ground. It is only a matter of time before people wake up to this and they realize that this narrative that they've all been sold is a crock of sh*t.”
1:42

David Friedberg: The AI Jobs Panic Is a Crock of Sh*t Why? The revenue potential outweighs the cost savings by 100x. “There is no job loss with AI. I've said it a thousand times, and I will say it again, and again, and again. What I see on the ground, and what I've seen at dozens of companies, including my company that I run, there are two sides to a business. There is revenue and there’s costs. On the cost side of the equation, AI can be used to reduce humans doing things that cost money, to some extent. The effect there, I would argue, is nominal. The real opportunity with AI is on the revenue side, where suddenly one engineer can do 100x or 1000x what they used to be able to do, meaning you can make more products at your company, whether those are agricultural seed products, or boats and ships, or software for companies, or clothing, or what have you. Because of AI, everyone has the ability to expand their revenue base to create more products, and that is the foundation of good economic prosperity. It is called productivity. We can grow productivity in this country with AI. So where I see AI being used is on the revenue side 100x more than the cost side. And in that equation, people are hiring like crazy. We cannot hire enough people. I just had a review meeting with my product and engineering team two days ago, and they're like, ‘We want to add an extra 15 headcount to our engineering squads because we have all this opportunity to do stuff that we couldn't otherwise do.’ So we are going to hire more people. And to Sacks' point, we are seeing that show up in the jobs numbers. The idea that AI is going to destroy jobs is a Luddite idea that is being disproven every single day, and I see it on the ground. It is only a matter of time before people wake up to this and they realize that this narrative that they've all been sold is a crock of sh*t.”

The All-In Podcast

152,399 次观看 • 2 个月前

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”
2:15

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”

Corinne Marie Riley

119,410 次观看 • 8 个月前

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."
2:04

AI is changing the software engineering craft. Anders Hejlsberg (Anders Hejlsberg) - creator of C#, TypeScript and industry legend - on why code review needs to get more enjoyable in response: #1 - AI is shifting the craft from writing code, to reviewing code: "In a sense, we're all turning into project managers. We can have an army of junior programmers, called agents, that will just spit out reams of code but someone's got to have the big picture and review all of that. And so, increasingly, our craft is going from one of writing the code, to one of reviewing the code and building the architecture of the code and overseeing the work. It's a different kind of craft. It's a different kind of enjoyment. I've always liked writing the code. To me that was the fulfilling part, seeing it work. In a way, AI robs a little bit of that, because I am less interested in reviewing code." #2 - The code review experience should be improved: "I think we could also make the process of reviewing code much more interesting than it is today. I mean, today, you see a list of diffs in alphabetical order and now it's up to you to make heads or tails of it. There are more pedagogical ways of presenting that. And you could have commentary generated by the AI that tells you what the changes are and whatever, and then tries to guide you along. So that symbiotic relationship, I think we need to work on that more and to keep the enjoyment in there."

The Pragmatic Engineer

39,011 次观看 • 3 个月前

Knowing your nature is critically important to understanding what success is for you. I can't tell you literally what is best for you, but I can tell you that success is not having a lot more money or status than you need. Having the time and freedom to do what you most want to do is far more important. What is success for most people? It is a matter of having meaningful work and meaningful relationships. If you can make your work and your passion one and the same, and do it with people who you care about and care about you, you will have a happy, successful life. Explore Principles for the Graduating Class of 2026 with my AI Twin, Digital Ray in Beta, here:
1:06

Knowing your nature is critically important to understanding what success is for you. I can't tell you literally what is best for you, but I can tell you that success is not having a lot more money or status than you need. Having the time and freedom to do what you most want to do is far more important. What is success for most people? It is a matter of having meaningful work and meaningful relationships. If you can make your work and your passion one and the same, and do it with people who you care about and care about you, you will have a happy, successful life. Explore Principles for the Graduating Class of 2026 with my AI Twin, Digital Ray in Beta, here:

Ray Dalio

99,275 次观看 • 2 个月前

The importance of giving back is something I learned from my parents. They taught my siblings and me that we had a responsibility to help make life better for others, in whatever way we could. What I love most about giving back is how it can deepen our connection to each other and to our communities—something I think many of us need right now. And best of all, anyone can be a giver, because everyone has something to offer the world. Giving Tuesday is just around the corner, and I can’t wait to share more about my plans. Stay tuned 😊
0:54

The importance of giving back is something I learned from my parents. They taught my siblings and me that we had a responsibility to help make life better for others, in whatever way we could. What I love most about giving back is how it can deepen our connection to each other and to our communities—something I think many of us need right now. And best of all, anyone can be a giver, because everyone has something to offer the world. Giving Tuesday is just around the corner, and I can’t wait to share more about my plans. Stay tuned 😊

Melinda French Gates

31,042 次观看 • 1 年前

David Friedberg: “Gaming is the future of entertainment, and the future of gaming is AI.” @jason: “Friedberg, what are your thoughts on the gaming industry versus social media versus traditional media?” david friedberg: “One way to answer that question is to think about how people spend their time.” “Do you spend more minutes on social media, or on traditional media, or playing games? And how is that trending?” “But importantly, which of those will accrue more benefit, and as a result, drive more hours spent from AI?” “One way to think about this thesis is that AI is going to ultimately accrue to video game entertainment far more than social media entertainment or traditional content.” “If you believe in AI, and you believe in the improvements in productivity, generally speaking, people in the industrialized world will generally have more free time on their hands and be able to support themselves with the deflationary effects of AI over time.” “So if there's more time on people's hands, the general market for entertainment is growing, and if the general market for entertainment is growing, gaming is the future of entertainment, and the future of gaming is AI.” “Because I think you can create dynamic, more engaging experiences that will benefit from a back and forth sort of relationship than you can with traditional content or with social media.” “If you're a noob in Fortnite, like you're an early player in Fortnite, you're mostly playing against AI, because what they do is they tune the AI to be easier to beat so that you can slowly develop your skills.” “What was happening early was they were seeing a high degree of churn in Fortnite because kids would go on and play for the first time and they'd get paired up with kids that were better than them, and so they would never win, and they would get frustrated and they would quit the game and stop.” “So the churn rate was high. So AI unlocked higher engagement and higher retention, and I think we're seeing that in a lot of different gaming platforms now.” “So AI can be used, for example, to maximally increase time, engagement, satisfaction, happiness.”
1:43

David Friedberg: “Gaming is the future of entertainment, and the future of gaming is AI.” @jason: “Friedberg, what are your thoughts on the gaming industry versus social media versus traditional media?” david friedberg: “One way to answer that question is to think about how people spend their time.” “Do you spend more minutes on social media, or on traditional media, or playing games? And how is that trending?” “But importantly, which of those will accrue more benefit, and as a result, drive more hours spent from AI?” “One way to think about this thesis is that AI is going to ultimately accrue to video game entertainment far more than social media entertainment or traditional content.” “If you believe in AI, and you believe in the improvements in productivity, generally speaking, people in the industrialized world will generally have more free time on their hands and be able to support themselves with the deflationary effects of AI over time.” “So if there's more time on people's hands, the general market for entertainment is growing, and if the general market for entertainment is growing, gaming is the future of entertainment, and the future of gaming is AI.” “Because I think you can create dynamic, more engaging experiences that will benefit from a back and forth sort of relationship than you can with traditional content or with social media.” “If you're a noob in Fortnite, like you're an early player in Fortnite, you're mostly playing against AI, because what they do is they tune the AI to be easier to beat so that you can slowly develop your skills.” “What was happening early was they were seeing a high degree of churn in Fortnite because kids would go on and play for the first time and they'd get paired up with kids that were better than them, and so they would never win, and they would get frustrated and they would quit the game and stop.” “So the churn rate was high. So AI unlocked higher engagement and higher retention, and I think we're seeing that in a lot of different gaming platforms now.” “So AI can be used, for example, to maximally increase time, engagement, satisfaction, happiness.”

The All-In Podcast

70,365 次观看 • 10 个月前

$GOOG's Ruth Porat says the biggest risk in the AI buildout is the ROI question, and focusing on short-term tactics at the cost of building a great company. "I think the biggest risk for everyone is when the question is asked, which it often is, how do you get to an ROI? That's going to force our teams to do something short-term and tactical. You can always do a cost cut to get something to drop to the bottom line, and that is not a way to build a great company." "So I think the risk is don't be so in the moment maniacally focused on what is it, but have a right mix of an engine that's continuing to drive the business, which also explains why you need to start building early for growth so that you can grow into it." ____ Follow Fireside Alpha for more daily highlights of key business and technology conversations.
0:32

$GOOG's Ruth Porat says the biggest risk in the AI buildout is the ROI question, and focusing on short-term tactics at the cost of building a great company. "I think the biggest risk for everyone is when the question is asked, which it often is, how do you get to an ROI? That's going to force our teams to do something short-term and tactical. You can always do a cost cut to get something to drop to the bottom line, and that is not a way to build a great company." "So I think the risk is don't be so in the moment maniacally focused on what is it, but have a right mix of an engine that's continuing to drive the business, which also explains why you need to start building early for growth so that you can grow into it." ____ Follow Fireside Alpha for more daily highlights of key business and technology conversations.

Fireside Alpha

38,361 次观看 • 1 个月前

“what are your dreams for the future?” 🆑: my ambition is to try and have the simplest life, keeping all those passions alive, and this is really where i’m spending my time now. i don't really need anything more because i’m happy the way i am, and my only ambition now is to obviously be a world champion. this is all i’ve ever dreamed about and this is all i’ve ever worked for and what i will continue to do so. but just having a relatively simple life and of course, simple is relative and it's more simpler than what it is now because obviously my life is quite hectic and i travel from everywhere. i go everywhere in the world every week for racing, for different kind of events. but trying to keep a simpler life and at the end of the day, i think what really matters in life is mostly people that you love most and to just keep that in mind, even though life is obviously quite hectic. 🎥: eight sleep
1:16

“what are your dreams for the future?” 🆑: my ambition is to try and have the simplest life, keeping all those passions alive, and this is really where i’m spending my time now. i don't really need anything more because i’m happy the way i am, and my only ambition now is to obviously be a world champion. this is all i’ve ever dreamed about and this is all i’ve ever worked for and what i will continue to do so. but just having a relatively simple life and of course, simple is relative and it's more simpler than what it is now because obviously my life is quite hectic and i travel from everywhere. i go everywhere in the world every week for racing, for different kind of events. but trying to keep a simpler life and at the end of the day, i think what really matters in life is mostly people that you love most and to just keep that in mind, even though life is obviously quite hectic. 🎥: eight sleep

clara

15,190 次观看 • 2 个月前