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Most companies don't realize they have a documentation problem until everyone already depends on it. Customers use it. New hires use it. Engineers use it. And when documentation falls out of date, the whole system starts working against itself People stop trusting what they're reading Teams lose context And... nobody can fix it, because the problem is everywhere at once The longer it goes unfixed, the harder it is to untangle That's the opportunity Manicule saw They're building an AI-native DevRel company for developer tools - owning documentation, technical content, GEO, and distribution across social channels. The premise isn't that AI should replace expertise. It's that expertise should scale. Their AI agents audit and test at scale. Their humans own the architecture, the writing, and the creative direction. Every review improves the system Every project creates more context. Every iteration raises the bar What started as a highly manual business - helping developer companies create better technical content - has become a scalable AI-native operation Today, Manicule works with some of the fastest-growing developer tool companies - including Supermemory, Greptile, and Reducto - has scaled fast over the last few months, and has more demand than it can take on. Not because they publish more content. Because they help developer companies create content developers actually trust. 🎙️ Naman Bansal & Shreyans Jain , Co-founders Manicule (YC P26) on Fondo.com START Huge thanks to Numeral for making this episode possible!show more

David J Phillips

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48,703 次观看 • 2 个月前 •via X (Twitter)

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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?

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

2,967,551 次观看 • 2 个月前

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,757 次观看 • 4 个月前

🚨BIGGEST ANNOUNCEMENT OF MY LIFE: I've been working on something big these last few months It's called Creator Buddy, and it's the best AI content tool you'll ever use It's an AI content coach that will help you create WAY better content on X It's jam packed with awesome AI tools that will help you create content that attracts you a massive audience: • AI coach that reviews all your old content to let you know what's working • AI content repurposing tool that helps you take a tweet and turn it into videos, blogs, and other forms of content • Brain dumping tool that takes what's on your mind and turns it into content • A bookmarking tool that's like X bookmarks except actually useful This is the most important project of my life and I can't wait for you to try it out The biggest challenge I see for content creators on X is they struggle to find their voice. They struggle to figure out what their niche should be or what they should talk about Creator Buddy solves this It's not an AI tool that creates you content. It's an AI tool that helps you discover who you are and what makes you unique and interesting. I'm launching Creator Buddy in the next month or so and want you to test it out. Beta testing begins in the next week and the first wave of users will be my subscribers on X I'll also be choosing 10 random people who like/RT this post to beta test it early Second wave will be through my newsletter (link in bio) Third wave will be to my reply guys (make sure to reply early and often) Creator Buddy is going to be the single greatest AI content creation tool and I can't wait for you to use it!

🚨BIGGEST ANNOUNCEMENT OF MY LIFE: I've been working on something big these last few months It's called Creator Buddy, and it's the best AI content tool you'll ever use It's an AI content coach that will help you create WAY better content on X It's jam packed with awesome AI tools that will help you create content that attracts you a massive audience: • AI coach that reviews all your old content to let you know what's working • AI content repurposing tool that helps you take a tweet and turn it into videos, blogs, and other forms of content • Brain dumping tool that takes what's on your mind and turns it into content • A bookmarking tool that's like X bookmarks except actually useful This is the most important project of my life and I can't wait for you to try it out The biggest challenge I see for content creators on X is they struggle to find their voice. They struggle to figure out what their niche should be or what they should talk about Creator Buddy solves this It's not an AI tool that creates you content. It's an AI tool that helps you discover who you are and what makes you unique and interesting. I'm launching Creator Buddy in the next month or so and want you to test it out. Beta testing begins in the next week and the first wave of users will be my subscribers on X I'll also be choosing 10 random people who like/RT this post to beta test it early Second wave will be through my newsletter (link in bio) Third wave will be to my reply guys (make sure to reply early and often) Creator Buddy is going to be the single greatest AI content creation tool and I can't wait for you to use it!

Alex Finn

337,313 次观看 • 1 年前

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

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

84,381 次观看 • 2 个月前

Feargal Sharkey, "Ofwat the regulator has already highlighted and confirmed. We have already paid them all of the funding they ever needed for 30 years to fix this problem. "We should be asking where our money's gone and what's happened to it and how you fix it" "Water companies have learned they can pollute with impunity because the regulatory system simply isn't up to it and it has failed. And that's created the situation which has been exposed today" Krishnan Guru-Murthy, "But that's going to require billions of spending, isn't it?" Feargal Sharkey, "And here's the thing. We've already paid this money. The regulators confirmed it. Water companies have admitted it" Krishnan Guru-Murthy, "But it's not sitting in a bank account somewhere ready to spend it. It is" Feargal Sharkey, "The shareholders that have left those companies £60 billion in debt that have taken £72 billion of our money. They're the ones should be held accountable. Not a penny more of public money should go into these companies"

Feargal Sharkey, "Ofwat the regulator has already highlighted and confirmed. We have already paid them all of the funding they ever needed for 30 years to fix this problem. "We should be asking where our money's gone and what's happened to it and how you fix it" "Water companies have learned they can pollute with impunity because the regulatory system simply isn't up to it and it has failed. And that's created the situation which has been exposed today" Krishnan Guru-Murthy, "But that's going to require billions of spending, isn't it?" Feargal Sharkey, "And here's the thing. We've already paid this money. The regulators confirmed it. Water companies have admitted it" Krishnan Guru-Murthy, "But it's not sitting in a bank account somewhere ready to spend it. It is" Feargal Sharkey, "The shareholders that have left those companies £60 billion in debt that have taken £72 billion of our money. They're the ones should be held accountable. Not a penny more of public money should go into these companies"

Farrukh

550,483 次观看 • 2 年前

AI shouldn't replace therapists. It should help them spend more time being therapists. Not because AI won't get smarter Because good therapy isn't just information It's trust It's compassion It's the human element. Most therapists spend only about half their time doing therapy The other half? Running the business Notes. Paperwork. Marketing. Insurance claims. Klarify automates the work around the session so therapists can focus on the work only they can do Today, more than 7,000 therapists use the platform The result is clear: they spend less time running a business, and more time doing the work only they can do 🎙️Moody Abdul, CEO & Co-Founder Klarify (YC P26) on Fondo.com START Huge thanks to Rippling for making this episode possible!

AI shouldn't replace therapists. It should help them spend more time being therapists. Not because AI won't get smarter Because good therapy isn't just information It's trust It's compassion It's the human element. Most therapists spend only about half their time doing therapy The other half? Running the business Notes. Paperwork. Marketing. Insurance claims. Klarify automates the work around the session so therapists can focus on the work only they can do Today, more than 7,000 therapists use the platform The result is clear: they spend less time running a business, and more time doing the work only they can do 🎙️Moody Abdul, CEO & Co-Founder Klarify (YC P26) on Fondo.com START Huge thanks to Rippling for making this episode possible!

David J Phillips

88,762 次观看 • 2 个月前

On Bloomberg last night, I was asked why Amazon drives more than a third of U.S. e-commerce. It is not because of their storefront. It is not because of their payments. It is not even because of product selection. It is because consumers trust their deliveries. That trust is built on physical infrastructure at scale with intelligence layered on top. And that is what Stord is building for every independent merchant. The most defensible companies in the world combine software intelligence with physical execution. Today, 9 of the 10 largest U.S. companies by revenue operate physical networks at scale. Software alone cannot replicate the outcomes created when you control the movement of physical atoms. That is the physical intelligence layer for commerce. And that is what Stord Labs is built to advance. Deploying AI and robotics against real operational complexity, in the real world, across a vertically integrated network operating at scale. Bloomberg TV

On Bloomberg last night, I was asked why Amazon drives more than a third of U.S. e-commerce. It is not because of their storefront. It is not because of their payments. It is not even because of product selection. It is because consumers trust their deliveries. That trust is built on physical infrastructure at scale with intelligence layered on top. And that is what Stord is building for every independent merchant. The most defensible companies in the world combine software intelligence with physical execution. Today, 9 of the 10 largest U.S. companies by revenue operate physical networks at scale. Software alone cannot replicate the outcomes created when you control the movement of physical atoms. That is the physical intelligence layer for commerce. And that is what Stord Labs is built to advance. Deploying AI and robotics against real operational complexity, in the real world, across a vertically integrated network operating at scale. Bloomberg TV

Sean Henry

1,275,331 次观看 • 2 个月前

JENSEN HUANG WAS ASKED WHO IS USING AI BETTER THAN ANYONE ELSE IN THE WORLD His answer wasn't OpenAI. It wasn't Google. It wasn't even Nvidia itself. Here's what he said on CNBC: "Nobody uses AI better than Meta." He explained that Meta went from a classical recommender system running on CPUs to a generative AI agentic system making recommendations across the entire platform. "Everything from the way social media works and the way they recommend ads and help advertisers create content has fundamentally been changed. And their earnings show it." "That's the reason why they're investing so hard. They see a much larger future potential for it." Mark Zuckerberg $META is spending $70B+ on AI this year.

JENSEN HUANG WAS ASKED WHO IS USING AI BETTER THAN ANYONE ELSE IN THE WORLD His answer wasn't OpenAI. It wasn't Google. It wasn't even Nvidia itself. Here's what he said on CNBC: "Nobody uses AI better than Meta." He explained that Meta went from a classical recommender system running on CPUs to a generative AI agentic system making recommendations across the entire platform. "Everything from the way social media works and the way they recommend ads and help advertisers create content has fundamentally been changed. And their earnings show it." "That's the reason why they're investing so hard. They see a much larger future potential for it." Mark Zuckerberg $META is spending $70B+ on AI this year.

WOLF

724,740 次观看 • 2 个月前

Wow. This is the coolest AI tool I've ever used It's an AI that can take ANY post you've ever put out and turn it into hundreds of different pieces of content instantly You'll never run out of content again Put in a single post and you can turn it into: • Video scripts • Long form posts • Blog posts • Articles • Short form posts It's actually mind blowing. On top of that you can use the AI to repurpose the content as different people on X Choose an ANY account on X then hit repurpose, the AI will write the post AS THAT person I've been writing some posts as Trump lately and they're fire You can find inspiration accounts and start creating content just like them So sick O forgot to mention: I built the tool myself It's called Creator Buddy and it's the single greatest AI content creation tool of all time This is one of 8 different AI content tools built in that will help you: • Increase your following • Explode your engagement • Get paid more on X It launches THURSDAY JANUARY 23rd! Hit the link in the bio to get more details and get notified when it releases Right now over 100 people are beta testing it and the feedback and growth has been insane The best part is, it's not just a super tool, it's also an exclusive community. I'll be meeting with members every single week! Can't wait for you to use it!

Wow. This is the coolest AI tool I've ever used It's an AI that can take ANY post you've ever put out and turn it into hundreds of different pieces of content instantly You'll never run out of content again Put in a single post and you can turn it into: • Video scripts • Long form posts • Blog posts • Articles • Short form posts It's actually mind blowing. On top of that you can use the AI to repurpose the content as different people on X Choose an ANY account on X then hit repurpose, the AI will write the post AS THAT person I've been writing some posts as Trump lately and they're fire You can find inspiration accounts and start creating content just like them So sick O forgot to mention: I built the tool myself It's called Creator Buddy and it's the single greatest AI content creation tool of all time This is one of 8 different AI content tools built in that will help you: • Increase your following • Explode your engagement • Get paid more on X It launches THURSDAY JANUARY 23rd! Hit the link in the bio to get more details and get notified when it releases Right now over 100 people are beta testing it and the feedback and growth has been insane The best part is, it's not just a super tool, it's also an exclusive community. I'll be meeting with members every single week! Can't wait for you to use it!

Alex Finn

15,357 次观看 • 1 年前

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

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

Mike Fishbein

10,141 次观看 • 2 个月前

Mark Cuban just told every software company on Earth they’re already dead. The people inside them are still building roadmaps. Cuban: “Software is dead because everything’s going to be customized to your unique utilization.” Every SaaS company was built on one bet. Software stays rigid. Humans stay adaptable. You learn the tool. You bend to it. You pay for someone else’s version of your solution. AI just inverted that. The tool bends to you or it dies. Cuban: “33 million companies aren’t going to have AI budgets, aren’t going to have AI experts.” 33 million businesses feel something shifting beneath them. None can name it. The distance between what AI can do and what small companies can access is the most mispriced gap in markets today. Not a technology problem. A translation problem. Cuban: “Learn all you can about AI but learn more on how to implement them in companies.” Everyone is racing to build intelligence. Almost nobody is racing to deploy it where the pain is deepest. The person who walks into a 40-person company and rewires their entire operation captures more value than the team that trained the model. Understanding pain is now worth more than building intelligence. Cuban: “Every single job available for kids coming out of school because every single company needs that.” The most important career of the next decade has no title. No degree path. No university knows it needs to exist yet. It belongs to whoever learns two languages fluently. The language of a business that can’t articulate what’s breaking. And the language of an AI that doesn’t know where to aim. 33 million companies. Zero translators. Whoever arrives first doesn’t enter a market. They create one.

Mark Cuban just told every software company on Earth they’re already dead. The people inside them are still building roadmaps. Cuban: “Software is dead because everything’s going to be customized to your unique utilization.” Every SaaS company was built on one bet. Software stays rigid. Humans stay adaptable. You learn the tool. You bend to it. You pay for someone else’s version of your solution. AI just inverted that. The tool bends to you or it dies. Cuban: “33 million companies aren’t going to have AI budgets, aren’t going to have AI experts.” 33 million businesses feel something shifting beneath them. None can name it. The distance between what AI can do and what small companies can access is the most mispriced gap in markets today. Not a technology problem. A translation problem. Cuban: “Learn all you can about AI but learn more on how to implement them in companies.” Everyone is racing to build intelligence. Almost nobody is racing to deploy it where the pain is deepest. The person who walks into a 40-person company and rewires their entire operation captures more value than the team that trained the model. Understanding pain is now worth more than building intelligence. Cuban: “Every single job available for kids coming out of school because every single company needs that.” The most important career of the next decade has no title. No degree path. No university knows it needs to exist yet. It belongs to whoever learns two languages fluently. The language of a business that can’t articulate what’s breaking. And the language of an AI that doesn’t know where to aim. 33 million companies. Zero translators. Whoever arrives first doesn’t enter a market. They create one.

Dustin

473,239 次观看 • 1 个月前

Google is a vaccine company?! "One thing people need to know, is that Google is a vaccine company. Google's parent company alphabet and Verily have this this a series of sister subsidiaries of Google's, actually manufactured vaccines flu vaccines and other vaccines and they're making and they're partnering with all the pharmaceutical companies. So Google has a seven hundred and sixty one million dollar partnership with Glaxo and what they're doing is they're harvesting medical information. That's a huge part of their business and then they're selling it to the pharmaceutical companies. But they're partnering with pharmaceutical companies that they can use that medical they can tell what you're buying habits are they can hear you cough listening to the theory and that all of these, you know, the surveillance systems they're erecting and the informational systems and how we use them and what we purchase is Is all turned into sellable data. Now going to the vaccine companies and the big pharmaceutical companies and they're partnered with them. They're really just part of the big part of the industry."

Google is a vaccine company?! "One thing people need to know, is that Google is a vaccine company. Google's parent company alphabet and Verily have this this a series of sister subsidiaries of Google's, actually manufactured vaccines flu vaccines and other vaccines and they're making and they're partnering with all the pharmaceutical companies. So Google has a seven hundred and sixty one million dollar partnership with Glaxo and what they're doing is they're harvesting medical information. That's a huge part of their business and then they're selling it to the pharmaceutical companies. But they're partnering with pharmaceutical companies that they can use that medical they can tell what you're buying habits are they can hear you cough listening to the theory and that all of these, you know, the surveillance systems they're erecting and the informational systems and how we use them and what we purchase is Is all turned into sellable data. Now going to the vaccine companies and the big pharmaceutical companies and they're partnered with them. They're really just part of the big part of the industry."

Bridgett Fertig

49,678 次观看 • 7 个月前

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud

the IBM earnings reinforced something I think Wall Street is still underestimating: IBM is becoming an operating system for enterprise AI. The initial earnings reaction focused on the familiar metrics—revenue growth, infrastructure, margins and quarterly expectations. But that misses the bigger transformation underway. The AI market is bifurcating. On one side are companies building frontier models. On the other are companies helping enterprises operationalize AI—integrating it with existing systems and data, governing it, securing it, and turning AI into measurable business outcomes. IBM is increasingly positioning itself for that second market. And that's important because the enterprise AI conversation is moving from models to tools, from experimentation to outcomes, and ultimately toward autonomy and agents. IBM has decades of enterprise infrastructure, software, data and consulting relationships sitting underneath that transition. The mainframe isn't necessarily legacy in this world—it can become part of the context and transaction infrastructure AI acts upon. Wall Street initially looked at the quarter. I think the more important question is what IBM is becoming. My commentary below. #IBM #AI #EnterpriseAI #AgenticAI #HybridCloud

John Furrier

47,663 次观看 • 16 天前

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."

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

393,776 次观看 • 6 个月前

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

The Peel

209,752 次观看 • 1 年前

Ray Dalio: “AI is eating everything… and it might eat itself.” Ray Dalio: “Right now it looks to me like AI basically is eating everything and it might eat itself.” “What I mean by that is not produce adequate profits.” “When we look at the bubble question on AI, what a lot of people don't realize in bubbles is that through all technologies, they think that they are betting on the technology when they buy the stocks in the companies.” “That's not true. There's a giant difference between the behavior of the companies and the behavior of the technologies.” “The norm is that a lot of companies won't survive at the start. Very small percentage, and they'll all fight and so on.” “But the technologies will go on and it'll be great. That has big implications.” ---------------------------------------------- Thanks to our partner for making this happen! Airwallex is a leading global payments and financial platform for modern businesses,offering trusted solutions to manage everything from business accounts, payments, treasury, and spend management to embedded finance. Airwallex

Ray Dalio: “AI is eating everything… and it might eat itself.” Ray Dalio: “Right now it looks to me like AI basically is eating everything and it might eat itself.” “What I mean by that is not produce adequate profits.” “When we look at the bubble question on AI, what a lot of people don't realize in bubbles is that through all technologies, they think that they are betting on the technology when they buy the stocks in the companies.” “That's not true. There's a giant difference between the behavior of the companies and the behavior of the technologies.” “The norm is that a lot of companies won't survive at the start. Very small percentage, and they'll all fight and so on.” “But the technologies will go on and it'll be great. That has big implications.” ---------------------------------------------- Thanks to our partner for making this happen! Airwallex is a leading global payments and financial platform for modern businesses,offering trusted solutions to manage everything from business accounts, payments, treasury, and spend management to embedded finance. Airwallex

The All-In Podcast

74,077 次观看 • 5 个月前

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.

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

230,594 次观看 • 1 年前

Every marketing leader I talk to is facing the same challenge: Scale 📶 The demand for content has exploded, but the systems behind it haven’t kept up. Teams are expected to launch more campaigns, in more markets, with more data, all while maintaining brand integrity and speed. Chat-based AI tools help individuals move faster. But they don’t solve the harder problem: scaling content creation across markets and audiences. Over the past year, we’ve seen the limits of chat-based AI inside large organizations. 📢 Today, we’re introducing ⊞ Jasper Grid ⊞ — the backbone of Content Pipelines. ⊞ Grid is a unified, no-code system that lets AI agents plan, produce, and optimize content at scale. It transforms structured inputs like briefs, data, and brand guidelines into launch-ready assets with speed and precision. Under the hood, Grid runs on a network of modular AI agents, a resilient scheduler, and deep integrations with the tools marketers already use — all governed by brand guardrails so teams can move fast without losing control. 🏆 The result: faster time to market, consistent brand execution, and true operational scale. AI is becoming infrastructure. The next generation of enterprise marketing will run on systems built for speed, precision, and trust. Proud of the Jasper team and our early customers who helped shape this moment.

Timothy Young

1,307,286 次观看 • 9 个月前

.Naval on Finding Technical Co-founders Join the conversation: "It's getting harder and harder because these days those people just go and start their own company. So what do you bring to the table that a good technical co-founder would not have? That's number one. And that would either mean you have some particular deep insight and relationships into a market and you have capital because you've got to bring a lot to the table because you're not creating the product anymore. Or secondly, you have to have a relationship that's pre-existing. You worked together before at some company or you've gone to school together. Outside of those cases, it's incredibly hard to recruit a quality technical co-founder. And I'm not even sure you're in a position to start a company. This is why so many companies come out of universities or come out of the Bay Area because there you're just very likely to have business founders and technical founders next to each other. The business founders at least have enough technical experience that they can properly evaluate the technical co-founder. And then the business founder has to hustle and raise money, recruit people, have a vision, have a customer base, have a market ready to go, and have proven themselves. And now lots and lots of other things to balance out the skills a technical founder brings to the table. There are now more successful cases of solo technical founders starting companies than there are of solo non-technical founders starting companies. So it is an uphill climb. I'm not going to sugarcoat it for you.”

.Naval on Finding Technical Co-founders Join the conversation: "It's getting harder and harder because these days those people just go and start their own company. So what do you bring to the table that a good technical co-founder would not have? That's number one. And that would either mean you have some particular deep insight and relationships into a market and you have capital because you've got to bring a lot to the table because you're not creating the product anymore. Or secondly, you have to have a relationship that's pre-existing. You worked together before at some company or you've gone to school together. Outside of those cases, it's incredibly hard to recruit a quality technical co-founder. And I'm not even sure you're in a position to start a company. This is why so many companies come out of universities or come out of the Bay Area because there you're just very likely to have business founders and technical founders next to each other. The business founders at least have enough technical experience that they can properly evaluate the technical co-founder. And then the business founder has to hustle and raise money, recruit people, have a vision, have a customer base, have a market ready to go, and have proven themselves. And now lots and lots of other things to balance out the skills a technical founder brings to the table. There are now more successful cases of solo technical founders starting companies than there are of solo non-technical founders starting companies. So it is an uphill climb. I'm not going to sugarcoat it for you.”

Airchat

14,611 次观看 • 2 年前

Imagine an AI application that can type anywhere you can and use the full context of what's on your screen. This is the application we all deserve (at least if you have macOS.) Check out Omnipilot. It's an app that works with every other macOS application and uses Claude Sonet 3.5 in the background—it also supports Gemini and GPT-4o. Here is the idea: You can use the tool to ask questions about anything on your screen. Or you can use it to autocomplete the text you are typing. You don't need to copy and paste anymore or waste your time providing context to a model. It sees what you see. It works right where you are. That's pretty cool. Here are a couple of cool examples: • Use it to reply to an email • Use it in the terminal to autocomplete a command • Use it to finish a document • Use to send a message on Slack AI at the system level is bonkers. You can read a ton more on their Product Hunt launch page: Thanks to the Omnipilot team for collaborating with me on this post!

Imagine an AI application that can type anywhere you can and use the full context of what's on your screen. This is the application we all deserve (at least if you have macOS.) Check out Omnipilot. It's an app that works with every other macOS application and uses Claude Sonet 3.5 in the background—it also supports Gemini and GPT-4o. Here is the idea: You can use the tool to ask questions about anything on your screen. Or you can use it to autocomplete the text you are typing. You don't need to copy and paste anymore or waste your time providing context to a model. It sees what you see. It works right where you are. That's pretty cool. Here are a couple of cool examples: • Use it to reply to an email • Use it in the terminal to autocomplete a command • Use it to finish a document • Use to send a message on Slack AI at the system level is bonkers. You can read a ton more on their Product Hunt launch page: Thanks to the Omnipilot team for collaborating with me on this post!

Santiago

72,306 次观看 • 2 年前