46 Y Combinator Video Summaries, Bullet Points, TL;DWs & More to Copy & Paste - Page 8

Aaron Levie: Why Startups Win In The AI Era

AI isn't just changing jobs, it's creating entirely new opportunities for startups! 🚀 Aaron Levie, Box CEO, breaks down why this era is the best time to build. Don't miss his insights on new business models & riding the AI tailwind. #AI #Startups #Entrepreneurship

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

1/ The AI era is here, and it's not just about job replacement. Aaron Levie, Box CEO, reveals why this is a golden age for startups. \n\n2/ Levie argues that AI agents will automate 'unstrategic' tasks, freeing up human time for high-impact innovation and creating massive new categories of work. \n\n3/ Unlike the cloud transition, enterprises are already bought into AI. The challenge now is implementation, not conviction. This creates unique entry points for nimble startups. \n\n4/ Forget traditional SaaS seats! Levie predicts new consumption-based business models where AI agents handle unlimited work volume, charging for outcomes, not just licenses. \n\n5/ For founders, the message is clear: ride the AI tailwind, build a great team, and read 'Innovator's Dilemma.' This window of opportunity won't last forever!

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

Hook: Are you worried AI will steal your job? Think again! \n\nContent: Aaron Levie, Box CEO, says AI isn't just replacing work—it's creating a massive wave of *new* opportunities, especially for startups! He explains how AI agents can tackle tasks software never could, unlocking unprecedented value for businesses. This is the moment to build, innovate, and ride the AI tailwind. \n\nCTA: Want to know how to seize this opportunity? Watch the full Y Combinator video now! Link in bio!

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

Aaron Levie, CEO of Box, provided a compelling analysis at Y Combinator's AI Startup School on the strategic implications of AI for enterprises and startups. Drawing parallels to the cloud transition, Levie emphasized that while enterprises are convinced by AI's potential, the real opportunity lies in automating 'unstrategic' work within organizations, thereby creating new, high-value roles for humans. He underscored unstructured data as AI's primary domain for disruption. For entrepreneurs, Levie highlighted the emergence of entirely new 'nouns and verbs' for software and the necessity to rethink traditional SaaS business models towards consumption-based pricing for AI agents. His advice for founders centered on leveraging market tailwinds, building strong teams, and understanding disruptive innovation to capitalize on this unique, time-limited window of opportunity.

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

Mind blown by Aaron Levie's insights on AI and startups at Y Combinator! 🤯 He's not just talking hype; he's talking real-world transformation. From Box's journey through the cloud era to predicting AI will create *more* work (the strategic kind!), this video is a must-watch for anyone building or working in tech. He even drops some incredible advice for young founders on finding new market opportunities and why now is the time to go BIG. \n\nWhat's your biggest takeaway from this AI revolution? Let us know in the comments! 👇\n\n#AIRevolution #StartupLife #AaronLevie #YCombinator #TechTrends #FutureOfWork #Entrepreneurship #Innovation #CloudComputing #BoxCEO

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

AI taking your job? 🤔 Aaron Levie says NO! 🙅‍♂️ It's creating NEW work & HUGE startup opportunities! 🚀 This window won't last, so build that dream company NOW! #AI #Startups #Tech #FutureOfWork #Innovation #Entrepreneur

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

There's a very, very long list of things that software never did before that AI agents are perfectly primed to go do now. And that's basically the opportunity set.

— Y Combinator

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

- AI is not primarily coming for jobs, but rather for 'unstrategic' tasks, freeing up human time for high-value work.\n- Unstructured data (documents, contracts) represents AI's biggest opportunity in the enterprise, enabling automation previously impossible.\n- The current period (approx. 2023-2026) is a unique, time-limited window for startups to create multi-billion dollar companies by finding new 'nouns and verbs' for AI-powered solutions.\n- SaaS business models will shift from seat-based licensing to consumption-based pricing for AI agents, reflecting the volume of work performed.\n- Incumbents will likely focus AI on their existing customer base, leaving vast greenfield opportunities for startups in new markets and use cases.

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

AI will create massive new startup opportunities by automating unstrategic tasks and enabling new work, shifting business models, and opening a unique window for founders.

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

Your job isn't safe... but not for the reason you think.

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

Imagine all the boring, repetitive parts of grown-up jobs, like sorting through piles of papers or sending the same email over and over. Computers used to be bad at that. But now, special smart computer helpers called 'AI agents' can do those boring tasks! This means people can spend their time on fun, creative, and important stuff. And for people who want to start new companies, it's like a whole new world of problems they can solve with these smart computer helpers, making things better and faster for everyone.

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

Did you know that the vast majority of time spent inside large companies is on 'unstrategic' but necessary activities, which AI agents are perfectly suited to automate?

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

Problem: Most companies spend a vast amount of time on necessary but unstrategic work that software couldn't automate.\nSolution: AI agents can now automate these tasks, freeing up human time for breakthrough innovation and creating new, economically viable work.

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

How do you see AI transforming your daily work or industry in the next 3-5 years? What 'unstrategic' task in your current role would you most want an AI agent to handle?

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

Aaron Levie: Why Startups Win In The AI Era\nBox co-founder and CEO Aaron Levie discusses the profound impact of AI on work and the unparalleled opportunities it presents for startups. He shares lessons from Box's journey through technological shifts and explains why AI represents the most transformative wave yet, challenging traditional business models and creating new categories of work.\n\nTopics Covered:\n- 00:00 AI hype vs. reality inside companies\n- 01:00 Early days: starting Box in 2005\n- 03:30 Pivoting from consumer storage to enterprise cloud\n- 05:45 Cloud transition lessons vs. AI today\n- 07:10 Why unstructured data is AI’s biggest opportunity\n- 09:05 Will AI replace jobs or create more work?\n- 13:15 Startups vs. incumbents in the AI era\n- 15:20 Finding the next “nouns and verbs” for startups\n- 20:25 Rethinking SaaS business models for AI agents\n- 27:05 Why companies won’t just build everything in-house\n- 31:15 Advice to young founders\n- 35:40 Fulfillment and meaning of life\n- 36:40 Enterprise design and craft in SaaS\n- 39:00 Competing with incumbents like Workday\n\nKey Quotes:\n"There's a very, very long list of things that software never did before that AI agents are perfectly primed to go do now. And that's basically the opportunity set." "This window, between a year ago and 3 years or so, plus or minus from now, this is when the next hundreds of great companies will get started." \nResources Mentioned: [Placeholder for any books, tools, or websites mentioned]

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

00:00 AI Hype vs. Reality & The Startup Window 01:00 Box's Early Days & Consumer to Enterprise Pivot 04:11 Cloud vs. AI Transition: Key Differences 07:09 Unstructured Data: AI's Biggest Opportunity 09:05 Will AI Replace Jobs or Create More Work? 13:14 Startups vs. Incumbents in the AI Era 15:20 Finding New "Nouns and Verbs" for Startups 20:25 Rethinking SaaS Business Models for AI Agents 27:05 Why Companies Won't Build Everything In-House 31:15 Advice to Young Founders & Seizing the Moment

https://youtube.com/watch?v=uqc_vt95GJg

Aaron Levie: Why Startups Win In The AI Era

1. Focus on automating 'unstrategic' work: Identify tasks within industries that are necessary but not core to a company's differentiation, as these are prime targets for AI agent solutions.\n2. Embrace new business models: Shift thinking from traditional seat-based SaaS to consumption or outcome-based pricing for AI agents to capture the true value of their capabilities.\n3. Leverage unstructured data: Recognize that AI thrives on unstructured data (documents, media), offering immense opportunities to unlock value from corporate content that was previously unsearchable or unautomatable.\n4. Ride the AI tailwind: For founders, ensure your startup idea is fundamentally transformed by AI, as fighting against market headwinds in this era is an unnecessary struggle.\n5. Seize the current window: Understand that the next 2-3 years represent a unique, time-limited opportunity to build significant AI-driven companies, so be ambitious and act decisively.

https://youtube.com/watch?v=uqc_vt95GJg

The Future of Software Creation with Replit CEO Amjad Masad

Replit CEO Amjad Masad predicts the value of traditional software will approach ZERO! 🤯 AI agents are reshaping how companies are built & work gets done. Watch the full talk from Y Combinator to see the future of coding. #AI #SoftwareDevelopment #FutureofWork

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

1. The future of software is wild! 🤯 Y Combinator features Replit CEO Amjad Masad on how AI agents are making coding accessible to EVERYONE & changing the game for businesses. Get ready for a paradigm shift! 👇 2. Amjad traces computing's evolution: from expert-only mainframes to today's PCs. Now, software engineering is undergoing the same transition, moving from specialized pros to generalists empowered by AI. 3. Replit's vision: make programming so easy, you don't even need to code. AI agents are the ultimate expression of this mission, tackling complex tasks and even creating entire apps autonomously. 4. Prediction: The value of traditional application software will go to zero. Companies will shift from buying SaaS to using AI agents to solve problems on demand, leading to highly specialized, 'sovereign individuals.' 5. This isn't just about code; it's about how we work. Prepare for a world of generalist employees and fluid, project-based teams. Dive into the full Y Combinator talk to understand the implications! #AI #FutureOfWork

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

Hook: What if I told you the software you buy today will be worthless tomorrow? Content: Y Combinator presents Replit CEO Amjad Masad, who reveals how AI agents are making software creation so cheap and easy that traditional SaaS is becoming obsolete. Imagine an HR professional building a custom app in days, or businesses run by generalist employees empowered by AI. This isn't science fiction; it's happening now. CTA: The future of work is here! Watch the full Y Combinator video to learn how to prepare for this massive shift!

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

In a compelling Y Combinator presentation, Replit CEO Amjad Masad articulated a profound shift in the software industry, positing that the value of traditional application software will inevitably trend towards zero. This disruption is driven by the maturation of AI agents, which enable anyone to generate complex software on demand, democratizing programming. Masad emphasized that this evolution necessitates robust, scalable, and secure infrastructure for these agents, a core focus for Replit. The economic implications are significant: businesses will move from purchasing specialized SaaS to leveraging AI for bespoke problem-solving, fostering a workforce of generalist employees and 'sovereign individuals' who can rapidly create and deploy value. This analysis provides critical foresight for founders, investors, and professionals navigating the evolving landscape of technology and work.

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

Mind. Blown. 🤯 Replit CEO Amjad Masad just dropped some serious truth bombs about the future of software at Y Combinator's AI Startup School! Imagine a world where AI agents build ALL your apps, and traditional software costs almost nothing. 🚀 He dives deep into how AI is making programming accessible to literally *everyone*, shifting us from specialized roles to a world of 'generalist employees' and 'sovereign individuals' who can create massive value with just an idea. This isn't just about tech; it's about how we'll work, build companies, and innovate. Seriously, this talk is a must-watch if you're curious about where the tech world is heading! Get ready to rethink everything you know about coding and careers. ✨ #FutureOfWork #AI #SoftwareDevelopment #Replit #YCombinator #TechTrends #Innovation #ArtificialIntelligence #CreatorEconomy #DisruptiveTech

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

AI is coming for your code! 🤯 Replit CEO Amjad Masad says traditional software value is going to ZERO. Get ready for a world where AI agents build EVERYTHING! 💻🤖 #AI #FutureOfCode #Replit #TechTok #MindBlown

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

My prediction is that all application software will go to zero. Software will be dirt cheap, that no one will be making money on the traditional type of SAS software.

— Y Combinator

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

- AI agents are making software creation universally accessible, fulfilling Replit's mission to 'solve programming.' - The value of traditional application software is predicted to approach zero due to on-demand AI generation. - This shift will lead to a workforce of generalist employees and the rise of 'sovereign individuals' empowered by technology. - Robust, scalable, and sandboxed infrastructure is crucial for the effective operation of AI agents. - Replit is evolving from an application builder to a universal problem solver for companies and individuals.

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

Replit CEO Amjad Masad predicts AI agents will drive traditional software value to zero, making programming universally accessible and transforming how businesses operate and how people work.

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

The software you pay for today? It's about to be worthless.

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

Imagine building with LEGOs. Right now, only special builders know how to make cool stuff. But soon, we'll have super-smart robots (AI agents) that can build *anything* you ask them to, just by telling them what you want! This means everyone can make amazing things, and the old way of buying pre-made LEGO sets will become less important because your robot can build a custom one for free.

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

An HR professional at Replit, with no prior coding experience, built a custom HR software in less than a week using AI agents, which could otherwise cost tens of thousands of dollars annually on the market.

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

Problem: Traditional software is expensive and often doesn't perfectly fit unique business needs. Solution: AI agents will allow anyone to generate custom software on demand, making application software dirt cheap and highly personalized.

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

How do you envision your job changing when AI agents can perform most cognitive and physical tasks? What new types of businesses or creative endeavors will emerge when software creation costs next to nothing?

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

The Future of Software Creation with Replit CEO Amjad Masad Amjad Masad, co-founder & CEO of Replit, shares his vision for the future of software, driven by AI agents. Recorded at AI Startup School, he discusses how AI will make programming accessible to everyone, fundamentally alter business models, and shift the workforce towards generalist roles. This Y Combinator talk offers a compelling look at the impending disruption in the tech industry. Topics Covered: - 00:00 Introduction to Replit's vision - 00:52 Evolution of computing and software engineering - 02:48 AI as the ultimate enabler for software creation - 04:27 Building the habitat for AI agents - 06:05 Essential infrastructure for agent-built software - 07:34 Future Replit roadmap: universal model access, payments, agent-to-agent - 09:11 Levels of autonomy in AI agents - 11:46 The future of computer use and end-to-end testing - 12:43 Sampling, simulations, and generative testing for agents - 15:39 Prediction: Application software value goes to zero - 17:15 Real-world example: HR professional building software - 18:16 Impact on work, businesses, and the rise of generalist employees - 21:39 The "Sovereign Individual" in the intelligence age - 23:21 Universal access to opportunity and rapid company formation - 25:39 Replit's shift to problem-solving, not just app-making - 26:38 Q&A: Multiple agents and data fragmentation - 28:18 Q&A: What's left for humans to do? - 30:39 Q&A: Liberal arts vs. STEM education - 31:44 Q&A: Replit's innovation in the tech stack - 33:29 Q&A: Preparing for generalist roles - 35:03 Q&A: Autonomous agent development time horizons - 36:43 Q&A: Agent market saturation and domain knowledge - 38:09 Q&A: Replit's long-term monetization strategy - 39:58 Q&A: Preventing error accumulation in agent-generated code - 41:17 Q&A: Replit's transactional file system and open source Key Quotes: - "My prediction is that all application software will go to zero. In other words, software will be dirt cheap, that no one will be making money on the traditional type of SAS software." - "The information age will be the age of upward mobility. The brightest, most successful and ambitious of these will emerge as truly sovereign individuals." Resources Mentioned: [Placeholder]

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

00:00 Introduction & Replit's Vision 00:52 The Evolution of Software & AI's Role 04:27 The Essential Infrastructure for AI Agents 09:11 Levels of Autonomy in Software Agents 15:39 Prediction: The Value of Application Software Goes to Zero 18:16 How AI Reshapes Work & Business Structure 21:39 The Rise of the "Sovereign Individual" 25:39 Replit's Shift to Universal Problem Solving 28:18 Q&A: The Human Condition in an AI World 38:09 Q&A: Replit's Future & Monetization

https://youtube.com/watch?v=lWmDiDGsLK4

The Future of Software Creation with Replit CEO Amjad Masad

1. Embrace generalist skills: Seek roles in early-stage startups to gain diverse experience, rather than specializing narrowly. 2. Focus on problem-solving: Shift your mindset from building specific apps to using software (or agents) to solve real-world problems. 3. Develop clear thinking: In an age of abundant AI, the ability to generate novel ideas and think critically will be your most valuable asset. 4. Understand AI agent infrastructure: Recognize that the 'habitat' an agent lives in (sandboxing, scalability, testing) is as crucial as the AI model itself. 5. Explore domain-specific agent opportunities: Don't just build general software agents; identify areas where your unique domain expertise can create highly specialized and valuable agents.

https://youtube.com/watch?v=lWmDiDGsLK4

The FDE Playbook for AI Startups with Bob McGrew

The Forward Deployed Engineer (FDE) model is exploding in AI startups! 🚀 Bob McGrew, Palantir pioneer & ex-OpenAI CRO, explains on Y Combinator's The Lightcone why FDEs drive product discovery and scale in new markets. Essential viewing for founders! #AI #Startups #FDE

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

1. The FDE model is exploding in AI startups, but what *is* it? 🤔 Y Combinator's The Lightcone features Bob McGrew, a pioneer of the Forward Deployed Engineer approach at Palantir, explaining its resurgence. Here's why it's critical: 2. FDEs are technical experts embedded with customers, bridging the gap between product capabilities and unique client needs. At Palantir, this was vital for building software for complex, varied intelligence community demands. 3. It's 'doing things that don't scale at scale.' Instead of minimizing services, FDEs act as product discovery, identifying generalized solutions from specific customer problems, turning 'gravel roads into paved superhighways.' 4. For AI agent startups, there's no incumbent product, leading to massive product discovery needs. FDEs help land & expand by solving top-priority problems and driving contract size up, focusing on outcomes not just software installation. 5. This approach fosters a 'learning company' culture, crucial for navigating rapid AI capability improvements and adoption challenges. Learn how to apply these lessons to your own venture! #AI #Startups #FDEModel

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

Hook: Ever heard of the Forward Deployed Engineer model? It's how Palantir scaled, and now it's powering the AI boom! Content: Bob McGrew, a Palantir pioneer and ex-OpenAI CRO, reveals on Y Combinator's The Lightcone how FDEs embed with customers, driving deep product discovery and solving unique problems. It's about 'doing things that don't scale at scale' to build valuable, generalizable products. For AI agents, with no incumbent solutions, this hands-on approach is key to finding product-market fit and expanding contracts. CTA: Want to master this strategy for your AI startup? Watch the full Y Combinator video now for Bob McGrew's full playbook!

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

In a compelling discussion on Y Combinator's 'The Lightcone,' Bob McGrew, a key architect of the Forward Deployed Engineer (FDE) model at Palantir and former OpenAI Chief Research Officer, elucidates its strategic significance, particularly for emergent AI agent companies. The FDE model, characterized by technical experts embedded at customer sites, enables profound product discovery in heterogeneous markets where traditional scaling approaches falter. McGrew emphasizes that FDEs transform bespoke solutions into generalizable product features, driving significant contract value by focusing on delivering measurable outcomes. This methodology is proving indispensable for AI startups navigating a landscape devoid of incumbent products, fostering a 'learning company' culture essential for rapid capability adoption and market leadership. The insights offered are crucial for founders seeking sustainable growth in complex, high-value enterprise AI deployments.

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

Dive deep into the FDE model with Bob McGrew on Y Combinator's 'The Lightcone'! 🚀 This former Palantir exec and OpenAI CRO breaks down how the Forward Deployed Engineer model, pioneered at Palantir, is now the secret sauce for AI agent startups. It's all about embedding with customers, discovering new products, and turning unique problems into scalable solutions. From building Palantir's ontology to driving outcome-based pricing, McGrew shares invaluable lessons for any founder tackling new markets. Don't miss this masterclass in 'doing things that don't scale at scale'! #FDE #AIStartups #Palantir #OpenAI #TechInnovation #StartupLife #ProductDiscovery #YCombinator #FutureofAI #BobMcGrew

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

AI startups are doing WHAT?! 🤯 Bob McGrew (ex-OpenAI, Palantir legend) reveals the FDE model is taking over! 🛠️ It's all about engineers embedded with customers, doing 'things that don't scale at scale' to build products. Essential for AI agents with no incumbents! #AI #StartupHacks #FDE #Palantir #YCombinator #TechTok

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

The FD model effectively is doing things that don't scale at scale.

— Y Combinator

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

• The Forward Deployed Engineer (FDE) model embeds technical experts at customer sites to bridge product gaps and facilitate deep product discovery.
• Palantir pioneered the FDE strategy to navigate complex and heterogeneous markets, effectively transforming bespoke solutions into generalizable platform features.
• AI agent startups are widely adopting the FDE model because there are no incumbent products, necessitating extensive, hands-on product discovery.
• FDE success metrics focus on increasing contract size and delivering valuable outcomes, rather than just selling software installations.
• The model encourages a 'learning company' culture, crucial for adapting to rapid AI capability advancements and driving real-world adoption.

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

The Forward Deployed Engineer (FDE) model, pioneered by Palantir, is now critical for AI startups. It involves embedding engineers with customers to drive product discovery and scale by delivering high-value outcomes.

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

The secret strategy powering AI startups? It's not what you think.

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

Imagine you have a super cool toy, but every kid wants to play with it a little differently. Instead of just sending the toy, you send a special friend (the FDE) with it. This friend plays with each kid, figures out exactly what they need, and then tells the toy makers how to make the toy better for *all* kids, even if it starts with just one. This helps new AI tools, which are like brand new toys, figure out how to be super useful for everyone.

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

The Y Combinator job board lists over 100 startups currently hiring for Forward Deployed Engineer roles, a figure that was virtually zero just three years ago, signaling the FDE model's rapid adoption in the AI sector.

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

Problem: AI agent startups lack incumbent products and face immense product discovery challenges in complex, heterogeneous markets. Solution: The Forward Deployed Engineer (FDE) model embeds technical experts with customers to drive deep product discovery, deliver outcome-based value, and scale solutions by transforming custom work into generalizable product features.

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

How might the FDE model evolve as AI capabilities become even more sophisticated and integrated into existing systems? What are the biggest cultural shifts companies need to make to successfully implement an FDE strategy?

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

The FDE Playbook for AI Startups with Bob McGrew Y Combinator welcomes Bob McGrew, an early engineer at PayPal, executive at Palantir, and former Chief Research Officer at OpenAI, to discuss the Forward Deployed Engineer (FDE) model. McGrew, a pioneer of the FDE approach, explains how it originated at Palantir, its unique application in today's AI boom, and offers invaluable insights for founders navigating complex, emergent markets. Topics Covered: 00:00 Introduction to Bob McGrew and the FDE Model 02:19 What is a Forward Deployed Engineer? 03:19 How Palantir Invented the FDE Strategy 07:56 FDEs as Product Discovery vs. Traditional Sales 09:51 Echo and Delta Teams Explained 13:34 FDE as a Training Ground for Founders 14:35 FDE Model: Consulting or Real Software? 17:54 The Birth of Palantir’s Ontology 23:04 Why AI Companies are Adopting the FDE Model 30:11 Pricing Outcomes and Contract Growth 41:14 Building with Demo-Driven Development 44:56 Bob McGrew Joins the US Army Reserve 47:43 Opportunities for Founders in AI Adoption Key Quotes: "The FD model effectively is doing things that don't scale at scale." "With AI agents, there is no incumbent product... there's so much product discovery to do." Resources Mentioned:

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

00:00 Introduction to the FDE Model 02:19 Defining a Forward Deployed Engineer 03:19 How Palantir Invented the FDE Strategy 07:56 Product Discovery vs. Sales in FDE 09:51 Understanding Echo and Delta Teams 14:35 FDE: Consulting or Real Software Business? 23:04 Why AI Companies are Adopting FDE 36:17 Key Success Metrics for FDE 41:14 Demo-Driven Development in FDE 47:43 Opportunities for AI Founders

https://youtube.com/watch?v=Zyw-YA0k3xo

The FDE Playbook for AI Startups with Bob McGrew

1. Prioritize outcome-based pricing: For AI agents, focus on the value delivered to the customer, not just usage or subscription fees, to drive contract growth. 2. Embrace FDEs for product discovery: Embed technical teams with customers to understand unique needs and generalize solutions, especially in new market categories like AI where incumbent products are absent. 3. Cultivate a 'learning company' culture: Continuously adapt and learn from customer interactions, as rapid AI capability improvements demand constant exploration and ingenuity for real-world adoption. 4. Build product leverage for FDEs: Design your core product to empower FDEs to deliver more value to customers with less custom work over time, making subsequent deployments easier. 5. Secure executive buy-in: For large enterprise deployments, ensure your solution addresses one of the CEO's top priorities to overcome organizational inertia and secure necessary authority to operate.

https://youtube.com/watch?v=Zyw-YA0k3xo

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

At 24, Michael Truell built Cursor into an AI coding powerhouse, hitting $100M ARR in a year! 🤯 Learn how he took on GitHub Copilot & revolutionized developer workflows. A must-watch for founders & engineers! #AICoding #StartupSuccess

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

1. At just 24, Michael Truell built Cursor into an AI coding powerhouse, hitting $100M ARR in a year! How did he do it? A thread 👇 2. Y Combinator's interview reveals Michael's journey started with early programming & failed projects. He and his co-founders pivoted from mechanical engineering AI to coding, despite GitHub Copilot's dominance. 3. Their conviction: all software development will flow through models. They built their own editor, iterated fast, and eventually based it on VS Code to focus on core AI innovation. 4. Growth exploded from $1M to $100M ARR in 2024 by relentlessly improving the product, making it 'codebase aware' and faster. Word-of-mouth fueled this wildfire adoption! 5. Michael's advice for students: work on what you love, with people you respect. Programming skills remain crucial, even as AI transforms the landscape. Watch the full Y Combinator interview for his incredible story!

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

Hook: How do you build a $100M company by 24? 🚀 Content: Michael Truell, co-founder of Cursor, shares his incredible journey with Y Combinator. He talks about challenging GitHub Copilot by envisioning a future where all code flows through AI models. From early pivots to relentless product improvement, learn how Cursor scaled from $1M to $100M ARR in just one year, driven purely by word-of-mouth! CTA: Don't miss the full Y Combinator video for his strategic insights and advice for aspiring founders!

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

Michael Truell's discussion with Y Combinator provides a compelling case study in market disruption and product-led growth. Cursor's rapid ascent to $100M ARR, particularly at the age of 24, highlights the strategic decision to pivot into AI coding with the conviction that all software development will transition to AI models. This bold move, directly challenging an incumbent like GitHub Copilot, was fueled by a relentless focus on product improvement, user feedback, and pragmatic model development. The success underscores the power of identifying an unmet need and building a superior solution, even in a competitive landscape, proving that deeply understanding user preferences and continuously enhancing the core offering can lead to exponential growth.

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

Mind blown by Michael Truell's story on Y Combinator! 🤯 Building Cursor to $100M ARR by just 24 is no small feat. He shares how they dared to take on GitHub Copilot, believing that AI would completely transform coding. From early coding experiments to strategic pivots and explosive product-led growth, this video is a masterclass for anyone interested in startups, AI, or just sheer determination. You'll hear about the power of word-of-mouth and why programming skills are still essential. Go watch the full Y Combinator interview! #StartupJourney #AICoding #CursorAI #TechFounder #YCombinator #Innovation #ProductLedGrowth #FutureOfCode #EngineerLife #Inspiration

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

Built a $100M AI coding company by 24?! 🤯 Michael Truell's Cursor story is WILD! 🔥 Taking on GitHub Copilot, pivoting hard, & hitting insane growth. Product-led growth is REAL! 🚀 #AICoding #StartupLife #TechTok #FounderStory #CursorAI #YCombinator

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

There was going to be an opportunity for all of coding to change in the next 5 years and for all of software development to flow through models.

— Y Combinator

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

• Michael Truell and his co-founders pivoted through several failed projects before landing on Cursor. • Cursor's core conviction is that all software development will eventually flow through AI models. • They initially built their own editor from scratch before basing it on VS Code to focus on AI features. • Cursor achieved explosive growth, going from $1M to $100M ARR in a single year (2024), driven by product improvements and word-of-mouth. • Michael believes programming skills remain crucial for professional engineers, even as AI transforms coding.

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

Michael Truell, 24, built Cursor to $100M ARR by challenging GitHub Copilot, believing all coding will flow through AI. His journey highlights pivots, product-led growth, and the future of programming.

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

He built a $100M company by 24. How?

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

Imagine coding is like building with LEGOs. GitHub Copilot helps you find the right LEGOs faster. Michael Truell's company, Cursor, believes AI can eventually build entire LEGO structures for you, or even design new types of LEGOs! So, they made a special LEGO building tool that learns what you want and helps you build much, much faster, even better than the older tools. They worked super hard, tried many ideas that didn't work, but kept going until they made a tool so good, everyone started using it, making their company grow super fast.

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

Did you know that the original Codex paper, which inspired GitHub Copilot's autocomplete, cost only about $100,000 to train, according to Michael Truell's calculations?

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

Problem: Existing AI coding tools weren't seriously aiming for a future where all software development flows through models. Solution: Cursor was built with the conviction that AI would automate and transform coding entirely, leading to a new paradigm for software development.

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

What are your thoughts on Michael Truell's vision that all software development will flow through AI models in the next five years? How do you think the role of a software engineer will evolve in this AI-first coding landscape?

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students Michael Truell, co-founder of Cursor, sits down with YC General Partner Diana Hu to discuss his incredible journey. From early programming experiments to building a company that hit $100M ARR in a year, this episode dives into Cursor's audacious challenge to GitHub Copilot and Michael's vision for the future of AI in coding. Topics Covered: 00:00 The bold vision for AI to transform all of coding 00:28 Michael's origin story: first code and YC essays 02:32 Early AI projects as a teenager 06:54 Failed startup attempts and the path to Cursor 09:54 The crucial pivot after ideas weren't working 12:04 Why Cursor decided to take on GitHub Copilot 13:10 Shipping the first version of the Cursor editor 15:24 Key lessons from early product iteration and the VS Code shift 16:26 The role of custom models and pragmatic bets in Cursor's development 17:58 The 'wandering' phase of 2023 before rapid growth 20:00 The breakthrough: $1M to $100M ARR in one year 22:46 How word-of-mouth fueled Cursor's wildfire growth 25:24 Michael's thoughts on the future of coding and advice for students Key Quotes: "There was going to be an opportunity for all of coding to change in the next 5 years and for all of software development to flow through models." "We're in a market where if you make the product better, you kind of see it in the numbers immediately where, you know, things start to grow more." Resources Mentioned: N/A

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

00:00 Introduction: The Vision for AI in Coding 00:28 Michael's Early Programming Journey 02:32 High School AI Projects and Learning 06:54 From Hemisphere to Cursor: Early Startup Attempts 09:54 The Pivotal Moment: Desperation and New Direction 12:04 Taking on GitHub Copilot 13:10 Shipping the First Cursor Editor 15:24 Early Lessons: Features, Feedback, and VS Code Shift 17:58 The 'Wandering' Year of 2023 20:00 Breakthrough: $1M to $100M ARR in a Year 22:46 Word-of-Mouth and Wildfire Growth 25:24 The Future of Coding and Advice for Students

https://youtube.com/watch?v=TrXi3naD6Og

Michael Truell: Building Cursor At 23, Taking On GitHub Copilot & Advice To Engineering Students

1. Embrace bold visions: Don't just incrementally improve existing solutions; aim for a future where the entire paradigm shifts. 2. Iterate rapidly and pivot fearlessly: Michael's journey shows the importance of trying multiple ideas and pivoting hard when something isn't working. 3. Focus on core product improvement: Cursor's explosive growth was largely driven by making the product better, leading to organic word-of-mouth adoption. 4. Programming skills remain vital: Even with advanced AI, understanding the fundamentals of coding and logic will be crucial for professional engineers. 5. Work on what you love with trusted people: Choose projects you're genuinely interested in and collaborate with individuals you respect and enjoy being around.

https://youtube.com/watch?v=TrXi3naD6Og

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

OpenAI's GPT OSS is here! 🚀 But how does it stack up against DeepSeek V3 and Qwen 3? Y Combinator breaks down their unique architectures, from MoE to long-context training. Discover the surprising similarities despite different approaches. #LLMs #OpenSourceAI #TechDeepDive

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

1. OpenAI's GPT OSS, DeepSeek V3, and Qwen 3 are leading the open-source LLM race. But what makes them tick? This Y Combinator video dives deep into their architectural differences and surprising performance similarities. Thread 👇 2. GPT OSS uses a mixture-of-experts architecture with 120B/20B parameters, grouped query attention, and Yarn scaling from pre-training for its 131K token context window. It's built for efficiency and immediate use. 3. Qwen 3, from Alibaba Cloud, offers both dense and MoE models. It innovates with QK norm for stable attention scores and a unique three-stage training process, including synthetic data generation and a "thinking mode fusion" post-training step. 4. DeepSeek V3, a massive 671B parameter MoE model, prioritizes efficiency, training natively in 8-bit. Its V3.1 update enhances reasoning and tool use, leveraging MLA (Multi-head Latent Attention) for memory savings in long contexts. 5. Despite varied approaches to long-context handling (pre-training vs. staged fine-tuning) and attention mechanisms, all achieve impressive, often similar, benchmarks. Y Combinator highlights the empirical nature of current deep learning and the crucial, often opaque, role of data engineering.

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

🤯 OpenAI just dropped GPT OSS! But is it *really* better than DeepSeek or Qwen? Y Combinator unpacks the architectural secrets of these open-source LLM giants. We're talking mixture-of-experts, wild long-context hacks like Yarn, and mind-blowing post-training techniques. Want to know which one wins for your next project? Tap the link for the full breakdown! #LLM #OpenSourceAI #TechExplained

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

In a recent Y Combinator analysis, Ankit Gupta provided a comprehensive comparison of OpenAI's GPT OSS, Alibaba's Qwen 3, and DeepSeek V3, shedding light on their underlying architectural philosophies. The video meticulously detailed their distinct implementations of mixture-of-experts (MoE) models, long-context handling strategies (e.g., GPT OSS's pre-training Yarn application vs. DeepSeek's staged fine-tuning), and advanced post-training for reasoning and alignment. A key insight was the observation that despite these varied engineering choices, including unique attention mechanisms like DeepSeek's MLA, the models often achieve remarkably similar benchmark performance. This underscores a prevailing empirical approach in deep learning development and highlights the strategic importance of proprietary dataset engineering as a competitive moat, even with open-weights releases. Understanding these nuances is critical for developers and organizations evaluating leading open-source LLMs for strategic deployment.

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

Ever wonder what's really under the hood of the latest open-source LLMs? 🤔 Y Combinator just dropped a deep dive comparing OpenAI's GPT OSS, Alibaba's Qwen 3, and DeepSeek V3! 🤯 This video breaks down their unique architectural choices – from how they handle massive context windows with techniques like Yarn, to their innovative mixture-of-experts designs, and even their secret sauce for post-training alignment. It's fascinating to see how different paths lead to surprisingly similar top-tier performance! Whether you're a developer, researcher, or just curious about the future of AI, this is a must-watch to understand the cutting edge of LLM engineering. Swipe up to decode the tech! 👇 #OpenSourceAI #LLMComparison #GPTOSS #DeepSeekV3 #Qwen3 #AIArchitecture #TechExplained #YCombinator #MachineLearning #DeepLearning

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

OpenAI just dropped GPT OSS 🤯! But how does it compare to DeepSeek & Qwen 3? Y Combinator breaks down the *real* tech secrets: MoE, long-context magic ✨, & wild training hacks! You won't believe the differences & similarities! #LLM #OpenSource #AI #TechTok #DeepLearning #GPTOSS #MustWatch 🤖

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

It's interesting that even though most of these models have similar topline benchmark statistics... they achieve these similar results using often very different techniques.

— Y Combinator

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

* OpenAI's GPT OSS, Alibaba's Qwen 3, and DeepSeek V3 represent leading open-source LLMs with distinct architectural approaches. * Models differ significantly in mixture-of-experts implementation, long-context training (e.g., Yarn application during pre-training vs. fine-tuning), and post-training strategies. * Despite varied engineering choices, these LLMs often achieve surprisingly similar top-line performance benchmarks. * Current deep learning research leans heavily on empirical findings, with less emphasis on first-principles justifications for architectural choices. * Data set engineering plays a crucial, often opaque, role in differentiating these models and creating a competitive advantage.

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

Y Combinator compares OpenAI's GPT OSS, Qwen 3, and DeepSeek V3, revealing their distinct architectures, training, and long-context handling, yet surprisingly similar performance.

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

OpenAI vs. DeepSeek vs. Qwen: Who built the ULTIMATE open-source LLM?

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

Imagine you have three super-smart robot friends who can write stories and answer questions. Y Combinator looked inside their brains to see how they're built. One robot (GPT OSS) learned how to understand really long stories from the start. Another (Qwen 3) learned in stages and can even switch how it thinks. The third (DeepSeek V3) is super big and learned to save memory. Even though they learned in different ways, they all ended up being really good at similar things!

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

Did you know that some advanced LLMs, like Qwen 3, can achieve impressive reasoning capabilities with surprisingly small amounts of reinforcement learning data, sometimes as little as 4,000 data pairs?

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

Problem: Evaluating the best open-source LLM for specific applications is challenging due to similar top-line benchmarks and opaque architectural differences. Solution: Y Combinator provides a detailed architectural comparison of leading models (GPT OSS, Qwen 3, DeepSeek V3), highlighting their unique engineering choices in MoE, long-context handling, and post-training, offering a framework for informed selection.

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

Which of these architectural approaches—OpenAI's native long-context training, DeepSeek's staged fine-tuning, or Qwen's inference-time scaling—do you think offers the most sustainable path for future LLM development? Given the empirical nature of current deep learning research, how much should we prioritize first-principles understanding versus practical performance in building new models?

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures Y Combinator's Ankit Gupta breaks down the intricate architectural differences and surprising performance similarities between OpenAI's GPT OSS, Alibaba's Qwen 3, and DeepSeek V3. This episode explores their unique approaches to mixture-of-experts, long-context training, and post-training techniques, offering a deep dive into the cutting edge of open-source LLM development. Topics Covered: * 00:00 OpenAI's GPT OSS Launch and Architectural Overview * 00:35 GPT OSS Mixture of Experts and Key Features * 01:37 GPT OSS Long Context and Tokenizer * 02:21 GPT OSS Post-Training and Quantization * 02:52 Qwen 3 Family and Architectural Similarities * 04:03 Qwen 3 Training Data and Stages * 05:20 Qwen 3 Post-Training Pipeline and Innovations * 06:46 DeepSeek V3 Overview and Optimizations * 07:35 DeepSeek V3.1 Updates and Attention Mechanism (MLA) * 08:52 Comparing Model Sizes and Architectures * 09:34 Long Context Strategies: Yarn Application Differences * 10:39 Reflections on Empirical Findings and Data Engineering * 11:55 Key Takeaways for Understanding Open Source LLMs Key Quotes: * "It's interesting that even though most of these models have similar topline benchmark statistics and use broadly the same tools... they achieve these similar results using often very different techniques." * "The big takeaway when reading these papers is you shouldn't focus too much on just the benchmark performance or topline stats like context size. Instead, look at the specific methods that these labs are using to achieve those results." Resources Mentioned: Placeholder for any specific papers or links mentioned in the video's description or by the speaker, if applicable, otherwise state "None mentioned."

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

00:00 Introduction to Open Source LLM Comparison 00:35 Deep Dive into GPT OSS Architecture 02:52 Qwen 3 Models: Dense, MoE, and Training Innovations 06:46 DeepSeek V3 and V3.1: Efficiency and MLA Attention 08:39 Comparing Model Sizes and Architectures 09:34 Different Approaches to Long Context Training 10:39 Empirical Nature of Deep Learning Research 11:43 The Importance of Data Set Engineering 11:55 Key Takeaways for LLM Evaluation

https://youtube.com/watch?v=raTbhtKZTZA

OpenAI vs. Deepseek vs. Qwen: Comparing Open Source LLM Architectures

1. When evaluating open-source LLMs, look beyond top-line benchmarks and context size; focus on the specific architectural methods and training strategies employed by the labs. 2. Understand how different models implement mixture-of-experts (MoE), as this significantly impacts inference efficiency and parameter usage. 3. Pay attention to long-context handling strategies (e.g., native pre-training vs. staged fine-tuning vs. inference-time scaling) to choose a model best suited for your specific long-input needs. 4. Recognize the empirical nature of current deep learning: often, different engineering choices yield similar results, highlighting the need for practical experimentation. 5. Consider the impact of data set engineering, which remains a crucial and often opaque differentiator between even open-source models, influencing their capabilities and alignment.

https://youtube.com/watch?v=raTbhtKZTZA