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OpenAI pays $785K/year to developers who know how to apply Forward Deployed Engineering in AI. in 40-minute talk, Head of FDE at OpenAI revealed full roadmap for how they actually use FDE internally: • 10% → 2:46 - why Morgan Stanley was their first FDE case • 30% →... show more

Codez

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110,140 views • 3 days ago •via X (Twitter)

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Anthropic pays $785K/year for Forward Deployed Engineers In this 20-minute talk, an Anthropic FDE engineer explained the full roadmap: • step 1 → you're not selling software or time. you're selling an outcome. "a leader in oil and gas cares about throughput, not how the data is organized" • step 2 → FDE exists for one case only: technical product + non-technical buyer. everything else runs on DevRel or sales • step 3 → every AI platform is now agentic & customizable - every AI company is in that case now. "your customers have no idea what you actually do" • step 4 → the result: Palantir's ACV is $4M per customer. ServiceNow $1.2M. Workday $600K. no other public SaaS cracks $500K watch, then read the guide below ↓

Anthropic pays $785K/year for Forward Deployed Engineers In this 20-minute talk, an Anthropic FDE engineer explained the full roadmap: • step 1 → you're not selling software or time. you're selling an outcome. "a leader in oil and gas cares about throughput, not how the data is organized" • step 2 → FDE exists for one case only: technical product + non-technical buyer. everything else runs on DevRel or sales • step 3 → every AI platform is now agentic & customizable - every AI company is in that case now. "your customers have no idea what you actually do" • step 4 → the result: Palantir's ACV is $4M per customer. ServiceNow $1.2M. Workday $600K. no other public SaaS cracks $500K watch, then read the guide below ↓

Codez

161,731 views • 4 days ago

'India can be the FDE factory for the world.' What is a Forward Deployed Engineer (FDE) and why is it becoming AI's hottest new job? Speaking to Shereen Bhan on Young Turks Reloaded, Fundamentum Co-founder Sanjeev Aggarwal says FDEs combine the skills of an engineer, product manager and AI architect to solve real business problems. 'It's almost like a unicorn... a 10x engineer.' He says a company can potentially build a $100 million business with just 100 FDEs, compared to 2,000–2,500 employees in a traditional IT services model. 'The gross margins can be as high as 70-80-90%.' As AI rewrites software development and companies like Microsoft ramp up hiring for FDE roles, Aggarwal believes India's decades of IT expertise could make it the world's biggest talent hub for this new category. Shereen Bhan

'India can be the FDE factory for the world.' What is a Forward Deployed Engineer (FDE) and why is it becoming AI's hottest new job? Speaking to Shereen Bhan on Young Turks Reloaded, Fundamentum Co-founder Sanjeev Aggarwal says FDEs combine the skills of an engineer, product manager and AI architect to solve real business problems. 'It's almost like a unicorn... a 10x engineer.' He says a company can potentially build a $100 million business with just 100 FDEs, compared to 2,000–2,500 employees in a traditional IT services model. 'The gross margins can be as high as 70-80-90%.' As AI rewrites software development and companies like Microsoft ramp up hiring for FDE roles, Aggarwal believes India's decades of IT expertise could make it the world's biggest talent hub for this new category. Shereen Bhan

CNBC-TV18

101,423 views • 1 month ago

The hottest role in tech — the forward-deployed engineer — was "the ugliest duckling" for a decade. In this conversation, Akshay Krishnaswamy, Chief Architect of Palantir, joins a16z GP Erin Price-Wright to cover: - Why a good team of engineers is like a hive mind - The archetypes of people that thrive as FDEs - Why pain tolerance is a hiring filter - Managing high-agency engineers without hierarchy and more. 00:00 Introduction 02:17 Defining forward-deployed engineering 04:49 Differences between FDE and other roles 06:09 Building and managing teams 09:55 Challenges and evolution of FDE 15:27 Maintaining product focus and customer relationships Akshay Krishnaswamy Erin Price-Wright

The hottest role in tech — the forward-deployed engineer — was "the ugliest duckling" for a decade. In this conversation, Akshay Krishnaswamy, Chief Architect of Palantir, joins a16z GP Erin Price-Wright to cover: - Why a good team of engineers is like a hive mind - The archetypes of people that thrive as FDEs - Why pain tolerance is a hiring filter - Managing high-agency engineers without hierarchy and more. 00:00 Introduction 02:17 Defining forward-deployed engineering 04:49 Differences between FDE and other roles 06:09 Building and managing teams 09:55 Challenges and evolution of FDE 15:27 Maintaining product focus and customer relationships Akshay Krishnaswamy Erin Price-Wright

a16z

134,494 views • 6 months ago

At first, prompting seemed to be a temporary workaround for getting the most out of large language models. But over time, it's become critical to the way we interact with AI. On the Lightcone Podcast, Garry, Harj, Diana, and Jared break down what they've learned from working with hundreds of founders building with LLMs: why prompting still matters, where it breaks down, and how teams are making it more reliable in production. They share real examples of prompts that failed, how companies are testing for quality, and what the best teams are doing to make LLM outputs useful and predictable. 0:58 - Parahelp’s prompt example 4:59 - Different types of prompts 6:51 - Metaprompting 7:58 - Using examples 12:10 - Some tricks for longer prompts 14:18 - Findings on evals 17:25 - Every founder has become a forward-deployed engineer (FDE) 23:18 - Vertical AI agents are closing big deals with the FDE model 26:13 - The personalities of the different LLMs 27:26 - Lessons from rubrics 29:47 - Kaizen and the art of communication

At first, prompting seemed to be a temporary workaround for getting the most out of large language models. But over time, it's become critical to the way we interact with AI. On the Lightcone Podcast, Garry, Harj, Diana, and Jared break down what they've learned from working with hundreds of founders building with LLMs: why prompting still matters, where it breaks down, and how teams are making it more reliable in production. They share real examples of prompts that failed, how companies are testing for quality, and what the best teams are doing to make LLM outputs useful and predictable. 0:58 - Parahelp’s prompt example 4:59 - Different types of prompts 6:51 - Metaprompting 7:58 - Using examples 12:10 - Some tricks for longer prompts 14:18 - Findings on evals 17:25 - Every founder has become a forward-deployed engineer (FDE) 23:18 - Vertical AI agents are closing big deals with the FDE model 26:13 - The personalities of the different LLMs 27:26 - Lessons from rubrics 29:47 - Kaizen and the art of communication

Y Combinator

352,288 views • 1 year ago

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 views • 1 month ago

Bob McGrew (Bob McGrew) helped build some of the most influential technologies of the past two decades. He was an early engineer at PayPal, an early executive at Palantir—where he helped pioneer the Forward Deployed Engineer (FDE) model— and was recently Chief Research Officer at OpenAI - where he led the development of ChatGPT, GPT-4, and o1. On this episode of Lightcone Podcast, he explains how FDEs became central to today's startups, why "doing things that don't scale at scale" works, and where he sees the biggest opportunities for founders working in AI. 00:29 – From PayPal to Palantir to OpenAI 02:19 – The Role of a Forward Deployed Engineer 03:19 – How Palantir Invented It 07:56 – Product Discovery in the Field vs. Sales 09:51 – Echo and Delta Teams Explained 13:34 – Training Ground for Founders 14:35 – Consulting or Real Software? 17:54 – The Birth of Palantir’s Ontology 23:04 – Why AI Companies Adopt It 36:17 – What Success Metrics Look Like 41:14 – Building with Demo-Driven Development 44:56 – Joining the US Army Reserve 47:43 – Opportunities for Founders

Bob McGrew (Bob McGrew) helped build some of the most influential technologies of the past two decades. He was an early engineer at PayPal, an early executive at Palantir—where he helped pioneer the Forward Deployed Engineer (FDE) model— and was recently Chief Research Officer at OpenAI - where he led the development of ChatGPT, GPT-4, and o1. On this episode of Lightcone Podcast, he explains how FDEs became central to today's startups, why "doing things that don't scale at scale" works, and where he sees the biggest opportunities for founders working in AI. 00:29 – From PayPal to Palantir to OpenAI 02:19 – The Role of a Forward Deployed Engineer 03:19 – How Palantir Invented It 07:56 – Product Discovery in the Field vs. Sales 09:51 – Echo and Delta Teams Explained 13:34 – Training Ground for Founders 14:35 – Consulting or Real Software? 17:54 – The Birth of Palantir’s Ontology 23:04 – Why AI Companies Adopt It 36:17 – What Success Metrics Look Like 41:14 – Building with Demo-Driven Development 44:56 – Joining the US Army Reserve 47:43 – Opportunities for Founders

Y Combinator

409,024 views • 10 months ago

BREAKING: Unpacking the largest investment in the history of Altimeter → OpenAI Reaching a $500B valuation & 700M WAUs, Apoorv Agrawal, AI & Software Partner at Altimeter, breaks down how to value OpenAI's insane growth We dig into the numbers. + PLTR FDE, XBOW, Power Laws, Supercycles Highlights - AI Supercycle Mirrors Past Technology Shifts - “Speed Is the Only Moat” - Power Law of VC - XBOW & the Future of Cybersecurity - Palantir Culture → Forward Deployed Engineers Timestamps (00:00) Altimeter’s $10B+ AUM strategy (01:57) Why Altimeter concentrated its largest investment ever in OpenAI (04:45) VC Power Law: Why 0.3% of companies drive 99% of returns (09:25) Consumer adoption of ChatGPT & enterprise spillover (12:08) Velocity: Why speed is the ultimate moat in AI (14:18) Parasocial relationships with ChatGPT (19:29) Adoption curves: Raising the ceiling vs. raising the floor (23:30) Defining AGI & DOOM (25:40) GPT-5 release: Volatility, iteration, & anti-fragility (32:00) XBOW AI: When AI becomes the world’s top hacker (38:22) Where AI value accrues: Chips, infrastructure, or applications (42:50) $1B talent wars & Meta’s AI strategy (44:12) Palantir’s billion-dollar FDE culture (50:10) Lessons from Shyam Sankar & Alex Karp (53:52) Evaluating founder performance & IPO readiness (55:36) IPO Predictions: Klarna, Discord, Databricks, Cerebras, Anduril Apoorv Agrawal | Altimeter Capital

BREAKING: Unpacking the largest investment in the history of Altimeter → OpenAI Reaching a $500B valuation & 700M WAUs, Apoorv Agrawal, AI & Software Partner at Altimeter, breaks down how to value OpenAI's insane growth We dig into the numbers. + PLTR FDE, XBOW, Power Laws, Supercycles Highlights - AI Supercycle Mirrors Past Technology Shifts - “Speed Is the Only Moat” - Power Law of VC - XBOW & the Future of Cybersecurity - Palantir Culture → Forward Deployed Engineers Timestamps (00:00) Altimeter’s $10B+ AUM strategy (01:57) Why Altimeter concentrated its largest investment ever in OpenAI (04:45) VC Power Law: Why 0.3% of companies drive 99% of returns (09:25) Consumer adoption of ChatGPT & enterprise spillover (12:08) Velocity: Why speed is the ultimate moat in AI (14:18) Parasocial relationships with ChatGPT (19:29) Adoption curves: Raising the ceiling vs. raising the floor (23:30) Defining AGI & DOOM (25:40) GPT-5 release: Volatility, iteration, & anti-fragility (32:00) XBOW AI: When AI becomes the world’s top hacker (38:22) Where AI value accrues: Chips, infrastructure, or applications (42:50) $1B talent wars & Meta’s AI strategy (44:12) Palantir’s billion-dollar FDE culture (50:10) Lessons from Shyam Sankar & Alex Karp (53:52) Evaluating founder performance & IPO readiness (55:36) IPO Predictions: Klarna, Discord, Databricks, Cerebras, Anduril Apoorv Agrawal | Altimeter Capital

Molly O’Shea

207,506 views • 11 months ago

in 20 minutes, OpenAI's Head of AI Deployment Engineering gave a Live Lecture on Voice Agents. OpenAI already shipped one of the most ambitious speech-to-speech APIs in production. Tonight Toki and Anoop show how every serious company is actually building audio agents in 2026. bookmark & watch this. then read the complete article below

in 20 minutes, OpenAI's Head of AI Deployment Engineering gave a Live Lecture on Voice Agents. OpenAI already shipped one of the most ambitious speech-to-speech APIs in production. Tonight Toki and Anoop show how every serious company is actually building audio agents in 2026. bookmark & watch this. then read the complete article below

Avid

23,821 views • 2 months ago

Jane Street pays $750k/year for engineers who build AI trading systems. The Head of AI at Man Group Hedge fund ($200B AUM) just revealed in 24 minutes how they run AI-built signals with Claude on real client money. AI ideates → gets data → backtests → productionizes signal. worth more than any $500 quant-trading course. Bookmark & watch. Then read the article below.

Jane Street pays $750k/year for engineers who build AI trading systems. The Head of AI at Man Group Hedge fund ($200B AUM) just revealed in 24 minutes how they run AI-built signals with Claude on real client money. AI ideates → gets data → backtests → productionizes signal. worth more than any $500 quant-trading course. Bookmark & watch. Then read the article below.

Movez

58,017 views • 2 months ago

Google just released free 1-hour course on building agentic knowledge Graphs from 0% to 100%: 10% → 4:01 - how to build a GraphRAG agent 30% → 15:00 - Graph Engineering explanation 55% → 30:00 - Agentic search Engineering 80% → 35:48 - Graph Engineering practice 100% → 47:06 - self-improving agents in graphs this free Google course mass replaces a $500 graph engineering bootcamp - learn it in 60 min to 100% watch it today - then read the full graph playbook in the article below ↓

Google just released free 1-hour course on building agentic knowledge Graphs from 0% to 100%: 10% → 4:01 - how to build a GraphRAG agent 30% → 15:00 - Graph Engineering explanation 55% → 30:00 - Agentic search Engineering 80% → 35:48 - Graph Engineering practice 100% → 47:06 - self-improving agents in graphs this free Google course mass replaces a $500 graph engineering bootcamp - learn it in 60 min to 100% watch it today - then read the full graph playbook in the article below ↓

codila

242,471 views • 7 days ago

Anthropic engineer just dropped a 1-hour workshop on “Graph Engineering” for agentic systems: “80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.” • 00:00 - Introduction to graph engineering for agents • 08:40 - Building a simple graph for agents • 30:36 - Agent memory-based graphs • 58:40 - Long-term memory for agents • 01:21:54 - Agentic graph deployment This 1-hour workshop will replace your $500 agentic memory course. Watch it today, then read how to become a graph engineer in the article below.

Anthropic engineer just dropped a 1-hour workshop on “Graph Engineering” for agentic systems: “80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.” • 00:00 - Introduction to graph engineering for agents • 08:40 - Building a simple graph for agents • 30:36 - Agent memory-based graphs • 58:40 - Long-term memory for agents • 01:21:54 - Agentic graph deployment This 1-hour workshop will replace your $500 agentic memory course. Watch it today, then read how to become a graph engineer in the article below.

Codez

56,877 views • 5 days ago

For the first time ever: OpenAI's official definition of an agent for AI Engineers; plus case studies, design patterns, & lessons learned from building Swarm, Operator, Deep Research, and beyond. This is the multi-million dollar talk you don’t want to miss with advice typically reserved for their enterprise clients. Presented by prashant, Member of Technical Staff at OpenAI, & Toki Sherbakov, Head of Solutions Architecture at OpenAI. YT version in next post. Chapters: 00:00 Opener 00:17 Intro and overview 00:40 How OpenAI is structured 1:44 Enterprise AI customer journey 3:02 AI as part of your business strategy 6:54 Morgan Stanley case study 8:00 2025 is the year of agents 9:00 Defining Agents 9:54 Lessons OpenAI learned building Agents in the field 9:56 Lessons: Abstraction is a tool, not a crutch 11:43 Lessons:: Start Simple 13:00 Lessons: Use a network of agents for complex tasks 14:52 Lessons: Keep prompts simple; use guardrails to handle edge cases 16:05 Recap

For the first time ever: OpenAI's official definition of an agent for AI Engineers; plus case studies, design patterns, & lessons learned from building Swarm, Operator, Deep Research, and beyond. This is the multi-million dollar talk you don’t want to miss with advice typically reserved for their enterprise clients. Presented by prashant, Member of Technical Staff at OpenAI, & Toki Sherbakov, Head of Solutions Architecture at OpenAI. YT version in next post. Chapters: 00:00 Opener 00:17 Intro and overview 00:40 How OpenAI is structured 1:44 Enterprise AI customer journey 3:02 AI as part of your business strategy 6:54 Morgan Stanley case study 8:00 2025 is the year of agents 9:00 Defining Agents 9:54 Lessons OpenAI learned building Agents in the field 9:56 Lessons: Abstraction is a tool, not a crutch 11:43 Lessons:: Start Simple 13:00 Lessons: Use a network of agents for complex tasks 14:52 Lessons: Keep prompts simple; use guardrails to handle edge cases 16:05 Recap

AI Engineer

47,285 views • 1 year ago

$PLTR People should really watch the interview by Alex Karp in trying to understand what the future of enterprise AI is heading towards. The difference between value creation for each enterprise can be and has to be tailored to get the most use. The FDE model that Palantir pioneered is being used by everyone these days but the end goal for every FDE seems to be different. Companies like Palantir are trying to drive value creation for the companies they work with vs. OpenAI and Anthropic who are more about trying to lock enterprise customers down into their respective ecosystems and endlessly consume tokens. I think its becoming clear that businesses are a bit ticked off by the sheer cost of these models without the tangible returns to justify the expense. Companies like Palantir dont sell tokens, they sell value. Palantir is helping customers by delivering value but maximizing efficiency through model selection and limiting spend. The ROI therefore is much more tangible and dare I say predictable. The incentive structure long term I believe ends up in the hands of those who can provide value and not just a blackbox models whose focus is to pad the bottom line at their respective model company. This move by OpenAI and Anthropic to create custom tailored solutions that feed one token budgets scream, "I better pad my profitability up before the IPO"

$PLTR People should really watch the interview by Alex Karp in trying to understand what the future of enterprise AI is heading towards. The difference between value creation for each enterprise can be and has to be tailored to get the most use. The FDE model that Palantir pioneered is being used by everyone these days but the end goal for every FDE seems to be different. Companies like Palantir are trying to drive value creation for the companies they work with vs. OpenAI and Anthropic who are more about trying to lock enterprise customers down into their respective ecosystems and endlessly consume tokens. I think its becoming clear that businesses are a bit ticked off by the sheer cost of these models without the tangible returns to justify the expense. Companies like Palantir dont sell tokens, they sell value. Palantir is helping customers by delivering value but maximizing efficiency through model selection and limiting spend. The ROI therefore is much more tangible and dare I say predictable. The incentive structure long term I believe ends up in the hands of those who can provide value and not just a blackbox models whose focus is to pad the bottom line at their respective model company. This move by OpenAI and Anthropic to create custom tailored solutions that feed one token budgets scream, "I better pad my profitability up before the IPO"

Kris Patel 🇺🇸

74,384 views • 1 month ago

Netflix pays AI PMs up to $900K/year. How do you become (and succeed) as one? Marily Nika Marily Nika is an AI PM at Google, and was an AI PM at Meta. In today's episode, she breaks it down: How AI PM Roles Differ at Big Tech Companies – 06:56 How Technical an AI PM Needs to Be – 08:08 Data Handling Responsibilities for AI PMs – 13:10 How PMs Can Improve Collaboration with Researchers – 18:06 Interviewing for AI PM Roles at Big Tech Companies – 22:08 The Role of a PhD in AI Product Management – 26:03 Optimizing LinkedIn for Aspiring AI PMs – 31:55 Roadmap to Becoming an AI PM – 37:05 Transitioning into AI Product Management – 40:52 The Process of Boomeranging Back to a Company – 44:00 AI Tools Every PM Should Know – 48:08 What It’s Like to Be an AI PM at Google – 57:46 How to Develop a Strong Product Strategy – 01:01:21 The Evolution of PM Roles in the Age of AI – 01:05:11 How PMs Can Stay Relevant in an AI-Driven World – 01:08:12 Balancing a Side Business with a Full-Time Job – 01:13:42 The Process of Creating a Course – 01:17:12 LinkedIn Tips for Aspiring AI PM Creators – 01:21:21 Lessons from Product Failures – 01:25:27 Paid promotions included

Netflix pays AI PMs up to $900K/year. How do you become (and succeed) as one? Marily Nika Marily Nika is an AI PM at Google, and was an AI PM at Meta. In today's episode, she breaks it down: How AI PM Roles Differ at Big Tech Companies – 06:56 How Technical an AI PM Needs to Be – 08:08 Data Handling Responsibilities for AI PMs – 13:10 How PMs Can Improve Collaboration with Researchers – 18:06 Interviewing for AI PM Roles at Big Tech Companies – 22:08 The Role of a PhD in AI Product Management – 26:03 Optimizing LinkedIn for Aspiring AI PMs – 31:55 Roadmap to Becoming an AI PM – 37:05 Transitioning into AI Product Management – 40:52 The Process of Boomeranging Back to a Company – 44:00 AI Tools Every PM Should Know – 48:08 What It’s Like to Be an AI PM at Google – 57:46 How to Develop a Strong Product Strategy – 01:01:21 The Evolution of PM Roles in the Age of AI – 01:05:11 How PMs Can Stay Relevant in an AI-Driven World – 01:08:12 Balancing a Side Business with a Full-Time Job – 01:13:42 The Process of Creating a Course – 01:17:12 LinkedIn Tips for Aspiring AI PM Creators – 01:21:21 Lessons from Product Failures – 01:25:27 Paid promotions included

Aakash Gupta

42,853 views • 1 year ago

$1.5M ARR in 2 weeks. Zero (human) teammates. Solo Founders Podcast is live with of Polsia, an AI that runs your company while you sleep. His solo founder rule: 80% AI, 20% taste. 0:00 — How "solo founder" has changed with AI 2:40 — Scoping and trusting AI agents to ship 5:30 — Cloud Kitchens with Travis Kalanick 9:52 — Mount Fuji: Where Polsia was born 13:55 — 80/20 rule: 80% AI, 20% taste 21:24 — Building for yourself, not imaginary customers 23:10 — The game that inspired Polsia 29:55 — Polsia as an economy: The bigger vision 40:59 — How Polsia actually works today 51:35 — Why not just build businesses yourself? 57:55 — Advice for new builders: Push AI to the edge

$1.5M ARR in 2 weeks. Zero (human) teammates. Solo Founders Podcast is live with of Polsia, an AI that runs your company while you sleep. His solo founder rule: 80% AI, 20% taste. 0:00 — How "solo founder" has changed with AI 2:40 — Scoping and trusting AI agents to ship 5:30 — Cloud Kitchens with Travis Kalanick 9:52 — Mount Fuji: Where Polsia was born 13:55 — 80/20 rule: 80% AI, 20% taste 21:24 — Building for yourself, not imaginary customers 23:10 — The game that inspired Polsia 29:55 — Polsia as an economy: The bigger vision 40:59 — How Polsia actually works today 51:35 — Why not just build businesses yourself? 57:55 — Advice for new builders: Push AI to the edge

weisser

523,629 views • 5 months ago

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

Shane Parrish

450,952 views • 3 months ago

My friend applied to 200 tech jobs in two years. No PhD. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me a course that was never supposed to get out. A 3-hour video to build a full LLM from scratch. A developer teaches you exactly how LLMs like ChatGPT and Claude are actually built. I watched it last night. Halfway through, I realized it's embarrassingly simple to break into an AI lab. Bookmark this and read the article below. • 00:00 - intro to LLMs • 05:43 - LLM transformer architecture • 40:24 - training the LLM • 1:30:27 - modernizing the LLM • 2:33:53 - scaling the LLM

My friend applied to 200 tech jobs in two years. No PhD. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me a course that was never supposed to get out. A 3-hour video to build a full LLM from scratch. A developer teaches you exactly how LLMs like ChatGPT and Claude are actually built. I watched it last night. Halfway through, I realized it's embarrassingly simple to break into an AI lab. Bookmark this and read the article below. • 00:00 - intro to LLMs • 05:43 - LLM transformer architecture • 40:24 - training the LLM • 1:30:27 - modernizing the LLM • 2:33:53 - scaling the LLM

Roan

276,389 views • 25 days ago

Anthropic just dropped a 3-hour course on how to actually master Claude Code, taught by the engineer who built it: 00:07 – everything new in Claude Code 24:39 – Boris Cherny builds a full app live 55:38 – the complete prompting playbook 1:29:07 – the "thinking" lever almost everyone misses 1:52:58 – how to pick the right model for the job 2:24:22 – how Anthropic itself codes with Claude This 3-hour watch will replace 10 paid Claude Code courses on the internet. Watch it now, then read how to build an AI that codes while you sleep, in the article below.

Anthropic just dropped a 3-hour course on how to actually master Claude Code, taught by the engineer who built it: 00:07 – everything new in Claude Code 24:39 – Boris Cherny builds a full app live 55:38 – the complete prompting playbook 1:29:07 – the "thinking" lever almost everyone misses 1:52:58 – how to pick the right model for the job 2:24:22 – how Anthropic itself codes with Claude This 3-hour watch will replace 10 paid Claude Code courses on the internet. Watch it now, then read how to build an AI that codes while you sleep, in the article below.

Phosphen

101,986 views • 24 days ago

Anthropic engineer showed how one person can run 5 AI agents, that code, test, review, and deploy at the same time. In 30 minutes they built the whole thing live in one session. Here's what they cover: > when to use one agent vs a full team > how to split work so agents don't step on each other > the exact framework for deciding what each agent handles that's exactly why, I put together a guide on building agent teams that actually work. full guide in the article below 👇

Anthropic engineer showed how one person can run 5 AI agents, that code, test, review, and deploy at the same time. In 30 minutes they built the whole thing live in one session. Here's what they cover: > when to use one agent vs a full team > how to split work so agents don't step on each other > the exact framework for deciding what each agent handles that's exactly why, I put together a guide on building agent teams that actually work. full guide in the article below 👇

darkzodchi

841,885 views • 2 months ago

"The pace of building has changed so quickly with AI. You have to give somewhere. And we gave for speed." Ashwin Sreenivas. Co-founder & President of Decagon. You'd be hard pressed to find another startup moving as ferociously as this one. Knuckle Up ↓ 00:00 Intro 02:10 How did Jesse and Ashwin decide what to work on at Decagon? 04:19 Why did they reject the top-down market-sizing approach? 13:16 What does Decagon give up to keep moving fast? 17:54 6 days in-office for founders, 5 for everyone else. 23:33 Why didn't Decagon hire a single employee until $1M in ARR? 27:12 How do you hire 450 people in three years without compromising quality? 31:20 Are Decagon engineers even writing code anymore? 35:08 How does the IC engineer role change with Claude Code and Cursor? 36:32 How does EPD leadership change in the AI era? 40:15 Two types of FDE, and which one do most AI companies actually need? 49:21 How will the human role at Decagon evolve over three years? 57:15 Why is Decagon building its own models with Decagon Labs? 1:00:50 What worries Ashwin most about Decagon today? 1:03:52 How does Ashwin manage his psyche while running this fast? 1:08:00 Hiccups in the startup journey. 1:09:50 Quickfire: overrated advice, AI products, books, red flags 1:12:43 What would Ashwin tell his younger self about Decagon's journey?

"The pace of building has changed so quickly with AI. You have to give somewhere. And we gave for speed." Ashwin Sreenivas. Co-founder & President of Decagon. You'd be hard pressed to find another startup moving as ferociously as this one. Knuckle Up ↓ 00:00 Intro 02:10 How did Jesse and Ashwin decide what to work on at Decagon? 04:19 Why did they reject the top-down market-sizing approach? 13:16 What does Decagon give up to keep moving fast? 17:54 6 days in-office for founders, 5 for everyone else. 23:33 Why didn't Decagon hire a single employee until $1M in ARR? 27:12 How do you hire 450 people in three years without compromising quality? 31:20 Are Decagon engineers even writing code anymore? 35:08 How does the IC engineer role change with Claude Code and Cursor? 36:32 How does EPD leadership change in the AI era? 40:15 Two types of FDE, and which one do most AI companies actually need? 49:21 How will the human role at Decagon evolve over three years? 57:15 Why is Decagon building its own models with Decagon Labs? 1:00:50 What worries Ashwin most about Decagon today? 1:03:52 How does Ashwin manage his psyche while running this fast? 1:08:00 Hiccups in the startup journey. 1:09:50 Quickfire: overrated advice, AI products, books, red flags 1:12:43 What would Ashwin tell his younger self about Decagon's journey?

Nakul Mandan

440,533 views • 2 months ago