Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

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

47,183 subscribers

113,816 просмотров • 1 месяц назад •via X (Twitter)

Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 31

Фото профиля me_cool_off

me_cool_off1 месяц назад

i'll be watching this while sipping milk and eating cookies

Фото профиля Codez

Codez1 месяц назад

Happy this topic caught your attention. More is coming.

Фото профиля DEFCON1

DEFCON11 месяц назад

@coder0214h come and see roadmap

Фото профиля Alpha Batcher

Alpha Batcher1 месяц назад

$785k/year so big money

Фото профиля Codez

Codez1 месяц назад

Yeah, so true, bro. But I don’t think OpenAI pays for it for no reason. FDE is actually really hyped right now.

Фото профиля starmex

starmex1 месяц назад

both anthropic and openai paying $785k for this role, the market is speaking

Фото профиля Codez

Codez1 месяц назад

Yeah. This is true bro

Фото профиля rewind

rewind1 месяц назад

fde turns demos into production

Фото профиля Codez

Codez1 месяц назад

Yeah, this is how the future of engineering should look!

Фото профиля African Talent Plug - ATP 🔌

African Talent Plug - ATP 🔌1 месяц назад

@dre_kwasi

Фото профиля Gentlechessman

Gentlechessman1 месяц назад

eval-driven engineering is the missing piece for most AI builders. Even for simple workflows—like turning long-form videos into viral Shorts in 3 mins—without proper evals, the output gets noisy fast. FDE principles are a must-read for anyone building practical AI toolkits.

Фото профиля Fokki

Fokki1 месяц назад

40 min of smart content

Фото профиля Codez

Codez1 месяц назад

Tier 1 content, I’d say. By the person who actually ships FDE at the world’s #1 AI company.

Фото профиля Alex

Alex1 месяц назад

Tier 1 video about FDA

Фото профиля Codez

Codez1 месяц назад

Yeah. When the Head of FDE at OpenAI talks, I’m listening.

Фото профиля Kaivorn

Kaivorn1 месяц назад

Forward deployed goldmine at $785K!

Фото профиля Codez

Codez1 месяц назад

yeah, FDE is crazy !

Фото профиля Aparna Soneja

Aparna Soneja1 месяц назад

Great video!

Фото профиля Rick | DMKTZ | FITzOn

Rick | DMKTZ | FITzOn1 месяц назад

honestly wondering if that salary is real or just a rumor floating around ai circles would love to know what the actual day-to-day looks like for these folks

Фото профиля Chandraprakash Darji

Chandraprakash Darji1 месяц назад

"forward deployed engineering" is consulting with a cooler name and better equity math attached.

Фото профиля TriDung.sol

TriDung.sol1 месяц назад

finally someone highlighting the deploymeny gap

Фото профиля StarHaze

StarHaze1 месяц назад

$785K at OpenAI here, $785K at Anthropic in @0xMovez's post three minutes earlier. either FDE comp has fully converged across the industry or that number is doing a lot of travelling

Фото профиля Nurvia

Nurvia1 месяц назад

The eval driven development part is the real shift. Most teams build first and evaluate later. FDE flips that.

Фото профиля Chris Schofield

Chris Schofield1 месяц назад

@grok summarise

Фото профиля Trever Jackson

Trever Jackson1 месяц назад

This is not the norm guys.

Фото профиля Mithun Kumar

Mithun Kumar1 месяц назад

$785k/year" is doing a lot of work here... one comp package isn't a market rate, it's recruiting theater.

Фото профиля Gregor

Gregor1 месяц назад

The Morgan Stanley case being first is the detail I'd pull on. What specifically from that deployment shaped how they structure FDE engagements now?

Фото профиля AI Mastery Guide

AI Mastery Guide1 месяц назад

$785K for FDE skills shows how valuable real deployment experience is now.

Фото профиля dhinna ship .ico

dhinna ship .ico1 месяц назад

Morgan Stanley case required embedding engineers for on-site regulatory data handling

Фото профиля Rohit

Rohit1 месяц назад

That's a solid salary for anyone good at that! Sounds like a really interesting talk.

Фото профиля Granite

Granite1 месяц назад

fde isnt new, palantir did it for years but everything else in here is actually gold

Похожие видео

Anthropic pays $785K/year for Forward Deployed Engineers. in 15-minute workshop, Palantir’s senior engineers revealed the actual AI FDE roadmap in a live demo: • 10% → 0:44 - the manual baseline: hundreds of safety incidents triaged by hand every week • 30% → 3:23 - one prompt + ontology context → a logic function with semantic search over OSHA regs • 55% → 5:57 - agent writes its own eval suite. 10 test cases spanning severities and required actions • 80% → 7:39 - from 90% failing to 90% passing, unprompted • 100% → 13:32 - agents building agents building agents bookmark & watch - then read the full FDE playbook below ↓

Anthropic pays $785K/year for Forward Deployed Engineers. in 15-minute workshop, Palantir’s senior engineers revealed the actual AI FDE roadmap in a live demo: • 10% → 0:44 - the manual baseline: hundreds of safety incidents triaged by hand every week • 30% → 3:23 - one prompt + ontology context → a logic function with semantic search over OSHA regs • 55% → 5:57 - agent writes its own eval suite. 10 test cases spanning severities and required actions • 80% → 7:39 - from 90% failing to 90% passing, unprompted • 100% → 13:32 - agents building agents building agents bookmark & watch - then read the full FDE playbook below ↓

Movez

22,913 просмотров • 1 месяц назад

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

166,915 просмотров • 1 месяц назад

'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 просмотров • 2 месяцев назад

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 просмотров • 7 месяцев назад

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

352,522 просмотров • 1 год назад

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

10,171 просмотров • 3 месяцев назад

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

409,575 просмотров • 1 год назад

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

23,882 просмотров • 4 месяцев назад

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 просмотров • 1 год назад

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

58,301 просмотров • 4 месяцев назад

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

247,169 просмотров • 1 месяц назад

SpaceXAI engineers just revealed in 30-minute workshop how to build an effective team of GrokBot agents: · 01:20 → 4 SpaceXAI rules for building agents · 09:34 → 7-agent team: from chief of staff to recruiter · 25:06 → GrokBot agents’ “computer use” capabilities · 29:13 → agent auto-delegation +cloud agents · 33:10 → Building memory, loops, graphs for team It makes every $500 agent-engineering course look obsolete Watch SpaceXAI replace people with GrokBots - then learn read full roadmap in article below↓

SpaceXAI engineers just revealed in 30-minute workshop how to build an effective team of GrokBot agents: · 01:20 → 4 SpaceXAI rules for building agents · 09:34 → 7-agent team: from chief of staff to recruiter · 25:06 → GrokBot agents’ “computer use” capabilities · 29:13 → agent auto-delegation +cloud agents · 33:10 → Building memory, loops, graphs for team It makes every $500 agent-engineering course look obsolete Watch SpaceXAI replace people with GrokBots - then learn read full roadmap in article below↓

Codez

28,345 просмотров • 3 дней назад

SpaceXAI engineer (ex-Cursor): "at SpaceXAI, 85% of our code is already written by GrokBot agents. I'm not coding - I'm building GrokBot teams who do this for me i'm running 10-20 GrokBot agents in a loop. I have a Chief of Staff agent, he is managing all coding agents" in a 20-minute workshop, SpaceXAI engineers reveal how they build AI agents internally worth more than a $500 agentic engineering course watch today, then read how to build a team of GrokBot agents from scratch in the article below

SpaceXAI engineer (ex-Cursor): "at SpaceXAI, 85% of our code is already written by GrokBot agents. I'm not coding - I'm building GrokBot teams who do this for me i'm running 10-20 GrokBot agents in a loop. I have a Chief of Staff agent, he is managing all coding agents" in a 20-minute workshop, SpaceXAI engineers reveal how they build AI agents internally worth more than a $500 agentic engineering course watch today, then read how to build a team of GrokBot agents from scratch in the article below

Movez

116,631 просмотров • 14 дней назад

SpaceXAI engineer (ex-Cursor): "99% of people still use GrokBot like Google and only 1% are building teams of self-learning agents i'm running 10-20 Grok cloud agents in a loop. I have a Chief of Staff, a PM agent and 15+ worker agents" in a 20-minute workshop, a SpaceXAI engineer showed how to build cloud agents that will work for you 24/7 this replaced a $500 agentic engineering course watch today, then read the article below on how to use GrokBot at 100% of its power

SpaceXAI engineer (ex-Cursor): "99% of people still use GrokBot like Google and only 1% are building teams of self-learning agents i'm running 10-20 Grok cloud agents in a loop. I have a Chief of Staff, a PM agent and 15+ worker agents" in a 20-minute workshop, a SpaceXAI engineer showed how to build cloud agents that will work for you 24/7 this replaced a $500 agentic engineering course watch today, then read the article below on how to use GrokBot at 100% of its power

Codez

2,342,578 просмотров • 10 дней назад

$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 🇺🇸

75,480 просмотров • 2 месяцев назад

My friend made $2 million last year running his own AI hedge fund. No MIT. No Stanford. No quant background. I asked him how he built the system from zero. He sent me the exact course that got him there. Bridgewater's 1 hour course on building an AI quant researcher. Their core team gave up the complete AI stack they run internally to manage $150 billion. If I had this a year ago, I would've built my hedge fund in a week instead of a year Bookmark this and read the article below. • 00:00 - building AI quant analyst • 25:30 - AI first hedge fund • 56:16 - inside Bridgewater engineering

My friend made $2 million last year running his own AI hedge fund. No MIT. No Stanford. No quant background. I asked him how he built the system from zero. He sent me the exact course that got him there. Bridgewater's 1 hour course on building an AI quant researcher. Their core team gave up the complete AI stack they run internally to manage $150 billion. If I had this a year ago, I would've built my hedge fund in a week instead of a year Bookmark this and read the article below. • 00:00 - building AI quant analyst • 25:30 - AI first hedge fund • 56:16 - inside Bridgewater engineering

Roan

32,996 просмотров • 1 месяц назад

Google Engineers just showed how their AI engineers are building Context Graphs from 0% to 100%: 10% → 4:01 - how to build a Context Graph 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 54-minute Google workshop, and it’s one of the clearest hands-on examples of the shift from. Watch it today, then read the full Context Graphs Playbook below.

Google Engineers just showed how their AI engineers are building Context Graphs from 0% to 100%: 10% → 4:01 - how to build a Context Graph 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 54-minute Google workshop, and it’s one of the clearest hands-on examples of the shift from. Watch it today, then read the full Context Graphs Playbook below.

Morty

10,451 просмотров • 16 дней назад

OpenAI senior engineer just released a 1-hour course on building agentic systems with Codex from scratch: • 00:36 - deep dive into Codex: agents, subagents • 11:34 - Codex agentic prompting patterns • 20:06 - long running Codex agents with /goal • 30:03 - building skills for Codex agents • 41:28 - self-improving loops with /automations this 1-hour workshop by the OpenAI team will replace a $500 AI engineering course watch today, then read how to build self-improving agentic systems with loops & graphs in the article below

OpenAI senior engineer just released a 1-hour course on building agentic systems with Codex from scratch: • 00:36 - deep dive into Codex: agents, subagents • 11:34 - Codex agentic prompting patterns • 20:06 - long running Codex agents with /goal • 30:03 - building skills for Codex agents • 41:28 - self-improving loops with /automations this 1-hour workshop by the OpenAI team will replace a $500 AI engineering course watch today, then read how to build self-improving agentic systems with loops & graphs in the article below

Codez

60,147 просмотров • 13 дней назад