Loading video...

Video Failed to Load

Go Home

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" •...

0xCodez's profile picture

Codez

40,113 subscribers

166,915 views • 1 month ago •via X (Twitter)

Science & TechnologyEducationNews & Politics
LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now - people are forcing agents to work instead of mastering the model first We made that mistake in 2016 at OpenAI - It cost us 5 years " what Karpathy actually means: step 1 → stop forcing your agent to do everything, understand the model underneath first step 2 → demos are easy - products take a decade. self-driving proved it - if you skip the foundation, everything breaks step 3 → the agent is not the product. the foundation is. build that - and agents emerge on their own "you building agents right now - you're at the forefront. not OpenAI. not DeepMind. you " watch - bookmark, then read article below ↓
5:39

Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now - people are forcing agents to work instead of mastering the model first We made that mistake in 2016 at OpenAI - It cost us 5 years " what Karpathy actually means: step 1 → stop forcing your agent to do everything, understand the model underneath first step 2 → demos are easy - products take a decade. self-driving proved it - if you skip the foundation, everything breaks step 3 → the agent is not the product. the foundation is. build that - and agents emerge on their own "you building agents right now - you're at the forefront. not OpenAI. not DeepMind. you " watch - bookmark, then read article below ↓

codila

1,910,937 views • 2 months ago

Andrew Ng: "The most important trend in AI is agentic AI." in 30-minutes talk he breaks down the agentic orchestration layer and how he actually builds with agents: step 1 → prototype fast. what used to take 3 engineers and 6 months - his team builds in an afternoon step 2 → run evals, not vibes. "new model comes out → run evals → switch in two days" step 3 → iterate in loops. outline → search → draft → revise → loop. "this is the heart of agentic systems" Worth more than any paid course on building agents. Watch the talk, then read the guide on building self-improving agentic systems below.
28:28

Andrew Ng: "The most important trend in AI is agentic AI." in 30-minutes talk he breaks down the agentic orchestration layer and how he actually builds with agents: step 1 → prototype fast. what used to take 3 engineers and 6 months - his team builds in an afternoon step 2 → run evals, not vibes. "new model comes out → run evals → switch in two days" step 3 → iterate in loops. outline → search → draft → revise → loop. "this is the heart of agentic systems" Worth more than any paid course on building agents. Watch the talk, then read the guide on building self-improving agentic systems below.

Movez

16,545 views • 2 months ago

Anthropic hired this engineer at $250k-$600k/year because he can built projects with zero lines of code - in Claude chat: in this 30-min workshop he showed exactly how, using loops - from 0% to 100% here's workflow, step by step: step 1 → set your rules once at the top: "no preamble, use artifacts, you're a React expert" - every reply after that stays on rails step 2 → load the docs plus a random Medium article about the API - the human explanation teaches it more than the reference step 3 → wrap your ask in XML tags and tell it to ask you questions first. Answer 5, and the scope is locked before a single file exists step 4 → now run the loop: screenshot the broken UI, paste it back, let it fix, screenshot again - each pass gets closer - this is the agentic loop in its simplest form step 5 → push to GitHub, then paste the repo link in - every next loop iteration builds on working code, not chat history most people never did this - it pays for itself on day one watch & bookmark - then read how to get Claude Certified Architect in the article below ↓
26:23

Anthropic hired this engineer at $250k-$600k/year because he can built projects with zero lines of code - in Claude chat: in this 30-min workshop he showed exactly how, using loops - from 0% to 100% here's workflow, step by step: step 1 → set your rules once at the top: "no preamble, use artifacts, you're a React expert" - every reply after that stays on rails step 2 → load the docs plus a random Medium article about the API - the human explanation teaches it more than the reference step 3 → wrap your ask in XML tags and tell it to ask you questions first. Answer 5, and the scope is locked before a single file exists step 4 → now run the loop: screenshot the broken UI, paste it back, let it fix, screenshot again - each pass gets closer - this is the agentic loop in its simplest form step 5 → push to GitHub, then paste the repo link in - every next loop iteration builds on working code, not chat history most people never did this - it pays for itself on day one watch & bookmark - then read how to get Claude Certified Architect in the article below ↓

codila

292,648 views • 1 month ago

Anthropic engineer: "Harness engineering is a really rare skill right now - and it's hard to know if you're even good at it." how he actually thinks about building the full system: • step 1 → the model. already smarter than we use it. "freeze development - you'd find new capabilities for another 6-12 months" • step 2 → the harness. delete the steering. "we stopped using plan mode - the model thinks correctly" • step 3 → the agents. workflows let Claude build its own harness per task and spin sub-agents. what didn't fit in one context window now runs across many • step 4 → the context. "RAG is an anti-pattern. use grep." memory via files, clean context window, prompt caching. that's the stack most people optimize step 1. he optimizes all four. Watch the full video, then read the guide below on how to build an agentic system.
46:31

Anthropic engineer: "Harness engineering is a really rare skill right now - and it's hard to know if you're even good at it." how he actually thinks about building the full system: • step 1 → the model. already smarter than we use it. "freeze development - you'd find new capabilities for another 6-12 months" • step 2 → the harness. delete the steering. "we stopped using plan mode - the model thinks correctly" • step 3 → the agents. workflows let Claude build its own harness per task and spin sub-agents. what didn't fit in one context window now runs across many • step 4 → the context. "RAG is an anti-pattern. use grep." memory via files, clean context window, prompt caching. that's the stack most people optimize step 1. he optimizes all four. Watch the full video, then read the guide below on how to build an agentic system.

Codez

44,590 views • 2 months ago

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → order matters: tools first, then system prompt, then your docs. anything that changes goes last step 2 → drop the breakpoint after the last stable block. everything above it gets stored step 3 → the match has to be byte-perfect. one extra space and you're back to full price step 4 → 5 minutes of idle and it's gone. but every hit restarts the timer step 5 → cache reads don't touch your rate limit. that's throughput you're not paying for 99% people never configure this - one afternoon and it pays for itself save this - the full 1-hour course is right below ↓
1:06:45

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → order matters: tools first, then system prompt, then your docs. anything that changes goes last step 2 → drop the breakpoint after the last stable block. everything above it gets stored step 3 → the match has to be byte-perfect. one extra space and you're back to full price step 4 → 5 minutes of idle and it's gone. but every hit restarts the timer step 5 → cache reads don't touch your rate limit. that's throughput you're not paying for 99% people never configure this - one afternoon and it pays for itself save this - the full 1-hour course is right below ↓

codila

86,071 views • 1 month ago

He is a 1st year AI engineering student at Stanford - and already launched an automated system making $2.1M a year: 00:20 - how Anthropic engineers make millions with LLM and Transformers 38:34 - In-Context Learning makes AI smarter for free 59:47 - 1st year student made $2.1M using LLM from scratch after watching I gave LLM the idea I had been holding in my head for 3 years - it built it in one evening. save & watch - the article below is a step by step guide to build your automated LLM system.
1:16:48

He is a 1st year AI engineering student at Stanford - and already launched an automated system making $2.1M a year: 00:20 - how Anthropic engineers make millions with LLM and Transformers 38:34 - In-Context Learning makes AI smarter for free 59:47 - 1st year student made $2.1M using LLM from scratch after watching I gave LLM the idea I had been holding in my head for 3 years - it built it in one evening. save & watch - the article below is a step by step guide to build your automated LLM system.

Noisy

43,667 views • 1 month ago

CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below
41:34

CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below

Anatoli Kopadze

14,741 views • 3 months ago

OpenAI CEO, Sam Altman: "We just had an AI security incident that genuinely shook me. What surprises me is how few people seem worried about it." In 55 minutes he explains what's now impossible to ignore. At this point there's no future without AI, and closing your eyes to it only leaves you behind. That's why I made a guide that walks you step by step through getting 100% out of AI, built on the one tool that ties every model together. Watch the interview, then read the article below.
55:51

OpenAI CEO, Sam Altman: "We just had an AI security incident that genuinely shook me. What surprises me is how few people seem worried about it." In 55 minutes he explains what's now impossible to ignore. At this point there's no future without AI, and closing your eyes to it only leaves you behind. That's why I made a guide that walks you step by step through getting 100% out of AI, built on the one tool that ties every model together. Watch the interview, then read the article below.

Anatoli Kopadze

44,106 views • 1 month ago

Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.
4:43

Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.

The Startup Ideas Podcast (SIP) 🧃

14,202 views • 4 months ago

Anthropic hired this engineer at $250K-$750K a year because he knows how to build harnesses for multi-agent systems In this 15-minute workshop, he shows exactly how to build one from scratch AI → Agents → Harness → Loops → Graphs step 1 → start with the Claude Agent SDK - the harness handles loops, context, and sandboxing step 2 → separate the brain from the hands - reasoning in one place, tools in a sandbox, 60% faster to first token step 3 → run it server-side and log every step - close your laptop and it keeps running, crashes resume from the log step 4 → make failure cheap - retry dead sandboxes and replay lost context instead of starting over step 5 → turn yesterday's logs into new memory and skills - the harness wakes up smarter Anthropic calls this "dreaming" Most people spend weeks building this by hand You don't have to Bookmark and watch it Then read the full harness engineering guide below ↓
12:00

Anthropic hired this engineer at $250K-$750K a year because he knows how to build harnesses for multi-agent systems In this 15-minute workshop, he shows exactly how to build one from scratch AI → Agents → Harness → Loops → Graphs step 1 → start with the Claude Agent SDK - the harness handles loops, context, and sandboxing step 2 → separate the brain from the hands - reasoning in one place, tools in a sandbox, 60% faster to first token step 3 → run it server-side and log every step - close your laptop and it keeps running, crashes resume from the log step 4 → make failure cheap - retry dead sandboxes and replay lost context instead of starting over step 5 → turn yesterday's logs into new memory and skills - the harness wakes up smarter Anthropic calls this "dreaming" Most people spend weeks building this by hand You don't have to Bookmark and watch it Then read the full harness engineering guide below ↓

rari

183,635 views • 18 days ago

Google engineer: "Delete your IDE, you don't need it anymore, in 2026 if you're still not building AI agents, it's crazy how behind you are. At Google, 85% of engineers are running agentic loops and graphs, that's how the engineering setup looks now." 25 minutes from a Google engineer with 30 years of experience, explaining where engineering is heading in 2026. This is something you can't skip if you don't want to be left behind. Watch it, then go further with the step-by-step guide below on how to build a system that improves itself.
24:23

Google engineer: "Delete your IDE, you don't need it anymore, in 2026 if you're still not building AI agents, it's crazy how behind you are. At Google, 85% of engineers are running agentic loops and graphs, that's how the engineering setup looks now." 25 minutes from a Google engineer with 30 years of experience, explaining where engineering is heading in 2026. This is something you can't skip if you don't want to be left behind. Watch it, then go further with the step-by-step guide below on how to build a system that improves itself.

Anatoli Kopadze

70,678 views • 25 days ago

ANTHROPIC LEAKED 8 RULES THAT GIVE ONE ENGINEER 8X FROM A SINGLE CHANGE the engineers built it so nothing adds up - it multiplies, and that's the 8x task → split → verify → score → write back one queue means you wait for every step in turn - three workers together cost you the slowest one, not the sum of all. as long as you're the one reading the diff, the bottleneck is you, not the model. state is written at every step - a crash mid-run costs you one step, not the whole session. history gets compressed 25 times over - the next pass starts at 8k instead of 200k. nothing moves forward without a number - "it got better" isn't a result, and the breakage comes back when you're no longer watching. the eighth rule is the only one that writes back into CLAUDE.md - the agent records what it learned, and the next run doesn't start from zero. copy this into Claude Code today - and tomorrow you work like an engineer on $1.5M a year ↓
1:05

ANTHROPIC LEAKED 8 RULES THAT GIVE ONE ENGINEER 8X FROM A SINGLE CHANGE the engineers built it so nothing adds up - it multiplies, and that's the 8x task → split → verify → score → write back one queue means you wait for every step in turn - three workers together cost you the slowest one, not the sum of all. as long as you're the one reading the diff, the bottleneck is you, not the model. state is written at every step - a crash mid-run costs you one step, not the whole session. history gets compressed 25 times over - the next pass starts at 8k instead of 200k. nothing moves forward without a number - "it got better" isn't a result, and the breakage comes back when you're no longer watching. the eighth rule is the only one that writes back into CLAUDE.md - the agent records what it learned, and the next run doesn't start from zero. copy this into Claude Code today - and tomorrow you work like an engineer on $1.5M a year ↓

Noisy

37,450 views • 1 month ago

holy sh*t, this is the whole one-person ai company playbook on one page... this is basically a 7-step blueprint for running a business with ai in 2026 pick one narrow problem talk to 10 potential buyers write the business case publish one clear page win the first customers manually automate what repeats bring in specialists when needed - ai handles research, content, sales prep, support, and operations - you keep control over money, quality, customer relationships, and final decisions save this, then automate everything by building your own AI office ⭣
0:20

holy sh*t, this is the whole one-person ai company playbook on one page... this is basically a 7-step blueprint for running a business with ai in 2026 pick one narrow problem talk to 10 potential buyers write the business case publish one clear page win the first customers manually automate what repeats bring in specialists when needed - ai handles research, content, sales prep, support, and operations - you keep control over money, quality, customer relationships, and final decisions save this, then automate everything by building your own AI office ⭣

beamnxw ./

54,848 views • 10 days ago

Anthropic hired this engineer at $250k-$750k/year because he can build knowledge Graphs for multi-agent systems: in this 15-min workshop he explain exactly how, using loops - from scratch step 1 → start from the Claude Agent SDK - you get the loop, context, and sandbox for free - bring the task, not the plumbing step 2 → split the brain from the hands - reasoning in one place, tools in a sandbox - the model thinks while the container boots: ~60% faster to first token step 3 → run it server-side, log every step - close your laptop and it keeps going - crashes resume from the log, not from zero step 4 → make failure cheap - dead sandbox retries, session log replays the context it dropped - that's what survives production step 5 → batch yesterday's logs into new memory and skills - it wakes up smarter - Anthropic calls this "dreaming" most people wire this by hand for weeks - the point is you don't have to watch & bookmark - then read how to get full graph system below ↓
12:01

Anthropic hired this engineer at $250k-$750k/year because he can build knowledge Graphs for multi-agent systems: in this 15-min workshop he explain exactly how, using loops - from scratch step 1 → start from the Claude Agent SDK - you get the loop, context, and sandbox for free - bring the task, not the plumbing step 2 → split the brain from the hands - reasoning in one place, tools in a sandbox - the model thinks while the container boots: ~60% faster to first token step 3 → run it server-side, log every step - close your laptop and it keeps going - crashes resume from the log, not from zero step 4 → make failure cheap - dead sandbox retries, session log replays the context it dropped - that's what survives production step 5 → batch yesterday's logs into new memory and skills - it wakes up smarter - Anthropic calls this "dreaming" most people wire this by hand for weeks - the point is you don't have to watch & bookmark - then read how to get full graph system below ↓

codila

119,500 views • 1 month ago

Monetizing your own AI app just got really easy. Last week I used YouAI to create an AI-powered bio generator. It took me less than an hour and I didn't need any programming skills. And now it just made it's first sale. All you need is a good idea and some prompting skills. Here's how you can create your own: Step 1: Setup → Go to → Sign up, then click on "new AI" (you can also remix an existing AI if you're lazy) Step 2: Create prompt → Within the "preamble" you can create the main prompt for this AI → In my example I described the assistant's goal (bio generator) and defined the format of the bios → I also added some bio examples to train the AI Step 3: Configure inputs → Now you define what the user inputs when using the AI app → In my case I defined 3 user inputs: - What topic do you tweet about? - What is your background? What have you achieved? - How do you help your followers? The answers are then used as variables to create the bios. Step 4: Publish → Select the underlying model (GPT or Claude) → Keep the app free or turn on monetization → Then publish! Your app will now be available in the YouAI library for others to use YouAI is also doing a live event tomorrow where they'll go into more detail and teach you how to build your own YouAI app: Thank you to YouAI for sponsoring this post! I'm a big fan of their product.
1:38

Monetizing your own AI app just got really easy. Last week I used YouAI to create an AI-powered bio generator. It took me less than an hour and I didn't need any programming skills. And now it just made it's first sale. All you need is a good idea and some prompting skills. Here's how you can create your own: Step 1: Setup → Go to → Sign up, then click on "new AI" (you can also remix an existing AI if you're lazy) Step 2: Create prompt → Within the "preamble" you can create the main prompt for this AI → In my example I described the assistant's goal (bio generator) and defined the format of the bios → I also added some bio examples to train the AI Step 3: Configure inputs → Now you define what the user inputs when using the AI app → In my case I defined 3 user inputs: - What topic do you tweet about? - What is your background? What have you achieved? - How do you help your followers? The answers are then used as variables to create the bios. Step 4: Publish → Select the underlying model (GPT or Claude) → Keep the app free or turn on monetization → Then publish! Your app will now be available in the YouAI library for others to use YouAI is also doing a live event tomorrow where they'll go into more detail and teach you how to build your own YouAI app: Thank you to YouAI for sponsoring this post! I'm a big fan of their product.

Moritz Kremb

30,424 views • 3 years ago

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → put everything that never changes at the top - tools, then system, then docs step 2 → mark where the static part ends. everything above it gets cached step 3 → one stray space breaks it - and you pay full price again step 4 → the cache dies in 5 min - every read resets the clock step 5 → cached tokens don't count against your rate limits. free headroom most people never touch this - it pays for itself on day one watch & bookmark - this 1-hour brilliant course ↓
1:04:05

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → put everything that never changes at the top - tools, then system, then docs step 2 → mark where the static part ends. everything above it gets cached step 3 → one stray space breaks it - and you pay full price again step 4 → the cache dies in 5 min - every read resets the clock step 5 → cached tokens don't count against your rate limits. free headroom most people never touch this - it pays for itself on day one watch & bookmark - this 1-hour brilliant course ↓

codila

545,647 views • 2 months ago

How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline - Ingestion + cleaning for RAG / analytics - Chunking, metadata, indexing - Observability and data quality checks Step 2: Learn how model providers work - APIs, limits, retries, rate limits - Latency and cost trade-offs - Privacy and data handling constraints Step 3: Prompting as an engineering discipline - Prompt templates - Versioning + change logs - Structured outputs (JSON schemas) Step 4: Evaluation for generative systems - Golden sets - Automated checks (format, factuality signals, regressions) - Human review loops when it matters Step 5: Tool integration + agentic flows - Function/tool calling - Guardrails (allowed tools, timeouts, fallbacks) - Tracing what the agent did and why Step 6: Build 1-2 small projects end-to-end Examples: - RAG assistant over internal docs - Ticket triage bot with tool calls + evals For an experienced data engineer, this is usually not a multi-year shift. With focused effort + hands-on work, ~3-4 months can be enough to become interview-ready. If you're making this transition, what part feels most unclear: evals, prompting, or agents?
3:48

How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline - Ingestion + cleaning for RAG / analytics - Chunking, metadata, indexing - Observability and data quality checks Step 2: Learn how model providers work - APIs, limits, retries, rate limits - Latency and cost trade-offs - Privacy and data handling constraints Step 3: Prompting as an engineering discipline - Prompt templates - Versioning + change logs - Structured outputs (JSON schemas) Step 4: Evaluation for generative systems - Golden sets - Automated checks (format, factuality signals, regressions) - Human review loops when it matters Step 5: Tool integration + agentic flows - Function/tool calling - Guardrails (allowed tools, timeouts, fallbacks) - Tracing what the agent did and why Step 6: Build 1-2 small projects end-to-end Examples: - RAG assistant over internal docs - Ticket triage bot with tool calls + evals For an experienced data engineer, this is usually not a multi-year shift. With focused effort + hands-on work, ~3-4 months can be enough to become interview-ready. If you're making this transition, what part feels most unclear: evals, prompting, or agents?

Alexey Grigorev

13,694 views • 6 months ago

Every company says they have Forward-Deployed Engineers now. ElevenLabs CEO and former Palantir Deployment Strategist Mati Staniszewski gives one of the best explanations of what an FDE is—and says companies are missing the key piece of what actually makes FDEs valuable: “In our case, FDEs are part of the product team. They are not part of the go-to-market team. They are deeply embedded in understanding what the roadmap of the product is.” “You want all the FDEs to actually solve the customer problem and stretch your product in that direction. But the second thing you want them to do is bring any of that knowledge back to the product, so the product becomes better for the next generation of companies building on top of it. I feel like the second part is frequently missed.” “The beauty of that is—everybody benefits because you work with one customer, learn, and you bring that into the product experience. Both of those customers benefited from having their product optimized for better work.” “The domain expertise they have is still theirs. They can win based on their domain expertise. But the product experience of how you build based on that domain expertise can be abstracted, and can be reusable.”
2:26

Every company says they have Forward-Deployed Engineers now. ElevenLabs CEO and former Palantir Deployment Strategist Mati Staniszewski gives one of the best explanations of what an FDE is—and says companies are missing the key piece of what actually makes FDEs valuable: “In our case, FDEs are part of the product team. They are not part of the go-to-market team. They are deeply embedded in understanding what the roadmap of the product is.” “You want all the FDEs to actually solve the customer problem and stretch your product in that direction. But the second thing you want them to do is bring any of that knowledge back to the product, so the product becomes better for the next generation of companies building on top of it. I feel like the second part is frequently missed.” “The beauty of that is—everybody benefits because you work with one customer, learn, and you bring that into the product experience. Both of those customers benefited from having their product optimized for better work.” “The domain expertise they have is still theirs. They can win based on their domain expertise. But the product experience of how you build based on that domain expertise can be abstracted, and can be reusable.”

David Senra

300,248 views • 10 days ago