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How does an FDE map the processes to deploy AI in a real business? We use 3 sources. Source 1 is people. - Companies tell us: "this person's been at the firm for 20 years and they just handle it." - We hear this from Fortune 50 companies and... from SMBs. - So in finance, you talk to the head of AP, AR, reconciliations, billing, banking, and FP&A. You make a "human API." Source 2 is the systems of record. - With real-time access to Salesforce for 3 to 4 weeks, you see what data comes in and how often people correct it. Source 3 is the documents in SharePoint, Drive, Notion, Slack, Teams, and Gmail. If you use the system data without the interviews, you miss half of the picture. The split is different for each company. A large company can have 10 years of Salesforce data. An SMB has most of it in people's heads. Choose your angle for each company before you build.show more

The Startup Ideas Podcast (SIP) 🧃

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35,097 views • 8 days ago •via X (Twitter)

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The Startup Ideas Podcast (SIP) 🧃8 days ago

Shoutout to @brexHQ for helping us spread the sauce!

Bruce Dando | Finance Transformation8 days ago

@vasuman Is FDE just a new name for transformation specialist? This is bread and butter stuff.

Hussain Hashim | Building SundayBack7 days ago

@startupideaspod I'd add that mapping out the dependencies upfront is crucial. Skipping this can derail everything.

vas8 days ago

Fire

Jules Malin7 days ago

Talk to the "20 years, just handles it" person before you open a single table. They know where the bodies are buried.

Rafe7 days ago

interview that person before you automate the job. their exception handling is probably the real process

Grad Conn7 days ago

Source 4 should be Pendo or whatever product analytics tool they have installed -- that shows the full clickstream and all the *actual* interactions occurring on applications (both Web and installed and DIY) throughout the organization.

Stefan Palm8 days ago

To find where their processes breaks, talk to new employees: What does not work as they told you, and what did no one told you that you had to figure out anyway?

Related Videos

This is how we streamline AI implementation across 26 companies in a PE portfolio. Each company has 10 departments, and each department has 10 workflows. If you do them one at a time, you need "50,000 forward deployed engineers across three years." Our method is to group the companies by system of record. For finance, the 5 companies on NetSuite are one group. The 4 companies on Dynamics are a different group. Then you find what NetSuite can do and what it cannot do. You build only for the gaps. In one group, the processes are still different. The 5 NetSuite companies used 12, 9, 15, 18 and 13 steps for their finance process. The target is 6 steps, with agents at specific points. You will not often make all 5 exactly equal. You can come close if you map each process with the stakeholders before you change it. A single company or 26, sort the work by its system before you build.

This is how we streamline AI implementation across 26 companies in a PE portfolio. Each company has 10 departments, and each department has 10 workflows. If you do them one at a time, you need "50,000 forward deployed engineers across three years." Our method is to group the companies by system of record. For finance, the 5 companies on NetSuite are one group. The 4 companies on Dynamics are a different group. Then you find what NetSuite can do and what it cannot do. You build only for the gaps. In one group, the processes are still different. The 5 NetSuite companies used 12, 9, 15, 18 and 13 steps for their finance process. The target is 6 steps, with agents at specific points. You will not often make all 5 exactly equal. You can come close if you map each process with the stakeholders before you change it. A single company or 26, sort the work by its system before you build.

The Startup Ideas Podcast (SIP) 🧃

16,803 views • 4 days ago

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 views • 11 months ago

"If you're an analytical SaaS company, it's over. It's over." - Palo Alto Networks CEO Nikesh Arora "What is an analytical SaaS company? It is a company that says, “I'm going to collect a lot of data for you and analyze it for you.” I don't need you to analyze it for me. I can run models against the data and analyze it myself. Think about it: almost every SaaS company has a marketplace. You can buy applications from the Salesforce Marketplace. What do those applications say? “You have Salesforce data. I'm a marketplace app. Use me, and I'll help you analyze that data.” But I don't need to do that. You don't need those applications anymore. I can simply run an LLM against the data. Therefore, all the additional functionality that has traditionally been sold to us as incremental software modules may no longer need to be sold separately. I would much rather have an LLM run against the data." ---- From "All-In Podcast" YouTube channel, (full video link in comment)

"If you're an analytical SaaS company, it's over. It's over." - Palo Alto Networks CEO Nikesh Arora "What is an analytical SaaS company? It is a company that says, “I'm going to collect a lot of data for you and analyze it for you.” I don't need you to analyze it for me. I can run models against the data and analyze it myself. Think about it: almost every SaaS company has a marketplace. You can buy applications from the Salesforce Marketplace. What do those applications say? “You have Salesforce data. I'm a marketplace app. Use me, and I'll help you analyze that data.” But I don't need to do that. You don't need those applications anymore. I can simply run an LLM against the data. Therefore, all the additional functionality that has traditionally been sold to us as incremental software modules may no longer need to be sold separately. I would much rather have an LLM run against the data." ---- From "All-In Podcast" YouTube channel, (full video link in comment)

Rohan Paul

52,190 views • 25 days ago

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Santiago

39,101 views • 2 years ago

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

Raoul Pal

79,047 views • 2 months ago

In the next 2-3 years, the skills you need to build an ETL process will fundamentally differ from anything we've done in the last decade. The EXTRACT and LOAD steps of an ETL pipeline have been solved forever. They are boring, and I suspect they will continue to be boring. But the TRANSFORM step is where the money is at (and where innovation will continue happening!) An ETL pipeline that can use Large Language Models effectively to transform data is a whole new ballgame. I recorded a video to talk about this. And more importantly, I talk about how you can build ETL pipelines to transform UNSTRUCTURED data (the kind of data that's really hard to handle).

In the next 2-3 years, the skills you need to build an ETL process will fundamentally differ from anything we've done in the last decade. The EXTRACT and LOAD steps of an ETL pipeline have been solved forever. They are boring, and I suspect they will continue to be boring. But the TRANSFORM step is where the money is at (and where innovation will continue happening!) An ETL pipeline that can use Large Language Models effectively to transform data is a whole new ballgame. I recorded a video to talk about this. And more importantly, I talk about how you can build ETL pipelines to transform UNSTRUCTURED data (the kind of data that's really hard to handle).

Santiago

56,412 views • 1 year ago

AI agents should add to the systems your client already uses. One client spent several years and $10 million on one ERP migration. Offer that client a move to an AI-native CRM, and "you've lost them." So we build our agents inside the systems of record. They read the data in Salesforce and do the work in Salesforce. When a human must be in the loop, the agent sends a message in Slack. The staff need no new training, so they use the agents faster. SMB clients with no CRM are the exception. You can put them on one. If you want to work as a forward deployed engineer, build on what your client already uses.

AI agents should add to the systems your client already uses. One client spent several years and $10 million on one ERP migration. Offer that client a move to an AI-native CRM, and "you've lost them." So we build our agents inside the systems of record. They read the data in Salesforce and do the work in Salesforce. When a human must be in the loop, the agent sends a message in Slack. The staff need no new training, so they use the agents faster. SMB clients with no CRM are the exception. You can put them on one. If you want to work as a forward deployed engineer, build on what your client already uses.

The Startup Ideas Podcast (SIP) 🧃

15,730 views • 5 days ago

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Santiago

40,643 views • 2 years ago

Meta Chief AI Officer Alexandr Wang on why your product can be better and still lose, because perception is more real than reality: Most founders assume the best product wins. Build something that works, prove it with data, and customers will see it. Wang says that assumption breaks down when you sell to large organisations. "Probably a lot of the companies that you all have worked with, they're very data-driven companies. It feels like the truth makes its way... everybody serves a shared sense of reality and whatnot. That's not true at most large companies and also not true within the government, unfortunately." So what do they run on instead? Perception. "At a lot of large customers, perception is more real than reality. The reality is just so ugly most of the time that very rarely do people actually confront reality, and most of the time they just sort of choose to believe the perceptions that they live in." That is how a better product loses. If buyers aren't judging reality, being better in reality isn't enough. The deal goes to whoever owns the perception. Wang spells out what this means for anyone building or selling to enterprises: "If you end up doing enterprise sales or you end up building enterprise products... just as much as your job is to improve the reality, it is to shape the perception." The product still matters, but by his reckoning, it's only half the job. One company he singles out for mastering the other half is Palantir: "One of Palantir's superpowers is that they shape the perception better than most other technology companies do, because I think they view themselves... it's like a combination of a sort of acting troupe combined with a software company." And Alexandr Wang means it literally: "They literally give an acting book to all of the new hires, or they did for a very long time." A software company that trained its people in performance. That tells you how seriously Palantir took the perception half of the job.

Meta Chief AI Officer Alexandr Wang on why your product can be better and still lose, because perception is more real than reality: Most founders assume the best product wins. Build something that works, prove it with data, and customers will see it. Wang says that assumption breaks down when you sell to large organisations. "Probably a lot of the companies that you all have worked with, they're very data-driven companies. It feels like the truth makes its way... everybody serves a shared sense of reality and whatnot. That's not true at most large companies and also not true within the government, unfortunately." So what do they run on instead? Perception. "At a lot of large customers, perception is more real than reality. The reality is just so ugly most of the time that very rarely do people actually confront reality, and most of the time they just sort of choose to believe the perceptions that they live in." That is how a better product loses. If buyers aren't judging reality, being better in reality isn't enough. The deal goes to whoever owns the perception. Wang spells out what this means for anyone building or selling to enterprises: "If you end up doing enterprise sales or you end up building enterprise products... just as much as your job is to improve the reality, it is to shape the perception." The product still matters, but by his reckoning, it's only half the job. One company he singles out for mastering the other half is Palantir: "One of Palantir's superpowers is that they shape the perception better than most other technology companies do, because I think they view themselves... it's like a combination of a sort of acting troupe combined with a software company." And Alexandr Wang means it literally: "They literally give an acting book to all of the new hires, or they did for a very long time." A software company that trained its people in performance. That tells you how seriously Palantir took the perception half of the job.

Big Brain Business

269,925 views • 9 days ago

Spirit Airlines stopped flying in May. Second bankruptcy in two years. Yet Google still wants to pay $10Million for the dead body. The planes are gone, the airport slots are sold. One asset left. At the bankruptcy auction, Google opened at 5 million dollars. An AI data company called Mercor countered at 7 and a half million. Then Google closed it at 10 Million. But the bids weren't for the planes or Airport slots. They were for 100 million internal emails. 500 million Teams messages. 30 million lines of code. Employee records that go back to 1986. And no, its not passenger data. This is purely internal: decades of how a real business thought, argued, and made decisions. Why pay that much for a dead company's inbox? Because it's the one thing AI can't fake. The most valuable data in the world right now is just real people thinking out loud… real decisions, real mistakes, real cause and effect. Which is why after the auction closed, another AI company came in with a 12 and a half million offer for it. Here's how you can leverage this kind of data for your business without a bankruptcy auction. People type their real, unfiltered questions into a search bar every single day… for free. And for business owners, those raw questions are a free roadmap: they tell you exactly what to create, what to fix on your site, and what to sell next. Tools like AnswerThePublic mine and present that exact data by looking at the different ways people search for a product. The AI data gold rush is just getting started, follow to not miss out!

Spirit Airlines stopped flying in May. Second bankruptcy in two years. Yet Google still wants to pay $10Million for the dead body. The planes are gone, the airport slots are sold. One asset left. At the bankruptcy auction, Google opened at 5 million dollars. An AI data company called Mercor countered at 7 and a half million. Then Google closed it at 10 Million. But the bids weren't for the planes or Airport slots. They were for 100 million internal emails. 500 million Teams messages. 30 million lines of code. Employee records that go back to 1986. And no, its not passenger data. This is purely internal: decades of how a real business thought, argued, and made decisions. Why pay that much for a dead company's inbox? Because it's the one thing AI can't fake. The most valuable data in the world right now is just real people thinking out loud… real decisions, real mistakes, real cause and effect. Which is why after the auction closed, another AI company came in with a 12 and a half million offer for it. Here's how you can leverage this kind of data for your business without a bankruptcy auction. People type their real, unfiltered questions into a search bar every single day… for free. And for business owners, those raw questions are a free roadmap: they tell you exactly what to create, what to fix on your site, and what to sell next. Tools like AnswerThePublic mine and present that exact data by looking at the different ways people search for a product. The AI data gold rush is just getting started, follow to not miss out!

Neil Patel

12,729 views • 1 month ago

I just found out that Luel AI, the company that completely copied my company Kled's entire website, is misrepresenting their compliance practices, using Delve to outsource it, fabricating their website user numbers, and pulling in massive amounts of fraud data from exclusively fraud oriented regions. Skip to 1:15 if you want to skip context. You truly cannot make this up. This is egregious, disgusting behavior from everyone involved. This is people's data. It cannot be taken lightly. The only way a company like this can truly succeed is by investing the time and resources to commit 100% to the trust and compliance of their users' data. Luel AI is doing the exact opposite. Disgusting behavior for a company trusted with people's data.

I just found out that Luel AI, the company that completely copied my company Kled's entire website, is misrepresenting their compliance practices, using Delve to outsource it, fabricating their website user numbers, and pulling in massive amounts of fraud data from exclusively fraud oriented regions. Skip to 1:15 if you want to skip context. You truly cannot make this up. This is egregious, disgusting behavior from everyone involved. This is people's data. It cannot be taken lightly. The only way a company like this can truly succeed is by investing the time and resources to commit 100% to the trust and compliance of their users' data. Luel AI is doing the exact opposite. Disgusting behavior for a company trusted with people's data.

Avi Patel

1,765,939 views • 4 months ago

Building with AI gets easier every day. Here is an open-source library that makes integrating AI into an application extremely easy: Star the repository! This library alone can make React the best front-end framework out there! There are a bunch of cool things I like about CopilotKit. Here are 3 of them: 1. It allows you to take any -powered agent and bring it into your application. (This is a brand-new feature!) 2. You can build an AI-powered chatbot in your application. The chatbot will have access to your context and can act on the application. 3. You can build a RAG workflow to process and answer questions from a real-time knowledge base. I recorded a video to show you how simple it is to make some of this happen. A few lines of code, and you are in business. Here is a link to the sample application: CopilotKit is open-source. You can self-host it. You can use it with any LLM. Thanks to the team for showing me their tool and collaborating with me on this post!

Santiago

108,884 views • 2 years ago

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.

🇺🇸RealRobert🇺🇸

18,982 views • 8 months ago

This is very true. AI integration is both hard and necessary for companies. And AI-native junior employees can drive this adoption. Earlier this year Aaron Levie explained with martin_casado on the a16z podcast how AI fluency is one of the most powerful skills a new grad can have. The real value comes from knowing how to make AI work inside an organization. Most companies, both large and small, are slow to adopt new technology. They need people who can explain what AI can do, show them how to use it, and help them move faster. If you are fluent in AI, you can be that person. You can walk into a company, spot the areas where AI can save time or create new opportunities, and make it happen. That is a wedge into your first job and a way to stand out from everyone else.

This is very true. AI integration is both hard and necessary for companies. And AI-native junior employees can drive this adoption. Earlier this year Aaron Levie explained with martin_casado on the a16z podcast how AI fluency is one of the most powerful skills a new grad can have. The real value comes from knowing how to make AI work inside an organization. Most companies, both large and small, are slow to adopt new technology. They need people who can explain what AI can do, show them how to use it, and help them move faster. If you are fluent in AI, you can be that person. You can walk into a company, spot the areas where AI can save time or create new opportunities, and make it happen. That is a wedge into your first job and a way to stand out from everyone else.

a16z

366,027 views • 1 year ago

Today, we're bringing shift to Europe. We start with completely free cleaning, and skilled trade services come soon. The AI economy is creating trillions in value. Most people get none of it. We're here to change that. After a successful launch in New York, we couldn't wait to expand to Europe. Here's how it works. Book a shift cleaning. A vetted shift operator comes to your home wearing one of our devices. They clean. They leave. You pay nothing. In exchange, we record the cleaning. Robotics is built on data about how people do everyday tasks, and the value of that recording funds the service. Anything personal is anonymized before processing. Tech companies have used our data for free for years. But your data has value. Everyone should benefit from it. That is what shift is about. Comment your city and we'll send you an early access link.

Today, we're bringing shift to Europe. We start with completely free cleaning, and skilled trade services come soon. The AI economy is creating trillions in value. Most people get none of it. We're here to change that. After a successful launch in New York, we couldn't wait to expand to Europe. Here's how it works. Book a shift cleaning. A vetted shift operator comes to your home wearing one of our devices. They clean. They leave. You pay nothing. In exchange, we record the cleaning. Robotics is built on data about how people do everyday tasks, and the value of that recording funds the service. Anything personal is anonymized before processing. Tech companies have used our data for free for years. But your data has value. Everyone should benefit from it. That is what shift is about. Comment your city and we'll send you an early access link.

shift

17,438 views • 3 months ago

Sequoia founder Don Valentine: “The art of storytelling is incredibly important” “The art of storytelling is incredibly important. And many—maybe even most of the entrepreneurs who come to talk to us can’t tell the story. Learning to tell a story is incredibly important because that’s how the money works. The money flows as a function of the stories.” The founder of Sequoia founder explains that the story is how you explain what you want to do, how long it’s going to take, who the competition is, and how much money you need. a16z cofounder Ben Horowitz shared a similar view in a 2014 Forbes interview: “Storytelling is the most underrated skill… Companies that don’t have a clearly articulated story don’t have a clear and well thought-out strategy. The company story is the company strategy.” He continues: “The story must explain at a fundamental level why you exist. Why does the world need your company? Why do we need to be doing what we’re doing and why is it important?… You can have a great product, but a compelling story puts the company into motion. If you don’t have a great story it’s hard to get people motivated to join you, to work on the product, and to get people to invest in the product.” This is the job of the founder and CEO: “The CEO must be the keeper of the story. The CEO is responsible for getting the story right, that it’s up to date, compelling, and can move the hearts of men and women. That’s the fundamental responsibility of the chief executive… The mistake people make is thinking the story is just about marketing. No, the story is the strategy. If you make your story better you make the strategy better.” Video source: Stanford Graduate School of Business (2010)

Sequoia founder Don Valentine: “The art of storytelling is incredibly important” “The art of storytelling is incredibly important. And many—maybe even most of the entrepreneurs who come to talk to us can’t tell the story. Learning to tell a story is incredibly important because that’s how the money works. The money flows as a function of the stories.” The founder of Sequoia founder explains that the story is how you explain what you want to do, how long it’s going to take, who the competition is, and how much money you need. a16z cofounder Ben Horowitz shared a similar view in a 2014 Forbes interview: “Storytelling is the most underrated skill… Companies that don’t have a clearly articulated story don’t have a clear and well thought-out strategy. The company story is the company strategy.” He continues: “The story must explain at a fundamental level why you exist. Why does the world need your company? Why do we need to be doing what we’re doing and why is it important?… You can have a great product, but a compelling story puts the company into motion. If you don’t have a great story it’s hard to get people motivated to join you, to work on the product, and to get people to invest in the product.” This is the job of the founder and CEO: “The CEO must be the keeper of the story. The CEO is responsible for getting the story right, that it’s up to date, compelling, and can move the hearts of men and women. That’s the fundamental responsibility of the chief executive… The mistake people make is thinking the story is just about marketing. No, the story is the strategy. If you make your story better you make the strategy better.” Video source: Stanford Graduate School of Business (2010)

Startup Archive

258,577 views • 1 year ago

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 views • 1 year ago

I miss building simple, working software. At some point, we decided to complicate everything for no reason. Today, people can't build anything without using three frameworks, 17 libraries, and a swarm of microservices. And here is a funny paradox: To understand how these complex systems work, we've had to build systems and tools that generate data we can later analyze. But the more data we produce, the harder it is to process and make sense of it. We are in the middle of an observability crisis. The tools we have are inefficient, and we don't have enough people to keep systems running. A few weeks ago, I met the team Resolve AI, and they have built a fundamentally new approach to observability and incident management: Instead of depending on humans to run a system, Resolve built a Production Software Engineer who runs the system using AI while letting people supervise. And it's not only crazy, but I think this will fundamentally change how we monitor and maintain systems in production for years to come. I recorded a quick video to showcase a simple example of how Resolve works behind the scenes. There are two main things I'd like you to notice: 1. The tool can correlate data across logs, metrics, and traces coming from different systems. You don't have to do any work to get the information that matters right in front of you. 2. (This is the big one!) The tool can diagnose what's happening and give you instructions on how to solve it. It can produce causal relationships across the entire system stack. Resolve is backed by investors like Replit's founder Amjad Masad, Reid Hoffman, Jeff Dean, Fei Fei Li, Andy Price, among others. They are currently working with a select number of companies and want to onboard a few more. If you are interested in trying them out, go to this link: Honestly, this is one of the most impressive uses of AI I've seen.

I miss building simple, working software. At some point, we decided to complicate everything for no reason. Today, people can't build anything without using three frameworks, 17 libraries, and a swarm of microservices. And here is a funny paradox: To understand how these complex systems work, we've had to build systems and tools that generate data we can later analyze. But the more data we produce, the harder it is to process and make sense of it. We are in the middle of an observability crisis. The tools we have are inefficient, and we don't have enough people to keep systems running. A few weeks ago, I met the team Resolve AI, and they have built a fundamentally new approach to observability and incident management: Instead of depending on humans to run a system, Resolve built a Production Software Engineer who runs the system using AI while letting people supervise. And it's not only crazy, but I think this will fundamentally change how we monitor and maintain systems in production for years to come. I recorded a quick video to showcase a simple example of how Resolve works behind the scenes. There are two main things I'd like you to notice: 1. The tool can correlate data across logs, metrics, and traces coming from different systems. You don't have to do any work to get the information that matters right in front of you. 2. (This is the big one!) The tool can diagnose what's happening and give you instructions on how to solve it. It can produce causal relationships across the entire system stack. Resolve is backed by investors like Replit's founder Amjad Masad, Reid Hoffman, Jeff Dean, Fei Fei Li, Andy Price, among others. They are currently working with a select number of companies and want to onboard a few more. If you are interested in trying them out, go to this link: Honestly, this is one of the most impressive uses of AI I've seen.

Santiago

82,074 views • 2 years ago

Here is how you can install an open-source, enterprise-grade RAG system on your server (with the best document understanding I've seen.) First, something obvious to anyone trying to sell RAG in the market: You are crazy if you think companies will let their data travel to a hosted model. No one wants to send their data anywhere (those who do haven't found an alternative.) Every single company would rather have an air-gapped system with no internet access. GroundX is an open-source RAG system that you can run on your servers (or any cloud provider, as long as you have access to GPUs) and works without a network. (If the military wants to do RAG, this is precisely what they will be looking for.) I installed GroundX on my AWS account and recorded a video to show you how to use it. There are two services you can use: 1. Ingest: This service uses a pretrained vision model to ingest and understand your knowledge base. 2. Search: This service combines text and vector search with a fine-tuned re-ranker model to retrieve information from your knowledge base. A quick note about the Ingest service: 99% of people think they need better "retrieval" mechanisms. I think they need better "ingestion." That's where this service comes in! Ingest "understands" your documents in a way I haven't seen before. After you try it, you'll realize why showing your LLM your raw documents is a bad idea. In the video, I use a free tool called X-Ray to test a document and understand how the Ingest service breaks it down. You can access this tool by signing up for a free GroundX cloud account and uploading your documents. You'll see a bit more about this in the video.

Here is how you can install an open-source, enterprise-grade RAG system on your server (with the best document understanding I've seen.) First, something obvious to anyone trying to sell RAG in the market: You are crazy if you think companies will let their data travel to a hosted model. No one wants to send their data anywhere (those who do haven't found an alternative.) Every single company would rather have an air-gapped system with no internet access. GroundX is an open-source RAG system that you can run on your servers (or any cloud provider, as long as you have access to GPUs) and works without a network. (If the military wants to do RAG, this is precisely what they will be looking for.) I installed GroundX on my AWS account and recorded a video to show you how to use it. There are two services you can use: 1. Ingest: This service uses a pretrained vision model to ingest and understand your knowledge base. 2. Search: This service combines text and vector search with a fine-tuned re-ranker model to retrieve information from your knowledge base. A quick note about the Ingest service: 99% of people think they need better "retrieval" mechanisms. I think they need better "ingestion." That's where this service comes in! Ingest "understands" your documents in a way I haven't seen before. After you try it, you'll realize why showing your LLM your raw documents is a bad idea. In the video, I use a free tool called X-Ray to test a document and understand how the Ingest service breaks it down. You can access this tool by signing up for a free GroundX cloud account and uploading your documents. You'll see a bit more about this in the video.

Santiago

89,664 views • 1 year ago