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You see this SSRF attack yeah? Big companies with strong engineering teams like Capital One, GitHub, Microsoft, Alibaba, and others, have all suffered from it in the past. Once an attacker successfully exploits SSRF, they can pivot from the application into the underlying infrastructure, using credentials (keys, tokens, metadata...

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Ghost St Badmus

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92,636 views • 7 months ago •via X (Twitter)

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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!
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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

108,835 views • 2 years ago

I really want to show off the cutting edge tech the team is building and how we're turning AI image generation into real living environments using gaussian splats and at the :19 mark of this video you can see how we're blending splats together to create a real world. What this looks like from a gaming perspective is this all happens behind the players view to simulate a real world environment. As you move through the space, distance becomes a variable to consider on how images update and are experienced. We are creating a new reality in real time.
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I really want to show off the cutting edge tech the team is building and how we're turning AI image generation into real living environments using gaussian splats and at the :19 mark of this video you can see how we're blending splats together to create a real world. What this looks like from a gaming perspective is this all happens behind the players view to simulate a real world environment. As you move through the space, distance becomes a variable to consider on how images update and are experienced. We are creating a new reality in real time.

𝔹𝕃𝕍ℂ𝕂𝕃!𝔾ℍ𝕋

25,084 views • 2 months ago

This is a standard practice for almost all Tier-1 banking applications in Nigeria, and for some fintech applications I’ve previously performed pentests on. Client-side encryption isn’t a total waste, or a waste of compute, as some people have claimed, but rather a measure to protect against API tampering or API request/response manipulation between the client and the server when implemented properly. Even with HTTPS, attackers can capture a decrypted version of web or mobile API data in transit because the browser and the server establish a level of trust during the TLS handshake. Attackers can leverage this trust to capture & proxy already-decrypted traffic, tamper with it, and then forward it to the server. This allows them to override what the user interface or client is originally supposed to send and replace it with data of their choosing. That is why validation needs to be performed on both the client and the server side. To wrap up, encrypting API requests and responses makes it significantly harder for attackers to tamper with data, even if they capture the traffic, unless they have access to the encryption details (algorithm, encryption mode, key size, secret key, and initialization vector), assuming asymmetric encryption is used. In the demo below, you can see how I discovered additional parameters (balance, is_admin) in the API response, captured the registration API request, despite it being sent over HTTPS from the interface, added the discovered parameters, and successfully inflated my balance to 50 billion and also escalated my privileges to admin, and ultimately deleted the accounts of two live users/customers. In the second slide, I captured an API traffic of a bank app, and you can see how difficult the payloads are to read.
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This is a standard practice for almost all Tier-1 banking applications in Nigeria, and for some fintech applications I’ve previously performed pentests on. Client-side encryption isn’t a total waste, or a waste of compute, as some people have claimed, but rather a measure to protect against API tampering or API request/response manipulation between the client and the server when implemented properly. Even with HTTPS, attackers can capture a decrypted version of web or mobile API data in transit because the browser and the server establish a level of trust during the TLS handshake. Attackers can leverage this trust to capture & proxy already-decrypted traffic, tamper with it, and then forward it to the server. This allows them to override what the user interface or client is originally supposed to send and replace it with data of their choosing. That is why validation needs to be performed on both the client and the server side. To wrap up, encrypting API requests and responses makes it significantly harder for attackers to tamper with data, even if they capture the traffic, unless they have access to the encryption details (algorithm, encryption mode, key size, secret key, and initialization vector), assuming asymmetric encryption is used. In the demo below, you can see how I discovered additional parameters (balance, is_admin) in the API response, captured the registration API request, despite it being sent over HTTPS from the interface, added the discovered parameters, and successfully inflated my balance to 50 billion and also escalated my privileges to admin, and ultimately deleted the accounts of two live users/customers. In the second slide, I captured an API traffic of a bank app, and you can see how difficult the payloads are to read.

Ghost St Badmus

217,666 views • 8 months 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.
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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

🚨This is how you turn a crash into a great depression. This is a public bailout of the worst bubble in history. “It’s a concept out there that’s so much money and it’s so big… where the American public essentially becomes a partner with the companies… the American people can benefit from the success of AI and by doing that they’re going to like it better…”
0:54

🚨This is how you turn a crash into a great depression. This is a public bailout of the worst bubble in history. “It’s a concept out there that’s so much money and it’s so big… where the American public essentially becomes a partner with the companies… the American people can benefit from the success of AI and by doing that they’re going to like it better…”

Jim Stewartson, Decelerationist 🇨🇦🇺🇦🇺🇸

119,258 views • 2 months ago

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Seth Thompson

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How to Create Music That Matches Your Video's Sound with Suno Studio I made a quick video showing how I use Suno Studio to generate background music that fits the audio and mood of a video, similar to the example in the quoted post. This is the method I use at the moment but if you have a better approach, feel free to share it. Before starting, export the original audio from your video. I usually do this in CapCut. After generating a track you like in Suno Studio, you can import it back into CapCut and add it to your video. *This is not a paid partnership.

Kōda

37,597 views • 2 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.
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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

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English is the most powerful language in the world. MongoDB just added support for MCP, and literally anyone can now query data without knowing anything about databases. If you know English, you are all set. I recorded a quick video to show you how I connect to a sample Airbnb database and run different queries: • How many listings? • How many listings in the US? • What's the list of countries in these listings? It takes 10 seconds to configure Claude, Cursor, WindSurf, or any other AI assistant to connect to MongoDB's MCP Server. By the way, the server supports much more than queries: • You can explore your collections • You can fetch schema information • You can generate app code snippets • You can manage your databases • You can handle cluster/user/admin tasks Here is a link to the MCP server GitHub repository: This is huge for developers. It's even bigger for non-developers. Thanks to the MongoDB team for collaborating with me on this post.
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English is the most powerful language in the world. MongoDB just added support for MCP, and literally anyone can now query data without knowing anything about databases. If you know English, you are all set. I recorded a quick video to show you how I connect to a sample Airbnb database and run different queries: • How many listings? • How many listings in the US? • What's the list of countries in these listings? It takes 10 seconds to configure Claude, Cursor, WindSurf, or any other AI assistant to connect to MongoDB's MCP Server. By the way, the server supports much more than queries: • You can explore your collections • You can fetch schema information • You can generate app code snippets • You can manage your databases • You can handle cluster/user/admin tasks Here is a link to the MCP server GitHub repository: This is huge for developers. It's even bigger for non-developers. Thanks to the MongoDB team for collaborating with me on this post.

Santiago

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I was just messing around with the animation, then I noticed that the swimming animation would leave the ripple effect behind, and you can change the size of it, too And of course, you can do this even on a land surface (Imagine a scene with you standing on land, but have a water ripple effect, something like that) Also, in this video, I just learn how to use the sky environment It's really helped me a lot to find the perfect cloud and sky color And I'm really proud of what the final picture has turned into #GPOSER #GPOSERS
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I was just messing around with the animation, then I noticed that the swimming animation would leave the ripple effect behind, and you can change the size of it, too And of course, you can do this even on a land surface (Imagine a scene with you standing on land, but have a water ripple effect, something like that) Also, in this video, I just learn how to use the sky environment It's really helped me a lot to find the perfect cloud and sky color And I'm really proud of what the final picture has turned into #GPOSER #GPOSERS

Lightny Lightningstorm

12,525 views • 6 months 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.
8:15

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

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. ​ Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. ​ If you are a Python developer, there is an open-source alternative! ​ Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. ​ Quix Streams is all Python, and it's open-source! ​ The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. ​ Here is what the video covers: ​ • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) ​ • How to efficiently stream that data into Kafka ​ • How to optimize Kafka producer settings like batch size and compression for maximum throughput ​ A few other notes about Quix Streams: ​ • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity ​ You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. ​ Thanks to the Quix team for collaborating with me on this post.
27:43

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. ​ Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. ​ If you are a Python developer, there is an open-source alternative! ​ Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. ​ Quix Streams is all Python, and it's open-source! ​ The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. ​ Here is what the video covers: ​ • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) ​ • How to efficiently stream that data into Kafka ​ • How to optimize Kafka producer settings like batch size and compression for maximum throughput ​ A few other notes about Quix Streams: ​ • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity ​ You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. ​ Thanks to the Quix team for collaborating with me on this post.

Santiago

89,230 views • 2 years ago

Why aren't more people talking about how difficult it is to turn documents into structured data? This is literally a problem that every single company I talk to is trying to solve. They have a buttload of documents with forms and tables, and they want to turn them into usable data. Let me show you how you can do this using Tensorlake. Probably the best parser I've found:
6:41

Why aren't more people talking about how difficult it is to turn documents into structured data? This is literally a problem that every single company I talk to is trying to solve. They have a buttload of documents with forms and tables, and they want to turn them into usable data. Let me show you how you can do this using Tensorlake. Probably the best parser I've found:

Santiago

46,413 views • 1 year ago

Lightspeed's Bucky Moore says the real opportunity in the AI app layer is in large industries far enough afield from where the model providers are today — and where the context engineering to get customer data into the model is extremely nuanced and messy. "I think this is kind of the elephant in the room right now — whether post-training open-source models combined with the unique user feedback you get from being an application provider is defensible enough." "That is going to be an inevitable challenge for any of these industries that hit a maturation point of AI adoption, like legal and software engineering have." "But on the other hand, there are some industries where they're very large, they're far enough afield from where the model providers are today — and probably will continue to be — and the context engineering to actually get the customer data into the model is just so messy. It requires going across different business functions, it requires a lot of hands-on forward-deployed engineering." "Those are the kind of companies that we get really excited about. Because I think being really good at that is not only defensible, but it also allows you to generate a feedback loop with your customers, where you hear a lot of their secrets. And those secrets allow you to feed that back into how you make your product better at the expense of anyone else playing in the space. Because if you're serving the customer, they're only serving you those secrets." "I think Palantir is a good example of this in the pre-AI era, and I think we're going to see many companies ascend in that same way."
2:28

Lightspeed's Bucky Moore says the real opportunity in the AI app layer is in large industries far enough afield from where the model providers are today — and where the context engineering to get customer data into the model is extremely nuanced and messy. "I think this is kind of the elephant in the room right now — whether post-training open-source models combined with the unique user feedback you get from being an application provider is defensible enough." "That is going to be an inevitable challenge for any of these industries that hit a maturation point of AI adoption, like legal and software engineering have." "But on the other hand, there are some industries where they're very large, they're far enough afield from where the model providers are today — and probably will continue to be — and the context engineering to actually get the customer data into the model is just so messy. It requires going across different business functions, it requires a lot of hands-on forward-deployed engineering." "Those are the kind of companies that we get really excited about. Because I think being really good at that is not only defensible, but it also allows you to generate a feedback loop with your customers, where you hear a lot of their secrets. And those secrets allow you to feed that back into how you make your product better at the expense of anyone else playing in the space. Because if you're serving the customer, they're only serving you those secrets." "I think Palantir is a good example of this in the pre-AI era, and I think we're going to see many companies ascend in that same way."

TBPN

46,862 views • 4 months ago

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.
4:50

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

Santiago

384,510 views • 3 years ago

How I have Claude automate anything I do on my computer just by showing it: I use the free Clips app and hit record to start recording. Then I just do the action - like navigating to Rippling and approving any unapproved time off - and I’ll often just talk out loud as I do it explaining any add’l details like I would when showing a human. The Clips app captures everything on your screen and everything you say, and transforms it into a format Claude can “see and hear”. You'll then get a link that you can easily share with Claude (or humans too). I’ll paste it to Claude and say something like, "automate this for me every day at 8:00 AM." The link has special metadata that agents like Claude can discover. It exposes APIs to see the full transcript with timestamps and grab screenshots of what was on your screen at any of those timestamps. Once it's created the routine, you can go to the Routines tab in Claude Code desktop app. This also works with the CLI, Claude Cowork, Codex etc. You’ll see your new routine there, and you can just hit Run Now to have it start running it. Video attached shows this step by step. The Clips apps is free and open source - I’ll link to it and the source code in the thread.
1:54

How I have Claude automate anything I do on my computer just by showing it: I use the free Clips app and hit record to start recording. Then I just do the action - like navigating to Rippling and approving any unapproved time off - and I’ll often just talk out loud as I do it explaining any add’l details like I would when showing a human. The Clips app captures everything on your screen and everything you say, and transforms it into a format Claude can “see and hear”. You'll then get a link that you can easily share with Claude (or humans too). I’ll paste it to Claude and say something like, "automate this for me every day at 8:00 AM." The link has special metadata that agents like Claude can discover. It exposes APIs to see the full transcript with timestamps and grab screenshots of what was on your screen at any of those timestamps. Once it's created the routine, you can go to the Routines tab in Claude Code desktop app. This also works with the CLI, Claude Cowork, Codex etc. You’ll see your new routine there, and you can just hit Run Now to have it start running it. Video attached shows this step by step. The Clips apps is free and open source - I’ll link to it and the source code in the thread.

Steve (Builder.io)

25,473 views • 1 month ago

Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?
0:51

Here's how I would learn data engineering basics in 2025: - Find a data source you care about (examples: gaming APIs, stock market, web scraping, etc) - Use Python to interact and ingest your source. Initially just write the data to a CSV. - Setup an account with Snowflake or Google BigQuery. - update your Python script to load a table in Snowflake/BigQuery - schedule your script with CRON in the cloud with a service like Heroku. - build aggregations and visualizations on top of your ingested data Only thing this misses is data quality and complex job orchestration which you can learn later! How would you learn data engineering nowadays?

Zach Wilson

20,368 views • 1 year ago

GPT-4o is bad at processing PDF documents. Whoever tells you otherwise is not living in the real world. In 2024, people fill out forms using pen and paper. Try to answer questions from those forms using modern models, and you'll be disappointed. I recorded a video to show you how to fix this. The answer is simple: stop letting the model see your PDF document. Instead, preprocess it and stick to showing the model text. Watch the video. You'll go from "THIS IS CRAP" to "EXCELLENT" in no time. I'm using Unstract to turn the documents into text while keeping the original format (this is crucial!) You can use them to process up to 100 pages for free. They collaborated with me on this post. You can find the code I wrote right here:
6:27

GPT-4o is bad at processing PDF documents. Whoever tells you otherwise is not living in the real world. In 2024, people fill out forms using pen and paper. Try to answer questions from those forms using modern models, and you'll be disappointed. I recorded a video to show you how to fix this. The answer is simple: stop letting the model see your PDF document. Instead, preprocess it and stick to showing the model text. Watch the video. You'll go from "THIS IS CRAP" to "EXCELLENT" in no time. I'm using Unstract to turn the documents into text while keeping the original format (this is crucial!) You can use them to process up to 100 pages for free. They collaborated with me on this post. You can find the code I wrote right here:

Santiago

196,847 views • 1 year ago