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+data/out/_86tEHCyMsY_segment_0000.wav|Hamner|EN|While recently learning about CubesOS, I was amazed at how containerization of apps, files, and workflows could add in so much extra security.
+data/out/_86tEHCyMsY_segment_0001.wav|Hamner|EN|If you don't know, CubesOS is an OS where a VM called DomZero runs a GUI and can start apps from other VMs.
+data/out/_86tEHCyMsY_segment_0002.wav|Hamner|EN|You control which VMs have access to the internet, and they all have their own files.
+data/out/_86tEHCyMsY_segment_0003.wav|Hamner|EN|Anyway, it's a cool OS that uses lots of VMs.
+data/out/_86tEHCyMsY_segment_0004.wav|Hamner|EN|Just because I like messing around with containers and graphical environments, I set out to create
+data/out/_86tEHCyMsY_segment_0005.wav|Hamner|EN|a similar setup for the Librem 5.
+data/out/_86tEHCyMsY_segment_0006.wav|Hamner|EN|Cube's OS uses a base OS of Fedora, but I'll just use PureOS as a base and build on top
+data/out/_86tEHCyMsY_segment_0007.wav|Hamner|EN|of that.
+data/out/_86tEHCyMsY_segment_0008.wav|Hamner|EN|This script checks you're running as root, then runs a Debian build script.
+data/out/_86tEHCyMsY_segment_0009.wav|Hamner|EN|This builds a Debian 11 chu root and installs fosh.
+data/out/_86tEHCyMsY_segment_0010.wav|Hamner|EN|It's then copied to var lib container. The script finishes
+data/out/_86tEHCyMsY_segment_0011.wav|Hamner|EN|by setting up the systemd and spawn files. With all that setup, we can start our half-baked DOM0
+data/out/_86tEHCyMsY_segment_0012.wav|Hamner|EN|as an app in our normal fosh session. This kind of nesting is super cool.
+data/out/_86tEHCyMsY_segment_0013.wav|Hamner|EN|It seems to be using the x-wayland window to display the second pha session. Okay, but the base OS isn't really meant to do anything except run VMs.
+data/out/_86tEHCyMsY_segment_0014.wav|Hamner|EN|The GUI is supposed to be run inside of DOM0.
+data/out/_86tEHCyMsY_segment_0015.wav|Hamner|EN|While this is kind of cheating, it was pretty simple just to edit the userbin pha script.
+data/out/_86tEHCyMsY_segment_0016.wav|Hamner|EN|With a simple edit and the systemctl command, the GUI is off but foc is running.
+data/out/_86tEHCyMsY_segment_0017.wav|Hamner|EN|From within the DOM0 container, this is all that's needed to start the foc session.
+data/out/_86tEHCyMsY_segment_0018.wav|Hamner|EN|It does display a mouse cursor, and you have to disable docked mode in order to get the
+data/out/_86tEHCyMsY_segment_0019.wav|Hamner|EN|on-screen keyboard icon to appear.
+data/out/_86tEHCyMsY_segment_0020.wav|Hamner|EN|Still, this is really cool.
+data/out/_86tEHCyMsY_segment_0021.wav|Hamner|EN|While this is a far cry from the advanced CubesOS DOM0, it's a start that could be built
+data/out/_86tEHCyMsY_segment_0022.wav|Hamner|EN|up on to add a similar security model that cubesOS has.
+data/out/_86tEHCyMsY_segment_0023.wav|Hamner|EN|All of this is GPL and simple code.
+data/out/_86tEHCyMsY_segment_0024.wav|Hamner|EN|If you have use of setting up an end spawn container, or if you need a Debian build script,
+data/out/_86tEHCyMsY_segment_0025.wav|Hamner|EN|this is a decent project to dig into.
+data/out/_86tEHCyMsY_segment_0026.wav|Hamner|EN|Thanks for watching, bye.
+data/out/OnOUb2RMOa8_segment_0000.wav|Hamner|EN|Hello everyone, David Hamner here and today I'm excited to share with you the journey of
+data/out/OnOUb2RMOa8_segment_0001.wav|Hamner|EN|developing VertWorker, a powerful framework for managing large language models. Let's dive into
+data/out/OnOUb2RMOa8_segment_0002.wav|Hamner|EN|how this project evolved and what it can do. It all started with VertWorkforce, a visual workflow
+data/out/OnOUb2RMOa8_segment_0003.wav|Hamner|EN|editor for AI models. While it was great for those just starting to play with AI, I quickly realized
+data/out/OnOUb2RMOa8_segment_0004.wav|Hamner|EN|the limitations. As the project grew more complex, the visual interface became cluttered. Even a little bit of logic resulted in a bunch of interconnected nodes, making it a challenge
+data/out/OnOUb2RMOa8_segment_0005.wav|Hamner|EN|to scale it up. To address these issues, I went back to the drawing board. The result,
+data/out/OnOUb2RMOa8_segment_0006.wav|Hamner|EN|VertWorker, a Python library and AI install scripts that maintain the same core philosophy
+data/out/OnOUb2RMOa8_segment_0007.wav|Hamner|EN|of modular, chainable LLM operations, but with much greater flexibility and power.
+data/out/OnOUb2RMOa8_segment_0008.wav|Hamner|EN|As you can see, VertWorker allows you to create complex workflows with just a few lines of code. chainable LLM operations, but with much greater flexibility and power.
+data/out/OnOUb2RMOa8_segment_0009.wav|Hamner|EN|As you can see, VertWorker allows you to create complex workflows with just a few lines of
+data/out/OnOUb2RMOa8_segment_0010.wav|Hamner|EN|code.
+data/out/OnOUb2RMOa8_segment_0011.wav|Hamner|EN|No more tangled webs of visual nodes.
+data/out/OnOUb2RMOa8_segment_0012.wav|Hamner|EN|It also turns out AI is a lot better at generating these AI scripts than it would be at creating
+data/out/OnOUb2RMOa8_segment_0013.wav|Hamner|EN|YAML or XML for a visual interface.
+data/out/OnOUb2RMOa8_segment_0014.wav|Hamner|EN|This framework is incredibly powerful.
+data/out/OnOUb2RMOa8_segment_0015.wav|Hamner|EN|You can use it to create short stories, process
+data/out/OnOUb2RMOa8_segment_0016.wav|Hamner|EN|logs, or even build a text adventure. As it's rather interesting, let's dive into how an LLM
+data/out/OnOUb2RMOa8_segment_0017.wav|Hamner|EN|powered text adventure game would work using this framework. Imagine a text adventure game where
+data/out/OnOUb2RMOa8_segment_0018.wav|Hamner|EN|you're not limited by pre-programmed options. You can explore, examine objects in detail,
+data/out/OnOUb2RMOa8_segment_0019.wav|Hamner|EN|or take actions that aren't explicitly defined. It's like having a dungeon master that
+data/out/OnOUb2RMOa8_segment_0020.wav|Hamner|EN|can adapt to any situation you throw at it. Of course, there are some limitations that have
+data/out/OnOUb2RMOa8_segment_0021.wav|Hamner|EN|added. For example, if you try to fly within the world, the script will come back and tell you
+data/out/OnOUb2RMOa8_segment_0022.wav|Hamner|EN|that you don't have anything to fly with. The very first version of this had no such catch and would
+data/out/OnOUb2RMOa8_segment_0023.wav|Hamner|EN|allow you to turn into a bird and start flying willy-nilly, kind of making it more like a lucid dream simulator. A lot of the credit for
+data/out/OnOUb2RMOa8_segment_0024.wav|Hamner|EN|quality here goes to Llama 3.1. Giant thank you to the meta team for
+data/out/OnOUb2RMOa8_segment_0025.wav|Hamner|EN|releasing that as open source. I'm continually using and improving Vert
+data/out/OnOUb2RMOa8_segment_0026.wav|Hamner|EN|Worker. Right now there are text-based nodes and text-to-voice nodes, but I want
+data/out/OnOUb2RMOa8_segment_0027.wav|Hamner|EN|to add in a bunch of other types of nodes. You can find the source code on my github
+data/out/OnOUb2RMOa8_segment_0028.wav|Hamner|EN|and thanks for watching. Bye!
+data/out/kx6tMCeUmaY_segment_0000.wav|Hamner|EN|Hi everyone, today I'm excited to share the progress of a falling sand game powered by neural nets.
+data/out/kx6tMCeUmaY_segment_0001.wav|Hamner|EN|Let's dive into the evolution of this project and the fascinating challenges I've encountered along the way.
+data/out/kx6tMCeUmaY_segment_0002.wav|Hamner|EN|My journey begins with an ambitious idea inspired by a paper about using stable diffusion to run Doom.
+data/out/kx6tMCeUmaY_segment_0003.wav|Hamner|EN|Intrigued, I thought I might be able to do something similar, but scaled down with my RTX 3090.
+data/out/kx6tMCeUmaY_segment_0004.wav|Hamner|EN|After trying stable diffusion for myself, the results were
+data/out/kx6tMCeUmaY_segment_0005.wav|Hamner|EN|interesting. While I'm sure you could coax something meaningful out of this approach,
+data/out/kx6tMCeUmaY_segment_0006.wav|Hamner|EN|doing so with 24 gigs of VRAM proved to be quite the challenge.
+data/out/kx6tMCeUmaY_segment_0007.wav|Hamner|EN|Undeterred, I decided to pivot and use a more tailored approach.
+data/out/kx6tMCeUmaY_segment_0008.wav|Hamner|EN|Enter our first convolutional neural network model.
+data/out/kx6tMCeUmaY_segment_0009.wav|Hamner|EN|This model uses convolutional layers with batch normalization. It takes in seven
+data/out/kx6tMCeUmaY_segment_0010.wav|Hamner|EN|channels, one for each element, and outputs predictions for the next state of each cell.
+data/out/kx6tMCeUmaY_segment_0011.wav|Hamner|EN|This version showed great promise. It had a basic set of elements, sand, water, plant, wood, acid,
+data/out/kx6tMCeUmaY_segment_0012.wav|Hamner|EN|and fire, and acted somewhat realistically. However, it did have its quirks
+data/out/kx6tMCeUmaY_segment_0013.wav|Hamner|EN|Liquids were a bit chunky, shall we say
+data/out/kx6tMCeUmaY_segment_0014.wav|Hamner|EN|With a working proof of concept, I decided to up the ante
+data/out/kx6tMCeUmaY_segment_0015.wav|Hamner|EN|Enter the improved sand model
+data/out/kx6tMCeUmaY_segment_0016.wav|Hamner|EN|This model introduces several improvements
+data/out/kx6tMCeUmaY_segment_0017.wav|Hamner|EN|Increased input channels to 14 as we added more element types
+data/out/kx6tMCeUmaY_segment_0018.wav|Hamner|EN|Used a larger initial kernel size of 5x5s to capture more context
+data/out/kx6tMCeUmaY_segment_0019.wav|Hamner|EN|Implemented a bottleneck architecture expanding and then contracting the number of channels
+data/out/kx6tMCeUmaY_segment_0020.wav|Hamner|EN|The results we hit a smooth 60 frames per second and saw improved interaction between elements
+data/out/kx6tMCeUmaY_segment_0021.wav|Hamner|EN|However, we faced some challenges with time dependent phenomenon like fire fading over time
+data/out/kx6tMCeUmaY_segment_0022.wav|Hamner|EN|There's also this strange waterfall effect that's been happening where the bottom part of your drawings fall faster than the
+data/out/kx6tMCeUmaY_segment_0023.wav|Hamner|EN|top part. Sometimes less is more, and our next iteration, Simple Sand Model, took that to heart.
+data/out/kx6tMCeUmaY_segment_0024.wav|Hamner|EN|This only uses three convolutional layers, keeps a consistent hidden size throughout,
+data/out/kx6tMCeUmaY_segment_0025.wav|Hamner|EN|removes batch normalization layers, but the big kicker
+data/out/kx6tMCeUmaY_segment_0026.wav|Hamner|EN|is it only uses the training set data that has the highest movement. And wow, did that deliver.
+data/out/kx6tMCeUmaY_segment_0027.wav|Hamner|EN|We're still getting our 60 frames per second and we're seeing much improved behavior with temporal
+data/out/kx6tMCeUmaY_segment_0028.wav|Hamner|EN|elements like fire. Fire now fades naturally over time, creating a much more realistic effect.
+data/out/kx6tMCeUmaY_segment_0029.wav|Hamner|EN|Overall, this version feels much more natural and responsive Capturing the dynamic nature of our falling sand world
+data/out/kx6tMCeUmaY_segment_0030.wav|Hamner|EN|But we still have the water falling issue and some elements don't work right
+data/out/kx6tMCeUmaY_segment_0031.wav|Hamner|EN|Looking into the future, I'm excited to try out curriculum learning
+data/out/kx6tMCeUmaY_segment_0032.wav|Hamner|EN|The plan is to gather data for each element individually
+data/out/kx6tMCeUmaY_segment_0033.wav|Hamner|EN|Then in pairs, and then finally all together
+data/out/kx6tMCeUmaY_segment_0034.wav|Hamner|EN|We'll also use three frames to try to give the model more time context
+data/out/kx6tMCeUmaY_segment_0035.wav|Hamner|EN|During training, we'll start with the simple scenarios and work our way up.
+data/out/kx6tMCeUmaY_segment_0036.wav|Hamner|EN|It's a work in progress, but I have high hopes for this version.
+data/out/kx6tMCeUmaY_segment_0037.wav|Hamner|EN|The most exciting takeaway for this journey is that we've basically created an accelerator for a Falling Sand game.
+data/out/kx6tMCeUmaY_segment_0038.wav|Hamner|EN|Traditional versions of the Falling Sand game tend to slow down the more elements and more interactions that are on screen.
+data/out/kx6tMCeUmaY_segment_0039.wav|Hamner|EN|But the AI version doesn't have this drawback.
+data/out/kx6tMCeUmaY_segment_0040.wav|Hamner|EN|You can have a full or empty screen with as many interactions as you want, and it'll
+data/out/kx6tMCeUmaY_segment_0041.wav|Hamner|EN|run at the same frame rate.
+data/out/kx6tMCeUmaY_segment_0042.wav|Hamner|EN|All the source code for each of these versions is available on my GitHub.
+data/out/kx6tMCeUmaY_segment_0043.wav|Hamner|EN|Feel free to play with each version or mess around with the model architecture.
+data/out/kx6tMCeUmaY_segment_0044.wav|Hamner|EN|Thanks for watching.
+data/out/DI4VYUv4vFQ_segment_0000.wav|Hamner|EN|Welcome tech enthusiasts, David Hamner here and today I want to show you a project that's set to change the way we interact with our command line interfaces
+data/out/DI4VYUv4vFQ_segment_0001.wav|Hamner|EN|Say hello to the AI powered Terminal Assistant
+data/out/DI4VYUv4vFQ_segment_0002.wav|Hamner|EN|This intelligent assistant interprets natural language queries and executes commands
+data/out/DI4VYUv4vFQ_segment_0003.wav|Hamner|EN|Making terminal interactions more intuitive and user friendly
+data/out/DI4VYUv4vFQ_segment_0004.wav|Hamner|EN|Let's take this thing for a spin
+data/out/DI4VYUv4vFQ_segment_0005.wav|Hamner|EN|First, let's see what's in our current directory
+data/out/DI4VYUv4vFQ_segment_0006.wav|Hamner|EN|Notice how we didn't have to type in
+data/out/DI4VYUv4vFQ_segment_0007.wav|Hamner|EN|the ls command. The assistant understood the natural language query and executed the appropriate
+data/out/DI4VYUv4vFQ_segment_0008.wav|Hamner|EN|command. Next, we ask it to display the license file. The assistant correctly used the cat command
+data/out/DI4VYUv4vFQ_segment_0009.wav|Hamner|EN|to show us the content. Now, let's create a directory and navigate into it. Notice how
+data/out/DI4VYUv4vFQ_segment_0010.wav|Hamner|EN|seamlessly the AI was able to understand the natural language queries and understand the context of the previous command.
+data/out/DI4VYUv4vFQ_segment_0011.wav|Hamner|EN|You can also ask it to create multiple files at once without the need for complex syntax.
+data/out/DI4VYUv4vFQ_segment_0012.wav|Hamner|EN|Navigating back to the previous folder and searching for text files is just as easy.
+data/out/DI4VYUv4vFQ_segment_0013.wav|Hamner|EN|The assistant translated our queries into the correct cd and find commands.
+data/out/DI4VYUv4vFQ_segment_0014.wav|Hamner|EN|But it's not just about file management, our AI assistant can also retrieve
+data/out/DI4VYUv4vFQ_segment_0015.wav|Hamner|EN|system information. Oops, looks like we hit a small snag here. It interpreted our query a bit
+data/out/DI4VYUv4vFQ_segment_0016.wav|Hamner|EN|too literally. But don't worry, that's the beauty of natural language processing. We can simply
+data/out/DI4VYUv4vFQ_segment_0017.wav|Hamner|EN|rephrase our command. There we go. By asking how big is the disk, we got the information we were
+data/out/DI4VYUv4vFQ_segment_0018.wav|Hamner|EN|looking for. This shows the flexibility of our AI assistant in handling different phrasings.
+data/out/DI4VYUv4vFQ_segment_0019.wav|Hamner|EN|The assistant can even help with simple coding tasks.
+data/out/DI4VYUv4vFQ_segment_0020.wav|Hamner|EN|Look how easily it creates a Python script and executes it.
+data/out/DI4VYUv4vFQ_segment_0021.wav|Hamner|EN|When we encounter an error, the AI assistant doesn't just give up, but instead it provides
+data/out/DI4VYUv4vFQ_segment_0022.wav|Hamner|EN|debugging information and alternative commands.
+data/out/DI4VYUv4vFQ_segment_0023.wav|Hamner|EN|That's the power of AI in action. As you can see, this AI
+data/out/DI4VYUv4vFQ_segment_0024.wav|Hamner|EN|powered terminal assistant makes command line interfaces more accessible and efficient. It's
+data/out/DI4VYUv4vFQ_segment_0025.wav|Hamner|EN|perfect for beginners learning the ropes of terminal commands and it can speed up workflows
+data/out/DI4VYUv4vFQ_segment_0026.wav|Hamner|EN|for experienced users too. Whether you're managing files, retrieving system information, or even
+data/out/DI4VYUv4vFQ_segment_0027.wav|Hamner|EN|coding, this AI assistant's got your back. It's not just about executing commands, it's about understanding your intent and providing
+data/out/DI4VYUv4vFQ_segment_0028.wav|Hamner|EN|helpful feedback.
+data/out/DI4VYUv4vFQ_segment_0029.wav|Hamner|EN|As always, this is Open Source and the links are in the description.
+data/out/DI4VYUv4vFQ_segment_0030.wav|Hamner|EN|Thanks for watching, bye!
+data/out/VRh6cQM8Kio_segment_0000.wav|Hamner|EN|I tested three open source game engines.
+data/out/VRh6cQM8Kio_segment_0001.wav|Hamner|EN|Here's what I learned from the fastest option to the option with the best graphics.
+data/out/VRh6cQM8Kio_segment_0002.wav|Hamner|EN|In Armory 3D, UpBlender Game Engine, and Godot,
+data/out/VRh6cQM8Kio_segment_0003.wav|Hamner|EN|I created roughly the same 3D game based on the gameplay of Baldr 2D.
+data/out/VRh6cQM8Kio_segment_0004.wav|Hamner|EN|Baldr 2D is a simple zero-g game where you can grab walls, jump off walls, and shoot.
+data/out/VRh6cQM8Kio_segment_0005.wav|Hamner|EN|Ironically, Baldr was originally going to be 3D before the dev scaled it down to be a 2D
+data/out/VRh6cQM8Kio_segment_0006.wav|Hamner|EN|game. Armory 3D was the first game engine I really took the time to learn. As a developer who writes
+data/out/VRh6cQM8Kio_segment_0007.wav|Hamner|EN|mostly Python code, the node-based logic was a big turnoff for me. I always considered any graphical
+data/out/VRh6cQM8Kio_segment_0008.wav|Hamner|EN|language to be less powerful or not worth my time. After learning Armory, I was proved wrong.
+data/out/VRh6cQM8Kio_segment_0009.wav|Hamner|EN|It's actually a very powerful, if a bit clumsy, programming format. Once you understand what the
+data/out/VRh6cQM8Kio_segment_0010.wav|Hamner|EN|nodes do, wiping out a bit of game logic with just a few nodes can be very fun. What ultimately
+data/out/VRh6cQM8Kio_segment_0011.wav|Hamner|EN|drove me away from Armory was the bugs. One such bug is I can't seem to get sound working without
+data/out/VRh6cQM8Kio_segment_0012.wav|Hamner|EN|resorting to building a full release of the game, but when you do build the game, the performance is excellent. I mean, just look at this gameplay.
+data/out/VRh6cQM8Kio_segment_0013.wav|Hamner|EN|Here's 100 enemies, and Armory has no issue with all those AIs and laser balls. 200 is too much,
+data/out/VRh6cQM8Kio_segment_0014.wav|Hamner|EN|but I bet you could get even more with a bit of tuning. The graphics are okay, but they're not
+data/out/VRh6cQM8Kio_segment_0015.wav|Hamner|EN|as good as other engines I tested. Models inside of Blender tended to look a lot different in-game, resulting in a lot of time tuning.
+data/out/VRh6cQM8Kio_segment_0016.wav|Hamner|EN|Armory 3D has the best set of exportable platforms.
+data/out/VRh6cQM8Kio_segment_0017.wav|Hamner|EN|From the web, to console, to mobile, this has you set up.
+data/out/VRh6cQM8Kio_segment_0018.wav|Hamner|EN|Next, I gave the UpBlender game engine a go.
+data/out/VRh6cQM8Kio_segment_0019.wav|Hamner|EN|I decided to use logic nodes inside of UpBlender game Engine because they were similar to Armory with one massive advantage.
+data/out/VRh6cQM8Kio_segment_0020.wav|Hamner|EN|These nodes can execute Python scripts that can do anything within your game.
+data/out/VRh6cQM8Kio_segment_0021.wav|Hamner|EN|One downside to the UpBlender Game Engine nodes is Logic seems to be forced to be handled per frame rather than at a trigger.
+data/out/VRh6cQM8Kio_segment_0022.wav|Hamner|EN|Compared to Armory 3D, the nodes are clumsy and a bit harder to use, but they're still effective.
+data/out/VRh6cQM8Kio_segment_0023.wav|Hamner|EN|Another big advantage to UpBlender Game Engine is the ability to use geometry nodes.
+data/out/VRh6cQM8Kio_segment_0024.wav|Hamner|EN|These can come in handy for things like terrain generation.
+data/out/VRh6cQM8Kio_segment_0025.wav|Hamner|EN|Performance seems to be completely usable for smaller numbers,
+data/out/VRh6cQM8Kio_segment_0026.wav|Hamner|EN|but at around 30 AIs, the game really starts to struggle.
+data/out/VRh6cQM8Kio_segment_0027.wav|Hamner|EN|Though bits could be redone in a more efficient
+data/out/VRh6cQM8Kio_segment_0028.wav|Hamner|EN|way, the performance is what drove me to look for something else.
+data/out/VRh6cQM8Kio_segment_0029.wav|Hamner|EN|Because this is built directly into Blender and utilizes the Blender rendering framework,
+data/out/VRh6cQM8Kio_segment_0030.wav|Hamner|EN|the games look just how you made them modeling. The graphics in UpBlender GameEngine is the
+data/out/VRh6cQM8Kio_segment_0031.wav|Hamner|EN|best of all three game engines I tested. The big downside is the complete lack of 2D tools
+data/out/VRh6cQM8Kio_segment_0032.wav|Hamner|EN|like basic buttons and menus. In other
+data/out/VRh6cQM8Kio_segment_0033.wav|Hamner|EN|words, you have to use 3D assets to build out your menus. Exporting is limited. You can run your games
+data/out/VRh6cQM8Kio_segment_0034.wav|Hamner|EN|on Mac, Windows, and Linux. Next, I tested Godot. My main reason for avoiding Godot was because of
+data/out/VRh6cQM8Kio_segment_0035.wav|Hamner|EN|the use of GDScript. I wanted to write Python code or no deal. Fortunately, working with the other game engines broke me of my stubbornness.
+data/out/VRh6cQM8Kio_segment_0036.wav|Hamner|EN|So I swallowed the GDScript shaped pill.
+data/out/VRh6cQM8Kio_segment_0037.wav|Hamner|EN|After using GDScript, I realized why the developers of Godot made it instead of using a pre-built
+data/out/VRh6cQM8Kio_segment_0038.wav|Hamner|EN|option like Python or Lua.
+data/out/VRh6cQM8Kio_segment_0039.wav|Hamner|EN|GDScript is built for games.
+data/out/VRh6cQM8Kio_segment_0040.wav|Hamner|EN|Vectors, colors, and game objects are first class.
+data/out/VRh6cQM8Kio_segment_0041.wav|Hamner|EN|While everything is still object oriented, the keyword self is nowhere to be seen as
+data/out/VRh6cQM8Kio_segment_0042.wav|Hamner|EN|it's implied by being attached to a game object.
+data/out/VRh6cQM8Kio_segment_0043.wav|Hamner|EN|GDScript is just like Python if Python were rewritten for game dev.
+data/out/VRh6cQM8Kio_segment_0044.wav|Hamner|EN|Godot was the fastest of all the engines I tested.
+data/out/VRh6cQM8Kio_segment_0045.wav|Hamner|EN|Buttery smooth gameplay with normal numbers of AIs and it can handle 100 AIs just fine.
+data/out/VRh6cQM8Kio_segment_0046.wav|Hamner|EN|Like with Armory, 200 was about the limit.
+data/out/VRh6cQM8Kio_segment_0047.wav|Hamner|EN|I already have a few ideas on how to limit the number of active script, which could increase
+data/out/VRh6cQM8Kio_segment_0048.wav|Hamner|EN|the AI count a lot.
+data/out/VRh6cQM8Kio_segment_0049.wav|Hamner|EN|Graphics are on par with UpBlender GameEngine, but with a disadvantage of things looking
+data/out/VRh6cQM8Kio_segment_0050.wav|Hamner|EN|slightly different when imported from other tools like Blender.
+data/out/VRh6cQM8Kio_segment_0051.wav|Hamner|EN|Godot lets you export to Mac, Windows, Linux, mobile, and the web. Better
+data/out/VRh6cQM8Kio_segment_0052.wav|Hamner|EN|than UpBlender Game Engine, but not as good as Armory 3D. Overall, I had a lot of fun learning
+data/out/VRh6cQM8Kio_segment_0053.wav|Hamner|EN|these engines. Armory is a good starting place and has excellent node logic, perfect for someone
+data/out/VRh6cQM8Kio_segment_0054.wav|Hamner|EN|hesitant to dive into programming but still wanting to learn game dev. Armory also has a
+data/out/VRh6cQM8Kio_segment_0055.wav|Hamner|EN|very good set of export platforms. The UpBlender game engine has all the
+data/out/VRh6cQM8Kio_segment_0056.wav|Hamner|EN|power of Blender and Python at your fingertips. Perfect for a Python fanatic like I used to be.
+data/out/VRh6cQM8Kio_segment_0057.wav|Hamner|EN|Godot is well-rounded with decent performance. Perfect for making bigger games. Godot also has
+data/out/VRh6cQM8Kio_segment_0058.wav|Hamner|EN|a lot of community plugins, some of which are crazy amazing.
+data/out/73yowNLO3Y8_segment_0000.wav|Hamner|EN|Hi everyone, David Hamner here, and today I'm excited to introduce VIRT Workforce,
+data/out/73yowNLO3Y8_segment_0001.wav|Hamner|EN|a virtual workforce editor for large language models that I've been developing.
+data/out/73yowNLO3Y8_segment_0002.wav|Hamner|EN|This tool aims to make AI more accessible and easier to use, even for those without
+data/out/73yowNLO3Y8_segment_0003.wav|Hamner|EN|extensive coding experience.
+data/out/73yowNLO3Y8_segment_0004.wav|Hamner|EN|Now let me walk you through some examples of what virtual workforce can do.
+data/out/73yowNLO3Y8_segment_0005.wav|Hamner|EN|Let's start with a simple example.
+data/out/73yowNLO3Y8_segment_0006.wav|Hamner|EN|Here we have three basic components for our workflow, a prompt node, a regular node,
+data/out/73yowNLO3Y8_segment_0007.wav|Hamner|EN|and a display node.
+data/out/73yowNLO3Y8_segment_0008.wav|Hamner|EN|The prompt node is where we input our initial request.
+data/out/73yowNLO3Y8_segment_0009.wav|Hamner|EN|In the regular node, we'll instruct the AI
+data/out/73yowNLO3Y8_segment_0010.wav|Hamner|EN|to create a joke about a given subject.
+data/out/73yowNLO3Y8_segment_0011.wav|Hamner|EN|And there we have it, a tux-related joke
+data/out/73yowNLO3Y8_segment_0012.wav|Hamner|EN|generated by our AI workflow.
+data/out/73yowNLO3Y8_segment_0013.wav|Hamner|EN|Now let's make things a bit more interesting. We'll add an if-else node to our AI workflow. Now let's make things a bit more interesting.
+data/out/73yowNLO3Y8_segment_0014.wav|Hamner|EN|We'll add an if-else node to our logic workflow.
+data/out/73yowNLO3Y8_segment_0015.wav|Hamner|EN|This node will search for the word tux in our generated joke.
+data/out/73yowNLO3Y8_segment_0016.wav|Hamner|EN|If it finds it, we'll display the joke and analyze what it's about.
+data/out/73yowNLO3Y8_segment_0017.wav|Hamner|EN|If not, we'll check if the joke's funny.
+data/out/73yowNLO3Y8_segment_0018.wav|Hamner|EN|This showcases how Vert Workforce can create complex branching workflows.
+data/out/73yowNLO3Y8_segment_0019.wav|Hamner|EN|While the specific example might not be directly applicable
+data/out/73yowNLO3Y8_segment_0020.wav|Hamner|EN|to business needs, it demonstrates the flexibility
+data/out/73yowNLO3Y8_segment_0021.wav|Hamner|EN|of the system.
+data/out/73yowNLO3Y8_segment_0022.wav|Hamner|EN|Let's take it one step further by creating a loop
+data/out/73yowNLO3Y8_segment_0023.wav|Hamner|EN|in our workflow.
+data/out/73yowNLO3Y8_segment_0024.wav|Hamner|EN|In this setup, we're generating a joke about an OS mascot.
+data/out/73yowNLO3Y8_segment_0025.wav|Hamner|EN|If the joke contains tux, it's displayed and analyzed.
+data/out/73yowNLO3Y8_segment_0026.wav|Hamner|EN|This is our success condition.
+data/out/73yowNLO3Y8_segment_0027.wav|Hamner|EN|If tux isn't mentioned, the workflow loops back and tries against specifically asking
+data/out/73yowNLO3Y8_segment_0028.wav|Hamner|EN|for a joke about Tux this time.
+data/out/73yowNLO3Y8_segment_0029.wav|Hamner|EN|This type of workflow can be incredibly useful for real-world applications.
+data/out/73yowNLO3Y8_segment_0030.wav|Hamner|EN|For example, you could ensure generated content meets specific criteria before it's approved
+data/out/73yowNLO3Y8_segment_0031.wav|Hamner|EN|for use.
+data/out/73yowNLO3Y8_segment_0032.wav|Hamner|EN|What we've seen here is just scratching the surface of what's possible with Vert Workforce.
+data/out/73yowNLO3Y8_segment_0033.wav|Hamner|EN|This tool can be adapted for a wide range of applications from data analytics to content generation.
+data/out/73yowNLO3Y8_segment_0034.wav|Hamner|EN|The visual nature of the workflow makes it easy to understand and modify complex AI
+data/out/73yowNLO3Y8_segment_0035.wav|Hamner|EN|processes, potentially saving significant time and resources in development.
+data/out/73yowNLO3Y8_segment_0036.wav|Hamner|EN|Vert Workforce is open source and available on GitHub. I encourage developers and businesses
+data/out/73yowNLO3Y8_segment_0037.wav|Hamner|EN|alike to explore its capabilities and consider how it might streamline your AI-related tasks.
+data/out/73yowNLO3Y8_segment_0038.wav|Hamner|EN|Thanks for watching. Bye!
+data/out/MaHEcggsMTc_segment_0000.wav|Hamner|EN|Hi there tech enthusiasts, David Hamner here and oh boy do I have a treat for you today.
+data/out/MaHEcggsMTc_segment_0001.wav|Hamner|EN|Ever think to yourself, gee, I wish I could use Linux commands without all the hassle of actually
+data/out/MaHEcggsMTc_segment_0002.wav|Hamner|EN|using Linux? Well, hold on to your keyboards because I've got just the thing, an AI-powered
+data/out/MaHEcggsMTc_segment_0003.wav|Hamner|EN|Linux terminal simulator that's currently about as accurate as a weather forecast in the middle of a
+data/out/MaHEcggsMTc_segment_0004.wav|Hamner|EN|hurricane. So, what exactly is Hamnix? Picture this, you're sitting at a computer typing commands into what
+data/out/MaHEcggsMTc_segment_0005.wav|Hamner|EN|you think is a Linux terminal, but it's actually an AI that's trying to pretend it knows what
+data/out/MaHEcggsMTc_segment_0006.wav|Hamner|EN|Linux is. It would be a bit like having a golden retriever as a system administrator,
+data/out/MaHEcggsMTc_segment_0007.wav|Hamner|EN|eager to please and prone to chasing squirrels mid-command.
+data/out/MaHEcggsMTc_segment_0008.wav|Hamner|EN|Now let me take you through the illustrious history of Hamnix.
+data/out/MaHEcggsMTc_segment_0009.wav|Hamner|EN|It all started when I had the brilliant idea to simulate raw VT100 on the biggest
+data/out/MaHEcggsMTc_segment_0010.wav|Hamner|EN|QN model my poor, overworked GPU could handle. Spoiler alert, it didn't go well. My GPU started
+data/out/MaHEcggsMTc_segment_0011.wav|Hamner|EN|sending me smoke signals that translated to, why are you doing this to me? To gather up all the
+data/out/MaHEcggsMTc_segment_0012.wav|Hamner|EN|training for data, I set up a trueurute script that builds a Debian root file
+data/out/MaHEcggsMTc_segment_0013.wav|Hamner|EN|system and runs a bunch of commands. It was like teaching a parrot to recite Shakespeare,
+data/out/MaHEcggsMTc_segment_0014.wav|Hamner|EN|technically possible, but the results were, let's say, creatively interpreted.
+data/out/MaHEcggsMTc_segment_0015.wav|Hamner|EN|The first version of Hamnex was about as useful as a chocolate teaspoon.
+data/out/MaHEcggsMTc_segment_0016.wav|Hamner|EN|It mostly outputted gibberish, occasionally stumbling upon a real command like a drunk
+data/out/MaHEcggsMTc_segment_0017.wav|Hamner|EN|person finding their keys.
+data/out/MaHEcggsMTc_segment_0018.wav|Hamner|EN|I asked for a Linux simulator and got a digital version of infinite monkeys typing on infinite
+data/out/MaHEcggsMTc_segment_0019.wav|Hamner|EN|typewriters.
+data/out/MaHEcggsMTc_segment_0020.wav|Hamner|EN|So, I went back to the drawing board, or in this case back to my keyboard to bang my head
+data/out/MaHEcggsMTc_segment_0021.wav|Hamner|EN|against it repeatedly.
+data/out/MaHEcggsMTc_segment_0022.wav|Hamner|EN|In the end, I updated the training set to look like this.
+data/out/MaHEcggsMTc_segment_0023.wav|Hamner|EN|I also switched to a smaller Quen model.
+data/out/MaHEcggsMTc_segment_0024.wav|Hamner|EN|My GPU breathed a sigh of relief, but then realized it still had to run Hamnex and started
+data/out/MaHEcggsMTc_segment_0025.wav|Hamner|EN|plotting its escape.
+data/out/MaHEcggsMTc_segment_0026.wav|Hamner|EN|Let me show you this current masterpiece in action.
+data/out/MaHEcggsMTc_segment_0027.wav|Hamner|EN|Ladies and gentlemen, I present you with...
+data/out/MaHEcggsMTc_segment_0028.wav|Hamner|EN|HAMNIX!
+data/out/MaHEcggsMTc_segment_0029.wav|Hamner|EN|It's like playing Pin the Tail on the Donkey, but instead it's Pin the Directory on the
+data/out/MaHEcggsMTc_segment_0030.wav|Hamner|EN|file system.
+data/out/MaHEcggsMTc_segment_0031.wav|Hamner|EN|And we're all blindfolded.
+data/out/MaHEcggsMTc_segment_0032.wav|Hamner|EN|And the donkey's actually a cardboard cutout of Tux the Penguin.
+data/out/MaHEcggsMTc_segment_0033.wav|Hamner|EN|The good news!
+data/out/MaHEcggsMTc_segment_0034.wav|Hamner|EN|It recognizes CD, LS, and PWD.
+data/out/MaHEcggsMTc_segment_0035.wav|Hamner|EN|The bad news! It recognizes CD, LS, and PWD. The bad news!
+data/out/MaHEcggsMTc_segment_0036.wav|Hamner|EN|It thinks the file system is a game of musical chairs,
+data/out/MaHEcggsMTc_segment_0037.wav|Hamner|EN|and every time you ask where you are, it's like throwing a dart at a list of directories.
+data/out/MaHEcggsMTc_segment_0038.wav|Hamner|EN|But fear not, we are making progress.
+data/out/MaHEcggsMTc_segment_0039.wav|Hamner|EN|My next steps include teaching Hamnix that Slashbin is not the answer to everything,
+data/out/MaHEcggsMTc_segment_0040.wav|Hamner|EN|and that maintaining context is not just a suggestion,
+data/out/MaHEcggsMTc_segment_0041.wav|Hamner|EN|it's a lifestyle. The dream is to make an AI that can infer programs just on their name.
+data/out/MaHEcggsMTc_segment_0042.wav|Hamner|EN|Imagine typing, make everything awesome, and the AI just does it based on the context of whatever you're doing at the time. We're not there yet. Right now, if you type make everything awesome,
+data/out/MaHEcggsMTc_segment_0043.wav|Hamner|EN|Hamnix might tell you that you're in VAR local and call it a day.
+data/out/MaHEcggsMTc_segment_0044.wav|Hamner|EN|If you want to follow along this journey of digital self-flagitation, then hit that subscribe
+data/out/MaHEcggsMTc_segment_0045.wav|Hamner|EN|button. And if you're a developer with experience in large language models or Linux systems and
+data/out/MaHEcggsMTc_segment_0046.wav|Hamner|EN|you're thinking, I could do better than that, well you're probably right and drop a comment below
+data/out/MaHEcggsMTc_segment_0047.wav|Hamner|EN|with your ideas or just a point and laugh. I'll take either at this point. This is David Hamner signing off. I'm
+data/out/MaHEcggsMTc_segment_0048.wav|Hamner|EN|going to go have a long talk with my GPU about its life choices.
+data/out/KkRmQQZQDOU_segment_0000.wav|Hamner|EN|If you haven't noticed, Raspberry Pis that used to cost $35 back in 2016 now cost much more than that if you can even find them.
+data/out/KkRmQQZQDOU_segment_0001.wav|Hamner|EN|With my 3D printer collection and my use of automation growing, I needed a way to control multiple 3D printers from a single computer.
+data/out/KkRmQQZQDOU_segment_0002.wav|Hamner|EN|Fortunately, this is not the first time I've dealt with serial and this is a pretty basic protocol.
+data/out/KkRmQQZQDOU_segment_0003.wav|Hamner|EN|In most cases, you send a command and wait for an OK. There are a few
+data/out/KkRmQQZQDOU_segment_0004.wav|Hamner|EN|edge cases, sometimes an OK looks like it came back out of order, meaning it would randomly
+data/out/KkRmQQZQDOU_segment_0005.wav|Hamner|EN|hang waiting for an OK that came in too quick. As a workaround, I keep track of when the
+data/out/KkRmQQZQDOU_segment_0006.wav|Hamner|EN|last OK was seen. If a command needs an OK, and we've recently seen one, we simply move
+data/out/KkRmQQZQDOU_segment_0007.wav|Hamner|EN|on. This seems to work pretty well and prevents random hanging. With that worked out, I now have a way to talk to many printers attached to a single
+data/out/KkRmQQZQDOU_segment_0008.wav|Hamner|EN|computer. In the end, the script takes a device path, character rate, and some gcode to spoon
+data/out/KkRmQQZQDOU_segment_0009.wav|Hamner|EN|feed to your printer.
+data/out/KkRmQQZQDOU_segment_0010.wav|Hamner|EN|Next I added a wrapper script to simplify the printing process. This script creates
+data/out/KkRmQQZQDOU_segment_0011.wav|Hamner|EN|a few folders for each configured printer. The folders are named after the dev files. The character ray or baud ray is configured here.
+data/out/KkRmQQZQDOU_segment_0012.wav|Hamner|EN|Inside each of the printer's main folder, you'll find an upload, printing and done directory.
+data/out/KkRmQQZQDOU_segment_0013.wav|Hamner|EN|You simply save your gcode to the upload folder to start a print.
+data/out/KkRmQQZQDOU_segment_0014.wav|Hamner|EN|It will be moved to the printing folder and the gcode send script will be started. If
+data/out/KkRmQQZQDOU_segment_0015.wav|Hamner|EN|you want to cancel a print, simply delete it from the printing folder.
+data/out/KkRmQQZQDOU_segment_0016.wav|Hamner|EN|When a print finishes, it will be moved to the done directory.
+data/out/KkRmQQZQDOU_segment_0017.wav|Hamner|EN|To help automate some of this, I added a few helper scripts.
+data/out/KkRmQQZQDOU_segment_0018.wav|Hamner|EN|This one looks for the last completed print and moves it back to the upload directory.
+data/out/KkRmQQZQDOU_segment_0019.wav|Hamner|EN|I tied this into a Zigbee switch using my home butler and home assistant setup.
+data/out/KkRmQQZQDOU_segment_0020.wav|Hamner|EN|I also have a few helpful small gcode scripts. These can come in handy when loading filament or turning off the hot end. and Home Assistant setup.
+data/out/zEXWN1BCieA_segment_0000.wav|Hamner|EN|Why is it so hard to upgrade or downgrade just one package?
+data/out/zEXWN1BCieA_segment_0001.wav|Hamner|EN|What is a package dependency?
+data/out/zEXWN1BCieA_segment_0002.wav|Hamner|EN|And just what would it look like to visualize all of Debian's package requirements in 3D space?
+data/out/zEXWN1BCieA_segment_0003.wav|Hamner|EN|Debian is a robust distro of GNU Linux based on the .deb package format.
+data/out/zEXWN1BCieA_segment_0004.wav|Hamner|EN|These packages are made of files that get extracted to the system and scripts that can be run if needed. .debs are a convenient and proven way to install the base OS, new applications, and required libraries.
+data/out/zEXWN1BCieA_segment_0005.wav|Hamner|EN|In just about every case, an application packaged as a .deb will have dependencies.
+data/out/zEXWN1BCieA_segment_0006.wav|Hamner|EN|A dependency is another package your package relies on. A good example is a graphical GTK3
+data/out/zEXWN1BCieA_segment_0007.wav|Hamner|EN|application, which needs libgtk to function. In many cases, your
+data/out/zEXWN1BCieA_segment_0008.wav|Hamner|EN|dependencies will also have dependencies, which could also have dependencies, and
+data/out/zEXWN1BCieA_segment_0009.wav|Hamner|EN|so on. The connections between these interdependent packages make up what's
+data/out/zEXWN1BCieA_segment_0010.wav|Hamner|EN|known as a dependency tree. This dependency tree is a very common image
+data/out/zEXWN1BCieA_segment_0011.wav|Hamner|EN|used to depict basic package relationships. Digging into the origin,
+data/out/zEXWN1BCieA_segment_0012.wav|Hamner|EN|it seems this image was created with apt-rdepends.
+data/out/zEXWN1BCieA_segment_0013.wav|Hamner|EN|It turns out apt-rdepends is a really handy way of outputting .graph data, which can be
+data/out/zEXWN1BCieA_segment_0014.wav|Hamner|EN|piped into a few different programs to create cool looking graphs.
+data/out/zEXWN1BCieA_segment_0015.wav|Hamner|EN|Asking Spring Graph to graph my entire Debian system really didn't work.
+data/out/zEXWN1BCieA_segment_0016.wav|Hamner|EN|But I was able to create this cool looking guy with sfdp, which is part of the package
+data/out/zEXWN1BCieA_segment_0017.wav|Hamner|EN|graphiz.
+data/out/zEXWN1BCieA_segment_0018.wav|Hamner|EN|SFDP put everything in a nice circle and can also output data in detailed dot graph format.
+data/out/zEXWN1BCieA_segment_0019.wav|Hamner|EN|So I used SFDP to create detailed graph data from the output of apt artipans run on the whole system.
+data/out/zEXWN1BCieA_segment_0020.wav|Hamner|EN|Next, I built a handy blender script to render that .graph data in 2D.
+data/out/zEXWN1BCieA_segment_0021.wav|Hamner|EN|To give it a third dimension, I calculated the height based on the number of other packages
+data/out/zEXWN1BCieA_segment_0022.wav|Hamner|EN|using it as a dependency.
+data/out/zEXWN1BCieA_segment_0023.wav|Hamner|EN|So the most dependent on packages ended up the highest on the Z axis.
+data/out/zEXWN1BCieA_segment_0024.wav|Hamner|EN|This makes libc6 the top tree in this cool visualization I'm calling a dependency forest,
+data/out/zEXWN1BCieA_segment_0025.wav|Hamner|EN|as this is a tight grouping of trees in 3D space.
+data/out/zEXWN1BCieA_segment_0026.wav|Hamner|EN|The links between packages are really just a list of the package names and the needed versions.
+data/out/zEXWN1BCieA_segment_0027.wav|Hamner|EN|Here's an example from the also utils package.
+data/out/zEXWN1BCieA_segment_0028.wav|Hamner|EN|By removing or changing any of these packages, you could break also utils as well as any other packages that depend on the changed package.
+data/out/zEXWN1BCieA_segment_0029.wav|Hamner|EN|I bet you could guess why messing with any of these higher dependent on packages could cause a problem for the lower parts of the tree.
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