You have hit the mark with this series! My only suggestion that will generate additional content is to create a fork at step 4 for those who would want to continue with the N8N tools and those who would move to Python. This way you keep both audiences engaged with unique content. I’ll be watching this space!
Thank you and I appreciate the suggestion a lot! I'll have to think about how I could do that, but that would certainly be the best way to keep everyone engaged.
Awesome. This video and your upcoming series are what I’m looking for. Among all of the people posting videos about building AI agents, I found your videos are the best: simple enough for non tech people to understand, also have in-depth technical details. Can’t wait to see your new videos.
@@ColeMedin l was talking to one of my senior Developers and said i was going to create another version of your video's called baby steps for old devs ... Thanks for packing so much in its a great direction.
@@ColeMedin Environment setup is essential or people will get lost in the Rush that is the new AI. Take us from New Laptop to ready to start coding AI Agents and I bet that will get some views. Assume we know nothing. Those that know will can skip it those that are stuck will point here as the source to start the journey. 25 years now and I've only needed SSMS to earn a living...
Even being an non tech person with very little coding knowledge (few courses of pythonin 2015), i am able to understand what u r saying @cole ! I m curious person and have seen tons of vids so in order to select a good teacher before i transit to AI world. I can say now, i have found me teacher after watching this video. Would love to be part of ur team! Amazing
Oh, you're a real deal developer! So many of these new AI channels are ran by people who are not devs. Nothing wrong with that but real devs offer more sometimes. I'd like to make an agent that can make money!
I appreciate it a lot! Yeah one of my main goals is to get more technical with a lot of my content for people like you who want to really dive into building agents to make money and solve real-world problems.
Awesome video! I'm really excited about this series. Building an AI agent that can consume entire GitHub repositories is going to be incredibly useful. Can't wait to see how you go from the basics all the way to production. Thanks for sharing your expertise in such a clear and engaging way!
Thank you for building this tutorial. What about in the RAG preprocessing stage to clean data, redact PII information before sending to LLM for chunk enrichment.
Hey @ColeMedin, Wonderful video.....can u share how did u make that roadmap, like what software did u use....cause its looking outstanding....and works wonderfully for meetings....
Good question! Open WebUI is certainly a great option and one of the other platforms I was referring to when I mentioned Telegram. I generally prefer making custom frontends because it's so easy with AI tools out there and those give more customization, but there is still a lot you can do with Open WebUI so I also not knocking it by any means!
As someone who already has real platforms running for thousands of customers for years, this is my sincere opinion: Why can't I just use OpenAI assistants with memory, RAG, function calling, and schemas already built-in, instead of creating my own agent structure with completions? I understand the flexibility of choosing different models and setups, but honestly, customers don't care about that-they just want an app that works. At least for me, I believe OpenAI assistants will become even more powerful, always at the cutting edge. This significantly reduces the complexity of building and allows you to focus on what truly matters: Solving customers' problems FAST and shipping FAST. PS: I know many developers enjoy building locally and working on complex setups for learning purposes, which is awesome. However, when it comes to consistently shipping quickly and solving real-world problems, I don't think it's practical.
@othon.rodrigues Bro, you hit the nail on the head! I've been following all those complex AI agent-making videos, and what I've understood is that sometimes people like to overcomplicate things. 😂 But I can't blame them; everyone enjoys things differently. From a purely business perspective, the OpenAI Assistant with RAG (Retrieval Augmented Generation) and memory is the best because, as you said, clients don't care about the backend if there's no significant difference in the output. They want fast results. However, these videos are focused on people who enjoy the freedom of choice and exploring different AI approaches. BTW, I'd like to know if you use only the OpenAI Assistant?
@@pressplayg I use mulitple openAI assistants and integrate each other in layers. But I also use google vertex for pdf OCR reading (I dont know why, but openAI is not too good). And also perplexity for searching the web. I currently have a platform for lawyers, and drafting 40-50 page documents its not easy, so I combine all of them to achieve the final result. Currently I dont use function calling (yet), but I have some specific asssitants with structured outputs. My goal now is to integrate all my +35 tools (create documents, analise documents, search and etc..) into one big super smart agent, and unify my customers experience, you know...
@@pressplayg I currently use many openAI assistants integrated in layers, together with google vertex for pdf reading (I dont know why, but openAI doesnt perform too good in this) and also Perplexity for searching the web... All of them integrated with each other in a small group of Agents/Assistants. My platform is for lawyers, so I need to craft sometimes 40-50 page documents, and thats not easy at all! haha My goal now is to integrate all of my +35 tools (each tool is a group of assistants) into a super smart Agent, and unify my users experience, you know.... Sometimes showing many tools can confuse the users.. And the market seems going in that direction too
Thank you for your question - I actually really appreciate this pushback a lot! For a lot of use cases, you're totally right that sticking to something super simple like OpenAI assistants is more than enough and it's important to not overengineer for the use case and just focus on delivering for your end user. That's something I will be sure to cover and should have in this video, so I'm glad you called it out. For a lot of use cases, especially ones revolved around a knowledgebase, OpenAI assistants are enough. Where custom agents really start to come in is when you need to integrate your agent with a lot of third party services or perform more complex tasks like interact with a database. Sure OpenAI assistants have function calling, but when your integrations require you to code or use automation tools, at that point you might as well bring the agent into code (Python with Pydantic AI for example) or the automation tool (i.e. n8n) as well since that's the easy part. As a clear example, I had a client a couple months back that built an OpenAI assistant as a tax expert. For the knowledgebase, he gave it these massive publicly available government tax documents that had thousands of records and had a super odd structure that had to be parsed. And the assistant performed TERRIBLY answering any questions about this data (even after giving it properly parsed documents). So I created a custom agent that was able to parse these documents, put them into a SQL database, and then actually write SQL queries to answer questions that a RAG approach wouldn't be able to (hence why the OpenAI assistant failed). And then I still implemented RAG for when that approach would be better. I definitely wouldn't have wanted to try all of that just as a better OpenAI assistant! There is also the whole limitation you called out where if you want to use local LLMs (privacy, the ability to fine tune on your data, lot of reasons to do so!) then you can't use OpenAI assistants.
thanks a lot men!! Gr8 video Question: Why start with n8n and then replace to python code? If u design it well you will not do a double work and decide n8n or python code. Btw how to move easily code from n8n to python code?
Thanks man! The reason I start with n8n first is because it's SO fast to build a POC and just wrap my brain around what I want my agent to be able to do. It's the fastest way for me to test that the tools I want to give my agent actually allow it to accomplish what I want it to do. Then moving from n8n to Python code is super easy because the n8n workflow already gives you the entire structure for your agent workflow. And you can use AI coding assistants like Windsurf to create the code based on the JSON workflow for your n8n agent.
@ColeMedin oh sorry for framing that question stupidly , autocorrect messed it up. What my question is , people say flowise is not production ready do u agree with that ? And flowise + n8n + pydantic ai isn't that deadly powerful combo .. Like pydantic ai is main and flowise and n8n api is connecting to pydantic ai . How is this workflow is it powerful what the benefits of this workflow ? Can we compelete remove voiceflow which is expensive comparing to this open source tools
Yes I agree in general Flowise isn't production ready, mostly because it doesn't have the best logging/monitoring/error handling that you would need. I would say Flowise is for prototyping and then Pydantic AI + n8n is the powerful combo. Pydantic AI for the agent logic and n8n for the agent tools. You could certainly use that instead of Voiceflow. Voiceflow is just great because of how much it takes care of for you so it's faster to get things up and running.
It would make your AI Agent Blueprint even more versatile and accessible! By integrating i18n, the frontend and even AI responses could support multiple languages, enabling non-English speakers to interact seamlessly. This would open up the project to a global audience-do you think this could fit within your roadmap? Thank you for your great content!😃
Great video boss please teach us this 10 steps without skipping any i was looking for something like this and the fact you using pydantic ai and fastapi other python stuff make this a truly master calls for production readt chatbot❤ thank u mahn cant wa8 please do this vidoe❤btw i got doubt isnt Flowise+n8n+flowise , isnt this combo production ready ?
Jeeze I’m a non techy person. But quick question at some point will we even be able to surpass this level of technical effort and just be able to speak our ideas into the ai and it will go through this process for us to create?
I think at some point it'll get close, but you'll still need some level of technical expertise to evaluate what the LLM creates and fix anything it doesn't get right (either from a technical or requirements standpoint).
Great question and Flowise is a great option for prototyping agents fast in general! I assume you're asking why not Flowise instead of Voiceflow? The main reason is just the sheer power that Voiceflow gives you out of the gate, especially for building RAG into your AI agents. It would take hours longer to build a solid RAG agent with Flowise compared to Voiceflow which is often well worth the cost in my eyes.
Hi Cole, thank you for this video! You nailed my needs, as I was contemplating, if I should keep doing my AI Agents with n8n or switch to Langchain / Langraph. Now I understand, prototype in n8n and move production to code! You earned my sub with this :) One question - are you moving only the "AI Agent" node to Langchain / Pydantic AI or move entire n8n backend? Thanks! Looking forward to the rest of the series!
You bet! I'm glad this clicked with you and thank you for the support! So for this series I'll probably move everything from n8n. Just to give a full demonstration of the process. Though a lot of times I do keep a lot of the agent tools in n8n because it's really nice to not have to manage as many credentials for third party services in my code!
I mean, I don't know that I would call it building in AI agent if you're just using n8n. You're not really building much at that point. Why not actually use an agent framework and build the agents yourself and show people how to do that. Obviously, I understand that using n8n is faster and it keeps you from having to do much work at all. But at that point, I wouldn't say you are building agents in this series. More so you are building n8n workflows.
Building an agent with n8n is just the beginning for prototyping! I almost always move on to building the agent with Pydantic AI or some other Python framework after I've prototyped with n8n to create a POC and get my head around everything I want the agent to do. And in this series I'll be building with Pydantic AI too!
Join me on December 28th at 9:00 AM CST for my livestream continuing this mini series! ua-cam.com/users/live56D91EcaUnM Also check out Voiceflow, I guarantee you'll be able to build some awesome AI agents fast with it. I'm also doing something cool with them very soon as a part of my Christmas gift to you! Use the link below to support the Hackathon: partners.voiceflow.com/dj9khewo5u34
This video might actually have some information but a good 75% of it is all marketing for services you're trying to provide or are involved with You keep referencing a stream or other videos series or website or service that you are involved in rather than the actual meat of of the content people are here for
I reference my stream, community, and the Hackathon which are all completely free to participate in! So it's a bit hard for me to understand where you are coming from.
Hi, I really enjoy your videos and your insightful discussions about artificial intelligence. Your content is engaging, and I’ve learned a lot from your unique perspectives. However, I noticed that while you dive deep into explaining concepts, you don't provide tutorials or practical guides. It would be amazing to see some step-by-step tutorials for beginners or even intermediate learners who want to explore AI hands-on. I’m sure many of your viewers would appreciate learning how to apply some of the concepts you talk about. Thank you for the amazing work you do, and keep it up!
Thank you for the kind words! And I hear you! That is actually one of the main goals of this series - to provide a super practical guide going through the entire process of building an AI agent.
You have hit the mark with this series! My only suggestion that will generate additional content is to create a fork at step 4 for those who would want to continue with the N8N tools and those who would move to Python. This way you keep both audiences engaged with unique content. I’ll be watching this space!
Thank you and I appreciate the suggestion a lot! I'll have to think about how I could do that, but that would certainly be the best way to keep everyone engaged.
Awesome. This video and your upcoming series are what I’m looking for. Among all of the people posting videos about building AI agents, I found your videos are the best: simple enough for non tech people to understand, also have in-depth technical details. Can’t wait to see your new videos.
Thank you so much! I'm really glad you found it helpful and I appreciate the kind words.
Thanks!
You are so welcome! Thank you for your support!
@@ColeMedin l was talking to one of my senior Developers and said i was going to create another version of your video's called baby steps for old devs ... Thanks for packing so much in its a great direction.
Haha I love it! Maybe I should do that too, I want my content to be super digestible. You are so welcome!
@@ColeMedin Environment setup is essential or people will get lost in the Rush that is the new AI. Take us from New Laptop to ready to start coding AI Agents and I bet that will get some views. Assume we know nothing. Those that know will can skip it those that are stuck will point here as the source to start the journey. 25 years now and I've only needed SSMS to earn a living...
Even being an non tech person with very little coding knowledge (few courses of pythonin 2015), i am able to understand what u r saying @cole ! I m curious person and have seen tons of vids so in order to select a good teacher before i transit to AI world. I can say now, i have found me teacher after watching this video. Would love to be part of ur team! Amazing
Thank you so much! I'm really glad I could break it down in a way that made sense for you!
These Videos Rock! you are killing it!
Thanks Rory, I appreciate it a lot!
Awesome video for the one who's inclined towards AI and ahents in general.
Looking forward for the whole series.
Thank you! I hope you enjoy the series!
Great content. That was the overview I needed.
Oh, you're a real deal developer! So many of these new AI channels are ran by people who are not devs. Nothing wrong with that but real devs offer more sometimes.
I'd like to make an agent that can make money!
I appreciate it a lot! Yeah one of my main goals is to get more technical with a lot of my content for people like you who want to really dive into building agents to make money and solve real-world problems.
@@ColeMedin super exited for you. You are on the front lines of what's next in tech. I'll be following along.
Wow! Awesome scope for your mini series 🎉
Thank you! :D
Awesome video! I'm really excited about this series. Building an AI agent that can consume entire GitHub repositories is going to be incredibly useful. Can't wait to see how you go from the basics all the way to production. Thanks for sharing your expertise in such a clear and engaging way!
Wow.. totally heartfelt comment.
Auto comment. Paid for. AI.
@reedickyaluss It might be... but also I did not pay for it.
Can't wait for it,
Thank you Cole.
Keep going bro
Thank you! :)
Thank you for building this tutorial. What about in the RAG preprocessing stage to clean data, redact PII information before sending to LLM for chunk enrichment.
You bet! And that will actually fit into this series since it'll be important for the GitHub agent!
Hey @ColeMedin,
Wonderful video.....can u share how did u make that roadmap, like what software did u use....cause its looking outstanding....and works wonderfully for meetings....
2nd that.
Thank you! I built the roadmap using Prezi
Thank you Cole amazed by you content one more time 😊
You bet! I appreciate it a lot!
This is epic Cole! Keep it up! 🎉
Thanks so much, I really appreciate it! :D
Is the community edition of N8N enough for prototyping?
Yes it 100% is!
Can I just ask why would you not recommend open webui for frontend?
Good question! Open WebUI is certainly a great option and one of the other platforms I was referring to when I mentioned Telegram.
I generally prefer making custom frontends because it's so easy with AI tools out there and those give more customization, but there is still a lot you can do with Open WebUI so I also not knocking it by any means!
Can you clarify about why can't we use Bolt for Streamlit? vs Cursor or Windsurf?
I guess it's because you cannot test your python code in those, but you can test python on your computer.
Good question! @ginocote is correct!
As someone who already has real platforms running for thousands of customers for years, this is my sincere opinion:
Why can't I just use OpenAI assistants with memory, RAG, function calling, and schemas already built-in, instead of creating my own agent structure with completions?
I understand the flexibility of choosing different models and setups, but honestly, customers don't care about that-they just want an app that works.
At least for me, I believe OpenAI assistants will become even more powerful, always at the cutting edge. This significantly reduces the complexity of building and allows you to focus on what truly matters:
Solving customers' problems FAST and shipping FAST.
PS: I know many developers enjoy building locally and working on complex setups for learning purposes, which is awesome. However, when it comes to consistently shipping quickly and solving real-world problems, I don't think it's practical.
PS 2: I am one of those like to dive deeper to understand tech. That's why I am here LOL
@othon.rodrigues
Bro, you hit the nail on the head! I've been following all those complex AI agent-making videos, and what I've understood is that sometimes people like to overcomplicate things. 😂 But I can't blame them; everyone enjoys things differently.
From a purely business perspective, the OpenAI Assistant with RAG (Retrieval Augmented Generation) and memory is the best because, as you said, clients don't care about the backend if there's no significant difference in the output. They want fast results.
However, these videos are focused on people who enjoy the freedom of choice and exploring different AI approaches.
BTW, I'd like to know if you use only the OpenAI Assistant?
@@pressplayg I use mulitple openAI assistants and integrate each other in layers. But I also use google vertex for pdf OCR reading (I dont know why, but openAI is not too good). And also perplexity for searching the web. I currently have a platform for lawyers, and drafting 40-50 page documents its not easy, so I combine all of them to achieve the final result. Currently I dont use function calling (yet), but I have some specific asssitants with structured outputs.
My goal now is to integrate all my +35 tools (create documents, analise documents, search and etc..) into one big super smart agent, and unify my customers experience, you know...
@@pressplayg I currently use many openAI assistants integrated in layers, together with google vertex for pdf reading (I dont know why, but openAI doesnt perform too good in this) and also Perplexity for searching the web... All of them integrated with each other in a small group of Agents/Assistants.
My platform is for lawyers, so I need to craft sometimes 40-50 page documents, and thats not easy at all! haha
My goal now is to integrate all of my +35 tools (each tool is a group of assistants) into a super smart Agent, and unify my users experience, you know.... Sometimes showing many tools can confuse the users.. And the market seems going in that direction too
Thank you for your question - I actually really appreciate this pushback a lot! For a lot of use cases, you're totally right that sticking to something super simple like OpenAI assistants is more than enough and it's important to not overengineer for the use case and just focus on delivering for your end user. That's something I will be sure to cover and should have in this video, so I'm glad you called it out.
For a lot of use cases, especially ones revolved around a knowledgebase, OpenAI assistants are enough. Where custom agents really start to come in is when you need to integrate your agent with a lot of third party services or perform more complex tasks like interact with a database. Sure OpenAI assistants have function calling, but when your integrations require you to code or use automation tools, at that point you might as well bring the agent into code (Python with Pydantic AI for example) or the automation tool (i.e. n8n) as well since that's the easy part.
As a clear example, I had a client a couple months back that built an OpenAI assistant as a tax expert. For the knowledgebase, he gave it these massive publicly available government tax documents that had thousands of records and had a super odd structure that had to be parsed. And the assistant performed TERRIBLY answering any questions about this data (even after giving it properly parsed documents). So I created a custom agent that was able to parse these documents, put them into a SQL database, and then actually write SQL queries to answer questions that a RAG approach wouldn't be able to (hence why the OpenAI assistant failed). And then I still implemented RAG for when that approach would be better. I definitely wouldn't have wanted to try all of that just as a better OpenAI assistant!
There is also the whole limitation you called out where if you want to use local LLMs (privacy, the ability to fine tune on your data, lot of reasons to do so!) then you can't use OpenAI assistants.
Hi, what tool have you used to make this kind of scheme? Thanks
Prezi!
What tool did you use for your presentation?
It has a very nice rendering!
I use Prezi!
thanks a lot men!! Gr8 video
Question:
Why start with n8n and then replace to python code?
If u design it well you will not do a double work and decide n8n or python code.
Btw how to move easily code from n8n to python code?
Thanks man!
The reason I start with n8n first is because it's SO fast to build a POC and just wrap my brain around what I want my agent to be able to do. It's the fastest way for me to test that the tools I want to give my agent actually allow it to accomplish what I want it to do.
Then moving from n8n to Python code is super easy because the n8n workflow already gives you the entire structure for your agent workflow. And you can use AI coding assistants like Windsurf to create the code based on the JSON workflow for your n8n agent.
what platform did you used for creating the presentation?
I used Prezi!
Hi Cole, great video thank you!
I would love to see a video where you clarify how embeddings and vector DBs work in indexing codebase
Great stuff! Super looking forward already. Nice graphics!, what are you using for this beautiful roadmap?
Thank you very much! I built the roadmap with Prezi.
@ColeMedin oh sorry for framing that question stupidly , autocorrect messed it up. What my question is , people say flowise is not production ready do u agree with that ? And flowise + n8n + pydantic ai isn't that deadly powerful combo .. Like pydantic ai is main and flowise and n8n api is connecting to pydantic ai . How is this workflow is it powerful what the benefits of this workflow ? Can we compelete remove voiceflow which is expensive comparing to this open source tools
Yes I agree in general Flowise isn't production ready, mostly because it doesn't have the best logging/monitoring/error handling that you would need.
I would say Flowise is for prototyping and then Pydantic AI + n8n is the powerful combo. Pydantic AI for the agent logic and n8n for the agent tools. You could certainly use that instead of Voiceflow. Voiceflow is just great because of how much it takes care of for you so it's faster to get things up and running.
Great project! Congrats. Just one question, do you plan to integrate i8n internationalization for different languages?? That whould be awesome
Thanks! I would love to - what would that involve to you?
It would make your AI Agent Blueprint even more versatile and accessible! By integrating i18n, the frontend and even AI responses could support multiple languages, enabling non-English speakers to interact seamlessly. This would open up the project to a global audience-do you think this could fit within your roadmap? Thank you for your great content!😃
Yeah that makes sense! Honestly I'm not familiar with integrating it though... so I'll have to look into it!
Great video boss please teach us this 10 steps without skipping any i was looking for something like this and the fact you using pydantic ai and fastapi other python stuff make this a truly master calls for production readt chatbot❤ thank u mahn cant wa8 please do this vidoe❤btw i got doubt isnt Flowise+n8n+flowise , isnt this combo production ready ?
Thank you very much! Could you clarify your question a bit?
Why hindi audio track is not available 😢
You mean the Hindi subtitles? Are they usually for UA-cam videos? UA-cam generally takes care of this stuff automatically for different languages.
Muito obrigado por disponibilizar esse conteúdo em áudio multilíngue.
Might want to add smolagents to the blueprint..
I'm definitely making a video on smolagents in the near future!
@ColeMedin I'm looking forward to it
I appreciate it!
I hope to get more detail into each step
That's the goal of the series! :D
Jeeze I’m a non techy person. But quick question at some point will we even be able to surpass this level of technical effort and just be able to speak our ideas into the ai and it will go through this process for us to create?
I think at some point it'll get close, but you'll still need some level of technical expertise to evaluate what the LLM creates and fix anything it doesn't get right (either from a technical or requirements standpoint).
why not use flowise similar and open source
Great question and Flowise is a great option for prototyping agents fast in general! I assume you're asking why not Flowise instead of Voiceflow? The main reason is just the sheer power that Voiceflow gives you out of the gate, especially for building RAG into your AI agents. It would take hours longer to build a solid RAG agent with Flowise compared to Voiceflow which is often well worth the cost in my eyes.
What would be the absolute quickest and minimalistic way of creating an entire AI agent from scratch that anyone could do?
Good question! I'd recommend using n8n to create your agent if you want something super fast, simple, and still powerful!
Nice one dude
Can’t believe they call it n 8 n when I think it would be cooler if they called it NATE-in
Hi Cole, thank you for this video! You nailed my needs, as I was contemplating, if I should keep doing my AI Agents with n8n or switch to Langchain / Langraph. Now I understand, prototype in n8n and move production to code! You earned my sub with this :)
One question - are you moving only the "AI Agent" node to Langchain / Pydantic AI or move entire n8n backend? Thanks!
Looking forward to the rest of the series!
You bet! I'm glad this clicked with you and thank you for the support!
So for this series I'll probably move everything from n8n. Just to give a full demonstration of the process. Though a lot of times I do keep a lot of the agent tools in n8n because it's really nice to not have to manage as many credentials for third party services in my code!
I mean, I don't know that I would call it building in AI agent if you're just using n8n. You're not really building much at that point. Why not actually use an agent framework and build the agents yourself and show people how to do that. Obviously, I understand that using n8n is faster and it keeps you from having to do much work at all. But at that point, I wouldn't say you are building agents in this series. More so you are building n8n workflows.
Those workflows have agents...
@kinetic_nexus Yes they have agents made by n8n that are just being consumed in the workflow... He isn't actually creating the agents.
Building an agent with n8n is just the beginning for prototyping! I almost always move on to building the agent with Pydantic AI or some other Python framework after I've prototyped with n8n to create a POC and get my head around everything I want the agent to do.
And in this series I'll be building with Pydantic AI too!
@@ColeMedinhopefully u vl, first do it wd scratch concepts then use ready made ones.
Hi cole; i will ake a serie on how build agent with this same roadmap? Please when you start ?
I'm starting this month!
Dope vid- man please share ur slide show zoom in and out tool
Thanks! I built it with Prezi!
Join me on December 28th at 9:00 AM CST for my livestream continuing this mini series!
ua-cam.com/users/live56D91EcaUnM
Also check out Voiceflow, I guarantee you'll be able to build some awesome AI agents fast with it. I'm also doing something cool with them very soon as a part of my Christmas gift to you! Use the link below to support the Hackathon:
partners.voiceflow.com/dj9khewo5u34
post ana affiliate link to that desk behind you and ill use it $$$
Phenomenal
Exelent !!
*move your agent to Go. ;)
I personally haven't used Go much and Python is a lot simpler, but I have huge respect for anyone who builds agents with Go!
I love your video. Can you share me your ia development roadmap?
wow!
Excelente esto se pone muy pero muy bueno.
Nice! I just tried loveable
Thanks! Yeah Loveable works great
This video might actually have some information but a good 75% of it is all marketing for services you're trying to provide or are involved with
You keep referencing a stream or other videos series or website or service that you are involved in rather than the actual meat of of the content people are here for
I reference my stream, community, and the Hackathon which are all completely free to participate in! So it's a bit hard for me to understand where you are coming from.
Really ❤
👏👏🤞
Thanks, man. i was wondering around to take a grasp of this topic. Dying to understan.
My pleasure! I hope this helps you a ton!
❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤
Hi, I really enjoy your videos and your insightful discussions about artificial intelligence. Your content is engaging, and I’ve learned a lot from your unique perspectives.
However, I noticed that while you dive deep into explaining concepts, you don't provide tutorials or practical guides. It would be amazing to see some step-by-step tutorials for beginners or even intermediate learners who want to explore AI hands-on. I’m sure many of your viewers would appreciate learning how to apply some of the concepts you talk about.
Thank you for the amazing work you do, and keep it up!
Thank you for the kind words! And I hear you! That is actually one of the main goals of this series - to provide a super practical guide going through the entire process of building an AI agent.
@ColeMedin hello Freind, I am waiting for a complete step-by-step tutorial designed for a total beginner with no programming knowledge
Stock Investing, thank you
Omg that’s your AI avatar and not real you
Haha sorry what? I'm not using an AI avatar for anything!
first :)