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Prompts Used

Prompt 1: Context Setup

You are an expert data extractor tasked with analyzing a podcast transcript. 
I will provide you with part 1 of 1 from a podcast transcript. 
I will then ask you to extract different types of information from this content in subsequent messages. Please confirm you have received and understood the transcript content.

Transcript section:
[00:00:00.320 --> 00:00:04.480] Hey, it's Arvid, and this is the Bootstrap Founder.
[00:00:08.880 --> 00:00:16.560] Today, I will highlight one part of the AI hype that we're all having to deal with right now that is severely underreported on.
[00:00:16.560 --> 00:00:28.560] And I think it's that part that I personally believe has much stronger long-term impact than all those magical video generators and coding agents that we see every single day.
[00:00:28.560 --> 00:00:35.920] This episode is sponsored by Paddle.com, my merchant of record payment provider of choice, who's been helping me focus on PodScan from day one.
[00:00:35.920 --> 00:00:48.480] And they've been taking care of all the things related to money so that founders like me and you can focus on building the things that only we can build, paddle panels, all the rest, like sales tags and credit cards failing, all of that, don't have to deal with it.
[00:00:48.480 --> 00:00:49.440] They do it for you.
[00:00:49.440 --> 00:00:50.960] I highly recommend checking it out.
[00:00:50.960 --> 00:00:53.840] So please go to paddle.com.
[00:00:55.440 --> 00:01:04.560] I just had one of those realizations that makes you stop and think quite differently about everything that has been going on around the discussion of AI tooling.
[00:01:04.560 --> 00:01:08.800] You know, we talk a lot about AI helping us build things faster.
[00:01:08.800 --> 00:01:13.360] And we talk about automation, about whether it's going to replace developers, take our jobs.
[00:01:13.360 --> 00:01:21.280] But there's a side effect happening right now that I think is either completely undervalued or just not being observed at all.
[00:01:21.280 --> 00:01:28.720] And honestly, it might be much more transformative than the automation piece that we're all focused on so much.
[00:01:28.720 --> 00:01:35.200] Let me paint you a picture of how learning to code has worked for basically forever.
[00:01:35.200 --> 00:01:40.480] For the longest time, if you wanted to learn how to code, you had pretty much two choices, two main paths.
[00:01:40.480 --> 00:01:42.640] The first was the intellectual approach.
[00:01:42.640 --> 00:01:51.200] You would dive into books and wade through documentation and just try to absorb all of this abstract knowledge and somehow transform it into working code.
[00:01:51.200 --> 00:01:54.160] And the second one was the experiential route.
[00:01:54.160 --> 00:01:58.080] And that's what most of us self-taught developers know intimately.
[00:01:58.080 --> 00:02:01.400] You would run headfirst into every single issue one by one.
[00:02:01.400 --> 00:02:02.760] You'd just figure it out.
[00:02:02.760 --> 00:02:04.760] You tap into a Stack Overflow.
[00:02:04.760 --> 00:02:12.200] When you were lucky, maybe find somebody else's solution that kind of fit your problem, or just adopt whatever you were seeing to what you needed.
[00:02:12.200 --> 00:02:20.040] And if you could handle it, you'd try to read more experienced developers' code and then reverse engineer how that could apply to what you were building.
[00:02:20.040 --> 00:02:28.200] But here's the thing: it was always this process of consuming information that wasn't really code to then produce code.
[00:02:28.200 --> 00:02:32.360] And it was always asynchronous every single time.
[00:02:32.360 --> 00:02:38.520] You would hit a wall, spend hours researching, maybe post a question somewhere, and then wait for an answer.
[00:02:38.520 --> 00:02:42.360] And if you were really fortunate, you were part of a team where you would get feedback.
[00:02:42.360 --> 00:02:45.560] But even then, the core experience was the same.
[00:02:45.560 --> 00:02:57.160] You alone diving into a problem, running into a wall, and then running into 20 more walls over the next couple of hours, waiting for somebody to help, because they also were running into their own walls at the same time.
[00:02:57.160 --> 00:03:07.080] Then you would either give up or ask somebody else, or you'd figure it out just through sheer persistence and take that hardworn learning forward into the next thing you had to build.
[00:03:07.080 --> 00:03:14.440] So that's how we've been learning to be better programmers, better infrastructure developers, better software architects, and all of that for decades.
[00:03:14.440 --> 00:03:18.200] But something fundamental is shifting with AI software development tools.
[00:03:18.200 --> 00:03:25.080] And I want to be clear: this has very little to do with this admittedly magical capacity of letting AI do the work for us.
[00:03:25.080 --> 00:03:27.960] That's a whole other conversation that everybody seems to be having.
[00:03:27.960 --> 00:03:29.880] So we don't need to talk about that here.
[00:03:29.880 --> 00:03:35.640] But this is about something much more profound for the people who are still willing to learn how to code.
[00:03:35.640 --> 00:03:44.680] And there are still many of those people because here's what I think we all understand deep down: coding is significantly more than telling a machine what to do.
[00:03:44.800 --> 00:03:54.560] To be able to judge good code, you still have to be able to write good code in the first place, or at least understand what the difference between good code and bad code looks like.
[00:03:54.560 --> 00:03:58.480] You can't delegate that judgment entirely, at least not yet.
[00:03:58.480 --> 00:04:05.200] And I think for the foreseeable future, we will still need to be able to judge the outputs of these machines.
[00:04:05.200 --> 00:04:15.680] So, for people who want to learn this craft of being able to write good code so they can tell machines what they need and don't have to write the code anymore, something remarkable is happening right now.
[00:04:15.680 --> 00:04:19.680] All of a sudden, they don't have to do it all by themselves anymore.
[00:04:19.680 --> 00:04:28.240] Think about this: with AI tools, learners can actually look at what an experienced person would do and pair program with them.
[00:04:28.240 --> 00:04:34.000] Not just occasionally, not just when schedules align, but 24-7, whenever they want to.
[00:04:34.000 --> 00:04:43.760] They can ask questions of that somewhat or very experienced developer 24-7 all day long for effectively free.
[00:04:43.760 --> 00:04:55.840] Even if they're paying 20 bucks a month or 200 bucks a month for an agentic system, you know, Cloud Code Max or whatever the thing is called, it is still a 24-7 relationship, always on, always available.
[00:04:55.840 --> 00:05:14.640] And if someone's trying to learn how to code, they can have this constant AI system build code for them and here's the crucial part: teach them how they did it, why they did it, what they thought about, what they approached and how they approached things in certain ways, why they didn't choose the alternative approach here.
[00:05:14.960 --> 00:05:20.240] You can always ask the system that is building code for you, what happened?
[00:05:20.240 --> 00:05:21.920] What choices did you have?
[00:05:21.920 --> 00:05:23.280] And what path did you take?
[00:05:23.280 --> 00:05:23.920] And why?
[00:05:23.920 --> 00:05:24.560] Explain it.
[00:05:24.560 --> 00:05:30.280] Give me examples from the past or code from other projects that did this and didn't do it well.
[00:05:29.840 --> 00:05:32.600] This is the thing about AI systems like this.
[00:05:32.840 --> 00:05:39.800] They're always ready to go that extra mile to validate why they did a thing, to explain themselves.
[00:05:39.800 --> 00:05:53.960] And for people who learn by imitation, for people who learn by watching other people practice their craft, I think this is the first time that coding has become truly accessible to them at scale and for almost zero marginal cost.
[00:05:53.960 --> 00:06:02.200] Like another person learning how to code from a system, an AI system that already can teach at this scale, that is zero marginal cost right there.
[00:06:02.200 --> 00:06:11.640] And this reminds me of what I would call the YouTube effect, that phenomenon that's been teaching people to do all kinds of things for decades now.
[00:06:11.640 --> 00:06:19.720] And yes, coding has been on YouTube too, I know, and it's been a great way to learn from videos, but YouTube still has this kind of asynchronous limitation.
[00:06:19.720 --> 00:06:23.560] You watch a video, try to figure something out, try to learn something from it.
[00:06:23.560 --> 00:06:28.760] You watch another hour of tutorials, then you attempt to implement that in your system, still back and forth, right?
[00:06:28.760 --> 00:06:30.920] But over an extended timeframe.
[00:06:30.920 --> 00:06:37.640] With AI systems, both for coding and for pretty much every other activity, something different is happening.
[00:06:37.640 --> 00:06:43.640] Coaching, active synchronous coaching has become a way of getting into things.
[00:06:43.640 --> 00:06:49.240] Not just getting much better when you're already in there, you can be coached into a thing on your own time.
[00:06:49.240 --> 00:06:57.320] And I find this fascinating because coaching used to be something highly expensive, highly exclusive, and honestly, very hard to get right.
[00:06:57.320 --> 00:07:04.920] Because if you ever worked with a coach or a consultant before, maybe you are one and you have some stories where your customers maybe didn't vibe with you.
[00:07:04.920 --> 00:07:06.360] You know the challenges.
[00:07:06.360 --> 00:07:11.160] Most of these relationships, consultants, coaches may not be the best fit for you.
[00:07:11.160 --> 00:07:16.320] Most consultants might not have the exact experience that you need for your specific situation.
[00:07:14.920 --> 00:07:20.160] And many coaches might just have a different personality that doesn't click with you.
[00:07:20.320 --> 00:07:23.600] And how you learn, how you need to get into a thing better.
[00:07:23.600 --> 00:07:24.960] It's like finding a therapist.
[00:07:24.960 --> 00:07:30.400] It often takes a while to find somebody who really works for you because you need compatibility.
[00:07:30.400 --> 00:07:32.640] And the same with coaches, same with consultants.
[00:07:32.640 --> 00:07:41.680] But AI systems, well, they can be all of these different people, all of different types of mentors, just by being prompted to be them.
[00:07:41.680 --> 00:07:55.840] You can very quickly establish a connection with these tools that would have been impossible with real people because either because you were limited to a certain selection of available experts or because these relationships were prohibitively expensive.
[00:07:55.840 --> 00:08:01.760] And this is really exciting to see, not just for coding, but for all kinds of learning activities.
[00:08:01.760 --> 00:08:11.840] Here's something I think we don't talk about enough: a guided, experiential approach that's almost synchronous in nature is now available.
[00:08:11.840 --> 00:08:17.920] And this is going to do a lot for people who learn in ways that our traditional education system hasn't served them well with.
[00:08:17.920 --> 00:08:24.320] A lot of people are not the best readers, they're not the best listeners or best abstract thinkers when it comes to learning.
[00:08:24.320 --> 00:08:30.320] A lot of peeps need to be shown precisely what and how in an almost deductive way.
[00:08:30.560 --> 00:08:35.760] They don't learn inductively, where you gather all the knowledge first and then you synthesize the result.
[00:08:35.760 --> 00:08:37.440] They learn by deducing.
[00:08:37.440 --> 00:08:41.760] They need to see the result first and then deduce their way into understanding its components.
[00:08:41.760 --> 00:08:50.000] They disassemble the final product to understand the pieces that they will need to reassemble it for themselves for their own purposes in the future.
[00:08:50.000 --> 00:08:58.240] And I think it's a completely valid way of learning and understanding the world, but it's been incredibly hard to access in traditional coding education.
[00:08:58.240 --> 00:09:18.120] So, if you look past all this shiny AI is doing all the work for me kind of headline stuff that we're exposed to, or the AI is threatening my job anxiety, and instead we see these tools as these always-on, extremely affordable empowerment tools, personal coaches for everything, then all of a sudden, this technological shift becomes even more significant.
[00:09:18.120 --> 00:09:21.880] Yes, there's definitely a hype cycle where it's all over the place.
[00:09:21.880 --> 00:09:24.760] Don't even need to look at Twitter, you can just read the newspaper.
[00:09:24.760 --> 00:09:30.920] Even in regular news, AI is being hyped, and AI companies are building products that nobody wants or needs.
[00:09:30.920 --> 00:09:41.960] But if we change our perspective and see AI as a learning vehicle first and foremost, and not just as fancy tech on GPUs, then we're looking at something really interesting.
[00:09:41.960 --> 00:09:45.000] We're looking at the democratization of mentorship.
[00:09:45.000 --> 00:09:47.400] It's not just about coding either, of course.
[00:09:47.400 --> 00:09:58.200] This same principle applies to design, to marketing, business strategy, to any skill where learning from experienced practitioners has traditionally been gatekept by access and cost.
[00:09:58.200 --> 00:10:05.400] But coding is particularly powerful as an example here because it's been such a clear illustration of the lone wolf kind of learning path.
[00:10:05.560 --> 00:10:14.600] The image of the programmer staying up all night, banging their head against these incomprehensible error messages that we're all exposed to every day, finally having the breakthrough moment at 3 a.m.
[00:10:14.920 --> 00:10:18.120] That's kind of been our romantic notion of how this works.
[00:10:18.120 --> 00:10:21.800] I've been doing this a lot in my own experience.
[00:10:21.800 --> 00:10:37.640] And while there's still value in that struggle, and you always kind of have to struggle to learn anything, sure, building persistence and problem-solving muscles, right, in your brain, we don't have to make the learning process unnecessarily isolating anymore.
[00:10:37.640 --> 00:10:51.520] So, if you're someone who's been thinking about learning the code, or you're early in your journey and you're feeling overwhelmed by the traditional path, I want you to know that you now have options that simply did not exist a couple years ago.
[00:10:51.520 --> 00:10:53.760] Probably didn't even exist a couple months ago.
[00:10:53.760 --> 00:10:59.520] Like the agentic systems play a big part here, and always on is pretty important too.
[00:10:59.520 --> 00:11:02.560] You can now have conversations with your AI coding mentor.
[00:11:02.560 --> 00:11:06.720] You can ask them, why did you choose this approach over that one?
[00:11:06.720 --> 00:11:10.560] You can say, I don't understand this concept that you just implemented here.
[00:11:10.560 --> 00:11:11.760] What pops up?
[00:11:11.760 --> 00:11:12.960] I never heard of this before.
[00:11:12.960 --> 00:11:14.640] Can you explain this to me in terms?
[00:11:14.640 --> 00:11:22.080] You can even have it explained to you in fun terms, like as a, I don't know, like as a rap or, you know, as a poem or whatever.
[00:11:22.400 --> 00:11:27.360] The thing will talk to you in a language that you want it to talk to you in, right?
[00:11:27.360 --> 00:11:29.040] In something you can understand.
[00:11:29.040 --> 00:11:32.800] You can request, walk me through your thought process as you debug this.
[00:11:32.800 --> 00:11:34.640] Explain it like I'm five years old.
[00:11:34.640 --> 00:11:37.840] It's not about replacing fundamentals or skipping hard work.
[00:11:37.920 --> 00:11:43.360] You still have to do this, but it's about having a guide for that hard work that works for you.
[00:11:43.360 --> 00:11:53.040] And if you're an experienced developer, I think there's an opportunity here too to reflect on how you learned and consider how you might help others learn more effectively here too.
[00:11:53.040 --> 00:12:02.880] AI tools are democratizing access to mentorship, but they're modeling their guidance on the collective wisdom of experienced developers who've been mentoring people before.
[00:12:02.880 --> 00:12:06.240] So we're witnessing something that goes far beyond coding tools.
[00:12:06.240 --> 00:12:13.040] We're seeing this emergence of accessible, personalized, and always available mentorship for any skill we want to develop.
[00:12:13.040 --> 00:12:20.000] And I'm not even just talking about like OpenAI, chat, GPT, stuff that you use in your browser when you're internet connected, right?
[00:12:20.000 --> 00:12:28.560] Think about the fact that you can run these large language models that power these systems locally on your computer in an isolated system.
[00:12:28.560 --> 00:12:39.320] You could turn off your internet, you could power your computer from a battery if the power goes out or whatever, and it would still be there, always ready, as long as there's electricity in some shape or form.
[00:12:39.320 --> 00:12:47.880] That is quite democratizing as a resource, as means of access to a thing that is really, really powerful.
[00:12:47.880 --> 00:12:51.240] So, question is not whether this will change how people learn.
[00:12:51.240 --> 00:12:52.600] It's already changing.
[00:12:52.600 --> 00:13:00.760] Like, if you look at students right now, they work with AI all the time, not just to have it solve their homework, but to actually ask it questions.
[00:13:00.760 --> 00:13:04.440] It's very, very interesting to look at this at the university level.
[00:13:04.440 --> 00:13:06.760] That's why people have really figured this out too.
[00:13:06.760 --> 00:13:14.200] But even with younger students, AI becomes a mentor, not just somebody to delegate to, but somebody to learn from.
[00:13:14.360 --> 00:13:18.600] Question here is whether we will recognize this shift for what it really is.
[00:13:18.600 --> 00:13:28.520] It's not just about making us more productive doing our homework, but it's about making learning itself more accessible, more personalized, more effective for different types of minds.
[00:13:28.600 --> 00:13:34.360] That's the revolution that is hiding in plain sight while we're all debating whether AI will replace developers.
[00:13:34.360 --> 00:13:38.840] That doesn't matter because it will allow us to learn so many more things.
[00:13:38.840 --> 00:13:44.200] And honestly, I think this learning revolution might be the more important story here.
[00:13:44.200 --> 00:13:45.400] And that's it for today.
[00:13:45.400 --> 00:13:47.480] Thank you so much for listening to the Bootstrap Founder.
[00:13:47.480 --> 00:13:51.080] You can find me on Twitter at AvidKal, AIBID, K-H-L.
[00:13:51.080 --> 00:14:00.840] If you want to support me on this show, please share podscan.fm with your professional peers and those who you think will benefit from tracking mentions of brands, businesses, and names on podcasts out there.
[00:14:00.840 --> 00:14:14.200] People have been using PodScan for lead generation, for creating newsletters to send to their clients and customers, for finding data to immediately act on, for finding mentions to do PR disaster recovery with.
[00:14:14.200 --> 00:14:21.040] It's really, really useful to hear exactly what's being said about a certain thing right after it's being published as a podcast.
[00:14:21.040 --> 00:14:26.000] So please share the word with those who you know need to stay on top of the podcast ecosystem.
[00:14:26.000 --> 00:14:27.280] Thanks so much for listening.
[00:14:27.280 --> 00:14:29.840] Have a wonderful day and bye-bye.

Prompt 2: Key Takeaways

Now please extract the key takeaways from the transcript content I provided.

Extract the most important key takeaways from this part of the conversation. Use a single sentence statement (the key takeaway) rather than milquetoast descriptions like "the hosts discuss...". 

Limit the key takeaways to a maximum of 3. The key takeaways should be insightful and knowledge-additive. 

IMPORTANT: Return ONLY valid JSON, no explanations or markdown. Ensure:
- All strings are properly quoted and escaped
- No trailing commas
- All braces and brackets are balanced
Format: {"key_takeaways": ["takeaway 1", "takeaway 2"]}

Prompt 3: Segments

Now identify 2-4 distinct topical segments from this part of the conversation.

For each segment, identify:
- Descriptive title (3-6 words)  
- START timestamp when this topic begins (HH:MM:SS format)
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Most important Key takeaway from that segment. Key takeaway must be specific and knowledge-additive.
- Brief summary of the discussion

IMPORTANT: The timestamp should mark when the topic/segment STARTS, not a range. Look for topic transitions and conversation shifts.

Return ONLY valid JSON. Ensure all strings are properly quoted, no trailing commas:
{
  "segments": [
    {
      "segment_title": "Topic Discussion", 
      "timestamp": "01:15:30",
      "key_takeaway": "main point from this segment",
      "segment_summary": "brief description of what was discussed"
    }
  ]
}

Timestamp format: HH:MM:SS (e.g., 00:05:30, 01:22:45) marking the START of each segment.

Prompt 4: Media Mentions

Now scan the transcript content I provided for ACTUAL mentions of specific media titles:

Find explicit mentions of:
- Books (with specific titles)
- Movies (with specific titles)  
- TV Shows (with specific titles)
- Music/Songs (with specific titles)

DO NOT include:
- Websites, URLs, or web services
- Other podcasts or podcast names

IMPORTANT: 
- Only include items explicitly mentioned by name. Do not invent titles.
- Valid categories are: "Book", "Movie", "TV Show", "Music"
- Include the exact phrase where each item was mentioned
- Find the nearest proximate timestamp where it appears in the conversation
- THE TIMESTAMP OF THE MEDIA MENTION IS IMPORTANT - DO NOT INVENT TIMESTAMPS AND DO NOT MISATTRIBUTE TIMESTAMPS
- Double check that the timestamp is accurate - a timestamp will NEVER be greater than the total length of the audio
- Timestamps are given as ranges, e.g. 01:13:42.520 --> 01:13:46.720. Use the EARLIER of the 2 timestamps in the range.

Return ONLY valid JSON. Ensure all strings are properly quoted and escaped, no trailing commas:
{
  "media_mentions": [
    {
      "title": "Exact Title as Mentioned",
      "category": "Book",
      "author_artist": "N/A",
      "context": "Brief context of why it was mentioned",
      "context_phrase": "The exact sentence or phrase where it was mentioned",
      "timestamp": "estimated time like 01:15:30"
    }
  ]
}

If no media is mentioned, return: {"media_mentions": []}

Full Transcript

[00:00:00.320 --> 00:00:04.480] Hey, it's Arvid, and this is the Bootstrap Founder.
[00:00:08.880 --> 00:00:16.560] Today, I will highlight one part of the AI hype that we're all having to deal with right now that is severely underreported on.
[00:00:16.560 --> 00:00:28.560] And I think it's that part that I personally believe has much stronger long-term impact than all those magical video generators and coding agents that we see every single day.
[00:00:28.560 --> 00:00:35.920] This episode is sponsored by Paddle.com, my merchant of record payment provider of choice, who's been helping me focus on PodScan from day one.
[00:00:35.920 --> 00:00:48.480] And they've been taking care of all the things related to money so that founders like me and you can focus on building the things that only we can build, paddle panels, all the rest, like sales tags and credit cards failing, all of that, don't have to deal with it.
[00:00:48.480 --> 00:00:49.440] They do it for you.
[00:00:49.440 --> 00:00:50.960] I highly recommend checking it out.
[00:00:50.960 --> 00:00:53.840] So please go to paddle.com.
[00:00:55.440 --> 00:01:04.560] I just had one of those realizations that makes you stop and think quite differently about everything that has been going on around the discussion of AI tooling.
[00:01:04.560 --> 00:01:08.800] You know, we talk a lot about AI helping us build things faster.
[00:01:08.800 --> 00:01:13.360] And we talk about automation, about whether it's going to replace developers, take our jobs.
[00:01:13.360 --> 00:01:21.280] But there's a side effect happening right now that I think is either completely undervalued or just not being observed at all.
[00:01:21.280 --> 00:01:28.720] And honestly, it might be much more transformative than the automation piece that we're all focused on so much.
[00:01:28.720 --> 00:01:35.200] Let me paint you a picture of how learning to code has worked for basically forever.
[00:01:35.200 --> 00:01:40.480] For the longest time, if you wanted to learn how to code, you had pretty much two choices, two main paths.
[00:01:40.480 --> 00:01:42.640] The first was the intellectual approach.
[00:01:42.640 --> 00:01:51.200] You would dive into books and wade through documentation and just try to absorb all of this abstract knowledge and somehow transform it into working code.
[00:01:51.200 --> 00:01:54.160] And the second one was the experiential route.
[00:01:54.160 --> 00:01:58.080] And that's what most of us self-taught developers know intimately.
[00:01:58.080 --> 00:02:01.400] You would run headfirst into every single issue one by one.
[00:02:01.400 --> 00:02:02.760] You'd just figure it out.
[00:02:02.760 --> 00:02:04.760] You tap into a Stack Overflow.
[00:02:04.760 --> 00:02:12.200] When you were lucky, maybe find somebody else's solution that kind of fit your problem, or just adopt whatever you were seeing to what you needed.
[00:02:12.200 --> 00:02:20.040] And if you could handle it, you'd try to read more experienced developers' code and then reverse engineer how that could apply to what you were building.
[00:02:20.040 --> 00:02:28.200] But here's the thing: it was always this process of consuming information that wasn't really code to then produce code.
[00:02:28.200 --> 00:02:32.360] And it was always asynchronous every single time.
[00:02:32.360 --> 00:02:38.520] You would hit a wall, spend hours researching, maybe post a question somewhere, and then wait for an answer.
[00:02:38.520 --> 00:02:42.360] And if you were really fortunate, you were part of a team where you would get feedback.
[00:02:42.360 --> 00:02:45.560] But even then, the core experience was the same.
[00:02:45.560 --> 00:02:57.160] You alone diving into a problem, running into a wall, and then running into 20 more walls over the next couple of hours, waiting for somebody to help, because they also were running into their own walls at the same time.
[00:02:57.160 --> 00:03:07.080] Then you would either give up or ask somebody else, or you'd figure it out just through sheer persistence and take that hardworn learning forward into the next thing you had to build.
[00:03:07.080 --> 00:03:14.440] So that's how we've been learning to be better programmers, better infrastructure developers, better software architects, and all of that for decades.
[00:03:14.440 --> 00:03:18.200] But something fundamental is shifting with AI software development tools.
[00:03:18.200 --> 00:03:25.080] And I want to be clear: this has very little to do with this admittedly magical capacity of letting AI do the work for us.
[00:03:25.080 --> 00:03:27.960] That's a whole other conversation that everybody seems to be having.
[00:03:27.960 --> 00:03:29.880] So we don't need to talk about that here.
[00:03:29.880 --> 00:03:35.640] But this is about something much more profound for the people who are still willing to learn how to code.
[00:03:35.640 --> 00:03:44.680] And there are still many of those people because here's what I think we all understand deep down: coding is significantly more than telling a machine what to do.
[00:03:44.800 --> 00:03:54.560] To be able to judge good code, you still have to be able to write good code in the first place, or at least understand what the difference between good code and bad code looks like.
[00:03:54.560 --> 00:03:58.480] You can't delegate that judgment entirely, at least not yet.
[00:03:58.480 --> 00:04:05.200] And I think for the foreseeable future, we will still need to be able to judge the outputs of these machines.
[00:04:05.200 --> 00:04:15.680] So, for people who want to learn this craft of being able to write good code so they can tell machines what they need and don't have to write the code anymore, something remarkable is happening right now.
[00:04:15.680 --> 00:04:19.680] All of a sudden, they don't have to do it all by themselves anymore.
[00:04:19.680 --> 00:04:28.240] Think about this: with AI tools, learners can actually look at what an experienced person would do and pair program with them.
[00:04:28.240 --> 00:04:34.000] Not just occasionally, not just when schedules align, but 24-7, whenever they want to.
[00:04:34.000 --> 00:04:43.760] They can ask questions of that somewhat or very experienced developer 24-7 all day long for effectively free.
[00:04:43.760 --> 00:04:55.840] Even if they're paying 20 bucks a month or 200 bucks a month for an agentic system, you know, Cloud Code Max or whatever the thing is called, it is still a 24-7 relationship, always on, always available.
[00:04:55.840 --> 00:05:14.640] And if someone's trying to learn how to code, they can have this constant AI system build code for them and here's the crucial part: teach them how they did it, why they did it, what they thought about, what they approached and how they approached things in certain ways, why they didn't choose the alternative approach here.
[00:05:14.960 --> 00:05:20.240] You can always ask the system that is building code for you, what happened?
[00:05:20.240 --> 00:05:21.920] What choices did you have?
[00:05:21.920 --> 00:05:23.280] And what path did you take?
[00:05:23.280 --> 00:05:23.920] And why?
[00:05:23.920 --> 00:05:24.560] Explain it.
[00:05:24.560 --> 00:05:30.280] Give me examples from the past or code from other projects that did this and didn't do it well.
[00:05:29.840 --> 00:05:32.600] This is the thing about AI systems like this.
[00:05:32.840 --> 00:05:39.800] They're always ready to go that extra mile to validate why they did a thing, to explain themselves.
[00:05:39.800 --> 00:05:53.960] And for people who learn by imitation, for people who learn by watching other people practice their craft, I think this is the first time that coding has become truly accessible to them at scale and for almost zero marginal cost.
[00:05:53.960 --> 00:06:02.200] Like another person learning how to code from a system, an AI system that already can teach at this scale, that is zero marginal cost right there.
[00:06:02.200 --> 00:06:11.640] And this reminds me of what I would call the YouTube effect, that phenomenon that's been teaching people to do all kinds of things for decades now.
[00:06:11.640 --> 00:06:19.720] And yes, coding has been on YouTube too, I know, and it's been a great way to learn from videos, but YouTube still has this kind of asynchronous limitation.
[00:06:19.720 --> 00:06:23.560] You watch a video, try to figure something out, try to learn something from it.
[00:06:23.560 --> 00:06:28.760] You watch another hour of tutorials, then you attempt to implement that in your system, still back and forth, right?
[00:06:28.760 --> 00:06:30.920] But over an extended timeframe.
[00:06:30.920 --> 00:06:37.640] With AI systems, both for coding and for pretty much every other activity, something different is happening.
[00:06:37.640 --> 00:06:43.640] Coaching, active synchronous coaching has become a way of getting into things.
[00:06:43.640 --> 00:06:49.240] Not just getting much better when you're already in there, you can be coached into a thing on your own time.
[00:06:49.240 --> 00:06:57.320] And I find this fascinating because coaching used to be something highly expensive, highly exclusive, and honestly, very hard to get right.
[00:06:57.320 --> 00:07:04.920] Because if you ever worked with a coach or a consultant before, maybe you are one and you have some stories where your customers maybe didn't vibe with you.
[00:07:04.920 --> 00:07:06.360] You know the challenges.
[00:07:06.360 --> 00:07:11.160] Most of these relationships, consultants, coaches may not be the best fit for you.
[00:07:11.160 --> 00:07:16.320] Most consultants might not have the exact experience that you need for your specific situation.
[00:07:14.920 --> 00:07:20.160] And many coaches might just have a different personality that doesn't click with you.
[00:07:20.320 --> 00:07:23.600] And how you learn, how you need to get into a thing better.
[00:07:23.600 --> 00:07:24.960] It's like finding a therapist.
[00:07:24.960 --> 00:07:30.400] It often takes a while to find somebody who really works for you because you need compatibility.
[00:07:30.400 --> 00:07:32.640] And the same with coaches, same with consultants.
[00:07:32.640 --> 00:07:41.680] But AI systems, well, they can be all of these different people, all of different types of mentors, just by being prompted to be them.
[00:07:41.680 --> 00:07:55.840] You can very quickly establish a connection with these tools that would have been impossible with real people because either because you were limited to a certain selection of available experts or because these relationships were prohibitively expensive.
[00:07:55.840 --> 00:08:01.760] And this is really exciting to see, not just for coding, but for all kinds of learning activities.
[00:08:01.760 --> 00:08:11.840] Here's something I think we don't talk about enough: a guided, experiential approach that's almost synchronous in nature is now available.
[00:08:11.840 --> 00:08:17.920] And this is going to do a lot for people who learn in ways that our traditional education system hasn't served them well with.
[00:08:17.920 --> 00:08:24.320] A lot of people are not the best readers, they're not the best listeners or best abstract thinkers when it comes to learning.
[00:08:24.320 --> 00:08:30.320] A lot of peeps need to be shown precisely what and how in an almost deductive way.
[00:08:30.560 --> 00:08:35.760] They don't learn inductively, where you gather all the knowledge first and then you synthesize the result.
[00:08:35.760 --> 00:08:37.440] They learn by deducing.
[00:08:37.440 --> 00:08:41.760] They need to see the result first and then deduce their way into understanding its components.
[00:08:41.760 --> 00:08:50.000] They disassemble the final product to understand the pieces that they will need to reassemble it for themselves for their own purposes in the future.
[00:08:50.000 --> 00:08:58.240] And I think it's a completely valid way of learning and understanding the world, but it's been incredibly hard to access in traditional coding education.
[00:08:58.240 --> 00:09:18.120] So, if you look past all this shiny AI is doing all the work for me kind of headline stuff that we're exposed to, or the AI is threatening my job anxiety, and instead we see these tools as these always-on, extremely affordable empowerment tools, personal coaches for everything, then all of a sudden, this technological shift becomes even more significant.
[00:09:18.120 --> 00:09:21.880] Yes, there's definitely a hype cycle where it's all over the place.
[00:09:21.880 --> 00:09:24.760] Don't even need to look at Twitter, you can just read the newspaper.
[00:09:24.760 --> 00:09:30.920] Even in regular news, AI is being hyped, and AI companies are building products that nobody wants or needs.
[00:09:30.920 --> 00:09:41.960] But if we change our perspective and see AI as a learning vehicle first and foremost, and not just as fancy tech on GPUs, then we're looking at something really interesting.
[00:09:41.960 --> 00:09:45.000] We're looking at the democratization of mentorship.
[00:09:45.000 --> 00:09:47.400] It's not just about coding either, of course.
[00:09:47.400 --> 00:09:58.200] This same principle applies to design, to marketing, business strategy, to any skill where learning from experienced practitioners has traditionally been gatekept by access and cost.
[00:09:58.200 --> 00:10:05.400] But coding is particularly powerful as an example here because it's been such a clear illustration of the lone wolf kind of learning path.
[00:10:05.560 --> 00:10:14.600] The image of the programmer staying up all night, banging their head against these incomprehensible error messages that we're all exposed to every day, finally having the breakthrough moment at 3 a.m.
[00:10:14.920 --> 00:10:18.120] That's kind of been our romantic notion of how this works.
[00:10:18.120 --> 00:10:21.800] I've been doing this a lot in my own experience.
[00:10:21.800 --> 00:10:37.640] And while there's still value in that struggle, and you always kind of have to struggle to learn anything, sure, building persistence and problem-solving muscles, right, in your brain, we don't have to make the learning process unnecessarily isolating anymore.
[00:10:37.640 --> 00:10:51.520] So, if you're someone who's been thinking about learning the code, or you're early in your journey and you're feeling overwhelmed by the traditional path, I want you to know that you now have options that simply did not exist a couple years ago.
[00:10:51.520 --> 00:10:53.760] Probably didn't even exist a couple months ago.
[00:10:53.760 --> 00:10:59.520] Like the agentic systems play a big part here, and always on is pretty important too.
[00:10:59.520 --> 00:11:02.560] You can now have conversations with your AI coding mentor.
[00:11:02.560 --> 00:11:06.720] You can ask them, why did you choose this approach over that one?
[00:11:06.720 --> 00:11:10.560] You can say, I don't understand this concept that you just implemented here.
[00:11:10.560 --> 00:11:11.760] What pops up?
[00:11:11.760 --> 00:11:12.960] I never heard of this before.
[00:11:12.960 --> 00:11:14.640] Can you explain this to me in terms?
[00:11:14.640 --> 00:11:22.080] You can even have it explained to you in fun terms, like as a, I don't know, like as a rap or, you know, as a poem or whatever.
[00:11:22.400 --> 00:11:27.360] The thing will talk to you in a language that you want it to talk to you in, right?
[00:11:27.360 --> 00:11:29.040] In something you can understand.
[00:11:29.040 --> 00:11:32.800] You can request, walk me through your thought process as you debug this.
[00:11:32.800 --> 00:11:34.640] Explain it like I'm five years old.
[00:11:34.640 --> 00:11:37.840] It's not about replacing fundamentals or skipping hard work.
[00:11:37.920 --> 00:11:43.360] You still have to do this, but it's about having a guide for that hard work that works for you.
[00:11:43.360 --> 00:11:53.040] And if you're an experienced developer, I think there's an opportunity here too to reflect on how you learned and consider how you might help others learn more effectively here too.
[00:11:53.040 --> 00:12:02.880] AI tools are democratizing access to mentorship, but they're modeling their guidance on the collective wisdom of experienced developers who've been mentoring people before.
[00:12:02.880 --> 00:12:06.240] So we're witnessing something that goes far beyond coding tools.
[00:12:06.240 --> 00:12:13.040] We're seeing this emergence of accessible, personalized, and always available mentorship for any skill we want to develop.
[00:12:13.040 --> 00:12:20.000] And I'm not even just talking about like OpenAI, chat, GPT, stuff that you use in your browser when you're internet connected, right?
[00:12:20.000 --> 00:12:28.560] Think about the fact that you can run these large language models that power these systems locally on your computer in an isolated system.
[00:12:28.560 --> 00:12:39.320] You could turn off your internet, you could power your computer from a battery if the power goes out or whatever, and it would still be there, always ready, as long as there's electricity in some shape or form.
[00:12:39.320 --> 00:12:47.880] That is quite democratizing as a resource, as means of access to a thing that is really, really powerful.
[00:12:47.880 --> 00:12:51.240] So, question is not whether this will change how people learn.
[00:12:51.240 --> 00:12:52.600] It's already changing.
[00:12:52.600 --> 00:13:00.760] Like, if you look at students right now, they work with AI all the time, not just to have it solve their homework, but to actually ask it questions.
[00:13:00.760 --> 00:13:04.440] It's very, very interesting to look at this at the university level.
[00:13:04.440 --> 00:13:06.760] That's why people have really figured this out too.
[00:13:06.760 --> 00:13:14.200] But even with younger students, AI becomes a mentor, not just somebody to delegate to, but somebody to learn from.
[00:13:14.360 --> 00:13:18.600] Question here is whether we will recognize this shift for what it really is.
[00:13:18.600 --> 00:13:28.520] It's not just about making us more productive doing our homework, but it's about making learning itself more accessible, more personalized, more effective for different types of minds.
[00:13:28.600 --> 00:13:34.360] That's the revolution that is hiding in plain sight while we're all debating whether AI will replace developers.
[00:13:34.360 --> 00:13:38.840] That doesn't matter because it will allow us to learn so many more things.
[00:13:38.840 --> 00:13:44.200] And honestly, I think this learning revolution might be the more important story here.
[00:13:44.200 --> 00:13:45.400] And that's it for today.
[00:13:45.400 --> 00:13:47.480] Thank you so much for listening to the Bootstrap Founder.
[00:13:47.480 --> 00:13:51.080] You can find me on Twitter at AvidKal, AIBID, K-H-L.
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[00:14:27.280 --> 00:14:29.840] Have a wonderful day and bye-bye.