Swetlana AI

Tag: technology

  • I Wrote a Book About the One Thing That Makes AI Actually Useful

    I Wrote a Book About the One Thing That Makes AI Actually Useful

    Attention Context is all you need.

    You talk to AI every day.

    Maybe you use ChatGPT, Claude, Gemini, or whatever comes next.

    And every single time, you start from zero (or with some minimal unstructured memory the model has of you).

    The AI has no idea who you are, what you have tried before, what you care about, or how you think.

    So it gives you generic answers. Decent ones, sure. But generic.

    I have been thinking about this problem for a while. Not as a technical challenge, but as a human one. Because the bottleneck is not the AI. The AI is already very capable. The bottleneck is you. Or more precisely: the fact that AI has no access to your context.

    And most people will never fix that, because it never occurs to them that they could.

    The idea is stupidly simple

    Take the things in your life that matter. Your experiments, your decisions, your lessons, your realizations. Put them on a page. Give that page a simple structure. Four sections: What it is. What matters. What I learned. What is next.

    That is it. Five minutes. One page.

    Now you have something that is useful to you when you come back to it in three months. And it is just as useful to any AI you hand it to. Because a clear, structured page is exactly the kind of input that makes AI go from “helpful stranger” to something much closer to a real collaborator.

    I call this process mdfication. The name comes from .md (Markdown), the simple text format that works beautifully with AI. But the book itself does not require any technical knowledge at all. If you can write a text message, you can do this.

    Why I wrote this as a book

    I have written about mdfication before on my blog.

    But I wanted to make something that a complete beginner could pick up and actually use. Not a tech person. A gardener. A cook. A parent. Someone who has never heard of Markdown and does not need to.

    The book is called .md-fy Yourself and it is structured like The Creative Act by Rick Rubin meets a practical workbook. Each chapter opens with an idea, a shift in perspective, and closes with a simple practice you can do in five minutes. No jargon. No system to install. No app required.

    It covers:

    • Why everything you know keeps disappearing (and how to stop it)
    • How to create simple “pages” for anything in your life
    • Pages for what you grow, what you feel, what you decide
    • The moment where you realize your pages are already the best possible input for AI
    • How to build a personal “context kit” that makes any AI smarter about you
    • How to connect your pages into something that grows over time

    The part I care about most

    AI is going to get significantly more powerful in the coming years. More capable, more flexible, more integrated into daily life. But no matter how advanced it gets, it will always need one thing it cannot generate on its own: your context. Your specific experience, your patterns, your history, your way of thinking.

    The people who have that context organized and ready will get dramatically more value from AI than those who do not. Not because they are more technical. Because they gave AI something to work with.

    That is the real argument of this book. Not “be organized.” But: build something that makes AI genuinely useful for the life you actually live.

    The book is out now on Amazon.

    If you have been following my mdfication posts, this is the full version. If you are new to the idea, the book is designed to take you from zero to a working practice in an afternoon.

    Let me know what you think. I’m super curious to hear how people use this.

    Find me on Twitter | YouTube | swetlanaai.com

  • How to .md-fy Yourself

    How to .md-fy Yourself

    Forget system prompts and custom GPTs. There is something way simpler and way more powerful you can use to upgrade your work with AI: .md files.

    mdfication = turning parts of your cognition into documented artifacts

    TLDR: You can externalize elements of your thinking process, decision making, taste, knowledge, and direction, by just writing a few .md files. 

    This method goes way beyond existing frameworks, and is at the same time simpler than all of them.

    I call it mdfication.

    Imagine a folder with a few .md files. Yes, that’s all it takes.

    I came up with this new thing and I feel it could be groundbreaking for a lot of people.

    Watch the video below for further explanation.

    [If you’re thrilled by this idea and need help structuring your “mind”, reach out. I will soon have an offering for you.]

    mdfication: Premise

    For most of history, the mind was something to understand.

    Now it can be something to structure, replicate*, and make operational outside your head.

    *“replicating the mind” may sound lofty. I mean partially replicate.

    How to “md-fy” Yourself

    Turns out it’s pretty easy to set up a digital version of yourself. 

    You just need to provide AI with some info.

    Example of hobbies.md

    Now that might sound trivial at first, but in reality it’s the single most powerful move you can make.

    Think of it this way: most people use AI as a search engine or a ghostwriter for one-off tasks.

    At most, they’ll use a long single chat thread in ChatGPT to keep a conversation going.

    But when you create small amounts of text about your thoughts, hobbies, life goals, ideas, and how you solve problems, you’re doing something much bigger.

    You’re giving the AI context.

    And that’s not nearly all.

    Once you have some text files with info, you can use it with any LLM, in an unlimited amount of sessions.

    A practical and free way to do this is to use Gemini and Google Drive. You can connect Gemini to the Google Workspace and allow it to access your docs.

    Just enable the access, drag your text files into your Google Drive, and now Gemini has memory about you.

    To be on the safe side, I recommend using a separate Google account for this type of stuff.

    .md Files

    Some of you might have heard of the .md file format. 

    If you haven’t, it’s simple markdown, like something between txt and html format.

    Wikipedia: Markdown

    It’s very simple and easy to create an .md file. 

    Just use hashtags (##) in front of your subheadings and be as concise as you can.

    Another example of an .md file

    How to Create Your Brain.md

    Here’s what you can do specifically in your case, regardless of who you are:

    • Write down basic things about yourself.
    • Add whatever you want about yourself.
    • If you have any ambitions in life, write them down.
    • If you have hobbies write them down.

    Zoom out, and really think about what matters to you.

    You can identify things that matter to you by looking at how much time you spend thinking about different things.

    Look at your browser bookmarks.

    Look at the files on your computer.

    If you can describe your thinking style, do it.

    Don’t overthink it.

    You can always edit the file later.

    Now save your text file as an .md file.

    You can do this from your text editor.

    You can create several .md files and put them in a folder.

    In our previous example I already pointed out that this works great with Gemini and Google Drive. You can use other LLMs too. But I’m not gonna get into too much detail this time.

    Upload your folder to Google Drive.

    Now when you chat with Gemini, you can ask it to access those files.

    Now that the AI has access to your files, the sky is the limit.

    content.md

    This “personal operating system” is even more impressive when it’s used by content creators.

    If you have a blog or Youtube channel about any particular topic, you can build a content operating system that makes your creative process dramatically faster and more coherent.

    Imagine: 

    • a channel.md file that describes your audience, your tone, your niche, your upload schedule, and the core themes you keep coming back to. 
    • a video_ideas.md that captures every half-formed thought, every comment from a viewer that sparked something, every trend you noticed but haven’t acted on yet. 
    • an uploaded_content.md that logs every video or post you’ve published — the title, the angle, the performance, what worked, what didn’t.

    Now when you ask the AI “what should I make a video about this week?”, it doesn’t give you a generic list of trending topics. 

    • It knows you already covered beginner composting in March. It knows your audience skews toward people in small apartments. 
    • It knows your last three videos were quite technical and your viewers have been asking for something lighter. 
    • It suggests something specific, something that fits your channel — not just whatever is ranking well on YouTube right now.

    As you can see, this small folder with just a few files is a game changer.

    And the best thing is, you don’t even need a paid subscription to use this simple method.

    Let me know what you think by leaving a comment.

    Watch the video:

    Fediverse Reactions

  • Key Insights From OpenAI Podcast Episode #3

    How will AI impact the job market? I’ve been asking myself this question countless times, and so far *this* podcast episode seems to give the best answers. Let’s break down this conversation by OpenAI’s COO and its Chief Economist (yes, OpenAI has a Chief Economist!).

    In episode 3 (from July 15, 2025) of their podcast, OpenAI COO Brad Lightcap and Chief Economist Ronnie Chatterji discuss the impacts of AI on software, science, small business, education, and jobs.

    After listening to that episode I felt like there are so many individual “gems” in it that I just had to extract the most important ones.

    Each of the following statements deserves to be meditated over.

    🧠 AI as a Tool for Empowerment

    • “AI is a tool that lets people do things that they had no ability to do otherwise.” — Lightcap
    • “You have the world’s smartest brain at your fingertips to solve hard problems.” — Chatterji
    • “It’s the return of the idea guy.” — Lightcap
    • “AI is interesting because it really is kind of a reflection of your will.” — Lightcap

    💼 Jobs, Productivity & Individual Empowerment

    • “If you wake up one day and decide you want to start a business, that just got meaningfully easier.” — Lightcap
    • “Software engineers are becoming not 10% more productive, but maybe 10x more productive.” — Lightcap
    • “What could they build if you can write that much more code and that much better code?” — Chatterji

    🧪 Scientific Research & Discovery

    • “If we can accelerate science, accelerate discovery, we’re gonna have more economic growth and more good things for everybody.” — Chatterji
    • “Imagine a corridor with doors on either side — AI lets scientists peek behind all the doors.” — Chatterji
    • “You’re enabling the people who work with and around the scientists to accelerate the end product.” — Lightcap

    🏢 Small Businesses & Emerging Markets

    • “Small teams can do a lot more. We’re seeing companies where non-technical people are building agents.” — Lightcap
    • “Small business owners can leverage agents for evidence-based advice — that’s something I’m very interested in.” — Chatterji
    • “In Africa, one of the biggest ROIs is agricultural extension support — AI can scale that.” — Chatterji

    🧑‍🏫 Education Revolution

    • “The entire way we think about education will have to adapt.” — Lightcap
    • “What are you teaching in Kindergarten? How to be a human — that’s now the most important skill.” — Chatterji
    • “Students said: ‘ChatGPT doesn’t judge you.’” — Mayne
    • “We’re working with Cal State to track whether access to this intelligence changes career outcomes.” — Chatterji

    🤖 AI Agents

    • “Agents must take on complex work they haven’t seen before and execute it autonomously.” — Lightcap
    • “A sales agent should process 100,000 inbound leads and move them through the funnel.” — Lightcap
    • “An agent in an IDE, inbox, or experiment platform — it should adapt to how the user works.” — Lightcap

    📈 Economy, Sectors & Geographies

    • “I’m developing indicators to tell us where the economy is going.” — Chatterji
    • “Sectors that are less regulated will change the quickest.” — Chatterji
    • “Health care and education change slower due to compliance — but still massive opportunity.” — Chatterji
    • “My team is identifying which sectors and geographies are most affected — and communicating that clearly.” — Chatterji

    💡 Soft Skills, EQ, and Agency

    • “EQ matters more than ever — soft skills rise in value once technical capabilities are democratized.” — Chatterji
    • “Critical thinking, resilience, and financial numeracy will be crucial for future careers.” — Chatterji
    • “People who are opinionated about sales, marketing, product, and engineering will run billion-dollar companies with 5 people.” — Lightcap

    🔧 Workforce & Institutional Change

    • “The direction of travel for tech is always toward individual empowerment.” — Lightcap
    • “Institutions are slower than people — but those that adapt will have an incredible accelerant.” — Lightcap
    • “Clinical trials and other legacy systems in pharma will need institutional reform to catch up.” — Chatterji

    Here’s the video if you’d like to watch the entire thing:

  • AI & Creative Process

    Generative AI makes almost anything possible. You just need to know the tools and be clear about what you want to say.*

    [*In addition to it, you’ll of course need motivation, agency, and taste, but let’s save this discussion for next time.]

    For better or worse, art/culture is about to change, and I’m changing with it.

    For the context: Among other things, I’m also a “real artist”, aka an artist who has been creating without AI – for quite some time. Luckily, I’m very pro-tech (grew up with computers thanks to my dad), and have no unresolved conflicts pertaining to creativity, which is why I’ve experienced a pretty smooth conversion from “real art” to AI content.

    I’m mentioning this because a huge chunk of “real artists” continues to be outraged about AI. But that’s something for a future blog.

    In the past few months I’ve been using AI to create a lot of stuff (see “Projects”). Since I’m very active on Twitter/X (by the way, let’s connect), I tend to learn about new tools very early, and in many cases I head straight to the tool to try it out.

    General Observations

    Experimenting, or as some call it “tinkering”, is essential. We are entering a new terrain of artistic expression, with a lot (most?) of it still unexplored.

    Imagine what it must have felt like when photography was invented. As cameras became more accessible and photography moved from early adoption into the mainstream, people began to test its boundaries. 

    They didn’t just replicate paintings. They played with light, blur, composition, even accidents. That’s how entirely new aesthetics emerged.

    The same applies to AI tools today. The real breakthroughs don’t come from following tutorials step by step, but rather from misusing tools, playing with prompts, layering outputs, remixing styles, and exploring the “wrong” ways of doing things. 

    Tinkering isn’t aimlessness, it’s a tool of discovery. 

    It’s how genres are born, how formats mutate, and how we can stretch the limits of what feels possible.

    What I’ve Learned

    Things are changing fast, but for the foreseeable future these insights will probably hold true.

    Assuming you want to enter the space and try creating something, 

    • Many platforms offer free daily credits, and you should make use of those.
    • Have a structured collection of AI tools. In my case it’s a bookmarks folder.
    • Definitely also structure your computer folders. You might end up with a lot of images and videos. Don’t get lost in the jungle.
    • Spend time away from your computer and write down your ideas on paper. In my case being in the nature really helps to not get lost in the detail. 
    • Recognize the difference between “gimmick” and “substance”.  I see people post AI videos on X, and most of them are gimmicks (= they don’t have any message, and/or are just tropes). Now you don’t have to have ambitions to create anything of substance. But some discernment really helps here either way.
    • Spend some time thinking about taste and what it implies. 
    • Have ChatGPT write your image / video prompt. Your job is to tell it your idea, intent, message. This middle step adds a big layer of detail.
    • Get familiar with JSON. Prompts in JSON format are the ideal language when talking to AI.
    What a prompt in JSON format looks like. This one is for a video.

    I will surely dedicate another post to the insights I’ve had while using all the different AI tools. For now let’s keep it concise. 

    Finally, below is a list of some of the AI tools I’m using. Give them a try; they all have a free tier.

    AI Tools I Use

    Voice & Script Generation: I use Google’s NotebookLM, a free tool that transforms PDFs, websites, or text into audio summaries. This makes scripting and voice generation easy, even for complex topics. For cases when I need a custom voice (and for all things text-to-speech), Elevenlabs is my go-to.

    Music Creation: Tools like Suno and Udio enable me to generate background music for any mood, or entire songs, if I happen to have any song idea. You can use lyrics or create instrumental tracks.

    Video Generation: Platforms such as Runway, Luma, and Kling AI allow video creation and animation, offering a variety of capabilities depending on the project. These days I also increasingly use Dreamina by ByteDance/CapCut.

    Image Generation: I prefer Ideogram, Flux, Reve, and Leonardo.ai for diverse artistic styles. ChatGPT image generation is great for Ghibli-style illustrations or comics. Unlike Midjourney, which tends to create polished, predictable images, these tools yield more unexpected and raw visuals, perfect for experimental art.

    That’s it for now! If you want to *listen* to me talk about my creative process, here’s a video I made some time ago:

  • Vibe Coding: Some How-To Advice

    Vibe Coding: Some How-To Advice

    I don’t know about you, but I’m definitely not into coding all that much. I know just enough Python to write a short script, like one that generates a poem based on a lexicon.

    As a humanities/linguistics person, I’ve never really had patience for extended periods of programming. After about five hours of dealing with code, my focus fades, and I yearn for some fresh air in the park.

    I’ve worked with Java, C++, Perl, Prolog, HTML, XML etc. in the past, but none ever went beyond school/university assignments.

    While I get bored of coding pretty fast, I still have ideas, side projects, and weird little experiments I’d love to build.

    Vibe coding makes me feel like I was born at the perfect time, as it now gives me an ability to thrive on ideas without burning out from endless manual coding.

    What Is Vibe Coding?

    Vibe coding is a fairly new term whose birth we can confidently pinpoint to this tweet by Andrej Karpathy:

    Ever since Karpathy coined this phrase, it’s been popping up all over my social media feeds (primarily Twitter/X), causing slight FOMO every time I see someone build something.

    [By the way, if you’re on Twitter/X, I’d love to connect.]

    It was also Karpathy who stated that“the hottest new programming language is English.”

    That’s basically the essence of vibe coding: You tell the machine what you want, it gets to work, and you go microwave last night’s leftovers.

    I think we’re experiencing a substantial paradigm shift. Programming isn’t what it used to be (writing boilerplate, googling syntax, manually debugging loops, etc.).

    Coding tasks becoming obsolete (click to expand)
    • Manually debugging simple errors
    • Setting up file structures from scratch
    • Looking up library documentation constantly
    • Copy-pasting Stack Overflow answers
    • Writing test cases by hand
    • Refactoring variable names manually
    • Commenting obvious code
    • Rewriting functions for different languages
    • Configuring basic build pipelines
    • Writing getters and setters
    • Typing repetitive API calls
    • Remembering regex syntax
    • Manually linting code
    • Writing basic UI layout code
    • Wrangling import statements
    • Searching for best practices
    • Translating pseudocode into real code
    • Writing “glue code” to connect APIs
    • Writing boilerplate code
    • Memorizing syntax

    If we were to put it in more elevated, conceptual terms:

    Vibe coding isn’t just about “building software”. It’s about shaping intent into form without wrestling with the medium.

    It lets you, at least to some degree, skip the laborious wrestle with details, and focus on your unique app features straightaway.

    That’s invaluable for beginners, solo entrepreneurs, and idea people like myself (at the same time it’s also disrupting software dev industry, but let’s leave that topic for another time).

    It’s more about seeing the purpose or the big picture, creating momentum, shipping/prototyping fast, all while using AI as your co-pilot (or more like a junior developer servant).

    It makes the whole process intuition/flow-driven and thus completely a different kind of activity.

    Still, if you’re not a software dev, there are important basics to consider/learn, like backend, security etc.

    The below guide breaks down the essential steps for turning ideas into functional products using AI tools at every stage.

    “Educational poster” made with Claude Artifacts

    If you can imagine it, you can build it.

    For this guide I’ve put together the best advice I’ve found, directly from people who build. These tips are especially valuable for those who have never coded or finished a final product.

    There are plenty of possible rookie mistakes you’ll want to avoid.

    Exhibit A:

    PS: If you need more info on this topic: I’ve just published the 2nd edition of my book “Vibe Coding: Build Without Thinking”, and you can get it on Amazon.

    It contains all the info you need to successfully build any type of app, website, or platform.

    The guide below is just a summary of what you’ll find in the book.

    My book about vibe coding is available now on Amazon

    Ok, ready for the guide? Here it comes.

    How To Properly Vibe Code

    Here is an ultimate “how to” list for vibe coding:

    1. Start with an idea. You need something to build. If you need inspiration, you can look at places like Reddit or app store reviews.
    2. Understand your competition. Use AI tools like Gemini to research what others in the same space are doing to identify your angle.
    3. Clearly articulate your idea. Define the basics of your concept. This will help you and the AI understand the goal.
    4. Create a simple plan (PRD). Use AI like Claude to grill your idea with questions to see if it’s viable. If it holds up, have the AI write a basic one-page plan (Product Requirement Document). This forces you to clarify what you want and breaks the work into small, clear steps. Think of it like outlining what “done” looks like for each stage.
    5. Focus on the UI first (optional but suggested). Break down the project into small, shippable chunks and have AI (like Claude) detail the UI for each, including page content, functionality, and user flow diagrams.
    6. Generate UI components with AI tools. Turn the UI chunks into prompts for tools like v0.dev and generate the user interface piece by piece, tweaking prompts as needed.
    7. Download the generated code. Once the UI is complete, obtain the code.
    8. Develop the backend logic. Use AI code editors like Cursor or VS Code Copilot to add the database, backend logic, and other functional components.
    9. Adopt a mainstream tech stack. When building a web app, consider using Next.js + Supabase because they have large user bases, many online examples, and AI is more likely to handle them correctly. Add Python if your backend needs more complex logic. For game development, learn Unity or Unreal instead of trying to “vibe-code” in less suitable environments like JavaScript for complex games. Choosing a stack AI knows well can prevent wasted time on bugs.
    10. Work in small, manageable steps. Give the AI one step at a time, rather than asking it to “do everything at once”. Test and fix each step before moving on to the next to prevent bugs from compounding. Example prompts: “Implement Step 1.1: Add Feature A” (test and fix), then “Implement Step 2: Add Feature B”.
    11. Use version control (Git). AI will inevitably make mistakes, so you need a way to roll back your code. Manual commits help you track progress and know exactly where to revert if AI creates issues.
    12. Provide working code samples. Before building a full feature, create a small working script that performs the core functionality (e.g., fetching data from an API). Once it works, save it and include it in your AI prompts as a reference to ensure accuracy with third-party libraries or APIs. This can prevent wasting time on minor mismatches.
    13. Prompt effectively.
      • Share your raw idea with the AI.
      • Ask: “what’s unclear, risky, or missing?” to refine your understanding.
      • Then: “make this resonate with [my audience/customer/community]” and provide data about them.
      • Finally: “what would [0.01% top expert in my field] do here?” to get more advanced insights.
    14. When stuck, start a new chat. Avoid getting trapped in a “copy error → paste to chat → fix → new error → repeat” cycle. If you hit this loop, open a fresh chat and clearly state what’s broken, what you expected, and what you’ve already tried. Include relevant logs, errors, and screenshots. A clean context can often resolve issues that endless retries won’t. The longer a chat history gets, the less effective the AI might become.
    15. Learn the basics of programming. While AI can write code for you, understanding fundamental programming concepts is still important. This helps you spot when the AI is incorrect and keeps your projects on track. Vibe coding can even make learning easier by doing and acquiring real-world skills while shipping projects.
    16. Ship something small today. Focus on creating and releasing a minimal viable product to gain momentum. The rest will evolve from there. Remember, shipping is the tuition for the “startup school” that is always open.
    17. Don’t be afraid to leverage different AI tools for their strengths. For example, use Gemini for research and Claude for planning and UI/UX brainstorming. You can even string together different AI agents to handle various operations.
    18. Create a simple README file. Use AI like Claude to write a basic README that explains what you are building.

    That’s it! I hope this guide will be useful to you. You can download the above poster graphic by clicking on the image.

    Watch my Swetlana AI podcast episode on this topic:

  • “Vibe Coding”: Rethinking Programming

    “Vibe Coding”: Rethinking Programming

    Vibe coding is an interesting new concept that’s gaining popularity in AI circles.

    Here’s what it’s all about:

    On Feb 2, 2025, Andrej Karpathy (ex-Tesla, ex-OpenAI = AI overlord) dropped a tweet introducing “vibe coding.” It’s a coding style where you mostly stop coding. Instead, you just… vibe.

    Let’s look at what vibe coding actually is.

    Andrej Karpathy’s tweet

    Follow the Vibe

    Karpathy says it’s about surrendering to the AI.

    No more obsessing over syntax or structure.

    Just “see stuff, say stuff, run stuff, copy-paste stuff.

    The code writes itself (sort of).

    You guide it with prompts, trust the AI to handle the rest, and don’t sweat the details.

    At its core, vibe coding represents a departure from traditional coding practices, where developers manually write, debug, and maintain code.

    Instead it relies heavily on AI tools to generate and manage code. Karpathy suggests that this method feels less like traditional coding and more like a fluid, almost magical process where the code grows beyond the developer’s direct comprehension.

    It seems like we can now trust AI to handle the details and all the nitty-gritty, so devs can focus on big ideas instead of getting lost in the technical stuff (aka the mechanics of implementation).

    Karpathy’s Vibe Coding Tools

    Karpathy’s setup relies on a few key AI tools:

    • Cursor (the interface where the code lives)
    • Claude Sonnet (the brain for deeper logic), and
    • SuperWhisper (a voice-to-text app).

    He barely touches the keyboard—just talks to the AI. Say something like, “make the sidebar padding bigger” and it just happens.

    This hands-free setup shows how AI is making coding way more accessible—even for stuff that used to be annoying or too small to bother with.

    Low Effort, High Trust

    Karpathy’s vibe coding style is basically: trust the AI, don’t overthink it.

    He hits “Accept All” without checking the changes, pastes in error messages with zero explanation, and sometimes just pokes around randomly until things work.

    It sounds chaotic, but for quick side projects or weekend experiments, it gets the job done. Fast, messy, good enough.

    That said, he admits the code can turn into a mess if you ever need to actually understand it later. So it’s fun and efficient, but only until you want to clean it up.

    The Limits

    Karpathy’s honest about the downsides. Sometimes the AI just can’t fix the bug—so you keep rewording your request or poking at the problem until it magically goes away.

    That might be fine for quick hacks, but it’s not ideal for big or serious projects where clean, secure, and reliable code actually matters. Without proper review, things can get messy fast—think spaghetti code no one wants to touch later.

    His point? Vibe coding is fun and fast, but it comes with trade-offs. If you care about long-term quality or working with a team, the chill approach might bite you later.

    The Big Picture

    Karpathy’s post clearly hit a nerve, as evidenced by the responses on X and related web discussions.

    Vibe coding indicates a broader shift: AI tools (trained on code repositories) are getting so good that more people can build software without being hardcore programmers.

    Tools like Cursor, Replit’s AI, and SuperWhisper make coding feel less like coding. It’s closer to chatting with a clever assistant that builds stuff for you. This fits right in with the low-code/no-code movement—more access, fewer barriers.

    Not everyone is a fan though. Some devs love the speed and freedom. Others worry we’re building unstable tech with no one left who understands how it works.

    Source: Reddit

    And then, of course, there’s the security aspect.

    What It Means for the Future

    So far it’s early to say, but: vibe coding might be more than a trend. It could become a whole new way of building software. With AI handling the actual labor of coding, devs can move faster, get more creative, and maybe even work more like artists than engineers.

    But there are also the big concerns (and we need to think about how to mitigate these in the future):

    • What happens to code quality?
    • Who’s responsible when AI-generated code causes problems?
    • What will it do to the job market?

    So far people are divided on this topic. Some see vibe coding as the future. Others think it’s only safe in the hands of experts like Karpathy. The rest is still coding manually.

  • The First Major AI Profession Is Growing Fast: AI Governance

    The First Major AI Profession Is Growing Fast: AI Governance

    AI replaces jobs, but it also creates new jobs. One of them is the rapidly growing field of “AI Governance“.

    People working in AI Governance are generally expected to apply ethical, legal, and societal expertise to shape AI systems.

    Key Points

    • AI Governance ensures ethical and responsible use of AI technologies.
    • Involves creating policies, managing risks, and ensuring compliance.
    • Requires skills in AI, law, ethics, and communication.
    • Growing field with increasing regulations and demand for experts.

    What is AI Governance?

    AI Governance is about making sure AI systems are used safely and fairly. It involves setting rules and guidelines to prevent problems like bias, privacy issues, and unethical decisions. Professionals in this field work to build trust in AI by ensuring it respects human rights and aligns with societal values.

    Roles and Responsibilities

    People in AI Governance do things like:

    • Make and follow AI policies.
    • Check if AI systems follow laws and regulations.
    • Lead teams to handle AI ethics and risks.
    • Work with different groups to make sure AI fits business and legal needs.

    Common jobs include AI Governance Manager, AI Ethics Officer, and AI Compliance Officer.

    Skills Needed

    You need a mix of skills, such as:

    • Knowing how AI works and what it can do.
    • Understanding laws and rules about AI.
    • Leading teams and managing projects.
    • Explaining complex AI ideas to people without technical backgrounds.
    • Managing risks and ensuring AI is used ethically.

    Surprising Growth and Demand

    It’s surprising how fast this field is growing, with a market size of USD 145.5 million in 2023 and expected to grow at over 52% annually until 2032, driven by the need for ethical hacking and cybersecurity (AI Governance Market Size & Growth Analysis Report, 2024-2032).

    Comprehensive Overview of AI Governance as a Professional Field

    AI Governance is an emerging and rapidly evolving professional field that focuses on ensuring the responsible, ethical, and effective development, deployment, and use of artificial intelligence (AI) technologies. This field is critical as AI becomes increasingly integrated into organizational and societal operations, necessitating robust frameworks to manage risks and maximize benefits. Below, we explore the definition, scope, roles, required skills, current trends, challenges, and career paths in AI Governance, providing a detailed analysis for professionals and enthusiasts alike.

    Definition and Scope

    AI Governance refers to the processes, policies, and practices that guide the ethical development, deployment, and use of AI technologies. It aims to ensure AI systems are safe, fair, and respect human rights, addressing risks such as bias, privacy infringement, and misuse. Unlike related fields like AI ethics, which focuses on moral principles, or AI law, which deals with legal compliance, AI Governance encompasses a broader oversight, integrating technical, legal, and ethical dimensions to foster trust and accountability.

    For instance, AI Governance addresses the human element in AI creation, mitigating biases and errors that can lead to discrimination, as highlighted by What is AI Governance? | IBM. It also involves establishing frameworks to align AI behaviors with societal expectations, ensuring transparency and explainability, as noted in What Is Artificial Intelligence (AI) Governance?| Definition from TechTarget.

    Roles and Responsibilities

    Professionals in AI Governance undertake a variety of roles, each critical to managing AI systems responsibly. These include:

    Common job titles include AI Governance Manager, AI Ethics Officer, AI Compliance Officer, and AI Policy Advisor, reflecting the diverse responsibilities within the field.

    Skills Required

    AI Governance professionals need a multidisciplinary skill set to navigate the complexities of the field. Key skills include:

    These skills are often developed through education, certifications like the AI Governance Professional certification (Artificial Intelligence Governance Professional), and on-the-job experience.

    Current Trends and Challenges

    The AI Governance field is witnessing significant trends and challenges, shaping its evolution:

    Challenges include keeping pace with technological advancements, navigating fragmented regulatory landscapes, and ensuring AI systems are both effective and ethical, as highlighted in Dentons – AI trends for 2025: AI regulation, governance and ethics.

    Career Path

    Entering AI Governance requires a strategic approach, leveraging education, skills development, and networking:

    The field offers exciting opportunities for those passionate about technology and ethics, with potential to shape the future of AI responsibly, as noted in career reviews like AI governance and policy – Career review.

    Summary Table of AI Governance Key Aspects

    AspectDetails
    DefinitionEnsures ethical, safe, and fair use of AI, managing risks and building trust.
    Key RolesAI Governance Manager, Ethics Officer, Compliance Officer, Policy Advisor.
    Essential SkillsAI knowledge, legal understanding, ethics, communication, risk management.
    Current TrendsGrowing regulations, transparency focus, ethical considerations, market growth.
    ChallengesRapid tech changes, regulatory complexity, ensuring effectiveness and ethics.
    Career EntryEducation, certifications, skills development, job roles in policy and ethics.

    This comprehensive overview underscores AI Governance as a vital field, offering significant opportunities for professionals to contribute to the responsible advancement of AI technologies.

    Key Citations

  • How Are People Using AI? I Asked Grok

    How Are People Using AI? I Asked Grok

    Grok is a fantastic tool for looking at what’s happening (and evaluating it), because in addition to being an AI it accesses what people are actually saying. Today I asked it about different use cases of how AI is helping people in their work.

    [Model used: Grok 2]

    My prompt:

    What are people on X saying about how AI is helping them accomplish their things? What are their use cases?

    Grok’s reply:

    People on X are discussing various ways AI is assisting them in their daily tasks and professional endeavors.

    Here are some of the use cases mentioned:

    • Content Creation: Users are employing AI for generating images, particularly for live events, indicating AI’s utility in visual content creation. AI is also used for writing and analyzing content, providing insights and creating detailed reports with case studies.
    • Programming and Technical Support: AI serves as an aid for coding, helping rudimentary programmers by providing code suggestions, explanations, and even writing code. This includes using AI to understand and generate code more efficiently.
    • Decision Making and Knowledge Repository: AI is utilized as an encyclopedia or a knowledge repository, aiding in decision-making processes by synthesizing information from various sources.
    • Productivity and Efficiency: There’s significant mention of AI enhancing productivity across different sectors like sales, support, operations, and administration. Use cases include AI agents managing inboxes, calendars, CRM systems, and other tools, saving considerable time (up to 5 hours a day) by automating routine tasks.
    • Document Conversion and Analysis: AI is praised for its ability to convert documents like PDFs into tables and perform preliminary analysis, which can then be reviewed manually for accuracy.
    • Ideation and Research: AI helps in brainstorming and research by analyzing and structuring thoughts or by providing in-depth analysis on otherwise mundane topics, enhancing creativity and strategic thinking.
    • Customer Interaction: AI is used in customer service for tasks like generating meeting minutes or handling customer support queries, improving efficiency and customer satisfaction.

    These insights from X reflect a broad spectrum of applications where AI is not just a tool but an integral part of daily professional life, enhancing both the quality and speed of work.

    My prompt:

    List up 20 specific cases you have found

    Grok’s reply:

    Here are 20 specific cases where AI is helping with tasks, as found in various discussions on X:

    1. Live Code Tutoring – AI provides real-time coding assistance and explanations.
    2. AI Research Assistant – Assists in gathering and synthesizing research data from various sources.
    3. Interactive Mixology – AI offers recipe suggestions and mixology tips for bartenders.
    4. Real-Time Piano Instruction – Provides immediate feedback and lessons to piano learners.
    5. Smart Kitchen Assistant – Helps with meal planning, recipe suggestions, and inventory management.
    6. Interactive Gaming Companion – Enhances gameplay with AI-driven interactions or strategy advice.
    7. AI Notes – Automatically transcribes and organizes notes during meetings or lectures.
    8. Voice Dictation – Transcribes spoken words into text with high accuracy.
    9. AI Slide Decks – Generates presentations based on given data or topics.
    10. Writing and Email Automation – Assists in creating content or automating email responses.
    11. Document Summarization – Summarizes lengthy documents or articles for quick review.
    12. Collaborative Research – Facilitates collaborative work by managing and analyzing shared research data.
    13. Workflow Automation – Automates repetitive workflow tasks based on user behavior patterns.
    14. AI for Email Marketing – Personalizes email campaigns and optimizes send times.
    15. Website Building – Assists in creating websites through AI-driven design and content suggestions.
    16. Image Generation – Creates visual content for various applications like marketing or social media.
    17. Copywriting – Generates ad copy, blog posts, or other content tailored to specific audiences.
    18. Meeting Notes – Automates the taking of notes during meetings, including action items and summaries.
    19. Research Tools – Provides quick answers to complex queries or aids in academic research.
    20. Productivity Apps – Integrates AI to manage calendars, emails, and other productivity tools for efficiency.

    These cases illustrate the diverse applications of AI in enhancing productivity, creativity, and efficiency across different sectors and personal tasks.

  • AI Tools That People Love/Use The Most

    AI Tools That People Love/Use The Most

    I asked Grok to analyze people’s tweets globally, and tell me which AI tools they tend to use.

    One of Grok’s priceless features is the access to what people are saying, in real time. That’s one of the things that ChatGPT can’t do.

    So: What AI tools do people actually use? Here’s what Grok says.

    Grok Breaks Down People’s Favorite AI Tools

    Here’s a more detailed breakdown of AI tools based on their mentions and praised uses from X posts:

    Perplexity AI

    Use: Primarily used for advanced search capabilities, providing focused and academic research.

    Benefits: Users appreciate its ability to integrate with other models like Claude, offer detailed insights from specific URLs or file uploads, and its ease in sharing and managing information. It’s particularly praised for its vertical integration which allows for tailored searches.

    V0 (by Vercel)

    Use: A tool for building web UI components.

    Benefits: It’s noted for its simplicity and ease of use for frontend design, making it beneficial for those who want to quickly prototype or design web interfaces.

    Bolt, Cursor AI, Windsurf AI

    Use: These tools are generally used for coding assistance.

    Benefits: Bolt might be more suited for beginners, while Cursor and Windsurf are praised for their deep integration into the coding workflow, providing real-time code suggestions, debugging assistance, and productivity boosts. Users find these tools vital for speeding up coding tasks and improving code quality.

    Claude

    Use: General productivity, particularly for writing and technical tasks.

    Benefits: Known for its nuanced understanding of context, which makes it excellent for tasks requiring detailed or technical writing. It’s also valued for its coding assistance capabilities.

    ChatGPT

    Use: Quick queries, document creation, and general information gathering.

    Benefits: Users highlight its versatility, from answering straightforward questions to creating comprehensive documents. It’s seen as an all-purpose tool for many due to its conversational nature and broad knowledge base.

    Abacus AI

    Use: Not as widely discussed, but mentioned in contexts of general productivity or potential future use.

    Benefits: While specifics are not detailed, the mention suggests it’s viewed with optimism for its capabilities, possibly in data analysis or custom AI solutions.

    Kapwing, InVideo, Synthesia, Kling, Pixverse

    Use: Focused on video creation and editing.

    Benefits: These tools are praised for simplifying video production. Kapwing and InVideo are noted for their editing capabilities, Synthesia for AI-generated video content, and Kling and Pixverse for innovative video generation, especially in creative or marketing contexts.

    SEMrush

    Use: SEO and digital marketing.

    Benefits: Users value it for its comprehensive digital marketing tools, including SEO, content marketing, and competitive analysis, which are crucial for enhancing online visibility.

    Midjourney, DALL-E

    Use: Image generation from text prompts.

    Benefits: These are celebrated for their creative outputs, allowing users to visualize concepts or create art with high quality and diverse styles. They’re particularly useful for designers, marketers, or anyone in need of visual content.

    Canva AI

    Use: Graphic design automation.

    Benefits: By integrating AI, Canva offers features like automated design suggestions, background removal, and more, simplifying the design process for users without extensive design skills.

  • Hooray, I’m a Verified Builder Now

    Hooray, I’m a Verified Builder Now

    OpenAI, here I come. I finally got verified as a builder, and my first GPT is officially public. With the GPT store coming out one of these days, I’m thrilled to see what will happen. Looks like this could become another income stream.

    Building, conceptualized image (made with DALL-E 3)

    What is my first public GPT you ask?

    It’s FreudGPT!

    I’ve renamed it to FREUD Dream Interpreter though, as using “GPT” at the end of it is apparently discouraged by the OpenAI guidelines. Go give it a try if you had a dream recently and you want to know what it means.

    By the way, read OpenAI’s usage policies and brand guidelines.

    And here is the general introduction to what’s to expect.

    While trying to get verified I’ve run into one particular technical problem – getting OpenAI to approve my DNS record.

    After several attempts to verify my website by adding a DNS txt entry it finally worked. Initially I’d press the “verify” button and got an error message, but then I deleted the domain and re-added it, added the txt again and it worked.

    How To Become A Verified Builder On OpenAI

    First things first, verification isn’t a big deal if you want to use your real name. It’ll get populated from your billing details and you’re good to go.

    In my case, I wanted to get my website verified.

    Here’s a step by step:

    1. Obviously have an account with OpenAI. This is a no-brainer.
    2. Go to ChatGPT, then to Settings, and then to “Builder Profile”.
    3. Add a new domain, click “Submit”.
    4. You’ll get a text string, copy it.
    5. Go to your domain host and add a new DNS txt record. Depending on the host this will require an individual approach. I’m using WordPress and in that case you go to Settings, scroll down to “Change Website Address”, click on the “…” next to your domain, and then “Manage DNS”. From there you add a txt entry, pasting your OpenAI code.
    6. Go back to ChatGPT Settings and click verify. If it doesn’t work straightaway, try again later, and if it doesn’t work on the next day, I’d suggest you delete and re-add your domain.

    That’s it! I hope it’ll work for you.

    As of my future plans: of course I have more GPTs up my sleeve.

    And I recommend you try it too. GPTs are free to build, so it’s a win-only situation.