Everyone is talking about storytelling (and desperately looking for it), and while “storytelling” is a terrible buzzword, I decided to attempt a little case study and turn something boring into something interesting via story.
Our Use Case #1 is a generic office stapler. In the future I might create a story for industries such as:
PS: Of course I used ChatGPT to write the below story, as the point of this article is to illustrate what “storytelling” does.
The Quiet Cohesion
Every morning, when the coffee machine hums and keyboards chatter to life, a small hero waits at the edge of the desk:
The stapler.
No one cheers when it arrives. No one tweets about it. But take a moment and watch its rhythm — plink-plink — like a heartbeat syncing scattered pages into unity.
The stapler doesn’t rewrite documents. It doesn’t design slides or craft strategy. What it does is deeper: it creates cohesion from chaos. A report without it is a forest of loose leaves. With it: a story with structure.
In every stapled stack there’s intention. A contract is ready to be signed. A pitch is ready to be shared. A brainstorm is now a blueprint.
Some might see a tool. Others see a token of order in a world that often threatens to fragment into noise. The stapler doesn’t just bind pages — it binds purpose to people.
And at the end of the day, when the office quiets, it rests — not unnoticed, but quietly proud. Because what it does? It’s the first ritual of every idea that’s taken seriously.
The Takeaway
This small storytelling case study shows what business storytelling and corporate storytelling actually do: this is not about “embellishing” reality, but more about creating meaning.
If a stapler can carry a narrative, then so can payroll software, waste management, or water treatment plants. This is the core of brand storytelling and narrative strategy (turning functional, overlooked products into stories people remember).
And especially in the era of AI-generated content, what still differentiates companies is human storytelling: context, judgment, and the ability to make boring businesses feel coherent, intentional, and real.
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:
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:
There's a new kind of coding I call "vibe coding", where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It's possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper…
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.
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:
Start with an idea. You need something to build. If you need inspiration, you can look at places like Reddit or app store reviews.
Understand your competition. Use AI tools like Gemini to research what others in the same space are doing to identify your angle.
Clearly articulate your idea. Define the basics of your concept. This will help you and the AI understand the goal.
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.
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.
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.
Download the generated code. Once the UI is complete, obtain the code.
Develop the backend logic. Use AI code editors like Cursor or VS Code Copilot to add the database, backend logic, and other functional components.
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.
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”.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
Are you clueless about all those new terms (like “e/acc”, AGI etc.) popping up here and there? Or maybe you haven’t even heard of any of these. Here’s a little glossary to update your knowledge.
I’ve put together this info with the help of Grok 3 / DeepSearch. The glossary is based on terms frequently mentioned within the AI community.
Detailed Survey Note on AI Community Terms
This section provides a comprehensive analysis of the key terms used by insiders in the e/acc and tpot communities on X, aimed at informing individuals outside the AI space (“normies”). The following details the process of identifying these terms, their definitions, and their relevance, ensuring a thorough understanding for lay readers.
Glossary of Terms with Definitions
Below is a detailed table of the identified terms, their definitions, and their relevance to the e/acc and tpot communities:
Term
Definition
Relevance to Community
Accelerationism (acc)
Belief in speeding up technological progress, especially AI, for a better future.
Core to e/acc, seen in posts advocating rapid AI growth.
Decelerationism (decels)
Belief in slowing AI progress to manage risks and ensure safety.
Opposed by e/acc, often labeled negatively in discussions.
Doomers
Pessimists fearing AI could cause catastrophic societal harm.
Frequently mentioned in e/acc posts as counterpoints.
AI Risk
Potential negative impacts of AI, from bias to existential threats.
Central to e/acc debates on AI’s societal impact.
Artificial General Intelligence (AGI)
AI capable of any human intellectual task, a key goal in AI development.
Discussed in both communities, especially in future predictions.
Singularity
Point where AI surpasses human intelligence, leading to rapid changes.
A focal point in e/acc, linked to utopian or dystopian scenarios.
Effective Altruism (EA)
Using evidence to maximize good, often focusing on AI safety.
Related to e/acc, contrasted in discussions with accelerationism.
Techno-optimism
Belief that technology will improve human life and society.
Common in both e/acc and tpot, emphasizing AI’s benefits.
Kardashev Scale
Measure of civilization’s advancement by energy use, with AI aiding higher levels.
Unique to e/acc, seen in posts like @BasedBeffJezos ‘s on civilization.
Post-rationalism
Blending rationalism with intuitive or mystical ideas, often in tpot discussions.
Central to tpot, reflecting broader intellectual interests.
Grey Tribe
Tech-savvy, health-focused group interested in new-age ideas, linked to tpot.
Describes tpot community members, seen in web articles.
Biohacking
Optimizing biology through diet, supplements, etc., for peak performance.
Popular in tpot, linked to personal optimization discussions.
Nootropics
Substances believed to enhance cognitive function, like memory or focus.
Frequently mentioned in tpot posts on productivity.
Productivity Hacks
Techniques to increase efficiency, like time management or apps.
Common in tpot, reflecting interest in personal optimization.
Rationality
Using logic and evidence for decision-making, a tpot and e/acc focus.
Foundational to both communities, seen in discussions on AI.
LessWrong
Community and website for rationalist thought and AI safety, linked to tpot.
Referenced in tpot discussions, a hub for rationalist ideas.
Slate Star Code
Blog by Scott Alexander on science and philosophy from a rationalist view.
Popular in tpot, mentioned in web articles on community interests.
Surprising Connections and Insights
One interesting finding is the connection between AI discussions and broader concepts like the Kardashev Scale, which measures civilization’s energy use and is tied to e/acc’s vision of AI-driven advancement. Similarly, Post-rationalism in tpot bridges AI with philosophy and spirituality, showing how these communities extend beyond pure technology into cultural and existential realms.
The term AI Agent has been popping up more and more recently. But what exactly are AI agents, and how can we create them?
Imagine you’re a novelist, trying to come up with the perfect plot twist.
Now envision an AI agent as your literary companion, not just a tool but a collaborator in storytelling.
This agent is specialized on language. It analyzes literary corpora to suggest synonyms that capture the exact emotion, propose plot developments that respect your narrative’s integrity, or offer prompts inspired by your style to help you out of writer’s block.
It’s like having a literary critic, research assistant, and creative muse in one, enhancing your writing process by sifting through historical details for authenticity, recommending culturally resonant character names, or mimicking stylistic elements from admired authors to enrich your work, making every word count and every sentence sing.
What Can AI Agents Do?
Many different things! Here are some examples:
Automate Email Management: Sort, respond to, or schedule emails based on content or priority.
Personal Shopping Assistant: Make purchases or suggest items based on user preferences, style, or past behavior.
Smart Home Control: Manage lighting, temperature, security systems, and appliances with voice or scheduled commands.
Financial Advisor: Track spending, provide budget advice, invest in stocks according to user-defined strategies, or alert on market trends.
Health Monitoring: Remind users to take medication, track vital signs, suggest workouts, or monitor diet for nutritional balance.
Entertainment Curator: Recommend movies, books, music, or games based on mood, preferences, or current trends.
Travel Planner: Organize travel itineraries, bookings, visa reminders, and local weather updates tailored to your travel.
Customer Service: Handle inquiries, complaints, or support tickets autonomously across multiple communication channels.
Language Translation: Offer real-time translation services in conversations or for document translations.
Educational Tutor: Provide personalized learning experiences, quizzes, explanations, or homework assistance.
Project Management: Schedule tasks, manage team assignments, track progress, and predict project timelines.
Social Media Manager: Schedule posts, analyze engagement, respond to comments or messages, and curate content.
Legal Assistant: Assist in drafting simple legal documents, researching case law, or managing document due dates.
Event Organizer: Plan events by coordinating with vendors, managing guest lists, and sending out reminders or invitations.
Mental Health Support: Offer daily affirmations, mood tracking, or connect users with resources or professionals when needed.
Gaming Companion: Adapt game difficulty, provide tips, or even play alongside or against the user in certain scenarios.
Recipe and Meal Planner: Suggest recipes based on dietary restrictions, pantry inventory, or nutritional goals.
News Aggregator: Curate personalized news feeds, summarize articles, or alert users to breaking news relevant to their interests.
Job Seeker Helper: Scan job listings, match them with user’s resume, apply automatically where applicable, or prepare for interviews.
Environmental Monitoring: Track energy usage, suggest ways to reduce carbon footprint, or manage waste in smart homes or offices.
What Is an AI Agent?
Let’s get our definition straight:
An AI agent is a type of artificial intelligence software designed to perform tasks autonomously on behalf of a user or another system.
Here’s a more detailed breakdown of what AI agents are and what they can do:
Definition:
Autonomy: AI agents operate with a degree of independence, making decisions based on their programming, data, and objectives without needing continuous human intervention.
Interaction: They can interact with users, other agents, or systems through various interfaces like text, voice, or API calls.
Capabilities:
Task Automation:
Routine Tasks: Handling repetitive tasks such as scheduling, sending reminders, or managing emails.
Complex Tasks: Solving problems or making decisions based on complex data analysis, like financial portfolio management or customer support.
Learning and Adaptation:
Machine Learning: Some AI agents can learn from data, improving their performance over time. This includes recognizing patterns in user behavior to personalize services.
Adaptation: They adapt to new situations, environments, or user preferences, enhancing their utility.
Communication:
Natural Language Processing (NLP): Understanding and generating human language for more intuitive interaction, like chatbots or virtual assistants.
Voice Recognition: Enabling voice commands, making interaction more natural and accessible.
Decision Making:
Data Analysis: Using data to make informed decisions, from recommending products in an e-commerce setting to suggesting medical treatments based on patient data.
Strategy Implementation: In gaming or simulation environments, AI agents can strategize and adapt to opponents.
Integration:
API Use: Interacting with other software systems or services, pulling data from various sources or pushing data to different platforms.
IoT Control: Managing smart home devices or industrial equipment by interpreting sensor data and executing commands.
User Assistance:
Personal Assistants: Helping with daily tasks, navigation, information retrieval, or setting up appointments.
Customer Service: Providing 24/7 assistance in customer support scenarios, answering FAQs, or troubleshooting.
Examples:
Virtual Assistants: Like Siri, Google Assistant, or Alexa, which handle a variety of user queries and commands.
Bots in Messaging Apps: Offering customer service, booking services, or even companionship.
Financial Trading Bots: Making real-time trading decisions based on market data.
AI agents are thus versatile tools that can significantly enhance efficiency, decision-making, and user experience across various domains, from personal use to enterprise solutions.
However, their effectiveness depends on the quality of their programming, the data they have access to, and ethical considerations in their deployment.
The 7 Levels of AI Agents
I’ve come across an interesting classification of AI agents. It’s outlined in this Forbes article.
The article describes seven levels of AI agents:
Level 1—Reactive Agents: These agents operate only in the present moment and follow pre-defined rules to respond to specific inputs. They do not retain memories or learn from past experiences. An example is a basic chatbot that answers questions based on keyword matching.
Level 2—Task-Specialized Agents: These agents excel in narrow domains, often exceeding human performance in specific tasks by collaborating with domain experts. They are used in many modern AI applications, from fraud detection to medical imaging. For example, a task-specialized agent might power an e-commerce recommendation engine.
Level 3—Context-Aware Agents: These agents can handle ambiguity and complexity by analyzing historical data, real-time streams, and unstructured information. Examples include systems that analyze medical data to assist doctors and systems that evaluate financial transactions to detect fraud.
Level 4—Socially Savvy Agents: These agents understand and interpret human emotions, beliefs, and intentions, enabling richer interactions. For example, in customer service, they can identify frustration in a caller’s tone and adjust responses accordingly.
Level 5—Self-Reflective Agents: These speculative agents would be capable of introspection and self-improvement, refining their algorithms autonomously. For example, in manufacturing, they could monitor production line inefficiencies and recalibrate machinery or workflows to enhance output.
Level 6—Generalized Intelligence Agents: Also known as artificial general intelligence (AGI), these agents would be capable of performing any intellectual task a human can. Recent progress in large language models (LLMs) hints at the potential for AGI. For example, an AGI agent could analyze financial trends, coordinate business functions, and handle stakeholder relationships.
Level 7—Superintelligent Agents: This hypothetical system would surpass human intelligence in all domains.Superintelligent agents could potentially discover cures for diseases, design sustainable solutions for environmental challenges, and optimize economic systems.
How to Build an AI Agent
Building an AI agent can be simplified using various tools and platforms, especially those designed for users with less technical expertise or for rapid prototyping.
Steps for a Simple AI Agent Build:
Define Your Goal: Clearly outline what you want your AI agent to do.
Choose Your Platform: Based on your coding skills and the complexity of the task, select a platform from the no-code or low-code options mentioned.
Configure or Program the Agent:
No-Code: Use the platform’s visual interface to set up triggers, actions, and data flows.
Low-Code: Follow tutorials or use existing templates, modifying them for your specific needs.
Test and Iterate: Run tests to ensure the agent performs as expected. Adjust based on performance.
Deploy: Once satisfied, deploy your agent within your workflow or make it accessible to users.
These methods allow you to build AI agents with significantly less complexity than traditional coding from scratch, making AI agent development more accessible.
To have an AI agent in simple terms, you’ll need:
Input Mechanism: A way for the agent to receive information or commands, like text input, voice recognition, or data from sensors.
Processing Unit: This includes:
AI Model: Often a machine learning or deep learning model (like neural networks) for decision-making or prediction.
Memory: To store data, learn from past interactions, or remember preferences.
Output Mechanism: How the agent communicates results or actions, whether through text, voice, or by controlling other systems or devices.
Decision-Making Logic: Rules or algorithms that dictate how the agent interprets data and decides on actions.
Learning Algorithm: If the agent is meant to improve over time, you’ll need a method for it to learn from new data or feedback, like reinforcement learning or updating neural network weights.
Interface or API: For integration with other systems or for user interaction, allowing the agent to operate within broader ecosystems or applications.
Environment Interaction: Ability to interact with the world, whether virtual (like managing files on a computer) or physical (like controlling a smart home device).
These components together allow an AI agent to perceive its environment, make decisions based on its programming or learning, and act autonomously to achieve specific goals or assist users.
Here’s a guide on simpler approaches to build an AI agent:
No-Code or Low-Code Platforms:
Zapier Central:
Use: Zapier has introduced a no-code AI agent builder that integrates with its vast ecosystem of apps. You can create AI agents to automate tasks based on triggers from different applications.
Advantage: User-friendly interface, no coding required, easy to connect live data for dynamic interactions.
Relevance AI:
Use: A platform where you can build and recruit teams of AI agents to automate tasks. It offers a no-code environment for integrating AI into business workflows.
Advantage: Provides a suite of integrations, tools, and AI agent templates for quick deployment.
Vertex AI Agent Builder by Google Cloud:
Use: This tool allows you to create AI agents using natural language or a code-first approach, making it accessible for both non-coders and developers.
Advantage: Grounding in enterprise data with various options, including pre-built templates for rapid prototyping.
Langflow:
Use: An open-source tool for building AI agents visually, where you can drag and drop components to create complex workflows.
Advantage: Comes with reusable components, ideal for those looking to experiment with agent building without coding.
Low-Code with Some Technical Knowledge:
LangChain:
Use: While LangChain is typically used by developers, it provides frameworks and examples that can be adapted with less coding if you’re familiar with Python. There are tutorials and resources for building agents which can simplify the process for those with basic programming skills.
CrewAI or similar platforms:
Use: These platforms allow for the creation of AI agents with a focus on collaboration between multiple AI entities, which can be configured with some basic coding or through UI-based setups.
Advantage: Focuses on team dynamics among AI agents, potentially simplifying the management of complex tasks.
Remember though, the simplicity of the process might come at the cost of customization or control over minute details, but for many applications, these platforms offer a good balance of simplicity and functionality.
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:
Live Code Tutoring – AI provides real-time coding assistance and explanations.
AI Research Assistant – Assists in gathering and synthesizing research data from various sources.
Interactive Mixology – AI offers recipe suggestions and mixology tips for bartenders.
Real-Time Piano Instruction – Provides immediate feedback and lessons to piano learners.
Smart Kitchen Assistant – Helps with meal planning, recipe suggestions, and inventory management.
Interactive Gaming Companion – Enhances gameplay with AI-driven interactions or strategy advice.
AI Notes – Automatically transcribes and organizes notes during meetings or lectures.
Voice Dictation – Transcribes spoken words into text with high accuracy.
AI Slide Decks – Generates presentations based on given data or topics.
Writing and Email Automation – Assists in creating content or automating email responses.
Document Summarization – Summarizes lengthy documents or articles for quick review.
Collaborative Research – Facilitates collaborative work by managing and analyzing shared research data.
Workflow Automation – Automates repetitive workflow tasks based on user behavior patterns.
AI for Email Marketing – Personalizes email campaigns and optimizes send times.
Website Building – Assists in creating websites through AI-driven design and content suggestions.
Image Generation – Creates visual content for various applications like marketing or social media.
Copywriting – Generates ad copy, blog posts, or other content tailored to specific audiences.
Meeting Notes – Automates the taking of notes during meetings, including action items and summaries.
Research Tools – Provides quick answers to complex queries or aids in academic research.
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.
Swetlana AI 