October was pretty nuts and I have a list of things to do so I’m gonna keep this short!!

Subscribe to join the ride, and if you’ve got questions (or just want to talk), drop me a line at pointone@barbaros.ca

Coffee’s on me if I take more than 24 hours to reply.

If you want to learn a bit more about me -- check out this intro to both me and this blog:

Some reflection on October:

You can just build. I know everyone says this, and I don’t want to sound like an old head; but with what we have access to now, you can literally do anything.

I’m not saying you can achieve something great in a week. Time still matters. However, there used to be barriers to build amazing things -- which are now broken due to our ease of access to endless brilliant information and tools.

The most valuable skill today is learning how to learn -- because it enables you to master anything else efficiently. Equally important is figuring out how to apply what you learn. And with today’s increasingly powerful tools -- both those already available and the ones we build ourselves, like ML systems -- the real challenge is learning how to optimize them to reach our potential as effectively as possible.

HABGuard:

You can check out a 3 min demo here.

I want to build meaningful things, so I started looking into some of the world’s biggest problems. Which is where I found water scarcity -- I can’t solve it all. But by zooming in, and getting really in-the-details, I can find overlooked micro-problems, the unlock macro-solution.

This philosophy is what led us to water desalination plants, and the issue of harmful algal blooms (HABs).

These blooms can shut plants down for over a week, cutting output by up to 40% and costing $200K–$700K per day in lost production and maintenance. If detected early, operators can adjust intake, switch sources, or activate pre-treatment systems before contamination hits.

Our goal is to predict harmful algal blooms before they form, and we believe machine learning and AI poses undoubtedly the clearest pathway forward to achieve just that.

So I made a real-time HAB monitoring + prediction system pulling from 6 NOAA oceanographic datasets + Sentinel-2 satellite imagery.

I built and trained three ML models from scratch (Random Forest + Neural Network + Anomaly Detection) that were trained on prev. HABs + environmental data. These models I made analyze how those conditions evolve, to predict when + where blooms are likely to form -- achieving 93.8% risk classification and 98.5% HAB prediction accuracy.

Antifragility Labs:

If you haven’t read one of these yet, you likely don’t know that I’ve been building Antifragility Labs for the past year. We’re a software-first agency turning startup helping brands rank on AI and search engines alike.

Current clients include YC-backed startups, leading podcasts, ecom businesses, and major VC firms; building software defined by real tangible experience, not just theory.

Our thesis is that it’s too early to bet on a single software solution for Generative Engine Optimization (GEO). It’s silly to say what works today will work tomorrow when the market is so volatile. We can prove this through both recent events, and through studying the beginning of SEO.

So through staying a software-first agency, whenever a client faces a problem, we break it down into it’s first principles and build a software solution to it. Clients often face similar problems, so the software automates processes, therefore allowing us to take on more clients, while inputting less work. This holds us to a higher level of accountability compared to any other company in the GEO space today, and once we’ve built software we truly believe is sustainable, we will make the pivot to SaaS startup.

• Our thesis is working out great with everything that’s been going on with Reddit.

• Got our first press feature!! Small outlet, but fun milestone. Huge thanks to TechBeat Canada.

• Was able to get free tickets to Elevate through cold outreach (smaller Canadian version of Web Summit). Me and my co-founder saw an empty startup booth so we grabbed it -- got a ton of clients to the point where we had to start rejecting people. We even got some folks asking if we were hiring haha.

• Still shipping software for clients minimum 1x a week.

• I’m working on an extremely interesting product and I think this will push our complete shift from agency >> startup.

Wealthsimple:

As you know, I’ve been taking a gap year after high school to intern at Wealthsimple. Some really exciting things happened this month!

For starters, our latest Wealthsimple presents, For Nerds Only.

It felt insane to be here for that. You really feel integrated within the team here, and what we’re building towards -- truly feel like something much bigger than yourself and it’s a great feeling, especially when it’s around such amazing people.

Not only that, but recently Wealthsimple closed a $750M round putting them at a $10B valuation!!

Very cool that I’m able to here for these super exciting moments, both humbled and grateful for that. I’m really getting a feel of what it’s like to be apart of a rocket-ship company that has found PMF, and I have no doubt it’ll help me in the future to understand how real speed looks like in building great companies.

• Building AI automation software for AI experiences team + working on product under recent acquihire Emily Luk, Plently.

Hypesonic:

• Started doing some work for an exciting startup, Hypesonic -- who recently closed a $3M round from Accel. The founder who I’m working under is SO cool -- Ananya Chadha -- a literal genius, and an exceptionally clear thinker. She’s been dedicating a lot of time to mentoring me and it’s been such a great learning experience + time getting to know her -- super excited to continue doing so!!

• Main work here is helping GTM + building internal software + whatever Ananya needs done.

Side projects // everything else:

• Building a SaaS for self-published authors -- though lately it’s mainly been Nitya building it haha.

• So sad that the Jay’s lost -- ruined my Saturday night. Toronto needed it.

• The #1 thing I want to learn within the next month: machine learning.

Final Reflections

This month I’ve been trying to practice radical optimism -- life gets extremely fun and enjoyable that way.

Along with that, I’ve came to some pretty deep realizations in turn of building in such volatility and depth.

One of which is that I believe it’s impossible to solve an entire problem. Because the biggest problems are just an umbrella of many other problems.

We need to zoom in, and get really in-the-details.

Because through that, we can find overlooked problems, that often lead to the greatest solutions.

This especially applies to complex problems like cancer. Yes, cancer itself is a problem, but the problem is so big, that within it, there is a number of many other problems; the length it takes to identify it, the technology available to cure it, etc.

It’s almost vague. The way to solve it is tackling it bit-by-bit by finding the small problems that others don’t see as they yield the greatest returns.

Thank You

If you gave this the time of your day to read, thank you. As I had noted at the beginning of this update, this blog is built to help me meet new people, and get my ideas out there.

So if at all you’d like to chat, don’t be a stranger and drop me a line at pointone@barbaros.ca

I promise to respond within 24 hours -- if I don’t I’ll buy // et you a coffee!

Later!!
- Noah B

If you’re new here, welcome! I started this blog to make it easier for people to get to know me and to open up more conversations. Subscribe to join the ride, and if you’ve got questions (or just want to talk), drop me a line at pointone@barbaros.ca

Coffee’s on me if I take more than 24 hours to reply.

If you want to learn a bit more about me — check out this intro to both me and this blog:

This month was packed — I honestly wouldn’t be shocked if I forget a couple things.

Some reflection on September:

Every morning I woke up this month, I found myself feeling nothing but a concoction of gratitude, excitement, and happiness. I mean how many people can say that about their life? I’m truly so lucky to be surrounded by incredible people — from family to friends to co-workers.

It is with full humility when I say that I’m so grateful for everything I have in life right now. And by no means is it to say “I’ve made it,” or “this is it!”

It is to say that I’m lucky.

I’ve truly found myself in such a great position; I’m not just talking about work // opportunity-wise, but I’m talking about the people I’m around.

If I felt one thing this month it would be gratitude. This months newsletter is entirely in honour of all of the amazing people in my life. This spans from family, to friends, and co-workers.

I really felt the need to get that off my chest — now we can head into the update.

Interning @ Wealthsimple:

Gun to my head: I wouldn’t be able to think of a bad thing to say about Wealthsimple.

My hour-thirty-long commute to work doesn’t have a single moment where I second-guess my choice to come here — in fact, it only has moments of excitement. The number of nights where I left past 11:00pm didn’t have a single feeling of regret either, it only has feelings of obsession for the work I was doing.

I love the everything, but more importantly, everyone here. It’s truly such a special community with an even better culture. One of a kind.

The Launchpad group is amazing — in case you haven’t heard of it, Wealthsimple chose 4 high school graduates to take a gap year and intern there. You can see us above! All three of these people are so sharp, seriously. This is what I meant when I talked about the amazing people I’m around.

Apart from the Launchpad group, I’m currently working on AI automation software for the AI experiences team where I’m working under a founding team member from CloudKitchens. The first thing I asked him was completely unrelated to work and was about working with Travis Kalanick haha. But in all seriousness, he is an amazing guy, and the team as a whole is so bright.

What I love most about Wealthsimple is the pro-builder environment and the company values — which isn’t just to say on paper, it’s true.

On my first day I reached out to Diana McLachlan, the Chief People Officer at Wealthsimple (unrelated: she will be the fastest reader you’ve met), asking about an idea with their product team, and if so, can I help work on it. She got back to me right away and said they recently did an acquihire for Emily Luk, CEO + co-founder of Plenty, and connected me to her.

When I said earlier that this months newsletter is in complete honour of the amazing people in my life that I’m grateful for; Emily is one of those co-workers I’m grateful for. Although in full humility and honesty, she’s been more of a teacher // mentor than co-worker haha.

She has consistently gone out of her way to let me learn from her. Working on projects under her, shadow calls, etc. How many brilliant people out there would do that for an intern? Not a lot. And I couldn’t put it in words as to how grateful I am for it.

Apart from work here, I’ve met even more amazing people, currently trying to start a soccer team, and will likely join the running club (should I join the pickleball club? My mom + dad would be so proud).

**Co-founder** @ Antifragility Labs:

Most notable thing this month with Antifragility Labs was that I brought on a co-founder. If you’ve read prior monthly updates his name’s familiar — Nitya — a very talented person but even better friend. Great guy and super sharp. Extremely excited to see where this goes.

In addition to expanding, I won a couple of pitch comps — all cash going towards API credits ha. Got some really cool interest from VC firms as well — one of which has agreed to somewhat advise Antifragility Labs and help us grow (for no equity!).

My thesis for Antifragility Labs is working out great right now. The tl;dr: there’s a massive bubble in GEO, and the market is incredibly volatile. I think it’s too early to bet on singular software solutions when it’s a cat and mouse game. Which is why we’re a software-first agency — building new tools every week for our clients and adapting quickly. Learning a lot from this and loving it.

Applying to YC! If you have any tips, I would love it! Hit me up: noah@antilabs.ai

Side Projects // Everything Else:

• Currently working on a side project to help optimize how the world currently desalinates water. Something I’ve realized is there’s many unicorn startups out there, but only a handful make an impact. My goal is to become financially free as soon as I can because only then can I control my time and try changing parts of the world. I’m not financially free by any means yet, but I’m interested in water desalination, so I’ll build a side project in it. I’m hoping to participate in the XPRIZE comp.

• Released a bunch of new essays (subscribe!!!) — writing has been fun, I can’t do it as much as I want to though unfortunately.

• SAT + university applications: not the best part of my day haha. Since I was born in California there might be a way for me to pay in-state tuition for Berkeley (if I get in of course, send your good luck wishes please I’ll need them haha). If you have any tips, please send them my way I’m in desperate need I’m doing this so last-minute. Email: pointone@barbaros.ca

• Building out a tool for self-publishing authors — a bit early to speak too much on this, but I think it’s a really underrepresented // overlooked market with a huge opportunity waiting to be pounced on with AI -- I hate that I’m using those words together but it’s true.

• Working on a mini-project for one of the hottest startups in SF right now. I’m directly reporting to the founder, who is just such a brilliant mind and clear thinker. I don’t want to say too much about this, but this person is beyond cool and if I told you some of the stuff they’ve done // accomplished your jaw would be on the floor.

Back to gratitude

Work has just been fun — everything is fun. This month every feeling comes back to gratitude. I’m grateful especially for Rachel Zimmer (Co-Founder & CEO, Simple Ventures) who went completely out of her way to recommend me for Wealthsimple. Had it not been for her, I wouldn’t have applied to the program, and I’d be at university right now. That’s not to say it’s a bad-case-scenario, but imagine what would’ve happened had she not done that? Because the average person wouldn’t.

There’s very few people in the world who go out of their way for people and expect nothing in return, and somehow I know so many. Rachel is a golden example of one of them.

I’m so grateful for everything I have in life right now, the time you took to read this is one of them.

I claim to love startups, to love entrepreneurship - but how do I define it?

Well, our friends at Google and OpenAI define it quite simply:

And although both these definitions are true, they avoid what startups are at their core.

At it’s core, a startup is made to solve a problem nobody has ever solved before, or to solve a problem better than the current person solving it.

- From the wise man himself, and yours truly, me - Noah

What a startup really is

Through following the principle that a startups’ core reason for existence is to solve the unsolved, it makes it so that if your truly “founding” a startup, your navigating in a constant unknown space - one that hasn’t been grasped by anything in humanity yet.

Because of this principle and belief, I view startups as its own inherent world.
A world that is constructed by all of societies problems.

This world is it’s own kind of utopia - a world that can’t ever be fully explored, because in order for us to have fully explored it, we would’ve had to solve every problem humanity faces, both now (present), and in the future.

And it’s just that, there will never be a time we stop facing problems as a society. Which means intrinsically, there will never be a time we have fully explored this “utopian world” of startups.

With that being said, what does it mean?

It means that there’s no playbook because no one’s ever been here before. There’s no set rules, there’s no guide, and there’s no built structure for when building a startup.

You’re building something nobody has ever done before. There is just you, and potentially the people you’ve brought along with you to go on about solving this problem.

How to Succeed & Navigate in a World of Unknown

OK. We’ve now settled down on a problem, how do you move forward? It’s pitch black, in this new world, where’s our flashlight? And how do we know we’re moving in the right direction?

IMO there’s three things you need:

1. Lifelong learning

2. The three mindsets to success (A “figure it out” (FIO) + antifragile mindset + beginner mindset)

3. Straight up luck — but both kinds

Each one of these stages to success come one after another, and have a particular order of which they come.

1. What is it to be a lifelong learner?

“In times of change the learners will inherit the earth” - Eric Hoffer

To learn is to keep on moving forward, it is to be adaptive for both the present, and for the future. By choosing to not learn, you’re choosing to do a great number of many other things; we call that opportunity cost.

The most dangerous outcome of refusing to learn, is that you will remain stagnant, to reach a peak and remain just that, until you erode and break.

If we focus on startups specifically, and study them, we can see that one of the most proprietary reasons giants fall is because they fail to adapt in volatile markets and times. A few companies come to mind when I think of this:

1. Blackberry

2. Blockbuster

3. MySpace

Blackberry especially, is an amazing case study on what happens when you don’t innovate, adapt, and refuse to learn.

Something else relevant to this context of being a “lifelong learner” is a quote my friend had mentioned the other day,

“Right now I’m the smartest I’ve ever been, and the dumbest I’ll ever be.”

I believe that if you want to reach some unconventional level of success, you have to live through this philosophy. Because through living in it, we humble ourselves, and remind ourselves that through learning, we will always be a beginner in some sort of way. And as a beginner, you gain a thirst to learn - which in turn, activates “The Three Mindsets.”

2. What the hell are “The Three Mindsets” ??

Great question.

The three mindsets are what follows after being a “lifelong learner,” they are mindsets that are naturally achieved from having such hunger for knowledge.

The three frames of mind include being a beginner, having a figure it out (FIO) approach, and an antifragile core.

You’ll see that through training each of these frames of mind, by virtue, greater things will occur almost by nature. So much that through doing so, the outcome of success builds upon itself and brings us to the next stage of creating your own luck (partially, but more on this after).

We’ll kick off with the first frame, being a beginner;
One of the GOATs said it best, the one and only Brian Chesky,

The bigger he gets, the more of a beginner he feels.

It took Picasso’s four years to paint like Raphael, but a lifetime to paint like a child. To be a beginner is to be curious, and that is to see the world with fresh eyes, to live // work without judgment, and never feeling like you’ve “made it.” Because once you believe you’ve arrived, you stop growing (which in turn is to stop learning - hopefully you get the connection and see the pattern).

Through being a beginner, we can see things that would’ve typically been overlooked. Because we are not blinded yet by expertise, assumptions, and our own arrogance.

When you’re new to something you question everything there is to it. You don’t accept “that’s how it’s always been done,” so you spot flaws veterans ignore. Whereas experts often filter reality. Experienced people see patterns - which lets them think quick, but also lead blindly. They skip over the depth of the issue because they assume the answer. Beginners will notice the awkward UX button, clunky onboarding process, the little friction, and so forth.

Those “naive” observations often lead to breakthrough design. After all, Brian Chesky was the one that said, “it’s in the tiny details that delight lives. Most people gloss over them. Beginners don’t.”

In correlation to this, something a good friend of mine recently said is “just when you reach the top of one mountain, you notice someone else standing in the clouds.”

You can’t have the mindset of believing that there even is an endpoint, or a peak; sure, you can be a big fish in a small pond, living on the mountain top.

It’s all about optimizing for what you want at the end of the day.

But I kinda want to see what the clouds are like, that’s for sure. Maybe I’ll see someone in space haha (hellooo Elon).

Figure it out (FIO) mindset:
In startups we don’t have the time, nor do we have the resources to not do the work. Simply put, if you don’t do the work, quickly and to a high standard, you will fail.

It is both a race and a marathon simultaneously.

The way we can possess a FIO mindset, is through holding such conviction in ourselves, that no matter what cards we’re handed, we trust ourselves to always find some sort of way to make ends meet, and finish the work needed.

The startup world moves quick, it’s speed is not relative to our time, and we cannot calculate it. This world can be thriving one day, and moving at an unprecedented pace - but then humanity can fail to keep up which leads to a bubble among many other things. It is an external factor to our success as founders, and all we can do is work within it, which is figuring things out is so important.

The greatest founders were amazing at “FIO” when it seemed impossible at face value. So many examples come to mind when talking about this, a great one is the beginning of DoorDash. Spun up “PaloAltoDelivery.com,” answered a Google Voice line, and manually delivered first orders.

Another good one is Reddit, the founders created tons of fake accounts to populate early posts/comments until real users arrived.

When Airbnb’s runway was getting short - they sold custom cereal boxes!! The opportunities are endless. You just have to think differently.

These are all great examples and reasons as to why you need to be thinking in a system where you refuse underperforming. You have to get the work done, it doesn’t matter - to any means possible.

There were so many opportunities to quit and fail in these examples listed, yet these amazing founders created opportunities out of nothing, and found the few things that could’ve actually worked out. All through thinking differently, and having the mindset that they could figure out whatever was thrown there way - the mindset capable for greatness.

Side note ::Would you believe me if I said my old LinkedIn bio used to say “Figure It Out Enthusiast”? So this one is held close to my heart… Hits home.

The antifragile core
The startup world is unpredictable. As mentioned before, it is a world of unknown. A world that can never be fully explored, as it is only somewhat explored by subject to the problems we know of - that have actually been solved.

Because of this unpredictability, we have to get comfortable with being in these unknown spaces - it’s an external environment that we cannot control, but in ways can manipulate it, and take advantage of the mass volatility.

This third mindset is derived fully from Antifragile: Things That Gain from Disorder, by Taleb - one of my favourite reads out there.

Taleb says, “Wind extinguishes a candle and energizes a fire. Likewise, with randomness, shock, and volatility: you want to be the fire, not the candle.”

Both the candle and fire cannot control the wind, it is an external force. However, they can place themselves in unique environments to grow, and thrive from the wind. This same philosophy applies to startups - we can use these external forces of being in an unknown environment, and take advantage of the volatility - not to scare from it, but to grow from it. Just as a fire would to the wind.

Through building an antifragile core, we can navigate the startup world to our best ability. Especially when combined with the other two mindsets of being a beginner, and to figure it out - we can combine these three systems to craft a great founder.

Luck in success + making your own (partially…)

There’s no doubt that luck plays a huge role in success - it’s in every aspect of winning in startups. Various factors like timing, people you meet, current economy, etc.

Instagram (or at that time “Burbn”) - literally raised there seed round from a16z and Baseline ventures from meeting them at a Hunch party.

Inversely, and more recently, Google got rid of the num=100 parameter - Reddit used to be a powerhouse in citations for AI. However with this new update, there rankings plummeted. Reddit’s citation share on ChatGPT had fell from approximately 29.2% to just 5.3%. In doing so, there stock tanked 15% in the span of 5 days - roughly $6.5B gone.

How could Reddit have seen that coming? They couldn’t. There’s just things out of anyones control because of the uncertainty.

It’s a big “right person, right time” game. There’s so many ways the “wind” could blow - that sometimes you get unlucky. Many can argue that since there are too many variables that goes into building a successful startup (worth $10M+), even if it’s not random, it’s too complex to be controlled.

And I agree with this, but to an extent.

There are external factors we cannot control, but that is in everything we do - you can be at a cushy corporate job, however a recession can occur and you can get laid off, or perhaps get passed on a promotion. I know is startups a far more volatile market than the corporate one - so luck plays a much larger factor, but you get the point.

I believe there are ways that we can make our own luck - I actually think a good portion of our luck is earned.

If we break down what luck is, it’s a fraction, odds. “How many times out of x number of times will I strike out?”

The odds of founding a unicorn through scaling a lemonade stand is minimal, yet the odds of founding a unicorn through scaling a startup in an interesting space, solving a genuine problem, with some sort of emerging technology, is a lot higher.

Not only that, but factors like how many startups you’ve worked on prior, or the number of people you’re working with play huge roles as well. This isn’t hyperbole either, it’s backed by real data.

According to First Round’s 10-Year Project, teams of more than one founder outperformed solo founders by 163%.

Ali Tamaseb, who analyzed 195 unicorns, the odds of succeeding nearly double when moving from a single-person team to a two-person or three-person:

I can go on, but the point I’m trying to make is that there are things we can do to increase our odds, and naturally, increase our luck. We create our own luck through building our own opportunities when there’s none to exist - I love the concept of “The Third Door” for this exact reason.

The third door talks about the two ways we can enter a club - you can wait in the long line with the normies, or alternatively you can go to the VIP line.

Couple problems,

A: it’s cold, and waiting in a line sucks.
B: we’re not cool enough to be in VIP.

So what do we do? We make the third door ourselves - whether that door be paying off the bouncer, kicking in a window at the bathroom, acting like a famous person, etc.

Opportunities are rare because they’re out of our control, but we can make them more common if we create them ourselves, therefore putting them within our control, and within our reach.

For instance, one of my friends needed to raise VC money » no VC would sit down with him » he ordered pizzas, dressed as a delivery guy, and put a QR code inside each box. Physically WENT to the VC offices and delivered them » when the VCs opened the lid, it linked to his demo, pitch deck, and one-pager.

He got a call back from every VC firm he went to. And then raised some money.

If you still want to build…

At the end of the day, startups aren’t about pitch decks, or seed rounds. It’s about choosing to build something in a place where nobody can tell you what to do, because nobody’s figured it out yet. A startup is just the tool - the real thing is deciding to live in uncertainty on purpose. As long as there are problems, there will be people trying to solve them. And I’d rather be out there trying, even in the dark, than sitting around waiting for someone else to turn on the lights.

If you made it this far, thank you! This essay in particular was especially long, so the time you’ve given me means so much more than you might realize. I’m sure your a very busy person.

I’m using this blog experiment to let people get to know me + what I believe - and hopefully connect with more of you along the way. If you have any questions about what I’ve done, or would just like the chat - hit me up at pointone@barbaros.ca - if I don’t get back within 24 hours, coffee’s on me.

Once again, thanks for your time.

With such a beginner level understanding, how did I create a robust Twitter Sentiment Analyzer that can:

• Fetch real-time tweets based on user-defined queries

• Clean and preprocess tweet text for analysis

• Analyze sentiments using natural language processing

• Visualize sentiment distribution with intuitive charts

• Save and debug processed data for future reference?

I’ll walk you through each step below! However feel free to check out my tutorial for a full walkthrough on how this works!

Before you start, ensure you have Python installed on your machine along with the necessary libraries: tweepy, textblob, matplotlib, and re. You can install these using pip:

pip install tweepy textblob matplotlib

Step 1: Loading Configuration

Configurations like API keys and default settings are crucial for accessing Twitter’s API and customizing the behavior of our analyzer.

Comparable to setting up your workspace, loading configurations ensures everything is in place before diving into the code.

import json

def load_config():
    with open("config.json", "r", encoding="utf-8") as f:
        data = f.read()
        print("File content:", data)  # Debugging: ensure the JSON is read correctly
        return json.loads(data)

Explanation:

• Purpose: Reads the config.json file containing sensitive information like API keys.

• Debugging: Prints the file content to verify correct reading (remove in production).

• JSON Parsing: Converts the JSON string into a Python dictionary for easy access.

Sample config.json:

{
    "bearer_token": "YOUR_TWITTER_BEARER_TOKEN",
    "default_tweet_count": 50,
    "polarity_positive_threshold": 0.2,
    "polarity_negative_threshold": -0.2
}

Visual Aid: Imagine a neatly organized toolbox where each tool has its designated place — this is what config.json achieves for your project.

Step 2: Authenticating with Twitter API

Authentication is like getting your ticket to access Twitter’s data. Without it, you can’t fetch any tweets.

import tweepy

def authenticate(config):
    # For Twitter API v2, we primarily use the bearer token
    client = tweepy.Client(bearer_token=config["bearer_token"])
    return client

Explanation:

• Tweepy Client: Utilizes the bearer token from the config to authenticate requests.

• Security: Keeps API keys secure by storing them in a separate configuration file.

Best Practice Tip: Never hard-code your API keys in your scripts. Always use environment variables or configuration files excluded from version control.

Step 3: Fetching Tweets

Fetching tweets based on user-defined queries is the core of our analyzer.

def fetch_tweets(client, query, count):
    # Twitter API v2: search_recent_tweets can fetch up to 100 tweets per request
    max_results = min(count, 100)
    # Append `lang:en` to the query to filter English tweets
    query = f"{query} lang:en"
    response = client.search_recent_tweets(query=query, max_results=max_results, tweet_fields=['text'])
    return response.data if response.data else []

Explanation:

• Query Parameters: Filters tweets in English to maintain consistency in analysis.

• Rate Limits: Caps the number of tweets per request to 100, adhering to Twitter’s API limits.

• Error Handling: Returns an empty list if no tweets are found to prevent crashes.

Visual Comparison:

Before Fetching:

[Empty]

After Fetching:

["I love sunny days!", "Feeling sad about the news...", ...]

Step 4: Preprocessing Tweets

Raw tweet data can be messy. Preprocessing cleans the text, making it suitable for sentiment analysis.

import re
def preprocess_tweet(text):
    text = re.sub(r'RT\s+', '', text)           # Remove retweets
    text = re.sub(r'@\w+', '', text)           # Remove mentions
    text = re.sub(r'http\S+', '', text)        # Remove URLs
    text = re.sub(r'\s+', ' ', text).strip()   # Remove extra whitespace
    return text

Explanation:

• Regex Patterns: Efficiently removes unwanted parts of the tweet.

• Clean Text: Ensures the sentiment analysis focuses solely on meaningful content.

Analogy: Think of preprocessing as cleaning ingredients before cooking — removing impurities to enhance the final dish’s flavor.

Step 5: Analyzing Sentiment

Using natural language processing to determine the sentiment of each tweet.

from textblob import TextBlob

def analyze_sentiment(tweets, pos_threshold, neg_threshold, debug=False):
    positive_count = 0
    neutral_count = 0
    negative_count = 0
    processed_tweets = []
    for tweet in tweets:
        cleaned_text = preprocess_tweet(tweet.text)
        blob = TextBlob(cleaned_text)
        polarity = blob.sentiment.polarity
        if polarity > pos_threshold:
            positive_count += 1
        elif polarity < neg_threshold:
            negative_count += 1
        else:
            neutral_count += 1
        if debug:
            processed_tweets.append((cleaned_text, polarity))
    return positive_count, neutral_count, negative_count, processed_tweets

Explanation:

• TextBlob: Analyzes the polarity of the tweet, ranging from -1 (negative) to +1 (positive).

• Thresholds: Customizable to define what constitutes positive, neutral, or negative sentiments.

• Debugging: Optionally stores processed tweets and their polarity for further inspection.

Visual Aid: Imagine a scale where tweets are placed based on their sentiment score — some leaning towards positive, some neutral, and others negative.

Step 6: Plotting Results

Visualizing the sentiment distribution helps in quickly understanding the overall mood of the tweets.

import matplotlib.pyplot as plt

def plot_results(positive, neutral, negative):
    labels = ['Positive', 'Neutral', 'Negative']
    counts = [positive, neutral, negative]
    colors = ['green', 'blue', 'red']
    
    plt.figure(figsize=(8, 6))
    plt.bar(labels, counts, color=colors)
    plt.title('Sentiment Distribution')
    plt.ylabel('Count')
    plt.xlabel('Sentiment')
    plt.ylim(0, max(counts) + 10)
    
    for i, count in enumerate(counts):
        plt.text(i, count + 1, str(count), ha='center', va='bottom')
    
    plt.show()

Explanation:

• Bar Chart: Clearly displays the number of positive, neutral, and negative tweets.

• Customization: Colors and labels enhance readability and visual appeal.

• Annotations: Adds count labels above each bar for precise information.

Best Practice Tip: Always label your axes and provide a title to make charts self-explanatory.

Step 7: Running the Main Function

Integrating all components to execute the sentiment analysis workflow.

def main():
    config = load_config()
    client = authenticate(config)

query = input("Enter a search term: ")
    count_input = input(f"Number of tweets to fetch (default: {config['default_tweet_count']}): ")
    try:
        count = int(count_input) if count_input.strip() else config["default_tweet_count"]
    except ValueError:
        print("Invalid input. Using default tweet count.")
        count = config["default_tweet_count"]
    tweets = fetch_tweets(client, query, count)
    if not tweets:
        print("No tweets found. Please try a different query.")
        return
    positive, neutral, negative, _ = analyze_sentiment(
        tweets,
        pos_threshold=config.get("polarity_positive_threshold", 0.2),
        neg_threshold=config.get("polarity_negative_threshold", -0.2),
        debug=False
    )
    print("\nSentiment Analysis Results:")
    print(f"Positive: {positive}")
    print(f"Neutral: {neutral}")
    print(f"Negative: {negative}")
    show_chart = input("\nShow chart? (y/n): ")
    if show_chart.lower() == 'y':
        plot_results(positive, neutral, negative)
    debug_print = input("Print cleaned tweets for debugging? (y/n): ")
    if debug_print.lower() == 'y':
        _, _, _, debug_tweets = analyze_sentiment(
            tweets,
            pos_threshold=config.get("polarity_positive_threshold", 0.2),
            neg_threshold=config.get("polarity_negative_threshold", -0.2),
            debug=True
        )
        print("\nSample Cleaned Tweets:")
        for t, p in debug_tweets[:10]:
            print(f"\"{t}\" - Polarity: {p}")

Explanation:

• User Interaction: Prompts users to input search terms and desired tweet counts.

• Error Handling: Validates user input and handles scenarios where no tweets are found.

• Conditional Execution: Offers options to display charts and debug information based on user preference.

• Sample Output: Displays a subset of cleaned tweets with their polarity scores for deeper insights.

Best Practice Tip: Always validate user inputs to make your program robust against unexpected or invalid data.

Conclusion

Building a Twitter Sentiment Analyzer from scratch was an enlightening experience that bridged my understanding of data fetching, natural language processing, and data visualization. By integrating Python’s powerful libraries — Tweepy, TextBlob, and Matplotlib — I was able to create a tool that not only analyzes sentiments but also presents the data in an intuitive manner.

What Did I Learn?

• API Integration: Gained hands-on experience with Twitter’s API using Tweepy, understanding how to authenticate and fetch real-time data.

• Data Cleaning: Learned the importance of preprocessing raw data to ensure accurate sentiment analysis.

• Natural Language Processing: Utilized TextBlob to analyze and interpret the sentiment of textual data effectively.

• Data Visualization: Enhanced my ability to present data through clear and informative charts using Matplotlib.

• Error Handling: Implemented robust error handling to make the application more user-friendly and resilient.

What’s the Bigger Picture?

In an era where data drives decisions, sentiment analysis tools like this one can be invaluable for businesses, researchers, and individuals seeking to gauge public opinion or emotional trends. Imagine scaling this project to:

• Real-Time Monitoring: Integrate with live data streams to monitor sentiment in real-time.

• Advanced Analytics: Incorporate machine learning models for more nuanced sentiment detection.

• User Interface: Develop a web-based interface for broader accessibility and user interaction.

• Multi-Language Support: Expand the analyzer to handle tweets in multiple languages, increasing its global applicability.

Future Enhancements:

• Subscription Models: Offer premium features such as historical data analysis, detailed reports, and personalized dashboards.

• Integration with Other Platforms: Extend the tool to analyze sentiments across various social media platforms like Facebook, Instagram, and Reddit.

• AI and Machine Learning: Leverage more sophisticated models to improve sentiment accuracy and context understanding.

Thank You,

This is an old project of mine from ~ a year ago! It was originally posted on Medium but a good friend told me it helps to cross-post. If you took the time to read all of this, I want to say thank you for your time — I’m sure your a very busy person, so the time you’ve given me means a lot more than you might realize.

I want to use this blog experiment as a way for people to learn more about me and, hopefully, connect with more people. If you have any questions about what I’ve done, or would just like the chat — hit me up at pointone@barbaros.ca — if I don’t get back within 24 hours, coffee’s on me.

---

Additional Resources

Source Code (Github Repo)

Twitter Developer Portal

Tweepy for interacting with the Twitter API

TextBlob for sentiment analysis

Matplotlib for data visualization

Regex (re) for text preprocessing

We like to think we’re rational investors, driven by data and diligent analysis. But let’s be real: markets don’t just move on numbers — they swing wildly on emotion, hype, and headlines. Think about it. One tweet, one scandal, one new product launch, and a company’s stock can skyrocket or tank, sometimes for reasons that have nothing to do with the balance sheet. This is the hidden power of sentiment in the stock market: it’s not about what is happening, but how people feel about it.

In a world where perception often outweighs reality, the ability to read market sentiment becomes a critical skill. That’s where this project steps in. With a unique combination of sentiment analysis and real-time data scraping, we’re capturing the heartbeat of the market for companies like Amazon, Google, and Meta— seeing beyond the financial reports to understand the emotion driving the prices.

Whether you’re an investor looking for an edge or simply fascinated by the psychology of finance, this project lets you see the market as more than numbers on a screen. Get ready to dive into the minds of the market movers and see what makes prices tick.

TL;DR:

Sentiment analysis tools like VADER provide a powerful yet imperfect solution for extracting emotional insights from text data. While capable of classifying sentiments as positive or negative, they can fall short in scenarios involving sarcasm or conflicting language.

A Classical Approach to Stock Analysis

In the world of traditional investing, tracking a stock’s sentiment means staying updated with news, reports, and analyst opinions — all of which take considerable time to read and interpret. Often, investors rely on financial advisors, news summaries, or quantitative analysis platforms to stay informed, but each of these is either time-consuming or expensive. What if we could combine the power of natural language processing (NLP) and sentiment analysis to speed up this process?

Manual Sentiment Analysis: A Tedious Process

A manual approach to reading stock sentiment looks something like this:

1. Read the Headline: Manually go through each piece of financial news.

2. Interpret the Tone: Decide if the news is positive, neutral, or negative.

3. Record the Sentiment: Track these interpretations in a database.

This method quickly becomes impractical. The speed and sheer volume of financial news demand a faster, more automated solution.

Sentiment Analysis with VADER: A Modern Solution

Enter VADER (Valence Aware Dictionary and sEntiment Reasoner). VADER is an NLP tool specifically designed for analyzing the sentiment of short texts, making it ideal for headlines. Here’s how VADER transforms headlines into scores:

• Positive, Neutral, and Negative Scoring: Each headline is broken down into scores across positive, negative, and neutral categories.

• Compound Score: The overall sentiment is expressed as a compound score ranging from -1 to +1, with positive scores indicating optimism and negative scores signaling concern.

Let’s consider a basic example:

• A headline like “Amazon breaks sales records this quarter” might receive a high positive score.

• In contrast, “Market challenges affect Meta’s profits” could score negatively.

VADER’s algorithm sorts these scores, allowing us to quickly see which companies are trending positively or negatively in the market.

The Naive Bayes Algorithm: The Engine Behind Sentiment Analysis

Our project taps into the fundamentals of sentiment analysis through a statistical approach known as the Naive Bayes Algorithm. This algorithm, grounded in probability, predicts whether a headline is likely to impact a stock positively or negatively. It works by evaluating the likelihood of certain words or phrases appearing in either positive or negative reviews based on historical data.

In this model, we let “X” represent all the headlines or news snippets, while “Y” becomes the final output score, where a positive score indicates an optimistic sentiment and a negative one a pessimistic sentiment. Here’s a look at the process:

• P(Y=Positive | X) gives us the probability that the market sentiment is positive given a particular headline.

• P(Y=Negative | X) does the same for negative sentiment, allowing us to gauge the influence of news on a stock’s public perception.

VADER: Quick, Rule-Based Sentiment Scoring

We use VADER (Valence Aware Dictionary and sEntiment Reasoner), a rule-based model built for general sentiment analysis. VADER scores text for positive, neutral, negative, and compound (overall) sentiment. The beauty of VADER is its efficiency: it doesn’t analyze every word but focuses on those that carry emotional weight, skipping filler words like “the” or “and.”

This tool enables us to calculate sentiment on a massive scale — quickly scoring each headline without heavy computational demands.

Web Scraping and Real-Time Analysis

Of course, analyzing sentiment would be meaningless without real-time data. Using Python’s web scraping tools, we fetch live headlines and stock news directly from Finviz, a leading financial website. Our scraper identifies and extracts headlines tied to specific tickers — AMZN, GOOG, META— pulling them into our analysis pipeline. By examining the latest news, we can dynamically update the sentiment score based on recent events.

Our project then structures the scraped data, linking each headline with its timestamp and sentiment score to create a live snapshot of how the market feels about each company. The final result? An insightful, constantly updating bar chart displaying sentiment over time for each stock.

Coding the Project: Step-by-Step Implementation

This section breaks down how I coded a simple sentiment analysis tool that scrapes headlines from Finviz, analyzes them with VADER, and visualizes the results.

Step 1: Data Collection — Gathering Headlines from Finviz

The first part of our process involves collecting data. For this project, we pull stock headlines directly from Finviz for tickers like Amazon (AMZN), Google (GOOG), and Meta (META), which serve as the foundation for our sentiment analysis.

Here’s the code for scraping headlines:

from urllib.request import urlopen, Request
from bs4 import BeautifulSoup

tickers = ['AMZN', 'GOOG', 'META']
finviz_url = 'https://finviz.com/quote.ashx?t='news_tables = {}
for ticker in tickers:
    url = finviz_url + ticker
    req = Request(url=url, headers={'user-agent': 'my-app'})
    response = urlopen(req)
    html = BeautifulSoup(response, 'html.parser')
    news_table = html.find(id='news-table')
    news_tables[ticker] = news_table

We start with a request to Finviz for each ticker symbol. Once we gather the HTML data, we use BeautifulSoup to isolate the table containing each company’s news. This table is stored for further processing, ready to be analyzed for sentiment.

Step 2: Applying Sentiment Analysis with VADER

Once we’ve gathered the news data, we dive into sentiment analysis using VADER. VADER, or Valence Aware Dictionary and sEntiment Reasoner, provides a compound sentiment score that categorizes text as positive, negative, or neutral.

Here’s the code for processing sentiment:

from nltk.sentiment.vader import SentimentIntensityAnalyzer
import pandas as pd

parsed_data = []
vader = SentimentIntensityAnalyzer()for ticker, news_table in news_tables.items():
    for row in news_table.findAll('tr'):
        title = row.a.text.strip()
        date_data = row.td.text.strip().split(' ')
        date = date_data[0]
        time = date_data[1] if len(date_data) > 1 else 'N/A'
        compound = vader.polarity_scores(title)['compound']
        parsed_data.append([ticker, date, time, title, compound])df = pd.DataFrame(parsed_data, columns=['ticker', 'date', 'time', 'title', 'compound'])

This step applies VADER’s compound score to each headline, converting sentiment into a numeric value that is easy to interpret: positive, negative, or neutral. We now have structured data that reflects the emotional tone behind each headline, making it possible to track shifts in market sentiment over time.

Step 3: Visualizing the Results

Finally, we visualize our sentiment scores to reveal trends and patterns. This step involves grouping data by date, calculating average sentiment for each stock, and plotting it as a bar graph to showcase sentiment shifts over time.

Here’s how we generate this visual insight:

import matplotlib.pyplot as plt

df['date'] = pd.to_datetime(df['date'])
mean_df = df.groupby(['ticker', 'date']).mean().unstack()
mean_df = mean_df.xs('compound', axis="columns")
mean_df.plot(kind='bar')
plt.title("Daily Sentiment for Stocks")
plt.xlabel("Date")
plt.ylabel("Mean Compound Sentiment Score")
plt.show()

The bar plot effectively communicates sentiment shifts by date and ticker. A positive bar height indicates optimism, while a negative bar height signals caution or pessimism — making it easy to identify which stocks are receiving favorable or unfavorable attention

AI isn’t perfect

AI is powerful, but it’s not perfect. There are times when sentiment analysis models misinterpret the tone of reviews. For example, a headline may earn a sentiment score that doesn’t align with the true intent behind the text, leading to results that may not accurately reflect the market’s mood. This becomes especially evident in cases of sarcasm or complex phrasing, where the literal words don’t match the implied sentiment.

Let’s look at some cases where sentiment scores and actual sentiment diverge:

# Identifying positive sentiment in news that should be negative
positive_sentiment_negative_review = df.query('compound > 0 and title.str.contains("loss|decline|problem")', engine='python')
print(positive_sentiment_negative_review[['title', 'compound']].head(3))

# Identifying negative sentiment in news that should be positive
negative_sentiment_positive_review = df.query('compound < 0 and title.str.contains("growth|gain|success")', engine='python')
print(negative_sentiment_positive_review[['title', 'compound']].head(3))

By querying for specific keywords that may contradict the sentiment score, we can identify examples of sentiment discrepancies. Here’s an example of where this might occur:

• “Company reports strong growth, but a decline in specific sectors”: VADER might score this as positive due to keywords like strong and growth, despite the mention of decline, which tempers the news sentiment.

• “Revenue gains lead to success, although facing minor issues”: The model may interpret success and gains positively, overlooking the sentiment-shifting impact of minor issues.

These nuances illustrate how AI models, including VADER, can stumble with sentiment detection due to the inability to gauge context fully, especially for nuanced expressions like sarcasm.

Thank You,

This is an old project of mine from ~ a year ago! It was originally posted on Medium but a good friend told me it helps to cross-post. If you took the time to read all of this, I want to say thank you for your time — I’m sure your a very busy person, so your time means more than you would know to me.

I want to use this blog experiment as a way for people to learn more about me and, hopefully, connect with more people. If you have any questions about what I’ve done, or would just like the chat — hit me up at pointone@barbaros.ca — if I don’t get back within 24 hours, coffee’s on me.

It’s a pretty self explanatory post — but eye awakening. Why is it that it took us 68 years to reach a terawatt of solar, yet just 2 years to double it?

The answer’s simple, compounding — and it applies to human potential just as much, if not more.

"The First Step is the Hardest"

I’m sure we’re all familiar with this phrase;

And it couldn’t be more true. The hardest part in developing human growth is the first breakthrough — learning a skill, building credibility, or getting that first win.

Building the foundation.

Early progress especially, feels painfully slow because your creating an entirely new framework — things that were not at all once there, things that are new to you: habits, knowledge, systems, and confidence, the list goes on.

Infrastructure and Flywheels + Doubling Potential

Once that basis // framework is established, effort compounds. The system of success (network, skills, reputation, resources) works like solar manufacturing at scale: cost declines, and the process gets quicker.

With each iteration, you uncover flaws in what once seemed brilliant and refine systems that once felt revolutionary.

Each new step builds on what came before, so doubling becomes both faster and easier.

The first $10K saved is slow, the next $10K is probably still slow… but a bit faster haha.

This isn’t hyperbole either; Theodore Paul Wright, was a U.S. aeronautical engineer and educator, and he made up this law — called “Wright’s Law” (probably could’ve guessed that).

This law can be found in this lithium-ion batteries example:

In the early 2000s, every doubling of production took years. Costs were high, adoption was slow, and scaling felt painful. But as production piled up, the curve bent — doubling came faster, costs fell, and batteries suddenly seemed to be “everywhere.”

Compounding occurred.

Wright’s Law explains why solar, batteries, and other technologies scale so fast once they break through. Every doubling of production drives costs down, making adoption easier, which fuels the next doubling. That’s why the second terawatt of solar took 2 years instead of 68.

Now zoom out: humans operate on a similar curve. Our currency, in “cost” of achieving something (whether it be in energy, effort, time) declines with each doubling of experience.

The relative cost per unit of progress drops. You’ve built the systems, habits, and pattern recognition that make each additional doubling faster. Just like solar panels get cheaper the more we produce, your “unit cost” of achievement falls the more you practice and accumulate wins.

Building becomes easier and growth becomes exponential.

Thank You,

These are my latest thoughts! If you took the time to read all of this, I want to say thank you for your time — I’m sure your a very busy person, so your time means so much to me.

I want to use this blog experiment as a way for people to learn more about me and, hopefully, connect with more people. If you have any questions about what I’ve done, or would just like the chat — hit me up at pointone@barbaros.ca — if I don’t get back within 24 hours, coffee’s on me.

To compress ~6 months into one post while actually making it readable, will be difficult. So I’m going to be very very concise.

If your new here, welcome! I started this blog to make it easier for people to get to know me and to open up more conversations. Subscribe to join the ride, and if you’ve got questions (or just want to talk), drop me a line at pointone@barbaros.ca

Coffee’s on me if I take more than 24 hours to reply.

If you want to learn a bit more about me — check out this intro to both me and this blog:

From where I left things off in March, here’s everything you’ve missed on since:

Shut down CoVenture:

If you don’t remember, at the time I was very very passionate about building the first ever social network for Gen Z — the most entrepreneurial generation out there.

I hated LinkedIn — every connection felt like an empty handshake, so I built a better one revolved around building projects not fake connections. I had some really exciting ups and downs building CoVenture. Met with some awesome people, got some amazing mentors through it, and got some opportunities to expand to the states, and UWaterloo! However I ultimately decided to shut it down because I felt like I had created the issue in my head rather than actually experiencing it… among with a list of other things.

Launched Antifragility Labs:

If I’m shutting something down it means I’m starting something new up haha. I love work, and I love being busy with work even more.

For the past 5 months now (since May), I’ve been running Antifragility Labs — an agency (turning) startup that helps businesses rank in both AI and search engines alike. More formerly known as GEO (generative engine optimization) and SEO (search engine optimization). I first started off as an agency acquiring clients like YC-backed startups, e-commerce businesses, well-known podcasts, and pretty big VC firms.

I’ve now been working on internal software — it’s not that I can’t speak too much on this, it’s more that I don’t want to. However I’ve been “dogfooding” it to my clients and I’ll GTM very very soon. More recently (as in a few days ago) I pitched this at a pitch competition at Waterloo Tech Week and won!

I first got into GEO when I did SEO for Armilla AI (YC-backed AI insurance startup w/ $5M+ raised). Released a few articles about it to both showcase and further my understanding in the industry.

Article 1

Article 2

Article 3

I also had the chance to present about Antifragility Labs in-front of 400+ people in Toronto’s Bluma Appel Theatre at the TKS showcase event — heres’s a pic of me presenting:

Samsung Pitch Competition:

At that time I was pitching a project to Samsung in a competition with TKS. My team competed against 1,000+ participants to rethink how Samsung could use AI to make accessibility more inclusive.

We won! Came first place.

My role in this project was to research, develop a prototype, and pitch on behalf of my team.

If you want to check out our final deck, feel free to check it out here:

Samsung Pitch Deck

And feel free to check out the interactive prototype I made here (open it on your phone!):

Samsung Prototype

The prototype followed One UI 6 Guidelines design principles—toast stack, spacing, and all — the current design guideline from Samsung at the time.

Overall was such an amazing experience — being able to work with Nitya, Aydin, and Naman again — absolute blast.

School Soccer Varsity Finals

Made it to the finals with my school varsity team!

Unfortunate rough ending but man did I love this team. Grateful for my time here. Truly.

Graduated High School!

Super crazy time. Probably some of the most fun I’ve ever had. So many moments that I lived in thinking “wow this is gonna be an awesome memory” — sure was. Pretty surreal to think I’m done with high school. Of course, I know I'm still young, and have much more school ahead, but I've gotten so used to growing up with the same people and seeing them every day — and now, come September, most of us will all be in different places — it's pretty wild to think about. Exciting but scary.

🏃‍♂️🏃‍♂️🏃‍♂️

Recently started really getting into running — ran a half marathon couple months ago and a 10k just a month ago. Training for an upcoming marathon! Ironmans the goal.

Taking a gap year to intern @ Wealthsimple

Ya… pretty self explanatory. Only thing I can say about this is both Rachel Zimmer + Lyn Chen absolutely rock!

Wrapped up internship @ Armilla AI

Armilla AI is a YC-backed startup with $5M+ raised tackling AI insurance. This is bittersweet — they really took a bet on me and I will always owe them one for that.

I’m very fortunate to say that not only did I leave with amazing new skills learnt, but I leave with new friendships with some of the most brilliant, and kind people I’ve ever met.

I remember when I first called the founder I told him that he could throw anything at me and I’d figure it out. He told me that they weren’t getting optimized Google and needed help with SEO! It was pretty ironic cause I was searching up what it meant on call — but nonetheless I had to take it on. So I learnt quick, and learnt well.

Got them awesome results in < 4 months without spending any money on SEO tools — just making a ton of alternate email accounts signing up for free trials.

My results carried in a 4x increase on clicks, nearly 20x increase in impressions, and ranked them on page 1 for all relevant and competitive KWs where they were previously unindexed.

Before:

After:

After I optimized their SEO I wondered how SEO would work for AI search — which led me to GEO, where I then got them cited on Google’s AI Overviews and all relevant LLMs like GPT, Claude, Perplexity, etc.

Some Reflections on These Past Few Months

Over these past few months I’ve met some incredible people and have truly felt what it is to be grateful.

From graduating high school, finishing TKS, and wrapping up my internship at Armilla AI, to making the choice to take a gap year while many of my friends head off to university — I’ve found myself pausing often, reflecting on gratitude, learning how to truly savour the moment, and figuring out how to make difficult decisions when many people are involved.

I’ll likely write about these reflections in some upcoming essays — but two of my favourite reflections have been:

1. The two types of wealth — wealth in money, and wealth in people. the kind of wealth where their love fills a room and leaves a mark on everyone they meet. both equally matter, but it’s easy to forget the second kind.

2. there's a true beauty + advantage to being a beginner -- almost a kind of ignorance to not knowing what's ahead and taking it on head first anyways.

More on these in the near future for sure.

Thank You.

This is what I’ve been up to! If you took the time to read all of this, I want to say thank you for your time — I’m sure your a very busy person, so your time means so much to me. I’ve had an amazing past few months and I couldn’t be more grateful for where I’m at, and the people I’m friends with.

I want to use this blog experiment as a way for people to learn more about me and, hopefully, connect with more people. If you have any questions about what I’ve done, or would just like the chat — hit me up at pointone@barbaros.ca — if I don’t get back within 24 hours, coffee’s on me.

After personally creating content for businesses that consistently gets featured in Google’s AI Overviews and cited by leading language models like ChatGPT, Perplexity, and Claude, I’ve identified the key strategies that drive both AI visibility and human engagement. The shift from traditional SEO to AIO (AI Optimization) / GEO (Generative Engine Optimization) requires a fundamental rethinking of content strategy.

Here’s what we’ll cover: crafting content that AI systems prioritize and how to leverage the right optimization tools.

TL;DR: What I Learned Getting My Content Cited by AI

After getting cited in Google’s AI Overviews, Perplexity, and ChatGPT, I found a clear pattern:
AI systems reward structured, scannable, and trust-rich content — not keyword dumps or long-winded narratives.

The key drivers?

• Answer-first formatting

• Credibility signals within the first 200 words

• Brand authority over backlinks

• Schema, sentiment, and multimodal presence (especially video)

Bottom line: The brands getting cited aren’t necessarily creating better content — they’re creating content that matches how AI systems are designed to extract and reference information.

This article breaks down the exact playbook I use to get my content featured by AI, while still making it valuable for human readers.

Writing Content That Both Humans & AI Systems LOVE

To crack this code, we first need to understand how and why AI systems cite specific sources. If you’re interested in the technical breakdown of the inference stage in AI, I’ve covered that in detail elsewhere.

Build “Answer-First” Pages

Here’s what I’ve learned: AI systems favour content that’s concise, direct, and scannable — qualities that also happen to drive human engagement.

The key is front loading your answers (ie. incorporating a TL;DR at the beginning). Instead of burying insights deep in paragraphs, lead with the solution, then provide supporting context. This “answer-first” approach satisfies both AI’s need for quick pattern recognition and human readers’ desire for immediate value.

The most effective formats include:

• TL;DR at the top of content

• FAQ sections

• Structured how-to guides

• Definition-heavy content that provides immediate value

AI models process structured content more effectively than dense paragraphs. The formats that consistently get cited include descriptive headings that telegraph content, FAQ sections that directly answer common queries, step-by-step processes that break down complex procedures, and bullet points that highlight key information.

This isn’t coincidence — it’s how AI systems are trained to extract information. Google’s AI Overviews frequently pull from FAQ schema, while Perplexity and Claude often reproduce well-formatted lists and step-by-step guides. By structuring your content to match these patterns, you’re essentially speaking the AI’s native language while simultaneously improving human readability.

The Clarity Advantage

Content clarity directly correlates with AI citation rates. Through testing, I’ve found that simplifying language and improving readability consistently outperforms keyword-heavy approaches. AI systems struggle with jargon-dense content the same way humans do — they favour clear explanations over technical complexity.

This means writing shorter sentences, explaining concepts in plain language, and maintaining strong grammatical flow. The goal isn’t dumbing down your content; it’s making expertise accessible. When you can explain complex ideas simply, both AI systems and human readers gravitate toward your content as the definitive source.

Building Trust Signals That AI Systems Recognize

Trust isn’t just about human perception — AI systems actively evaluate credibility markers when selecting content to cite. Through extensive testing, I’ve identified the specific signals that boost both AI visibility and human confidence.

Strategic Citation Integration

Content that references external sources receives significantly higher AI selection rates.

But there’s a methodology here:

• Cite authoritative sources within the first 200 words

• Use diverse source types (academic papers, industry reports, reputable publications)

• Ensure citations directly support your core arguments.

Expert Voice Integration

Direct quotes from recognized authorities create immediate credibility signals. The most effective approach involves weaving expert perspectives throughout your content, not just dropping them in as isolated blocks.

For instance, a cybersecurity article that integrates insights from security researchers, official reports, and industry leaders throughout the narrative demonstrates comprehensive sourcing that AI systems recognize and prioritize.

Data-Driven Substantiation

Statistical evidence serves as content currency in the AI economy. Specific numbers, percentages, and research findings provide concrete value that AI systems can extract and reference. The key is presenting data that’s both current and contextually relevant — “95% of users reported improved efficiency” carries more weight than vague claims about “significant improvements.”

Technical Depth Without Density

Including relevant industry terminology and technical concepts signals topical authority to AI systems. In cloud computing content, naturally incorporating terms like Kubernetes, REST APIs, and containerization demonstrates subject matter expertise. The critical balance is maintaining readability while showcasing depth — technical accuracy that doesn’t sacrifice accessibility.

E-E-A-T Optimization for AI

Experience, Expertise, Authoritativeness, and Trustworthiness aren’t just Google ranking factors — they’re AI selection criteria.

This means:

• Clear author credentials

• Demonstrated first-hand experience

• Confident, authoritative tone throughout your content

AI systems favor content that reads as expert-written and thoroughly researched.

Digital Ecosystem Presence

Perhaps most importantly, AI systems heavily weight cross-platform mentions and discussions. Your content’s likelihood of being cited increases dramatically when your brand or insights appear across forums, social media, and industry discussions.

This means strategic community engagement and thought leadership efforts directly impact AI visibility — a factor many content creators overlook.

Review Velocity as Trust Currency

AI systems perform sentiment analysis on your brand’s digital footprint, making reviews a critical ranking factor.

The combination of review count, velocity, and quality across platforms like Yelp, Google Business, and industry-specific sites creates powerful trust signals. LLMs actively crawl these review sources to assess credibility, meaning your off-page reputation directly influences AI citation rates.

Brand Authority > Backlinks

Unlike traditional SEO, AI systems prioritize brand authority and topical relevance over link building. Mentions in trusted sources — even without direct links — shape how AI models summarize and cite your content. This represents a fundamental shift where earned media and thought leadership carry more weight than traditional link acquisition strategies.

Stack That Got Me into AI Overviews

Traditional SEO tools weren’t built for AI optimization. While keyword research and backlink analysis remain useful, the metrics that matter most for AI visibility require entirely different measurement approaches. After testing dozens of tools and platforms, I’ve identified the essential stack that drives real AI citation results.

Schema Markup and Structured Data Implementation

Just as traditional Google algorithms rely on clear signals, AI systems need structured data to interpret your content effectively. Schema markup serves as a direct communication channel with LLMs, providing context that dramatically improves citation rates.

As mentioned earlier, the most impactful schema types for AI optimization include FAQ schema for question-based queries, How-to schema for process-driven content, Review schema for trust signals, and Organization schema for brand authority. Tools like Schema Pro, Structured Data Markup Helper, and JSON-LD generators streamline implementation, but the key is strategic deployment rather than comprehensive coverage.

Content Optimization for AI Readability

Earlier, I mentioned the importance of clarity — something we can actively test and improve. Tools like Grammarly offer baseline readability checks, but optimizing for AI requires deeper analysis. I often use LLMs themselves (specifically Claude) to fine-tune grammar, tone, and structure for AI readability.

YouTube Optimization for AI Visibility

Gemini heavily favors YouTube content in AI Overviews, making video optimization a critical component of AI strategy.

Focus on short, educational videos tied to core queries, optimized titles that match conversational search patterns, and strategic keyword placement in descriptions and transcripts. The goal isn’t viral reach — it’s becoming the authoritative video source that AI systems cite when answering specific questions.

The Future is Already Here

The shift from traditional SEO to AI optimization isn’t coming — it’s already happened. While most marketers are still optimizing for search engines from 2019, the brands getting cited by AI systems today are the ones that will dominate tomorrow’s search landscape.

The playbook is straightforward: structure your content like AI systems are trained to process it, build trust signals that algorithms can recognize, and optimize for the tools that actually matter in an AI-first world. It’s not about gaming the system — it’s about understanding how information flows in the age of generative AI.

What we’ve covered here — from answer-first formatting to schema implementation to cross-platform brand building — these aren’t advanced tactics. They’re foundational strategies that will only become more important as AI systems become the primary way people discover and consume information.

The businesses that adapt to this reality now will have a massive advantage. The ones that don’t will find themselves invisible in an AI-driven search world.

Let’s Keep The Conversation Going!

Hey! I’m Noah, a 17 y/o who’s been deep in the GEO space for the past year, where I’ve been running Antifragility Labs to help brands rank in both search engines and AI systems.

If you found this useful or have questions about any of these strategies, feel free to reach out. I love meeting people who are thinking about these problems and always enjoy swapping notes on what’s working in the AI optimization world.

The future of content is being written right now, and the more of us who understand it, the better off we’ll all be.

Thanks for reading this post! I love talking to you guys—if you have any questions, opinions, or just wanna chat, hit up my email at: noah@barbaros.ca anytime or comment below!

The AI wave has fundamentally shifted how we work, communicate, and live. But here's what nobody talks about: the magnitude of AI's risk matches the magnitude of its promise.

The problem isn't the technology itself - it's that we don't understand its strengths and weaknesses, and we're not leveraging what makes us uniquely human: judgment, taste, and our ability to handle the unknown.

Let me explain: every innovation exists for one reason: to solve problems - that's the whole point. But I believe there are two types of “innovation”:

“Regular innovation,” solves specific problems for specific people. “Disruptive innovation” solves problems for everyone. AI didn't just impact a niche group of people, it impacted everyone.

That's incredible. So very few things accomplish that. AI will go on to do so much good for the world - areas like cancer research will improve at a multiplier in contrast to before. I mean, people use AI for everything in their day-to-day lives asking prompts like:

“Make me an image on xyz”

“Write me this essay on abc”

“Draft my breakup text but make me sound like the mature one.”

“What’s the fastest way to lose weight?"

AI is revolutionizing cancer research… and also helping Chad figure out what to text his ex Rachel.

Jokes aside, at face value AI is great - “i can get amazing results without thinking?? noice.”

But behind said ‘face value’ we find a world where AI replaces the human mind, and that, is a very dangerous world.

What happens to the very things that make us human?

We’re in a time that AI is being frequently used to replace the human mind - replace our critical thinking, creativity, imagination.

AI is reshaping who we are.

And it’s happening everywhere. We have tools that could make us superhuman, but instead we're using them to think less, solve fewer problems ourselves, and basically cruise through life on autopilot in “zombie mode”, because these innovations “think” for us. It almost feels like we’re one step away from asking an LLM “how do i breathe again?”

Now don’t get me wrong, a lot of people are absolutely crushing it with AI. But most of us are using revolutionary technology to maintain comfortable mediocrity instead of pushing the boundaries of what "normal" looks like.

And that's slowly killing innovation itself.

Why is This Happening?

Human psychology.
The tools changed, but we didn’t.

A couple months ago, MIT released this study. It’s about how our brains work when using AI to write essays (which in my opinion… is the highest level of critical thinking). The results reveal exactly why we’re headed for a world where AI makes us stupid.

We're wired to conserve effort - why work harder when you can work smarter? When AI can write your emails, why learn to write better? When GPS handles navigation, why memorize routes? It makes perfect sense from an individual perspective.

EEG data from that MIT study backs this up. EEG (think a heart‑rate monitor for your neurons) showed that frontal‑theta (big‑picture planning) and beta bands (active thinking/problem‑solving) all but flatlined when people wrote with ChatGPT. Literal proof we work less hard once the bot steps in.

But here's the thing: we don't measure ourselves against our potential, we measure ourselves against everyone else. As long as we're keeping up with our peers, we feel contempt with our output.

So when everyone starts using the same shortcuts, the whole baseline shifts down together.

“Brain connectivity systematically scaled down with the amount of external support: the Brain‑only group exhibited the strongest, widest‑ranging networks, Search Engine group showed intermediate engagement, and LLM assistance elicited the weakest overall coupling.”

From the same experiment: brain engagement scaled Brain‑only > Google > ChatGPT. When every student grabbed the shortcut, the whole neural curve sank together - exactly the collective mediocrity Jim Rohn warned about.

It's like muscle atrophy, but for your brain. Stop using a skill, lose the skill. Except now we're outsourcing thinking itself to machines, and we're all getting mentally weaker without realizing it. The researchers literally call it planning off‑load: alpha/theta activity (linked to structuring thoughts and memory) disappeared once the LLM took over.

“The LLM group also fell behind in their ability to quote from the essays they wrote just minutes prior.”

83 % of the ChatGPT group couldn’t quote a single sentence they’d just written, while the ‘brain‑only’ writers rattled theirs off without thinking. That’s cognitive muscle loss in real time.

The scariest part? We think we're getting smarter because we can access more information and solve problems faster - 17/18 ChatGPT users still rated their essays ‘great.’

Access != understanding (haha get it…), and using tools isn't the same as developing capability.

Final Words

When we rely on technology to think for us, we lose critical thinking, creativity, and the humanity of who people are. We become consumers of solutions rather than creators of them. I’m not trying to preach “AI is the end of the world”, what I’m trying to share is that technology is very strong - imagine how much stronger it could be in addition to our critical thinking, creativity, and humanity, not as a replacement for them.

This use of technology is exactly how the world's most effective startups were made. The founders didn't just use existing tools to automate their way to success. They combined innovative technology with uniquely human insights - understanding problems that data couldn't capture, making creative connections that algorithms couldn't see, and building solutions that felt genuinely human.

Steve Jobs didn't just use computers; he reimagined what computers could be by applying taste and design thinking that no machine could replicate. The best AI companies today aren't just training bigger models - they're figuring out how to make AI actually useful by understanding human psychology and behaviour in ways that pure computation can't.

The opportunity isn't to let technology do everything for us. It's to use technology to amplify what makes us human, not replace it. When we do that, we don't just get better results - we get results that were impossible before.

We need to begin approaching these innovations with a sense of urgency, and ambition. Human potential is at the highest it has ever been - yet we're settling for the same results our grandparents got with way less powerful tools.

**Full disclaimer: I’m a 17 y/o and all of these points made are subject to change as I take on more life experiences. These essays are published as a way for me to push my current ideas out there as a true beginner.**

Do you disagree? If so, great! Reach out to me :: noah@barbaros.ca

I’m a 17 y/o -- the timing couldn’t be better to be wrong about things + learn! I’m not looking for a debate - I’m looking for new perspectives and ideologies. I’d love to chat.

Usually, I hear things like, “I wasn’t doing this at your age,” or “You’re so ahead!” But this time, it was different. It was a question about who.

“Who kickstarted your journey?”

I couldn’t answer. No clear person came to mind. Sure, I’ve had amazing mentors, but if we’re talking about the one person who truly kicked it all off? I drew a blank.
Then, on the train ride home, it hit me: Mr. Knowles—my Grade 11 computer science teacher.

I think it started with how genuinely enthusiastic he was about building. I still vaguely remember a story he told me about one of his old students who built something really cool with a Raspberry Pi over the summer—I forget what exactly, haha, but I remember thinking it was super sick. At the time, the idea of "working" during summer felt a bit wild, but once you find something you love, it becomes addicting.

We had conversations like that often, and naturally, it made me curious—made me wonder what I could do with entrepreneurship.
Funny enough, this all happened in a computer science course, but the two go hand-in-hand in a lot of ways. That’s really where it all began.

Mr. Knowles taught me for barely two months before he got promoted and left. Yet in that short time, he sparked something essential: curiosity and ambition. Here’s to the mentors who plant seeds they might never see grow—and to the quiet moments that change everything.

Let me introduce myself! 👋

Hey! I’m Noah. If we haven’t met before, or you want to learn a bit more about me—I recommend you check out this quick 30 second intro to both me, and this newsletter:

What I’ve Been Up To Lately

• Working on a project for Samsung, where I placed top 5 in Toronto and now competing globally against 1000+ people

• Attended the Socratica Symposium + a YC partner at a fireside chat.

• Competing in one of Canada’s largest hackathons—my first of many!

• Kicking off the #BoardyFellowshipCountdown

• Working on SEO @ Armilla AI as an intern

• Working on SEO and product @ Built for Impact as an intern

• Attending the third GenAI Collective Event

• Learning—a ton.

Working on a Consulting Project for Samsung 📱

My Second Challenge at TKS: The Samsung Project

Last month, I started my second TKS challenge—a global project with Samsung. My team competed against 1,000+ participants to rethink how Samsung could use AI to make accessibility more inclusive.

Our Solution: Galaxy Focus – A Neuro-Inclusive AI Smartwatch

The Problem:
Current smartwatches don’t support neurodivergent users who need real-time, personalized cognitive assistance.

• 1.3 billion people globally live with cognitive or sensory challenges

• 576 million live with ADHD, autism, or anxiety

• 20–25% of this group owns a smartwatch, yet no wearable adapts in real time to focus or sensory needs

• Inaccessible tech experiences cost companies $6.9B+ in lost revenue annually

The Solution:
Galaxy Focus—the first Galaxy Watch that senses cognitive states and transforms environments using AI:

• Detects stress, fatigue, distraction

• Adjusts environment via SmartThings + Bixby:

  • Dims lights during sensory overload

  • Silences non-essential notifications to help with focus

  • Prompts cognitive breaks to reduce burnout

• Built on existing Galaxy Watch hardware with ~$15/unit sensor upgrade

The Outcome:

• Galaxy Watch sales projected to grow by 5% = +$7.5B in revenue

• Samsung becomes leader in neuro-inclusive tech

• AI personalization boosts user satisfaction by 80%

• Galaxy Focus becomes a daily essential for millions

We recently found out we placed top 5 in Toronto out of 200+ participants—now advancing to global semi-finals.

My role in this project was to research, develop a prototype, and pitch on behalf of my team.

If you want to check out our final deck, feel free to check it out here:

Samsung Pitch Deck

And feel free to check out the interactive prototype I made here (open it on your phone!):

Samsung Prototype 📱

The prototype followed One UI 6 Guidelines design principles—toast stack, spacing, and all.

Overall was such an amazing experience—being able to work with Nitya, Aydin, and Naman again—absolute blast.

Attending Socratica Symposium & a YC Fireside Chat

Socratica Symposium was unreal—my first time in such a builder-heavy space. I met folks from Cansbridge Scholars & Fellows (check them out if you're entrepreneurial—the team behind it is fricking awesome!).
Also got to connect with people from UWaterloo and beyond.

It was surreal. I was building CoVenture (a network for ambitious teens to connect + build), but struggling with things like "The Cold Start" problem. I remember bringing it up—and people would sit with me in total silence for 10 minutes brainstorming real ideas.

It blew me away.

I left completely inspired.

My First Hackathon! 🧑‍💻

In March, I did my first hackathon: GenAI Genesis 2025, Canada's largest AI hackathon (2,200+ applicants, 600+ hackers from 25+ countries and 100+ schools).

I had made a team with three TKS fellows: Nitya, Yakshith, and Parsa.

That was us, minutes before pitching—dead tired haha. One of the most fun weekends I’ve had. It was 3 days straight—Friday evening to Sunday morning.
I slept twice: once on the TTC and once for 30 mins at 4:00 AM. Drank more caffeine than I care to admit.

Worth every second.

We built HackBuddy (check out our Devpost here) which is a personalized voice AI agent.

For most of us, it was our first hackathon—and the biggest issue we had was finding the right team. We were placed in a discord server but it was just nonstop sound and spam, it felt almost surreal that there wasn’t a more efficient alternative—that’s why we build HackBuddy! HackBuddy is your personal AI wingman for hackathons. Instead of stressing out over awkward intro messages or last-minute team scrambles, hackers can just hop on a call with HackBuddy, tell it their interests, skills, and ideas, and get matched with other hackers looking for teammates.

Two Channels:

• B2B: Hackathons upload a CSV of participants and get a unique code to distribute

• B2C: Users enter the code → call the AI agent → answer questions like:

  • “Spell out your name.”

  • “Your email?”

  • “Tell me about your experience. Any past projects or hackathons?”

  • “What are you proud of?”

  • “What kind of teammates are you looking for?”

  • “Looking for specific skills?”

  • “What team vibe do you prefer—fast and intense or chill and collaborative?”

The AI transcribes and stores the data in Supabase.
Matching used a super basic ChatGPT wrapper—it worked well, even if it was simple haha.

Incredible experience. Here's a photo of me completely passed out afterwards.

This month, I paused work on CoVenture to explore a new project—can’t share much yet since I’m deep in the learning phase. But expect more soon 👀

As always, reach out with questions, ideas, or if you just want to connect. Your input means a lot.

See you next month,
Noah

P.S. If you want to read more about any of these projects, check out my Medium, LinkedIn, or my new X / Twitter account!

Earlier this month I was listening to this awesome episode of Lenny's Podcast—which was featuring Brian Chesky (co-founder & CEO @ Airbnb).

There was a handful of amazing things Chesky said, but one thing really stuck out to me,

The bigger he gets, the more of a beginner he feels.

He referenced Picasso’s idea that it took him four years to paint like Raphael, but a lifetime to paint like a child. Curiosity is about seeing the world with fresh eyes, without judgment, and never feeling like you’ve “made it.” Because once you believe you’ve arrived, you stop growing.

In order to become successful in anything, you must be curious. Otherwise you’ll remain stagnant—left behind.

Let me introduce myself! 👋

Hey! I’m Noah. If we haven’t met before, or you want to learn a bit more about me—I recommend you check out this quick 30 second intro to both me, and this newsletter:

In this month you’ll see a lot of curiosity—a lot of “fresh eyes”.

What I’ve Been Up To Lately

• Building the first network for ambitious teens to connect and build

• Working on SEO @ Armilla AI as an intern

• Working on SEO and product @ Built for Impact as an intern

• Marketing for brands & implementing AI into their business

• Learning—a ton.

🧑‍💻 The First Network for Ambitious Teens

Gen Z is the most entrepreneurial generation ever. Yet have the least support.

76% of Gen Z are entrepreneurial. The most ambitious generation in history.

Yet, when it comes to resources and networks to support their journey? Almost nothing.

College students enter a built-in ecosystem that expands over time. But for entrepreneurial teenagers? The landscape is fragmented. You're scattered across Reddit, Discord, Twitter, or selective programs like TKS. While these platforms are valuable, is that really all we have for the most entrepreneurial generation ever?

There has to be more.

The Networking Gap

LinkedIn? It’s useless for teens.

It’s an online resume database where every connection feels like an empty handshake. Teenagers don’t need corporate networking; they need real connections. They need a space to build alongside others, not just accumulate useless contacts that hold no real value.

Because at the end of the day, almost every ambitious Gen Z entrepreneur is aiming for the same few things:

• A cool internship

• Admission into their dream university

• Launching a startup with real momentum and traction

All incredible goals—but how do you actually get there?

It’s easy to say “I want an internship / Ivy league admission / unicorn”, but not so easy to go on and do it. As a teen, you’re already starting with a credibility gap.

You don’t have a university degree, let alone a high school diploma, to back up your hard work, skills, and so on.

Not only that, but it’s competitive. Your often competing with people either years ahead of you, or competing with fellow cracked teenagers.

This is where the outlier concept comes in—to differentiate yourself. If you want to succeed, you can’t just blend in—you need to stand out.

Now I want to add a second part to my question—how do you get there? and how do you become an outlier?

building.

If you want to differentiate yourself and unlock top-tier opportunities, you need to build endlessly. Explore anything that piques your interest. Create projects. Launch startups. Experiment. Fail. Iterate.

So now we’ve recognized a few things:

1. Gen Z is the most entrepreneurial generation ever—with no designated network. Often aiming for the same few things.

2. LinkedIn sucks.

3. You need to build if you want to achieve great things.

This is where CoVenture comes in. The first network for ambitious teenagers to connect and build amazing projects together.

Building CoVenture was an incredible experience—it reminded me just how much I love building, and now I can’t get enough of it. Even with a few all-nighters and countless redos, I learned so much, and I’m excited to keep going.

Apple started in a garage. OpenAI started as a small research team. The next generation of great companies will be built by teenagers, and CoVenture will be the platform that makes it happen.

I’m doing the #BoardyFellowshipChallenge where I see if AI can bring me from 0 to 1 with CoVenture! Make sure to tap in and check it out.

Also: if your an entrepreneurial teenager—check it out! Post a project, build with others, create the future.

One Pager 📄

CoVenture 🦄

Marketing Consulting

I thought to myself a few months ago: “How sick would it be if I did consulting for brands as a teenager”.

So I did it.

After a few months of working with free clients, I’ve finally built up the credibility to start charging. Excited to share that I’m now developing AI tools that analyze a brand’s Instagram analytics to uncover key patterns behind their most successful posts.

I’m also starting to run paid ads for a client and may have just landed my first paying SEO project.

Reach out to me if your interested—noah@barbaros.ca

Learning

Learning isn’t just a key to success—it is the engine behind it. The world moves fast. It evolves, reinvents, and never pauses. If you’re standing still, you’re already behind.

So how do you keep up?

You become obsessed with learning.

Over the past month, I’ve made it my mission to chase curiosity with everything I’ve got. If something sparks even the slightest interest, I dive headfirst into it—no hesitation. I’ve swapped out mindless scrolling for deep exploration, and the difference is unreal. Every day, I feel myself compounding—growing, stretching, leveling up—and honestly, it’s addicting.

But my favorite way to learn? Through doing. That’s why I’ve become hooked on CoVenture. Every single problem is a lesson in disguise. And if startups are anything, they’re a never-ending series of problems. Which means... I get to learn forever. And that’s the best part.

My ask to you!

My ask:

Know anyone passionate about empowering teenage entrepreneurs? Or someone who believes in building better networks for young founders? Maybe someone at a startup accelerator, incubator, or venture firm that supports the next generation of builders? I'd love an intro!

As always, feel free to reach out if you have questions, ideas, or want to connect. Your feedback and ideas are invaluable as I continue exploring how the world works.

See you next month,

Noah

P.S. If you want to read more about any of these projects, check out my Medium, LinkedIn, or my new X / Twitter account!

This newsletter is my way of building in public—sharing what I’m working on, what I’m learning, and my journey from 0 to 0.1. (And yes, it’s a play on Zero to One haha).

Every month, you’ll see that I’m relentless about learning through action, jumping at every opportunity, and even creating my own when none exist.

Before I kick off anything, I wanted share a bit about my goals this year. A lot of the experiences I just began taking on this past year were only possible because of my willingness to get uncomfortable. I want to keep that going. The second goal I have is to continue to be unconventional in every aspect possible. To oversimplify it: Never be comfortable, and always be unconventional.

What I’ve Been Up To 📈

• Finished exams & university applications: Despite having finals and university applications, I’ve managed to make good progress on my projects and internships this month.

• Working on SEO @ Armilla AI: Armilla AI is a YC-backed AI insurance startup.

• Working on SEO, GEO, and product @ Built for Impact: Built for Impact is a startup supported by U of T & Western.

• Marketing consulting for brands & assisting in implementing AI into their business: Currently working with Lost Kids Find Hope, an SME. Right now I’m building an AI program that analyzes and tracks their social media analytics. Along with that I’m running paid ads.

• Built a personal website: By no means am I a developer. Sure, I’ve done some coding, and I love doing it, but I’m not a software engineer in any way possible. However, with the help of AI, I was able to build a personal website. Feel free to check it out here:

  Personal Website 🧑‍💻

Achievements & Highlights 🚀

• IKEA Challenge Semifinalist: Me and my team didn’t make finals, but the learning we underwent was so invaluable. I deployed the website I built for our pitch, and you can play around with it here:

  IKEA Prototype 🇸🇪🧑‍💻

Some of My Thoughts 💭

The Intersection of Creating Opportunities For Yourself & The Power of Grit: Opportunities don’t just appear—they’re created by those bold enough to chase them. No one will hand you your dream internship, no one will gift you a Stanford acceptance letter, and no one will place a billion-dollar company in your lap overnight. Luck can only go so far, and the path to greatness is paved with relentless action.

You have to become your own outlier. You have to break convention, defy expectations, and create your own doors when none exist. Whether it’s tirelessly building projects that set you apart or reaching out to hundreds of startups for that one breakthrough interview, success is reserved for those who refuse to wait for permission.

Grit is the secret weapon that separates the extraordinary from the ordinary. It’s the willingness to persist when others quit, to learn when others plateau, and to push forward when doubt creeps in. If you want unconventional success, you must be willing to take an unconventional path.

Contentment and comfort is the enemy of progress. The moment you settle, you lose. Stay uncomfortable. Stay hungry. Keep pushing to become the best version of yourself, because the future belongs to those who dare to create it.

My ask to you!

My ask:

Know anyone passionate about empowering teenage entrepreneurs? Or someone who believes in building better networks for young founders? Maybe someone at a startup accelerator, incubator, or venture firm that supports the next generation of builders? I'd love an intro!

As always, feel free to reach out if you have questions, ideas, or want to connect. Your feedback and ideas are invaluable as I continue exploring how the world works.

See you next month,

Noah

P.S. If you want to read more about any of these projects, check out my Medium, LinkedIn, or my new X (formerly Twitter) account!

Hey! If we haven’t met before, I’m Noah. A 17 y/o obsessed with entrepreneurship.

I love learning, especially through building. A quote I live by is, “In times of change the learners will inherit the earth.”

I believe the most successful people are lifelong learners, and that’s exactly who, and what, I aim to be.

In blatant honesty, I’m unsure what this Substack experiment is right now -- it started as a monthly newsletter in November 2024. However, I now enjoy writing and want to begin putting my ideas out there.

Whatever this “experiment” turns out to be, it’s main purpose is to serve as a way for me to build in public, so that people can see what I’m working on, and what I believe.

Through this, I’m going to be able to share my journey from 0 to 0.1 (yes, it’s a play on Zero to One hah).

For a brief tl;dr on my current state,

What I’m working on right now ::

> Co-Founder @ Antifragility Labs: building a software-first agency turning startup helping businesses rank on both AI and search engines. working with YC startups, top podcasts, e-commerce brands, and major VC firms.

> Intern @ Wealthsimple: recent high school grad taking a gap year to intern @ Wealthsimple, building great things with even greater people. currently building AI software + working in product.

> Habguard AI: working on ML models to help detect harmful algal blooms for water desalination plants.

> Forma: more on this soon -- a software project for an overlooked market.

--

What I’ve done in the past ::

> Intern @ Armilla AI: Armilla AI is a YC-backed AI insurance startup with $5M+ raised, where I performed both there SEO + GEO. Non-indexed to first page, 20x impressions, 5x clicks, ranked across all LLMs, etc.

> Founder @ MovitIative: charity I launched for my childhood best friend. Very proud to say it’s became one of Ontario’s biggest youth-led clubs raising thousands every November for cancer research.

> Builder @ CoVenture: first-ever network for entrepreneurial teens to connect and build cool stuff. Hustled my way to 100+ users and came close to partnering with some universities like UWaterloo + UClemson.

> Intern @ Built for Impact: in high school I didn’t know what I liked in startups, or how they would work. so I joined a university startup as an intern where I scaled outreach from 0 to 10k+ users in less than 2 weeks.

I’ve built way too many projects to count, from executive proposals for Samsung to fun hackathon builds.

If you’re curious about more, you can explore my full portfolio // personal website.

I'm super stoked to kick things off here and grow this newsletter. This isn't going to be another "how I made my first million" story -- those are linear, this is happening now.

Thank you so much for taking the time to read this little intro, and I hope you’ll tune in for more of my journey from 0 to 0.1.

I love chatting with likeminded people -- if you ever want to chat about anything (startup related or not), don’t be a stranger and drop me a line :: noah@thepointone.co

I promise to respond within 24 hours -- if I don’t I’ll buy // et you a coffee!

Later!!
- Noah B

Incase you couldn’t tell, I’m trying to do A LOT. But I’m not gonna cry about having a lot on my plate when my goal was to eat. Recently, I was talking to someone about everything I’ve got going on, and they hit me with that common saying: “If you chase two rabbits, you won’t catch either one.”

Guess what I said,

“I have a gun”

And my gun? A lot of it comes from my ignorance being a teenager haha, but also my curiosity to learn a ton. This isn’t impossible by any means. This next month is going to be a lot, but quite frankly I’m excited for all of this to come at me, I want to see where I am after January. A lot can happen in a month.

Let me introduce myself! 👋

Hey! I’m Noah. I’m a huge “figure it out” enthusiast. I firmly believe that anyone can find a way to solve anything if the incentive and/or desire is strong enough.

Speaking of realizations, last month I had a big one. I discovered that entrepreneurship isn’t some straight, predictable path where everyone already knows the answers. Instead… it’s so much better.

It’s messy and unconventional—nobody knows what’ll happen from let alone a month, but a week from now. Something awful like a 2007 recession could pop up and flip this economy upside down, whereas on the flip side, some crazy underground AI startup could go viral and… honestly, who knows? Maybe it would do something revolutionary—or maybe it’ll flop. And that’s the best part: I don’t know. Nobody knows.

In entrepreneurship, uncertainty is the norm, nobody really knows what they’re doing. Most of the time, it feels like we’re navigating uncharted territory, figuring things out as we go. And that’s what I love about it.

Welcome to my 0 to 0.1 newsletter (and incase your wondering.. yes it’s a play on Zero to One by Peter Thiel & Blake Masters haha). This newsletter is my space to share the process of my journey: the wins, the lessons, and everything in between, as I work toward my ultimate goal—becoming a unicorn and “figuring it out”.

Here’s my unconventional and messy way of “figuring it out”, month by month.

Getting Started | Highlights of the Month

I feels like it’s been a while, so let’s catch up and get on the same page. Here’s a quick rundown of what’s been happening and what to expect moving forward. These are just brief summaries for now, but don’t worry—I’ll dive deeper into the important topics later:

This month was packed:

1. Kicked Off Two Internships 🚀

   • Armilla AI: A Y Combinator-backed startup and the world’s first AI insurance company. My assigned project is to boost SEO.

   • Built For Impact (BFI): A promising startup supported by UofT’s Rotman School of Management and Western Ivey Business School. Working with them to outreach and work towards our upcoming pilot launch.

2. Worked on a Project Alongside IKEA 🇸🇪

   In Innovate, we tackle two major challenges throughout the year, and this was our first one—a two-week opportunity. It was a chance to train on solving real-world unstructured problems, where there isn’t a definite answer. For this challenge, we worked with IKEA, focusing on finding ways to attract more Gen Z customers.

3. Beginning of a Super Exciting Project 👀🦄

   Can’t wait for this one, tune back in next month and check it out.

4. Finding a Way to Make Learning Addictive & Getting Into Rabbit Holes 💡🐇

   I’ve become obsessed with learning and even more obsessed with falling into rabbit holes while doing so.

5. Attended The GenAI Collective’s First Event in Toronto 🤖

   Became the youngest person ever to attend one of these events. Awesome opportunity to connect with people who have already jumpstarted their careers.

6. Worked on and Submitted University Applications 🤞

   Stressful. The timed interviews and essays preparation kill me.

7. AI Improvements 🧑‍💻

   I built some really cool AI programs, I’ll go into these later.

8. AI Marketing Consulting

   Who would’ve thought this. I’m beginning to do marketing consulting with small businesses, keep on reading to find out more about this—I can’t wait to see where this goes.

9. Turned 17 🎂

   Crazy. I feel wiser haha.

Kicking Off Two Internships! (Guess Who’s Adulting Now)

I’m going to break this down into two:

Armilla AI:

• At Armilla AI I’m addressing our website’s SEO to improve visibility for terms like “Insurance for AI.” My focus is on assessing current performance, identifying relevant search terms, optimizing content and technical elements, and implementing automated monitoring for sustainable growth.

I'll be completely transparent: I had never worked with SEO before this internship—honestly, I only learned what it the term “SEO” actually meant a month ago. But that’s not going to stop me.

If there’s one thing I’ve learned from my past projects, it’s this: to succeed, you figure it out. So it doesn’t matter that many might not find me “prepared” for this. What I’m doing right now is the best way of learning. Learning by action.

This internship just started, so I don’t have too much to share, but tune back in a month from now and I’ll be able to speak on it a lot more.

Built for Impact:

• At Built for Impact I’ve been focused on executing our outreach to high school students, strategically finding ways to engage them and identify potential partner organizations for growth. In just two weeks, I successfully reached out to well over 10,000 students and sparked interest from two non-profit organizations that foster high school communities eager to collaborate with us.

I’m happy to say I’ve been offered a contract extension! This time however, in various fields for whichever I find interesting and want to get better at. I’m debating between dev or marketing.

Hej! - Working on a Project for IKEA

In TKS Innovate they do two major challenges throughout the year.

This was the first one.

I got the awesome opportunity to work on a project for IKEA!

To sum it up: our prompt was to figure out a way to attract more people from Gen Z. Everyone in TKS was in a group and competing against over 4000 TKS students globally, learning unstructured problem solving.

I’ve never wanted to win more.

TKS is filled with the most critical thinking, and innovative teenagers across the world, AKA: My top competition.

This was one of the very few opportunities that I will ever get, to compete, and prove that I’m the best at what I do. I needed to win.

In this project, everyone was given two weeks to develop an idea, business plan, pitch deck, and prototype. I was part of a team of three, including myself.

Here are the most important things I learnt:

• Never be afraid to ask for help—iteration, and repetition is crucial for success

• You should seek failure and problems in your work—don’t hide from them, because they’re what differentiate the good from the great.

• First principle thinking is key. Break things down and rebuild them, you can find so many more opportunities and answers that way.

• Love winning. Hate losing.

So, what was our idea? Reinventing IKEA As-Is to Capture Gen Z’s $360 Billion Market through Recommerce.

If you’re unfamiliar with the IKEA As-Is Marketplace, it’s a section where customers can purchase slightly damaged products at a discount. With the massive push toward recommerce among Gen Z, this platform represents an untapped opportunity to make used products more accessible and appealing.

Here’s the twist, and our competitive edge: The biggest issue with current recommerce platforms—like Facebook Marketplace or Kijiji—is the risk of scams, broken items, or unreliable sellers. But if IKEA could step in, offering authentication and verification for products, it would gain a significant competitive edge in this booming market.

In my group my role was to evaluate the factors contributing to the success of recommerce, explore its growth as an emerging trend, and analyze case studies and reports from successful platforms like Facebook Marketplace and eBay.

However my main role? To make a fully interactive prototype. This prototype would serve to be the core of our pitch, our competitive edge. I made a fully interactive frontend user interface website, so the judges could understand how impactful our product and idea was, from our perspective.

Check it out yourself:

IKEA x TKS Pitch Deck

IKEA Prototype Demo

Click it… 👀👆

I’m super happy to say that me and my team have been selected as a semi-finalist for the IKEA Challenge! When I found out I fell out of my seat in class I kid you not.

Me and my team have ranked in the top 35 out of over 4000 TKS students! Locally, we made it to the Top 7 out of 200+ students.

We’re one of the six Toronto teams out of 200+ students moving forward to represent TKS Toronto, as well as 1/19 that IKEA executives are currently reviewing in detail based on our slideshow & prototype we submitted (that you should really check out! 👆)

We’re going to find out soon whether or not we made it to the finals!

Make sure to subscribe so you can stay updated! I’m more determined than ever to win.

Attending The First GenAI Collective Event in Canada!

Had such an awesome time at the first ever GenAI Collective Event in Toronto! It was an amazing event hosted and held by Chappy Asel, and Radical Ventures.

As you might be able to tell, I was the youngest person there—but that was the best part. I got to speak with so many people who are so much further into their career.

I made so many amazing connections! I learnt some awesome insights on AI and entrepreneurship, it was so much fun and an awesome way to put myself out there and network.

When AI Projects Take Over My To-Do List

Let me start off by saying: by no means am I a developer.

Up until a month ago, my coding experience with programming was limited to basic scripting—the furthest I’d gone was a step by step YouTube tutorial to put it into perspective.

Yet somehow I managed to develop and code a Twitter Sentiment Analyzer that can:

• Fetch real-time tweets based on user-defined queries

• Clean and preprocess tweet text for analysis

• Analyze sentiments using natural language processing

• Visualize sentiment distribution with intuitive charts

• Save and debug processed data for future reference?

How’d I do it? Just utilizing the right mindset! I know I’ve said it a lot, I should honestly make it my motto. But I’m really passionate about figuring things out, finding a way to make ends meet no matter my skillset.

If your interested about it, you can check out my step by step tutorial here:

I also made an article about it that you can check out:

Check Out My Article! 📝

Feel free to check out my GitHub repo as well:

Check Out The Code! 🐦🧑‍💻

AI Marketing Consulting

If you haven’t read my last newsletter for November, then let me update you:

• Last month, I got bored. And I wanted to do something cool, and something none of my friends had done. I wanted to create an opportunity rather than sit around and wait.

• I cold-pitched 11 clothing brands on Instagram that I found cool. Offering to develop an AI program to analyze their pages for common patterns of success. Along with this, I proposed marketing their brand using display ads and providing consulting services. Out of the 11, only one responded—but that was all I needed. With that response, I landed my first real-world coding project, along with an awesome marketing & consulting project.

• Everything was going great, I was in contact with the brand, getting all the information I needed, and then suddenly they cut communication. Randomly they stopped replying and I was ghosted two weeks into the project.

• I wasn’t going to stop, and I wanted this so bad, so I sent a few follow up messages, nothing. I went back to the outreach stage.

• Now I’ve confirmed a project with a growing clothing brand that I’m going to take on post exams—subscribe so you can stay updated!

I’ll share all the details of the project when it’s finished (I plan to write an article about it here, so follow my account to stay updated).

For now, all I can say is this: the fear of starting is so much bigger than the task itself. Once you take that first unconventional step, a whole new world opens up.

What’s Next? 💭

• Action! (Then learn)—I’m going to be taking on everything I can, and just learn on the way.

• TKS Challenge updates.

• Wrapping up the pilot launch at BFI.

• Finishing up SEO work with Armilla AI.

• Begin the consulting project.

• Release my MVP for my project, I can’t even explain how excited I am for this—it’s all I think about.

• Wrap up semester 1 at school + wrap up exams.

• Finish university applications.

• And lastly, a ton of learning and research about entrepreneurship. It’s gone to the point where I can’t picture myself doing anything different. I don’t want anything more.

I want all of you to hold me to a high standard too. So if next month, or any other month, you expected more from me, let me know.

Thank you so much for reading! Feel free to reach out on my LinkedIn if you’d like to have a chat on anything and everything entrepreneurship!

See you next month with even more to share and compounded growth toward 0.1! 👋

Earlier this month I had a call with the CEO and Co-Founder of Armilla AI, Karthik Ramakrishnan. He shared something that stuck with me: in the startup world, nobody really knows what they’re doing. Most of entrepreneurship is uncharted territory. It was a reassuring reminder that figuring things out as you go is part of the journey. But it also sparked something in me to work towards marking this ‘uncharted territory’.

I know I don’t have all the answers yet—but I’m committed to the process. This newsletter is my space to share the process of my journey: the wins, the lessons, and everything in between, as I work toward my ultimate goal—becoming a unicorn and “figuring it out”.

Let me introduce myself! 👋

Hey! If we haven’t met before; I’m Noah, a 16 year old aspiring entrepreneur obsessed with start ups and artificial intelligence from Toronto. I’ve founded and launched an AI chatbot—CostCulture, launched a charity that raised over $1000 in less than 30 days—while giving talks to schools across the Toronto & Peel school boards about our cause, in a position of internships at two start ups, a SHAD fellow, and an Innovator at TKS. Welcome to my 0 to 0.1 newsletter (and incase your wondering.. yes it’s a play on Zero to One by Peter Thiel & Blake Masters haha).

Every month you’ll see that I’m very persistent in taking action and jumping at every opportunity. I’ve personally never seen the downside of taking on challenges—worst case scenario, I’m not ready, but I learn a ton along the way and come out stronger, no need to be comfortable all the time. No matter how unqualified I might be—I don’t care! Cause I’ll figure it out!

This month, I dove headfirst into countless spirals and rabbit holes in the world of startups. It’s probably been the most I’ve worked ever, and the funny thing is, it’s still not even remotely close to the productivity and work rate that I want to be at. But isn’t that the point? Always chasing growth, learning from every stumble, and raising the bar higher each time—it’s a privilege to be in a position where growth and opportunity are always within reach.

I’m excited to share the lessons, failures, and tiny wins from this past month as I navigate the wild world of startups and AI.

Let’s dive in and grow from 0 to 0.1 together,
Starting at 0.01

Starting from 0.01 | Highlights of the month

This month was a roller coaster, and before I get started I feel like I should catch you all up so we’re on the same page, I’ll go deeper into everything later so don’t worry:

1. Re-Launched and Co-Ran a Charity Initiative & Spoke at Schools Across Peel and Toronto
   Raised over $1,000—while delivering impactful talks to students, spreading our mission, and building meaningful connections within multiple communities

2. Secured Two Internship Opportunities

   • Armilla AI: A groundbreaking Y Combinator-backed startup and the world’s first AI insurance company

   • Built For Impact (BFI): A promising startup backed by UofT’s Rotman School of Management and Western Ivey’s entrepreneurship group

3. Personal Growth Lessons
   Realized the power of putting yourself out there and creating your own opportunities instead of waiting for them to appear

4. The TKS Impact
   Reflecting on how just a few months in TKS have completely reshaped my life’s trajectory

5. AI Exploration & Focus

   Exploring the world of AI, through projects and real world opportunities.

MovItiative 🥸

Believe it or not, The MovItiative started as my very first entrepreneurial project—a passion-driven charity born in grade 7. It began as a way to support my best friend, who had been battling cancer for more than three years, and to honour my grandmother, a two-time cancer survivor, and my grandfather, who sadly lost his fight against cancer. Back then, my mission was simple yet deeply personal: raise awareness about the mental toll cancer takes—not just on those fighting it but also on the people who love them.

Fast forward to today: This month, I relaunched The MovItiative—but this time, I wasn’t doing it alone. I worked with my partner and close friend, Matthew, and together, we took what I started in grade 7 and elevated it to a whole new level. We began planning in August, and now we’ve grown into one of the largest student-led charities in the GTA. And honestly? I’ve never felt more accomplished. Seeing what started as a small project expand into something so big is humbling and exhilarating all at once and if you asked me anything about it, it would take me a few minutes to grab myself to say anything—because that’s how big it is to me.

Here are just a few milestones that make me pinch myself:

• Surpassing Our Goal TWICE 📈: We doubled our fundraiser target from $500 to $1,000 in less than a week—and then blew past $1000.

• Empowering Young Minds 💡: We gave talks at schools across the Toronto and Peel boards, sharing our message and inspiring students to take action and build community.

• Building Community 🥸:

  • We marketed, launched, and successfully initiated an entire school-wide trend, Movember Mondays, where students purchased and wore moustaches to support our cause. The funds? All donated to cancer research and mental health.

  • We organized a school-wide basketball tournament, creating a fun and impactful way to bring people together.

The process was exhausting and time-consuming—juggling school, extracurriculars, university applications, and running a charity was no joke. But honestly, it reminded me why I fell in love with entrepreneurship and taking action in the first place: the joy of creating, but more importantly, seeing something you built bring people together—still puts me in awe.

When I looked out at a sea of students rocking Movember moustaches or coming together for the basketball tournament, or the faces and smiles of the young students we worked with—I couldn’t help but think back to grade 7 me. I think he would be very happy.

The TKS Impact

TKS has been a transformative experience for me, and I’m grateful to be a part of the TKS community. Why? Because in just three months of weekly sessions, I’ve learned so much—and very different things than what is taught at a traditional high school.

In three months, I’ve had in person sessions on things like AI, brain-computer interfaces, medical devices, the MECE framework, and hackathons with some very smart people. But the greatest value has been the work ethic and the high standards that TKS expects from you. If you’re always doing the bare minimum and not meeting the challenges TKS throws your way, you’re not going to see results. That’s why I joined TKS Velocity—a program for the top 7% of students in TKS. Velocity provides more structure, higher expectations, and additional resources to immerse myself in my interest areas: AI & Startups.

More Intentional Work = More Intentional Outcomes.

Now beyond Velocity, what work did I do so far?

Explore Hackathon:

• This was one of my first-ever hackathons! The hackathon went alright, I didn’t think our idea was very realistic, but my group loved it so I had to adjust. My team and I focused on artificial organs, inspired by CGM technology. We developed a product to monitor organ efficiency and an app to track real-time health data post-transplant. My role was designing the app’s UI and pitch deck.

  
  But here’s the thing—I really wanted to present. I knew I was the best fit for the role and could have delivered a strong pitch, but fear held me back, and I let someone else take the lead who didn’t even want it. We made it to the top 8 but fell short of Best Overall purely because of our pitch. It’s a decision I’ve reflected on and it has taught me the importance of stepping into roles that scare you—because that’s where growth happens. It also reinforced something important: I have to trust myself to deliver when it matters.

Focus Hackathon:

• I loved my group for this hackathon—everyone was productive, and we were all laser-focused making our team successful. I had a lot more confidence going in. I also made sure I was doing the presentation. Our slides were stronger, our idea was more practical with a realistic timeline, and we even developed a chatbot for our project.
  

  We focused on minimizing GLP-1 side effects with better nutritional planning, using AI to guide users when they needed support. The chatbot provided instant help with health-related questions or issues, making the plan more accessible and user-friendly. We got much better feedback this time and I walked away proud.

Overall, TKS has transformed how I see the world and approach problems. Now, whenever I face a challenge—big or small—I think, “Could I create something to solve this?” It’s a gift to have that mindset, to constantly be ready to create and take action. TKS has also improved my work ethic and productivity in ways I couldn’t have imagined, and honestly, I can’t wait to see where I’ll be just a month from now (so subscribe).

When AI Takes Over My To-Do List

How does someone with zero coding experience dive into AI development?

This month, I went from having no clue about Python syntax—or even knowing if I liked coding—to discovering just how much joy it brings me. Now, building AI projects has become a top priority in my free time. Don’t let me fool you—I am not a coder haha, but I will be.

The first AI project I ever worked on was a stock sentiment analysis tool. Honestly, I followed a YouTube tutorial step by step, and whenever I hit a roadblock, I just turned to ChatGPT for help. Going into it, I thought coding would be the death of me—but to my surprise, it was kind of underwhelming how simple it was—especially with all the resources available online.

View Stock Sentiment Code 🧑‍💻📈

For the longest time, I’d built coding up in my head as this massive, intimidating obstacle. A lot of accelerators and VCs talk about successful co-founder dynamics, often split into two roles:

1. The technical co-founder

2. The business/marketing co-founder

I always saw myself as the second one—the business/marketing type—because coding seemed so out of reach. But after that first project, even though it was just a tutorial, I realized it wasn’t nearly as scary as I’d imagined. It gave me the confidence to take on any role, “figure it out” as I went, and still learn and deliver great results.

To push myself further, I cold-pitched 11 clothing brands on Instagram—brands I thought were cool—proposing a sentiment analysis project to track their social media trends. Out of the 11, only one responded. But honestly, that’s all I needed. Just like that, I landed my first real-world coding project.

Here’s the funniest part: I got a project before most of my friends who’ve been coding for years—and are, on paper, way more qualified than I am. The difference? They’ve been waiting for the perfect opportunity to land in their lap, while I’ve learned that waiting doesn’t work.

At TKS, we studied The Third Door approach—the idea that there’s always an alternative path to achieving your goals if you’re willing to hustle, take risks, and create your own opportunities. Ever since, I’ve stopped waiting and started building my own doors, no matter how unconventional. And I haven’t looked back since.

Do I know how to code well? Not yet. There are countless people better suited for this kind of project, with more expertise and experience. But that doesn’t bother me, because I know I’ll find a way to make it work—and in the process, I’ll push myself far beyond what anyone expects.

I’ll share all the details of the project when it’s finished (I plan to write an article about it here, so follow my account to stay updated). For now, all I can say is this: the fear of starting is so much bigger than the task itself. Once you take that first step through the third door, a whole new world opens up.

What’s Next? 💭

• Learn, learn, and learn—then learn even more. Learn what exactly? Anything entrepreneurship!

• Taking on the next TKS challenge

• Wrapping up my first ever real-world coding project

• University applications

• Launching new projects (to fail intentionally to grow)

• Kick off my internship at Armilla AI & BFI

• Going after just about any opportunity I see—even if I’m not deemed ‘prepared’

Thank you so much for reading! Feel free to reach out on my LinkedIn if you’d like to have a chat on anything and everything entrepreneurship!

See you next month with even more to share and compounded growth toward 0.1! 👋

Subscribe now