Hey, it's Bernie!

#music #playlist #ai

Nobody: What did the server say? Me: I got a feeling, somebody's pinging me.

#lame

After two months of diving into machine learning, I’ve realized that data science is like performing an autopsy on a business problem to find the clues hidden in the facts. By treating data like a detective mystery, we can stop guessing and start building solutions based on real-world evidence and predictive confidence.

At the start of 2026, I jumped head first into the world machine learning (ML) and data science (DS), and I realized that I'm starting to look at the world with a new lens, and add-on if you will, where every problem starts with data.

I know, some of you reading this might say “no Bernie, we always start with identifying the business problem, and ask ourselves what are we trying to solve.” Yes, that too, but after that, you would hire a data scientist right? That's where I'm starting my story.

Why Data Science is like a Medical Drama

The clues are in the data. With every business problem, the data is like a dead body on an autopsy table, ready for the data scientist to slice and dice, looking for clues as to why there's a problem in the first place. I've always loved a good detective and/or medical drama, and now I know why. ML and DS is also like that. Find the clues, solve the mystery. And after we solve the mystery we can start building a solution based on facts, which can then point us into the right direction to collect, not just more data, but the RIGHT data. Ultimately we would like to be able to predict with higher confidence, the consequences of our future decisions and actions, so we don't make the same mistakes that would cause said problems to arise again.

So many examples from the real-world, like

• Hotel Industry: Reducing “no-show” rates by identifying patterns in booking cancellations.
• FinTech: Detecting fraudulent transactions before they clear by spotting anomalies in spending.
• Education: Analyzing student performance data to intervene before grades drop.
• Marketing: Auditing marketing spend to see exactly where the ROI is failing and why.

It can help us create systems and processes for better business ROI.

I've always wondered about such things. Why are things the way they are. Almost everything we see around us is a consequence of someone's decision and action – what made that person come to that choice? And by understanding their motivations (through the data) we can make better decisions for a future that we want, whether good or bad.

With the right and big enough dataset, we can predict (and solve) almost anything.

#ai #data #reflection #MachineLearning #DataScience #BusinessROI

Now we code.

[UPDATE] I did a classic noob move to use Opus to help me tweak a css file. Session ended pretty fast. Note to self: Just use Sonnet.

#claude #ai

Deliveroo has exited the Singapore market. It impacts me directly because I've been a Deliveroo rider for the last three plus years – it was my side hustle as I sorted my shit out and figured out what I wanted to pivot to. Even though I'm also on Foodpanda, another food delivery platform, Deliveroo is the best as it has a more direct fee system, for example, it gives you fee boosts vs quests, and the ONE THING that makes it so much better is that it allows riders to “reject jobs without penalty”.

Anyway, the announcement about the exit gave little runway for us all in Singapore.

Announcement made on Feb 26, and exit on Mar 3. Read the article here

WOW. And Ouch.

I'm not even sure the execs at Door Dash (the guys who bought Deliveroo) even know where Singapore is. Anyway, to them we are just a non-performer on a spreadsheet, bound for deletion to make the “books” look better.

I decided to make a video where I travel to almost every zone in Singapore in the little time left they have here in our country, as a symbolic gesture to the platform that allowed me to earn some lunch money while I noodle away at life. I will link to the video here once it is done, so stay tuned.

#deliveroo #fooddelivery #reflection

#music #bts

As a self-proclaimed 'financial illiterate,' I used Gemini 3 to build a Python dashboard that tracks the Rule of 40, Altman-Z, and other metrics to find high-growth companies.

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I am no good with money and have never really invested in anything other than one piece of property in my life. But I was thinking, is there a way to identify high-growth companies and be able to predict their ability to stay as leaders and invest in them. What would be the benchmarks and metrics to measure their worth on?

So I wanted to try and create a dashboard, which will tell me at a glance a sort of a health-check, whether a particular company is doing well or not.

I chose Gemini 3's thinking model for this. Apparently it “solves complex problems”. But my problem isn't really complex, I just wanted a python script calling an API and visualization using Streamlit.

Here's my starting prompt what are the key indicators of companies when evaluating their business growth potential and revenue model, and what kind of competitive analysis data should I collect to compare similar companies to separate the winners from the losers

What followed was about 2-3 hours of Gemini and I coding out a script which connects to yfinance to get real-time data, but now I heard that it isn't that reliable. Here's version 1 of the dashboard.

Project Specs:

• Stack: Python, Streamlit, yfinance API
• AI Collaborator: Gemini 3 (Thinking Model)
• Build Time: 2 days (from concept to prototype)
• Goal: Demystifying complex financial health checks for non-experts.

Check it out —> Bernie's Finance Dashboard

What it does

1. You pick a ticker you want to analyze
2. The dashboard will show you some meaningful financial metrics
   • The Rule of 40: Balances growth vs. profitability—a must-have for SaaS companies.
   • S&M Efficiency Ratio: Measures how much revenue you get for every dollar spent on Sales and Marketing.
   • Altman-Z Score: A “crystal ball” formula used to predict the likelihood of a company going bankrupt within two years.
   • Cash Runway: Tells you exactly how many months a company can survive if it doesn't make another cent.
3. There's also a way for you to enter a benchmark ticker to compare with your target ticker
4. If you are unsure of the what the metrics mean, there's also a quick guide on what those metrics measure

On to my next project! Enjoy!

#finance #ai #built #Python #Streamlit #FinTech #Gemini3 #SoloBuilder #DataVisualization #FinancialLiteracy #GrowthMetrics

I've built my own image hosting server! Yay!

Hint: the proof is in the image URL :)

#built

I put Claude Sonnet 4.6 to the test by asking it help me build a Telegram Bot. I've always wanted to build a bot like that, and now it's possible. Yay. First I looked at all the possible real-time datasets on rainfall in Singapore, then zoomed in on the one from data.gov.sg by NEA. So here's my starting prompt:-

Hi, I would like to create a telegram bot called theraincheck_bot, which will send a message when there's rain approaching 30 minutes from the time sent. It will check real-time data from the API from data.gov.sg, NEA's realtime rainfall data. The country is Singapore and users can specify which part of Singapore they want to check, for example, downtown, yishun, woodlands, etc. What programming language should I use and how should I start building this?

Claude then proceeded to break down the steps I need to build it. Guided me through:

• libraries to install
• how to call the NEA API
• how to create a bot on Telegram using @botfather
• how to host my files on AWS Lightsail
• taught me how to secure my files
• helped me tweak the code to incorporate my new requests, like wording changes, logic updates (I wanted the user to be able to ping the bot for on-demand rainchecks vs just being ping'd are scheduled intervals)

The whole experience just took one day and another half a day for tweaks.

Want to check out my bot? The Rain Check Bot [by Bernie]

I'm going to build a transport bot next ...

#telegram #built

It's simply mind-boggling that AI developments changes on a daily basis, whether it's “who's built a new app?”, or “new AI model release!”, and “[insert job title here] is obsolete!“, there's never a boring day with AI news. Currently the two podcasts that I'm listening to are:

• The AI Daily Brief – provides daily updates on daily AI news. Very current and informative.

• AI & I – showcases how people and companies are leveraging AI for their work and personal lives.

#podcast #ai