May 31, 2025 — 2:10 PM
There are days when your mind drifts toward big questions—the kind that don’t have immediate answers. Today is one such day. I find myself wondering: What would the end look like? Would it be a peaceful closure or chaotic unraveling? Would we have abundance, or would scarcity shadow us? Will AI continue to serve us, or eventually outpace and outmaneuver our needs and values?
In the middle of these swirling thoughts, I’m asking myself a more immediate question: Should I just shut off the noise and do what I like? The issue is—I'm not even sure what that is anymore.
I’ve tried a few things that help: meditation, journaling, reading, even watching motivational videos—stories rooted in Buddhism or Ankur Warikoo’s perspectives. These do bring temporary clarity. But at times, even with all these tools, the world just doesn’t make sense. It feels disjointed, like I’m walking through a fog with no compass. I find myself asking: Which hobby should I pursue? What would bring joy?
Despite the inner chaos, life carries on. Time ticks. You complete your 400 words. You breathe. You live.
Today, I visited Tijara Ji with Akash (Honey Kaushik). A short visit. We meditated for 20 minutes inside the serene temple, bathed in the sounds of prayer chants. No elaborate rituals, no shopping, just silence—and perhaps, healing. Moni wasn’t with us, which made it feel even quieter, perhaps a bit empty in its own way.
Lunch was at the temple too, so no meals needed till later in the evening. I feel light, both physically and mentally.
“What now?” you may ask.
I’m turning my focus to something constructive. I plan to work on the social media presence of my blog—share posts on Twitter, Facebook, and LinkedIn. A small effort, but one that gives me direction.
In a fleeting moment, I wished there was someone I could talk to. Someone who would just listen—no advice, no interruptions, just presence. It passed, but the wish lingered for a heartbeat.
As for the weekend, it’s going to be about stillness and small wins. I’ll write, read, relax—and maybe even plan something for the days ahead.
God bless.
Life has a way of throwing curveballs. One moment, you might be riding high, your business flourishing, and the future looking bright. The next, circumstances beyond your control can pull the rug out from under you, leaving you scrambling to stay afloat. This is a reality many face, and the story of Adarsh, a young man from Delhi, serves as a poignant and relatable example of navigating financial hardship and the crucial lessons learned along the way.
The Rise and Fall
Not long ago, Adarsh was a successful entrepreneur. Before the pandemic, his business was thriving, bringing in a handsome income of around ₹2.5 lakh per month. He was young, ambitious, and seemingly on the right track. However, the lockdowns hit his travel-related business hard, forcing it to shut down completely. Overnight, his substantial income vanished, replaced by a job paying ₹35,000 per month. While his brother contributed another ₹25,000, the family's total income plummeted to ₹60,000 – a stark contrast to their previous earnings.
Caught in the Debt Cycle
The drastic income drop was compounded by a significant debt burden. Like many, Adarsh had taken out loans during better times and for significant life events. These included:
* A ₹5 lakh personal loan for his sister's wedding (EMI: ₹17,700).
* Another ₹3 lakh personal loan, also for the wedding (EMI: ~₹7,000).
* A consumer loan for a phone (EMI: ~₹4,080).
* Credit card debt, largely from wedding expenses (EMI: ₹7,000).
* A home loan on an investment property purchased when his business was doing well (EMI: ₹6,700).
Combined, these EMIs amounted to roughly ₹42,700 per month. Added to the family's essential living expenses of about ₹20,000 (groceries, bills, medicine, travel), their outgoings significantly strained their reduced income, even before accounting for any personal spending or savings.
Three Critical Mistakes to Avoid
Adarsh found himself trapped, struggling to manage. His situation highlights three common financial pitfalls that many fall into, especially when transitioning from high earnings to financial strain:
1. Lifestyle Inflation Creeps In: The advisor in Adarsh's story pointed out a crucial mistake: letting expenses rise unchecked as income grows. When Adarsh was earning well, his spending habits adjusted upwards. When his income crashed, the expenses didn't automatically follow suit. The purchase of a new phone on a consumer loan, even while facing financial difficulties, exemplifies this. The allure of maintaining a certain lifestyle or acquiring new things can be strong, but it's a dangerous path. True financial security comes not just from earning more, but from controlling spending relative to income. As the advisor noted, the mindset of the wealthy isn't about spending millions, but about using money to build independence, ensuring that life isn't solely dependent on the next paycheck.
2. Flying Blind Without a Budget: Adarsh admitted he didn't have a precise handle on where all his money was going. He had rough estimates, but lacked a detailed, day-to-day understanding of his cash flow. This is like trying to navigate a storm without a compass. Knowing exactly where every rupee goes is fundamental to financial control. Without tracking, it's impossible to identify wasteful spending, make informed decisions, or create a realistic plan to get out of debt. This financial literacy isn't just for individuals; it sets a precedent for the entire family.
3. Investing While Drowning in Debt: Despite his significant debt burden, Adarsh had recently started investing ₹7,000 per month in SIPs (Systematic Investment Plans). While investing is crucial for long-term wealth, doing so while carrying high-interest debt (like personal loans and credit cards, often charging 12-14% or much more) is usually counterproductive. The interest paid on these loans typically outweighs potential investment returns, especially after taxes. The 'guaranteed' return from paying off a high-interest loan is often far more beneficial than the uncertain gains from investments. Prioritizing the elimination of expensive debt frees up cash flow and provides a solid foundation for future, more effective investing.
Taking Control
Adarsh's story is a difficult one, born from hardship, responsibility, and perhaps a lack of guidance. He had to forgo education and hustle from a young age. Yet, his experience offers invaluable lessons. Recognizing these mistakes – unchecked lifestyle inflation, poor financial tracking, and premature investing under debt – is the first step towards recovery. The path forward involves making tough choices, creating a meticulous budget, prioritizing high-interest debt repayment, and potentially exploring options like selling lossy/unprofitable assets.
It's a reminder that financial well-being isn't just about income; it's about discipline, awareness, and making informed choices. By learning from experiences like Adarsh's, we can strive to build a more secure financial future for ourselves and our families.
Today started with something simple yet empowering—a driving class. I drove to the office parking lot in Sector 21, Dundahera village, about 24–25 kilometers away. With less traffic on the road than usual, the drive was smooth and comfortable. I managed to return halfway home before switching off near the Jaipur Highway. It was one of those drives that makes you feel just a little more in control of your life.
Later in the day, good news came in—my salary got credited. With that, I made an important decision: to pre-close my car loan. The loan had been dragging me down financially, especially with ₹24,000 vanishing each month. Looking at the numbers, I’ve already spent over a lakh on interest payments alone. Ending that cycle will be a financial relief and a step closer to financial clarity.
Work was technically productive. We managed to containerize the NEA app using Docker and deploy it to a Kubernetes cluster. However, the last piece of the puzzle—DNS setup—is still pending. We’re close to getting the whole thing functional. It’s one of those inch-by-inch battles.
Now, why am I journaling all of this? The simple answer: to rebuild my communication skills. Writing helps me pause and reflect. It brings structure to the chaos in my head.
That said, some parts of the day brought discomfort. Moni’s daughters—Mahi and Chulbul—are staying with us. But their stay has been more frustrating than helpful. Their academic performances have been lackluster, and their motivation seems lacking. Today, they were out late into the evening without much accountability, and that stirred some raw feelings in me. I don’t want to dwell too much on negativity, but writing it out helps process the emotion and let it go.
On a lighter note, Ankit and Akash called. There’s talk of a new freelancing opportunity where we’d split ownership—33%, 33%, and 34% respectively. It’s exciting but also daunting. Where do I find the time and energy? Still, it feels worth exploring.
Tomorrow’s another early morning, another drive, and maybe another small win. Let’s see where the road leads.
God bless.
Some days feel like a kaleidoscope of chaos, clarity, and contemplation. Today was one such day — full of tech trials, broken glass, unexpected conversations, and a few quiet life lessons.
The morning began slowly. I woke around 6:30 AM, but didn’t actually roll out of bed until 7:30. A quick driving lesson with Akash was on the schedule — just a trip to the office parking lot and back. It was a small accomplishment, but a grounding one.
Work-wise, things were off-track from the beginning. I missed the 9 AM IR project stand-up and didn’t attend any of the IR calls through the day. Instead, I was buried in a mountain of IT troubleshooting. Ubuntu 25.04 failed to install. Then 24.04.5 also refused to cooperate. I considered downgrading but instead gave MX Linux a shot — it's what’s running on Ankur’s Toshiba, so I figured it was worth exploring.
And then there was the center table. Smashed — by none other than Mahi, Chulbul, and Moni. A reminder, perhaps, that impermanence is everywhere. A lesson, albeit a sharp one.
Later in the evening, Binu from work messaged me, wondering why I missed a call and asking for testing updates. I pinged the relevant teammates — Bindu, Anusha, Varsha — and relayed the request. It was a small task, but one that pulled me back into the workstream after a disconnected day.
In between it all, I found moments for gratitude. I’m thankful to Shridhar and Shiben — two constants in the maze of day-to-day questions and management tasks. There are many more I could thank — mentors, colleagues, teammates — each playing a part in this journey.
And forgiveness? That too found its place. To those who’ve hurt me — intentionally or otherwise — I offer forgiveness. Not because I have to, but because I want to move forward lighter.
Three lessons stood out today:
Impermanence – whether in shattered glass or failed installations.
Respect – we bow not just to rank, but to the divinity in others.
Planning – shared some freelance advice with Ankit and Akash, reminding them (and myself) that thoughtful planning is often the difference between chaos and calm.
Life’s still a bit crazy. But I’m learning to walk through the madness with gratitude — and maybe a little Linux.
God bless.
Today was a digital disaster. It started with chaos—files being moved, deleted, and devices being wiped clean. Over the past week, I’ve lost more than 600GB of data, thanks to a perfect storm of file mismanagement and cleaning sprees on hard drives, pen drives, and memory cards.
The real kicker? My 1TB memory card nearly died during a file transfer gone wrong on Ubuntu. The USB port failed mid-transfer, and suddenly I was facing file corruption. In my frustration (and probably as a coping mechanism), I decided to upgrade my OS to Ubuntu 25.04.
That’s where the real trouble began.
I started the installation around 4–5 PM. It’s now past 11:30 PM, and I’m still battling with setup. The first installation failed, and the second one just gave up on me. I’m seriously considering downgrading to version 24.10—or even 24.04—just to regain a sense of normalcy.
Between these digital calamities, I was technically “working from home,” juggling AIP Support to get containerization done for a GenAI app. I also missed two calls with the IR team (5PM and 6PM), which now feel like minor blips in an already overwhelming day.
I managed to sneak out with Moni for some groceries around 8PM, which was the most peaceful part of the day. But once back, I returned to the wreckage—my first OS installation had failed. I tried to fix it. It wouldn’t budge. So I reinstalled, and... same result.
At this point, I’m exhausted. My head is heavy, I’m yawning constantly, and all I want is sleep. But I also know that if I push this off to tomorrow, I’ll regret it during work hours. It’s a lose-lose.
Despite the chaos, I’m grateful for one thing: I’m writing more regularly than I did in 2024. That alone marks some improvement in my health and habits.
Still, it’s hard not to ask: “What am I doing?” Not just with Ubuntu, but with these long, sleepless nights chasing clean installs and clean slates.
Time to call it a night. I’ll try again tomorrow—with Ubuntu 24.x if I must. Morning drive with Akash and Honey at 6AM is my only reward in sight.
Let’s hope tomorrow is quieter.
LLM Used: ChatGPT leveraging GPT-4.5 as in May 2025.
How do you choose between bias and variance in model training?
Great question! Choosing between bias and variance is essentially about managing the tradeoff between underfitting and overfitting during model training. Here's how you can approach it:
⚖️ Understanding the Trade-off
Term
Description
Consequence
Bias
Error due to overly simplistic assumptions in the model
Leads to underfitting
Variance
Error due to model being too sensitive to training data
Leads to overfitting
🧠 How to Choose / Balance Them
You don’t choose one over the other — you try to balance both to achieve low total error (bias² + variance + irreducible error).
✅ Steps to Handle It Practically:
Check Learning Curves
If both training and validation error are high → high bias (underfitting)
If training error is low, but validation error is high → high variance (overfitting)
Model Complexity
Simple models (like linear regression) = lower variance, higher bias
Small data → simpler model (to avoid high variance)
Large data → can use complex models (variance can be managed)
Use Regularization
Techniques like L1/L2 regularization reduce variance without increasing bias too much.
Dropout in neural networks is a variance-reducing method.
Cross-Validation
Helps estimate generalization error and pick models that minimize overfitting.
Bias–Variance Table (Intuition):
Model Type
Bias
Variance
Linear Regression
High
Low
Decision Tree
Low
High
Random Forest
Low
Moderate
Neural Networks
Low
High
🎯 Summary
You don’t "choose" one — you manage the balance by:
Selecting the right level of model complexity
Using regularization and cross-validation
Analyzing learning curves to guide model adjustments
Let me know if you'd like help visualizing this with a graph or example dataset!
What is regularization? Explain L1 vs. L2.
Great question! Regularization is a key concept in machine learning used to prevent overfitting by discouraging overly complex models.
✅ What is Regularization?
Regularization adds a penalty term to the loss function used to train a model. This penalty:
Constrains the magnitude of the model’s parameters (weights)
Encourages simpler models that generalize better to unseen data
🧮 Standard Loss Function (e.g., for regression)
text
Loss = Error (like MSE) + Regularization term
🔍 L1 vs. L2 Regularization
Feature
L1 Regularization (Lasso)
L2 Regularization (Ridge)
Penalty term
`λ * Σ
w
Effect on weights
Can shrink some weights to zero
Shrinks weights close to zero, but not exactly zero
Feature selection
Yes – leads to sparse models
No – keeps all features
Optimization
Not differentiable at 0
Differentiable, easier to optimize
🔧 λ (lambda) is the regularization strength – higher λ means more penalty.
🔢 Visual Intuition
L1 gives a diamond-shaped constraint – solution often lands on axes → some weights become 0
L2 gives a circular constraint – tends to shrink all weights smoothly
📌 When to Use
Situation
Recommended Regularization
You want feature selection
L1 (Lasso)
You want smooth/shrinkage of all weights
L2 (Ridge)
You want the best of both
ElasticNet (L1 + L2 combo)
🧠 Summary
Regularization reduces overfitting by penalizing large weights.
L1 (Lasso): Sparse models, good for feature selection.
L2 (Ridge): Keeps all features, smoother weight decay.
Let me know if you'd like a code example to visualize the difference!
Explain precision, recall, F1-score, and when you’d prioritize one over the other.
Great! Let’s break down Precision, Recall, and F1-score—they’re critical metrics for evaluating classification models, especially when classes are imbalanced or costs of false positives/negatives differ.
F1-Score: "Balance the two" ⚖️ — good when both errors are costly
Let me know if you'd like a small Python example or confusion matrix visualization!
Can you share one simple numerical on Precision and Recall?
Find Precision and Recall for the below span email classifier problem.
Out of 100 emails, 40 are spam mails
Model predicted 50 spam mails:
30 (correctly predicted spam)
20 (predicted spam, but not spam)
10 (actual spam, but predicted not spam)
Absolutely! Let's go through a simple numerical example to understand Precision and Recall.
🧮 Example Scenario
Imagine you're building a spam email classifier.
Out of 100 emails, here’s what your model predicted:
False Positives (FP) = 20 (predicted spam, but not spam)
False Negatives (FN) = 10 (actual spam, but predicted not spam)
Let’s calculate:
✅ Precision
Precision=TP+FPTP=30+2030=5030=0.6
So, 60% of the emails predicted as spam were actually spam.
✅ Recall
Recall=TP+FNTP=30+1030=4030=0.75
So, the model correctly identified 75% of all actual spam emails.
✅ F1 Score
F1=2⋅0.6+0.750.6⋅0.75=2⋅1.350.45=0.6667
F1 Score is 66.7%, showing the balance between precision and recall.
🎯 Interpretation
If your main concern is not marking real emails as spam → improve precision
If your goal is to catch all spam → focus on recall
Let me know if you'd like to see this visualized in code or a confusion matrix chart!
What is the ROC curve? How is AUC useful?
Great question! The ROC curve and AUC are essential tools for evaluating the performance of classification models, especially in binary classification.
✅ What is the ROC Curve?
ROC stands for Receiver Operating Characteristic curve.
