Thursday, February 5, 2026

ASME to SAME - Can you convert s into t (Easy)

Index of "Algorithms: Design and Analysis"

Attempt it on HackerEarth
You are given 2 strings s and t both containing only lowercase English characters with s containing some ? as well. You can swap characters at positions i and j in the string s (j > i) any number of times and can also replace any ? with any lowercase English character. Can you convert s into t ?

Input Format

The first line contains an integer T denoting the number of test cases. 

The first line of each test case contains an integer N, the length of the strings.

The second line of each test case contains N lowercase English characters with some ? (possibly 0) denoting string s.

The third line of each test case contains N lowercase English characters denoting string t.

Output Format

For each test case, print 'Yes' (without quotation marks) if it is possible to convert s into t. Otherwise, print 'No' (without quotation marks). Print the output in a new line.

Constraints

1 <= T <= 100
1 <= N <= 10**5

Sample Input

3
4
???n
aman
4
aacb
aabc
3
?po
oyo

Sample Output

Yes
Yes
No

Time Limit: 2
Memory Limit: 256
Source Limit:

Explanation

In the first sample, we can replace first 3 with characters a, m, a respectively to obtain the final string.

In the second sample, we can swap 3rd and 4th characters to get our final string.

In the third testcase, no matter what we do, we cannot convert s into t.


Initial Thoughts

1. The problem requires checking if we can transform string s into string t by swapping characters and replacing '?' characters.
2. Since we can swap characters freely, the order of characters in s does not matter.
3. We need to ensure that for every character in t, there are enough corresponding characters in s or '?' characters to replace.
4. We can use frequency counts to compare the characters in both strings.
5. If the count of any character in t exceeds the count in s plus the number of '?' characters, then the transformation is not possible.
6. Otherwise, it is possible to transform s into t.
7. We also need to check if there is some character in t that is not present in s (and otherwise: in s that is not in t) at all and there are no '?' characters in s to replace it with.


My Code

from collections import Counter

def can_convert(s, t):
    if len(s) != len(t):
        return "No"
    count_s = Counter(s)
    count_t = Counter(t)

    wildcards_s = count_s.get('?', 0)

    for char in count_t:
        if count_t[char] > count_s.get(char, 0):
            needed = count_t[char] - count_s.get(char, 0)
            wildcards_s -= needed
            if wildcards_s < 0:
                return "No"
        elif count_s.get(char, 0) > count_t[char]:
            return "No"
    return "Yes"

    

T = int(input())
for _ in range(T):
    N = int(input())
    s = input().strip()
    t = input().strip()
    print(can_convert(s, t))


Note: I have tested my code. And ChatGPT's code (below) is not tested for correctness, mistakes or hallucinations.
Download Test Cases


ChatGPT's Thoughts

Me: Explain the solution to this problem in layman terms. Give clear descriptions of what this problem is trying to teach/assess, what design pattern, what problem solving trick/technique.

Let’s explain this from first principles, in a way that a layman (or an early learner) can follow, and then connect it to what the problem is testing and the underlying technique.


๐Ÿงฉ Problem restated in simple words

You are given two strings of the same length:

  • s → may contain lowercase letters and ?

  • t → contains only lowercase letters

You are allowed to:

  1. Swap any two characters in s (as many times as you want)

  2. Replace each ? in s with any lowercase letter

Your task:
๐Ÿ‘‰ Decide whether it is possible to transform s into t.

You don’t need to show how, just say Yes or No.


๐Ÿง  Key observation (very important)

Since you can:

  • Swap characters freely

  • Replace ? with any letter

๐Ÿ‘‰ The order of characters in s does NOT matter at all.

Only the counts of characters matter.

This turns the problem into a frequency matching problem.


๐Ÿ” How to think about it (layman logic)

Think of string s as a bag of letters:

  • Some letters are fixed (a, b, c, …)

  • Some are wildcards (?) that you can turn into anything

String t is another bag of letters.

Your question becomes:

“Can I use the fixed letters + the ? wildcards in s to match exactly the letters in t?”


๐Ÿ’ก Step-by-step reasoning

Step 1: Count characters in t

Example:

ini
t = "aman" counts = {a:2, m:1, n:1}

Step 2: Count fixed letters in s and count ?

Example:

ini
s = "???n" fixed = {n:1} wildcards = 3

Step 3: Match fixed letters

For each letter in t:

  • If s already has that letter → use it

  • Otherwise → you must use a ?

If at any point:

  • Required letters > available fixed + ?

❌ Answer is No


Step 4: If all needs are satisfied

✔️ Answer is Yes


๐Ÿงช Example walkthrough

Example 1

ini
s = "???n" t = "aman"
  • Fixed in s: n

  • Wildcards: 3

  • Needed in t: a(2), m(1), n(1)

We already have n
Use ?a, m, a

✔️ Possible → Yes


Example 2

ini
s = "aadb" t = "aabc"
  • s fixed: a(2), d(1), b(1)

  • t needs: a(2), b(1), c(1)

We need c but:

  • No ?

  • No c in s

❌ Impossible → No


๐ŸŽฏ What this problem is trying to teach / assess

1️⃣ Abstraction

  • Ignore swaps → reduce problem to counts

2️⃣ Frequency counting

  • A core skill in string problems

3️⃣ Handling wildcards

  • ? acts as a flexible resource

4️⃣ Logical feasibility check

  • “Do I have enough resources to meet the demand?”


๐Ÿง  Design pattern used

Greedy + Frequency Matching

  • Greedy: use fixed letters first

  • Use wildcards only when needed

No backtracking. No brute force. No permutations.


๐Ÿงฐ Problem-solving trick / technique

When swaps are unlimited, order doesn’t matter — only counts do.

This trick appears in:

  • Anagram problems

  • String transformation checks

  • Inventory matching problems

  • Resource allocation puzzles


๐Ÿง  One-line intuition

“After ignoring order, can the letters (and ?) in s supply the letters required by t?”


๐Ÿ Final takeaway

This is a thinking problem, not a coding-heavy one.

It tests whether you can:

  • Simplify a problem

  • Spot what actually matters

  • Reduce it to a clean frequency check

If you want next:

  • Full Python code

  • Edge cases explained

  • Time/space complexity

  • Why swapping makes order irrelevant (proof)

Just tell me ๐Ÿ˜Š



Me: Full Python code

Here is the complete, clean, and efficient Python solution, followed by a short explanation.


✅ Full Python Code

python
def can_convert(s, t): from collections import Counter count_s = Counter(s) count_t = Counter(t) wildcards = count_s.get('?', 0) # Check each character needed in t for ch in count_t: available = count_s.get(ch, 0) needed = count_t[ch] if available >= needed: continue else: required_from_q = needed - available if required_from_q > wildcards: return "No" wildcards -= required_from_q return "Yes" # ---- Driver code ---- T = int(input()) for _ in range(T): N = int(input()) s = input().strip() t = input().strip() print(can_convert(s, t))

๐Ÿง  Explanation (simple & clear)

What the code does

  1. Counts letters in s and t

    • Uses Counter to track how many times each letter appears.

  2. Counts how many ? characters are in s

    • These are wildcards that can become any letter.

  3. For each letter in t

    • First use matching letters already in s

    • If more are needed, try to use ?

    • If not enough ?, conversion is impossible

  4. If all letters are satisfied → print Yes


๐Ÿงช Example

ini
s = "???n" t = "aman"
  • Need: a(2), m(1), n(1)

  • Have: n(1) + ?(3)

  • Possible → Yes


⏱️ Time & Space Complexity

MetricComplexity
TimeO(N)
SpaceO(1) (26 letters max)

๐ŸŽฏ What this solution demonstrates

  • Frequency counting

  • Greedy resource usage (? as wildcards)

  • Reduction of problem complexity by ignoring order


๐Ÿ Final takeaway

If swaps are unlimited, order doesn’t matter — only counts do.

If you want:

  • Step-by-step dry run

  • Edge cases

  • Proof of correctness

  • Alternative implementation without Counter

Just ask ๐Ÿ˜Š

Wednesday, February 4, 2026

The Secret of Happiness is Something to Do (1/6)


See other summaries on "Finding Purpose"
Chapter 1: from the book "Light From Many Lamps"
Chapter By: John Burroughs

The poet stood at his window and watched a neighbor walk by.
He walked—not as a man should, in joy and triumph—but with slow step and sagging shoulders, like a man with a great burden on his soul.

John Burroughs knew why. The man had no work he loved, nothing to keep him busy and content, to give his days purpose and direction.

“An idle man is a wretched man,” he thought, listening to the shrill cacophony of the birds, to the soft whispering of the trees. Surely no one knew better than he the blessedness of work, of life-giving and life-sustaining work!

Had he not been idle himself once, and one of the most utterly dejected of God’s creatures? Life had lost its savor for him, had become empty and stagnant; he couldn’t eat or sleep, couldn’t think or dream.

Work had saved him. Good hard work on a farm, with his hands, raking and hoeing, plowing and planting, feeling the good earth between his fingers… feeling his oneness with the universe.

The plow had done its perfect work on him, as on his fields. The bitterness and boredom had been plowed under, the stagnant pools of discontent drained off. The planting and pruning had shaped his life as surely as they had shaped his fruit trees. For seeing the rivers shine and dimple in the spring, watching the birds arrive and hearing their shrill, excited laughter, looking up from his work and seeing the skies and distant hills bathed in the magic of sudden beauty, he had found renewal and inspiration.

He had found his life’s work.

John Burroughs turned from the window, walked slowly to his desk. Wake-Robin had been the first of a series of books about birds, flowers, and rural scenes that had brought him world-wide recognition. But far more important, his poems and essays on out-of-door life had brought him joy and contentment.

If only he could make others realize that happiness was no elusive will-o’-the-wisp, that real happiness was the simplest thing in the world—and within reach of all! People all about him were reaching for happiness in hopeless ways, or, like the neighbor who had just walked by, were letting their lives empty into stagnant pools. He must try to make them realize that the secret of happiness was in work, congenial work, something to do.

He picked up his pen and began to write:

There is a condition or circumstance that has a greater bearing upon the happiness of life than any other. What is it? … It is one of the simplest things in the world and within reach of all. If this secret were something I could put up at auction, what a throng of bidders I should have, and what high ones! Only the wise ones can guess what it is. Some might say it is health, or money, or friends, or this or that possession, but you may have all these things and not be happy. You may have fame and power, and not be happy.

I maintain there is one thing more necessary to a happy life than any other, though health and money and friends and home are all important. That one thing is—what? The sick man will say health; the poor man, wealth; the ambitious man, power; the scholar, knowledge; the overworked man, rest.

Without the one thing I have in mind, none of these things would long help their possessors to be happy. We could not long be happy without food or drink or clothes or shelter, but we may have all these things to perfection and still want the prime condition of happiness.

It is often said that a contented mind is the first condition of happiness, but what is the first condition of a contented mind? You will be disappointed when I tell you what this all-important thing is—it is so common, so near at hand, and so many people have so much of it and yet are not happy. They have too much of it, or else the kind that is not best suited to them.

What is the best thing for a stream? It is to keep moving. If it stops, it stagnates. So the best thing for a man is that which keeps the currents going—the physical, the moral, and the intellectual currents. Hence the secret of happiness is—something to do; some congenial work. Take away the occupation of all men, and what a wretched world it would be!

Few persons realize how much of their happiness is dependent upon their work, upon the fact that they are kept busy and not left to feed upon themselves. Happiness comes most to persons who seek her least, and think least about it. It is not an object to be sought; it is a state to be induced. It must follow and not lead. It must overtake you, and not you overtake it.

How important is health to happiness, yet the best promoter of health is something to do.

Blessed is the man who has some congenial work, some occupation in which he can put his heart, and which affords a complete outlet to all the forces there are in him.

It was out of the richness and fullness of his own life that John Burroughs wrote the above words.

Wake-Robin had awakened him to his own destiny, had shown him what he wished most to do in life. It was followed by Birds and Poets, Locusts and Wild Honey, Signs and Seasons, The Ways of Nature—books that made him the successor of Henry David Thoreau as America’s most popular essayist on birds, plants, and animals.

Like so many others who lived the good life, Burroughs felt in later years the need to share his philosophy with others, to pass on his secret of happiness.

“Keep the currents moving,” he urged.
“Don’t let your life stagnate.”

Today he is perhaps as well remembered for his reflections on happiness as he is for his poems and essays. The selection given here has been quoted so many times it would be impossible to calculate its influence—an influence that increases with every reprinting.


Quoted Reflections

“The grand essentials to happiness in this life are something to do, something to love, and something to hope for.”
Joseph Addison


“Work and thou canst not escape the reward; whether thy work be fine or coarse, planting corn or writing epics, so only it be honest work, done to thine own approbation, it shall earn a reward to the senses as well as to the thought. No matter how often defeated, you are born to victory. The reward of a thing well done is to have done it.”
Ralph Waldo Emerson


“Happiness, I have discovered, is nearly always a rebound from hard work… She loves sweat, weariness, self-sacrifice… If you look up suddenly from hard work you will see her, but if you look too long she fades sorrowfully away.”
David Grayson


“The mintage of wisdom is to know that rest is rust, and that real life is in love, laughter, and work.”
Elbert Hubbard


“To awaken each morning with a smile brightening my face… to approach the night with weariness that ever woos sleep and the joy that comes from work well done—this is how I desire to waste wisely my days.”
Thomas Dekker


“Thank God every morning when you get up that you have something to do which must be done… Being forced to work, and forced to do your best, will breed in you temperance, self-control, diligence, strength of will, content, and a hundred other virtues which the idle never know.”
Charles Kingsley


Bitcoin-Led Crypto Rout Erases Nearly $500 Billion in a Week


Lessons in Investing

Bitcoin price today dropped sharply, falling to the $74,000 level and triggering another wave of selling across the crypto market. Ethereum slipped nearly 10% to around $2,100, while most major altcoins declined between 5% and 10% today.

The sudden move has raised fresh concerns about whether Bitcoin is entering a deeper correction phase after weeks of volatility.

Possible Reasons Behind the Bitcoin Crash Today

The latest Bitcoin crash is not linked to a single event. Instead, analysts point to multiple factors hitting the market at the same time, creating strong downward pressure.

Heavy Liquidations Accelerate Bitcoin Decline

One of the main reasons behind the drop is massive liquidations in the futures market. Market data shows that over $500 million worth of Bitcoin positions were liquidated in recent sessions.

Many traders were using high leverage. When Bitcoin slipped even slightly, automatic liquidations kicked in, forcing positions to close. This led to a chain reaction of selling, pushing prices lower within minutes.

After the U.S. market opened, Bitcoin dumped another 11USDT,700, wiping out more than $55 million in long positions in just two hours. The overall crypto market lost nearly $50 billion during the same move.

US Stock Market Weakness Hits Crypto Hard

The crypto sell-off mirrored weakness in traditional markets. The S&P 500 fell nearly 1.3%, as investors moved away from risk assets.

Historically, when global markets turn cautious, cryptocurrencies tend to react faster and more sharply. The same pattern played out this time, with Bitcoin and altcoins facing intense selling pressure.

Spot Bitcoin ETF Outflows Add Pressure

Another key factor weighing on prices is strong outflows from spot Bitcoin ETFs.

As per CoinGlass data, on February 3, spot BTC ETFs recorded $272 million in net outflows. BlackRock’s IBIT stood out as the only major buyer with $60 million in inflows, while other funds continued to see selling.

When ETF flows turn negative like this, it often signals reduced confidence among institutional investors, even if long-term interest remains intact.

Epstein Files Add to Market Uncertainty

Beyond macro pressure and liquidations, renewed discussion around the Epstein files has added another layer of uncertainty to the crypto market. Reports highlighting Jeffrey Epstein’s past connections to early Bitcoin research, funding linked to MIT’s Digital Currency Initiative, and ties to prominent crypto figures have resurfaced online. 

While there is no direct evidence linking these revelations to current price action, the narratives have fueled speculation on social media and increased short-term volatility. During already weak market conditions, such controversies often amplify fear and contribute to risk-off behavior among traders.

Geopolitical Tensions Increase Market Uncertainty

Rising global tensions have also played a role. Ongoing disputes involving the United States, Iran, and Venezuela, along with tariff-related concerns, have increased uncertainty across financial markets.

During such periods, large funds and ETF managers usually cut exposure to risky assets. This capital outflow has added further pressure to Bitcoin and the broader crypto market.

Profit-Taking After Bitcoin’s Massive Rally

Galaxy Digital CEO Mike Novogratz believes the recent decline is mainly driven by profit-taking, not panic.

According to him, many investors who bought Bitcoin at much lower levels started selling after prices crossed $100,000, locking in gains after a long rally. He described the move as a “seller’s wave”, rather than fear-driven selling.

Novogratz also dismissed concerns around emerging threats like quantum computing, saying price moves are still driven by basic supply and demand.

Bitcoin Price Analysis: Key Support and Resistance Levels

The market is sitting at a critical turning point. If Bitcoin slips below the $74,500 support, the next downside target is seen around $69,800–$68,000, a zone that previously acted as strong resistance. 

A deeper breakdown from there could drag prices toward the $53,000–$54,000 range, implying a correction of nearly 30% from current levels. 

On the upside, analysts believe a quick recovery is unlikely, as Bitcoin would need to reclaim the $90,000–$95,000 resistance zone and establish a clear higher-high structure before any sustained rebound can take shape.

FAQs

Why is Bitcoin price down today?

Bitcoin is down today due to leveraged liquidations, weak U.S. markets, ETF outflows, profit-taking after the rally, and rising global uncertainty.

How do U.S. stock market declines impact Bitcoin prices?

When stocks fall, investors reduce risk exposure. Bitcoin typically reacts faster, leading to sharper declines during market-wide sell-offs.

Is the current Bitcoin drop a healthy correction?

Yes. Many analysts view this move as a normal correction after a strong rally, helping reset leverage and excess speculation.

Does profit-taking mean Bitcoin’s bull market is over?

No. Profit-taking is common after major rallies and does not signal the end of a long-term bullish trend.

What could drive Bitcoin prices higher again?

Stabilizing markets, renewed ETF inflows, reduced leverage, and improving macro sentiment could support a recovery.

Ref Tags: Finance,Investment,

What is Anthropic's AI tool that wiped $285 billion off software stocks in a single day


See All Articles on AI

Anthropic's newly launched AI plugins have triggered what analysts are calling a 'SaaSpocalypse'—a brutal selloff that wiped out roughly $285 billion from software, legal tech and financial services stocks in a single trading session. The AI developer, known for its Claude chatbot, released 11 open-source plugins for its Claude Cowork tool on Friday, January 30. One of them targets legal workflows, and that alone was enough to send investors running for the exits.Thomson Reuters dropped over 15%. RELX, owner of LexisNexis, fell 14%. LegalZoom got hammered nearly 20%. A Goldman Sachs basket tracking US software stocks posted its worst single-day decline since April's tariff-driven selloff. The Nasdaq fell 1.4%, with ripple effects extending to Indian IT giants—Infosys ADRs slipped 5.5% and Wipro fell nearly 5%.

What exactly did Anthropic release

Claude Cowork is an agentic AI assistant that Anthropic launched earlier in January. Think of it as Claude Code—the company's developer-focused coding tool—but redesigned for non-technical professionals. It can read files, organise folders, draft documents and carry out multi-step tasks with user consent.

The plugins announced last Friday supercharge this capability. They let companies tailor Claude to specific job functions by specifying how work should be done, which tools and data to pull from, and what workflows to automate. Anthropic open-sourced 11 starter plugins covering productivity, sales, marketing, finance, data analysis, customer support, product management and biology research.But it was the legal plugin that spooked markets. It automates contract review, NDA triage, compliance checks and legal briefings. Anthropic was careful to note that all outputs should be reviewed by licensed attorneys and that the tool does not provide legal advice. That disclaimer did little to calm investors.

Why the market panic over a folder of prompts

Here's the thing that has caught many observers off guard: the legal plugin is essentially a set of prompts and configurations. There's no proprietary model fine-tuned on case law, no special legal reasoning engine. It's Claude being Claude, but with structured workflow instructions.The real signal, analysts say, is that Anthropic has moved from selling the model to owning the workflow. When Claude was just an API, companies like Thomson Reuters could build on top of it. Thomson Reuters literally runs CoCounsel on OpenAI. But when Anthropic starts publishing ready-made vertical solutions, the platform becomes the competitor.Jefferies dubbed the selloff a 'SaaSpocalypse', noting that investor sentiment has shifted dramatically. The narrative used to be that AI helps software companies. Now it's that AI replaces them. 'Trading is very much get-me-out style selling,' said Jeffrey Favuzza from Jefferies' equity trading desk. The brokerage also noted that OpenAI appears to be losing ground in the corporate market to Anthropic's Claude, with enterprises now accounting for 80% of Anthropic's business.

What does this mean for the software industry going forward

The damage extended well beyond legal tech. DocuSign fell 11%, Salesforce dropped nearly 7%, Adobe slid 7%, and ServiceNow declined 7%. Business development companies with exposure to software loans also got caught in the selling. Blue Owl Capital Corp fell 13%, marking a record ninth consecutive decline.Anthropic isn't the only player in legal AI. Startups like Harvey AI (valued at $5 billion) and Legora ($1.8 billion valuation) have been developing tools for years. But Anthropic's advantage is that it builds its own underlying models. Firms like Legora rely on models from developers like Anthropic, which means Claude's creator can potentially disrupt both traditional legal services and the startups trying to disrupt them.The company's growth trajectory underscores why investors are taking this threat seriously. Claude Code hit $1 billion in annualised recurring revenue by November, just months after its May launch. Anthropic is now reportedly raising $20 billion at a $350 billion valuation—up sharply from $61.5 billion in March 2025.The iteration speed is also striking. Cowork launched on January 12. The plugins dropped less than three weeks later. Enterprise software companies typically spend quarters on such releases.For software executives watching the carnage unfold, the takeaway is uncomfortable but clear: Anthropic didn't need a breakthrough product to rattle markets. It just needed to show what Claude could already do—and hit publish.

Ref

Tuesday, February 3, 2026

Maximize Minimal Potential -- What to Do When You Start with Nothing


See other Biographies & Autobiographies

Let me start with something uncomfortable but honest.

Most of us don’t begin life with ideal conditions. Some people are born into money, safety, and encouragement. Others—maybe you, maybe me—start with chaos, fear, neglect, or low expectations. And when you grow up like that, survival becomes the goal. Not growth. Not excellence. Just survival.

The problem is, survival mode has an expiration date.

At some point, you either evolve—or you stay stuck replaying the same story forever.

Born Behind the Line

There’s a lie we tell ourselves when life deals us a bad hand: “It’s not my fault.”
And here’s the thing—it’s usually true.

It’s not your fault if you grew up in poverty.
It’s not your fault if you were abused, neglected, bullied, or ignored.
It’s not your fault if you were never taught discipline, confidence, or self-respect.

But that truth can quietly turn into a trap.

Because if nothing is your fault, then nothing is your responsibility either. And that’s where potential goes to die.

Many people live their entire lives grading themselves on a forgiving curve. They carry their past like a permanent excuse slip. And the world—friends, family, coworkers—often reinforces it: “After what you’ve been through, it’s understandable.”

Understandable, yes. But survivable is not the same as sustainable.

The Moment of Reckoning

There’s usually a moment—sometimes loud, sometimes quiet—when you realize something is wrong inside you. Not broken in a dramatic way, but numb. Flat. Disconnected.

That numbness is dangerous.

Numb people quit easily.
Numb people stop dreaming.
Numb people settle for lives that don’t match their inner potential.

Numbness often starts as protection. When you grow up in pain, going emotionally cold can keep you alive. But what once saved you can eventually suffocate you.

And here’s the hard truth: no one is coming to wake you up.

Responsibility Without Blame

One of the most powerful mindset shifts you can make is this:

It may not be my fault—but it is my responsibility.

That sentence changes everything.

Responsibility doesn’t mean self-hatred.
It doesn’t mean denying your past.
It means refusing to let yesterday dictate tomorrow.

At some point, you have to stop collecting evidence about why your life turned out the way it did and start asking a harder question:

What am I doing today that keeps me here?

That question hurts. But it also liberates.

The Trap of “Distracting Injuries”

There’s a concept from emergency medicine called a distracting injury. It’s a wound that looks severe enough to pull attention away from the real threat.

In life, our past trauma often becomes that distracting injury.

We focus so intensely on what happened to us that we miss what’s happening because of us—our habits, our decisions, our avoidance, our lack of discipline.

Pain becomes our identity.
Victimhood becomes familiar.
And growth feels like betrayal.

But distraction is still distraction, no matter how justified.

Minimal Potential Is Still Potential

Here’s the good news.

You don’t need talent.
You don’t need confidence.
You don’t need permission.

You just need ownership.

Some people are planted in rich soil. Others sprout through concrete. That doesn’t make the concrete-grown seed inferior—it makes it resilient.

Resilience isn’t something you’re born with. It’s something you earn by choosing discomfort over stagnation, again and again.

Maximizing minimal potential means this:

  • Working with what you have, not waiting for what you wish you had

  • Trading excuses for effort

  • Accepting that progress will be lonely and misunderstood

The Accountability Shift

Real change begins when you stop negotiating with yourself.

You stop saying:

  • “I’ll start when I feel ready.”

  • “I’ll try once things calm down.”

  • “This is just who I am.”

Instead, you ask:

  • “What’s the hardest honest thing I can do today?”

  • “What am I avoiding?”

  • “What standard am I willing to live by—even when no one is watching?”

That’s where accountability lives—not in motivation, but in daily self-confrontation.

Urgency Is a Gift

One of the most sobering ideas in this chapter is urgency.

Your dreams have expiration dates.
Your windows of opportunity close quietly.
And time does not negotiate.

Hope without action is gambling.
Waiting for clarity is procrastination.
Comfort is often disguised as patience.

Urgency doesn’t mean panic. It means respecting time enough not to waste it.

Becoming the One Who Sharpens Himself

There’s a romantic idea that growth happens best in groups—teams, mentors, communities. And while support helps, eventually everyone faces a moment when they’re alone with their standards.

No coach.
No applause.
No safety net.

That’s when character is forged.

Some people wait to be sharpened by others.
Others sharpen themselves.

They don’t stop at “better than before.”
They don’t settle for “good enough.”
They evolve relentlessly.

Final Thought

Maximizing minimal potential is not about proving anyone wrong. It’s about refusing to stay trapped by the lowest expectations placed on you—by others or by yourself.

You may have started behind.
You may still feel behind.
But you are not finished.

And the moment you take responsibility—not for what happened to you, but for what happens next—you stop being a product of circumstance and start becoming a force.

That’s where everything changes.

You have been preoccupied by bullshit for way too long. It’s time to switch your focus to the things that will slingshot you forward. 
#DistractingInjuries
#NeverFinished

From the book: Never Finished (Unshackle Your Mind and Win the War Within)
Chapter 1: Maximize Minimal Potential
By David Goggins, 2022

Rook Attack (Easy) - Find the position where we should place the rook, where Rook has maximum impact

Index of "Algorithms: Design and Analysis"

Try out the problem on Hacker Earth
Rook Attack

Given a N x M chessboard, where every cell has a value assigned to it denoted by 
 (Cell in the ith row from top and jth column from left).

Find the position (x, y) where we should place the rook, just the sum of cells which are under attack of rook is maximum possible. The rook can not attack the cell on which it is placed.

If there are more than one positions for (x, y), return the position where x is as minimum as possible. If there are more than one positions with equal x, return the position where y is as minimum as possible.

Note:

-> 1 based indexing is followed.
-> A rook can attack all the cells which are either in horizontal or vertical direction to the rook's position.
Input

First line contains two space separated integers denoting N and M.

Next N lines contains M space separated integers denoting the values of array A.

Output

Print two space separated integers x and y denoting the rook's position.

Constraints

1 ≤ N, M ≤ 10**6
1 ≤ A[i][j] ≤ 10**4

Sample Input
2 2
4 1 
3 1

Sample Output
1 2

Time Limit: 1.5
Memory Limit: 256
Source Limit:

Explanation

If we place the rook at (1, 2) position, the cells under attack of rook will be
(1, 1), (2, 2)
The sum of these cells will be
4 + 1 = 5, which is maximum possible.



Brute-Force Thinking....

Iterate through each cell of the chessboard and for each cell, calculate the sum of values in its row and column, excluding the value of the cell itself. Keep track of the maximum sum found and the corresponding position. If a new maximum is found, update the position. If the same maximum is found, check for the smallest row and column indices.


My Code:


N, M = map(int, input().split())

A = [list(map(int, input().split())) for _ in range(N)]

rowSum = [0] * N
colSum = [0] * M

for i in range(N):
    for j in range(M):
        rowSum[i] += A[i][j]
        colSum[j] += A[i][j]

best_score = -1
best_row = -1
best_col = -1

for i in range(N):
    for j in range(M):
        score = rowSum[i] + colSum[j] - 2 * A[i][j]

        # print("Score: ", score)
        # print(best_score, best_row, best_col)

        if score > best_score or (score == best_score and (i + 1 < best_row or (i + 1 == best_row and j + 1 < best_col))):
            best_score = score
            best_row = i + 1
            best_col = j + 1

print(best_row, best_col)

BEFORE I GIVE YOU CHATGPT'S THOUGHT: I WOULD LIKE TO TELL YOU THAT CHATGPT MADE A MISTAKE -- SAME MISTAKE REPEATEDLY WHEN TALKING ON A POINT ON MULTIPLE OCCASIONS

You see that when score is computed a cell: the value A[i][j] should be subtracted twice because it is counted in both rowSum and colSum. However, in the code and hints provided by ChatGPT, it is subtracted only once. 

So the correct formula for score should be:
score = rowSum[i] + colSum[j] - 2 * A[i][j

ChatGPT's Thoughts

Explain the solution to this problem in layman terms. Give clear descriptions of what this problem is trying to teach/assess, what design pattern, what problem solving trick/technique.

Let’s break this down slowly, simply, and intuitively, without jumping into code first.


๐Ÿงฉ What is the problem saying (in plain English)?

You are given a chessboard (a grid) where each cell has a number.

You want to place one rook on the board.

A rook:

  • Can attack all cells in the same row

  • Can attack all cells in the same column

  • ❌ Cannot attack the cell it is placed on

Your goal is to:

Choose the cell where placing the rook gives the maximum total sum of attacked cells.

If multiple positions give the same maximum sum:

  1. Choose the smallest row index x

  2. If still tied, choose the smallest column index y


๐Ÿง  What does the rook “collect”?

If you place the rook at position (x, y):

  • It collects all values in row x

  • Plus all values in column y

  • Minus A[x][y] (because it cannot attack its own cell)

So effectively:

score(x, y) = rowSum[x] + colSum[y] - A[x][y]

๐Ÿงช Sample intuition

Input:

4 1 3 1

Grid:

4 1 3 1

At position (1,2):

  • Row 1 sum = 4 + 1 = 5

  • Column 2 sum = 1 + 1 = 2

  • Subtract cell (1,2) = 1

Final score = 5 + 2 - 1 = 6

This is the maximum possible → answer is (1,2)


๐Ÿš€ The naรฏve (slow) way — and why it fails

A beginner might think:

  • For each cell, scan its entire row and column

  • Add values

That would cost:

mathematica
O(N + M) per cell Total = O(N * M * (N + M))

This is too slow for large grids (up to 10⁶ cells).


๐Ÿ’ก The key insight (problem-solving trick)

Instead of recalculating row and column sums again and again, we can:

Step 1: Precompute

  • The sum of every row → rowSum[]

  • The sum of every column → colSum[]

This takes only:

scss
O(N * M)

Step 2: For every cell (x, y)

Compute the score in constant time:

css
rowSum[x] + colSum[y] - A[x][y]

๐ŸŽฏ What this problem is really testing

1️⃣ Precomputation / Caching

  • Don’t repeat work

  • Compute row and column sums once

  • Reuse them

2️⃣ Optimization mindset

  • Replace nested loops with smart preprocessing

  • Turn an expensive operation into O(1)

3️⃣ Grid / Matrix reasoning

  • Understanding how rows and columns interact

  • Applying formulas on 2D data

4️⃣ Tie-breaking logic

  • Careful comparison logic

  • Attention to problem constraints


๐Ÿง  Design pattern used

Precomputation + Enumeration

This is a very common competitive-programming pattern:

  1. Precompute useful aggregates (row sums, column sums)

  2. Enumerate all candidates efficiently

  3. Track the best result with tie-breaking rules


๐Ÿ“Œ Why subtract A[x][y]?

Because:

  • The rook cannot attack the cell it is placed on

  • That cell is included once in the row sum

  • And once in the column sum

  • So we subtract it once


๐Ÿง  One-sentence intuition

“For each cell, imagine the rook collects everything in its row and column — so we just need fast access to row and column totals.”


๐Ÿ Final takeaway

This problem teaches you:

  • How to optimize brute-force ideas

  • How to think in terms of precomputation

  • How to handle 2D data efficiently

  • How to apply clean mathematical reasoning to grids

If you want next:

  • Full Python code

  • Dry-run with a larger example

  • How this idea appears in other problems

  • Space/time complexity discussion

Just tell me ๐Ÿ˜Š



Full Python code

Here is the full, correct, and efficient Python solution, followed by a clear explanation of how it works.


✅ Full Python Code

python
def solve(): import sys input = sys.stdin.readline N, M = map(int, input().split()) A = [list(map(int, input().split())) for _ in range(N)] # Step 1: Compute row sums and column sums row_sum = [0] * N col_sum = [0] * M for i in range(N): for j in range(M): row_sum[i] += A[i][j] col_sum[j] += A[i][j] # Step 2: Find the best position max_score = -1 best_x = 1 best_y = 1 for i in range(N): for j in range(M): score = row_sum[i] + col_sum[j] - A[i][j] if (score > max_score or (score == max_score and (i + 1 < best_x or (i + 1 == best_x and j + 1 < best_y)))): max_score = score best_x = i + 1 # convert to 1-based indexing best_y = j + 1 print(best_x, best_y)

๐Ÿง  Explanation in Simple Terms

What are we doing?

We want to place a rook on a chessboard-like grid so that the sum of values it can attack (same row + same column) is as large as possible.

The rook:

  • Attacks everything in its row

  • Attacks everything in its column

  • ❌ Does NOT attack the cell it sits on

So for any cell (i, j):

ini
score = rowSum[i] + colSum[j] - A[i][j]

Step-by-step logic

๐Ÿ”น Step 1: Precompute row and column sums

Instead of re-adding rows and columns repeatedly (which would be slow), we compute:

  • Sum of each row

  • Sum of each column

This makes later calculations very fast.


๐Ÿ”น Step 2: Try placing the rook at every cell

For every position (i, j):

  • Compute the attack sum using the formula above

  • Keep track of the maximum score found

  • If scores tie:

    • Pick the smaller row index

    • If still tied, pick the smaller column index


⏱️ Time & Space Complexity

MetricComplexity
TimeO(N × M)
SpaceO(N + M)

This is optimal for a grid with up to 1 million cells.


๐Ÿงฉ What this problem teaches

  • Precomputation optimization (don’t repeat work)

  • 2D grid reasoning

  • Greedy comparison with tie-breaking

  • Turning brute force into efficient code


๐Ÿ Final intuition (one line)

“Precompute row and column totals, then test every cell using a constant-time formula.”

If you want:

  • A dry-run example

  • A version using less memory

  • Similar problems to practice

  • Visualization

Just say the word ๐Ÿ™‚



I am super excited to see GPT 5.2 make mistakes and hallucinate... so capturing a couple of screenshots below: