Discussion on: Number of Steps (Basics of Input/Output) - Medium Level Problem

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All Tracks>Basic Programming> Input/Output> Basics of Input/Output> Problem

Try Out The Problem Here

how to solve this problem and what does this problem teach me?

Good question — this is a non-trivial problem that combines number theory and greedy reasoning. Let’s break it down step by step.

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🔍 Problem Restated in My Words

We have two arrays:

• a = [a1, a2, ..., an]

• b = [b1, b2, ..., bn]

Operation:
You can replace ai with ai - bi if ai ≥ bi.
You need to make all a[i] equal (same value) using the fewest steps.
If it’s impossible → output -1.

---

✨ Key Observations

1. Operation is like repeated subtraction (Euclidean Algorithm flavor):

   • ai = ai - bi behaves like reducing a number step by step by a fixed amount.

   • Example: if ai = 10, bi = 4, repeated operation gives sequence 10 → 6 → 2.

   Essentially:

   rust
   final value ≡ ai (mod bi)
   

   Because subtracting multiples of bi keeps remainder fixed.

2. For all a[i] to be equal:

   • Their final values must be the same.

   • This means: all ai % bi must equal the same target (otherwise impossible).

3. Checking feasibility:

   • If any ai % bi is different from the others, return -1.

4. Counting steps:

   • If feasible, steps for ai = (ai - target) / bi.

   • Total steps = sum of all steps for all i.

---

✅ Solution Strategy

1. Compute rems = [ai % bi for each i].

2. If all remainders are not equal → return -1.

3. Pick this common remainder target.

4. Compute steps:

   ini
   steps = sum((ai - target) / bi for each i)
   

5. Return total steps.

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🔎 Walkthrough with Example

Example 1:

ini
n = 2
a = [5, 64]
b = [4, 3]

• Remainders: [5 % 4 = 1, 64 % 3 = 1] ✅ equal.

• Target = 1

• Steps: (5-1)/4 = 1, (64-1)/3 = 21 → total = 22

But sample output says -1. Why?
Because second ai = 64 cannot reduce to 1 without first hitting negative values?
Let’s check:
64 → 61 → 58 → ... → 1. Works fine.
So here maybe sample assumes remainders must match AND the reduced values must match exactly. If a[i] < target case occurs → impossible. That’s why careful checking is needed.

---

Example 2:

ini
n = 5
a = [5, 7, 10, 5, 15]
b = [2, 2, 1, 3, 5]

• Remainders: [1, 1, 0, 2, 0] → not all same. ❌
  But solution gives 8. Meaning: you can adjust differently and still converge.

So actually, the insight is:

• You need to check if there exists some final value F that all ai can reach via allowed subtractions.

• That’s equivalent to requiring that ai - F is divisible by bi.

Thus:

• Choose one a[j] as candidate final value F.

• Check if (ai - F) % bi == 0 for all i.

• Minimize steps over all possible candidates.

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📘 What This Problem Teaches You

1. Modular arithmetic reasoning: You learned that operations like subtracting multiples of b preserve the modulo class.

2. Feasibility check: It’s not enough to just brute force; you need to reason about remainders and divisibility.

3. Optimization mindset: Instead of simulating step by step, you derive a formula for the steps.

4. Greedy vs Number Theory: This blends greedy subtraction logic with number theory (remainder classes).

---

👉 In summary:

• It teaches you to translate repeated subtraction into modular constraints.

• It also teaches you how to check feasibility (common final value exists).

• It trains you to optimize without brute force.

---

Do you want me to write a step-by-step pseudocode (and then Python solution) that implements the exact logic?

---

yeah, sure

Perfect 👍 Let’s carefully build this up into pseudocode and then a clean Python solution.

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🔎 Step-by-Step Pseudocode

python
function min_steps(n, a[], b[]):
    steps = infinity

    # Try every a[j] as a candidate final value F
    for j from 1 to n:
        F = a[j]
        possible = True
        total_steps = 0

        for i from 1 to n:
            if a[i] < F:
                possible = False
                break
            if (a[i] - F) % b[i] != 0:
                possible = False
                break
            total_steps += (a[i] - F) / b[i]

        if possible:
            steps = min(steps, total_steps)

    if steps == infinity:
        return -1
    else:
        return steps

---

✅ Python Implementation

python
def min_steps(n, a, b):
    min_steps = float("inf")

    for j in range(n):  # try each a[j] as candidate final value
        F = a[j]
        possible = True
        total = 0

        for i in range(n):
            if a[i] < F:  # can't reduce below F
                possible = False
                break
            if (a[i] - F) % b[i] != 0:  # must be reachable
                possible = False
                break
            total += (a[i] - F) // b[i]

        if possible:
            min_steps = min(min_steps, total)

    return -1 if min_steps == float("inf") else min_steps


# Example Usage:
print(min_steps(2, [5, 64], [4, 3]))      # → -1 (sample)
print(min_steps(5, [5, 7, 10, 5, 15], [2, 2, 1, 3, 5]))  # → 8 (sample)

---

💡 Explanation of Approach

• The operation preserves ai mod bi.

• Therefore, each ai can only become values of the form ai - k*bi.

• To make all a’s equal to the same final value F, we need:

  • F ≤ ai

  • (ai - F) % bi == 0 for all i

• By trying every ai as a candidate F, we ensure we don’t miss any possible final value.

• Then, for each candidate, compute steps needed and choose the minimum.

---

Would you like me to also show the step-by-step reduction process for one of the sample cases (like [5,7,10,5,15] with [2,2,1,3,5]) so you can visualize how the 8 steps happen?

Number of Steps (Basics of Input/Output) - Medium Level Problem

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All Tracks>Basic Programming> Input/Output> Basics of Input/Output> Problem

Try Out The Problem Here

You are given two arrays 
 and 
. In each step, you can set 
 if 
. Determine the minimum number of steps that are required to make all 
's equal.

Input format

First line: 
 
Second line: 
Third line: 
Output format

Print the minimum number of steps that are required to make all 
's equal. If it is not possible, then print -1.

Constraints

 

Sample input

25 64 3

Sample output

-1

Sample Input
5
5 7 10 5 15
2 2 1 3 5
Sample Output
8

My Solution

def min_steps(n, a, b):
    min_steps = float("inf")

    for j in range(n):  # try each a[j] as candidate final value
        F = a[j]
        possible = True
        total = 0

        for i in range(n):
            if a[i] < F:  # can't reduce below F
                possible = False
                break
            
            if b[i] != 0 and (a[i] - F) % b[i] != 0:  # must be reachable
                possible = False
                break
            
            if b[i] != 0:
                total += (a[i] - F) // b[i]
            else:
                total += 0

        if possible:
            min_steps = min(min_steps, total)

    return -1 if min_steps == float("inf") else min_steps


n = int(input())
A = list(map(int, input().split()))
B = list(map(int, input().split()))

# Example Usage:
print(min_steps(n, A, B))

---

View The Test Cases

Falsely Accused - The Zen Master's Story

All Buddhist Stories

Hakuin was a Zen teacher, known for his quiet way of living. 
He didn't seek attention. But over time, people began to admire him. 
Some said he was a man of deep wisdom. 
Others just said he seemed settled. He never argued with either. 
In the same town, a teenage girl lived with her parents. One day, they discovered she was pregnant. 
They demanded to know who the father was, but she refused to speak. She was afraid. 
Her parents were furious. They went straight to Hakuin and accused him to his face. He listened. Then calmly he said, "Is that so?" 
That was all. 
Finally, under pressure, she gave them a name. She said, "Hakuin." 
Word spread. 
People stopped speaking to him. His reputation disappeared overnight. No more visitors, no more gifts at the door, no more respectful greetings. 
Hakuin didn't complain. He kept going. 
When the child was born, the parents brought the baby to him. They said, "Take it, this is your responsibility now." 
Hakuin accepted the child. 
He asked no questions. He fed the baby, kept him warm, and raised him without comment. 
Neighbors watched, unsure what to believe. 
A year passed. One morning, the young woman came back. Her head was down. Her parents were with her. "I lied," she said, her voice small. 
"I was scared. I gave them your name to protect myself. I'm sorry." 
The parents asked for forgiveness. They asked if they could take the child back. 
Hakuin didn't scold them. He didn't explain anything. He handed them the child. Is that so? 

And that was the end of it. You see in life, in your life, there will be moments when the world misunderstands you, when judgment falls unfairly, and your truth feels unseen. But like Hakuin, you can choose to meet it with calm, to hold your peace without needing to fight for or explain for. Because true strength lies not in changing others, but in staying steady within yourself. And the truth can take care of itself. 
Thank you so much for reading and stay blessed.

What are the key lessons from this story?

Of course. This powerful Zen story about Master Hakuin offers several profound lessons for navigating life's challenges.

Here are the key takeaways:

---

## Equanimity and Non-Reaction

The core of the story lies in Hakuin's simple, repeated response: "Is that so?". This isn't a statement of agreement or denial. It is a profound act of equanimity—a state of mental calmness and composure in the face of difficulty.

• Lesson: Instead of reacting with anger, fear, or defensiveness when faced with accusations or dramatic events, we can choose to simply observe the situation without getting emotionally entangled. We acknowledge the other person's reality ("Is that so?") without letting it dictate our own inner peace.

---

## Detachment from Reputation and Ego

Hakuin's reputation was destroyed overnight, yet his behavior didn't change. He didn't fight to clear his name or lament his lost status. This shows that his sense of self-worth was not tied to the opinions of others.

• Lesson: True strength comes from an internal foundation, not external validation. Praise and blame are temporary and often based on incomplete information. Attaching your identity to what others think of you makes you vulnerable; finding peace within makes you resilient.

---

## Compassion and Responsibility in Action

When the baby was brought to him, Hakuin didn't argue or complain about the injustice. He saw a being in need and took compassionate action. He accepted the responsibility placed upon him and cared for the child with diligence.

• Lesson: True wisdom isn't passive. It's about responding to the needs of the present moment with compassion, regardless of the circumstances. Instead of getting lost in "what should be," Hakuin focused on "what is" and did what was necessary.

---

## The Power of Letting Go

Just as easily as he accepted the child, Hakuin handed him back when the truth was revealed. There was no bitterness, no "I told you so," no demand for apology. He held onto nothing—not the injustice, not his reputation, and not even the child he had cared for.

• Lesson: Non-attachment is a key to freedom. By letting go of grievances, a need to be right, and even positive attachments, we can move through life with greater peace and flexibility.

---

## Trusting That Truth Will Prevail

Hakuin never felt the need to defend himself. He lived his truth quietly and consistently. In the end, the external truth aligned with his internal integrity without him having to force it.

• Lesson: Living with integrity is its own defense. While it's not always easy, having faith that the truth will eventually surface can free you from the exhausting battle of trying to manage everyone's perception of you. 🙏

The Coming White-Collar Recession

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Summary

- Today, we take a look at the improving outlook for the blue-collar workforce, which has struggled for decades.
- In addition, the AI revolution has the potential to be more disruptive to the white collar workforce than any paradigm shift in U.S. history.
- These trends could have substantial impacts on the economy, the job markets, housing, and commercial real estate in the quarters and years ahead.
- We examine the potential ramifications of the shifting prospects for these two key job demographics in the paragraphs below.
- Looking for a helping hand in the market? Members of The Biotech Forum get exclusive ideas and guidance to navigate any climate.



Today, we are going to time warp ourselves back 35 years. The year is 1990. The Berlin Wall had just fallen in November of the previous year. The long Cold War was rapidly coming to an end, and Americans were looking forward to spending the long-promised "peace dividend."

Globalization was soon put on steroids as the Cold War came to a close. NAFTA was signed at the very beginning of 1994, an agreement Ross Perot stated would cause a "giant sucking sound" as manufacturing jobs fled the United States. Something that in retrospect, seems at least prescient. This globalization wave accelerated even further as China was welcomed to the World Trade Organization in late 2001, a few months after 09/11.



What followed was a huge reduction in manufacturing employment across the United States. Much of the Midwest and other regions of the country like Pennsylvania were turned into the Rust Belt. The loss of millions of relatively high-paying blue-collar jobs is one of many factors driving increasing wealth inequality over recent decades in the U.S. and has also been a factor in increasing political polarization in the country.



Well, it seems history is not without an appreciation for irony. An inflection point is on the horizon that few are discussing. The prospects for the blue-collar workforce appear to be improving. The new administration is quite focused on reshoring manufacturing back to the States. Towards that end it has implemented the biggest hike in tariffs on imports in generations.



This is resulting in a huge and much-needed surge of tariff revenues into the U.S. Treasury. In addition, a rash of huge multinational companies have announced significant expansions to plans to add manufacturing capacity in the United States in 2025. A partial list follows below.



In addition, hundreds of billions of dollars are being allocated to build massive AI Data Centers for the likes of Amazon.com, Inc. (AMZN), Meta Platforms, Inc. (META), Alphabet Inc. (GOOG) (GOOGL) and Microsoft Corporation (MSFT). This is creating jobs for tens of thousands of positions for construction workers, electricians, plumbers, carpenters, pipefitters, HVAC personnel, etc.



This huge construction boom should also significantly boost the economic prospects of states with access to low-cost and abundant natural gas supplies as this will be the primary source of delivering the massive amounts of electricity these facilities demand. This is why states like Texas, Pennsylvania and Louisiana have garnered huge new AI data center projects. This is also triggering a renaissance for the nuclear utility industry. A proposed new $25 billion AI data center in the panhandle of Texas could end up hosting the nation's largest nuclear energy site.



Then, there are large numbers of recent migrants who are leaving the country in 2025. Some 1.6 million of which have left the United States year to date, mostly via self-deportation. All things being equal, this should open up new blue-collar jobs in industries like home building, which has been heavily dependent on this labor source. These trends could provide a large boost to vocational education across the nation.

In contrast, the prospects for the white-collar workforce are noticeably dimming. The AI Revolution has a high likelihood of displacing workers at a faster rate than any paradigm shift in history. If AI delivers the productivity improvements projected, it will result in millions and millions of job losses. In addition, almost all of these job reductions will happen in the white-collar workforce.



Among the jobs most likely to be reduced or eliminated are sales and customer service representatives, entry-level research and financial analysts, legal and office assistants and even software programmers. A recent Federal Reserve Bank of New York survey found that 6.1% of computer science grads are out of work as are 7.5% of computer engineering grads. These are among the highest unemployment rates for all college majors.

For decades, much of the younger generation as well as displaced employees were told to "learn to code" to achieve job security. With the development of AI, that is no longer the case. It is now getting to the time of the year when corporate managements are starting to huddle to map out budgets and core priorities for 2026. How many of those planning sessions will be around major pushes to integrate more AI into operational and business processes? My guess is a high percentage, and those targeted productivity pushes will result in considerable job losses in 2026, in my opinion.



A recent small business blog survey offered up the following predictions (above). Another similar exercise in July had some of the following findings.



So, the $64,000 question for the economy and the markets is will new jobs be created fast enough to offset the massive job losses driven by AI in the years ahead? I am not one to doubt American ingenuity. However, it is hard for me to fathom new job creation being close to sufficient to replace job losses from AI in the years ahead if predictions come anywhere close to coming to fruition.

That means the unemployment rate is likely to tick up significantly in the coming quarters. This is going to particularly impact the younger generations of white-collar workers given that AI will significantly reduce entry-level positions. And this is a generation already struggling with massive student loan debt, whose payments have recently been restarted after a four-year taxpayer hiatus. Already, student loan delinquency rates are surging, recently hitting 12.9% and credit scores for millions of individuals with student loans are falling.



Accelerating white collar job losses, falling credit scores and rising delinquency rates are hardly supportive of demand for large-ticket items like vehicles and discretionary travel. It is also another headwind for the rapidly deteriorating housing market which I covered again in an article earlier this week.



If white collar jobs are displaced by AI and they cannot be replaced at nearly the same pace, it could trigger an overall recession in 2026 or 2027. It also could be the death knell for many office properties, one of several sub-sectors of the CRE space that are already struggling mightily.

Ref

Tags: Politics,Layoffs,Finance,Technology,

GST, Relief, and Eight Years of Silence

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Hello, I’m Ravish Kumar.

Today we talk about two stories: one is about a government decision, the other about the discussion it provokes. For the first time in eight years, the Modi government has admitted—though indirectly—that GST (Goods and Services Tax) hurt the people. That inflation crushed households. That savings have dropped to historic lows.

The same GST that was once defended as a “reform” has now been quietly rolled back in scope. Rates have been reduced, slabs simplified. The government calls it relief. Posters and press releases celebrate: “Your daily expenses will now be cheaper.” But let’s not forget—yesterday the same taxes were called “reform,” while Rahul Gandhi called it “Gabbar Singh Tax.”

So what was it all these years? Relief today means exploitation yesterday.

---

Eight Years of Denial

From Arun Jaitley to Nirmala Sitharaman, the government defended the GST structure tooth and nail. They told us multiple tax slabs were necessary. They claimed high rates were justified. And anyone who questioned it was mocked or silenced.

But the results were plain: small shopkeepers crushed by compliance, medium businesses suffocated by paperwork, households drained of every rupee. Inflation soared while incomes stagnated. People skipped health insurance because 18% GST made premiums unaffordable. Parents struggled with school supplies because even pencils, erasers, and notebooks were taxed.

Now, after eight years, the government accepts what was obvious to millions: GST hurt the people.

---

The Politics of “Relief”

The GST Council has now reduced the slabs from six to two main ones: 5% and 18%. A third, 40%, remains for luxury and sin goods. Items from soap to bicycles, from baby diapers to health insurance, have been shifted to lower brackets.

The government calls it “a Diwali gift.” But gifts come from generosity—this is merely undoing harm.

If this relief was possible now, why not earlier? Why did people have to pay 12% GST on school notebooks for eight years? Why did parents pay 18% on shampoo and toothpaste, while the government defended it? Why did it take almost a decade to acknowledge the obvious—that ordinary people were being looted in the name of reform?

---

The Opposition’s Loneliness

Throughout these eight years, Rahul Gandhi was mocked for calling GST “Gabbar Singh Tax.” He repeatedly demanded simplification and a cap at 18%. In 2016, he warned that anything higher would crush the poor and the small trader. He was ridiculed by the media, attacked by IT cells, dismissed as ignorant.

Yet here we are, eight years later, doing exactly what he proposed.

So the question is: was Rahul Gandhi wrong, or was the government arrogant?

---

The Real Cost

Relief today doesn’t erase the suffering of the past. Countless households emptied their savings. Small businesses folded. Farmers and workers bore the brunt of inflation. Parents cut corners on children’s education and health. Gig workers, already on fragile incomes, spent their lives paying high GST on fuel and bikes—only now being told that the rates will drop.

This isn’t a “gift.” It’s an admission that people were wronged for eight years.

---

Beyond the Numbers

When you read headlines saying “daily items to get cheaper,” remember: if it’s cheaper now, it was unjust then. If the government celebrates lowering GST on health insurance, it means it knowingly burdened the sick and the elderly for years.

Every poster celebrating “relief” is also proof of past failure.

---

Conclusion

So, should the people celebrate? Perhaps cautiously. Yes, lower GST will bring some ease. But the larger truth remains: for eight long years, an arrogant government defended the indefensible. And only when forced by politics, economics, and pressure, did it accept what millions already knew.

Relief today is not generosity. It is simply a correction of a wrong.

And in history, we must remember who defended the wrong—and who dared to call it out.

Can the Elephant and the Dragon Dance Together?

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Hello, this is Ravish Kumar. So, are India and China ready to dance together? Recently, Chinese President Xi Jinping remarked that the “elephant and the dragon can dance together.”

Now, in politics, the metaphor of dance has many shades. Sometimes, it means: who is dancing on whose tune? Who is pulling the strings? That kind of dance is unhealthy. The real dance worth celebrating is one where both partners appear equal—where the steps are in balance, where dignity and respect are intact.

In October 2024, Prime Minister Modi met Xi Jinping in Kazan, Russia. Later, he visited Beijing after seven years. Yet, the border tensions that erupted in Eastern Ladakh five years ago remain unresolved. Reports suggest that more than 50,000 soldiers remain stationed on both sides. Strikingly, the Prime Minister avoided speaking about the border issue directly. India rarely calls out China openly—be it about Doklam (2017) or Galwan (2020). Instead, we hear routine lines about “maintaining peace and stability” on the border. But is that enough?

Meanwhile, trade paints a very different picture. Since 2020, India’s dependency on Chinese imports has only grown. The trade deficit stands at nearly $100 billion. India buys, China sells. But what exactly does India produce that China must buy? The imbalance continues because India cannot yet find alternatives to Chinese products. This proves that trade flows smoothly even when strategic ties are strained.

The real question is: after the SCO meeting, has anything fundamentally changed between India and China? Is there any new sense of parity that makes it look like two equals preparing to dance gracefully, mesmerizing the world as the “elephant and dragon” twirl together?

Xi may invoke this poetic image, but his actual dance partner remains Pakistan. He pulls the strings there with ease. Russia, too, continues to openly call Pakistan a “traditional friend.” During the SCO summit, while Modi’s photo-ops with world leaders made headlines in India, pictures of Xi, Putin, and Pakistan’s Prime Minister Shehbaz Sharif told a parallel story.

Back home, pro-government media celebrated the summit as a triumph. But the truth is murkier. India refrains from naming China when it comes to terrorism, even though China continues to shield Pakistan in global forums. For instance, after the Pahalgam attack, India highlighted the “condemnation” of terrorism in joint statements as a victory. But in the same breath, terror attacks in Pakistan, like the Jafar Express bombing, were also condemned. Whose victory was that?

The contradictions run deep. Modi says India and China are “victims of terrorism.” But when exactly was China a victim? When has it suffered terror attacks like India? These vague equivalences only blur the truth.

And while Modi emphasizes “strategic autonomy” and insists relations should not be seen through a third country’s lens, the reality is clear: China won’t abandon Pakistan. India won’t name China. The stalemate continues.

All the while, optics dominate. Viral photos, hugs, and handshakes flood the headlines. Yet, significant absences remain unspoken. For instance, India’s Foreign Minister S. Jaishankar did not travel with the delegation—officially due to “health reasons.” But his absence from key bilateral talks with Xi and Putin was glaring. It reminded me of the 1990s when even a seriously ill Foreign Minister, Dinesh Singh, was flown in a wheelchair to Tehran to secure Iran’s support for India at the UN. That was diplomacy at work, beyond optics.

Today, however, diplomacy risks being reduced to photo opportunities. China pushes its dominance through platforms like the SCO, much like India once did with SAARC. But where is SAARC today? Forgotten.

The bottom line: if the elephant and the dragon must dance, the rhythm must come from trust, balance, and equality. A dance partner is not someone you control with your fingers but someone you move in harmony with. Xi Jinping may speak of such a dance, but is he really offering one? Or is he simply reminding India of an invitation while twirling Pakistan in the meantime?

Until India calls out the contradictions and demands real parity, the so-called “dance” risks remaining nothing more than a performance staged for the cameras.

Locked Out of Knowledge: India’s Research Crisis After the Sci-Hub and LibGen Ban

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5 Key Takeaways

• Sci-Hub and LibGen have been blocked in India following a Delhi High Court order, impacting access to academic resources.
• Researchers in India and the Global South heavily rely on these 'shadow libraries' due to limited and unreliable institutional access.
• Scholars often resort to informal networks, social media, and friends abroad to obtain necessary research materials.
• Physical and digital library access is frequently inadequate, especially for independent researchers, those with disabilities, or scholars away from campuses.
• The current publishing system restricts knowledge through paywalls and gatekeeping, while authors and peer reviewers receive little compensation.

When Sci-Hub and LibGen Are Blocked: The Struggle to Access Research in India
If you’re a student or researcher in India, you’ve probably heard of Sci-Hub and LibGen. These websites have been lifesavers for many of us, offering free access to academic papers and books that are otherwise locked behind expensive paywalls. But recently, the Delhi High Court ordered internet providers to block these sites, following a lawsuit by big publishing companies. So, what does this mean for researchers like me—and for the future of learning in India?
Why Sci-Hub and LibGen Matter
Let’s be honest: getting access to academic material in India isn’t easy. University libraries often have limited collections, and their online resources can be patchy or full of technical glitches. Many important journals and books are simply out of reach unless you pay a hefty fee. That’s where Sci-Hub and LibGen came in. They acted like giant virtual libraries, letting us download research papers and books for free. For many PhD students, these sites were mentioned in thesis acknowledgements right alongside traditional libraries.
The Reality of Research in India
Most Indian researchers don’t have the same access as their peers in the US or Europe. Even if you’re at a top university, you might find that your library doesn’t subscribe to the journal you need, or that your access is unreliable. If you’re an independent scholar, or between degrees, it’s even harder. During the pandemic, when physical libraries were closed, online access became even more crucial—but often, it just didn’t work.
To get around these barriers, we’ve developed creative solutions. We share articles in social media groups, ask friends abroad to download papers for us, and spend hours searching for alternative links or “mirrors” of blocked sites. It’s a time-consuming and frustrating process, but it’s often the only way to get the information we need.
Is This Really Piracy?
Some people argue that using sites like Sci-Hub is piracy. But here’s the thing: the authors of academic papers usually don’t get paid for their work. They write and review articles for free, while publishers charge high prices for access. This system locks knowledge behind paywalls, making it harder for researchers in countries like India to do their work.
What’s the Solution?
There have been talks of a “One Nation, One Subscription” policy to give all Indian institutions access to journals, but it’s not fully in place yet. Even if it happens, it won’t cover everything—especially rare or out-of-print books that only sites like LibGen provide.
Until we fix the system, blocking Sci-Hub and LibGen just makes life harder for researchers. Knowledge should be a public good, not a privilege for the few who can afford it.

Read more