Sunday, November 8, 2026

Comparisons of the extreme

In a cryogenic engine shaft rotates with 40k rpm, to compare that I have a statistic like 36000 rpm means 600 rotations every second, just so that 40k rotations could be imagined, Are there other instances where figures and statistics go to such extremes that we would struggle to imagine those numbers in an ordinary way. Another example I can give is UPI payments crossing nearly 30 lakh rupees per month can be imagined as 1 lakh crore worth transactions every day, another example is from population increase in India between 2001 and 2010 was 182 million meaning 1.8 crore increase every year including 2.4 crore births and 60 lakh deaths..

ChatGPT:

What you are describing is a fascinating way of making unimaginably large or small numbers humanly comprehensible.

I would call it statistical imagination or making scale tangible.

The trick is to take a figure that our brains cannot intuitively process and translate it into a different dimension: time, distance, frequency, population, everyday activity, or something we can physically picture.

Your examples are particularly good because they use three different techniques:

Frequency conversion: 36,000 RPM becomes 600 rotations per second.
Financial scale conversion: Monthly transaction value becomes a daily economic flow.
Demographic scale conversion: A decade's population increase becomes an annual addition, births and deaths.

There are several other fascinating examples.

1. Engineering: Numbers that move faster than our imagination

Practice for the German F1 Grand Prix

Formula 1 engine

At 15,000 RPM, an engine completes 250 revolutions every second.

That's one revolution every 4 milliseconds. Imagine trying to count them!

How Far Can a Bullet Travel?

A bullet travelling at 1,000 m/s

In one second, it travels one kilometre. In the time it takes to blink (100 milliseconds), it travels 100 metres.

ESMC: Joint Venture mit TSMC feiert Spatenstich der Chip-Fabrik in Dresden - Notebookcheck.com News

A processor running at 5 GHz

Five billion clock cycles every second. During one ordinary second, it experiences as many clock cycles as there are seconds in approximately 158 years.

International Space Station News - NASA

International Space Station

Travels at roughly 28,000 km/h. It circles Earth approximately 16 times every day, or once every 90 minutes.

2. Astronomy: Distances and time scales

VIDEO: NASA presunula obriu raketu na rampu. Rozhodujúca fáza pre návrat ľudí k Mesiacu sa začala - Vesmír - Veda a technika - Pravda

Speed of light

Light travels approximately 300,000 km every second.

That's roughly 7.5 trips around Earth's equator in a single second.

UPSC Prelims 2007 | Geography: what is the average distance (approximate) between the Sun and the Earth?

Distance from Earth to the Sun

Approximately 150 million kilometres.

Even travelling at the speed of light, the journey takes about 8 minutes 20 seconds.

MilkyWay 2 by Santos Gonzalez | Fstoppers

Age of the universe

Approximately 13.8 billion years.

If the entire history of the universe were compressed into one calendar year, human civilisation would occupy only the final few seconds.

3. Human biology: Incredible activity inside our bodies

An hand drawn illustration of anatomy of the human heart with indicated major parts. Vector illustration in cartoon style

Heartbeats

At an average of 70 beats per minute, your heart beats approximately 100,800 times a day.

That's around 36.8 million beats in a year.

Normal red blood cells

Red blood cells

An adult human has roughly 20–30 trillion red blood cells circulating in the body.

Imagine counting them at one million cells per second. Even then, counting 25 trillion would take approximately 289 days.

1902 Best Science Images Generated by Flux AI | Science

DNA inside a human body

If the DNA from all the cells containing a nucleus were stretched out end to end, its total length would be of the order of tens of billions of kilometres.

That's an astronomical amount of information packed into something microscopic.

4. Economics and society: Numbers that become more meaningful when divided by time

These are particularly close to your UPI and population examples.

Statistic	Convert it into something tangible
1 million seconds	11.57 days
1 billion seconds	31.7 years
1 trillion seconds	31,700 years
₹1 lakh crore per day	₹1.16 crore every second
1 crore transactions per day	About 116 transactions every second
2.4 crore births per year	About 66,000 births every day, or 46 every minute

The first three are especially powerful.

A billion seconds is approximately 31.7 years. This is an excellent way to explain why a billion and a million are not merely different by three zeroes.

5. Computing and communication: The invisible scale

File:Fiber Optical Cable.jpg - Wikimedia Commons

Internet data transmission

A 1 Gbps connection can theoretically transmit 125 million bytes every second.

In one minute, that's 7.5 GB of data.

PROEM SECURITY S.A.S. | Seguridad electrónica en Bogotá y Barranquilla

A petabyte of storage

One petabyte is approximately one million gigabytes.

At 100 GB per day, it would take about 27 years to consume one petabyte.

Digital Microprocessor. Computer Controller Circuit Board closeup Main Central Processing Unit Electronic Chips with Data Signal Lane.

A billion operations

At one million operations per second, a billion operations take 1,000 seconds, or 16 minutes 40 seconds.

The difference between millions and billions becomes much easier to appreciate.

6. Some comparisons that are particularly good for teaching

These are examples where the choice of comparison makes all the difference.

Large number	A more imaginable comparison
1 million minutes	Approximately 1.9 years
1 billion minutes	Approximately 1,900 years
1 trillion minutes	Approximately 1.9 million years
1 million metres	1,000 kilometres
1 billion metres	1 million kilometres
1 million people	A city-sized population
100 billion neurons	Roughly the number of neurons in a human brain
1,000 tonnes	The mass of roughly 200 adult elephants (at 5 tonnes each)

7. Let's revisit your original examples

I particularly like your approach to the Indian population statistics.

India's population increase, 2001–2011

18.2 crore

Approximately 182 million additional people over the decade

2.4 crore

Annual births (approximate)

60 lakh

Annual deaths (approximate)

1.8 crore

Annual natural increase

~50,000

Net additions per day

Rounded illustrative figures. The actual decadal population change also reflects migration and differences in annual birth and death rates.

One small clarification about UPI: I assume you mean approximately ₹30 lakh crore per month, rather than ₹30 lakh. At that monthly value, the daily average would indeed be approximately ₹1 lakh crore.

And your cryogenic engine example has an interesting real-world connection: ISRO documented a 36,000 RPM turbopump in its C25 cryogenic engine. That is precisely 600 revolutions per second.

Press Information Bureau+1

For 40,000 RPM, the equivalent is approximately 667 revolutions per second.

8. An interesting idea: Build a library of human-scale comparisons

I think there is an educational concept here worth developing.

Instead of presenting statistics as numbers, present them as translations.

For example:

Speed: How many kilometres does something travel in the time it takes to blink?
Frequency: How many times does something happen during one second?
Money: How much money changes hands every second?
Population: How many people are added every minute?
Computing: How long would an ordinary computer take to perform the same work?
Astronomy: How long would it take to travel that distance using familiar transportation?
Time: What would happen if we compressed a million years into one day?

The deeper principle is that humans are much better at understanding comparisons than isolated numbers.

A number such as 40,000 RPM is an engineering specification. But 667 rotations every second is something we can begin to imagine.

Perhaps the most important skill in communicating science, technology and economics is not presenting bigger numbers, but finding better ways to make people understand them.

Next Prompt:

Can you focus on payments, economics and computers as I liked those three more.. and give some more examples like country comparisons between India and America in particular, an average American earns 56k dollars an average Indian does about $2800 and take another representative country like Nigeria from Africa to compare with India. Focus in current trends and which one's are going to change rapidly

ChatGPT... went about answering with interesting tidbits.

The Technological Monoculture: Are We Building a Forest of Identical Trees?

Is something fundamentally wrong with the way humans approach progress?

We seem to have an extraordinary ability to invent things, but a rather unfortunate tendency to surrender our understanding of them once they become convenient.

Consider computers.

What began as an exciting landscape of experimentation, competing architectures and possibilities gradually evolved into a largely standardised personal computing ecosystem. Mass production made computers affordable and accessible, undoubtedly one of the greatest technological achievements of our time. But somewhere along the way, we increasingly became consumers of computing rather than participants in it.

We stopped asking how things worked and became more interested in what was available off the shelf.

Now, with the arrival of Artificial Intelligence, we appear to be repeating the same pattern, perhaps on a much larger scale.

Everyone seems eager to climb the same enormous technological tree. A handful of platforms, models, cloud providers and ecosystems are attracting an extraordinary concentration of users, developers, businesses and capital.

But what happens when that tree falls?

Imagine a forest where every tree is genetically identical, has the same root structure, depends on the same water source and grows under the same conditions. It might look incredibly efficient. It might even outperform a diverse forest under favourable circumstances.

But introduce a disease, a pest or an environmental shock, and the entire forest becomes vulnerable.

Technological ecosystems are not very different.

When everyone depends on the same infrastructure, the same intelligence providers, the same software ecosystems and the same underlying assumptions, efficiency can quietly become systemic vulnerability.

The problem is not standardisation itself. Interoperability and common standards are essential for technological progress. The problem is allowing standardisation to eliminate alternatives.

We need interoperability without dependency, innovation without monoculture, and progress without surrendering technological independence.

Perhaps we should start asking some uncomfortable questions:

Do we understand the technologies we increasingly depend upon?
Can we reproduce essential systems independently?
What happens when the platforms we rely on become unavailable, unaffordable or inaccessible?
Are we creating a generation that knows how to use technology but increasingly has no idea how to build it?
Are we sacrificing architectural diversity for convenience?

There is a fundamental difference between knowing how to use a technology and understanding how it is constructed.

Imagine an engineering culture in which people are encouraged to build before they buy, experiment before they adopt, and understand before they depend.

Imagine students learning not just to use frameworks, APIs and cloud platforms, but also to understand the underlying protocols, operating systems, processors, networks and algorithms.

Imagine more independent implementations of computing systems, more locally operated AI, more distributed infrastructure and more open experimentation.

This isn't an argument against mass production or large technology companies. It is an argument against allowing a handful of architectures to become the only imaginable way forward.

A healthy technological civilisation should resemble a biodiverse forest.

Different architectures should coexist. Different approaches should be encouraged. Systems should communicate, but retain the ability to function independently. Components should be replaceable. Knowledge should remain accessible. Failure of one system should not bring everything else down with it.

Perhaps we have confused technological progress with technological adoption.

Perhaps the next great leap forward will not come merely from inventing more powerful machines, but from creating a society capable of understanding, questioning, rebuilding and independently improving those machines.

Humanity does not need fewer trees. It needs more kinds of trees.

We should stop building technological forests where the survival of everything depends on a single tree.

Friday, October 30, 2026

Should India use AI to draft or modify its laws?

Short answer: yes as an assistant to human drafters, no as the author. And "watertight" laws wouldn't fix most of what people mean by lawlessness.

Where AI would help

Consistency checking. Flagging contradictions with existing statutes, undefined terms, cross-reference errors, and conflicts between Central and State law. India has a huge, layered body of legislation, so this is tedious work that software handles well.
Spotting obsolete or overlapping laws and drafting repeals or consolidations.
Stress-testing. Having a model act as an adversary and look for loopholes or ambiguous clauses before a Bill is introduced.
Plain-language and multilingual versions of the text.
Commentators have already pointed to these uses, such as helping identify outdated laws and streamline the legislative process.

Where it fails

No law is watertight. Language is ambiguous, and facts and technology change. Courts exist partly to fill gaps, and over-precise drafting often creates new loopholes.
Drafting involves value choices, such as who bears the burden of proof, how to trade privacy against security, and what penalty fits. Those need democratic accountability, which a model can't supply.
Opacity and bias. Critics note that AI models can be so complex that it's hard to understand how they reach decisions, which matters for something that becomes binding law.
Hallucination. Models can invent plausible-sounding citations or misstate what an existing provision says. Every output needs expert verification.
Training data is skewed toward whatever is online and may miss local or customary context.

The bigger issue is the diagnosis. India's problem is less badly drafted law than weak implementation and enforcement. The National Judicial Data Grid showed roughly 4.92 crore pending cases across district and high courts as of 31 May 2026, with about 92,000 more in the Supreme Court. Analyses of that backlog point to causes like multiple appeals, frequent adjournments, vague laws, weak compliance and poor case management. Vague drafting is one cause among several. Judge vacancies, police capacity, procedure and compliance culture matter at least as much. A perfectly drafted statute still stalls in a court that takes a decade to hear it.

A sensible model for India

Use AI for internal vetting, consistency checks and adversarial review, with humans, Parliament and the Legislative Department owning every word.
Require disclosure of when and how AI was used in a draft, and publish the prompts or checks where feasible.
Keep the public consultation step human and real.
Spend equal effort on capacity: courts, prosecution, and digitised case management. AI-based case management is already part of the eCourts Phase III push.

Also worth noting, MeitY has said it will begin stakeholder discussions and drafting of AI regulation, so there's an open question of who governs AI used inside lawmaking itself.

This is a debated policy area, and reasonable people weigh the efficiency gains against accountability concerns differently. If you want, I can sketch what an AI-assisted drafting pipeline with human review checkpoints could look like, which might also suit a student capstone.

Sources:

Tuesday, October 27, 2026

Text Summarization Origins

Is it safe to argue that the necessity of text summarization was felt after someone sending an email wished they did not need to type in the subject?

Yes, but I would qualify the argument. It is safe as an illustration of how a need for automation emerges, but not as a historical claim about the origin of text summarization.

Your example captures an interesting progression:

A small inconvenience: Someone writes an email and wishes they did not have to type a subject line.
A need for inference: The system could infer the subject from the body of the email.
A more general capability: To infer the subject, the system must identify the central idea of the message.
A broader application: Once a system can identify the central idea, it can potentially summarize the entire message or a collection of messages.

There is an important distinction, though. Generating an email subject requires identifying the message's main topic, whereas summarization requires selecting and compressing its important information. The capabilities overlap, but neither necessarily implies the other.

The deeper argument

You could frame your idea as follows:

The need for text summarization may be understood as an extension of a much smaller human desire: not having to type an email subject. A request to eliminate one repetitive task can expose a more fundamental need—to extract the essence of information without manually processing all of it.

This is a useful example of how a local usability problem can reveal a more general intelligence problem.

If you are developing this as a research argument, I would avoid saying that summarization historically originated from this particular frustration unless you have evidence. Instead, present it as a thought experiment illustrating how the demand for a broader capability can emerge from a seemingly trivial interaction.

The most interesting question is: When a user asks a system to eliminate one small task, should the system automate that task alone—or discover the underlying capability that could eliminate an entire class of tasks?

Saturday, October 24, 2026

What is the dark side of falling in love?

Falling in love is beautiful, but it has a shadow side that people often don’t talk about. Here are the major “dark sides” — emotional, psychological, and practical — that can come with love:

🌑 1. Loss of Objectivity

When you're in love, your brain releases dopamine and oxytocin. These chemicals can:

Make you overlook red flags
Idealize someone who may not deserve it
Make poor decisions you wouldn’t make normally

It’s like a temporary blindness.

🌑 2. Emotional Vulnerability

Love opens you up — which is beautiful, but also risky.

Your partner now has the power to hurt you
Fear of losing the person can cause anxiety
Rejection or betrayal becomes far more painful

Love gives someone access to your deepest self.

🌑 3. Dependency & Attachment Issues

Strong emotional bonding can accidentally create:

Over-reliance on your partner for happiness
Loss of your individuality
Unhealthy attachment patterns (anxious, avoidant, etc.)

You can start prioritizing them over your own growth.

🌑 4. Losing Yourself

Some people reshape their identity to be lovable:

Abandon hobbies, friends, or goals
Change beliefs to please the other person
Forget personal boundaries

Love can sometimes dissolve your sense of self if you’re not careful.

🌑 5. Jealousy & Insecurity

Even secure people can feel:

Fear of being replaced
Comparison with others
Jealousy triggered by small things

Love amplifies insecurities you didn’t know you had.

🌑 6. Heartbreak Is Real Pain

Breakups can cause:

Anxiety and depression
Physical symptoms (chest tightness, loss of sleep, appetite changes)
Long-term trust issues

Because when love ends, the brain withdraws from dopamine like addiction withdrawal.

🌑 7. Sacrifice (Sometimes Too Much)

Relationships often require:

Time
Emotional labor
Compromise
Career or lifestyle changes

Sometimes one person gives more than the other.

🌑 8. The Past Comes Back Up

Love exposes old wounds:

Childhood trauma
Abandonment issues
Past relationship scars
Insecurities you thought you buried

Being loved forces you to confront parts of yourself you ignored.

🌑 9. Love Can Make You Tolerate Toxicity

People in love sometimes:

Forgive too quickly
Ignore disrespect
Stay in dysfunctional dynamics
Make excuses for bad behavior

Love can cloud judgment.

🌑 10. Fear of Losing Love

Even in good relationships, you may feel:

Anxiety about the future
Pressure to maintain the “perfect” connection
Fear of not being good enough

This fear can be exhausting.

🌕 But…

The dark side doesn’t mean love is bad. It means love is powerful — and anything powerful has risks.

Healthy love comes from:

Awareness
Boundaries
Communication
Emotional balance

If you want, I can also tell you:

How to avoid the dark side of love
How to know if your relationship is healthy
How to protect yourself emotionally while loving deeply

Would you like that?

Wednesday, September 30, 2026

TextBox handling using Python

I converted a simple text box input taking method to some modern python implementation

/* program to implement text box press esc key to terminate with comments

#include <graphics.h>

int poly[8]; // a polygon to display the textbox

int curposx=100,curposy=100; // current cursor position

int flag; // flag to indicate blinking of cursor

char str[20]; // string containing typed text

// Function to draw cursor while blinking it

void drawcursor()

{

if(flag==0)

setcolor(WHITE);

else setcolor(BLACK);

line(curposx,curposy,curposx,curposy+20);

}

// Function to display key(character) typed

void display(char key[2])

{

int len;

cleardevice();

setfillstyle(1,LIGHTGRAY);

setcolor(WHITE);

fillpoly(4,poly);

flag=(flag==0)?1:0;

switch(key[0])

{

case 8: // if backspace is pressed delete the last character from str

len=strlen(str);

if(strlen!=0)

{

str[len-1]='\0';

curposx-=7;

}

break;

case '\0': // if the key string is empty then do nothing

break;

default:

strcat(str,key);

curposx+=7;

}

outtextxy(100,106,str);

drawcursor();

}

int main( )

{

char key[2];

int keycount=0,enough=0,len;

/* our polygon array */

poly[0]=100;poly[1]=100;poly[2]=200;poly[3]=100;

poly[4]=200;poly[5]=130;poly[6]=100;poly[7]=130;

initwindow(400, 300, "Form");

key[0]='\0';

flag=0;// set the flag for cursor blinking

if(kbhit()) key[0]=getch();// get the character typed if any before the loop starts

key[1]='\0';

while(key[0]!=27) // if it is not escape be in loop

{

key[0]='\0'; // destroy the key code from previous loop run

delay(200); //delay for cursor blinking

if(kbhit()) { // if keyboard is hit

key[0]=getch(); //get the key code of pressed key

keycount++; // increment the key count

if(keycount>18) // If key count greater than 18 stop taking input

enough=1;

}

if(enough) key[0]='\0'; //if enough set key code to zero

display(key);

}

// set the last character as "\0" to terminate the str

len=strlen(str);

if(len>0)

{

str[len-1]='\0';

}

setcolor(WHITE);

outtextxy(200,200,str);

printf("%d,%s",len,str);

getch();

return 0;

}

Updated code:

"""

A small, reusable text box widget for pygame 2.

pip install pygame

python textbox.py

Click the box to focus it. Enter submits, Esc quits.

Supports: insertion at the cursor, Backspace/Delete, Left/Right/Home/End,

click-to-position, key repeat, max length, placeholder and horizontal scrolling.

"""

import pygame

BG = (30, 30, 36)

BOX_IDLE = (70, 70, 80)

BOX_ACTIVE = (90, 160, 255)

TEXT = (235, 235, 240)

HINT = (120, 120, 130)

BLINK_MS = 500 # cursor is visible for 500 ms, hidden for 500 ms

class TextBox:

PAD = 8

def __init__(self, rect, font, max_length=20, placeholder=""):

self.rect = pygame.Rect(rect)

self.font = font

self.max_length = max_length

self.placeholder = placeholder

self.text = ""

self.cursor = 0 # index in self.text where the caret sits

self.active = False

self._scroll = 0 # horizontal pixel offset of the text

self._blink_start = pygame.time.get_ticks()

# ---------- helpers ----------

def _touch(self):

"""Restart the blink cycle so the caret is visible right after input."""

self._blink_start = pygame.time.get_ticks()

def _caret_x(self):

return self.font.size(self.text[: self.cursor])[0]

def _update_scroll(self):

"""Keep the caret inside the visible area."""

visible = self.rect.width - 2 * self.PAD

caret = self._caret_x()

if caret - self._scroll > visible:

self._scroll = caret - visible

elif caret - self._scroll < 0:

self._scroll = caret

self._scroll = max(0, self._scroll)

def _index_at(self, mouse_x):

"""Character index nearest to a mouse x position."""

x = mouse_x - self.rect.x - self.PAD + self._scroll

best, best_dist = 0, float("inf")

for i in range(len(self.text) + 1):

dist = abs(self.font.size(self.text[:i])[0] - x)

if dist < best_dist:

best, best_dist = i, dist

return best

# ---------- events ----------

def handle_event(self, event):

"""Feed every pygame event here. Returns the text when Enter is pressed."""

if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:

self.active = self.rect.collidepoint(event.pos)

if self.active:

self.cursor = self._index_at(event.pos[0])

self._touch()

return None

if not self.active:

return None

if event.type == pygame.TEXTINPUT:

# TEXTINPUT already handles shift, layouts and IMEs correctly.

chunk = "".join(c for c in event.text if c.isprintable())

chunk = chunk[: self.max_length - len(self.text)]

if chunk:

self.text = self.text[: self.cursor] + chunk + self.text[self.cursor:]

self.cursor += len(chunk)

elif event.type == pygame.KEYDOWN:

if event.key == pygame.K_BACKSPACE and self.cursor > 0:

self.text = self.text[: self.cursor - 1] + self.text[self.cursor:]

self.cursor -= 1

elif event.key == pygame.K_DELETE:

self.text = self.text[: self.cursor] + self.text[self.cursor + 1:]

elif event.key == pygame.K_LEFT:

self.cursor = max(0, self.cursor - 1)

elif event.key == pygame.K_RIGHT:

self.cursor = min(len(self.text), self.cursor + 1)

elif event.key == pygame.K_HOME:

self.cursor = 0

elif event.key == pygame.K_END:

self.cursor = len(self.text)

elif event.key in (pygame.K_RETURN, pygame.K_KP_ENTER):

return self.text

else:

return None

else:

return None

self._touch()

return None

# ---------- drawing ----------

def draw(self, surface):

pygame.draw.rect(surface, (45, 45, 54), self.rect, border_radius=6)

pygame.draw.rect(surface, BOX_ACTIVE if self.active else BOX_IDLE,

self.rect, width=2, border_radius=6)

self._update_scroll()

inner = self.rect.inflate(-2 * self.PAD, -4)

old_clip = surface.get_clip()

surface.set_clip(inner)

if self.text:

img = self.font.render(self.text, True, TEXT)

else:

img = self.font.render(self.placeholder, True, HINT)

y = self.rect.centery - img.get_height() // 2

surface.blit(img, (inner.x - self._scroll, y))

# Blink using elapsed time, so the main loop never has to sleep.

blink_on = ((pygame.time.get_ticks() - self._blink_start) // BLINK_MS) % 2 == 0

if self.active and blink_on:

cx = inner.x - self._scroll + self._caret_x()

pygame.draw.line(surface, TEXT, (cx, y), (cx, y + img.get_height()), 2)

surface.set_clip(old_clip)

def main():

pygame.init()

screen = pygame.display.set_mode((480, 240))

pygame.display.set_caption("Text box")

font = pygame.font.SysFont("segoeui,arial", 24)

clock = pygame.time.Clock()

box = TextBox((40, 80, 400, 44), font, max_length=20, placeholder="Type here...")

box.active = True

pygame.key.set_repeat(400, 35) # hold a key to repeat it

pygame.key.start_text_input()

submitted = ""

running = True

while running:

for event in pygame.event.get():

if event.type == pygame.QUIT or (

event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE

running = False

result = box.handle_event(event)

if result is not None:

submitted = result

screen.fill(BG)

box.draw(screen)

if submitted:

msg = font.render(f"Submitted: {submitted!r} ({len(submitted)} chars)", True, HINT)

screen.blit(msg, (40, 160))

pygame.display.flip()

clock.tick(60)

pygame.quit()

if __name__ == "__main__":

main()

Thursday, September 3, 2026

Sankey chart of user abilities

Prompt: If there is a software development model where the features on a per user basis are enabled or disabled using remote config by orchestrating the parameters.. will we be able to generate a sankey chart that depicts users adept at a particular feature

Yes — absolutely, and it could actually be a very useful visualization for the kind of software-development model you're describing.

The key is to distinguish configuration from actual usage.

Your model

Imagine your application has:

Users: U1, U2, U3, ...
Features: F1, F2, F3, ...
Remote configuration determining whether a feature is enabled
Parameters controlling how a feature behaves

For example:

User	Feature A	Feature B	Feature C
U1	Enabled	Enabled	Disabled
U2	Enabled	Disabled	Enabled
U3	Disabled	Enabled	Enabled

You could then collect runtime telemetry such as:

User → Configuration → Feature → Action/Usage

and construct a Sankey such as:

                    ┌── Feature A ── Used
                    │
Users ──→ Config ───┼── Feature B ── Used
                    │
                    └── Feature C ── Not Used

But there's a more interesting version.

You can make the Sankey represent the user's "feature journey"

For example:

                 ┌── Search ────────┐
                 │                   ↓
Users ──→ Enabled Features ──→ Used Features ──→ Successful Outcome
                 │                   ↑
                 ├── Recommendations ┘
                 │
                 └── Analytics

The width of each flow represents the number of users.

So you might discover:

10,000 Users
   │
   ├── 8,000 → Feature A enabled
   │              └── 6,200 actually used
   │
   ├── 6,500 → Feature B enabled
   │              └── 1,100 actually used
   │
   └── 4,000 → Feature C enabled
                  └── 3,800 actually used

That immediately tells you something that a conventional feature-usage bar chart doesn't: where users are being enabled, filtered, and subsequently flowing through the system.

The really interesting research/software-engineering angle

If your remote configuration is parameterized, you can add another dimension:

User → Configuration → Feature → Parameter → Behaviour → Outcome

For example:

                     ┌─ Search ── threshold=0.7 ── Success
                     │
Users → Remote Config┼─ Search ── threshold=0.9 ── Abandoned
                     │
                     └─ Recommendations ────────── Success

Now the Sankey isn't merely saying "who uses Feature X?"

It can answer:

"How do different configuration decisions cause different populations of users to flow through different features and ultimately produce different outcomes?"

That's much more powerful.

One important caveat

A Sankey doesn't by itself tell you that users are "adept" at a feature.

It tells you about flows and quantities.

If by adept you mean something like:

users who successfully completed the feature,
users who use it repeatedly,
users with high proficiency,
users who require fewer attempts,
users who achieve a desired outcome,

then you need to define an adeptness metric first.

For example:

Adept user =
    Feature enabled
    AND used ≥ 5 times
    AND success rate ≥ 80%

Then your flow could become:

All Users
    ↓
Feature Enabled
    ↓
Feature Used
    ↓
Repeated Usage
    ↓
Successful Usage
    ↓
Adept Users

That is a very natural Sankey representation.

If you're thinking of this as a software architecture / research model, there's an even more interesting possibility: treating the remote configuration system as a feature orchestration layer, and the Sankey as a runtime observability model of user-feature affinity. That could be developed into a fairly concrete framework rather than just a visualization.

Prompt: Can I solicit user abilities from other websites to research on gaps in them? like competencies and suggest users some new features to try

Yes — technically and conceptually, this is possible, but there is an important distinction between soliciting abilities and silently collecting them.

What you're describing could become a very interesting extension of your remote-configuration idea:

Build a user competency profile from consented signals available across services, identify gaps, and use those gaps to recommend features or learning opportunities.

A possible architecture

External Sources
     │
     ├── GitHub ───────┐
     ├── LinkedIn ─────┤
     ├── Stack Overflow┤
     ├── Learning sites ┤
     └── Your application
                      │
                      ▼
             ┌─────────────────┐
             │ Competency      │
             │ Extraction      │
             │                 │
             │ JavaScript  4/5 │
             │ React      3/5 │
             │ Git        5/5 │
             │ GraphQL    1/5 │
             └────────┬────────┘
                      │
                      ▼
              Competency Graph
                      │
             ┌────────┴────────┐
             ▼                 ▼
       Strengths             Gaps
                              │
                              ▼
                    Feature Recommendations
                              │
                              ▼
                    Remote Configuration
                              │
                              ▼
                         User tries
                              │
                              ▼
                         Telemetry
                              │
                              ▼
                    Updated competency

The interesting part is that the recommendation itself becomes an experiment.

Suppose your system detects:

User competency

React        █████  5
JavaScript   ████   4
REST APIs    ███    3
GraphQL      █      1

Your application might decide:

"This user has strong React and REST experience but relatively little GraphQL exposure."

It could remotely enable a GraphQL exploration feature for that user.

Then you measure:

Recommended
     ↓
Feature enabled
     ↓
Feature opened
     ↓
Feature attempted
     ↓
Feature successfully completed
     ↓
Repeated usage

Now your Sankey idea becomes particularly powerful.

You could connect competency → recommendation → adoption

For example:

Users
 │
 ├──────────────→ Strong React
 │
 ├──────────────→ Strong REST
 │
 └──────────────→ GraphQL gap
                         │
                         ▼
                GraphQL feature offered
                         │
              ┌──────────┴──────────┐
              ▼                     ▼
          Tried it              Ignored it
              │
        ┌─────┴─────┐
        ▼           ▼
     Adept       Struggled

That gives you a feedback loop:

Observe → infer gap → recommend → enable → observe outcome → update profile.

But there's a significant privacy issue

I would not design this as:

"Go to other websites and collect information about what this person can do."

Instead, design it as:

"User-authorized competency aggregation."

For example, the user explicitly connects accounts or imports their competency information.

You also need to distinguish:

Evidence

User has 27 GitHub repositories involving React.

from:

Inference

User's React competency = 4.2/5.

The second is a model-generated inference and shouldn't be presented as an objective fact.

And ideally the user should be able to see:

Why am I being recommended GraphQL?

✓ 14 REST projects detected
✓ React experience detected
✓ No GraphQL activity detected

Confidence: Medium

That makes the system explainable.

There's an even bigger research question here

You could frame the research problem as:

Can consented cross-platform competency signals be used to identify latent user capability gaps and dynamically personalize feature exposure through remote configuration?

Then you have three measurable components:

Competency inference — what does the system think the user knows?
Gap detection — what potentially useful capability is missing?
Adaptive feature exposure — does exposing the appropriate feature actually improve adoption/competency?

Your earlier Sankey visualization could then become the evaluation mechanism showing:

User population → competency profile → detected gap → recommended feature → feature adoption → successful proficiency.

That is considerably more interesting than simply making a Sankey chart of feature usage. It starts looking like a closed-loop adaptive software system.