#Maa Ka Sum #Comedy #Drama #Movie #FilmWorld #Thriller #Hindi Dubbed #Season 1 #Mona Singh #Mihir Ahuja #Angira Dhar #Ranveer Brar #Hetal Gada #Manish Anand
Agastya, (Mihir Ahuja) a math prodigy, believes every problem has a formula - even love. When he sets out to find the perfect match for his mother (Mona Singh), his algorithm-fueled adventure spirals into a hilarious tangle of bad dates, big feelings, and one unexpected discovery: sometimes love can't be calculated, only felt.
Agastya, (Mihir Ahuja) a math prodigy, believes every problem has a formula - even love. When he sets out to find the perfect match for his mother (Mona Singh), his algorithm-fueled adventure spirals into a hilarious tangle of bad dates, big feelings, and one unexpected discovery: sometimes love can't be calculated, only felt.
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FunTranscript
00:28You
00:31You
01:0430 feet drop
01:07To
01:08Approx 50 kilos
01:10Impact speed
01:1213.4 meters per second
01:14Impact force
01:162200 newtons
01:18Nahi marega
01:19Back off
01:21Max 2-3 ribs tooting if you're lucky
01:24Leg fracture, hospital, surgery
01:26Wheelchair
01:27Just back off dude!
01:30Okay, okay
01:36Sutta
01:37What have you done with a cigarette?
01:41In the hospital, smoking is not allowed
01:47Look, you look like a cigarette
01:54You look like a cigarette
01:55I mean, you can't get an exam
01:59Groom nails
02:00Groom nails
02:01Hoodie or shoes
02:01Fancy perfume
02:04Cool watch
02:05Hush!
02:05IG reals are true
02:07Piaar mein sab apne best version bani zate hai
02:10Break up
02:35In the hospital
02:36Do you want to go?
02:39No, no, no, no, no
02:39No, no, no, no
02:40No, no, no, no
02:40No, no, no, no, no
02:40No, no, no, no, no, no, no
02:45What's the difference between semantics, bro?
02:49She wants to say that you both are incompatible.
02:51Fuck, semantics, dude. I love her.
02:54And I can't live without.
02:56And she cannot live with you.
03:01What is PR?
03:02PR is basically best case compatibility.
03:05Fuck that shit, dude. I don't care.
03:09She's the one.
03:12She's the one of many probabilities.
03:16What?
03:19Probability is a numerical value of...
03:21Hey, don't go to maths.
03:22Second-year maths on Azuma.
03:26No way!
03:27What are they all for?
03:30Sorry, sorry, sorry.
03:32You okay?
03:34I'm Agastya.
03:35So you are Agastya,
03:36I've heard about it, but I've never seen it in class.
03:40I have an attendance anxiety.
03:44If it's more, I get anxious.
03:47Anyway, will you play a game?
03:49Do you play a game?
03:52Are you going to party here?
03:53Not really, but...
03:56After five minutes, I'm going to play a game.
04:01So...
04:02After the game, I'm going to play.
04:04The last two days, my kids are a little more of a kid.
04:06The average for women in college is 47%.
04:09So, take out the boys and kids are 75,200 girls.
04:15Trust me, I know my maths.
04:29Now, let's take a look, 60% are taken, meaning 40% single, of course.
04:34So, 40% off 75,200, that's 30,080.
04:45Assuming 50% nerd is single by choice, lesbian, or simply, they have 5-6 women's interest.
04:525-6 and a half.
04:545-6 and a half women's interest.
04:58That's 15,040.
05:06Now, let's take a look, you are also choosy, because why not?
05:09And in eligible options, 60% you will reject, which is 60% off 15,040, but 6,016.
05:196,016 matches for you at North Campus, bro.
05:23Now, let's take a look, 5% on your campus.
05:27That's 301 girls.
05:30301 possibilities for the perfect girlfriend, dude.
05:39I mean, you should get one girlfriend.
05:44Dude, compatibility probability is 0.5.
05:47Now, one match probability is 99.999999.
05:53Now, if you can't make a girlfriend after that,
05:57I'm gonna do that.
05:58I don't want to eat them in the moonlight.
06:01I don't want to do that anymore.
06:10I don't know.
06:51I don't know.
07:29I don't know.
07:39I can't.
07:42I...
07:43Sorry.
07:46Sorry, sorry, sorry.
08:16I don't know.
08:17You're famished.
08:18Me too.
08:19Excuse me.
08:23Yes, sir?
08:24I'll have a portion of smoked tomatoes and mascarpone risotto.
08:29And mom ke liye peri peri pasta.
08:32And I add some chicken to it.
08:33Hey.
08:33Okay, sir.
08:34I think I raised you better.
08:35Man choose lo ga ra mera order.
08:37Oh, my bad.
08:38What is it?
08:39Hmm.
08:41I'll have the panseared fish.
08:46No, no, risotto.
08:48No, it'll be heavy.
08:49Shall I come back later, ma'am?
08:51That would be wise.
08:52No, no, wait.
08:52I've got it.
08:54I will have...
08:55I'll have the eggplant parmigiana.
08:59Hmm.
08:59Anything to drink?
09:00Drink?
09:05Uh...
09:06Tumhaari.
09:07Me?
09:07But it's fine.
09:08I'll drive.
09:09Gin and tonic.
09:10Small.
09:11Thank you, ma'am.
09:13Oh, so what were you talking about the maths bro?
09:16I was obviously listening.
09:17Were you?
09:18Hmm.
09:19Nothing.
09:19It's a cliched story of heartbreak.
09:22And you've seen that cliched knowledge of august knowledge.
09:25I've seen the right path.
09:27Using maths, of course.
09:29Yeah, of course.
09:34All these years and I still don't get it.
09:36Why do you celebrate your dad's anniversary?
09:39You don't need to make a joke of the people.
09:42You don't need to make a joke of the people.
09:43You need to make a joke of the people.
09:44You need to make a joke of the people.
09:45That's why.
09:46In the Hindi film reference,
09:48you can't explain anything, right?
09:50Life, right?
09:51Hollywood movies are not genre specific.
09:54You see, it's like a helicopter.
09:56In which action, drama, comedy, tragedy,
10:00everything happens.
10:01So essentially, life is like a Hindi film.
10:15That's my food.
10:16What kind of food?
10:17Yeah, I'm not eating this penguin.
10:25Oh, yeah.
10:25Anup Akharwal brand on me, our song.
10:27Nice, huh?
10:28It's so sad.
10:30It's so sad.
10:31There's no other word in Google.
10:32Let me tell you.
10:35Hey, I didn't have to drive the car, okay?
10:38Don't act too cool with me.
10:40Synth, when someone tries too hard in a relationship,
10:43you could have said,
10:44I'm sick. I mean, amazing.
10:46Sick.
10:48Oh.
10:51Is that from the big man?
10:53Mr. Shoff?
10:55Bingo.
10:56How's it going?
10:59It's good.
11:02Wow.
11:03That's something new.
11:04When are you getting married?
11:06I mean, I'm not going to get married.
11:08No.
11:09What are you talking about?
11:11First, let me be sure, sure.
11:13And how much will it be to be sure, sure?
11:15I'll be leaving in a few months.
11:17Remember?
11:17Yes.
11:22For now, yes.
11:23But eventually, I'll be moving abroad
11:25for my further studies.
11:35Oh, I can take care of myself, okay?
11:37I can't stay alone.
11:39Jinx.
11:54Jinx.
12:02Of course.
12:07You know, Vanita, I really like this, yeah.
12:13Us.
12:15Really do.
12:15Yes.
12:18Me too.
12:19Actually, I feel that everything with you is very easy.
12:24I love that.
12:26I've never met such an understanding relationship before.
12:30Wow.
12:32Now I'll take that as a compliment.
12:34You should.
12:38So?
12:40So?
12:43Have you given a thought to what's next?
12:50Dessert?
12:52Yeah, you're not good.
12:54I was talking about us.
12:56Oh, here you go, sir.
12:57Oh, okay.
12:58A little too soon, but thank you very much.
13:00Thank you, sir.
13:00Well, you asked for dessert.
13:01But what's the occasion?
13:03Take a wild guess.
13:05Mm-hmm.
13:06I-I don't know.
13:07Kya hai?
13:08Hundred days.
13:13Hundred days.
13:14Hundred days.
13:16I would've thought.
13:17But here we are.
13:20You're so romantic.
13:21Speaking.
13:22Speaking of which.
13:25Eek.
13:26Surprise.
13:28Oh.
13:29Dekho.
13:29I've made a lot of courage.
13:32I've written a song for you.
13:34Mm-hmm.
13:37Oh.
13:38And I'll listen to that.
13:44We completed a hundred days.
13:49I am glad in so many ways.
13:53It started when we swiped right.
13:57Right.
14:00Now our lives are happy and bright.
14:10Dating apps are so much.
14:11So, what is the heartset successful?
14:14My hard work.
14:15Dedication.
14:16Foresight.
14:18Eek.
14:19Chota asa.
14:20Complex algorithm.
14:21Eek.
14:21Maska kya seenin bro?
14:22And I spent a lot of time.
14:23Teer kukya.
14:24Okay.
14:25I'm no modern age cupid.
14:28Despite what summit review says.
14:30I bet he gets off to that magazine every morning.
14:33I bet he gets off to that magazine every morning.
14:33I bet he gets off to that magazine every morning.
14:33Yeah.
14:33I bet he gets off to that magazine every morning.
14:33I bet he gets off to that magazine every morning.
14:34Up the streetwear t-shirts can next drop me.
14:37In situation k total ad kare de.
14:40Right under it.
14:41Self-love.
14:43How much is too much?
14:47Hmm.
14:50Do you see?
14:52Kya?
14:54Thought level per connect hear.
14:57Remind me.
14:58Where we break up is.
15:03This was when, like you, I was a student, but not here, at Stanford on a full scholarship.
15:08That's right.
15:09The university's rating average is 20 weeks.
15:12We did far better.
15:15Went well over P50.
15:17What is it?
15:18It's an algorithm, right?
15:20P50.
15:21The 50th percentile, the median.
15:24You and me did better than half the college.
15:28Not bad.
15:28I just want that anyone and everyone can find their special someone.
15:36Thank you, everyone.
15:42Mr. Jatin Bhandari.
15:46And please, call me JB.
15:49JB, can you ask questions about JB?
15:54Sir, sir, sir.
15:55Sir, sir.
15:57Yes, Chitna.
15:58How does it feel to be a billionaire?
16:03Well, jitne time me, aapne ye sawaal pochae, utne time me, heartset pe, wada ni kitne or matches
16:10over.
16:11That's the real wealth.
16:17Yes, Agastir.
16:22It's a fact.
16:23It's a fact, heartset results in the highest number of first dates, no cap.
16:26But it has an 80% failure rate.
16:56Rarely, first dates are converted to second dates.
16:58All right, I think that's enough.
17:00Just one question per student, please.
17:01Let's move on.
17:02Anyone else?
17:03Yes.
17:09I thought I was late, but he is never on time.
17:14Yeah, but I told you.
17:44What are you looking for?
17:45What are you looking for?
17:46What are you looking for?
17:48What are you looking for?
17:49What are you looking for?
17:50Hello?
17:51What are you looking for?
17:52What are you looking for?
17:53What are you looking for?
17:54That's okay.
17:55Sir, what are you looking for in my personal life?
17:58Maths.
17:59On the basis of Maths, people can predict their patterns and decisions.
18:03Okay.
18:05Okay.
18:06Then you prove your hypothesis, right?
18:08Yes, sir.
18:10See that pretty lady with the books?
18:13That's right.
18:16It's pretty.
18:17Randu, focus.
18:18You predict which book will be the next book.
18:21On the basis of Maths.
18:23That's pushing it, huh?
18:25I can tell you which book will be the next book will be the next section.
18:29Done?
18:30Yes.
18:44I can tell you everything, man.
18:45See you all know everything, bro.
18:48So much to choose from.
18:51Besides, you don't know.
18:56I don't read those books, you know that.
19:07Hi.
19:12What happened?
19:14What are you doing?
19:15What are you doing?
19:17What are you doing?
19:20I want to do a little data analysis.
19:22The second book was for my book.
19:24Infinity.
19:31The part.
19:33The part.
19:34The part.
19:35Give me a rough sheet.
19:37Rough tissue for...
19:38It's fine.
19:40It's fine, man.
19:45You have to do it, Einstein.
19:51Until you do your calculation,
19:53you will get your book, man.
19:54It's fine.
19:551 by n minus 1.
19:571 by n minus 1.
19:59Historical fiction.
20:01Yeah?
20:03She'll pick the next book
20:05from historical fiction section.
20:11Excuse me.
20:13You will get your book in that section.
20:18History.
20:19Maybe if you can give me some details,
20:21I can help you find the precise book.
20:26Thank you so much.
20:28You're most welcome.
20:29The universe has a funny bone.
20:33Thanks.
20:34No, no, no.
20:35This is the wrong book.
20:37You will get your book in this section.
20:39History.
20:39Historical fiction.
20:42Sorry?
20:45I mean, this is the wrong book.
20:49It says who?
20:51It says maths.
20:52It says math.
20:53Maths means numbers, algebra, geometry.
20:58You know?
20:58Uh-huh.
20:59Yeah.
21:01Maths can help me to find the book.
21:03But which book I'll read?
21:05I won't tell you.
21:06Thank you, boys.
21:09I'm kidding you.
21:09That's the wrong book.
21:12Hey.
21:14That's why I'm saying.
21:15You always find the mouth of maths.
21:17Okay.
21:19Maths.
21:20The genius people are stupid, right?
21:23I mean, it doesn't matter in maths and real life.
21:26You'll get to, you'll get to.
21:28When?
21:33In due time.
21:34You know, you've been saying that a lot, right?
21:37When the time is right, you will meet him.
21:41And when will the time be right, Vinny?
21:44I mean, you've been met with my whole world.
21:46With my friends, with my parents.
21:48Who would say it?
21:49That's different, no?
21:51How?
21:53How is it different?
21:55Vin?
21:59Because...
22:01Gusta and I, we only have each other.
22:04And I want to be sure that he's ready to meet you.
22:08So you're saying that your relationship with your son
22:11is more important than my relationship with my parents?
22:15No.
22:17No, Shroff, I never meant that.
22:20You know what, Vin?
22:26I feel that you have your single-mother-only-child relationship
22:31more romanticized.
22:33What?
22:35Make no mistake, I've dated a lot of single mothers.
22:38And they never had a problem introducing me to their kids.
22:41But you've kept your relationship on a pedestal.
22:46Untouchable, also sacred.
22:49Shroff, what are you saying?
22:50I don't think that Gus is a genius kid.
22:54I mean, he's just a regular kid, which is not a problem, by the way.
22:57But you've made a genius in your mind.
23:00Think about it.
23:02Hi.
23:02Yes, sir.
23:03Table for two, please.
23:04Sure, sir.
23:05No.
23:28I don't want it.
23:34I don't want to talk about it.
23:51Happy?
24:08Happy?
24:16Not a lot, Mom.
24:17Give it some more time.
24:20How much time?
24:22I've been going on dates after dates for years!
24:27Years.
24:28Years.
24:30You started going on dates after I turned 18.
24:33I love you.
24:49Odd.
24:52Who drinks and you're already gone?
24:55What do you mean gone?
24:56I'm not...
24:58I'm fine.
25:01I'm not hungry.
25:02Why do you say not hungry?
25:03It's the most important meal in the night.
25:06You say breakfast for breakfast.
25:08It's the most important meal in the night.
25:10Yes, breakfast is also very important.
25:12Yes.
25:13Lunch too.
25:14I'll eat it.
25:15No, I'll take it away.
25:16No, I'll take it away.
25:17I'll take it away.
25:18Time to go to bed.
25:20Yes.
25:20Yes.
25:23I'll take it away.
25:26Good night.
25:27Good night.
25:31Super husband.
25:33Good night.
25:33You can understand my mind.
25:39You have to understand your mind again?
25:41I'll take it away.
25:42Men don't want to see yourself.
28:09Wow.
28:10Nice.
28:11That's really funny.
28:13Oh lovely.
28:14Nice.
28:14Really nice.
28:15I really like it.
28:16What is it?
28:16That's you.
28:17Let me see it.
28:19That's you.
28:21Only with a cuter's mic.
28:23Huh?
28:23Yeah.
28:24Okay.
28:25I see.
28:26Look at this.
28:27This.
28:28So cool.
28:28I love what you did with the colors here.
28:30I'm thinking of getting this printed.
28:31This is nice.
28:32I don't like this color.
28:33No, it's nice.
28:34No, there's something not good about it.
28:36It's good.
28:41What are you doing?
28:42Huh?
28:42You're doing no.
28:43You're doing no.
28:47No.
28:50No.
28:50No.
28:58You shouldn't be doing this.
29:01I shouldn't be doing that.
29:03Yeah.
29:03Let's go.
29:03Let's go.
30:22Let's go.
30:24Let's go.
30:25Oh, shit.
30:29Hello, hello.
30:30I'll call you later.
30:31Bye, bye.
30:34Hi.
30:35Hi, Vinny.
30:36Hi.
30:36Hi, I came to see you.
30:38Oh, what a surprise, Annie.
30:40Yeah.
30:43I love your pants.
30:46I love your t-shirt, top.
30:48Thanks.
30:50I love your drip.
30:53Drip?
30:54Drip.
30:54Oh, my God.
30:55I love your fashion sense.
30:56Drip.
30:57Don't sweat.
30:58Oh, I'm going to sweat.
30:59You're doing that.
31:07My bag.
31:07Yeah.
31:10Yeah.
31:15Okay.
31:15Oh, cool.
31:15I love to bounce.
31:16Hmm.
31:17I've got a new math professor.
31:18Apparently, super intelligent.
31:20Harvard and all.
31:31Yeah.
31:34Yeah.
31:35Bye.
31:36Bye.
31:37Bye.
31:37Bye.
31:38Bye.
31:40Bye.
31:41I'll see you.
31:42I need your feedback.
31:43Ha, ha.
31:44I'm coming and meeting you soon.
31:45Bye.
31:46Call and come.
31:47Bye, mom.
31:48Bye, bye.
31:49Bye.
31:53With a specialization in graph theory, Dr. Raina ne apna B.Tech IIT Delhi se kiya, followed by a research
32:00at Stanford in Behavioral Decision Science and graduating with a PhD across the Maths and Psychology department.
32:07Dr. Raina is also a Simon's Foundation Grant winner and a Dan David prize-winning researcher.
32:14We are privileged that Dr. Raina ne semester humare Maths department mein as a visiting faculty join karne ka faisla
32:21kiya hai.
32:22Please give her a warm welcome.
32:24Huh?
32:25Dr. Ira Raina.
32:32Marshmallow?
32:33Yeah, sure.
32:35You're most welcome.
32:37Ah.
32:39Does everybody know what a marshmallow is?
32:42Yes.
32:48Stanford University, 1970 mein, Dr. Walter Mischel ne ik experiment kiya tha.
32:56Unho ne ek bachche ko ek marshmallow ke saath ek kamre me rakha.
33:01A ganchye ka chuts name sirf ek shak di.
33:03Jhar pundra minute me agar usne woe marshmallow nahin khaya, toh marshmallow double hoote jaya gaining.
33:09A ek ka dhu, dhu ka chaar, so on and so four.
33:14A agar kha liya, toh experiment wehine khatam hoze jaya ga.
33:18Yane ke jitna zyada patients utna bada gratification.
33:21and in follow-up studies researchers found
33:25that joe bachche zyada gratification ke liye
33:28zadi der wait karne ko tayyaar te
33:30they had better life outcomes
33:32better jobs, more money, better education
33:36yeh research aaj tak chalti aarehi hai
33:39and the results, they are the same
33:43agar mein aapse kehti hun ki
33:44is patient study ke basis pe
33:46mein aapke life outcomes predict kar sati hun
33:49so you'll call me a hoax
33:52lekin jab psychology ko mathematical model se sadi gya jaye
33:56to outcomes magical hi lagte
33:58and to be able to see these models predict human behavior
34:06feels like being able to take a peek into the future
34:11agar aapke paas sahih data ho
34:13to ap har cheez pattern mein dek sakte
34:16jaisa ki the weather, the lottery, elections
34:19in general you can predict and even manipulate human behavior
34:24using mathematical models
34:25in my course mathematical models in behavioral psychology
34:30we will learn how to model human behavior
34:32using advanced math tools like neural network
34:36as a model with sina
34:40the water, kız, couldn't protect human behavior
34:42and animal behavior
34:59we can filter out
35:03once again
35:04perhaps we will have to modify
35:04alahan-so- soon
35:10I'm going to do it.
35:12What?
35:14My algorithm will give you the perfect partner.
35:35I'm going to do it.
35:38I'm going to do it.
36:06I'm going to do it.
36:08I'm going to do it.
36:20When this heart is hurt,
36:25it's your name.
36:36I'm going to do it.
36:40I'm going to do it.
36:45I am your host, and I am your host, I am my host, I am your host, and I am
37:11my host,
37:50You
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