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Ani Republishes Essay on Marrying the Wrong Person for Wrong Reasons

You're probably marrying the wrong person/the 2 am test no one told you about

Ani reshares an archived X article arguing most people marry for three impulses: checkbox compatibility, social pressure, or fear of being alone. The essay promotes a paid newsletter on money and power.

Original post · 9 min read
From my writing archives because some of you are about to propose for the wrong reasons during the upcoming holiday season and I care about you while also wanting engagement bait.
X ArticleYou're probably marrying the wrong person/the 2 am test no one told you about
Most people marry the wrong person for the wrong reasons and spend the rest of their lives telling themselves how right they were. Some end up seeing the light and separate while others go fully into the darkness as they head down the aisle, eyes not wide shut but horrifyingly clear sighted and aware of the incredible mistake they are making as it happens.
I write about money, power, and the ways people wreck their lives over both. Subscribe to read more: www.notyourtypicalfinancebro.com
I find myself at that awkward age where friends I saw get married are now getting divorced, friends who should get divorced are desperately fighting to stay married, and the rest are either panic-proposing or considering downloading Hinge for the seventh time pretending this round will be different. Since I'm in the grown-up position of not offering my opinions on anyone's spouses and you can usually find me at the buffet line or in a corner eating (I am so serious when I say most photos of me at events involve food, I am an equal opportunity eater), I've had extensive time to observe this whole tragicomedy while feasting on canapés and champagne. Which is why I have some thoughts.
I make no claims at being an expert. God knows I've destroyed enough relationships or potential along the way. But I've built this theory from observation and discussion with others, and I think it holds up to scrutiny. It's certainly not popular nor the fun way to do it, but that's also because most people don't want to think about who the person sleeping next to them is going to be when they get the worst phone call of their lives.
From what I've observed, most people marry out of three impulses:
They have met someone who meets certain checkboxes at a certain time in their life.
They feel pressure from society, family, social circles, the media, gossipy aunts, gross uncles, whatever, and they meet someone with which they can settle to remedy that ache.
They are just plain afraid to be alone.
Sometimes it can be all three at once really, a perfect storm of bad reasoning dressed up in white and in blooming flowers with church hall music streaming in. But here is the problem - the person you meet today may not be the person you are with in a few years, much less in 5, 10, 15, or 20 - and the person you are expecting to grow or to change may surprise you along the way in ways good, bad, and ugly. What is in front of you and what is possible is not a guarantee of someone's character when the going gets tough.
As for reasons 2 and 3, well, I'll gloss over them, but it is fairly obvious - you have a problem, you think this is a solution because you come across another person, but actually, this is a horrible way to go about it. Do you remember group projects in school when you were assigned a mandatory partner to find? Sometimes it worked if you ended up clicking, but most of the time, it was not enjoyable nor an experience worth repeating. But you did it only because you thought you had to because of an authority, right? Same applies here.
As for people who can't be alone, well, I'll spare them the indignity of being told there's only so much running one can do - and how painful it must be to be someone in a relationship but lonelier than ever before.
You are probably marrying the wrong person. You may have married the wrong person already. Let me back up.
The person you want to marry is not the person in front of you today. It is not the person they will be tomorrow. It is not the person who is the smartest or hottest or wealthiest or whatever-liest. It is not about their potential, it is not about their possibility, it is not about their psychology even. It is not about who they are, what they do, or where they are going. Those are all elements to consider along with the broader decision. They are not the finalizing factors that make or break a lifetime's commitment.
The person you should marry is the 2am person. You know what I mean when I say 2am, but in case it isn't clear, let me cut to the chase: I want you to imagine the worst possible phone call you will ever get in your life. That is the 2am call.
That's the moment you pick up the phone, you hear something said to you, and there's your life before and your life after the call. That is your 2am moment. Now I want you to think about who is going to support you through what happens after you get that phone call.
Your 2am person is the person you marry. Your 2am person is the person who will be your partner, who understands high highs and low lows and everything in between, the person who at 2am has your back even if you weren't the best person you could be in that moment (which is, of course, a nuanced thing and not something that gives anyone a carte blanche to do whatever they want however. My point is the 2am person will be the person who sits with you or deals with it to support you instead of making it a burden or worse in the ways and forms that takes on specific to you.)
Is the hottes… continue on X ↗
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More in Culture & Ideas

How Avery Wang's Shazam Algorithm Identified Songs Without AI

Aakash Gupta explains that Shazam's 2002 system, invented by Stanford PhD Avery Wang, used spectrogram peaks, constellation maps and hash lookups instead of AI, and was published openly in 2003. Apple acquired the company in 2018.

Original post · 2 min read
Shazam could name any song back in 2002, on flip phones, with zero AI. You dialed the number 2580, held your phone up to the speaker, and hung up. 15 seconds later, a text came back with the song name. That same core trick still runs the app today.

The inventor was Avery Wang, a Stanford PhD in audio signal processing. His problem was brutal. Match a short clip recorded on a 2002 cell phone mic, in a noisy bar, against a database of a million songs, in seconds, over a phone call.

His solution treated music as geometry instead of sound.

The algorithm converts audio into a spectrogram, a picture of the song, then throws almost all of it away. It keeps only the peaks, the loudest frequency points at each moment in time. Bar chatter and blown-out speakers can wreck most of a recording. The peaks survive. Shazam only ever needed the peaks.

Those surviving points form what Wang called a constellation map, because it looks like a star field. Pairs of peaks get converted into hash numbers, and identifying a song becomes a dictionary lookup rather than an audio comparison. That made it fast enough to search a million tracks on 2002 hardware.

Wang published the full method openly in 2003 in a paper called "An Industrial-Strength Audio Search Algorithm." Anyone could read exactly how the magic worked. The moat was the database and the deals with carriers, never the secret.

Apple bought the company in 2018 for a reported $400 million. People have tagged over 100 billion songs since the very first one, Jeepster by T. Rex, during the beta in April 2002.

One deterministic signal-processing trick, written before most people had heard the phrase machine learning, and it's still so good that in 2026 everyone assumes it must be AI.
Nathan Ruff @TheNathanRuff
Dude, how did Shazam work 15 years ago without AI!?
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FBI Arrests Fortnite Player Using Epic's Voice Chat Recording

Aakash Gupta reports that the FBI arrested Edward Frith after Epic Games reviewed a reported voice clip of his threat and sent it to authorities, and explains how Fortnite's rolling five-minute voice buffer and reporting system work.

Original post · 2 min read
The FBI just arrested a Fortnite player using a recording the game made of his own voice. He had no idea his headset was taping him. Almost nobody playing does.

Edward Frith, 29, had logged into his account over 1,700 times. In September he told another player that if the FBI showed up at his door he'd shoot them too. Someone in the lobby pressed the report button. Epic reviewed the clip, sent it to the FBI on September 20, and agents arrested him within days.

The design of the system is the clever part.

Recording every player would be a privacy disaster and a storage bill nobody wants. So Epic built voice reporting in 2023 to work like a flight recorder. The audio buffer lives on your own device, overwrites itself every five minutes, and never leaves your machine unless another player in the match reports it. The moment someone does, the clip gets packaged and sent to Epic's safety team with the speakers tagged.

He said it to one stranger in a lobby. That stranger had a button that turns the last five minutes into a federal exhibit.

I was a producer on Fortnite, and this is the part people outside the building never see. Threats of real-world violence got treated with the same urgency as a revenue outage. The game is full of kids, and Epic acts like it.

Fortnite is actually the conservative version of this. It still requires a human to press the button. Call of Duty has run AI moderation directly on live voice chat since 2023, no report needed. Every major platform with a microphone is converging on the same architecture.

The era where anything said into a gaming headset stayed in the lobby is ending one match at a time.
Dexerto @Dexerto
A Fortnite player who threatened to shoot the FBI in voice chat was arrested by the FBI

WagesOfNinja told another user "Let's see if the FBI is going to show up at my door because I'll shoot them motherf**kers too"
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Ohio's New Rome Dissolved Over Speeding Ticket Revenue

Aakash Gupta describes how New Rome, Ohio, and Macks Creek, Missouri, depended on speeding fines for most of their budgets, leading to dissolution in Ohio and bankruptcy in Missouri. Missouri subsequently capped ticket revenue at 20% of city budgets.

Original post · 2 min read
Ohio once dissolved an entire town because its main business was writing speeding tickets.

New Rome had 60 residents and a 14-officer police force. One cop for every four people in town. They collected around $400,000 a year in fines, 92% of the village budget, mostly by working a stretch of road where the speed limit dropped from 45 to 35.

A state audit then found the village was spending 82% of that budget on the police force. The town's only real industry was funding the thing that funded the town. In 2004 a judge ruled New Rome had effectively dissolved itself through corruption and erased it from the map. Its land got absorbed into the neighboring township.

Missouri ran the same experiment with Macks Creek, population 272, sitting on the highway to Lake of the Ozarks. The town wrote 2,900 tickets a year. Eight a day, almost all tourists who would never drive back to fight them. More than 75% of town revenue came from fines.

Then an officer there pulled over a state legislator. He went back to the capitol and passed a law capping how much of a city's budget can come from traffic tickets. Macks Creek lost its revenue stream, went bankrupt, laid off its entire police force, and disincorporated. The IRS seized the town's bank account.

Speed traps cluster wherever a highway full of out-of-towners meets a sudden limit drop in a state that lets the town keep the money. Missouri now caps ticket revenue at 20% of a city's budget. Ohio had to pass a law aimed at one specific village.

A speeding ticket heatmap doubles as a map of who's allowed to keep the fine money.
Terrible Maps @TerribleMaps
Where you’re most likely to get a speeding ticket in the U.S.
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Claire Vo Says Mothers Are Slowest to Adopt AI, Calls for Family-Focused Agents

Claire Vo says many mothers, including those in tech, are slow to adopt AI due to privacy, environmental and workplace concerns. She argues the market is missing agents that manage family logistics like calendars, bills and errands rather than one-off tasks.

Original post · 1 min read
As one of the most tagged AI mother of bots in this thread let me tell you a secret: a lot of moms (even moms in tech) are the slowest f-ing adopters of AI I know.

I’m in a bunch of random mom groups and I hear
- I won’t connect my email cal whatever
- water / data centers etc
- get all AI out of my kids schools
- here is my 10000 word prompt for chat
- I hate AI in my job, burn out, get me out of tech

If you get off X and into the real world, the adoption curve is slow. My YT channel is over 90% men. Women + moms are being left behind, and it’s not because someone hasn’t designed the right agent yet.

Layers here
Sonia Baschez @SoniaBaschez
Silicon Valley keeps building me a husband when what I really want is a wife lol

I don't want an agent that only does one-off, random tasks that are nice but not life-changing: booking flights, making dinner reservations, or buying tickets

I want an AI wife who knows what’s on the family calendar, remembers there’s a birthday party Saturday and we haven’t bought a gift then sends me options, realizes we’re almost out of diapers and buys them, knows which bills are coming up, remembers someone needs a dentist appointment and books it, figures out what the hell we’re eating all week and what w…
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Deedy Explains Shazam's Spectrogram Peak Hashing Method

Deedy Explains Shazam's Spectrogram Peak Hashing Method

Deedy outlines how Shazam extracts high-amplitude spectrogram peaks, hashes them with timestamp and track ID, and performs recognition via a hash table lookup. He notes that his college CS class built a version and recommends the original paper.

Original post · 1 min read
The serious answer to how Shazam worked is it took the peaks of a spectrogram of short clips of every song, find the peaks from the highest amplitude bits, hash it with the value being (time stamp, track id) and then the actual recognition is a hashtable lookup.

We did this for a college CS project. The original paper is fantastic:
Nathan Ruff @TheNathanRuff
Dude, how did Shazam work 15 years ago without AI!?
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Aakash Gupta Offers Formula for Valuing Private Company Equity Grants

Card comparing Google and OpenAI offers

Aakash Gupta shares a formula for valuing private equity grants by discounting quoted equity by payout probability and years to liquidity. He compares a $400K Google grant to a $400K OpenAI grant, estimating the latter at about $242K.

Original post · 1 min read
A $400K grant from Google is worth $400K.

A $400K grant from OpenAI is worth about $242K today.

Same number on the offer letter. You can sell Google stock the day it vests. OpenAI is private, so you sell only when the company runs a tender.

Here's the formula I use for any private grant:

Value = Quoted equity × P(payout) ÷ 1.15^years to liquidity

The 15% is your discount for money you can't touch. For a late-stage company with real revenue and a tender history, P(payout) is 70-90%.

OpenAI, 2 years to a sale at 80% odds: $400K × 0.8 ÷ 1.15² = ~$242K.
1 year at 90%: ~$313K. 3 years at 70%: ~$184K.

Earlier stage gets brutal. A $400K grant at a Series C with an IPO 5 years out at 40% odds: $400K × 0.4 ÷ 1.15^5 = ~$80K.

The formula gives no credit for growth past today's price, and OpenAI has had a lot of it: $157B to $852B in 17 months. So treat the number as your floor.

Compare offers on what the equity is worth today. Then negotiate the gap in base and sign-on.
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