Niran Abraham

Global Equity Investor. EX-TD bank, EX- Sun life.

[Niran Abraham @niranabraham]

@SawyerMerritt @elonmusk Come on @Wealthsimple can you bring this offer to Canada.

[Niran Abraham @niranabraham]

@arpitrage @kylascan Thumbs up for point 1 & 2

[Niran Abraham @niranabraham]

@unusual_whales Did Ray Dallio say who will win the FIFA World Cup?

[Niran Abraham @niranabraham]

@ActusDei If Godrej Wealth wants to compete with JioBlackrock, they should consider teaming up with Vanguard. Otherwise, my money is on Blackrock.

[Niran Abraham @niranabraham]

@nsitharamanoffc @monikahalan @nsitharaman @PIB_India @MIB_India @FinMinIndia @MCA21India @DDIndialive @airnewsalerts Hopefully there is a chapter on removing double taxation.

[Ming @tslaming]

Antonio Gracias, Elon Musk’s best friend, loaned Musk $1 million to keep SpaceX alive 🤝

Now he is poised to make as much as $140 billion from SpaceX’s IPO, making him one of the 50 wealthiest people alive, according to Fortune 💰

[Niran Abraham @niranabraham]

@livemint What about Indians overstaying their visas and remaining in the country for 10-plus years? I personally know a few.

Does Mint have anything to say about that?

[JeebsTX 🇺🇸 @JeebsTX]

If you’re having a bad year, just remember:

Mark Zuckerberg wasted $73 billion on the Metaverse.

Tim Cook wasted $10 billion on the Apple Car.

Jeff Bezos lost $10 billion chasing Alexa.

Masayoshi Son lost $14B investing in WeWork and missed out on $150 billion by selling Nvidia early.

Cathie Wood dumped Nvidia right before ChatGPT launched and missed $1.2B

Logan Paul bought an NFT for $630,000 in 2021. Today, it's worth only $140.

Even the RICH and famous get it completely wrong sometimes.

[Niran Abraham @niranabraham]

@GadSaad These days, I only watch movies recommended by @TheCriticalDri2. I can’t see Devil Wears Prada 2 being recommended.

[Niran Abraham @niranabraham]

@GadSaad Not in this life will I watch TDWP 2.

[Niran Abraham @niranabraham]

My copy of Gad's newest book Suicidal Empathy has arrived. Citizens of X, go out and grab your copy. You won’t regret buying this book @GadSaad

[Niran Abraham @niranabraham]

@tobi Just like the Nevada state pension fund which I hear is 100% invested in index funds/ passive strategies. I first heard about it on a Meb Faber podcast @MebFaber

[Niran Abraham @niranabraham]

@DeghTeghFateh01 @DanielBordmanOG Daniel is an honours graduate of Khalistan University of Brampton. He knows more about Sikh culture than anyone I know.

[Niran Abraham @niranabraham]

@ActusDei @rravindia Wherever u live, global diversification > none.

[Niran Abraham @niranabraham]

Meta is still the No1 social media platform

[Niran Abraham @niranabraham]

@jasonpereira Subscribed to your Substack

[Niran Abraham @niranabraham]

@MebFaber Some of these NBA floppers make excellent trampoline stars.

[Niran Abraham @niranabraham]

@gurjota Also proves @HindenburgRes was right all along about Adani. @gautam_adani Adani also learned a painful lesson not to mess with American regulators.

[Niran Abraham @niranabraham]

@scoopercooper Sam Cooper has exquisite taste for music.

[Nav Toor @heynavtoor]

Elon has open-sourced the algorithm that decides what you see on X.

Not a blog post explaining it. Not a press release describing it. The actual source code. With the actual model weights. On GitHub. Every line. Every signal that determines whether your post reaches 100 people or 100 million.

This is the most powerful recommendation engine on Earth. And now you can read it.

Here is what the algorithm actually looks at:

→ Fifteen actions. The model does not score "relevance." It predicts 15 separate probabilities for every post: favorite, reply, repost, quote, click, profile click, video view, photo expand, share, dwell, follow the author, not interested, block author, mute author, report. Your final reach is the weighted sum of all 15.

→ Negative actions hurt more than you think. Block, mute, report, and not-interested are weighted negatively. Every single mute on your post subtracts from your score. A post that triggers one mute and ten likes does not net ten likes. It nets ten likes minus one heavy penalty.

→ Bookmarks are not tracked. Bookmark is not in the 15 predicted actions. Every creator guru telling you to "write bookmark-worthy posts" is wrong, at least according to this released code. Bookmarks may matter for the user privately, but they do not feed the ranker.

→ Dwell time is a silent vote. If someone stops scrolling and lingers on your post without engaging, that counts as a positive signal. The model has a P(dwell) prediction. The algorithm knows when people pause.

→ Profile clicks are tracked separately. P(profile_click) is its own prediction. People clicking from your post to your profile is a distinct positive signal. Your post sells the click, your bio closes the follow.

→ Quote is worth more than repost. The model predicts P(quote) and P(repost) as two different actions with two different weights. A quote tweet, where someone adds their own commentary, carries information a silent repost does not. The algorithm rewards conversation, not amplification.

→ Follow-after-engagement is tracked. P(follow_author) is one of the 15 predictions. A post that converts a reader into a follower is rewarded directly by the ranker. Not because of a hardcoded rule, but because the model learned the signal.

→ The Author Diversity Scorer punishes spam posting. There is a specific component in the code called Author Diversity Scorer. Its job is to attenuate repeated scores from the same author. Posting twenty times a day cannibalizes your own reach. The algorithm enforces diversity automatically.

→ Candidate isolation. Each post is scored alone against your history. Posts cannot see each other during scoring. Your post is not competing with the post above it in the batch. It is competing with the user's taste model.

→ No hand-engineered features. There is no rule in the code that says "boost verified" or "suppress external links" or "promote subscribers." The Grok transformer learns everything from raw engagement sequences. Whatever the model has learned, it learned from user behavior, not from a hardcoded boost.

→ Out-of-network discovery. The Phoenix retrieval system uses a two-tower model. One tower encodes you. One tower encodes every post on X. They meet in a vector space. The closest matches surface in your For You feed even if you do not follow the author.

→ Visibility filters run last. After ranking, a final pass strips spam, violence, gore, and deleted posts. Ranking decides reach. Visibility decides whether you see it at all.

Here is what this means for you:

If your posts are not reaching people, it is not because the algorithm is broken. It is because the algorithm is working exactly as designed. It rewards:

1. Posts that get reactions across multiple action types (a like AND a profile click AND a follow beats five likes alone)
2. Conversation depth (quote tweets are worth more than reposts in the math)
3. Dwell time (write posts people stop to read)
4. Posts that convert viewers into followers (your bio is part of your post)
5. Variety from each author (post less, post better)

And it punishes:

1. Mutes
2. Blocks
3. Reports
4. "Not interested" clicks
5. Same-author saturation

For twenty years, the algorithm was a secret.