Aakash Gupta Profile picture
Apr 1, 2023 21 tweets 7 min read Read on X
Twitter revealed its algorithm to the world.

But what does it mean for you?

I spent the evening analyzing it.

Here’s what you need to know:
1. Likes, then retweets, then replies

Here’s the ranking parameters:

• Each like gets a 30x boost
• Each retweet a 20x
• Each reply only 1x

It’s much more impactful to earn likes and retweets than replies. Image
2. Images & videos help

Both images and videos lead to a nice 2x boost. Image
3. Links hurt, unless you have enough engagement

Generally external links get you marked as spam.

Unless you have enough engagement. Image
4. Mutes & unfollows hurt

All of the following hurt your engagement:

• Mutes
• Blocks
• Unfollows
• Spam reports
• Abuse reports Image
5. Blue extends reach

Paying the monthly fee gets you a healthy boost. Image
6. Misinformation is highly down-ranked

Anything that is categorized as misinformation gets the rug pulled out from under it.

Surprisingly, so are posts about Ukraine. Image
7. You are clustered into a group

The algorithm puts you into a grouping of similar profiles.

It uses that to extend tweet reach beyond your followers to similar people. Image
8. Posting outside your cluster hurts

If you do “out of network” content, it’s not going to do as well.

That’s why hammering home points about your niche works. Image
9. Making up words or misspelling hurts

Words that are identified as “unknown language” are given 0.01, which is a huge penalty.

Anything under 1 is bad.

This is really bad. Image
10. Followers, engagement & user data are the three data points

If you take away anything, remember this - the models take in 3 inputs:

• Likes, retweets, replies: engagement data
• Mutes, unfollows, spam reports: user data
• Who follows you: the follower graph Image
Shoutout to all the people analyzing:

@NFT_GOD
@amasad
@mxpoliakov
@0xCygaar
@xerocooleth
If you enjoyed this,

1. I write daily threads to help you grow. You may like to follow: @aakashg0

(But if you’re going to unfollow, go ahead and don’t!)

2. Consider RTing the first tweet so others can benefit:
As much as it's fun to analyze the Twitter algorithm, it's also fickle.

Most of my content doesn't make it to your feed.

Subscribe to the newsletter to get my best and deepest work: aakashgupta.substack.com
How to optimize for the algorithm:

Likes, then retweets, then replies
You are clustered - posting outside it hurts
Links hurt. Mutes & unfollows hurt
Misinformation is down-ranked
Images & videos help
Blue extends reach
Making up words or misspelling hurts
New learning: There’s also something known as “Heavy Ranker”

This heavily weights replies to replies and time spent on Tweet.
Additional learning:

Your follower to following ratio matters.

Following way more than follow you hurts.

Use lists. Image
The big open question is: what about bookmarks?

The predominant opinion right now is favcountparams() 30x multiplier's formula is:

Likes + Bookmarks = Favorites Count

It doesn't look to be in the code right now. Part of the problem here is what's on GitHub is incomplete.
Do you want to go the layer deeper to understand how all these code snippets and boosts work together?

I have spent the whole weekend going deeper than my Friday evening analysis.

Get the overall framework in today's newsletter: aakashgupta.substack.com/p/the-real-twi…
And here's why Twitter is the best place to get your information.

This thread was featured on the Reddit home page and Yahoo News: Image
Now that the algorithm is public, Elon Musk wants to update it every 24-48 hours.

Here's what he should do:

• • •

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More from @aakashg0

Dec 20
One thing that will remain the same in the 2025 job landscape:

Hiring managers will still ask, "Why this company?"

Nail it, and you’re halfway to landing your dream role.

Here’s how to master this: Image
Over the years, I’ve worked with countless aspiring and senior PMs.

99% of them give a 4/10 answer.

Here’s where they go wrong:

1. Generic fluff → They rattle off buzzwords like “innovative culture” or “market leader.” It’s uninspired.
2. No connection → They talk about the company, but not how they fit into the picture.

3. Vague details → It’s clear they didn’t research. No examples, no depth. Just surface-level compliments.

4. Lack of energy → The delivery feels more like a verbal LinkedIn endorsement.
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They embed quality into every step of the development process.

Here’s how you can do the same: Image
𝗙𝗶𝘃𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗖𝗵𝗲𝗰𝗸𝗽𝗼𝗶𝗻𝘁𝘀

𝗢𝗡𝗘 - 𝗕𝗲𝗳𝗼𝗿𝗲 𝗙𝗶𝗻𝗮𝗹𝗶𝘇𝗶𝗻𝗴 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀

→ When: Right after you’ve done your research and before locking down specs.
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→ Win: Fewer mid-sprint surprises and last-minute changes.
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PMs, you can add at least $50k to your salary by becoming an AI PM.

However, many people miss the mark, making the same mistakes over and over again.

Here are the 7 mistakes to avoid if you want to secure your dream AI role: Image
𝗠𝗶𝘀𝘁𝗮𝗸𝗲 #𝟭: 𝗧𝗵𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗼𝗿’𝘀 𝗖𝘂𝗿𝘀𝗲

→ Collecting certificates may feel productive, but it’s often just an illusion of progress.

→ Instead of paper chasing, identify a problem, build a prototype, and document your process.
𝗠𝗶𝘀𝘁𝗮𝗸𝗲 #𝟮: 𝗧𝗵𝗲 𝗖𝗼𝗱𝗲-𝗣𝗵𝗼𝗯𝗶𝗰 𝗣𝗮𝗿𝗮𝗹𝘆𝘀𝗶𝘀

→ Coding helps you to connect with your dev team and independently prototype ideas.

→ Start with Python, build simple models, and soon enough, coding will become your new best friend.
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Looking to land a PM role at Google, Amazon, or Meta?

Here’s the truth: It’s not enough to just secure the offer.

You need to nail the team matching process.

Here’s how to land the team (and role) you want: Image
𝗪𝗛𝗔𝗧 𝗜𝗦 𝗧𝗘𝗔𝗠 𝗠𝗔𝗧𝗖𝗛𝗜𝗡𝗚 𝗣𝗥𝗢𝗖𝗘𝗦𝗦?

At top tech companies, landing an offer is only the beginning.

After that, it’s time for team matching: the stage where PMs find their “home base” within the company.

Why this process is crucial for both parties?
Because it ensures that PMs are placed where their skills and expertise can be best leveraged to drive maximum impact and results for the team and the organisation as a whole.

There are two paths here:

→ Sometimes you’re hired for a specific team, like “PM, Instagram Growth.”
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The biggest bottleneck in scaling your product with enterprise clients?

Not getting your product ready.

Here’s what you need to know to transform for enterprise: Image
𝗧𝗛𝗘 𝗣𝗜𝗧𝗙𝗔𝗟𝗟𝗦 𝗜𝗡 𝗖𝗢𝗡𝗩𝗘𝗡𝗧𝗜𝗢𝗡𝗔𝗟 𝗪𝗜𝗦𝗗𝗢𝗠

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These are important but just the price of admission...
Companies like @Atlassian, @datadoghq, and @SnowflakeDB have shown that winning in the enterprise space requires a fundamental transformation.

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This founder dropped out of college and built a $20B company.

Here's the crazy story of how Figma went from nothing to one of the iconic startups: Image
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