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

Jul 17
There's a new PM interview in town.

But most PMs don't know it yet.

Last week, a PM messaged me in panic:

"I just got destroyed in a Google interview. They wanted me to prototype something with AI tools. I'd never even heard of 'vibe coding' before."

Here's what happening:
This isn't isolated. I'm coaching more PMs who are blindsided by this new interview format every week.

Here's what's happening behind the scenes:

The nature of the PM role is undergoing its biggest shift in years.

We're now moving to the era of AI PMs.
Companies are looking for PMs who can:

1. Build AI features (understanding evals & tech details)
2. Use AI to focus on high-leverage tasks
3. Prototype with AI tools like pros

And their interviews are changing to match.

At Google, this is only in India (for now).
Read 7 tweets
Jul 16
>50% of PM interviews now ask metrics questions.

But not all metrics questions are the same.

5 categories + 16 sub-categories to know: Image
𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 (𝟮𝟭% 𝗼𝗳 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀)

Three major flavors here:

Product launch success: Measuring launching GPT-5.

Feature success: Measuring a mobile payment feature.

Business model success: What would you look at if we launched a marketplace?
𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰𝘀 (𝟮𝟮% 𝗼𝗳 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀)

Investigating issues when metrics drop.

Sudden drops: IG Stories dropped 5% overnight. Approach?

Gradual declines: Monthly retention declining 6 mos. Why?

Unexpected changes: Feature 30% → 60% engagement. WDYD?
Read 7 tweets
Jul 14
Context engineering is the new prompt engineering.

Here's everything you need to know: Image
1. Etymology

It's unclear who coined the term, but two folks have been particularly important in its rise:

Andrej Karpathy and Dex Horthy.
2. What It Is

It's all about thinking beyond the prompt - thinking about ALL of the tools you have to drive success.

5 are the most important to understand:

a. RAG
b. Memory
c. State/ History
d. Prompt Engineering
e. Structured Outputs
Read 8 tweets
Jul 11
Which is it: use LLMs to improve the prompt, or is that over-engineering?

By now, we've all seen a 1000 conflicting prompt guides.

So, I wanted to understand:

• What do actual studies say?
• What do experts at OpenAI, Anthropic, & Google say?

Here are the answers: Image
I spent the past month in Google Scholar, figuring it out.

I firmed up the learnings with Miqdad Jaffer at OpenAI.

Some of my favorite takeaways from the research:
1. It's not just revenue, but cost

You have to realize that APIs charge by number of input and output tokens.

An engineered prompt can deliver the same quality with 76% cost reduction.

We're talking $3,000 daily vs $706 daily for 100k calls.
Read 10 tweets
Jul 5
If you're preparing for PM interviews in 2025, there's one question type you cannot afford to mess up: Metrics.

Here's the history of how it over took product hiring and why it's the silent killer of PM dreams: Image
PMs have been getting hit with questions like these...

And while they’re not as sexy as product sense or design...

They’ve quietly become non-negotiable in most interviews. Image
So, what happened?

In the late 2000s, Big Tech needed a way to simulate real PM work...

- Something they could ask in 45 minutes
- That showed judgment under pressure
- And gave them clean signals across a large candidate pool.

Metrics interviews were perfect.
Read 9 tweets
Jun 26
Jira Product Discovery (JPD) just launched their biggest update yet.

It will take them from 18K → 36K customers.

Here’s why I think so: Image
1. From Teams → Teams of Teams

I covered Jira Product Discovery (JPD) earlier this year.

They were really good for a product team.

Now, with this month’s launch of Premium, they are really good for multiple product teams.

This is a huge unlock…
2. The Lack of Standardization Problem

For a Head of Product or CPO that has multiple things they are taking care of, it’s hard to get a central view of everything.

Everyone has a slightly different roadmap template.

JPD allows you to centralize all of that!
Read 9 tweets

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