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:

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

Jun 10
There’s a paradox of seniority: people get more messages, yet they are even more responsive.

This is especially true for important emails.

Here’s why: Image

As an Individual Contributor, your work (outside of certain roles) mostly is internal to your company.

You can spend the vast majority of your day in Slack for responsive tasks.

Email responsiveness matters, but it’s not the most important thing.

As you become a middle manager, email starts to matter more.

You’re hiring folks externally, making calls on agencies… And there’s lots of rapid decision-making to be done cross-functionally.

Responsiveness ramps accordingly.
Read 9 tweets
Jun 1
For a company founded in '93, Nvidia's ascent to $2.7T market cap has been FAST. But what really is Nvidia's moat?

Let's break it down.


The story starts all the way back in the early 2000s. That's when Jensen Huang, Nvidia CEO, and his team were out meeting researchers using their products.

Most researchers were hacking graphics packages to run complex parallel compute tasks. It was not ideal. To say the least.

So, when the Nvidia team met Ian Buck, who had the vision of running general purpose programming languages on GPUs, they funded his Ph.D. After graduation, Ian came to Nvidia to commercialize the tech.

Two years later, in 2006, Nvidia released CUDA.

C ompute
U nified
D evice
A rchitecture

CUDA made all those parallelization hacks the researchers were doing available to everyone. Over time, CUDA became the default choice for researchers.

CUDA allowed accessible customization of the low-level hardware. So developers loved it.

Nowadays, when startups like MosaicML evaluate the available technology vs CUDA, they inevitably choose CUDA.

The ecosystem around CUDA has grown so robust that its lead is virtually unbeatable. This software layer is at the core of Nvidia's moat.


The other side of Nvidia's moat is hardware. But it's not graphics cards for crypto and gaming. The hardware that matters is AI supercomputers.

The story of these supercomputers begins in the late 2000s. As Nvidia was developing CUDA, Jensen asked the team to build a supercomputer to help him build better chips.

The result was a massive supercomputer that weighed 100 pounds and strung together many GPUs with world-class networking for ultra-fast computing.

In the early 2010s, Jensen gave a talk at a conference about this AI supercomputer. Elon Musk got wind of it and said, "I want one."

So, in 2016, Jensen actually donated one to Elon Musk's relatively unknown nonprofit, OpenAI. He hand delivered it, and there's photographic proof.

OpenAI quickly learned the supercomputer worked really well. Especially for training large neural networks. That 2016 Pascal architecture delivered an impressive 19 TFLOPS of FP16 operations.

That's 19 trillion floating point operations per second. It's a massive amount. But that was just the beginning.

Since then, Jensen and the Nvidia team have been lapping the industry in delivering more TFLOPS, growing them at an exponential rate.

The latest Blackwell architecture delivers a massive 5000 TFLOPS. That's >260x AI computer in 8 years. And sells for more than $75K. But buyers like Meta, OpenAI, Google, and Amazon just can't get enough, as their internal ASICs are nowhere near Nvidia's level.

As a result, Nvidia's profits and market cap continue to soar, cementing its position as a leader in the AI hardware and software space.Image
Jensen is one of the most impressive entrepreneurs alive.

He spotted the AI revolution before any other semiconductor CEO and bet the company on it.

That's a rare trait.
And he has a rare management style as well.

He has over 60 direct reports, and doesn't have 1:1s with any of them.

He believes in sharing feedback in public, so everyone can learn from it. And he also does it to remove layers.

Quite the maverick.
Read 5 tweets
May 25
There is no one-size-fits-all when it comes to GTM.

Maja Voje and I studied 12 leading B2B SaaS companies.

(including interviews with their teams)

Here’s what we learned: Image
1. PLG is eating the world

>80% of the companies in our study employ PLG in some fashion.

Even enterprise companies like Snowflake and Salesforce are adding free trials & freemium.

It’s the new normal.
Why is this working for them?

In 2024, the best marketing is often your product.

Users rarely want to lock in a $500K+ contract without trying the product first.

But you do need to layer on a strong product-led sales motion to make enterprise work.
Read 13 tweets
May 16
There's so many ways orgs mess up transforming the product team away from the feature factory.

Here's the top one's, as I see them: Image
1. Understanding and commitment

It's not enough to just have engineering, product, and design on board for transformation.

Transformation impacts marketing, sales, customer success, finance...

You need to drive top-down alignment from the CEO and Senior Leadership Team.
2. Shifting culture

Cultural shift is often the hardest element of a transformation:

• Sales is used to promising features
• Marketing is used to dates when features will arrive

The mistake people make is to gloss over the real tensions for the sake of meeting a schedule.
Read 7 tweets
May 12
The fractional CPO role is the hottest new role in product.

Here's what you need to know:

1/10 Image
1. Fractional v Interim v Consulting

It's easy to confuse the variety of non-traditional product leadership jobs these days.

You can think of a simple 2x2 to distinguish them.

You ask 2 questions:

• Do you lead product?
• Are you full-time?

This breaks down the four types:

1. Lead product & full-time: Interim CPO

2. Lead product & part time: Fractional CPO

3. Don't lead product & full-time: Regular PM

4. Don't lead product & part-time: Product Consultant or Coach

Read 11 tweets
May 11
"I'm in a feature factory. I hate my job."

Spoiler alert: You shouldn't just immediately try to transform the company.

That a recipe for disappointment at best, and getting exited at worst.

Instead, figure out which of these 3 approaches is best for you:

1. Guerilla Tactics
2. Soft Power
3. The Long Game

They all can make you happier, but are totally different.

OPTION 1 - Guerilla Tactics

The reality is that most PMs don’t have the clout to drive transformation for everyone on their own.

So instead of trying to change everyone, you can just change your immediate vicinity.

For instance:

1. Organize 'innovation labs' that champion product model practices.
2. Offer to manage small 'side projects' that run in the product model, showcasing rapid progress.
3. Embed Agile principles subtly, using phrases like 'quick syncs' and 'priority check-ins' to avoid bureaucratic pushback

These are all little, tactical things that you don't have to be super public with. But they make your life better.

The key with all of these is to have a mindset that “I can shape my job.”

OPTION 2 - Soft Power

Another option that has worked for quite a few people is not to go guerilla - but go soft power.

So you're not under cover. You're out in the open.

You do things like:

1. Bring customer discovery and solution iteration into the process
2. Empower your designers and engineers in the what and why
3. Think like an owner about outcomes to drive
4. Then, you document and share those wins.

This is a really nice middle ground that PMs use to get promoted by bringing state of the art practices to their product domain.

Instead of just bringing happiness to your job, you also try to move the company along.

Especially with that step 4.

OPTION 3 - The Long Game

This final option is a long-shot... and too many content creators jumpt to his.

But it can be done by PMs who want to stay at a company--and also effectuate change.

The idea is you:

1. Get in with some execs: you find and win sponsors
2. Patiently prove out the process: you slowly keep showing proof points of success
3. Celebrate, celebrate, celebrate: act as the on-the-ground cheerleader for the initiative

And you try to be that unicorn IC PM who helps drive transformation org-wide.

But beware: the odds are long. Options 1 and 2 also can make you happier.

Too many people underrate them.Image
I see so many PMs saying stuff like this. It's a shame.

The books idealized too much. Enjoy what you have. Image
Too many people get the advice, 'if you're not in this ideal, move to it' and I just think that's too one-size-fits all.

Adapt to what's realistic for your situation!

And be happy in your job. Image
Read 4 tweets

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