Femi Fashakin

Co-founder @zerotrustedai - Building the future of AI Security through the lens of game theory. WSOP champion. WR holder - won largest live poker tournament

[Atal @ZabihullahAtal]

🚨 BREAKING: A new role is quietly emerging and it’s about to dominate the next 5 years.

It’s not “AI engineer.”
It’s not “prompt engineer.”

It’s the Agent Operator.

And it will sit inside almost every organization.

Most people are still thinking about AI as a tool.

That framing is already outdated.

What’s actually happening is a shift from:
humans using software to humans managing autonomous agents that execute work

This is a fundamental redesign of how work gets done.

So what is an Agent Operator?

An Agent Operator is the person who:
• Designs how agents interact with real workflows
• Connects tools, data, and systems into agent pipelines
• Translates business problems into executable agent behavior
• Monitors, corrects, and improves agent performance over time

They don’t just “use AI.”

They orchestrate outcomes.

and this matter because
Every function marketing, legal, finance, biotech is becoming “agent-compatible.”

Not because companies want it.
Because they won’t have a choice.

Agents can:
• Run research loops
• Execute multi-step workflows
• Integrate across tools without APIs breaking the flow
• Operate 24/7 at near-zero marginal cost

The bottleneck is no longer capability.
It’s implementation inside real-world systems.

Required skills for AI Agent Operator role:
→ MCPs (Model Context Protocols)
Understanding how agents access tools, memory, and structured context.

→ CLIs (Command Line Interfaces)
Because serious agent workflows won’t live in GUIs—they’ll run in programmable environments.

→ Writing skills (the file kind)
Clear specs, instructions, and structured documents.
Agents run on precision, not vibes.

→ agents dot md fluency
The ability to define agent roles, constraints, memory, and tool usage in persistent formats.

→ Business acumen
Knowing what actually matters:
Where automation creates leverage, not noise.

What happens next
Enterprises will begin to redesign workflows:

Not around employees using dashboards…
But around agents executing tasks.

That means:
• SOPs → Agent playbooks
• Teams → Human + agent hybrids
• Tools → Composable agent systems

When that shift happens, companies won’t just need engineers.
They’ll need operators who understand both the system and the business.

The leverage is asymmetric

One strong Agent Operator can:

• Replace fragmented SaaS workflows
• Multiply team output without adding headcount
• Turn ideas into execution systems in days

This is not incremental productivity.

It’s operational transformation.

[Rimsha Bhardwaj @heyrimsha]

🚨BREAKING: Claude can now find your startup's fatal flaw the way Paul Graham does in the first 5 minutes of an interview (for free).

Most founders discover it at month 8. Some never do.

Here are 9 Claude prompts that surface the real problem before you build the wrong thing.

(Save before you hire)

[Dustin @r0ck3t23]

Andrew Ng just revealed why the AI companies throwing the most compute at the problem are going to lose.

The winner of the intelligence race won’t use the most compute.

They’ll waste the least.

Ng: “Most of your high-dimensional data lies on a lower-dimensional subspace. It’s just a fact of life.”

Here’s what that means in practice.

You have a 10,000-dimensional dataset.

Every dimension dragged through every calculation.

Every training cycle hauling dead weight the model will never use.

Ng: “You’re carrying around these 10,000-dimensional examples throughout your whole training process.”

That bloat isn’t just inefficient.

It’s a tax on every computation you run.

Memory bandwidth. Network bandwidth. Computational speed.

All of it eaten by dimensions that contribute nothing to intelligence.

They contribute noise.

The insight that separates the architects from the arms race: that 10,000-dimensional dataset is almost entirely captured by a much smaller subspace.

The signal lives in a fraction of the space you’re paying to process.

Compress it. 10,000 dimensions down to 1,000.

Ng: “You can run your learning algorithm on a much lower-dimensional set of data and it may be much more efficient.”

Same hardware. Same budget. A fraction of the friction.

Brute force is the strategy of whoever has the deepest pockets.

Compression is the strategy of whoever actually understands the problem.

The companies that master this don’t just build faster models.

They build models that find more truth in less data than anything scaling blindly ever will.

Intelligence was never about processing everything.

It’s about knowing what to cut.

[Xiaoyin Qu @quxiaoyin]

Stanford CS grads can’t find jobs right now.

A few years ago, that would’ve sounded absurd. Today, friends are texting me asking if I know anyone hiring interns. The resumes? Stanford. MIT. Top-tier CS. All struggling.

When I was in school, companies competed for CS majors. Signing bonuses. Exploding offers. Recruiters chasing students. That world is gone.

Big tech isn’t hiring junior talent the way it used to. Meta cut back on interns and entry-level engineers. OpenAI largely hires senior+ talent. The hiring bar shifted up. At the same time, most companies aren’t adding headcount — they’re trying to extract more productivity from existing teams.

But here’s what’s interesting: some 19–22 year olds are still getting hired — and getting paid more than engineers with years of experience.

What separates them?

They prove they’re exceptional early. They publish research. They ship real products, not just coursework. Some skip the traditional path entirely and go straight to OpenAI or Google. The credential filter is weakening. Proof of execution is replacing pedigree.

They dominate hackathons. A 19-year-old won xAI’s hackathon and Elon hired him on the spot. AI companies are looking for people who explore, build, and execute fast. Hackathons are becoming live auditions.

And many of them build in public. They create content, explain AI tools, grow audiences. Marketing and DevRel teams notice. If you can use AI well and communicate clearly, you’re suddenly more valuable than someone with a decade of silent experience.

The gap between “can’t find a job” and “multiple premium offers” has never been wider.

The old playbook was: get the degree and wait to be picked.
The new playbook is: build, ship, compete, publish.

AI didn’t just change the tools. It changed how talent gets discovered.

#TechCareers #AI #fyp #SiliconValley #FutureOfWork

[Math Files @Math_files]

Bayes’ theorem is probably the single most important thing any rational person can learn.

So many of our debates and disagreements that we shout about are because we don’t understand Bayes’ theorem or how human rationality often works.

Bayes’ theorem is named after the 18th-century Thomas Bayes, and essentially it’s a formula that asks: when you are presented with all of the evidence for something, how much should you believe it?

Bayes’ theorem teaches us that our beliefs are not fixed; they are probabilities. Our beliefs change as we weigh new evidence against our assumptions, or our priors. In other words, we all carry certain ideas about how the world works, and new evidence can challenge them.

For example, somebody might believe that smoking is safe, that stress causes mouth ulcers, or that human activity is unrelated to climate change. These are their priors, their starting points. They can be formed by our culture, our biases, or even incomplete information.

Now imagine a new study comes along that challenges one of your priors. A single study might not carry enough weight to overturn your existing beliefs. But as studies accumulate, eventually the scales may tip. At some point, your prior will become less and less plausible.

Bayes’ theorem argues that being rational is not about black and white. It’s not even about true or false. It’s about what is most reasonable based on the best available evidence. But for this to work, we need to be presented with as much high-quality data as possible. Without evidence—without belief-forming data—we are left only with our priors and biases. And those aren’t all that rational.

[Parimal @Fintech03]

ultimate irony: bayes, the man whose name is on the most important eqn in rationality, never published it & likely did not think it was a law of the universe. bayes wrote his thoughts on inverse probability in a private notebook to solve a specific problem about billiard balls. when he died in 1761, the notebook was stuffed in a drawer. his friend, richard price, found the papers & price spent 2 yrs obsessively refining the math cos he wanted to use it to prove the existence of god (by calculating the probability that the universe's order was not an accident). price is the one who presented it to the royal society; w/o his search for god, the math for modern AI & medical screening would have been thrown in the trash.

[Femi Fashakin @femitfash]

@calcsam BOOK

[Mitch Forest - edu/acc @MitchForest]

You have an idea. A SaaS tool you’ve been thinking about for months. You fire up Cursor and v0.
24 hours later, you have a working demo. Clean UI. Real features. You can barely believe it.

You post it on X. Replies flood in:
“this is sick”
“How did you build this so fast?”
“Let me know when it launches.”

You’re buzzing. This is it. You’re finally going to be a founder.

Week 2:
You add auth. Payments. A dashboard.
The AI is printing features.
Every night you go to bed feeling like you’ve hacked the matrix.

Week 3:
A user reports a bug. Simple fix, right?
You prompt the AI. It changes the code.
Now two other things are broken.
You fix those. Now the login flow is broken.

Week 4:
Every change detonates something else.
The codebase feels like a minefield.
You no longer understand how the pieces fit together.
You only know they barely do.

You tell yourself: I’ll rebuild it properly this time.
I know what I’m building now.

Month 2:
You haven’t opened the repo in three weeks.
The domain renewal email sits in your inbox.
Another idea is forming.
Maybe this one will be different.

It won’t be.

One night, doomscrolling, you find a post about why vibe-coded apps die.
Someone links a PDF from 1985.
A guy named Peter Naur.

You almost skip it.
But you’re desperate.
So you read.

Naur’s idea hits you like a brick:

Programming isn’t writing code.
Programming is building a theory.

A mental model of how your system works
and how it maps to real-world problems.

Without that theory, you don’t have software.
You have fragments that happen to compile.

Holy shit.
That was you.

The AI generated code, but you never built the theory.
You never designed the architecture.
You never understood how the pieces should fit.
You were just prompting and praying.

So you start over.
But this time actually is different.

You take a weekend with a whiteboard.
No code.
Just thinking.

What is this system?
How should it behave?
What are the core entities?
How do they relate?

You sketch the architecture.
You design the data model.
You write down the rules.
You build the theory in your head first.

Then you open Cursor.

Now when you prompt, you’re not asking the AI to figure things out.
You’re asking it to execute your vision.
You review every change.
You check that it fits the theory.
You keep the system coherent and simple.
You guard against drift.

The AI becomes a multiplier instead of a mind.

Three months later:
Real users.
Paying customers.
Feature requests that don’t break the world.
The app isn’t a demo anymore.
It’s a product.

Here’s the truth nobody tells you about vibe-coding:

It works beautifully when you own the theory.

When you’ve done the thinking.
When you understand the architecture.
When you know what you’re building and why.

AI can generate code at light speed.
But it cannot generate the vision behind it.

That shift from “AI do everything” to “I architect, AI executes” is everything.

Vibe code all you want. Just make sure someone is actually thinking.

The graveyard is full of people who mistook prompting for architecture.

[Gaurav Sen @gkcs_]

This video explains how diffusion models are overtaking Large Language Models for generation tasks like:
1. Code Generation
2. Image Generation
3. Video Generation

00:00 Agenda
00:20 How are they different from LLMs?
05:09 Internal Mechanism
10:09 How are vectors generated?
12:08 Conclusion
13:02 Opinion Piece

AI Engineering Course: https://t.co/uzJfgsGfou

#Diffusion #AI #LLMs

[Femi Fashakin @femitfash]

@henrythe9ths Super cool story.

Fundraise.

[Henry Shi @henrythe9ths]

I got rejected by 144 investors before raising $150M for my $200M+ rev/year startup.

After 144 rejections, I started questioning our approach.

Were we solving the right problem?

What were we doing wrong?

Why weren’t investors seeing what we were seeing?

Were we the right team to build this?

We tried everything: different pitch angles, new deck structures, and reframing the problem.

Then came the 145th meeting, where we closed our first growth round.

That yes made everything worth it. But getting there took years of mistakes and hard work.

We went through a lot of trial and error just to figure out what resonates with investors.

We tried dozens of approaches to figure out what made investors engage.

Some landed, most didn't. But each iteration taught us something about what builds conviction versus what just sounds good on paper.

And once we cracked that code, our Series C closed faster than expected.

And today, I see so many founders in the exact same position I was in 10 years ago: grinding through rejections, questioning everything, and trying to figure out what works.

So today I want to give you the resource I wish I had back then:

Something that shows you exactly how to structure these conversations and navigate the entire process

(because the fundraising cycle can be a big distraction and take a toll on you as a founder).

So I've partnered with Notion's Startups Team to create the essential fundraising resource that helps you avoid the mistakes that cost me years.

Here's what you are getting:

• The actual decks I used to raise $150M for Super[.]com (Series B, C)

• 50 real examples from funded startups like Eleven Labs and Artisan AI

• A searchable database of 10,000+ investors - angels, VCs, and accelerators you can reach out to immediately (this alone would take months to build manually)

• An AI-powered fundraising agent built into Notion with step-by-step prompts (no separate ChatGPT needed)

Want access?

• Like and share this post

• Comment "FUNDRAISE"

• Follow me so I can DM you the link

I'll send it over ASAP.

P.S.: If you are serious about fundraising (now or in the future), you should grab it right away.

[Andrej Karpathy @karpathy]

Agency > Intelligence

I had this intuitively wrong for decades, I think due to a pervasive cultural veneration of intelligence, various entertainment/media, obsession with IQ etc. Agency is significantly more powerful and significantly more scarce. Are you hiring for agency? Are we educating for agency? Are you acting as if you had 10X agency?

Grok explanation is ~close:

“Agency, as a personality trait, refers to an individual's capacity to take initiative, make decisions, and exert control over their actions and environment. It’s about being proactive rather than reactive—someone with high agency doesn’t just let life happen to them; they shape it. Think of it as a blend of self-efficacy, determination, and a sense of ownership over one’s path.

People with strong agency tend to set goals and pursue them with confidence, even in the face of obstacles. They’re the type to say, “I’ll figure it out,” and then actually do it. On the flip side, someone low in agency might feel more like a passenger in their own life, waiting for external forces—like luck, other people, or circumstances—to dictate what happens next.

It’s not quite the same as assertiveness or ambition, though it can overlap. Agency is quieter, more internal—it’s the belief that you *can* act, paired with the will to follow through. Psychologists often tie it to concepts like locus of control: high-agency folks lean toward an internal locus, feeling they steer their fate, while low-agency folks might lean external, seeing life as something that happens *to* them.”

[Sahil Bloom @SahilBloom]

Everyone needs to hear this…

[Tom Yeh @ProfTomYeh]

At MIT, I learned about RNNs in my NLP class with Prof. Michael Collins. He built a model from my keystrokes to predict who I was. To me, it felt like a magic box. Years later, when I had to teach RNNs, I forced myself to go inside the box. ⬇️ Download: https://t.co/pD5gFhOrpr

First, with a tiny example on paper by hand ✍️.
Then, a slightly larger one in Excel.

That’s when it finally clicked:
👉 The weights are reused (weight matrices on the left side)
👉 The hidden states are passed down (H's)

When I built it by hand and saw everything visually, it clicked, just math you can actually trace.

Now I try to give others the same “aha!” moment I had.

⬇️ Download Excel: https://t.co/pD5gFhOrpr

[Femi Fashakin @femitfash]

@PeterGFraz @FarazJaka Underrated comment!

[Femi Fashakin @femitfash]

@AdamHendrix10 @pokerorg Bro, you got a main event to ship. Forget about Kassouf. Sending you good vibes!!!

[Femi Fashakin @femitfash]

@DavidUvaydov Insane odds.

[Sahil Bloom @SahilBloom]

A lesson I wish I learned earlier: Talent and intelligence are abundant. Courage is not. The people you admire are the ones who had the courage to act. They aren’t more talented than you. They aren’t smarter than you. They just took action when you didn’t.

[Femi Fashakin @femitfash]

@Kevmath Can you please provide a link to lookout for satellite updates or schedules?

[Jason Cohen @asmartbear]

You’re overwhelmed by too many things to do, and not good enough at most of them.

Is it fatal?

Not yet. 1000s of successful startups faced the same constraints on time and ability.

You don’t need to be great at everything; execute the few most-important things.