leno_fred

AI/ML Engineer · Agent systems & LLM security researcher · Formalized the Puzzle Piece Privacy Problem (PZPP) · Energy & Finance AI ·Building things that matter

[Kirill @kirillk_web3]

https://t.co/IscgkTMDM9

[Lewis 🏴󠁧󠁢󠁷󠁬󠁳󠁿 @WaldronLewis]

Elon Musk literally sat down for a 45-minute talk with Y Combinator that explains how to build world-changing companies better than any business school on earth. This is the advice he gave a room full of young founders:

1. Don't try to build something great. Try to build something useful.

Everyone obsesses over greatness. Musk says that's the wrong target. "I didn't originally think I would build something great. I wanted to try to build something useful. I didn't think I would build anything particularly great. Seemed unlikely, but I wanted to at least try." Aim for useful first. Greatness, if it comes, is a byproduct.

2. When you can't get in the front door, build your own door.

Before Musk started his first company, he tried to get a job at Netscape. "I sent my resume into Netscape and nobody responded. I tried hanging out in the lobby to see if I could bump into someone, but I was too shy to talk to anyone. So I'm like, this is ridiculous, I'll just write software myself." He didn't set out to be a founder. He became one because no one would hire him.

3. He slept in the office and showered at the YMCA.

The origin of his first company was not glamorous. "We couldn't even afford a place to stay. The office was 500 bucks a month, so we just slept in the office and showered at the YMCA." He couldn't afford proper internet either, so he drilled a hole through the office floor and ran a cable to the internet provider downstairs. That was the founder of the future richest man on earth.

4. Keep the chips on the table.

When Musk sold his first company, he received a $20 million cheque. His bank balance went from $10,000 to $20 million overnight. Most people would have stopped. He put almost all of it straight back into his next company. "I kept the chips on the table." He did the same thing decades later, over and over. He hates money sitting idle. Money is fuel for the next mission.

5. Start with the mission, then work backwards to make it a business.

Musk didn't start SpaceX to make money. He went on the NASA website to find out when humans were going to Mars, and there was no plan. So he decided to build one. "There had been no prior example of a rocket startup succeeding. A small chance of success is better than no chance of success." The mission came first. The business model came later.

6. He started SpaceX expecting to fail.

He is brutally honest about the odds. "SpaceX started in mid-2002 expecting to fail. Probably 90% chance of failing. When recruiting people, I said, we're probably going to die, but small chance we might not die." The first three launches failed. The fourth one worked with no money left. "If the fourth launch hadn't worked, it would have been curtains. We made it by the skin of our teeth."

7. Break every problem down to physics.

This is the core of how Musk thinks. "First principles means break things down to the fundamental elements that are most likely to be true, then reason up from there, as opposed to reasoning by analogy." His example is rockets. Everyone priced them based on what old rockets cost. Musk asked what a rocket is actually made of, priced the raw metals, and found the materials were only 1-2% of the historical price. The rest was inefficiency he could attack.

8. When told something takes 24 months, break it down and do it in six.

Last year xAI needed a giant computer to train its AI. Suppliers said it would take 18 to 24 months. "It's like, well, we need to get that done in six months or we won't be competitive." So he broke it into parts. Needed a building, so he found an old factory. Needed power, so he rented generators. Needed cooling, so he rented a quarter of America's mobile cooling capacity. He slept in the data centre and ran cabling himself. It got done.

9. Watch your ego-to-ability ratio.

Musk's single sharpest piece of advice for young founders is about staying honest with yourself. "A major failure mode is when your ego-to-ability ratio gets too high. Then you break the feedback loop to reality." Keep the ego small, internalise responsibility for everything, and stay ruthlessly connected to what's actually true. "You want to close the loop on reality hard. That's a super big deal."

10. Chase work, not glory.

His closing philosophy ties it all together. "It's so hard to be useful. The area under the curve of total utility is how useful you've been to your fellow human beings times how many people. If you aspire to do true work, your probability of success is much higher. Don't aspire to glory, aspire to work."

He was ridiculed for years. The press called him "internet guy attempting to build a rocket company." He agreed it sounded absurd. He did it anyway, because a small chance of doing something useful beat no chance at all.

Here's the thing though....

Musk became the most followed founder alive because everything he does happens in public. The launches, the failures, the talks like this one. The companies made him powerful. The personal brand made his every word travel around the world before he finishes saying it.

We build massive distribution and grow personal brands on X and beyond without our clients lifting a finger.

If you're a founder or VC looking for that kind of exposure, book a call below.

We average 1.5M views a week.

https://t.co/UoXuYlkBQq

[Matt Dancho (Business Science) @mdancho84]

RIP document extractors.

Google just released LangExtract: Open-source. Free. Better than $100K enterprise tools.

Here’s what it does: 🧵

[jules @juleslogs]

reading ML papers without implementing anything gives a fake sense of progress

[diva @divaagurlxw]

People obsessed with engineering, computer science, robotics, AI, AGI, machine learning, deep learning, neural networks, LLMs, SLMs, transformers, RAG systems, vector databases, agents, automation, DevOps, MLOps, distributed systems, backend architecture, scalable systems, Linux, Arch Linux, NixOS, Docker, Kubernetes, Kafka, cloud computing, cybersecurity, cryptography, reverse engineering, system programming, low-level coding, Rust, C++, Python, APIs, startups, indie hacking, SaaS, product building, hacking ideas together at 2AM, UI/UX, minimalism, branding, typography, industrial design, cinematography, philosophy, existentialism, absurdism, stoicism, psychology, neuroscience, cognitive science, mathematics, physics, astronomy, quantum theory, futurism, transhumanism, biohacking, economics, game theory, geopolitics, Dostoevsky, Kafka, Camus, Nietzsche, Orwell, literature, poetry, storytelling, dark academia vibes, deep conversations, cafés, notebooks full of ideas, overthinking reality, and changing the world with weird ideas automatically become my friends

[Richard Socher @RichardSocher]

https://t.co/HjRCjERRGY

[Tim Shi @timshi_ai]

Excited to share that we’ve started Recursive Superintelligence to automate knowledge discovery, starting with AI that experiments on how to improve itself. We raised $ 650m+ led by GV and Greycroft.

[maja 🔭🍒 @majamediaco]

i listened to the people and turned my thoughts on finding side doors in career context into a full essay.

read here: https://t.co/AlMF6zMyQX

[Raul Junco @RaulJuncoV]

Push-based systems come up in 90% of system design interviews.

Here's the exercise you should be able to solve:

Design a notification system for 100M users. Some have 50 followers. Some have 10M.

The instinct is to hold a WebSocket connection open to every active user and push updates as they arrive. Clean mental model. It collapses the moment a celebrity posts.

When someone with 10M followers posts, you push to 10M open connections simultaneously. Your message broker saturates. Your WebSocket servers fall over. The system fails at the exact moment it needs to work.

That's the fan-out problem. And it kills more interview answers than any other mistake.

The production answer: push and pull aren't binary. You pick based on follower count. Users with fewer than 1,000 followers get push fan-out. Each follower gets notified immediately.

Users with millions of followers get pull fan-out. Their feed assembles on read. Nobody gets a push. Followers see the post when they open the app.

Twitter built exactly this: push-on-write for small accounts, pull-on-read for large ones.

But fan-out is only half the problem.

Push means stateful connections. Your servers now need to know which connection lives on which machine. You can't route blindly. Most teams reach for Redis pub/sub here; the WebSocket server subscribes, the backend publishes, the message finds the right node.

Add a 3-second network drop and you have another layer: what did the client miss? Now you need sequence IDs, a message buffer, and reconnect logic that replays missed events.

"Push-based" became push with a pull fallback, a message broker, sticky routing, and a replay buffer.

Most engineers stop at the first diagram.
The ones who get the offer keep pulling the thread until the system breaks.

[Dwarkesh Patel @dwarkesh_sp]

New blackboard lecture w @reinerpope

How do chips actually work – starting with basic logic gates, and working up to why GPUs, TPUs, FPGAs, and the human brain each look the way they do.

0:00:00 – Building a multiply-accumulate from logic gates
0:16:20 – Muxes and the cost of data movement
0:25:59 – How systolic arrays work
0:39:00 – Clock cycles and pipeline registers
0:51:40 – FPGAs vs ASICs
1:03:14 – Cache vs scratchpad
1:07:16 – Why CPU cores are much bigger than GPU cores
1:11:49 – Brains vs chips
1:15:22 – A GPU is just a bunch of tiny TPUs

Look up Dwarkesh Podcast on YouTube/Spotify/etc to watch. Enjoy!

[Dwarkesh Patel @dwarkesh_sp]

.@reinerpope explains the difference between a CPU and a GPU. A huge chunk of a CPU die is actually just predicting what code will run next. GPUs strip this out, which is part of why they can fit so much more compute on the chip.

[Sebastian Raschka @rasbt]

Added a DeepSeek Sparse Attention (DSA) from-scratch implementation to my LLMs-from-scratch repo thanks to an awesome new reader contrib.
With motivation, overview, and GPT-style model reference implementation as standalone example code: https://t.co/o2PMhjF0TN

[Elliot Arledge @elliotarledge]

Co-Founder of Cerebras explains their WSE simplified design compared to classical GPUs made by NVIDIA.

[Mustafa @oprydai]

how to become a modern polymath

not by randomly learning everything.
that’s just intellectual hoarding.

a modern polymath needs structure.

what actually matters:
• build a strong spine → math, physics, computer science, writing. these fields compound into everything else
• go deep in one domain → you need one hard skill where you can actually produce real work
• go wide around it → biology, economics, history, design, psychology, philosophy. breadth gives you pattern recognition
• connect fields aggressively → innovation usually happens between domains, not inside clean academic boxes
• build artifacts → apps, essays, robots, diagrams, simulations, systems. knowledge must leave your head
• teach what you learn → if you can’t explain it simply, you don’t own it yet
• study reality, not just books → markets, machines, people, nature, institutions. the world is the real textbook

the goal is not to look smart.
the goal is to become useful across problems.

a polymath is not someone who knows random facts.

it’s someone who can move between domains,
extract principles,
connect patterns,
and build something real from the synthesis.

[Uncle Ruckus @Emarged]

“Until death, all defeat is psychological."

[Elon Musk @elonmusk]

Where will AI be in 1, 2 or 3 years?

[leno_fred @Allen_fred1]

Every new capability granted to an agent is a new attack surface.
Every new inter-agent communication channel is a new compositional risk.

#AI #machinelearning #deeplearning #AIagent #agent #AISecurity #SecurityBreachRuin #security #Agentic

[Roan @RohOnChain]

The secret of Hedge Funds is revealed in a 17 page PDF.

Stanford released the complete Hidden Markov Model framework that quants at firms like Jane Street & Two Sigma are known to use & released it for free.

Bookmark this & read the article below before someone takes it down.

[Boring_Business @BoringBiz_]

Ali Ghodsi, CEO of Databricks, sits down with Stanford students to discuss the AI value chain

Worth the full 40 minute listen

Thought the most interesting takeaways from the conversation were

1/ AGI has already been solved based on prior definitions, but we keep moving the goalpost on it

2/ Despite AI being an incredible tool, it still lacks the context that sits inside of human brains at most organizations

3/ Software is not dead, but the barrier to entry has fallen dramatically. Companies need to innovate faster to stay ahead

4/ Value will accrue to the application layer eventually. The same thing happened with the internet (Uber, Airbnb, Amazon)

5/ Frontier models is an economies of scale game with shrinking margins over time

Shoutout to @apoorv03 for hosting another fantastic discussion. Please go follow him if you arent already

[Eyo Eyo, PhD @Eyowhite3]

2. Generative AI Fundamentals

https://t.co/p0TFaUJKtr