Win S retweeted
1/ Controversial take: hard work is more important than smart work.
It's a myth that we only have a few hours of good creative work per day. Train yourself to grind long hours first. You will surprise yourself. The work naturally become higher quality, less distracted.
Win S retweeted
The rewarding criteria for most blockchain dlt bug bounty programs need to be revisited in order to improve incentive for whitehats.
Let me explain:
Most dlt programs like polkadot and cosmos like chains have defi code, for example polkadot have their pallets while Cosmos chains have modules.
Injective for example have a complex defi focused modules, but this are the impacts in scope:
github.com/InjectiveFoundati…
This means, if you for example find a bug were every yield in their dex could be stolen it will fall under the low:
"A bug in the respective layer 0/1/2 network code that results in unintended smart contract behavior with no concrete funds at direct risk" impact scope, and you'll be paid $1,000
There are some projects that are also defi focused but don't even have this impact in scope, which means finding anything besides loss of funds when hunting in the defi aspects will likely not earn you any reward.
For example the @immunefi gear program(polkadot), and coreum(cosmos):
github.com/gear-tech/gear/tr…
github.com/CoreumFoundation/…
But the pallet is in scope:
When you feel they'll like at least compensate you for the effort by picking the closest impact in scope then you get a message like this:
As an ecosystem, projects need to aim to do better. Help us as we help you.
If the chances of getting paid are low, most researchers skip, personally if you don't have the saving impact in scope i won't even bother with you, after my experience with a program.
While we mostly aim to make web3 saver, the truth is the biggest incentive for most(me included) is money.
Win S retweeted
Whoever created this, huge respect.
webhacklist.com/
Win S retweeted
click your picture in natural light, no editing nothing...keep it real
And upload it on ChatGPT with this prompt!
Prompt:
Create a visual skincare analysis graphic using this portrait. Feature side-by-side comparisons to identify the subject’s likely skin type (oily, dry, combination, or sensitive), visible skin concerns, hydration levels, texture, pores, and overall skin condition. Include a clear skin undertone and skin barrier analysis. Showcase personalised skincare recommendations such as suitable cleansers, moisturisers, sunscreens, serums, and ingredients that would work best for the subject’s skin. The graphic should feel like a premium dermatology/skincare consultation sheet with visual callouts, charts, highlighted facial zones, and before/after-style comparisons. Keep it visual-first using only short labels, tags, icons, arrows, and minimal text – no long paragraphs.
Win S retweeted
Jane Street hired this junior at $220k-$600k /year because he uses AI to analyse TRILLIONS of data
in this 1-hour lecture - he show how to research trillion of data points thanks to his machine
Bookmark & watch it, instead of Netflix to learn how to do the same!
Claude is officially almost dead
- new agent LITERALLY does 99.9% of work for you
Why is Roman Ai better than Claude?
Essentially, roman is a wrapper around the same models (Claude, Gemini, Kimi), but with two key differences:
> Claude is a separate tab where you go to ask questions.
> Roman lives in Slack, where you already work.
You write a message in the channel, it does the task, and responds right there.
> Claude responds with text - then you copy, paste, and run it yourself.
> Roman has its own ecosystem (a virtual machine); it writes the code itself, runs it itself,
- connects directly to your tools (GitHub, Jira, HubSpot, Notion - 3,000+ integrations), and delivers the finished result.
Plus, it retains context across sessions for weeks, rather than losing it like Claude does after a long conversation
So, right now, this is the best Cowork agent,
- which will save you a huge amount of time while building your product!
Readers added context they thought people might want to know
The lecturer is Horace He, a guest speaker at Jane Street, not a hired employee. He worked at Meta on PyTorch and now works at Thinking Machines.
janestreet.com/tech-talks/bui…
x.com/cHHillee/statu…
linkedin.com/in/horacehe
Win S retweeted
SECRETLY READ ANYONE:
1. Status - revealed by their shoes.
2. Discipline - revealed by their nails.
3. Background - revealed by how their sit.
4. Intentions - revealed by their eyes.
5. Personality - revealed by how they laugh.
6. Confidence - revealed by how they walk.
7. Loyalty - revealed by where they look.
8. Intelligence - revealed by how they listen.
9. Maturity - revealed by how they argue.
10. Respect - revealed by how they treat people who can't help them.
Win S retweeted
The man who BUILT Claude Code just sat down and showed how he actually uses it.
Not a tutorial creator.
Not a vibe coder with 3 months of experience.
The CREATOR.
30 minutes of watching him work will restructure how your brain thinks about building with AI.
Most people are using Claude Code like a smarter autocomplete.
He uses it like a second engineering team.
The gap between those two mental models is the gap between shipping toys and shipping products people pay for.
Bookmark this.
Watch it twice.
The second watch hits different once you understand what he is actually doing.
Follow @cyrilXBT for more Claude Code breakdowns the moment they drop.
Win S retweeted
There are 2 career paths in AI right now:
The API Caller: Knows how to use an API. (Low leverage, first to be automated, $150k salary).
The Architect: Knows how to build the API. (High leverage, builds the tools, $500k+ salary).
Bootcamps train you to be an API Caller. This free 17-video Stanford course trains you to be an Architect.
It's CS336: Language Modeling from Scratch.
The syllabus is pure signal, no noise:
➡️ Data Collection & Curation (Lec 13-14)
➡️ Building Transformers & MoE (Lec 3-4)
➡️ Making it fast (Lec 5-8: GPUs, Kernels, Parallelism)
➡️ Making it work (Lec 10: Inference)
➡️ Making it smart (Lec 15-17: Alignment & RL)
Choose your path.
(I will put the playlist in the comments.)
♻️ Repost to save someone $$$ and a lot of confusion.
✔️ You can follow @techNmak, for more insights.
Win S retweeted
One day, a man went to a bank to withdraw money. He filled out a form for ₹1,40,000 and gave it to the cashier. The cashier counted the money and handed it to him.
The man took the money and put it in his bag without counting it. But he had already noticed that the cashier had made a mistake. Instead of ₹1,40,000, he had received ₹1,60,000.
He walked away quietly, pretending he didn’t notice anything. Soon, he started feeling uncomfortable. He thought, “Should I return the extra ₹20,000?” But another thought came, “When I make mistakes, no one returns my money.”
His mind and heart kept arguing. One moment he wanted to return the money, the next moment he wanted to keep it. But deep inside, his conscience told him, “Is it right to take advantage of someone’s mistake?”
He felt more and more restless. Finally, he took ₹20,000 out of his bag, put it in his pocket, and decided to go back to the bank.
As he walked back, he felt lighter and happier, like a big burden had been lifted. When he reached the bank, he returned the money to the cashier.
The cashier felt very relieved. He offered him ₹1,000 as a reward and said, “Thank you. Please buy sweets for your children.”
The man smiled and said, “No, thank you. I am the one who is grateful.”
The cashier asked, “Why?”
The man replied, “Your mistake gave me a chance to test myself. Today, I learned to control my greed. That is my biggest reward. There is no greater reward than honesty.”
Win S retweeted
There's a physicist at Stanford named Safi Bahcall who modeled this exact principle and the math is wild.
He calls it "phase transitions in human networks." When you're stationary, your probability of a lucky event is limited to your existing surface area: the people you already know, the places you already go, the ideas you've already been exposed to. Your opportunity window is fixed.
When you move, your collision rate with new nodes in a network increases nonlinearly. Double your movement (new conversations, new cities, new projects) and your probability of a serendipitous encounter doesn't double. It roughly quadruples. Because each new node connects you to their entire network, not just to them.
Richard Wiseman ran a 10-year study at the University of Hertfordshire tracking self-described "lucky" and "unlucky" people. The single biggest differentiator wasn't IQ, education, or family money. Lucky people scored significantly higher on one trait: openness to experience. They talked to strangers more, varied their routines more, and said yes to invitations at nearly twice the rate.
The "unlucky" group followed the same routes, ate at the same restaurants, and talked to the same 5 people. Their networks were closed loops. No new inputs, no new collisions.
Luck isn't random. Luck is surface area. And surface area is a function of movement.
The lobster emoji is doing more work than most people realize. Lobsters grow by shedding their shell when it gets too tight. The growth requires a period of total vulnerability. No protection, no armor, soft body exposed to the ocean.
That's the cost of movement nobody posts about. You have to be uncomfortable first. The new shell only hardens after you've already moved.
Win S retweeted
looked at how maple's deposits are actually structured on morpho. the numbers are wild.
maple's app shows ~$252M in deposits earning yield from corporate loans, looks clean. but onchain there's only $40m of actual equity behind all of it.
the rest is borrowed. 6.2-6.9x leverage across three markets and zero liquidations have ever happened on any of them.
the borrowed capital funding it is $115M in RLUSD and $90M in PYUSD.
maple's v1 pool defaulted in dec 2022 for ~$36M and that was 1x leverage.
at 6x, a 10% credit impairment wipes 62% of the equity. the same kind of event today would eat $23m of the $38m backing these positions.
syrupUSDC isn't a liquid spot asset, it's a yield bearing wrapper around maple's lending pools. if a credit event triggers liquidations, liquidators need to sell syrupUSDC and there might not be enough buyers at a fair price.
