bluecollarMD

[Justin Skycak @justinskycak]

Structure isn’t a cage. It’s a cache for your best decisions, so you can spend your energy living freely instead of renegotiating your life from zero every morning.

[Justin Skycak @justinskycak]

Expertise is what happens when thousands of prerequisite skills have become automatic. Education is the engineering challenge of making that happen efficiently.

[0xSlyth @0xSlyth]

A 24 YEAR OLD QUANT TRADER RELEASED A 55GB DATASET SHOWING HOW HEDGE FUNDS ARBITRAGE PREDICTION MARKETS

most traders stare at charts

quant funds stare at order books

The dataset contains:

-> 850M+ order book updates

-> Full L2 depth up to 20 levels

-> 100ms snapshots

-> Cross platform data from Polymarket and Kalshi

Here is what they do with it:

-> Find price gaps between identical markets on different exchanges

-> Model spreads using Ornstein Uhlenbeck mean reversion

-> Measure order book imbalance in real time

-> Calculate micro prices before trades happen

-> Predict which platform will move first

-> Capture the spread before retail traders even see it

the crazy part?

they don't care who wins the election

they don't care about the news

they don't care about the outcome

they are trading market structure itself

while retail traders predict events

quant funds predict other traders

the edge isn't information

It is mathematics data and execution speed

bookmark this so you can read the article in your spare time

[Andrew @s4yonnara]

Andrej Karpathy spent 70 minutes breaking down how top AI users actually work with LLMs.

The reality is simpler than people expect. You tell the model what you want in plain language and let it run.

No 40-line system prompts. No secret tricks.

By 2026 the engineer who writes off LLMs loses to the junior who just set one up properly.

70 minutes. Free. A rare straight look from an OpenAI co-founder.

Bookmark it and watch.

[Movez @0xMovez]

Creator of Claude Code:

"At Anthropic, almost 100% of our engineers are running 100+ agents with self-improving loops

self-improving loops help agents become better with each run."

in a 1-hour podcast, Boris explains how they build agents loops from sratch.

Claude + loops + routines + dynamic workflows - that’s the secret.

Watch the talk, then read how to apply the same playbook to quant trading below.

[Justin Skycak @justinskycak]

Status games become dangerous when they disconnect from genuine skill accumulation.

[codila @0xCodila]

Anthropic Quant Andrej Karpathy:

"Most people use tools that they don't understand

- the ones who strip everything down to basics - end up faster than everyone else "

"the best code is the code anyone can read "

he couldn't fix a bug in 2 hours, so instead of googling - he rewrote the entire system from scratch

no frameworks. no dependencies. it ended up faster

that's the difference between using AI and understanding it

25-min masterclass - bookmark and watch

[Hamza Khalid @humzaakhalid]

An asian guy has discovered a method to learn anything ten times faster using AI!

It just involves the Claude + Obsidian.

Most people learn the slow way: read, forget, re-read, forget again.

His flip: use Claude to turn anything you're learning into small, connected notes. Use Obsidian to link them so nothing you learn ever sits alone.

The slow way: highlight a book, move on, forget it in a week.
The fast way: Claude breaks it into atomic notes, and Obsidian links them into a growing web of knowledge.

Six months in, one new idea instantly connects to twenty things you already know.

I broke down every Claude resource you should try to master claude in 7 days with practical guide that most people have never found.

Article below ↓

[Rahul @sairahul1]

🚨 Karpathy was right.

He warned that 90% of AI advice dies in 6 months

spoiler: most tools will not even survive 90 days

this guy is literally giving away the exact 2026 playbook for AI Agents.

he covers what to learn, how to build, and when to skip 👀

↓ read this today

[Justin Skycak @justinskycak]

It's kind of wild how fast many kids will progress though grade levels once their instruction is adapted to their own personal knowledge profile and pace of learning.

Like one of those physics phenomena that makes sense in theory but is still incredible to witness firsthand.

I remember back when I was teaching in our original school program my incoming 6th graders would place into Prealgebra, fly through it, continue flying through IM1, before you know it they're in IM2, then IM3, and by the time you come to terms with the fact that they're a 7th grader with all of HS math under their belt, they're already cruising through calculus.

And then that opens up all sorts of opportunities that few people even understand are possible.

[Charly Wargnier @DataChaz]

🚨 @ElonMusk relies on a strict 5-step algorithm to avoid the deadliest engineering trap:

Optimizing things that shouldn't exist.

To stop wasting time, you must follow this exact order:

1/ Question the requirements
→ Never assume they are perfect, no matter who wrote them. Challenge them early to avoid flawlessly answering the wrong question.

2/ Delete parts or processes
→ Ruthlessly eliminate steps. The rule: If you aren’t forced to add back at least 10% of what you cut, you haven't deleted enough.

3/ Optimize and simplify
→ Only streamline a process once you are 100% sure it actually needs to exist.

4/ Speed it up
→ Things can always run faster, but never accelerate a step before you’ve questioned, deleted, and optimized it.

5/ Automate
→ This is your absolute final step. Don't automate backward.

By strictly enforcing this sequence, Elon Musk guarantees his teams only focus on what matters.

Want to dive a little deeper?

@jaynitx wrote an excellent, detailed breakdown of this framework 👀↓

[SITA @S1TA10]

A 22-YEAR-OLD FROM LONDON CLOSED A DEAL ON AN AI AGENT TEAM WITH ZERO DEVELOPERS ON HER TEAM. CLIENT SIGNED THE CONTRACT. SYSTEM RUNS ITSELF.

she is not a programmer. not technical. has no team.

but she has four agents and one pipeline that does what others pay $370,000 a year for.

agent 1 scrapes google maps and instagram while she sleeps. leads are already in the system by morning.

agent 2 creates a personalized plan and mockup for every potential client. automatically. no human involved.

agent 3 writes a personal outreach email for each lead and drops a ready draft directly into the client's gmail.

agent 4 coordinates the work of all three. tracks the status of every lead. signals when a human is needed. the rest of the time - full autopilot.

she did not write code. she made a proposal. negotiated the terms. got the contract signed.

claude code did everything else.

most businesses still keep people on tasks that require no decisions - only execution. lead generation. cold outreach. personalized mockups. emails. anything with a clear algorithm AI closes better. faster. without errors from exhaustion.

and while a competitor waits for a reply from a junior - her system already sent its hundredth email today.

[spect @spectnfa]

🚨 NVIDIA CEO, Jensen Huang said in an interview:

"Nobody writes prompts anymore"

The NVIDIA CEO says the real skill now isn't prompting it's writing and managing loops.

He's calling it the shift that defines the rest of 2026.

Full 23-minute talk below 👇

[liemandt @jliemandt]

This Monday, 100 students from Stanford, MIT, and UT start our Alpha School summer internship.

Their mission: fuse learning science with AI to build apps that transform how kids learn.

@gdb @demishassabis @elonmusk @karpathy - we'd love early access to your frontier models to put into the hands of these students.

[Chamath Palihapitiya @chamath]

There are many “simple” features that are more complicated than they look on the surface because of a cascade of dependencies.

For complex, enterprise systems, this is true of most features.

8090’s Software Factory is built to handle this flawlessly. We first help write requirements, expand and frame dependencies and then execute with a more global knowledge of the problem.

You can learn more here: https://t.co/fkfTXgdfXK

Also, I’m completely in love with our visual system. 😍

[Justin Skycak @justinskycak]

Stop turning every weakness into a permanent identity.

[Quant Science @quantscience_]

How to build your own algorithmic trading analyzer with Claude Code.

(bookmark this for later)

[Rohan Paul @rohanpaul_ai]

Big claim in this paper, pushes against the common idea that more test-time compute should keep helping.

Claims a code model gets much better when it rethinks once (i.e. by looping once) inside itself, but worse when it keeps rethinking.

The first loop builds context, the second loop refines it, and later loops mostly disturb it.

The paper studies a faster design called Parallel Loop Transformer, where loops can run almost in parallel and share memory, so the authors can ask a cleaner question about how many loops are actually useful.

They trained 7B code models with 1, 2, 3, and 4 loops on 18T tokens, then tuned and tested them on code writing, code reasoning, software engineering, and tool-use tasks.

The main result is that 2 loops worked best, raising SWE-bench Verified from 43.0 to 64.4, while 3 and 4 loops often got worse.

Their internal checks suggest loop 2 does the real useful refinement, because it changes the model’s hidden states, attention patterns, and predictions in meaningful ways.

After loop 2, the extra loops mostly add weaker, more repetitive changes, while a built-in position shift keeps adding the same kind of mismatch cost.

Overall, the paper gives a simple lesson for efficient test-time compute: adding 1 hidden loop can help a lot, but adding more is not automatically better.

----

Link – arxiv. org/abs/2606.18023

Title: "LoopCoder-v2: Only Loop Once for Efficient Test-Time Computation Scaling"

[rari @0xwhrrari]

Google CEO, Sundar Pichai:

"If you don't learn how to orchestrate agents now, you'll spend 2027 catching up to people who started today"

In 30 minutes, he explains why the best engineers are moving from writing code to running agents

One agent researches
One writes
One tests
One reviews
One fixes

The human becomes the operator, not the bottleneck

Bookmark and watch the interview

[David Sacks @DavidSacks]

This brilliant lesson on communication by @rabois is why the AI leaders are failing. It’s not sufficient just to “speak your truth.” You have to communicate in a way that elucidates your audience. Convincing the public that your company is a menace obviously fails that test.