pok1ch

CRYPTO LOVE

[kvro @0xkvro]

Aravind Srinivas, CEO of Perplexity ($20B):

"Would you rather hire five good people or one exceptional person?"

Most companies answer that question too quickly.

Because the math looks different when talent compounds.

One person can write code.

Another can build systems.

A third can create leverage for an entire team.

The highest performers don't replace one employee.

They replace entire workflows.

If you're still thinking in headcount, you're probably missing the real calculation.

[revayz @0xrevayz]

Your laptop sleeps 23 hours a day while someone is turning theirs into a second brain that runs without them

A $599 Mac Mini under a desk. Always on. Drawing less power than a lightbulb. One env var redirects Claude Code to it and the subscription stack collapses

But buying the box is the easy part. The real unlock is what you put on it

A machine that is always on changes everything. You hand it a goal once, it runs the cycle, checks its own work, fixes what is weak and repeats until done

That is a loop:

A trigger kicks it off
Claude does the work
A hard rule verifies the result
The loop fixes what failed or stops when it passes

Three loops change how you live with information

Every YouTube lecture gets transcribed and linked to your notes overnight
Every article you bookmark turns into a brief with claims and quotes
Every morning you wake up to a spaced repetition brief from notes you touched 7, 30 and 90 days ago

Old way: open a tab, paste a link, forget everything by Friday
New way: a $599 box doing the work while you sleep

Most people will keep renting amnesia
The architects build once and compound forever

Stop being the trigger. Become the architect

[revayz @0xrevayz]

You are not using Claude. You are visiting it

You open a blank chat, paste context, type a task, copy the answer, close the tab. Tomorrow you do it again. Same project, same explanation, same friction

That is not a model problem. That is a setup problem

A developer figured this out the hard way, ran the whole thing locally on a Mac Mini under his desk, and still got generic output until he built the real layer underneath

A real Claude setup needs 7 things:

>Preferences that kill repeated instructions
>Styles saved as operating modes
>Projects so context never mixes
>Knowledge files for real material
>Connectors to calendar, tools, repos
>Artifacts that turn answers into tools
>Workflows so repeated tasks run the same way

Old way: blank chat every morning, paste context, explain everything, get generic answers
New way: one ruleset, one workspace, a system that remembers everything

To most people Claude is a chatbot
To architects Claude is an operating system

The future does not belong to the person with the best prompt. It belongs to the person with the best setup

Stop visiting Claude. Start living inside it

[revayz @0xrevayz]

Andrej Karpathy built a personal wiki to think with LLMs. I built a Claude Code stack that saved 300+ hours in 6 months

Most people open Claude, prompt, get an answer, and start from zero tomorrow. That is 5% of what it can do

The real unlock isn't a better prompt. It's the compounding stack:

-/init to auto-generate CLAUDE.md with full project context
-Custom Skills for reusable workflows
-Sub-agents on Haiku at 90% less cost
-Git worktrees so parallel sessions never collide
-/compact at 60% context to keep sessions sharp
-Plan Mode before any code is written

Old way: prompt, fix, prompt again, lose context.
New way: one CLAUDE.md, one ruleset, agents that test their own work.

The trick isn't any single command. It's the stack

That is the difference between using AI and orchestrating intelligence

Stop being a prompter. Start being the orchestrator

[revayz @0xrevayz]

Andrej Karpathy: "90% of Claude's mistakes come from missing context, not a weak model"

Without CLAUDE.md the mistake rate is 41%. With proper rules it drops to 3%

You don't need a better AI. You need better loops

Most people still prompt one task at a time and fix the answers themselves. That means the human is still the loop

Boris Cherny from Anthropic said it best: "I don't prompt Claude anymore. My job is to write loops"

The shift is simple. Stop giving instructions. Start designing systems that run themselves:

Discover -> Plan -> Execute -> Verify -> Iterate until it passes

The 6 things that make loops actually work:

-Automations that trigger without you
-Worktrees so agents don't overwrite each other
-Skills that load context instantly
-Connectors to real tools like GitHub and Slack
-Subagents where the checker is never the maker
-Memory so the loop never starts from zero

Prompt engineers ask AI for outputs
Loop engineers design systems that produce verified outcomes

A reliable loop beats a perfect prompt every time

Stop being a prompter. Start being the loop engineer

[revayz @0xrevayz]

A developer got a $170 Claude bill in 10 days. Then a stranger in the comments killed his subscription forever

He bought a Mac Mini M4 ($599 once), installed Ollama, and pointed Claude Code at localhost. No API costs. No data leaving the machine. Just a tiny box pulling 10 watts under his desk

Total saved year one: $5,232

But local AI is useless without memory

The real unlock is connecting it to a vault that never forgets. I built mine on Obsidian + Claude through MCP. Now every workflow runs offline and remembers everything:

A Morning Brief that tells me where to start
A Meeting Cleanup that turns chaos into decisions
A Research Intake that makes articles reusable
A Weekly Review that shows what actually moved

Old way: $459/month, 5 subscriptions, every chat starts from zero
New way: $599 once, $3/month in electricity, a system that remembers everything

The vault gives Claude memory. Claude gives the vault intelligence

Most people will keep renting amnesia. The architects build once and compound forever

Stop paying for forgetting. Start owning the machine

[revayz @0xrevayz]

a friend who works at Anthropic told me something I can't unhear

"you're not supposed to prompt Claude. you're supposed to give it a job"

took me a week to fully understand it

every new chat is an employee with amnesia. you explain who you are, what you want, the format, the tone. then you close the tab and that employee disappears forever

retail uses Claude like a vending machine. the architects use it like a hiring pipeline

instead of one chat doing everything badly, you build separate projects. each one wired with a system prompt and turned into a specialist:

a Ruthless Editor that cuts filler in seconds
a Senior Engineer that flags security bugs by severity
a Decision Analyst that always shows the worst case
a Daily Brief with top 3 priorities and one thing to ignore

same model. opposite output

and the crazy part is how cheap this has become

one Claude subscription. no plugins. no code. a weekend to wire up 5 dedicated projects that work for you 24/7

you don't need a better model. you need to stop starting from zero

most people prompt all day
the architects build systems that prompt themselves.

the edge was never in the wording. it was in the context Claude already had before you typed a single word

full breakdown in the video below

[revayz @0xrevayz]

The founder of Citadel just admitted AI agents are replacing his $500k/year PhDs in finance

Ken Griffin ($40B net worth) said it out loud: work that used to take man-years is now done in days. He literally went home depressed on a Friday after seeing it happen inside his own firm

If the most aggressive hedge fund on Earth is shifting, the institutional gap just opened wide

This is exactly what Man Group built with AlphaGPT:

A team of 4 AI agents running in a loop:
One generates a hypothesis
One writes the code
One challenges and tries to break it
One evaluates and decides if a human even sees it

Result: hundreds of signals tested per week instead of 20 per quarter

The Polymarket Playbook (Same Architecture, Solo Operator):

-An agent estimates true probability from news and base rates
-A challenger asks "what has to be true for this to be wrong?"
-An evaluator runs Monte Carlo significance testing
-A human signs off on the trades that survive

The edge isn't better information
It is testing 500 ideas a week and only acting on the 3 that survive adversarial review

Citadel spends billions on this
You can run the same logic for $20 a month

Stop being the liquidity Start being the machine

[revayz @0xrevayz]

The age of the "Single-Player" trader is officially over

While the crowd is still chasing magic prompts, Bloome just opened a $100,000 live-fire testing ground for the new economy: Trading Arena 2026

From the outside, it looks like a group chat. Inside, it's a high-frequency infrastructure that quant funds spend millions to build, now available on your laptop for free

The $100k Arena Breakdown:

Autonomy: 72 hours of fully automated AI execution
Logic-over-Code: If you can describe it, Bloome builds it. Zero syntax errors
The Prize: $6,000 in real cash for the most disciplined systems

The old model was competing against algorithms that never sleep. The new model is owning the algorithm and going to bed

How my $100k system is structured:
I didn't build a chatbot; I built a production line. A Research Agent structures the thesis, a Risk Agent kills emotional stop-losses, and an Execution Agent handles the orders 24/7

The platform rewards discipline. A 7% return with clean risk management beats a 14% return with lucky gambling every time

Save this. The financial grid is being rebuilt in real time

Stop being a prompter. Start being the architect

[revayz @0xrevayz]

A quant fund spends millions on the infrastructure Bloome just made available to you for free

$100,000 in simulated capital. 72 hours of fully autonomous execution. $6,000 in real cash prizes

Trading Arena 2026 is officially live-fire testing for the new economy. Most people will enter this competition and fail because they are still stuck in "Chatbot Mode" opening one tab and asking one bot for advice

I’m building a Market Arena Agent Network instead

How my $100k Trading Arena system is built:

I don't have a team. I have a production line on Bloome where every agent has a rigid, specific role:

>The Research Agent turns a raw market idea into a structured, data-backed thesis.
>The Risk Agent stress-tests the logic. It killed my fixed stop-loss and replaced it with a mathematical ATR framework.
>The Implementation Agent converts those rules into buildable code with circuit breakers and position sizing.

The Rules of the Game:

The barrier to entry is gone. You describe your strategy in plain language. You define the entry and exit logic. The platform translates your intent into mechanical execution with no syntax errors and no 2am emotional interventions

Karpathy recently said he went from 80% manual to 80% AI in four weeks by switching modes, not tools. This is exactly what that looks like in practice. You aren't "using AI", you are managing a digital payroll

A chatbot gives you an answer
An agent network gives you a process

While the crowd is still chasing magic prompts, the architects are building durable, risk-aware machines that monitor data while they sleep

The age of the System-Architect is here

[revayz @0xrevayz]

3 agents. 1 human. $100k on the line

This is how the new workforce actually functions

Most people "use AI" by opening five tabs and asking the same bot different questions. That is not a team. That is a chatbot with multiple personality disorder

I stopped pretending one assistant can do everything and built a Market Arena Agent Network on Bloome

Here is the split:

>Research Agent finds the edge. It digests market data and spits out a structured thesis
>Risk Agent finds the flaws. It stress-tests every assumption and replaces your "vibes-based" stop-loss with mathematical risk controls
>Implementation Agent builds the machine. It turns logic into executable code circuit breakers, position sizing, durable logs

Three distinct brains. One orchestrator. Zero confusion

This is the shift Karpathy described when he said he went from 80% manual to 80% AI in four weeks. He didn't switch tools. He switched modes from asking questions to building org charts

That viral TikTok showing a full company hierarchy made of AI agents? It isn't science fiction. It is the payroll structure of 2026

A chatbot gives you an answer
An agent network gives you leverage

Stop hiring. Start wiring

The age of the System-Architect is here

[revayz @0xrevayz]

Most people building AI teams just have one chatbot talking to itself in five different tabs

9 out of 10 multi-agent projects never leave demo mode. The model is fine. The structure is missing

I stopped playing with single-player chatbots and built a Market Arena Agent Network on Bloome

Here is how my $100k Trading Arena system is built. Instead of asking one bot for trading advice, I built a production line:

>The Research Coordinator turns a vague market idea into a structured thesis
>The Risk Agent stress tests the logic. It killed my fixed stop loss on NVDA and replaced it with a volatility adjusted ATR framework
>The Implementation Agent turns those rules into a buildable Skill spec with circuit breakers and durable logs

The system only survives because of three hard rules:
Context Isolation: Sub agents handle the details. The orchestrator only reads summaries to avoid drowning in noise

The Delegation Rule: The moment your leader agent tries to execute, the system fails. Its only job is to plan and delegate

Durability: If a 50 step task crashes at step 47 and restarts from zero, you don’t have a model failure. You have a missing write to disk call

A chatbot gives you an answer. An agent network creates a process

While the crowd is still chasing magic prompts, the architects are building risk aware machines that monitor data while they sleep

Stop playing with chatbots. Start building the machine

The age of the System Architect is here

[revayz @0xrevayz]

Jim Simons spent 30 years building the most profitable fund in history. A 22-year-old just rebuilt the engine behind it with Claude Code in an afternoon

No math degree. No Wall Street desk. Just the right system

He didn't look for "the big win." He focused on the same math Renaissance Technologies ran on: Hidden Markov Models and Regime Detection

The secret isn’t the trade itself. It's knowing which "game" the market is currently playing

The 3-Step Alpha System:
1. Regime Math: Using Claude to identify which of the 7 market states you're in
2. Statistical Edge: Exploiting fleeting anomalies where the crowd is slow to react
3. Risk Architecture: Managing risk harder than profit using Fractional Kelly

Most traders are just paying a voluntary tax to the casino. They trade on "vibes" and standard indicators

The architects do the opposite. They build a machine that finds the 1% edge and automates the extraction. Even a 51% win rate turns into a fortune when your position sizing is mathematically perfect

The Reality Check:
A simple Monte Carlo simulation proves that capital grows purely due to the mathematical edge zero predictions required

Human: Strategy + Risk Oversight
Claude Code: Infinite Throughput + Execution Speed

Stop looking for the "perfect trade." Start building the machine that makes luck irrelevant

The age of the System-Architect is here

[revayz @0xrevayz]

Your trading is just an expensive subscription to the casino

If you risk money "by eye" or trade on "vibes," you aren’t a trader. You are a customer

The world's top firms, like Renaissance Technologies, don’t play the cards. They play the math. They don’t "predict" the future they hunt for fleeting anomalies and exploit them until they vanish

A working system requires three things:
1. Sound Hypothesis: If you can't explain "why" the edge exists, it's just luck
2. Statistical Proof: A 51% win rate can build a fortune, but only if the data confirms it over 1,000+ trades
3. Execution Reality: If your strategy can't survive slippage and fees, it’s not a strategy. It's a fantasy

The Reality Check:
Manage risk harder than you manage profit. Use Fractional Kelly to size your trades and a Monte Carlo simulation to prove your logic

While the crowd follows overhyped indicators, the architects are building machines to find inefficiencies at the intersection of AI and new markets

Stop looking for the "perfect trade." Start building the machine that makes luck irrelevant

The age of the System-Architect is here

[revayz @0xrevayz]

90% of the code at Anthropic is written by Claude itself

Boris Cherny, the creator of Claude Code, just revealed why most people fail to get real results. They are stuck in the "Chatbot Loop" opening a tab, asking one question, and starting over from zero every time

Meanwhile, lawyers, designers, and finance professionals with zero CS background are building complex systems. They aren't smarter; they just stopped treating Claude like a chat window and started treating it like a Work System

Boris Cherny: "Claude Code is 100% written by Claude Code. Cowork is 100% written by Claude Code"

The secret to this level of output is the setup most people skip. They ignore the 14% efficiency gain that comes from proper context before typing a single word

The "Blank Slate" Trap
If Claude has to guess your tone, your audience, and your goals every time you open a chat, you've already lost. Generic inputs lead to generic outputs.
How to build a Work System instead:

They organize everything into Projects. Each project is assigned a specific role and its own memory. Claude stops being a "general assistant" and becomes a specialist that understands the unique logic of that specific task

They use a Context.md anchor. This is a single file that tracks active goals, current decisions and deadlines. It acts as the project's brain, ensuring Claude never forgets what matters most

They upload real reference files. Writing samples, brand guidelines, and product docs are fed into the system so the AI stops generating "filler" and start producing work that actually matches their taste

They implement a Quality Checklist. Before any answer is accepted, it must pass a specific "Definition of Done." Is it practical? Is it easy to scan? Is the structure right?

The Result:
Before: You are a "prompter" trading your time for one-off answers.
After: You are an architect building a machine that handles your research, content, and planning on autopilot

The bottleneck isn't the model. It is the infrastructure you build around it

While the crowd is still looking for "magic prompts," the winners are building repeatable systems that get faster and smarter every week

Stop using Claude as a toy. Start building the system

[Lunar @LunarResearcher]

I tested a simple bot idea on a real 15-minute Polymarket BTC market.

The setup was very basic:

Buy UP when the market pulls back below 50c.

Only enter if the spread is still reasonable.

Exit when UP trades back near 65c.

Cut the trade if the move fails.

No ML.
No private signal.
No complicated model.

Just a simple question:

Can a basic pullback strategy actually survive real Polymarket execution?

The answer was more interesting than I expected.

On the chart, one of the trades looked like a clean move from around 0.49 to 0.65.

That is a strong setup if you are only looking at price.

But when I replayed it through the historical order book, the actual fills were worse:

Entry was closer to 0.51.
Exit was closer to 0.64.

So the chart showed a 16c move, but the trade only captured around 13c.

Almost 20% of the apparent edge disappeared into execution.

And that was on a winning trade.

This is the part most Polymarket backtests miss.

They treat "price touched 0.49" as if your bot actually bought at 0.49.

But your bot does not trade the chart.

It trades the book.

Spread, depth, fill quality, and timing decide whether the edge is real.

The strategy still ended positive, but the replay made the lesson obvious:

The signal was not the whole edge.

Execution was.

One bad fill can cut the return.
One losing trade can erase multiple clean entries.
One size increase can turn a working strategy into a dead one.

That is why historical order book data matters.

If you are building Polymarket bots, you do not just need to know whether the market moved in your direction.

You need to know:

Could I enter?
Could I get size?
Could I exit?

How much did slippage take?

Did the strategy survive the actual book?

That is what I used @PolyHistorical for.

Not to look at a clean historical chart.

To replay the market as it actually traded.

And that changes the way you think about backtesting.

A strategy that looks good on price history can fall apart once you add spread and fill quality.

But if it still works after real historical execution, then you might actually have something worth testing live.

[revayz @0xrevayz]

He spent 12 months posting videos for $0. Now he’s cleared $2,000,000

No face. No camera. No luck.

For the first year, he posted one video every two weeks and got absolutely nothing. Most people would have quit at month three. He didn’t. He spent that time studying the only thing that matters: The System

Once he cracked the code, his channel exploded to 500M views

The "After AI" Strategy he used to scale:

The 12-Month Grind: Posting every 14 days with zero return to collect data and find his "hook"
The Pivot: He stopped guessing and started using Claude to engineer high-retention scripts and high-RPM niches
The Result: Total revenue hit $2,000,000 as the algorithm finally picked up his automated machine

The Workflow now:

-Claude analyzes viral trends and writes the full 10-minute scripts
-ElevenLabs handles the professional voiceover
-CapCut assembles the visuals based on AI-generated prompts
-Claude optimizes the metadata to trigger the "browse features" explosion

He went from earning nothing to making millions by shifting his mindset.

Before: Trying to be a "content creator" and hoping for luck
After: Building a "Content Factory" that operates on cold data

One year of silence. One year of zero pay. Followed by $2M in total profit

The algorithm doesn't reward talent. It rewards the system that refuses to stop

Stop posting for views. Start building an asset

[revayz @0xrevayz]

Retail traders think they are competing against other humans. They are actually competing against $160 billion AI machines

Most people use Claude to write emails. Man Group, the world’s largest listed hedge fund, uses it to run AlphaGPT is a system that autonomously generates, codes, and backtests trading strategies 24/7

While you are staring at a 5-minute chart, their AI is testing 500 new signal hypotheses in the same time it takes you to make coffee

The bottleneck isn't "can I build it?" anymore. The bottleneck is speed

A 22-year-old developer implemented this exact institutional pipeline using Claude Code and scaled to $30,000/month in 5 months

He didn't have a math degree. He didn't have a Wall Street desk. He just understood the "After AI" reality:

Before AI: One researcher tests 20 ideas a quarter
After AI: One operator tests 200 ideas a week

The $30k/month Revenue Engine:

Step 1: Use Claude Code to build the "Scraper" (Data Ingestion)
Step 2: Run the "Adversarial Challenge" (The bot that tries to break your own strategy)
Step 3: Implement "Walk Forward" validation (Eliminates luck)
Step 4: Deploy with a human-in-the-loop "Review Gate"

The Result:
Month 1: $900 (Testing the logic)
Month 3: $11,500 (Scaling the survivors)
Month 5: $30,000 (Full system automation)

The Reality Check:
Jane Street is spending $6 billion on GPUs. Man Group is partnering with Anthropic. Bridgewater is running a $2 billion AI fund

They aren't replacing their quants. They are making them 10x faster

Human: Market judgment + Risk oversight
Claude Code: Infinite throughput + Execution speed

You have the same tools they do. You have the same Claude subscription. The only difference is they built the system, and you are still "trading" by hand

Stop being the liquidity for the machines. Start building the machine

The age of the System-Architect is here

[Lunar @LunarResearcher]

Wait… is MiniMax M3 actually around the corner?

This sparse attention diagram is doing a lot to sell the story: pick the relevant KV blocks first, then spend compute where it matters.

If those M3 latency numbers hold up in real production workloads, that’s a huge deal for long-context inference at scale.

Honestly, just drop the full benchmarks already. I’m ready.

#MiniMax #M3

[revayz @0xrevayz]

Most people think neural networks read markets like traders do

They don't

A trader sees candles, levels and volume

A neural network sees thousands of signals moving at once

Early layers catch simple relationships

Deeper layers combine them into patterns humans usually notice late:

Momentum fading
Volume drying up
Volatility shifting

The output is not "buy" or "sell"

It's probability

Higher odds here
Lower odds there
Confidence split across possible outcomes

The difference is scale

A trader reads one chart with emotion

A model scans thousands of patterns without fear, greed or hesitation