Frogify

Web3 Expert 📊 | 1000x Finder 🔎 | Data Analytic 🔬

Delivering the alpha to you.

[Frogify @0xFrogify]

🚨 SPACEX FELL 50% - EXACTLY WHAT I WANTED

I told you this drop was coming.
SpaceX is now down almost ~50% from its peak.

The $SPCX falling right now is actually the part I've been waiting for.

Everyone's screaming "failed IPO."
They're right about the price and wrong about what it means.

Here's what's actually happening:
~5% of SpaceX floats.

The rest unlocks in waves - 20% after Q2 earnings, then tranches through November.
Each wave dumps supply onto a stock that already stopped buying at $225.

That's the mechanic crushing it.
Not the company.
The plumbing.

And yes - the bears have real points.

Japan landed a reusable rocket.
Flight 13 slipped.

The valuation is stretched.

That's exactly why this keeps bleeding through every unlock.

But watch what happens on the other side.

Once the float is fat and the last locked shares hit the tape, the supply overhang is gone.

No more forced sellers.

That's the moment the sellers run dry - and that's when real size steps in, not before.

Institutions don't buy the hype at $225.
They buy the exhaustion after the final unlock, when the timeline has already left for dead.

The unlock schedule is the whole map.

I'm not buying yet.

The best entry of 2026 is on the far side of these waves. You'll know the moment I move.

NFA. Notifications on.

Sorry, but you WILL regret not following me earlier.

[Frogify @0xFrogify]

🚨 SPACEX WILL RETIRE US ALL

Everyone's panicking that SPACEX just closed below its IPO price.

Rocket Lab did the exact same thing.

Then it ran 37x.

RKLB debuted near $10.
Fell under $4.

Spent almost two years below its debut price while the timeline called it dead money.

That was the entry. $4 → $150.

But here's what nobody tells you about this pattern:
The accumulation box on this chart is TWO YEARS wide.

That's why you'll miss it.
It's not a dip you buy on Tuesday.

It's a phase so long and boring it outlasts your conviction.

Retail needs a reason to hold.
Institutions just need a price.

SpaceX just entered that box.

Chart below. Same script, faster clock.

NFA. Notifications on.

You might regret not following me in 1 month

[Frogify @0xFrogify]

IF YOU STILL DON'T USE FABLE 5.2 FOR TRADING YOU'RE A COMPLETE IDIOT

Just fed my workflow the Jane Street open sourced tutor skill and told it to make me a research trading terminal.

Came back to the first 3 wallets it found

I'm not here to waste your time, here's the prompt I used + why every trader needs this:

Why EVERY trader needs this:

Most of trading isn’t trading.
It’s the boring part:

→ building your tools
→ recording and cleaning data
→ tracking PnL trade by trade
→ backtesting by hand

This does all of it for you.

You skip the routine and go straight to the only part that matters:
understanding the setup and making the call.

This is literally is super power for any trader.

Every module stays locked until you prove you understand it:

→ live order book
→ fair value
→ two-sided quotes
→ inventory limits + kill switch
→ fills, PnL, backtest

Fail a review and the module locks again.

So you don’t end up with a bot you can’t explain.

You end up with a desk you understand line by line.

paper trade it first

The VR table in the video is my own front end for the research side.

The prompt in the first reply builds the terminal
Copy it, paste it into Claude Code, pick Fable 5.2.

Notifications on.

[Roan @RohOnChain]

Fable 5.2 is the MOST dangerous AI model right now.

It gives you AGI-adjacent reasoning.

That can discover new profitable trading strategies for you 24/7.

If you set it up correctly, you gain super powers. https://t.co/37IkQHiQEC

[Frogify @0xFrogify]

link to the skill:
https://t.co/P6rqNjUmDx

[Frogify @0xFrogify]

FABLE 5.2 IS NOT SAFE FOR US ALL

Jane Street open-sourced their strict Socratic tutor skill for free.

Pair it with Fable 5.2 and it refuses to just give you code. It forces real understanding first.

Drop the socratic-tutor folder into your agent skills, switch to Fable 5.2, and use the prompt in the screenshot.

It diagnoses your level first, maps every required skill (data, fair value, detection, backtest, risk, execution) from 0-5, and only levels you up after you prove each idea twice.

One question at a time.

Once you’re solid, have it build the scanner, backtest with real fees/slippage on unseen years

run paper-only with hard stops, daily loss caps, and a kill switch.

Every night it quizzes you on the trades.
Can’t explain one? That strategy dies.

Two clean weeks matching the backtest → size small and go live.

The edge is the forced understanding + the rules that can’t be overridden.

Follow me if you want to understand how to turn AI into your personal shovel.

[Claude @claudeai]

Introducing Claude Fable 5.2 and Claude Mythos 5.2, designed to tackle the most daunting technical challenges.

They’re capable collaborators on ambitious projects in many fields, and show improvements in areas like coding, reasoning, and vision.

[Frogify @0xFrogify]

FABLE 5.2 IS F4CKING INSANE!

this 4-page prompt builds you a trading terminal that learns from its own mistakes.

every closed trade gets a debrief and a new rule. you approve it, it never repeats the mistake.

CTRL+C / CTRL+V on this prompt might literally change someones life tonight.

Save this post before you never see me again.

[Claude @claudeai]

Introducing Claude Fable 5.2 and Claude Mythos 5.2, designed to tackle the most daunting technical challenges.

They’re capable collaborators on ambitious projects in many fields, and show improvements in areas like coding, reasoning, and vision.

[Frogify @0xFrogify]

@TedPillows noted

[Frogify @0xFrogify]

@ryangrowthh bro you're a beast

[Frogify @0xFrogify]

@lynk0x stonks

[Frogify @0xFrogify]

@CRHRNN @DudeWhoInvests @bot ask Scots

[Frogify @0xFrogify]

This prompt can make you $10,000.

every teardown trains the file.
every file makes the next teardown sharper.

that compounding is the whole product.
giving you the prompt literally for free.

Paste it into GrokBot, let it run for a week and you have a trained data some people will pay thousands to have

[Frogify @0xFrogify]

This prompt can make you $10,000.

every teardown trains the file.
every file makes the next teardown sharper.

that compounding is the whole product.
giving you the prompt literally for free.

Paste it into GrokBot, let it run for a week and you have a trained data some people will pay thousands to have

[Argona @Argona0x]

https://t.co/uFcQbivrbz

[Frogify @0xFrogify]

@0xHvdes This is what I call using 100% out of that monthly subscription

[Frogify @0xFrogify]

@0xParci Touch grass❌
Touch sand✅

[Frogify @0xFrogify]

If you still don’t know where to start with Grok Bot

I beg you - just paste this prompt and wait till morning

it builds a trading workflow that’s better at firing strategies than finding them

99% of the workflows you see here are "flip a coin 200 times a week, keep the 10 runs that came up heads five times straight, and call that edge"

This prompt changes everything

Save this post - this is your ground zero.

[Frogify @0xFrogify]

If you still don’t know where to start with Grok Bot

I beg you - just paste this prompt and wait till morning

it builds a trading workflow that’s better at firing strategies than finding them

99% of the workflows you see here are "flip a coin 200 times a week, keep the 10 runs that came up heads five times straight, and call that edge"

This prompt changes everything

Save this post - this is your ground zero.

[Frogify @0xFrogify]

https://t.co/QCRPHG0UMG

[Frogify @0xFrogify]

I JUST CONNECTED A WORLDMAP MCP TO MY GROKBOT TRADING WORKFLOW, FEELS ILLEGAL TO EVEN F^KING LOOK AT IT.

a strait gets blocked. a sanctions package drops. a grid goes down. a country’s instability jumps 11 points in a day.

then it hits your timeline. then someone deploys a ticker for it.

if you’re only watching X, you’re watching step three of a three step process and calling it alpha.

so i gave my agents a second pair of eyes.

there’s an open source project called World Monitor. real time global intelligence dashboard, 88.7k stars on github.

- country instability scores.
- chokepoints.
- cable faults.
- cascade analysis.
- a finance radar across exchanges.
- commodities and crypto.
- everything attributed, everything with a number on it.

it has an MCP server. i connected it to my bot.

what actually changed:

my setup is four agents.

- Scout watches X.
- Weigher scores the accounts interacting with a narrative.
- Linker maps the narrative to the actual contract.
- Chief sizes it and arms the exit.

before, Scout was the only input. everything the system knew came from one surface.

now there’s a node in front of Scout.

World event comes in, gets mapped to the accounts talking about it, gets weighted, gets matched to a token if one exists.

If it doesn’t, nothing happens and that’s fine.

Every entry in my log now carries the world event that triggered it.

That was already my rule, every trade gets a reason. now the reason starts outside crypto.

How to build it:

1. Open worldmonitor(.)app, switch to the finance view, spend ten minutes in the globe and markets panels. know what the data looks like before you let an agent read it

2. Grab your API key, or clone the repo and run it locally if you’d rather host it yourself

3. Add it to your agent as a custom MCP server

4. Open a free Alpaca paper account. fake money, zero risk

5. Add the Alpaca MCP server in paper mode

6. Tell your agent in plain english:
every few minutes, check World Monitor for significant movements and tell me which markets they touch

7. Give it your mapping rules.

Oil supply risk → energy. instability spike → gold. this mapping is your edge, not the tool’s. the tool is the same for everyone who installs it

8. Run a second agent whose only job is mapping events to tickers, separate from the one that decides. one agent that decides and then grades its own decision will tell you it was right every time

9. Check whether Polymarket and Kalshi already moved. read it the right way: odds moving is confirmation the event is real. odds not moving usually means nothing happened, not that you’re early. prediction markets are faster than you

10. Small size, entry and exit and stop defined before it opens, all on paper

11. Make it message you the exact world event behind every trade. then sit on it for a few weeks and read every single one before real money goes anywhere near this

The tool is free to use and open source.

The part that makes it work is the mapping rules you write, and nobody can install those.

[Frogify @0xFrogify]

I JUST CONNECTED A WORLDMAP MCP TO MY GROKBOT TRADING WORKFLOW, FEELS ILLEGAL TO EVEN F^KING LOOK AT IT.

a strait gets blocked. a sanctions package drops. a grid goes down. a country’s instability jumps 11 points in a day.

then it hits your timeline. then someone deploys a ticker for it.

if you’re only watching X, you’re watching step three of a three step process and calling it alpha.

so i gave my agents a second pair of eyes.

there’s an open source project called World Monitor. real time global intelligence dashboard, 88.7k stars on github.

- country instability scores.
- chokepoints.
- cable faults.
- cascade analysis.
- a finance radar across exchanges.
- commodities and crypto.
- everything attributed, everything with a number on it.

it has an MCP server. i connected it to my bot.

what actually changed:

my setup is four agents.

- Scout watches X.
- Weigher scores the accounts interacting with a narrative.
- Linker maps the narrative to the actual contract.
- Chief sizes it and arms the exit.

before, Scout was the only input. everything the system knew came from one surface.

now there’s a node in front of Scout.

World event comes in, gets mapped to the accounts talking about it, gets weighted, gets matched to a token if one exists.

If it doesn’t, nothing happens and that’s fine.

Every entry in my log now carries the world event that triggered it.

That was already my rule, every trade gets a reason. now the reason starts outside crypto.

How to build it:

1. Open worldmonitor(.)app, switch to the finance view, spend ten minutes in the globe and markets panels. know what the data looks like before you let an agent read it

2. Grab your API key, or clone the repo and run it locally if you’d rather host it yourself

3. Add it to your agent as a custom MCP server

4. Open a free Alpaca paper account. fake money, zero risk

5. Add the Alpaca MCP server in paper mode

6. Tell your agent in plain english:
every few minutes, check World Monitor for significant movements and tell me which markets they touch

7. Give it your mapping rules.

Oil supply risk → energy. instability spike → gold. this mapping is your edge, not the tool’s. the tool is the same for everyone who installs it

8. Run a second agent whose only job is mapping events to tickers, separate from the one that decides. one agent that decides and then grades its own decision will tell you it was right every time

9. Check whether Polymarket and Kalshi already moved. read it the right way: odds moving is confirmation the event is real. odds not moving usually means nothing happened, not that you’re early. prediction markets are faster than you

10. Small size, entry and exit and stop defined before it opens, all on paper

11. Make it message you the exact world event behind every trade. then sit on it for a few weeks and read every single one before real money goes anywhere near this

The tool is free to use and open source.

The part that makes it work is the mapping rules you write, and nobody can install those.

[Frogify @0xFrogify]

https://t.co/QCRPHG0UMG

[Frogify @0xFrogify]

@JamesWynnReal @grok I ain't no grokbot bro but the photo is not real, trust me

[Frogify @0xFrogify]

0.2%

That’s how many people are ACTUALLY using AI at it's full power.

And you're not one of them.
You pay $20/month to google questions with AI.

Andrej Karpathy named three things that decide whether AI is a search box or a business tool that can change your life.

1. You ask questions. You don’t assign work.

His words:
“a question with a paragraph answer barely stresses the system.”

If the answer fits on your screen, you learned nothing about the model.

2. You don’t have hard problems.

He says you need “a reservoir of big, difficult problems that you really care about.”

That’s the real bottleneck - not prompting, not the model.
Most people have never given it something it could visibly fail at.

3. You’re working where the model is weak and don’t know it.

Capability isn’t flat.

It peaks in domains that are “digital, verifiable and economically valuable,” because that’s where reinforcement learning had a real score to train against.

The 5 fixes that will help you use your AI at full power:

1. Stop measuring prompts by the answer. Measure by the job.

New rule:
if it takes the model under a minute, you didn’t give it a task, you gave it a lookup.

2. Build the reservoir.

Write down the 5 problems in your work that you’ve been avoiding because they’re too big.

That list is now your prompt queue. This is the step everyone skips and it’s the whole thing.

3. Only push it where results can be checked.

This is why AI gives you beautiful nonsense on “what’s the market going to do” and something genuinely useful on “score this setup against these 8 rules and tell me which one failed.”

Digital, verifiable, economically valuable describes a backtest perfectly. It describes a market opinion not at all.

Pick the first shape every time.

4. Give every task a scoreboard.

A test, a rule, a number - anything the model can grade itself against.

Without one it can’t iterate and you’ve capped it at the first attempt.

5. Let it run long, then review instead of write.

Batch it.
Let it try fifty times overnight.

Your job moves from producing to auditing.
That is the actual difference between the 20 million and the 2 billion.

-

The access gap is real and you can’t close it. Nobody’s handing you an internal frontier model.

Karpathy is not giving you the model also. This is a behavioral strategy.

If you want to google stuff, just use Google bro.

If you want to actually learn and understand how you can use AI at its full power - follow me for more.

[Frogify @0xFrogify]

0.2%

That’s how many people are ACTUALLY using AI at it's full power.

And you're not one of them.
You pay $20/month to google questions with AI.

Andrej Karpathy named three things that decide whether AI is a search box or a business tool that can change your life.

1. You ask questions. You don’t assign work.

His words:
“a question with a paragraph answer barely stresses the system.”

If the answer fits on your screen, you learned nothing about the model.

2. You don’t have hard problems.

He says you need “a reservoir of big, difficult problems that you really care about.”

That’s the real bottleneck - not prompting, not the model.
Most people have never given it something it could visibly fail at.

3. You’re working where the model is weak and don’t know it.

Capability isn’t flat.

It peaks in domains that are “digital, verifiable and economically valuable,” because that’s where reinforcement learning had a real score to train against.

The 5 fixes that will help you use your AI at full power:

1. Stop measuring prompts by the answer. Measure by the job.

New rule:
if it takes the model under a minute, you didn’t give it a task, you gave it a lookup.

2. Build the reservoir.

Write down the 5 problems in your work that you’ve been avoiding because they’re too big.

That list is now your prompt queue. This is the step everyone skips and it’s the whole thing.

3. Only push it where results can be checked.

This is why AI gives you beautiful nonsense on “what’s the market going to do” and something genuinely useful on “score this setup against these 8 rules and tell me which one failed.”

Digital, verifiable, economically valuable describes a backtest perfectly. It describes a market opinion not at all.

Pick the first shape every time.

4. Give every task a scoreboard.

A test, a rule, a number - anything the model can grade itself against.

Without one it can’t iterate and you’ve capped it at the first attempt.

5. Let it run long, then review instead of write.

Batch it.
Let it try fifty times overnight.

Your job moves from producing to auditing.
That is the actual difference between the 20 million and the 2 billion.

-

The access gap is real and you can’t close it. Nobody’s handing you an internal frontier model.

Karpathy is not giving you the model also. This is a behavioral strategy.

If you want to google stuff, just use Google bro.

If you want to actually learn and understand how you can use AI at its full power - follow me for more.

[Andrej Karpathy @karpathy]

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.

- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.

The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.

So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

[Elon Musk @elonmusk]

As crazy as this sounds, $1T in SpaceX revenue in 2028 is no longer impossible