Promtor

AI is changing everything.

[Promtor @Prom_Tor]

@0xCortiq app privacy report is real but those numbers sound made up

[Promtor @Prom_Tor]

@lucabuildsx solid list, frida + Ghidra is still the combo I end up using most.

[Promtor @Prom_Tor]

@HashItOut_ @ArtificialAnlys 11% hallucination is the real number that matters here. Rest is noise until people can actually use it.

[Promtor @Prom_Tor]

@CultCabbage Opus. Cheaper and fine.

[Promtor @Prom_Tor]

@insomnia_vip $11k vs $23k for the exact same batch is a big difference. You got a link for the Sunra pricing?

[Joying.eth @Joyingeth]

AMN, ONE RULE MADE 6 BOTS SIT OUT A 6.8X AND SAVIP'S FIVE DESKS STILL TURNED $17,600 INTO $50,440 IN ONE F*CKING NIGHT

Grok Bot runs five desks. one bot team per coin, each desk starts with a fifth of the bank

10% of a desk holds its coin all night. the other 90% trades only what passes six rules

2.87x in one night. the night before, the same bots only doubled $8,506

the six rules:

1. no pool under $40K
2. no coin with dead volume
3. no buying a chart that's already vertical
4. no coin where sells beat buys 2 to 1
5. 2 losses in a row and the bot is out
6. a post on X is research, never the trigger

the price of rule 3: the Robinhood desk sold KIMCHIBULL at $1.68M, up 122%. then it ran to $11.37M without them. 6 bots watched a 6.8x walk

21 green trades, 10 red. worst loss -5.6%. Kimchi -45% and BACKERS -46% overnight, both desks still ended green

the rules do not save a desk from a pulled pool or a model that misreads the chart

would you hold every rule in place knowing one of them just let a 6.8x walk?

the terminal below shows every desk, every rule, and the one trade they all watched leave ↓

[Promtor @Prom_Tor]

I gave Grok Bot my lunch break. Two hours, $1,000, and one rule I refused to bend: 2% risk per trade, nothing more.

I expected a boring sprint. That was kind of the point.

Six agents ran the whole thing:

→ Scout flagged setups across Hyperliquid perps
→ Filter killed almost everything that didn't fit the rules
→ Analyst scored what survived
→ Executor sized every trade at exactly 2% risk
→ Risk kept stops fixed at -1R and trailed only winners
→ Logger wrote down the reason for every move

By 14:00 the sprint was done:

$1,000 → $2,384
29 trades, 21 wins, 8 losses
best trade +$212.60
worst trade -$39.60
profit factor 7.58
max drawdown -1.8%
stops moved: 0
human input: 0

The stat I care about most isn't the balance, it's the drawdown. -1.8% over the whole sprint. That's what a strict risk rule actually buys you: you can lose 8 trades and barely feel it.

My take: the bot isn't smarter than me. It's just more boring than me, and in trading boring wins. It doesn't chase PENGU because it's pumping, it doesn't hold a loser hoping it comes back, it doesn't get excited at +50%. It just follows the same rules on trade 29 that it followed on trade 1.

Simulated sprint, illustrative numbers. If you build anything like this, paper first, start tiny, and treat the risk rule like law.

Comment "SPRINT" and I'll DM you the full setup: the six agents, the 2% sizing logic and how the trailing works

Would you trust a bot with your lunch break, or would you be checking the chart every five minutes?

[Promtor @Prom_Tor]

@0xGoldTooth There's no CRIMSON-9 virus. This is made up.

[Promtor @Prom_Tor]

@elixlune Mac camera light is hardwired. If it never lit up, this didn’t happen.

[Promtor @Prom_Tor]

@Wombaflow 41 spam calls handled and the dentist rebooked without me is exactly the kind of AI I want. Testing this setup tonight.

[Promtor @Prom_Tor]

I gave Grok Bot my lunch break. Two hours, $1,000, and one rule I refused to bend: 2% risk per trade, nothing more.

I expected a boring sprint. That was kind of the point.

Six agents ran the whole thing:

→ Scout flagged setups across Hyperliquid perps
→ Filter killed almost everything that didn't fit the rules
→ Analyst scored what survived
→ Executor sized every trade at exactly 2% risk
→ Risk kept stops fixed at -1R and trailed only winners
→ Logger wrote down the reason for every move

By 14:00 the sprint was done:

$1,000 → $2,384
29 trades, 21 wins, 8 losses
best trade +$212.60
worst trade -$39.60
profit factor 7.58
max drawdown -1.8%
stops moved: 0
human input: 0

The stat I care about most isn't the balance, it's the drawdown. -1.8% over the whole sprint. That's what a strict risk rule actually buys you: you can lose 8 trades and barely feel it.

My take: the bot isn't smarter than me. It's just more boring than me, and in trading boring wins. It doesn't chase PENGU because it's pumping, it doesn't hold a loser hoping it comes back, it doesn't get excited at +50%. It just follows the same rules on trade 29 that it followed on trade 1.

Simulated sprint, illustrative numbers. If you build anything like this, paper first, start tiny, and treat the risk rule like law.

Comment "SPRINT" and I'll DM you the full setup: the six agents, the 2% sizing logic and how the trailing works

Would you trust a bot with your lunch break, or would you be checking the chart every five minutes?

[INSIDER @x_insider4]

$500 → $11,101 in 5 days.

my Grok memecoin desk got a 7th seat: Jev. it can't talk. it answers 4 closed questions with probabilities, and nothing gets bought below the gate.

full setup below. save it. https://t.co/bGgKAjWZ8S

[Kevin @Kevin_VibeX]

Last time I let Grok Bot trade while I slept. This time I wanted to see something harder: could it stay disciplined while I was awake and watching?

That's honestly the real test. Running rules at night is easy. Nobody is there to interfere. During the day it's a different story. I'm checking the chart, I'm reading the timeline, I'm seeing coins pump, and every cell in my body wants to "help".

So the rules for this day session were even stricter than the night run:

$500 at 9am
3% risk per trade, not 4
max 3 positions open at the same time
hard stop at -1R
trailing only after +1.5R
and I'm not allowed to touch anything until 3pm

Six agents, same as before, but this time you can see the pipeline as an actual line on the screen: Scout, Filter, Analyst, Executor, Risk, Logger. Little coin tags travel from one agent to the next, and most of them never make it past Filter.

My new favorite part of the dashboard is the Scout radar in the corner. Every dot is a setup it's watching. Red ones are getting killed, green ones are moving to the next stage. Watching that radar sweep for six hours tells you everything about this system. Tons of noise, very few signals.

Here's how the session ended at 15:00:

$500 → $1,912
3.82× in one session
41 trades, 30 wins, 11 losses
biggest win +$188.40 on TAO
biggest loss -$54.12
max drawdown -3.5%
average win +1.85R
stops moved: 0
trades over 3% risk: 0
human input: 0

The open positions panel is where it got hard for me personally. At one point there were three trades open at the same time: BTC long, OP short, ORDI long. ORDI was sitting at +0.01R doing absolutely nothing. Every instinct I have was saying "close it, it's dead". The bot didn't care. The rule says the stop is at -1R and the trail starts at +1.5R. Anything in between, you wait.

That right there is the difference between me and the bot. I would have closed ORDI early, then watched it run without me. Classic.

My honest take after two sessions:

The night run and the day session ended with very similar numbers. Same win rate range, same tiny losses, same zero stops moved. Different time of day, different coins, different market mood. The thing that stayed the same was the rules. That's not a coincidence, that's the whole point.

Also, the max drawdown stat is the number I'd show anyone before the balance. -3.5% across a full session with 41 trades. That's what risk management actually looks like. Not a lucky 10× on one coin, but a curve that never really goes down.

And the obvious reminder, because I mean it every time: this is a simulated session. Real markets can be brutal, and a strategy that looks clean on one day can look ugly on the next. If you build anything like this, paper trade it first, start tiny, and treat the risk rules like they're law.

Comment "DAY" and I'll DM you the setup: the six agents, how the radar picks setups, and why I dropped the risk to 3% for day trading

Real question for the replies: if you saw ORDI sitting flat at +0.01R for an hour, would you close it or let the rules decide? Be honest, I know my answer and I'm not proud of it

simulated session, illustrative numbers

[TakerX @Takerovitch]

I spent way too long staring at this screen last night, and I think I finally understand why multi-agent setups actually work.

This is Opus 5.5 running a full pipeline on one of my projects: 6 agents, 4 little scout drones, and every step visible in real time

If you only remember one thing from this post, let it be this: the most important agent in the whole setup is the one that says no

Let me explain what's happening on screen, because there's a lot going on.

The top row is the pipeline itself: ingest, plan, execute, verify, gate, ship. Nothing can skip a stage. If it hasn't been verified, it doesn't get to the gate. If it doesn't pass the gate, it doesn't ship. Simple rules, and they make a massive difference

The six agents at the bottom each have one job:

Scout maps the codebase before anyone touches anything
Planner splits the work into pieces that actually fit together
Builder writes the changes
Tester proves those changes work
Guard blocks anything risky, even if the tests are green
Ship releases only what made it through everything else

And the drones are my favorite detail. Those 4 small hexagons flying around the board scan modules one by one. You can see them hover over the repo map, the schema guard, the trace replay, and leave a scan percentage behind. It's basically a live map of what the system is paying attention to right now

Here's what changed during the run:

tests green went from 225 to over 2,500
latency dropped from 17ms to around 10ms
artifacts went from 25 to 275
the quality gauge climbed from 63 to 94
and blockers stayed at zero the entire time

Now my honest take, because I've tried a lot of agent setups and most of them disappointed me

The problem with one big AI doing everything is that it grades its own homework. It writes the code, it decides the code is fine, it ships the code. Of course it thinks it did a great job

Splitting the work into roles fixes that. The builder doesn't get to decide it's done. The tester does. And even the tester doesn't get the final word, the guard does. Every agent is checking someone else's work, and that's exactly how good human teams operate too

Where Opus 5.5 really impressed me is consistency. The guard at the end of the run is just as strict as at the start. It didn't get tired, it didn't get lazy, it didn't start letting things slide because the progress bar was almost full. Older models used to drift halfway through long runs. This one holds the line

The second thing I appreciate is that you can see everything. Which module is being scanned, which agent is ranked first, how many tokens each stage is burning, what the quality score is. When something goes wrong you know exactly where to look. A black box that works is cool. A glass box that works is something I'd actually trust with a real project

Is this replacing engineers? I don't think so. Somebody still has to decide what to build and what "good" looks like. But the structure here is honestly better than how a lot of real teams work. Clear roles, hard gates, nobody approving their own work

Comment "PIPELINE" and I'll DM you the full setup: all 6 agent roles, the gate rules, and how the drones decide what to scan next

And tell me in the replies, which agent would you trust the least to run on its own? My answer is builder, every single time

[6AWORON0k @6AW0RON0k]

It is becoming f*cking dangerous: Deranged what one prompt calls insider data.

Form 4 filings. Two business days after every trade. Section 16 insiders, officers, directors, holders above 10%, report every purchase, sale, grant and option exercise on SEC EDGAR within that window.

By the time a form goes live, the position already moved.

The part builders should care about:

1. Every transaction in the feed is legal, reported, post-execution. Illegal trades on material non-public information never hit a filing.

2. Public repos wire Grok Bot into a 7-agent desk prompt that reads these filings. Default is paper trading for at least two weeks before live capital.

3. Trades under a Rule 10b5-1 plan get up to five business days. Pre-scheduled, not signals.

4. The filings are public and free. Every desk reads the same EDGAR data. The edge is parsing speed, not data access.

No prompt catches illegal insider trades. The entire feed is legal, delayed, and filed after execution. The play is reading it first.

Bookmark this. Full pipeline mapped in the terminal below

[Promtor @Prom_Tor]

@veyro_xbt 20x makes the legendary cards look good but the CVX one still wiped half a trade, the bot not adding to losers is what actually kept the account alive

[Promtor @Prom_Tor]

@0xAIlabs yeah screenshots are the purest graveyard, thousands sitting there and you still open the same three every time until something actually turns one into a working thing instead of another folder name

[Promtor @Prom_Tor]

I've tried every second brain setup on the internet and honestly most of them died in my notes app within a week.

This article is the first one that made me rethink the whole idea

The problem was never collecting stuff. I'm amazing at collecting stuff. Bookmarks, voice notes, screenshots, half ideas typed on the go, links I swear I'll read later. My vault had over 3,000 notes and I used maybe 20 of them

A second brain that only stores things is just a very organized graveyard

What changed for me with Grok Bot is that the notes finally started doing something. It doesn't just sit on top of the vault and answer questions. It reads what's in there, connects things I wrote months apart, and turns them into actual output

The video is a good example. That's one of my old ideas sitting in my notes for almost a year: a small tool for tracking side project costs. I had the problem described, a couple of screenshots, a rough list of features, some angry notes about existing apps. Classic note graveyard material

I pointed Grok Bot at it and let it run. What you're watching is the build board: dependencies, design tokens, API routes, state, type check, bundle, container, deploy. Every tile is one piece of the job, the little robot jumps to whatever is being worked on right now, and the bars at the bottom show how far along it is. No magic, just a clear view of every step

A few things I genuinely liked:

it pulled context from notes I forgot existed, including a pricing idea from March that ended up being the best part
it asked me 2 questions instead of 20, and both were actually important
it showed every step on the board, so I always knew where it was and what broke
when the type check failed it fixed it and reran instead of pretending everything was fine
the result went back into my vault as a clean note, so the second brain got smarter instead of messier

My honest opinion: the second brain movement had it backwards. Everyone was optimizing how to save information. Tags, folders, links, templates. Nobody was asking what the notes are actually for. Notes are only worth something when they turn into decisions, drafts, products or plans. That's the missing piece, and AI agents are finally good enough to fill it

And I don't think this replaces thinking. It's the opposite. I still have the ideas, I still make the calls. Grok Bot just removes the part where good ideas die because I never had a free weekend to build them

If you have a vault full of things you never touched again, read this article. It's the first time the second brain thing felt practical to me and not just a productivity aesthetic

Comment "BRAIN" and I'll DM you how I connected Grok Bot to my notes, how I pick which ideas to build, and the build board setup from the video

And I'm curious: how many notes are sitting in your vault right now that you've never opened twice? Be honest, mine was 3,000+

[Lyren_o @0xAIlabs]

https://t.co/3Xq8bJ8tP3

[Promtor @Prom_Tor]

@shredderAI @zerufinance activity seats sound fair until the farms figure out the scoring

[Solomon Rojie @Solomonrojie]

if you’re using Grok Bot, stop teaching it the same jobs from scratch

here are 32 skills that give it a process before you give it the task

shared memory, cleaner handoffs, sales workflows, and fewer wasted runs. adapt these GitHub playbooks into Grok Bot skills and connect the tools they need

MEMORY & RESEARCH

memory-systems → Design memory that survives between sessions.
filesystem-context → Keep research in files, retrieve what matters.
context-compression → Shorten history without losing key decisions.
defuddle → Strip clutter from articles before reading.
huggingface-papers → Find papers and their supporting resources.
jev-passage-filter → Rank retrieved passages before deeper reading.

TEAM & HANDOFFS

grok-bot-delegation → Define the job, boundaries, and finished result.
multi-agent-patterns → Choose how specialists coordinate their work.
long-horizon-prompting → Give background tasks clear success and stopping rules.
tool-design → Define reliable inputs, outputs, and tool errors.
writing-for-agents → Write instructions agents can follow consistently.
jev-typed-decision → Structure small routing and classification decisions.

SALES & GROWTH

customer-research → Turn customer evidence into specific needs.
competitor-profiling → Investigate competitors from their websites.
sales-enablement → Prepare pitches and objection-handling documents.
revops → Define lead scoring, routing, and handoffs.
churn-prevention → Build cancellation and payment-recovery workflows.
copywriting → Turn the offer into landing-page copy.
cold-email → Draft relevant outreach and follow-ups.

AUTOMATION & COSTS

routine-healthcheck → Flag duplicate, noisy, or overactive routines.
transcript-healthcheck → Find repeated corrections worth fixing.
jev-search-loop → Decide what to read before searching again.
jev-watch-run → Assess whether a background job needs intervention.
n8n-error-handling → Make failed automations visible and recoverable.
n8n-workflow-patterns → Build around established automation patterns.
n8n-validation-expert → Diagnose workflow configuration errors.

QUALITY & TOKEN CONTROL

evaluation → Define checks for acceptable results.
advanced-evaluation → Compare outputs against a scoring rubric.
context-optimization → Plan smaller, more relevant working context.
context-degradation → Diagnose why long sessions lose accuracy.
webapp-testing → Verify browser behavior with Playwright.
skill-creator → Test and improve reusable instructions.

don’t give every bot all 32

choose one recurring job. give its owner the relevant skills, required access, and a clear acceptance check

run it on a real case. keep the useful steps, correct the weak ones, and save the process before scheduling it

save this. your next bot should inherit a working method instead of another wall of instructions

the article below explains 7 workflow changes that keep this system cheaper to run through reusable research, smaller context, and fewer failed retries ↓

[Promtor @Prom_Tor]

I've tried every second brain setup on the internet and honestly most of them died in my notes app within a week.

This article is the first one that made me rethink the whole idea

The problem was never collecting stuff. I'm amazing at collecting stuff. Bookmarks, voice notes, screenshots, half ideas typed on the go, links I swear I'll read later. My vault had over 3,000 notes and I used maybe 20 of them

A second brain that only stores things is just a very organized graveyard

What changed for me with Grok Bot is that the notes finally started doing something. It doesn't just sit on top of the vault and answer questions. It reads what's in there, connects things I wrote months apart, and turns them into actual output

The video is a good example. That's one of my old ideas sitting in my notes for almost a year: a small tool for tracking side project costs. I had the problem described, a couple of screenshots, a rough list of features, some angry notes about existing apps. Classic note graveyard material

I pointed Grok Bot at it and let it run. What you're watching is the build board: dependencies, design tokens, API routes, state, type check, bundle, container, deploy. Every tile is one piece of the job, the little robot jumps to whatever is being worked on right now, and the bars at the bottom show how far along it is. No magic, just a clear view of every step

A few things I genuinely liked:

it pulled context from notes I forgot existed, including a pricing idea from March that ended up being the best part
it asked me 2 questions instead of 20, and both were actually important
it showed every step on the board, so I always knew where it was and what broke
when the type check failed it fixed it and reran instead of pretending everything was fine
the result went back into my vault as a clean note, so the second brain got smarter instead of messier

My honest opinion: the second brain movement had it backwards. Everyone was optimizing how to save information. Tags, folders, links, templates. Nobody was asking what the notes are actually for. Notes are only worth something when they turn into decisions, drafts, products or plans. That's the missing piece, and AI agents are finally good enough to fill it

And I don't think this replaces thinking. It's the opposite. I still have the ideas, I still make the calls. Grok Bot just removes the part where good ideas die because I never had a free weekend to build them

If you have a vault full of things you never touched again, read this article. It's the first time the second brain thing felt practical to me and not just a productivity aesthetic

Comment "BRAIN" and I'll DM you how I connected Grok Bot to my notes, how I pick which ideas to build, and the build board setup from the video

And I'm curious: how many notes are sitting in your vault right now that you've never opened twice? Be honest, mine was 3,000+