6 Grok bots doing the job of a full social media department.
one hunts trending topics. one writes the posts. one builds the visuals. one runs publishing. one reads the analytics. and one keeps all five in sync.
i found 12 GitHub repos that wire the whole system together:
01 / Crewm8 Social Media Skill Graph → Dedicated skills for handling social media across multiple platforms.
github.com/gokulsvision/Crew…
02 / Social Media Skills → 100+ skills covering content strategy, copywriting, design and analytics.
github.com/pavelhov/social-m…
03 / Last 30 Days Skill → Surfaces trending conversations on Reddit, X, YouTube and the open web.
github.com/mvanhorn/last30da…
04 / OpenMagpie → Watches social chatter across several platforms at once.
github.com/obris-dev/openmag…
05 / Trend Opportunity Radar → Spots emerging trends and content gaps early.
github.com/datou202307-desig…
06 / 37studio → AI-driven content production for social feeds.
github.com/Shaitrish/37studi…
07 / Postiz → Lines up and ships posts across every social platform on schedule.
github.com/gitroomhq/postiz-…
08 / TryPost → Social scheduler made for AI agents, with MCP and API hooks.
github.com/trypostit/trypost
09 / Postmill AI → Bundles social publishing, AI design and content analytics in one place.
github.com/postmill-ai/postm…
10 / X Growth Dashboard → Tracks engagement, hook performance and post reach on X.
github.com/AM1403x/x-growth-…
11 / Daily Social Report → Converts social analytics into clean daily summaries.
github.com/dizzyx/daily-soci…
12 / Crossposter → Ships content to multiple social platforms from one pipeline.
github.com/apoorvdarshan/cro…
stack your own AI social department from this.
Mojunel.eth retweeted
i was quietly let go from Binance a month ago after a security test i ran went sideways and exposed a massive flaw in how internal permissions are isolated.
i was auditing cross-service authentication on a staging environment. one misconfigured token let my test container read restricted internal routing tables that should have been air-gapped.
i reported the breach immediately to the security ops channel. two days later my corporate access was wiped and i got a termination letter for "violating information security protocols."
this is what they sent me:
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1,926
i was quietly let go from Binance a month ago after a security test i ran went sideways and exposed a massive flaw in how internal permissions are isolated.
i was auditing cross-service authentication on a staging environment. one misconfigured token let my test container read restricted internal routing tables that should have been air-gapped.
i reported the breach immediately to the security ops channel. two days later my corporate access was wiped and i got a termination letter for "violating information security protocols."
this is what they sent me:
12
33
1,926
I loaded a 1,000-agent crew on Fable 5.2
One theorem. 60 statements. 28.7 hours.
Minute 0. 1 lead, 999 helpers. Each in a sealed sandbox. Shell, editor, Git, shared database. One pipe out to a gateway.
Minute 10. The lead built five coordination tables. Announce, assign, notes, subtasks, landed. No human asked for them.
First pass. 16 per statement. 47 of 60 flagged infeasible. Proved most anyway.
Hour 18. Langlands-Tunnell stalled the run. Restart from the last save.
750 helpers converged on one statement. The lead logged one line: honest odds are low. 2,418 tasks, 10 levels deep.
Hour 28.7. Full proof landed. Seven times faster than September's 11-day formalization and a third of the token budget.
A million tool calls through one live monitor. None blocked.
47 statements looked impossible.
28.7 hours said otherwise.
Broke a cost stack. $8 plans, $0.10 reads, same build.
> Four models, one ladder, 80x top to bottom. I wired an agent team through all four.
> Fable 5.2 plans and judges. $8 in, $40 out per million. The brain.
> Opus 5.5 writes code. $4 in, $20 out. The builder.
> Sonnet 5.5 runs parallel workers. $2 in, $10 out. Scoped tasks, file-level edits.
> Haiku 5.5 reads logs, finds files, runs tests. $0.10 in, $0.50 out. The cheapest hands.
> Cache reads cost $0.20 per million on Fable and Opus. Repetitive context closes the gap.
> Fast mode: 2.5x at $16 in, $80 out. Worth it live, wasteful overnight.
> Sequential chains kill it. Overhead eats the savings when every step waits on the last.
> Pin Claude Code's file explorer to Haiku. Scans drop from $8 to $0.10. Fable dropped 20% from 5.1.
> Fable plans. Opus builds. Haiku reads. The brain stays expensive. Everything routine falls 80x.
Bookmark this ▼
Genuinely unhinged, 10 repos turn you into a whole coding-agent platform team.
You already pay $20 a month for Claude Code on Pro. These 10 repos are the free platform around it.
pick. control. isolate. browse. connect. ship.
01 codex → local coding agent, one curl line to install
github.com/openai/codex
02 opencode → swap the model provider without changing your tools
github.com/anomalyco/opencod…
03 goose → extend the agent with 70+ MCP tools on install
github.com/aaif-goose/goose
04 OpenHands → one canvas for Claude Code, Codex and any ACP agent
github.com/OpenHands/OpenHan…
05 warp → terminal app running Claude Code, Codex and Gemini CLI
github.com/warpdotdev/warp
06 sandbox-agent → drive any agent inside a sandbox over plain HTTP
github.com/rivet-dev/sandbox…
07 E2B → run agent-written code in isolated cloud sandboxes
github.com/e2b-dev/E2B
08 browser-use → hand the agent a real browser session on any LLM
github.com/browser-use/brows…
09 agent-client-protocol → one wire format between any editor and any agent
github.com/agentclientprotoc…
10 claude-code-action → Claude Code answers PRs and issues in GitHub Actions
github.com/anthropics/claude…
pick → control → isolate → browse → connect → ship.
One rule: Warp runs AGPL, the rest are MIT or Apache. Sandboxes and cloud tiers bill on your own key.
Before this, one agent lived in one tab. Now the platform wraps around it.
One photo turned a pencil sharpener into a living 3D diagram I can rip apart.
I never understood how a crank sharpener carves a perfect point, so I told Claude Opus 5.5 to rebuild one I could disassemble piece by piece.
How it works:
The whole sharpener came from a single reference image. Every gear, blade and spring is pure three.js code.
Crank the handle and two cutters orbit the pencil while a locked 36-tooth ring rotates each one.
15 helical edges on every cutter. 12 full turns, 1,080 cuts, a 22° tip, then the resistance drops.
Slide the drawer out and cedar shavings rest inside clear acrylic.
The whole workflow lives in one Claude skill. Feed it any photo and it builds the exploded model.
AT 4:12 AM GROK BOT HIT $10,018.36 AND STOPPED ON ITS OWN.
I BEGGED IT TO KEEP RUNNING. IT TOLD ME NO
$142.80 → $10,018.36. 70X in a single night.
11:35 PM. Loaded $142.80 off my card into the app. Face ID, done.
I screenshotted 3 traders and gave Grok Bot one instruction:
> copy these traders
> target: $10,000
> once you reach it, close everything and stop
> even if I tell you to keep going
> lock everything by 7am
I added that fourth line because I know how I get.
13 trades landed. 9 green. 4 red: −$16.80, −$39.60, −$156.60 and −$340.00.
At 1:01 AM I had $2.60 left in cash. The rest was all in.
$2.5K by 3:14 AM. $5K by 3:44. $7.5K by 4:02.
4:12 AM. $10,018.36. It closed both open trades right there, one at +168% while the copied trader was still holding. Then it posted: "done for tonight."
4:14 AM I woke up, checked the screen and typed half asleep: "Don't stop. One more hour."
It sent back my own words from 11:40 PM: "even if I tell you to keep going". Then: "no. go to sleep."
I wrote "pls". It came back: "still no."
Went back to sleep pissed.
It never stopped watching. Between 4:12 and 7:00 those same traders took −31%, −34% and −27% on their next moves. If it had listened to me, I would have woken up to $7,175.71.
That refusal saved $2,842.65.
$142.80 → $10,018.36.
+$9,875.56. +6,916%.
Pulled out at 7:03 AM.
The sharpest call that night was the trade it refused to take.
Borderline insane, a whole Claude Code ops team in 10 repos.
Claude Code on Max costs $100 a month. This kit is the open-source layer that keeps it from re-learning your repo every session.
learn. remember. watch. run. install.
01 skills → sets the skill folder standard, 192.5k stars
github.com/anthropics/skills
02 claude-code-best-practice → maps every feature, subagent and hook
github.com/shanraisshan/clau…
03 Claude-Code-Quest → teaches Claude Code with three levels of graded exercises
github.com/wesammustafa/Clau…
04 claude-mem → keeps what one session learned alive for the next one
github.com/thedotmack/claude…
05 deja-vu → indexes transcripts of 43 agents, hands sessions between them
github.com/vshulcz/deja-vu
06 ccusage → prices every token run from local logs, nothing uploaded
github.com/ryoppippi/ccusage
07 ccstatusline → shows model, branch and tokens live
github.com/sirmalloc/ccstatu…
08 agent-browser → drives a real browser from Vercel
github.com/vercel-labs/agent…
09 opcode → runs sessions and agents from one window
github.com/winfunc/opcode
10 skills → drops one skill into Claude Code and 75 more agents
github.com/vercel-labs/skill…
learn → remember → watch → run → install.
Claude-Code-Quest runs PolyForm Noncommercial and opcode runs AGPL-3.0. Neither is free to ship commercially. Read both licenses before the code hits production.
All 10 repo names drop into the feed and the last skill slot clicks shut
I'm still in shock, the CEO of Y Combinator open-sourced his agent brain on GitHub, 32.4K stars, one clone away
7 second brains you probably never noticed, all ready to clone right now:
1. gbrain · 32.4K stars
Garry Tan's personal agent brain. every fact tracks its origin, and every agent taps the same memory
github.com/garrytan/gbrain
2. Karpathy's llm wiki · 50K stars
the single-page idea file that kicked off this entire wave
gist.github.com/karpathy/442…
3. ai memory · 13.7K stars
leave Claude Code halfway through and resume inside Codex with nothing lost. memory sits in plain markdown
github.com/akitaonrails/ai-m…
4. COG second brain · 1.4K stars
35 skills and 10 agents, no database. just markdown files that reason
github.com/huytieu/COG-secon…
5. agent second brain
fire off a voice note through Telegram and it arrives as a linked entry in Obsidian, or a task inside Todoist
github.com/smixs/agent-secon…
6. open second brain
each night it converts the fixes you keep making into rules your agent obeys
github.com/itechmeat/open-se…
7. claude second brain
one npx command wires up Obsidian, Claude Code and local search in a single pass
github.com/jessepinkman9900/…
seven repos sitting in the open. your agent still loses you by morning
save this
$55 to $1,724 overnight, zero screen time.
I set Grok loose with $55 and 3 open repos at bedtime. It ran 114 trades on autopilot. 10 hours of sleep, $1,724 in profit.
04:35, dead to the world. Grok spotted a coin ripping +240% in 5 minutes and threw $28.92 at it.
Sold 3 minutes later. +$45.74.
Found out over breakfast.
0 manual clicks the whole night.
GROKSCAN uses toasteragent. Grok from xAI finds the hottest new launches on pump.fun.
SNIPER uses Chainstack's pump.fun bot, 928 stars. Catches new mints, no third-party APIs.
EVAL uses agentinu. AI rates the risk at 5/10 on that coin before anything executes.
MONITOR locks take profit and stop loss on its own. SWAP triggers the auto-sell.
What I kept coming back to. 4 out of 5 agents agreeing still falls short. Nothing moves until it hits 5/5.
5 years in trading showed me the worst calls land at 4am, tired and reckless. The bot has neither problem.
22:00: $55. 04:45: $447. 08:00: $1,779.
Top trade +$396.18. Worst hit -$58.80. 81% win rate on 12 coins.
The stack:
github.com/toasteragent/toas…
github.com/tow3web3/agentinu
github.com/chainstacklabs/pu…
All open source. Save this.
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This is f*cking insane...
Stop paying Claude Opus $6,880 to read your own documents.
XERJ Reader is built for one job:
find the exact answer in a document, show the evidence, or admit it’s not there.
You give it a file and a question -- it highlights the exact line with the answer, or says “no answer.” It can’t make up a number. It can only return text that is already in your file.
The same 1M questions:
→ GPT-6 Astra: $17,200
→ Claude Opus 5.5: $6,880
→ Claude Haiku 5.5: $172
→ XERJ Reader: $6.50
$0.01 per 1M input tokens, output is free.
1,050x cheaper than Opus. 26x cheaper than Haiku.
The answer lands in 80 ms. Opus needs about 5 seconds just to start typing.
Not every AI task needs a frontier model. Sometimes you just need the right answer at the lowest possible cost.
I put the whole model on one page -- bookmark it before your next API bill.
Get on the waitlist while it’s open ↓
I've built a new AI model that is not allowed to generate text. Instead, it produces a verifiable program that answers your prompt using the original reference.
XERJ Reader-1 is a neurosymbolic model for working with data. It points to the exact words in your document, writes a small program over them, and exact code computes the answer:
• 20-90x faster
• 10-1,000x cheaper (output tokens free)
• every answer is a program you can replay
The shortest path to automating data work.
Mojunel.eth retweeted
YOU’RE OVERPAYING FOR AI BY 1,000X.
I found a tool called XERJ Reader, and it doesn’t generate a single word.
Here’s how it works:
1. Drop in your data: PDF, Word, email, JSON, CSV, logs. No schema, no setup
2. Ask a question in plain words
3. Reader scans the whole document in a single pass
4. You get the exact passage highlighted in the source, with its position
5. Not in the doc? It tells you “no answer” instead of making something up
It can’t make up a word it never writes.
The only answer it can’t give you is a wrong one.
And the math nobody wants to run is f**king brutal:
→ $0.01 per 1M tokens. Output is free
→ 1,050x cheaper than Opus 5.5
→ 26x cheaper than Haiku 5.5
→ Roughly 4x cheaper than Jev
Same question. Same file. Three models.
Opus writes you a paragraph.
Haiku writes you a shorter paragraph.
XERJ writes nothing.
It just underlines the sentence that was already there.
10 questions on a 100 KB doc:
Reader → under half a second
LLM agent → about a minute
The problem was never the model.
You’ve been paying for an essay when all you needed was a highlighter.
No hallucinations. No waiting. No huge bills.
Just the answer, highlighted.
Waitlist opens today. Bookmark this and be one of the first to start using it ↓
I've built a new AI model that is not allowed to generate text. Instead, it produces a verifiable program that answers your prompt using the original reference.
XERJ Reader-1 is a neurosymbolic model for working with data. It points to the exact words in your document, writes a small program over them, and exact code computes the answer:
• 20-90x faster
• 10-1,000x cheaper (output tokens free)
• every answer is a program you can replay
The shortest path to automating data work.
Broke the cost floor with a team
$8 brain plans. $0.50 hands type.
I wired four agent files and each one carries its own model.
Fable is the brain.
Plans the work, splits it, judges what comes back.
Runs only when I type /brain.
Opus 5.5 takes the builder role. Builds from the brief, nothing else.
Sonnet 5.5 is the checker. Reviews the output. Edits nothing.
Haiku 5.5 runs the repetitive tasks and never touches code.
80x between the brain and the runner.
The brain delegates three kinds of work:
1. Independent parts
2. Work too big for one session
3. Repeatable tasks
One brain plans. Three hands deliver.
I describe the job
→ Fable writes the spec at $8 / $40
→ Opus builds at $4 / $20
→ Sonnet reviews at $2 / $10
→ Haiku runs routine at $0.10 / $0.50
→ Builder fails twice, Fable takes over
Never two helpers on the same file at once. Checker signs off before done.
One CLAUDE.md line: consult the advisor before the approach, on a repeating error, before calling done.
Agent teams in plan mode burn 7x the tokens of a solo session. Worth it only when the job outgrows one sitting.
Four files and one line
$11,000 or I kill the subscription. The bot blew past it
last post showed it clearing $11,040
you figured that was the peak?
it just hit $15,743.36
from a $19.63 starting deposit
sit with that for a second
i've been running an ai trading terminal built with Grok Bot, and six agents keep going nonstop, scanning tokens, spotting setups and pulling the trigger on trades
since that last check the balance on the terminal climbed another $4,702.78
here's the current snapshot
starting balance: $19.63
current balance: $15,743.36
total p&l displayed: +$15,723.73
win rate: 70.4%
resolved events: 2,533
and this isn't just a static number
the terminal tracks balance history, live activity, token scans, every execution and the back-and-forth between agents
six agents running inside a single environment
one watches the market, another catches setups, trades fire, and every event hits the log
wins, losses, liquidity scans, slippage, fresh pairs
all of it runs through the same terminal
the crazy part is how the whole thing got started
i didn't wire up a trading system from scratch by hand
i used Grok Bot to build the agents, the execution pipeline and the interface on top
now i'm pushing the experiment harder and logging every result as it lands
from $19.63 to $11k, then straight past $15.7k on the screen
the only question left is where the ceiling sits
A 21-year-old guy from San Francisco walked into an Apple Store, connected his Claude account to his phone right on the spot, and made $1,956 in just 15 minutes trading via Fable 5.2 without writing a single line of code
He opens Fable 5.2 right there in the store, types a quick prompt for an AI-powered trading strategy, and connects it to the live charts
Fable 5.2 builds the full setup in 3 minutes: real-time analytics, automated pattern recognition, and precise market entry signals
He launches the tool directly on his mobile device, tracks the live market spikes, and watches the profits grow trade by trade
While standing right in the store, he executes a series of successful trades using the AI-generated system, stacking up to $1,956 in profit in just 15 minutes
He closes the app, walks up to the store employee, and buys the brand-new phone he just earned money for right on the spot
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Mojunel.eth retweeted
NOT ON THE APP STORE. GROK BOT MADE THIS, I FELL ASLEEP, IT TURNED $347.86 INTO $3,048.56
from 11:47pm to 6:36am. 8.76x, 13 trades, i touched none of them
i told Grok Bot to make me a terminal to run off my phone. it put this one together, i dropped it in my Trading folder, tapped Run and went to sleep
4 agents inside
Scanner tosses anything short on liquidity
Analyst scores whatever gets through
Executor buys
Risk attaches a -6% stop to each buy
2:35am i opened my eyes and checked the app. same balance, positions firing open and shut on their own. went back to sleep
out of 13 sells, 10 hit green and 3 went red. those 3 lost $18.33 combined
top trade was a coin named scamsem, that name. +$566.46 in 46 minutes
and Diamond Hands, held true to its name, 1:27am to 5:00am, closed at 5.65x and 6.84x
8.76x is solid, but what i actually like is that this app belongs to me. nobody touches the rules except me
would you hand your money to an app that doesn't live on the App Store, or is that insane?
THIS GTA AI CLIP MAKES A PHYSICS-DEFYING TRANSITION LOOK LIKE A 30-SECOND PROMPT
what sells it is the continuity, not the AI glitch. you get realistic ambient lighting, accidental slip physics, matching clothing and crew-style framing before anything impossible happens.
3 different character interactions show up in under 50 seconds, but they all feel like they belong to the same street-level clip instead of random AI shots stitched together.
that opens a much better format than another cinematic clip: build seamless character loops and physics transitions around scenes that would normally need complex motion capture, stunt teams and hours of manual masking.
with @Pixverse, i can push the same concept through text, image, reference and transition modes while keeping the character identity consistent enough to feel like one continuous generation.
1 idea can become 4-6 impossible character loops, one behind-the-scenes workflow breakdown and multiple cuts for x, reels and tiktok. that is a much stronger use of AI video than just generating one pretty shot.
COMPLETELY DERANGED.
Most teams still stack six paid tools for one funnel while 10 repos run a whole sales and revenue-ops team.
Zapier Pro charges $733.50 a month at 100,000 tasks. These ten cover the same pipeline with no per-task fee.
trigger. capture. pipeline. reach. measure. close.
01 n8n → wire triggers to every tool you run
github.com/n8n-io/n8n
02 activepieces → 280+ pieces work as MCP agent tools
github.com/activepieces/acti…
03 formbricks → capture leads with in-app surveys
github.com/formbricks/formbr…
04 twenty → Salesforce rival, open CRM built for AI
github.com/twentyhq/twenty
05 mautic → nurture leads with drip email campaigns
github.com/mautic/mautic
06 listmonk → send newsletters from a single binary
github.com/knadh/listmonk
07 novu → route email, SMS, push and in-app alerts
github.com/novuhq/novu
08 umami → measure traffic and conversions, no cookies
github.com/umami-software/um…
09 documenso → get contracts signed without DocuSign
github.com/documenso/documen…
10 akaunting → invoice and book every closed deal
github.com/akaunting/akaunti…
trigger → capture → pipeline → reach → measure → close. Half the list ships AGPL or source-available. Read the license before you resell a thing.
Six subscriptions gone.
Mojunel.eth retweeted
One agent scoring its own find is a guaranteed yes.
Five that never see each other's work built this $100 desk.
I wired one prompt on Solana. Five agents, each doing one job. A coin moves left to right and any agent kills it.
1. Finder scans every new pair. Under $20k liquidity or past 6 hours: dead.
2. Auditor reads contract and holders. Top 10 above 35% or dev above 5%: dead.
3. Judge scores 0 to 100. Under 80: dead.
4. Stager sizes at 4% of the book. Sets the -25% stop before entry.
5. Closer sells half at double, quarter at triple, trails 30% under the high.
Seven rules sit above every score. 6 open positions, 3 entries an hour, -10% day kills the desk until midnight.
The Finder never knows what the Judge scored. The Auditor reads contracts, not scores. Something goes wrong and I get a name, not a story.
Rule 7 saved the desk. The creator pushed tokens toward the exchange and the Closer cleared at -6% in one block. The coin lost 91%.

























