Paris, France
Joined February 2010
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- Followers274
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Michel Bhasin retweeted
Linkavista Conference 2026: SEO, AI & GEO · Kyle Roof, SEO Jesus & more xitter.cf/i/broadcasts/1qJVmyPwm…
Michel Bhasin retweeted
A 19-year-old Japanese student built a trading bot with Claude Code in 2 days.
Used his iPad as a second monitor.
First night: $6,732 profit.
Starting capital: $68.
Total profit so far: $750,000.
Here's how it works:
The bot scans over 50 markets simultaneously.
Syncs live BTC data from Binance every second.
Spots price errors before humans even notice.
The edge is pure speed + pattern recognition.
While traders stare at charts trying to predict the next move, his bot is already executing on mispricing across dozens of markets.
No guessing.
No emotions.
No hesitation.
Just Claude Code logic finding gaps that close in seconds.
He built the entire system in 48 hours:
→ Claude Code handles the trading logic
→ Binance API feeds real-time BTC data
→ iPad displays multi-market monitoring
→ Executes trades when arbitrage windows open
The system runs 24/7.
Every price dislocation = profit opportunity.
Most people are still trading manually, refreshing charts, second-guessing entries.
Meanwhile this 19-year-old engineering student turned $68 into $750K by letting Claude Code do what humans can't: process 50 markets instantly and execute without fear.
Why are people still trading manually?
💡 I'm giving away the exact Claude Code setup for free.
24 hours only.
To get it:
1️⃣ Comment "Fable"
2️⃣ Like and Repost
3️⃣ Follow @anjum_ai
I'll DM you the complete setup.
Michel Bhasin retweeted
STOP DOWNLOADING LLMS YOUR MACHINE WAS NEVER GOING TO RUN
llmfit is an open-source tool that scans your hardware first, then tells you exactly which models will actually run well on your setup.
It inspects:
→ RAM
→ CPU
→ GPU(s)
→ VRAM / unified memory
Then it scores every model across four dimensions:
→ quality, based on parameter count and quantization
→ speed, estimating tokens per second for your exact backend
→ fit, matching memory usage to your hardware
→ context window support for your use case
Each model then gets a clear label: Perfect, Good, Marginal, or Too Tight.
It also picks the best quantization automatically, stepping down until something fits instead of leaving you to guess GGUF sizes by hand.
Coverage is broad too! 100's of models from Meta, Mistral, Qwen, DeepSeek, and more.
It works with Ollama, llama.cpp, MLX, and LM Studio out of the box.
And that’s not even it!
There’s also a lightweight TUI, a web dashboard, and REST API endpoints if you want to plug it into orchestrators or deployment pipelines.
This should be step zero before every local model download :)
100% free and open-source. macOS, Linux, and Windows.
Repo in 🧵↓
Michel Bhasin retweeted
Your $5,000/month ghostwriter just became a $20/month Claude folder.
That's $60,000/year you stop paying.
→ No more 14 hours/month briefing someone who still strips your specifics out
→ No more voice drift by month 2 where you sound like them, not you
→ No more 10+ hour revision cycles that kill real-time optimization
→ No more AI slop reaching your feed with no quality gate
Just 21 Claude skill files. 9 folders. Drop into Claude. The entire LinkedIn content pipeline runs from there.
Here's what's inside:
→ Brand Memory Foundation (6-section context block. Every post sounds like you, not AI)
→ Hook Patterns Library (15 patterns. Templates behind posts hitting 9,000+ comments)
→ 8 Post Template Categories (authority, lead gen, myth-busters, story frames, engagement. Each with a trigger breakdown)
→ Voice Firewall (7-dimension scorecard. Kills slop before it ships)
→ ICP Profile Builder (your buyer's exact language, fears, and psychological triggers)
→ DM Opener System (fires on keyword comment. Qualifying question first, pitch never)
→ Visual Brief Generator (GPT Image 2 or designer handoff in 2 minutes)
Results from the same system:
- Posts hitting 9,020 comments. 5,400. 4,600. 3,800. 3,700.
- 50M+ views across LinkedIn
- The content infrastructure behind a $150K/month operation
Most people are still prompting Claude like it's Google.
This is infrastructure.
Like + comment "SKILLS" + repost, and I'll DM it to you.
(must be following)
Michel Bhasin retweeted
Claude + Reddit intelligence + LinkedIn data = Content OS that replaced a $14,500/month team.
(48K followers. 500+ booked calls. $387K in attributed revenue)
This 6-skill system eliminates writers, strategists, researchers, and schedulers using brand memory + real audience intelligence + calibrated AI writers...
→ No more $14,500/month payroll for a 4-person content team
→ No more 20+ hours weekly building content calendars from scratch
→ No more guessing which hooks actually stop the scroll
→ No more AI slop that sounds nothing like you
→ No more starting every post from a blank page
Just 1 topic in → a finished month of content out.
Here's how it works:
→ Brand Memory (your voice, proof, and exact buyer in one file. every skill runs off it. nothing generic ships.)
→ Reddit Intelligence (mines your ICP's subreddits for the exact phrases they use. posts start from real words, not blank pages.)
→ LinkedIn Viral Signal (hooks already stopping the scroll in your niche, last 30 days, ranked by real engagement.)
→ Four Calibrated Writers (thought leadership, lead magnet, story, value. one writer per stage of the buyer journey.)
→ Voice Firewall (7-dimension QA that scores and rewrites every draft before it ships under your name.)
→ Finished Board (every post handed over with its visual and first comment. ready to approve.)
Built on real content infrastructure.
Zero payroll. Consistent output. Enterprise-grade quality.
Results from the system:
- $14,500/month content team eliminated
- 48K LinkedIn followers
- 500+ calls booked from content
- $387K in attributed revenue
Want the complete 6-skill build?
Like + comment "OS" + repost, and I'll DM it to you.
(must be following)
