Please don’t scroll past 🙏
This is my Dad.💔
He has been diagnosed with Stage 4 Colon Cancer, and we’re desperately trying to raise funds for his treatment.
Kindly take a moment to watch the video.
If you’re unable to donate, please help us repost it. God bless you 🙏❤️
I just open-sourced my entire n8n ad automation system...
- AI writes 5 ad variants in 30 seconds
- Auto-deploys to Meta Ads
- Monitors performance hourly
- Optimizes budgets automatically
- Creates lookalike audiences
95% less manual work
35% lower CPA
60% higher ROAS
Free & production-ready:
https://t.co/Rlw7gQgrN3
#MarketingAutomation #AI #OpenSource
I don’t even know which one to tweet. Is it Justice for Ochanya, End Child Marriage, Bring Back Our Kebbi Girls, End terrorism in Nigeria, Free Sudan, Congo or Palestine?
I’M EXHAUSTED!!!!
1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25.
25 school girls got kidnapped in Kebbi by terrorists
A whole 25 girls.
Let’s trend the hastag guys
#BringBackKebbiGirls
There is a Christian genocide in Nigeria.
There is a Christian genocide in Nigeria.
There is a Christian genocide in Nigeria.
There is a Christian genocide in Nigeria.
There is a Christian genocide in Nigeria.
There is a Christian genocide in Nigeria.
Please RT. 🙏🏾💔
I absolutely love how much more control being an ML developer comes with. For years, I studied the systems built by some of the most successful traders of the last century... Livermore’s trend logic, Wyckoff’s price/volume principles, O’Neil’s momentum rules, Minervini’s timing, Dalio’s risk frameworks. Those strategies worked before computers, before algos, before crypto. So I asked myself a question:
What would it look like to merge that century of trading wisdom with modern machine learning? Ultimately a dashboard that says one thing: BUY, SELL, or WAIT.
That question became the foundation of the system I’m building. Not another crypto bot. Not another random forest on GitHub. A model that thinks in the same rhythm as the strategies that have survived decades of market cycles.
So far, here’s what’s alive:
- A real-time BTC/USD ingestion engine
- Feature pipeline encoding 103+ indicators... not randomly, but chosen to match patterns these legendary traders relied on:
- trend (Livermore)
- momentum (O’Neil)
- volatility (Market Wizards)
- volume behavior (Wyckoff)
- mean reversion (Dalio)
- An LSTM model designed to read the last 60 minutes the way a seasoned trader reads a tape
- A target inspired by actual trading logic: predict if BTC will be meaningfully higher 5 minutes from now
- A dashboard that converts all of that into a single, simple signal: BUY, SELL, or WAIT
The difference isn’t the code.
It’s the philosophy behind it.
The greatest traders didn’t have GPUs or Python
but they deeply understood how price behaves.
My goal is to encode that understanding into a modern system yk, something that thinks with the discipline of a century of trading strategy and the speed of machine learning.
The data pipeline is running.
The features are live.
The model is ready.
More soon.
I just deployed an end-to-end ML pipeline predicting customer churn on 41k+ real records. Used XGBoost + Optuna, saved $417k in simulated revenue. Deployed live web app using FastAPI on Render.
Tech stack:
• Python, XGBoost + Optuna
• SMOTE for imbalance
• FastAPI web app
• MLflow tracking
• Render for deployment
GitHub: https://t.co/rkbxf9TeRP
All in ONE command: `python https://t.co/x8qvbVdVwI`
Built from scratch.
#MachineLearning #DataScience #Python #FastAPI
Sadly, People have moved on to trending issues; such is the nature of Nigerians. That said, this is the latest update on the Ochanya case as reported by @fijnigeria. The case has not been reopened like the rumors alleged. Click on the link to read. https://t.co/B00o5N91ge
there’s so much about this country that doesn’t add up, filthy rich in stones and oil and yet so many impoverished? And who’s responsible for our literature? Bunch of misleading lies everywhere smh.