BIG BIG Happy birthday to my little brother @ihate1999 and the youngest in charge!!! 🥳🥳
- Managing Partner, @RcrsvCapital
- CEO, @btrustteam
- @Forbes Digital Asset Contributor
- #Bitcoin Core Contributor
Forever proud of you!! Stay focused and keep building!! ❤️❤️❤️
#27 #BTC 🚀🚀
Last week, I had the opportunity to host a Bitcoin event on my campus where I educated students on Bitcoin, how wallets work, the different types of wallets, and the concept of the Bitcoin circular economy.
Engineering a Smarter RAG for Biochemistry
A few months ago, I was gripped by a single question: What does the skeleton of AI actually look like? What began as a deep dive into the mechanics of Large Language Models and tokenization quickly evolved into a personal challenge.
I had to pivot and implement a conversational memory buffer, ensuring the AI could maintain context and actually learn from previous steps of a student's inquiry.
Building this system shifted my view: engineering = finding loopholes & closing them with better logic.
Very proud of my Biochemistry RAG system, but in AI, learning never stops. Suggestions to boost retrieval precision or improve CRAG logic? Share in comments or DMs.
Merry Christmas from all of us at Tapnob. 🎄
The best gift we received this year was your trust. Thank you for believing in what we are building and for letting us serve you.
We appreciate every single one of you. Eat well and rest well!
Us when someone says Bitcoin is a trend:
We’re not leaving. Ever.
Orange-pilled for life—and bringing our sisters who are ready with us. 🧡
#BitcoinWeekend#BitcoinDada
I am done with the practical aspect. Now, I want to focus on the theoretical aspects of what I have learned: the use cases and how to choose the perfect model for the right dataset.
Well, yesterday I completed the @DataCamp course on supervised learning with scikit-learn. I learned about KNN, regression, Lasso regression, Ridge regression, pipelines, confusion matrices, and Standard-Scaler. I didn't just watch the videos.
I practiced everything myself without using YouTube or AI. I used the KNN model to predict diabetes progression with the load_diabetes dataset. I trained the KNN model using the standardized training features (X_train_scaled) and the corresponding target values (y_train).
Yes, I said I would document my journey here and I didn't forget.
I worked on 6 different projects using Python and performed EDA. It wasn't perfect or easy, but I did it. It's a big win for me and I'm eager to face what's next.
So I did some exploratory analysis on Monday. It wasn't bad, considering that I'm a data analyst who's scared of using Python for data cleaning and analysis. But I challenged myself to try it, I had nothing to lose, and I did it!
it is not perfect but i mean little beginnings