Samri-A

EOTCOpenSource ✝️ EOTCOpenSource is a collaborative community dedicated to uniting Ethiopian Orthodox Tewahedo Church (EOTC) believers with developers, designers, and contributors from around the world. Our mission is to build open-source digital tools that support the Church, its followers, and its sacred traditions. We create modern software rooted in the ancient values of the Orthodox faith, making spiritual and cultural resources more accessible for generations to come. Our projects aim to serve the diverse needs of the EOTC community. We focus on creating high-quality, reliable, and accessible tools, including: Mobile & Web Apps: Tools for prayer, learning, and community engagement. Digital Calendars: Liturgical calendars with feasts, fasts, and saints' days. Digital Texts & Archives: Digitizing liturgical books, hagiographies, and important texts in Amharic, Ge'ez, and other languages. Liturgical Tools: Software to assist clergy and laity in Church services and prayers. Chant & Hymn Resources: Platforms for learning and preserving the rich tradition of EOTC chants (Zema). Join community: https://t.me/EOTCOpenSource Github Link: https://github.com/EOTCOpenSource/EOTCOpenSource

They say

health is not valued till sickness comes

Aha lately 🫩 and today for some reason my migraine 😣 I m not even thinking straight.

😁 @samli_documentation

እንኳን አደረሳችሁ ✨

🚀 I Built a Checkers AI Agent That Learns Like a Brain! 🤖♟️ Hello everyone! I just built a Checkers AI Agent that learns to play using the NEAT (NeuroEvolution of Augmenting Topologies) algorithm! 🧠♟️ After a long time of learning and building, I finally created a Checkers AI Agent that mimics biology using the NEAT algorithm. This AI doesn’t just play; it learns through self-play and evolves its neural network structure over time, just like a brain. Inspired by AlphaGo, I wanted to explore how AI can mimic biological evolution — and watching it get smarter is honestly exciting. It’s still at a basic level, but seeing it grow and learn on its own makes it incredibly rewarding. You can play against the AI in your browser or train your own models. It is also open source. Try it online, check out the code, or contribute! 🔗 Play now: https://checkers-using-neat1.onrender.com/ 🔗 GitHub: https://github.com/benasphy/Checkers_Using_NEAT ⭐ Don’t forget to star, fork. For questions or contributions DM @benasphy

እንኳን ደስ አላችሁ፤ እንኳን ደስ አለን! የ2017 በጀት ዓመት እና የሶስት ዓመት መሪ የዕድገት ስትራቴጂያችንን በላቀ ስኬት ማጠናቀቃችንን በታላቅ ደስታ እንገልጻለን። እርስዎም የስኬታችን ተካፋይ ኢትዮ ቴሌኮም

Meanwhile the speed 🐌 It's B/s now😭 I only have few days left before my monthly ... ወይኔ ብሬ🤧

Meanwhile me 😭

Thought I optimized it but 👀

😁 but they forgot stat good night 🚶‍♀️‍➡️

These are youtube channels I found helpful for ML so far

computerphile he makes the logic behind complex algorithms easy to understand 🤌 Krish helped me to understand ada boost algorithm in minutes. the way he explains 👏 statquest made even the maths simple but not really simple yea I loop over like twice or three times to understand 😁

when ever I try to understand ML algorithm 😁

Don't waste your twenties... I think It's the time we plant the seed for the rest of our life.

The answer to how to approach your twenties "Everything you do is irrevocable, because time is finite✨ Which means your choices matter. So you have to choose a fig. You cannot sit “in the crotch of this fig tree starving to death.” But choosing one fig does not cause all the others to instantly fall from the tree. If you realize you’ve chosen the wrong fig—and I mean as soon as you realize, because your twenties are shorter than you think—go back to the tree and pluck another✨ And another And another Until you’ve found the right one🤍" ~Caroline

This GitHub Repo contains pretty much every use case that you can think of with n8n. https://github.com/Zie619/n8n-workflows.git

I used to remove outlier using percentile before Kifiya Ai good night 🤌

Z-score This is standard normal distribution. The 0 point is the mean of the data and it has standard deviation 1. A data above the average has positive Z-Score and negative for below. Z-Score is calculated like this Z = data point - mean of the data / Standard deviation So this indicate how far our data point is from the mean. The probability of a z-score tells you how likely a value is to appear in a normal distribution and from the image you can see zscore that fall between 3 and -3 have 99.9 probability to be in normal distribution. Here the use of zscore comes in Outlier is a data point that is unusual. like we have data of high school students and it is like getting 80 or 5 in age column and the other data is clustered between 15-20. Outliers affect your model performance. So the data point with z-score above absolute value of 3 or -3 has 0.1 % chance to be found in normal distribution of the data. So in the another word it is outlier. #SharingisCaring 😊

Always you have been told that work is a curse and labour a misfortune. But I say to you that when you work you fulfill a part of earth's furthest dream, assigned to you when that dream was born, And in keeping yourself with labour you are in truth loving life, And to love life through labour is to be intimate with life's inmost secret.

from the

The Prophet book

-- Khalil Gibran

Starting tomorrow I will post what I gained through this training. good night ☺️

So, week 2 I didn’t touch the IDE project. I started the Play Store app review project instead. It been almost 2 months since I joined the 10Academy AI Kifiya Mastery course. The thing is it is project based learning, and my week 2 project was to do Play Store review analysis for three selected mobile banking apps. Then, in week 6, the project was using RAG to build a customer complaint chatbot. So the idea for this project came to me like by combining week 2 and week 6 project why not owners get actionable insights from users review just by using play Store app id This is also in progress ... for improvements and my backend is like 👨‍🦽