Forwarded from GDG Addis
Google Africa is inviting members of Google Developer Group Addis to take part in an exclusive partnership event between Google and the African Union Commission taking place on February 17, 2026, at 11:00 AM at the AUC Headquarters. The event will be a great opportunity to learn about the latest AI technologies from Google and their relevance in strengthening Africaβs digital economy. To participate, please fill out this form before Wednesday, 11 February:- https://forms.gle/4aHK7K8NdhbX9jPz9
Google Docs
Google and African Union Partnership Event
Google Africa is inviting members of Google Developer Group Addis to attend a partnership event between Google and the African Union Commission taking place on February 17, 2026, at 11:00 AM at the AUC Headquarters.
This event is scheduled to take placeβ¦
This event is scheduled to take placeβ¦
Code It now
https://core.telegram.org/bots/api
The Bot API is an HTTP-based interface created for developers keen on building bots for Telegram.
Forwarded from Information Systems Hub π»π
π In-Person Workshop on RAG (Retrieval-Augmented Generation)
π Feb 25 β Mar 5, 2026 (Weekdays)
β° 2:00 PM β 5:00 PM
π 4 Kilo New Building, 2nd Floor
Join Telegram channel
Prerequisites:
β’ Basic Python (essential)
β’ Basic API knowledge
β’ Git (recommended)
β’ No prior AI/ML experience required
π Certificate Provided Fill out this Form
Telegram|LinkedIn|YouTube | Tiktok |
π Feb 25 β Mar 5, 2026 (Weekdays)
β° 2:00 PM β 5:00 PM
π 4 Kilo New Building, 2nd Floor
Join Telegram channel
Prerequisites:
β’ Basic Python (essential)
β’ Basic API knowledge
β’ Git (recommended)
β’ No prior AI/ML experience required
π Certificate Provided Fill out this Form
Telegram|LinkedIn|YouTube | Tiktok |
π₯4
Are you interested in learning Artificial Intelligence and building digital skills for the future? The Youth Empowerment and Economic Acceleration Program (YEEAP) is offering a Foundations of Artificial Intelligence course designed to help young people understand and apply AI in todayβs digital economy.
What You Will Gain
πΉUnderstanding of Artificial Intelligence, Machine Learning, and Generative AI
πΉHands-on experience using AI tools and platforms
πΉSkills in prompt engineering and human-AI collaboration
πΉInsights into career and entrepreneurship opportunities in the AI economy
Who Can Apply
πΈAnyone interested in learning AI
πΈHigh school graduates
πΈUniversity students and graduates
πApply: https://edutrack-yeeap.powerappsportals.com/
@code_it_now
What You Will Gain
πΉUnderstanding of Artificial Intelligence, Machine Learning, and Generative AI
πΉHands-on experience using AI tools and platforms
πΉSkills in prompt engineering and human-AI collaboration
πΉInsights into career and entrepreneurship opportunities in the AI economy
Who Can Apply
πΈAnyone interested in learning AI
πΈHigh school graduates
πΈUniversity students and graduates
πApply: https://edutrack-yeeap.powerappsportals.com/
@code_it_now
Forwarded from Chapi Dev Talks
π Introducing Dataset.ET
The future of AI should speak Ethiopian languages.
Today we are launching Dataset.ET β an open community initiative to build the largest dataset for Ethiopian languages.
Why this matters:
Most AI systems today barely understand Amharic, Afaan Oromo, Tigrinya, Somali, and many other Ethiopian languages. Without datasets, AI will ignore our languages.
Dataset.ET is changing that.
What we are building:
β’ Speech datasets
β’ Text datasets
β’ Community-validated language corpora
β’ Open infrastructure for Ethiopian AI
How you can help:
1οΈβ£ Record sentences using our Telegram bot
2οΈβ£ Validate recordings from other contributors
3οΈβ£ Help expand datasets for Ethiopian languages
No technical skills needed β just your voice and your language.
π€ Start contributing:
https://t.me/dataset_et_bot
π Learn more:
https://dataset.et
π¬ Join the community:
https://t.me/dataset_et
Together we can teach AI to understand Ethiopia.
π’ Community leaders & channel admins:
The future of AI should speak Ethiopian languages.
Today we are launching Dataset.ET β an open community initiative to build the largest dataset for Ethiopian languages.
Why this matters:
Most AI systems today barely understand Amharic, Afaan Oromo, Tigrinya, Somali, and many other Ethiopian languages. Without datasets, AI will ignore our languages.
Dataset.ET is changing that.
What we are building:
β’ Speech datasets
β’ Text datasets
β’ Community-validated language corpora
β’ Open infrastructure for Ethiopian AI
How you can help:
1οΈβ£ Record sentences using our Telegram bot
2οΈβ£ Validate recordings from other contributors
3οΈβ£ Help expand datasets for Ethiopian languages
No technical skills needed β just your voice and your language.
π€ Start contributing:
https://t.me/dataset_et_bot
π Learn more:
https://dataset.et
π¬ Join the community:
https://t.me/dataset_et
Together we can teach AI to understand Ethiopia.
If you run a Telegram group or community and believe Ethiopian languages should be part of the future of AI, we would really appreciate it if you could share this post with your community.
Your support can help thousands of people contribute their language and voice to this initiative. Thank you!
Please open Telegram to view this post
VIEW IN TELEGRAM
Forwarded from Information Systems Hub π»π
Special Announcement
π’ Adwa AI Assistant β βHistory Meets Intelligenceβ
IS HUB is launching an exciting challenge to build an AI Assistant inspired by the history of Adwa.
π Submission Deadline: March 17
π Prize: A reward will be given to the best project at Is hub tech event (Wednesday )
π₯ Open to all students
Organized by IS HUB
Telegram|LinkedIn|YouTube | Tiktok |
π’ Adwa AI Assistant β βHistory Meets Intelligenceβ
IS HUB is launching an exciting challenge to build an AI Assistant inspired by the history of Adwa.
π Submission Deadline: March 17
π Prize: A reward will be given to the best project at Is hub tech event (Wednesday )
π₯ Open to all students
Organized by IS HUB
Telegram|LinkedIn|YouTube | Tiktok |
Forwarded from Information Systems Hub π»π
We are hosting the IS HUB Tech Event!
π Location: Digital Library Building, 5th Floor β AAU 4 Kilo
β° Time: 7:00 LT
β’ Opening & Welcome
β’ IS Hub Introduction
β’ Guest Sessions & Pitches
β’ Panel Discussion
β’ Refreshment Break
β’ Competitions & Project Demos
β’ Winner Announcements
β’ Certificates & Recognitions
β’ Giveaways & Closing Telegram|LinkedIn|YouTube | Tiktok |
π Location: Digital Library Building, 5th Floor β AAU 4 Kilo
β° Time: 7:00 LT
β’ Opening & Welcome
β’ IS Hub Introduction
β’ Guest Sessions & Pitches
β’ Panel Discussion
β’ Refreshment Break
β’ Competitions & Project Demos
β’ Winner Announcements
β’ Certificates & Recognitions
β’ Giveaways & Closing Telegram|LinkedIn|YouTube | Tiktok |
Looking for people who can drop AI, ML, and deep learning resources, tips, and useful stuff in my channel. If thatβs you, DM me
@kipa_s
@kipa_s
AI / ML / Deep Learning Resources (Part 1)
Here are some high-quality resources to get you started or level up your skills π
π₯ YouTube Channels
β’ 3Blue1Brown β https://www.youtube.com/@3blue1brown
β’ StatQuest with Josh Starmer β https://www.youtube.com/@statquest
π Online Courses
β’ Kaggle Learn β https://www.kaggle.com/learn
β’ Applied Data Science Lab β https://www.wqu.edu/adsl
β’ Learn PyTorch for Deep Learning β https://www.learnpytorch.io/01_pytorch_workflow/
β’ TensorFlow for Deep Learning β https://dev.mrdbourke.com/tensorflow-deep-learning/00_tensorflow_fundamentals/
π Competitions & Practice
β’ Kaggle β https://www.kaggle.com/competitions
β’ Zindi β https://zindi.africa/
β’ ML Contests β https://mlcontests.com/
π‘ Start small, stay consistent, and build projects along the way.
More resources coming soon π
@code_it_now
Here are some high-quality resources to get you started or level up your skills π
π₯ YouTube Channels
β’ 3Blue1Brown β https://www.youtube.com/@3blue1brown
β’ StatQuest with Josh Starmer β https://www.youtube.com/@statquest
π Online Courses
β’ Kaggle Learn β https://www.kaggle.com/learn
β’ Applied Data Science Lab β https://www.wqu.edu/adsl
β’ Learn PyTorch for Deep Learning β https://www.learnpytorch.io/01_pytorch_workflow/
β’ TensorFlow for Deep Learning β https://dev.mrdbourke.com/tensorflow-deep-learning/00_tensorflow_fundamentals/
π Competitions & Practice
β’ Kaggle β https://www.kaggle.com/competitions
β’ Zindi β https://zindi.africa/
β’ ML Contests β https://mlcontests.com/
π‘ Start small, stay consistent, and build projects along the way.
More resources coming soon π
@code_it_now
Kaggle
Learn Python, Data Viz, Pandas & More | Tutorials | Kaggle
Practical data skills you can apply immediately: that's what you'll learn in these no-cost courses. They're the fastest (and most fun) way to become a data scientist or improve your current skills.
π1
AWS AI & ML Scholars Program 2026 is now open
β Amazon Web Services has launched applications for its AI & ML Scholars program, aiming to train 100,000 learners globally in foundational AI and generative AI skills.
β The program is designed for students and aspiring technologists with limited access to AI training. No prior experience is required, just curiosity and commitment.
β What youβll get:
π Hands-on experience with AWS tools like PartyRock, Amazon Q, and Amazon Bedrock
π Project-based learning through AWS Skill Builder
π Preparation for the AWS Certified AI Practitioner certification
β Program structure:
π Challenge Phase (Mar 24 β Jun 24, 2026): foundational training + certificate + 3-month AWS Skill Builder access
π Top 4,500 learners advance to a fully funded Udacity Nanodegree
π Nanodegree tracks include AI Programmer, Agentic AI Business Professional, and Agent Developer
β¨ If you're looking to break into AI or strengthen your practical skills, this is a strong starting point.
π Apply here: https://aws.amazon.com/about-aws/our-impact/scholars/?utm_source=aws_tc_blog&utm_medium=post&utm_campaign=launch_post
@code_it_now
β Amazon Web Services has launched applications for its AI & ML Scholars program, aiming to train 100,000 learners globally in foundational AI and generative AI skills.
β The program is designed for students and aspiring technologists with limited access to AI training. No prior experience is required, just curiosity and commitment.
β What youβll get:
π Hands-on experience with AWS tools like PartyRock, Amazon Q, and Amazon Bedrock
π Project-based learning through AWS Skill Builder
π Preparation for the AWS Certified AI Practitioner certification
β Program structure:
π Challenge Phase (Mar 24 β Jun 24, 2026): foundational training + certificate + 3-month AWS Skill Builder access
π Top 4,500 learners advance to a fully funded Udacity Nanodegree
π Nanodegree tracks include AI Programmer, Agentic AI Business Professional, and Agent Developer
β¨ If you're looking to break into AI or strengthen your practical skills, this is a strong starting point.
π Apply here: https://aws.amazon.com/about-aws/our-impact/scholars/?utm_source=aws_tc_blog&utm_medium=post&utm_campaign=launch_post
@code_it_now
β‘2
Forwarded from YearProgressET
ββββββββββββββββ αΆα% (55%)
Join the A2SV Remote Education G7 Program!
The A2SV Remote Education G7 community conversation has officially started! We are excited to invite brilliant members of the A2SV community to join our remote program and take part in this unique learning experience.
πWho Can Apply:
Students from all universities are welcome, except the following A2SV partner universities who are not eligible for the remote program (since A2SV provides in-person education at these institutions) :
AAiT, AASTU, ASTU, AUCA and University of Rwanda
Requirements:
Solved β₯60 problems across LeetCode & Codeforces (combined)
β₯30 active days across LeetCode and Codeforces combined.
Application Deadline: April 13, 2026
πApply here: Application Form
@code_it_now
The A2SV Remote Education G7 community conversation has officially started! We are excited to invite brilliant members of the A2SV community to join our remote program and take part in this unique learning experience.
πWho Can Apply:
Students from all universities are welcome, except the following A2SV partner universities who are not eligible for the remote program (since A2SV provides in-person education at these institutions) :
AAiT, AASTU, ASTU, AUCA and University of Rwanda
Requirements:
Solved β₯60 problems across LeetCode & Codeforces (combined)
β₯30 active days across LeetCode and Codeforces combined.
Application Deadline: April 13, 2026
πApply here: Application Form
@code_it_now
π2
Code It now
https://www.datacamp.com/blog/chunking-strategies
The importance of chunking extends far beyond simple data organization; it fundamentally shapes how AI systems understand and retrieve information.
Large language models and RAG pipelines require chunking due to their inherent limitations in context windows and computational constraints.
When I process large documents without proper chunking, the system often loses important contextual relationships and struggles to identify relevant information during retrieval. Effective chunking directly enhances retrieval precision by creating semantically coherent segments that align with query patterns and user intent.
In my experience, well-implemented chunking strategies significantly improve semantic search capabilities by maintaining the logical flow of information while ensuring each chunk contains sufficient context for meaningful embeddings. This approach allows embedding models to capture nuanced relationships and enables more accurate similarity matching during retrieval.
Large language models and RAG pipelines require chunking due to their inherent limitations in context windows and computational constraints.
When I process large documents without proper chunking, the system often loses important contextual relationships and struggles to identify relevant information during retrieval. Effective chunking directly enhances retrieval precision by creating semantically coherent segments that align with query patterns and user intent.
In my experience, well-implemented chunking strategies significantly improve semantic search capabilities by maintaining the logical flow of information while ensuring each chunk contains sufficient context for meaningful embeddings. This approach allows embedding models to capture nuanced relationships and enables more accurate similarity matching during retrieval.
π2π1