Introduction to AI Weekly
Good morning, AI enthusiasts! This week’s issue bridges two ends of the spectrum: the foundations you need to get started, and the nuanced tools and ideas shaping how we build with AI today. We begin with a clear, approachable guide to Python and core computer science concepts — ideal if you’re just starting out or brushing up on the basics. But from there, things go deeper.
What’s AI Weekly
This week in What’s AI, we dive into Python Fundamentals and CS Concepts. This is meant to be a one-stop starter guide for a total programming beginner. We’ll take things one step at a time and use examples to explain each concept. Don’t worry, if you don’t grasp all concepts from just this single article, you can always learn more about them in our Python course. Start your learning with this article or watch the video on YouTube, and practice these concepts to really understand them!
Learn AI Together Community Section
Featured Community Post from the Discord
Blondu0994 has built an all-in-one platform for translations, transcriptions, OCR, PDF/Word/Excel conversions, and electronic signatures. It is powered by AI, fully automated, and runs without commercial APIs. He is looking for feedback, go check it out and support a fellow community member. If you have any questions about the tool, reach out in the thread!
AI Poll of the Week
While the polls show most of you use 4o, the discussion in the thread has moved from OpenAI to Deepseek, Perplexity, and Gemini. Is price guiding this decision, or performance? Also curious to know why anyone still isn’t using Grok. Tell me in the thread on Discord!
Collaboration Opportunities
The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!
- Uwaix wants to do some research in AI and is looking for people who’d like to join them. If you have any topic ideas or want to pursue research, connect with them in the thread!
- _madarauchiha is exploring numpy and other Python libraries and is looking for an accountability partner available to study for three hours per day. If you have the time and are focusing on the same topics, reach out to him in the thread!
Meme of the Week
Meme shared by bin4ry_d3struct0r
TAI Curated Section
Article of the Week
From First Principles: Building Function Calling by Fine-tuning NanoGPT By Suyash Harlalka
This blog provides a detailed walkthrough for implementing function calling by fine-tuning a NanoGPT-like model using only PyTorch and Tiktoken. Unlike methods requiring function definitions in prompts, this approach trains the model to generate structured outputs directly, improving efficiency.
Our Must-Read Articles
- Extracting Actionable Rules from Raw Data By Nehdiii
This work details methods for extracting interpretable business rules from data using Decision Tree Classifiers, useful when speed or clarity is preferred over complex models. - Adaptive Decay-Weighted ARMA: A Novel Approach to Time Series Forecasting By Shenggang Li
This article presents Adaptive Decay-Weighted ARMA, a time series forecasting approach addressing the limitation of traditional models that treat all past data equally. - In-Context Learning Explained Like Never Before By Allohvk
This article examined In-Context Learning (ICL), an emergent capability where Large Language Models (LLMs) learn tasks from prompt examples without fine-tuning. - Data-Driven LLM Evaluation with Statistical Testing By Robert Martin-Short
This piece explored using empirical statistical methods, specifically bootstrap and permutation testing, to evaluate improvements in LLM applications. - DeepSeek-V3 Explained Part 3: Auxiliary-Loss-Free Load Balancing By Nehdiii
As the third part in a series on DeepSeek-V3’s architecture, this piece details its auxiliary-loss-free load balancing technique for Mixture-of-Experts (MoE) models.
Conclusion
In conclusion, this week’s AI Weekly issue covers a wide range of topics, from the basics of Python and CS concepts to more advanced topics like In-Context Learning and Adaptive Decay-Weighted ARMA. We also highlight some exciting projects and collaborations from the Learn AI Together community. Whether you’re a beginner or an experienced AI enthusiast, there’s something for everyone in this issue.
FAQs
Q: What is AI Weekly?
A: AI Weekly is a newsletter that covers the latest news and developments in the field of artificial intelligence.
Q: What is the Learn AI Together community?
A: The Learn AI Together community is a Discord community where people can come together to learn and discuss AI-related topics.
Q: How can I get involved in the Learn AI Together community?
A: You can join the Learn AI Together Discord community and participate in discussions, share your projects, and collaborate with others.
Q: What is In-Context Learning?
A: In-Context Learning is an emergent capability where Large Language Models (LLMs) learn tasks from prompt examples without fine-tuning.
Q: What is Adaptive Decay-Weighted ARMA?
A: Adaptive Decay-Weighted ARMA is a time series forecasting approach that addresses the limitation of traditional models that treat all past data equally.
