Hey everyone,

I've been going deep on RAG architectures lately and couldn't find a single resource that covered all the modern variants in one place, so I built one and open-sourced it.

What's in the repo:

• 10 sections covering every major RAG type
• 50 interview questions tagged [Basic] / [Intermediate] / [Advanced]
• Detailed answers with architecture diagrams, code snippets, and trade-off tables
• A cheatsheet with a decision tree ("which RAG should I use?")
• GitHub Pages site auto-deployed on every push

RAG types covered: Naive, Advanced, Modular, Agentic, Graph, Corrective (CRAG), Self-RAG, Speculative, Multi-modal, and Long-context RAG.

https://github.com/ather-techie/rag-interview-questions

Looking for contributors! If you've been in an ML/LLM interview recently and got a question not covered here, please open a PR or drop it in the comments. I'll add it with credit.

If this is useful, a star on GitHub goes a long way. it helps others discover it. Thanks!

Lightning-Fast, On-Device, Multilingual TTS — running natively via ONNX.

 Highlights

• ⚡ Blazingly Fast — Low-latency, real-time synthesis across desktop, browser, mobile, and edge — fast enough to turn an entire webpage into audio in under a second
• 🌍 31-Language Multilingual — Synthesize directly from text across 31 languages, or pass lang="na" to let Supertonic process the text language-agnostically when you don't know the input language — no separate language adapters needed
• 🪶 99M-Parameter Open-Weight Model — A compact, fully open-weight checkpoint — a fraction of the size of 0.7B–2B class open TTS systems — for smaller downloads, faster cold starts, and lower memory footprint
• 📱 Edge-Device Ready — Runs locally on desktop, mobile, browsers, and resource-constrained hardware like Raspberry Pi or e-readers, with zero network dependency, complete privacy, and no GPU required
• 🔊 44.1kHz High-Quality Audio — Outputs studio-grade 44.1kHz 16-bit WAV directly, ready for production playback without any external upsampler
• 🎭 Expression Tags — 10 inline tags (e.g. <laugh>, <breath>, <sigh>) bring natural human nuance into generated speech without prompt engineering or reference audio
• 🛠️ Multi-Runtime SDKs — Ready-to-use examples through ONNX Runtime across Python, Node.js, Browser (WebGPU), Java, C++, C#, Go, Swift, iOS, Rust, and Flutter

🌍 Supported Languages (31)

Arabic (ar), Bulgarian (bg), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), French (fr), German (de), Greek (el), Hindi (hi), Hungarian (hu), Indonesian (id), Italian (it), Japanese (ja), Korean (ko), Latvian (lv), Lithuanian (lt), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Slovak (sk), Slovenian (sl), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk), Vietnamese (vi)

The best part is it's 100% open source and comes under the MIT license.

Link: https://github.com/supertone-inc/supertonic

I was recently struggling to understand GRPO and how RL is applied on LLM's, the main problem was not the resources but the lack of visual explanations, so I generated a blog for you guys that has both. If you want any more blogs on RL topics then drop a request in the comments and I will add them.
https://www.feynmanwiki.com/library/grpo-and-rl-for-llms-vogl

Hi everyone, I'm 13 and new to machine learning, and people recommended learning linear regression first, I made one using C++, the code itself is probably not great since C++ isn't my main language, Python is, but I'm trying to learn it because I wanna use it in USACO later, so I thought doing projects in C++ would help me get familiar with the language. Anyway, here's the Github repo: https://github.com/hl0228057-cmd/Basic-Linear-Regression-Using-Cpp

I'm open to feedback because I wanna get better and learn, thanks!

I built a skin cancer classifier from scratch — here's what I learned 🧠

No pre-trained models. No transfer learning shortcuts. Just raw PyTorch, 10,015 images, and a lot of debugging.

→ Dataset: HAM10000 (benign vs malignant skin lesions)
→ Architecture: 3-block CNN built entirely from scratch
→ Result: 83.9% test accuracy | 85% validation accuracy
→ Trained on my own home GPU cluster

The hardest part wasn't building the model — it was understanding WHY it was learning what it was learning.

Key things I picked up:
✅ How convolutional layers learn spatial patterns
✅ Why dropout matters (overfitting hit hard at epoch 10)
✅ Class imbalance (80% benign, 20% malignant) and how it affects training ✅ The difference between validation and test accuracy

This was my first serious deep learning project built completely from scratch. It's not perfect — but it's real.

Full code 👇
https://github.com/Elijah-bino/skin_cancer_cnn-benign_vs_malignant

#MachineLearning #DeepLearning #PyTorch #DataScience #ComputerVision #MelbourneTech #AI #Monash