Pull down to go back
ksbisht941/ai-engineer — A full-stack AI & ML engineering lab covering Python, Statistics, NumPy, Pandas, Matplotlib, Machine Learning, Deep Learning (ANN, CNN), Computer Vision (OpenCV, YOLO), NLP, LangChain, LangGraph, and Generative AI (RAG, LLMs, Embeddings, Agents). Includes FastAPI and hands-on projects.
opensourcegithub

ksbisht941/ai-engineer — A full-stack AI & ML engineering lab covering Python, Statistics, NumPy, Pandas, Matplotlib, Machine Learning, Deep Learning (ANN, CNN), Computer Vision (OpenCV, YOLO), NLP, LangChain, LangGraph, and Generative AI (RAG, LLMs, Embeddings, Agents). Includes FastAPI and hands-on projects.

ksbisht941/ai-engineer — 全棧人工智慧與機器學習工程實驗室，涵蓋 Python、統計學、NumPy、Pandas、Matplotlib、機器學習、深度學習（ANN、CNN）、電腦視覺（OpenCV、YOLO）、自然語言處理、LangChain、LangGraph 和生成式人工智慧（RAG、大語言模型、嵌入向量、智能代理）。包含 FastAPI 和實作專案。

⭐ 1 stars | Jupyter Notebook | A full-stack AI & ML engineering lab covering Python, Statistics, NumPy, Pandas, Matplotlib, Machine Learning, Deep Learning (ANN, CNN), Computer Vision (OpenCV, YOLO), NLP, LangChain, LangGraph, and Generative AI (RAG, LLMs, Embeddings, Agents). Includes FastAPI and hands-on projects.

Read original →

Keywords

AI engineeringmachine learningdeep learningcomputer visionNLPgenerative AIRAGLLMsembeddingsagentsjupyter notebookhands-on projectsfull-stack

Related Articles

TRELLIS.2 image-to-3D now runs on Mac (Apple Silicon) - no NVIDIA GPU needed
Open SourceRead

TRELLIS.2 image-to-3D now runs on Mac (Apple Silicon) - no NVIDIA GPU needed

I ported Microsoft's TRELLIS.2 to run on Apple Silicon via PyTorch MPS. The original depends on five CUDA-only compiled extensions (flex_gemm, flash_attn, o_voxel, cumesh, nvdiffrast) that have no Mac equivalent. Wrote replacement backends from scratch: Pure-PyTorch sparse 3D convolution (replacing flex_gemm), Python mesh extraction using spatial hashing (replacing CUDA hashmap ops in o_voxel), SDPA attention for sparse transformers (replacing flash_attn), and GPU-accelerated trilinear interpolation (replacing cumesh and nvdiffrast).

Reddit
Show HN: Run TRELLIS.2 Image-to-3D generation natively on Apple Silicon
Open SourceRead

Show HN: Run TRELLIS.2 Image-to-3D generation natively on Apple Silicon

I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files.

Hacker News
157
Which Qwen 3.6 Quantization Works Best for M5 Pro with 24GB RAM?
Open SourceRead

Which Qwen 3.6 Quantization Works Best for M5 Pro with 24GB RAM?

A user with an M5 Pro machine featuring 24GB of RAM is trying to figure out the best quantization level for running Qwen 3.6 via Ollama. They're unsure whether to go with Q4 (4-bit quantization) but can't find a solid Q3 (3-bit) alternative. They're looking for recommendations on which version would run smoothly on their setup without compromising too much on quality.

Reddit