Pull down to go back
Movie Match Maker: AI-Powered Movie Recommendations That Actually Understand What You Want to Watch
opensourcegithub

Movie Match Maker: AI-Powered Movie Recommendations That Actually Understand What You Want to Watch

Movie Match Maker：終於有 AI 能懂你想看什麼電影了

Ever spent 20 minutes scrolling through Netflix trying to find something good? Movie Match Maker is a full-stack recommendation platform that gets it. Instead of clicking through endless categories, you just tell it what you're in the mood for in plain English—like "I want something funny but also kind of dark"—and it uses AI (NLP/TF-IDF machine learning plus Groq LLMs) to actually understand what you mean and suggest movies you'll probably love. It's built with FastAPI and Streamlit, pulls data from TMDB, and even has a smart watchlist analyzer that learns from what you've already watched. The interface looks sleek too. Basically, it's like having a friend who's seen every movie ever and actually listens to what you want.

Read original →

Keywords

movie recommendationAI-poweredNLPTF-IDFLLMfull-stacknatural language searchwatchlist analyzer

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