Alibaba

A Technical History of Generative Media

Sep 8, 2025 · 1:04:44

Fal.ai founders Gorkem and Batuhan detail their pivot from dbt pipelines to generative media inference, which now serves 2M developers and 350 models, crossed $100M ARR, and raised a $125M Series C. Key inflection points included Stable Diffusion 1.5 (company pivot), SDXL (first $1M revenue), Flux (jump from $2M to $10M monthly revenue), and Veo 3 (text-to-video with perfect lip-sync). They built a proprietary inference engine with 100+ custom kernels and a serverless GPU stack managing 10,000+ H100 equivalents across 6 cloud providers, typically delivering 1.5-10x speedups over stock PyTorch. Video models now drive 50% of revenue, up from 18% in February, fueled by open-source models like Hunyuan and partnerships with closed labs such as Play.ht and Google DeepMind. They argue advertising is the killer application for generative media and that image/video RL, specialized data pipelines, and cheaper conversational video models are underexplored startup opportunities.

[Paper Club] Embeddings in 2024: OpenAI, Nomic Embed, Jina Embed, cde-small-v1 - with swyx

Dec 1, 2024 · 41:46

Shawn Wang (swyx) surveys four key embedding developments: OpenAI's text-embedding-3 models, Nomic Embed, Jina Embed v3, and CDE-small-v1. OpenAI's models introduce Matryoshka embeddings, compressing output from 1024 to 64 dimensions (94% storage reduction) with only an 8% performance drop. Nomic Embed is a fully open-source BERT-based model trained on English data, offering reproducible code. Jina Embed v3 supports 89 languages and uses task-specific LoRA adapters—e.g., a retrieval adapter yields a huge boost over the base model. CDE-small-v1 (143M parameters) employs a two-stage adaptation: first conditioning on the corpus, then embedding; it outperforms 7B-parameter models on many tasks, though its stateful API complicates deployment.