Stability

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.

⚡️Mercury: Ultra-Fast Diffusion LLMs — Estefano Ermon, CEO Inception Labs

Aug 4, 2025 · 28:07

Stefano Ermon of Inception Labs explains why diffusion language models like their Mercury Coder can match GPT-4.1 Nano and Claude Haiku in intelligence while delivering 5–10× faster inference at 737–1109 tokens/sec on H100s, built on score-based generative model foundations he pioneered at Stanford. He details the coarse-to-fine generation process that modifies multiple tokens per network evaluation, the required end-to-end training from scratch (no fine-tuning existing LLMs), and how post-training pipelines use specialized DPO for preference alignment. Ermon identifies latency-sensitive applications—voice agents, IDEs, vibe coding—as the killer use case, acknowledges they aren't yet frontier-level but sees a future where diffusion dominates due to efficiency gains, and notes the challenge of open-sourcing given proprietary inference engines and kernels.

RWKV: Reinventing RNNs for the Transformer Era

Aug 31, 2023 · 1:56:51

Eugene, CTO of UILicious, introduces RWKV, a recurrent neural network that matches transformer-level performance while scaling linearly with context length, solving the quadratic cost of attention. He explains how RWKV uses Attention-Free Transformer layers and parallelizable training, achieving competitive results on 7B and 14B models. The community-driven project prioritizes multilingual support, with a tokenizer that handles languages without spaces. Eugene recounts his journey from GPU.js to finding RWKV, and highlights use cases like long HTML analysis where transformers fail. He also discusses the token crisis, diffusion models for text, and how the AI waifu community drives optimization and alignment research. Finally, he advises AI engineers to focus on practical prompting and data curation, noting that deep architecture knowledge is optional.