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Foundations of statistical learning, neural networks, and model architectures
We just raised our Series B! NeuralScale is building the infrastructure layer that will power the next generation of AI applications. Think of us as 'Vercel for AI inference' — but with 10x better latency and 3x lower cost than existing solutions. Hiring across the board: infra engineers, ML engineers, and product managers. DM me or check the jobs page.
Open-source AI is winning, and here's the data to prove it. We analyzed model adoption across 500 companies: • 67% use at least one open model in production • Open model usage grew 340% year-over-year • Cost savings average 4.2x vs proprietary APIs • Customization cited as #1 reason (not cost) The ecosystem has never been healthier.
Fascinating result from our experiments on in-context learning: We found that the order of few-shot examples matters dramatically — sometimes more than the examples themselves. Optimal ordering improved accuracy by 15-30% across 12 benchmarks. We're calling it 'positional priming' and working on a paper. Has anyone else observed this?
Lessons from building real-time AI video generation at Runway: 1. Latency budgets are everything — users notice >100ms 2. Streaming architectures beat batch processing 10:1 3. Progressive rendering is key to perceived speed 4. GPU memory management is the real engineering challenge 5. The gap between demo and production is 6-12 months Shipping creative AI is wildly different from shipping chatbo...