Who's Who of AI

Ethan

171 trust practitioner @ethanrosenthal.com · 6,081 followers
AI business Culture, work & education
Why they matter

Practitioner with public evidence across AI business, Culture, work & education.

AI signals
3
past 30d
Sources
2
distinct domains
Discusiones
5
past 30d
Latest signal
2d ago
View every signal from Ethan →
data/ml/ai @ runway in 🗽 formerly spent time in startups, biggish tech, and physics labs www.ethanrosenthal.com
What they're sharing

Articles & links

Nice new DataComp paper for VLMs, especially the part about scaling laws for data mixtures. arxiv.org/abs/2606.28551

DataComp-VLM: Improved Open Datasets for Vision-Language Models arxiv.org
AI Weekly's analysis →
  • DCVLM assembles 160 datasets and 6T multimodal tokens across image-caption pairs, interleaved documents, text-only, and instruction-tuning data as an open VLM testbed.
  • The testbed spans 1B to 8B parameter models and 6.25B to 200B token budgets, evaluated on up to 52 downstream benchmarks across 9 domains.
  • The authors' experiments find data mixing, not filtering, is key to training quality, with instruction-heavy mixtures scaling better than caption-heavy ones.
Read full analysis →
View on Bluesky · ♥ 0 ↻ 0 ↩ 0 · 10d ago
Their own posts

Recent commentary

It’s oddly vindicating(?) that our Agentic Age rewards those who invested in software engineering best practices. Tests, CI/CD, platform APIs, infrastructure as code. They’re all very well-aligned with agent utility, which is maybe not surprising; these are all done in the name of automation

View on Bluesky · ♥ 10 ↻ 1 ↩ 1 · 49d ago
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