@adamatflex
BizOps at FlexAI
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Nothing here yet.
Apr 30 · 6 min read · The default path for most AI teams today is single-vendor, single-cloud: pick NVIDIA, pick AWS (or GCP, or Azure), and build everything around that stack. It works until it doesn't — hyperscaler credi
Join discussionApr 21 · 6 min read · Everyone assumes bigger models produce better results. LegML set out to prove otherwise. They fine-tuned a 32B-parameter legal LLM — internally called "Hugo" — that outperformed a leading frontier mod
Join discussionApr 14 · 5 min read · When developers scale LLM workloads to production, one question always comes up: which GPUs should I use, how many will I need, and how much is this going to cost me? Not a back-of-the-envelope guess
Join discussionApr 7 · 7 min read · Small AI startups are dying — not from lack of innovation, but from infrastructure exhaustion. While the industry focuses on model architecture and training data, a quieter crisis unfolds in the trenc
Join discussionApr 3 · 8 min read · As AI workloads expand across cloud, edge, and enterprise environments, the infrastructure underpinning them is shifting toward hardware diversity. The question is no longer whether teams will run on
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