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Luis Velasco works as a Forward Deployed Engineer at OpenAI. For him, code is becoming very cheap to produce, while building the system around the model is becoming critical.

When the volume of code outpaces the cognitive budget of the people asked to read it, fatigue happens.

Dana Lawson leads R&D at Netlify, overseeing engineering, product, and design. From where she sits, she sees the real shift clearly: one person or a tiny team can now cover far more ground.

At 55,000 operations per second across a 20 TB sharded cluster, you stop theorizing about databases and start reading their source code.

Static runbooks break under pressure. Operational knowledge should live in executable tools instead of documents nobody can follow at 3 AM.

NVIDIA’s Igor Dmochowski explained that LLMs are no longer limited just by model size or benchmark scores; the cost of longer reasoning means runtime, context length, memory use, and throughput matter just as much.

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OpenAI FDE: “Code is very cheap to produce”

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Runbooks Are Dead; Make Operations Executable

LLMs Have a New Limit – It Costs More to Think Longer