@svpino: This looks pretty interesting: An end-to-end system to build, evaluate, deploy, monitor, and continuously improve your …

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Santiago Valdarrama highlights an end-to-end system for building, evaluating, deploying, and monitoring specialized AI models, noting that enterprises are willing to pay for custom small models despite the popularity of foundation models.

This looks pretty interesting: An end-to-end system to build, evaluate, deploy, monitor, and continuously improve your own specialized AI models. I know foundation models are great and everything, but trust me: companies are willing to pay a ton of money for their own specialized, small, fast, and cheap models.
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Cached at: 08/12/26, 08:33 AM

This looks pretty interesting:

An end-to-end system to build, evaluate, deploy, monitor, and continuously improve your own specialized AI models.

I know foundation models are great and everything, but trust me: companies are willing to pay a ton of money for their own specialized, small, fast, and cheap models.

Manos Koukoumidis (@Koukoumidis): Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥

Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors.

Their workflows and expertise are highly specialized, yet they

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@rhythmrg: https://x.com/rhythmrg/status/2066561780495896785

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The article argues that enterprises should post-train their own custom AI models for mission-critical, high-volume use cases to achieve differentiation, cost savings, and control over tradeoffs, rather than relying solely on general frontier models.