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@paulg Tracing code all the way down to the source has always been a pretty unrealistic feat, but today, an unread AI layer is actively writing new layers you also won't read. Turtles all the way down, and the turtles talk back. 😉

Before vibe coding became a thing, programming was already evolving in that direction. It already increasingly consisted of installing and configuring stuff other people wrote, without reading the source.

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Agreed. And the shape of work is changing too. Six months ago people were using AI to write a function, now we're describing a whole experience for an agent to assemble. And as the coding part gets faster and easier, it's becoming more apparent that the new bottleneck is what the agent codes against: data, workflows, permissions, etc.

It's mind-blowing how fast agentic coding has progressed in the past 6 month. It's a completely different world now.

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Customers are abandoning AI agents that feel unpredictable A clever chatbot persona isn't enough to drive adoption—brands need reliable AI that acts like a trusted employee Here are 3 ways to build agents customers actually trust: Define core company values upfront to dictate agent behavior before development begins Rigorously test edge cases and build plain-language fallbacks for seamless human handoffs Measure qualitative user satisfaction instead of just relying on closed support tickets Agentforce builds these guardrails directly into your agents using Agentforce Builder and Testing Center, ensuring every interaction stays secure, reliable, and perfectly on-brand.

@matei_zaharia The tasks that actually matter to each company also sit inside their unique data, workflows, and policies. So, every company needing its own loop also means every company needs its own eval set built from real work.

One reason we built custom coding and data agent benchmarks internally at Databricks (e.g. databricks.com/blog/benchmark…). Academic benchmarks are great and people will build better ones, but you also care about YOUR tasks, which are often different. Each company needs its own "loop".

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39% of consumers now let AI do the product discovery Shopping behavior has officially shifted, and agentic commerce changes how brands get discovered Here are 3 ways to stay visible to LLMs: → Clean up product data with structured details and natural search terms → Feed catalogs directly into AI platforms instead of relying on web scraping → Drive third-party reviews on social platforms to signal trust to AI models Salesforce B2C Commerce automatically syncs product catalogs to OpenAI and Google through new direct feed protocols, ensuring inventory sits right in front of the agents making the purchase decisions.

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