Today's Overview
Building with AI in production isn't the same as building with AI in a demo. The demo works because you wrote the happy-path prompt and the model complied. Production is when a developer hits the feature after you've shipped it, and they've had time to think about what they actually want the model to do-which is often not what you intended.
Making AI respect your company's rules
This week, a developer published a working implementation of what production AI governance actually looks like: an MCP server that sits between the developer and Claude, enforcing your company's tech policy in real-time. The insight is straightforward but changes the game: instead of hoping your system prompt is enough, you deliver your company's actual decision records, approved libraries, and security rules directly into the model's context at the moment it's generating code. The model doesn't guess what you prefer. It knows. If a developer asks it to use a deprecated library, it explains why that's forbidden and suggests the approved alternative.
This matters because the previous era of DevSecOps-where you catch bad decisions at pull request time-is too slow now. When an AI can generate a hundred lines of code in seconds, finding the mistakes after they're written means developers are already working with the code, tests are already written against it, and reversing course costs time. The MCP guardrail prevents the mistake from happening in the first place. It's not perfect enforcement; it's context-aware nudging. The model can still be pushed to ignore it. But in practice, when the rule is right there in context with a clear explanation, compliance rates jump from 60-70% to 90%+.
When models think in real-time
Meanwhile, a startup called Thinking Machines is building a model that listens while it generates. Today's models are turn-based: you talk, they listen. They respond, you listen. Thinking Machines is testing a model that processes your input and generates output simultaneously, like a phone call instead of an email chain. This is a different kind of governance problem-not about enforcing policy, but about building systems where the model can course-correct mid-response if it's going down the wrong path. It's early, and it's not clear yet how useful real-time bidirectional streaming actually is. But it's worth watching because it changes the shape of the conversation itself.
Production Flutter: the handbook no one reads until too late
If you're building AI features into a Flutter app, there's now a complete handbook covering everything between the demo and the app store rejection. It covers Firebase App Check (so your API key never leaves the server), streaming responses (so the UI doesn't feel frozen), handling safety blocks (when the model refuses to answer), rate limiting (so one user doesn't burn your quota), and the two critical policy compliance points: Google Play requires a user feedback mechanism for every AI-generated message, and Apple requires explicit consent before sending any data to third-party AI services. Most teams learn these requirements from a rejection letter. This handbook is how you learn them first.
The pattern across all three is the same: production AI isn't about making the model smarter. It's about building the infrastructure that keeps the model constrained, observable, and compliant. Guardrails enforce intent. Real-time communication prevents drift. And production checklists prevent shipping features that violate store policies. None of this is novel. All of it is necessary.