RobertSerber

5 ms·

1.
by RobertSerber 7mo ago
Yes — that layer is part of the runtime design. The AI never mutates structure directly. It only proposes a DSL change, which goes through a deterministic compile pipeline before it becomes canonical. Schema evolution is treated as a runtim
2.
by RobertSerber 7mo ago
Good point. In the model I'm experimenting with, identity and permissions end up being part of the runtime primitives rather than external services. In traditional SaaS architectures identity is usually handled by separate layers (auth
3.
by RobertSerber 7mo ago
For context, I'm experimenting with applying this architecture to a CRM system where the entire application is defined as a semantic JSON model executed by a runtime. The LLM proposes structural changes (entities, workflows, metrics),
4.
Ask HN: Rethinking SaaS architecture for AI-native systems
3 points by RobertSerber 7mo ago|5 comments
5.
Ask HN: What invariants matter most to prevent drift in AI-modified SaaS apps?
1 points by RobertSerber 7mo ago|1 comments
6.
by RobertSerber 8mo ago
Totally agree. We treat structural validation as a separate, deterministic “compiler phase” before anything executes. Draft (AI output) → normalize/canonicalize → link/type-check (cross-refs, relationship/FK semantics, datase
7.
by RobertSerber 8mo ago
This makes a lot of sense — especially the “LLMs as nondeterministic services” framing. I agree that versioned prompts, schema validators, regression evals, and contract tests are essential if you're shipping LLM-powered systems. What
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How do you keep AI-generated applications consistent as they evolve over time?
11 points by RobertSerber 8mo ago|4 comments