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From Demos to Production: Guardrails and Review Budget
Part 3/3 | Generators output code fast. Your bottleneck is review budget - the amount of code you can verify without losing confidence. Optimize for reviewability, not throughput. Turn architecture preferences into enforceable constraints.
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From Demos to Production: Distribution Mismatch
Part 2/3 | LLM generators were trained on public code - simple examples, minimal composition. Your Hydra-driven, config-first architecture is far from that center. Under ambiguity, the model drifts toward common patterns, not your invariants.
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From Demos to Production: The Generator Is Not Learning Your Architecture
Part 1/3 | Inference doesn't rewrite trained weights. When you tell an LLM 'use config-driven instantiation,' you're adding a prompt constraint - not teaching it your architecture. That gap explains why real-world, multi-file work remains fragile.
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2025: The Year of Toy Agents
Every vendor promised agents that 'just work.' Reality delivered toy agents - fragile in production, useless when correctness matters. AI doesn't remove complexity. It relocates it.
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OpenAI Reveals new Chat GPT-5 Flagship Models
OpenAI collapsed the lineup across both the API and the ChatGPT apps (browser, desktop, iOS, Android). The table below shows a simple before → after map so you can translate any prior model reference to the new GPT-5 family.