"What comes after vibe coding" is a different question to the one you're answering.
Feels like your answer is about better vibe coding: I'm using a tiered document system. It's just docs all the way down. Seems to be working for me, ask in another ten years.
What comes after vibe coding? At some point the AI will be asking humans if its new software saves us time and whether we'd pay money for it. Until then, there'll be some level of humans-asking-AI to solve a problem, probably with coarser and less detailed spec at each step.
Hi HN, I wrote this after spending the last two years on the AI coding adoption curve.
Most takes I'm reading are about treating AI failures as a memory or context window problem. I argue it's an orientation problem. LLMs are probabilistic-like generation engines; enterprise codebases are deterministic structures. Forcing probability into a deterministic system without strict boundaries generates compounding divergence between the end goals and what AI agents are coding.
I've been experimenting with building a deterministic orientation substrate (repo-graph) using a three-layer truth model to force agents to respect boundaries before they generate syntax.
I also argue that traditional safety-critical processes are about to become highly relevant to standard agentic development, not only for compliance, but because heavy process is a sound containment vessel for AI-generated entropy.
Curious to hear how other teams are preventing architectural drift when deploying agents at scale.
"What comes after vibe coding" is a different question to the one you're answering. Feels like your answer is about better vibe coding: I'm using a tiered document system. It's just docs all the way down. Seems to be working for me, ask in another ten years.
What comes after vibe coding? At some point the AI will be asking humans if its new software saves us time and whether we'd pay money for it. Until then, there'll be some level of humans-asking-AI to solve a problem, probably with coarser and less detailed spec at each step.
Hi HN, I wrote this after spending the last two years on the AI coding adoption curve.
Most takes I'm reading are about treating AI failures as a memory or context window problem. I argue it's an orientation problem. LLMs are probabilistic-like generation engines; enterprise codebases are deterministic structures. Forcing probability into a deterministic system without strict boundaries generates compounding divergence between the end goals and what AI agents are coding.
I've been experimenting with building a deterministic orientation substrate (repo-graph) using a three-layer truth model to force agents to respect boundaries before they generate syntax.
I also argue that traditional safety-critical processes are about to become highly relevant to standard agentic development, not only for compliance, but because heavy process is a sound containment vessel for AI-generated entropy.
Curious to hear how other teams are preventing architectural drift when deploying agents at scale.
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