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Show HN: AI-Native Architecture Series (Open-Source)
Hi HN,
Open-sourced my complete AI-native architecture series from a solo builder project (spare time).
4 articles (EN/ZH) covering:
- Constrained DSL for reliable LLM decisions
- 60/40 → 90/10 AI-driven development workflow
- "Boring" hybrid agent architecture
- Low-cost multi-vendor cloud scaling
Real scale: 28 repos, 538k LOC, 3k+ users, low four-digit RMB yearly infra.
Repo: https://github.com/myinvestpilot/ai-architecture https://github.com/myinvestpilot/ai-architecture
Pure sharing, no promo. Feedback, critiques, or your solo AI-native experiences welcome!
Thanks
- JB_5000 7mo agoactually respect this. feels like more solo builders are realising agents arent about intelligence, they’re about constraints — DSLs, deterministic layers, boring hybrid architectures. you stop writing code and start designing guardrails. curious how this scales though.
- versalog1460 7mo ago> Posted 1 minute ago > This comment posted 0 minutes ago This is very suspicious, I’m sorry.
- madawei2699 7mo agoThanks JB_5000 — really appreciate you putting it that way. You're spot on: the whole point is constraints over intelligence. Guardrails (DSL, schema, deterministic replay, boring hybrid) are what actually make it production-usable. On scaling: so far it's handling ~3k trial users + growing paid base with low four-digit RMB yearly infra (queue-driven scale-to-zero, Redis cache, R2 for artifacts). The real bottleneck is still alignment quality (good artifacts + human gates), not the constraint overhead itself. Haven't hit hard walls yet, but I'm sure 10x–100x load will expose new ones. How about you? Have you seen constrained agents / deterministic layers scale well (or break) at larger sizes? Any guardrails that worked surprisingly well for you? Thanks again!