Most streaming failures are not about “can it stream”, but “does it stay stable under load”: broken chunks, stuck clients, timeout cascades, and retry storms. ...
线上最容易把流式输出做坏的，不是“能不能流出来”，而是流量一上来就抖：token 断片、客户端卡死、超时雪崩、重试风暴。 ...
In production, the painful part is not “streaming is slow.” It’s “streaming breaks and then duplicates output after reconnect.” This guide gives you a practical recovery loop: delta persistence + resume token + idempotent dedup, so reconnection does not replay garbage. ...
生产里最难受的不是“流式返回慢”，而是“流式返回断了还重复”，用户看到半句、重连后又从中间重喷一遍。 这篇给一套可落地的恢复闭环：delta 持久化 + resume token + 幂等去重，目标是“断线可续，重放不重字”。 ...
Most multi-tenant AI platforms fail for two boring reasons: one tenant saturates shared capacity, and finance discovers the burn too late. This guide gives you a practical Go blueprint: token-bucket throttling, budget circuit breakers, and request-level cost attribution. ...
多租户 AI 服务最容易死在两件事：一个租户打爆全局配额，以及月底账单炸了才发现。 这篇给你一套可直接落地的 Go 方案：令牌桶限流 + 预算熔断 + 账单归因，目标是“先活下来，再精细化”。 ...
In production Go agents, the first thing that breaks is usually not model quality. It is memory management: context grows, bills spike, and answers drift. Use a 3-layer memory design: L1: short-term conversational window (seconds) L2: rolling summary (minutes) L3: long-term retrieval memory (days) ...
你在 Go 里做 Agent，最容易翻车的不是推理能力，而是“记忆”失控：上下文越来越长、账单越来越高、回答却越来越飘。 这篇给你一个可落地的三层方案： L1：短期会话上下文（秒级，强相关） L2：中期摘要记忆（分钟级，压缩） L3：长期检索记忆（天级，向量索引） ...
You don’t need an AI reviewer that “sounds smart.” You need a gate that stops risky PRs before they hit main. This post shows a production-ready minimum setup: OpenAI Responses generates structured risk output, GitHub Actions enforces tiered policies, and critical failures can trigger a one-click rollback. ...
你不需要一个“会聊天”的 AI 审查器，你需要一个能阻断坏改动进主干的风险闸门。 这篇给一套可上线的最小方案：OpenAI Responses 负责生成结构化审查结论，GitHub Actions 负责分级阻断，发现高风险时自动回滚到安全提交。 ...