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16:53

AI-Native Dev: Same Flow, New Roles — From Issue to Shipped Feature

AI 原生开发复盘：流程没变，角色变了——从 Issue 到功能上线

Using Claude Code (Fable 5), the author added a remote-transcription feature to BaoCut, a subtitle transcription app, and retraces the entire flow. The key claim: AI-native development does not invent a new process — feasibility analysis, design doc, prototyping, implementation, testing all stay. What changes is who executes: agents handle analysis and execution, while humans decide on the critical path. The post walks each gate: a feasibility study that leads to plan A (embedding the ASR service in the app), a design doc that becomes the context handoff for new agent sessions, a high-fidelity prototype that merges requirements, interaction and UI into one confirmable artifact, an implementation phase where the /goal command lets the agent hit milestones, self-run tests and screenshot results — so the author skips code review and relies on black-box testing — and a final QA round as a naive user. Two contrarian takeaways: most coding-focused Skills are unnecessary because the real bottleneck sits on both sides of code (design/confirmation and test/deploy), and documents gain new weight as both human confirmation substrate and inter-session memory. For engineers who already use AI coding tools but are still wrestling with the surrounding workflow.

baoyu.io · 9 min · Agent Engineering · AI Engineering · Claude Code · Context Engineering
16:53

Good Engineering Doesn’t Trust Engineers

好工程不信任工程师：AI时代，代码不再是护城河

Factory workers tell the author they don't trust software engineers, because clean models miss dusty sensors, part batch changes, and cold-morning valve stickiness. Good engineering agrees: NASA, aviation, and nuclear plants build processes around the assumption that engineers can be wrong. AI makes code cheap, exposing that mainstream software development treats code as source of truth, requirements as Jira tickets, and safety arguments as PR comments. The post argues that engineering means making intent explicit and attaching evidence to obligations, separating verification from validation, and applying rigor proportional to risk. It cites NASA's SWEHB, MC/DC coverage, and FRET project, then introduces ReqProof as an agent-driven lifecycle where obligations stay visible and evidence stays attached. The core ideas stand even if you never use the product.

blog.reqproof.com · 17 min · Agent Engineering · AI Engineering · Requirements Engineering · Safety-Critical · Software Engineering
06:00

KV, Prefix, Prompt and Semantic Caching in LLMs Explained

LLM 四级缓存全拆解：KV、前缀、提示词、语义缓存各自的命中与失效

This tutorial breaks down the four cache layers in an LLM serving stack from first principles: the KV cache for a single request, prefix caching for cross-request reuse on the server, provider-billed prompt caching, and a semantic cache that returns stored responses by embedding similarity. It includes runnable code for transformers DynamicCache/StaticCache, a compact vLLM-style chain-hash block scheduler, an Anthropic prompt-caching example, and a tiny semantic cache. Concrete numbers ground each trade-off: a 70B model holds roughly 40GB of KV tensors at 128K context; Anthropic bills reads at 0.1x and writes at 1.25x input rates; two sentences differing only by a negation score 0.952 cosine similarity yet need opposite answers. It also covers what silently breaks reuse: variable content in system prompts, reordered RAG chunks, partial tail blocks, eviction, and per-tenant salt separation. The piece is honest about failure modes, including a throughput regression on unique traffic and the false-positive risk of semantic caching. Useful for engineers operating inference, tuning RAG cost, or debugging prompt-cache misses.

x.com · 33 min · Context Engineering · Inference · LLM · LLM Caching