Surgical DevOps – Prevent LLM context drift and regressions
Surgical DevOps 🚀 Note: Clique aqui para ler a versão em Português. Surgical DevOps is an open-source, agnostic protocol ecosystem designed to govern and standardize the behavior of Large Language Models (LLMs) during the software development lifecycle, eliminating code regressions and context drift in long chat sessions. The ecosystem operates by coupling two core protocols: BH-SEP (Safe Evolution Protocol): Forces the AI to act with surgical precision.
- ▪Surgical DevOps 🚀 Note: Clique aqui para ler a versão em Português.
- ▪Surgical DevOps is an open-source, agnostic protocol ecosystem designed to govern and standardize the behavior of Large Language Models (LLMs) during the software development lifecycle, eliminating code regressions and context drift in long
- ▪The ecosystem operates by coupling two core protocols: BH-SEP (Safe Evolution Protocol): Forces the AI to act with surgical precision.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,266 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/bonushora/surgical-dev-ops/blob/main/README_EN.md |
| Publication time | Mon, 20 Jul 2026 14:41:52 +0000 |
| Retrieval time | 2026-07-20T15:26:58.882Z |
| Last seen | 2026-07-20T15:53:39.047Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 6b7j5cgEfuLO |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Surgical DevOps 🚀 Note: Clique aqui para ler a versão em Português. Surgical DevOps is an open-source, agnostic protocol ecosystem designed to govern and standardize the behavior of Large Language Models (LLMs) during the software development lifecycle, eliminating code regressions and context drift in long chat sessions. The ecosystem operates by coupling two core protocols: BH-SEP (Safe Evolution Protocol): Forces the AI to act with surgical precision. It is strictly forbidden from assuming context or rewriting entire functional files. The core focus is a total analysis of the target file (Inspect First) followed by isolated, tactical code changes (Minimal Diffs). BH-SDP (Snapshot & Delivery Protocol): Manages the model's short-term memory.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.