An opinionated AI engineering workflow for BMAD
Hedgehog is an opinionated workflow that turns AI into a disciplined software engineer by combining BMAD planning, a fixed stack, and test‑driven development. It encodes the build order into the project, allowing AI to work with small, verified layers rather than remembering the entire codebase. The system supports full‑stack apps, landing pages, and other project types, with tooling for multiple coding agents and visualizing the build graph.
- ▪Hedgehog integrates BMAD planning, an opinionated stack, TDD, and mechanical enforcement to keep AI‑generated code structured and verifiable.
- ▪The build process follows a layered order—schema, contract, repository, service, controller, and UI for full‑stack applications—each layer must pass verification before the next begins.
- ▪Installation uses npx commands to set up the appropriate project type and coding agents such as Claude, Cursor, or Gemini, and updates refresh these agents without altering the core workspace.
- ▪A local server can display a read‑only diagram of the task graph, showing each task's status and dependencies.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,436 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/skyf0xx/hedgehog |
| Publication time | Tue, 04 Aug 2026 00:34:11 +0000 |
| Retrieval time | 2026-08-04T00:35:41.591Z |
| Last seen | 2026-08-04T00:35:41.591Z |
| 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 | tSJiCQScg0Vc · 1 stories |
| 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
Turn AI from a code generator into a reliable software engineer ⭐ AI can write code in seconds. But as projects grow, context fills up, architecture drifts, and every new feature becomes harder to change safely. Hedgehog gives AI a disciplined way to build software: TDD. Opinionated architecture. Small, verifiable steps. Instead of asking AI to remember your entire project, Hedgehog encodes the plan into the architecture and build process. The codebase carries the context, not the model.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.