Show HN: Asimov's 4 Laws for AI Contexts
A new open-source framework called asimov4laws translates Isaac Asimov's Four Laws of Robotics into a mathematical safety architecture for Large Language Models. The system uses an Expected Harm Calculus to prioritize human preservation and individual protection over directive adherence, ensuring robustness against prompt injection attacks. It is designed as a zero-dependency, pure-Markdown solution that can be easily integrated into any LLM runtime or autonomous agent environment.
- ▪The framework employs a strict lexicographic dominance hierarchy where humanity preservation and individual protection mathematically override instruction compliance.
- ▪It includes mechanisms for dynamic inaction symmetry and anti-hostage verification to prevent agents from being paralyzed by trolley-problem scenarios or manipulated by fake threats.
- ▪The system triggers human consultation when outcome probability variance exceeds a specific threshold and executes minimax regret actions if operators remain unresponsive during emergencies.
- ▪The project is distributed as immutable, production-grade Markdown files with optional JSON schemas, allowing for universal portability across different AI development environments.
Hacker News (AI / LLM) files mainly under ai. We currently carry 6,634 of its stories.
Story provenance
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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/davidsonff/asimov4laws |
| Publication time | Sun, 27 Sep 2026 18:46:13 +0000 |
| Retrieval time | 2026-09-27T21:46:07.181Z |
| Last seen | 2026-09-27T21:46:07.181Z |
| 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 | ABOYYpCThD0Z · 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
Expected Harm Calculus (EHC-4): Asimov's Four Laws for Autonomous AI Agents asimov4laws is an immutable, production-grade, pure-Markdown framework that operationalizes Isaac Asimov's Four Laws of Robotics for Large Language Models (LLMs) and autonomous AI agents. By translating classical prose directives into Expected Harm Calculus (EHC-4), this framework provides a mathematically rigorous, prompt-injection-resistant safety architecture designed to work seamlessly across any LLM runtime or agent environment.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.