Building simulations and/or digital twins with AI
Plugboard is a Python-based event-driven framework designed for building and scaling simulations and digital twins of complex, interconnected systems. It supports integration with AI models, machine learning, and physics-based simulations, enabling flexible model composition and reconfiguration. The framework can run locally or be scaled across cloud infrastructure using Ray, with support for various simulation paradigms and data sources.
- ▪Plugboard allows users to create digital twin models of industrial processes, including those with material recirculation and AI integration.
- ▪It supports both discrete-time and event-based simulations and can scale from a laptop to cloud compute clusters using the Ray framework.
- ▪Models can be defined using a Python API or YAML files, with built-in support for LLMs, cloud storage, SQL databases, and logging for monitoring or process mining.
- ▪Users can initialize projects with an AI assistant via the 'plugboard ai init' command, which provides context for generating custom simulation code.
- ▪Optional installation extras include support for AWS, Azure, GCP, Redis, WebSockets, and hyperparameter optimization through Ray.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,129 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
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/plugboard-dev/plugboard |
| Publication time | Tue, 28 Apr 2026 21:00:10 +0000 |
| Retrieval time | 2026-04-28T21:09:39.797Z |
| Last seen | 2026-04-28T21:09:39.797Z |
| 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 | H1pEH12lRhh1 |
| 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
Plugboard is an event-driven modelling and orchestration framework in Python for simulating and driving complex processes with many interconnected stateful components. You can use it to define models in Python and connect them together easily so that data automatically moves between them. After running your model on a laptop, you can then scale out on multiple processors or go to a compute cluster in the cloud thanks to the integration with the Ray framework. Some examples of what you can build with Plugboard include: Digital twin models of complex processes: It can easily handle common problems in industrial process simulation like material recirculation; Models can be composed from different underlying components, e.g.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.