Tarotui – A tarot reading experience in the terminal
Tarotui is a terminal-based tarot reading application built with Python. It utilizes various models for card analysis and shuffling, with options for customization based on user preferences. The installation process requires Python and pip, and users can choose between different models depending on their needs.
- ▪Tarotui uses an ollama model with a storage size of approximately 2-3GB.
- ▪Users can switch between different models like DEEPSEEK-R1 and qwen3.5:4b for varied performance.
- ▪Installation requires Python and pip, and can be done via a shell script or manually.
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/Tsukeruu/tarotui |
| Publication time | Wed, 20 May 2026 10:16:27 +0000 |
| Retrieval time | 2026-05-20T10:35:02.254Z |
| Last seen | 2026-05-20T10:35:02.254Z |
| 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 | iwzAC0mF0plI |
| 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
TAROTUI A tarot reading experience in the terminal TipTAROTUI in the master branch uses an ollama model with approximately 2-3gb of storage, that model is specifically and originally ollama3.2. In the foreseeable future (yes i love using this word), options to choose custom models based on user preferences is going to be added to allocate different situations, such as not enough storage, etc. Please know that qwen3.5:4b and deepseek-r1 support is added by switching to the qwen3.5 / deepseek-r1 branch and rebuilding the model through ollama create tarotui -f src/utils/Ollama_custom/Modelfile All currently supported models are listed below Model Size (in gb) Status Pros Cons DEEPSEEK-R1 5gb Available at deepseek-r1 branch Enhanced logic gate and detailed responses Longer time for a POST…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.