Tg-Rich-Converter: Streaming LLM Markdown and LaTeX to Telegram Bot API 10.1
tg-rich-converter A lightweight, zero-dependency Python library that converts standard LLM Markdown, LaTeX formulas, thinking processes, and tables into native Telegram Bot API 10.1+ Rich HTML (sendRichMessage). Starting with Telegram Bot API 10.1, Telegram introduced Rich Messages (sendRichMessage) supporting: Messages up to 32,768 characters (no more 4,096-character limit!). Native interactive tables with borders and striping.
- ▪tg-rich-converter A lightweight, zero-dependency Python library that converts standard LLM Markdown, LaTeX formulas, thinking processes, and tables into native Telegram Bot API 10.1+ Rich HTML (sendRichMessage).
- ▪Starting with Telegram Bot API 10.1, Telegram introduced Rich Messages (sendRichMessage) supporting: Messages up to 32,768 characters (no more 4,096-character limit!).
- ▪Native interactive tables with borders and striping.
Hacker News (AI / LLM) files mainly under ai. We currently carry 5,752 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/kobaltgit/tg-rich-converter |
| Publication time | Sun, 20 Sep 2026 19:40:30 +0000 |
| Retrieval time | 2026-09-20T19:53:46.862Z |
| Last seen | 2026-09-20T19:53:46.862Z |
| 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 | n_GE4PtpMVDq · 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
tg-rich-converter A lightweight, zero-dependency Python library that converts standard LLM Markdown, LaTeX formulas, thinking processes, and tables into native Telegram Bot API 10.1+ Rich HTML (sendRichMessage). Why tg-rich-converter? Starting with Telegram Bot API 10.1, Telegram introduced Rich Messages (sendRichMessage) supporting: Messages up to 32,768 characters (no more 4,096-character limit!). Native interactive tables with borders and striping. Native LaTeX math rendering (both inline and display equations). Expandable spoiler/details blocks for reasoning models (DeepSeek-R1, OpenAI o1/o3, Qwen, Gemini). Native lists and advanced typography (<ul>, <ol>, <u>, <mark>). However, LLMs (OpenAI, Anthropic, DeepSeek, Ollama) still output plain Markdown and LaTeX.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.