Show HN: Germany's new sovereign AI model Kolibri
Huge congrats to everyone at Aleph Alpha who built it, my good friend Michael Hofmann among them! This post is about how Kolibri works, where it’s strong, where it isn’t, how to run it, and when it’s the right pick. Everything here comes from Aleph Alpha’s 189 page technical report, the model card and their launch post, plus one experiment I ran on its tokenizer.
- ▪Huge congrats to everyone at Aleph Alpha who built it, my good friend Michael Hofmann among them!
- ▪This post is about how Kolibri works, where it’s strong, where it isn’t, how to run it, and when it’s the right pick.
- ▪Everything here comes from Aleph Alpha’s 189 page technical report, the model card and their launch post, plus one experiment I ran on its tokenizer.
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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 | Tejas Kumar |
| Canonical URL | https://tej.as/blog/aleph-alpha-kolibri |
| Publication time | Sat, 03 Oct 2026 10:43:51 +0000 |
| Retrieval time | 2026-10-03T10:58:12.934Z |
| Last seen | 2026-10-03T10:58:12.934Z |
| 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 | XXrM2SqIzM6u · 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
Tejas Kumar/WritingAleph Alpha Kolibri: How the Sovereign German LLM WorksOct 3, 2026/10 min read/Share on 𝕏By Tejas Kumar, AI Engineer at IBMKolibri is an open-weight large language model (LLM) from Aleph Alpha for German and English: a mixture of experts with 78 billion parameters that only uses about 3.5 billion of them for each token it reads or writes. It came out on 3 October 2026 under the Apache 2.0 license, the weights are on Hugging Face, and it was trained from scratch on infrastructure in Germany and Finland.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Tejas Kumar.
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