TODO: one or two sentences on how I ended up on the trainer side of this. How I got into the Bielik community, why I did the trainer certification in 2025, and what a test engineer who spends his days on automotive hardware and Go services wanted from teaching people about a Polish language model.
What Eskadra Bielika actually is
Bielik is an open Polish language model built by the SpeakLeash community. Eskadra Bielika (“Bielik Squadron”) is the practical, hands-on side of that community: a series of workshops where people sit down with the model and wire it into something real instead of talking about it.
TODO: the edition(s) I trained at, what tracks were offered (e.g. RAG, agents, MCP, fine-tuning, cloud deployment), how the day was structured, and what the trainer role actually involves.
What I worked on
TODO: pick the one thing you actually built. Suggested structure:
- The problem. TODO: what was the input data, who was the imagined user.
- The stack. TODO: which Bielik variant, how it was served (vLLM? Ollama? hosted?), which retrieval/agent framework, where it ran.
- What broke. TODO: the honest part. Tokenisation quirks, context length, retrieval quality on Polish text, tooling that assumed English.
- What worked better than expected. TODO.
TODO: a small snippet worth keeping, e.g. the prompt template that finally
behaved, the MCP tool definition, or the docker-compose that served the model.
The embedded/backend engineer’s view
This is the part I care about most, because it’s where Bielik stops being “AI news” and becomes plumbing:
- Sovereignty is a feature, not a slogan. A model trained on Polish data, with open weights, that I can run on a box in a rack in Poland. That matters for the same reasons I self-host everything else.
- The interesting work is around the model, not in it. Retrieval, evaluation, guardrails, latency budgets, the boring pipeline that feeds it fresh data. That’s backend engineering with a new kind of dependency.
- Small models and small hardware get along. TODO: if I tried a quantised variant on modest hardware, note what ran where and how fast. If not, cut this bullet.
TODO: one honest paragraph on where I think this fits into KhazLabz work. The point of the “AI infrastructure and pipelines” service is exactly this: keep the model close to the data, on hardware you control, and treat it as one more component with an SLO.
People
TODO: a few names worth thanking (with their permission), the organisers, whoever fixed my CUDA drivers at 11 pm.
Takeaways
- TODO
- TODO
- TODO
If you were there and remember it differently, or you’re thinking about putting Bielik into a product and want a second pair of eyes on the infrastructure side, say hi.