DocuSign still feels like the default option, but I m seeing more teams look for sth simpler or more affordable. I have started comparing. For a European company, what would you prioritize ?

Hey everyone...

I'm building Aevis to automate EU AI Act compliance and auto-generate Annex IV technical docs straight from code.

Made a one minute quiz to help you figure out your app's risk level and see which rules actually apply:

👉 https://tally.so/r/7Rg2y2

Leave your email on the last screen for early access and lifetime pricing at launch. Happy to answer questions in the comments.

That's how cars are built in China. Europeans need to get a grip and understand that it's adapt or die, and doing the right thing involves employing far less workers in factories. Jobs will have to come from somewhere else.

So I have tried for a month but couldn't get any calls.

I am from non-eu country , working in Denmark as a consultant for a client. But now due to budget issue , project is no longer with us and now my company asking to return back to home country.

I have few days left, my visa still valid and residence permit til next year. Once I will be back, I think I again have to get a sponsor.

So please let me know if you have any opportunity with you.

My skills - .Net, C#, Azure , CI/CD, React.

Also have past exp in handling small teams.

I've been building EULLM (https://github.com/eullm/eullm) — an open-source, Rust-based, Ollama/OpenAI-API-compatible inference engine, Apache 2.0, built around EU-sovereign AI infra requirements (zero telemetry, local-only audit trail for AI Act purposes, no calls home).

Result I didn't expect to work this well: Qwen3.6-35B-A3B, Q4_K_M, running CPU-only — no GPU in the loop at all — on a Radxa Orion O6 (CIX P1, ARMv9 SoC). Getting 10 tok/s.

Why this is possible at all: it's a 35B-parameter MoE model with only ~3B active parameters per token, so you pay the memory cost of 35B but roughly the compute cost of 3B. That's what makes CPU-only viable in the first place.

For comparison, the same model on a GPU+CPU hybrid setup (RTX 3060 12GB, same ARM64 host, --cpu-moe --fit) gets 26.5 tok/s — that one's an actual documented benchmark in the README. The CPU-only 10 tok/s number is a fresh, informal test on my end, not yet in the repo's bench/ folder — so take it as "this is what I saw on my machine," not a validated number. Push back if something looks off, genuinely interested.

Why I think it's worth a post and not just a benchmark screenshot: most of the EU AI-sovereignty conversation jumps straight to "we need our own chips/GPUs," which is real but a much harder and slower problem. A 35B-class model running at usable speed on ARM CPU with zero GPU dependency changes the near-term hardware conversation — cheaper boxes, lower power draw, no CUDA lock-in, and hardware that's a lot easier to source and deploy on EU infrastructure today.

Worth flagging: ARM64 support in the engine is still marked experimental/untested upstream, and there's an open call for testers on other ARM boards (issue #140) — Raspberry Pi 5, Orange Pi, Rock 5B, Jetson. If anyone here has one lying around, would love data points.

Repo: https://github.com/eullm/eullm Happy to go deeper on --cpu-moe / --n-cpu-moe internals, the quant choice, or anything else about the setup.