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Hetzner is experimenting with LLM inference. That is not a sentence I expected to write, but I think it is pretty interesting :) Before anyone moves their production AI workloads to Hetzner: this is very much an experiment . There is no billing, no SLA, no production guarantee, and currently only one model. Hetzner says it wants to learn whether people actually want this, how the system scales, which features matter, and what kind of load it can handle. So this is not a finished product launch. It is Hetzner putting something early in front of users and seeing what happens. I really like that approach. What Is Hetzner Inference? Hetzner Inference is an OpenAI-compatible API running on Hetzner's own infrastructure. You create an API token in the Experiments dashboard, point an OpenAI client at Hetzner's base URL, and use it like most other inference APIs. Right now, the only available model is Qwen/Qwen3.6-35B-A3B-FP8 . It is a 35-billion-parameter Mixture-of-Experts model with 3 billion active parameters. It accepts text and images, has a 262K context window, and uses FP8-quantized weights. That is a perfectly reasonable model for an experiment. It is small enough to serve without a ridiculous GPU cluster, but still useful enough to test the API with real workloads. Hetzner also published a short tutorial for connecting OpenCode to the API , if you want to try it without writing any code. I Tried It Because the API is OpenAI-compatible, there is almost nothing special about the integration: pip install openai from openai import OpenAI client = OpenAI ( base_url = " https://inference.hetzner.com/api/v1 " , api_key = " YOUR_TOKEN " , ) response = client . chat . completions . create ( model = " Qwen/Qwen3.6-35B-A3B-FP8 " , messages = [ { " role " : " user " , " content " : " Explain why the sky is blue in one sentence. " } ], extra_body = { " chat_template_kwargs " : { " enable_thinking " : False , } }, ) print ( response . choices [ 0 ]. message . content ) The enabl

After Trump publicly backed pillow salesman turned election denier Mike Lindell for governor of Minnesota, the local GOP pushed back strongly.

The article discusses an experiment where a code assistant had to design an agentic system using LangChain4j documentation. The assistant created a coding framework capable of writing, testing, and debugging code autonomously. Results showed that two architectural patterns—supervisor and workflow—offered different trade-offs between flexibility and execution speed during debugging tasks. By Kevin Dubois, Mario Fusco

The teleprompter that actually listens Discussion | Link

All your coding agents, synced everywhere, free forever Discussion | Link

submitted by /u/mttd [link] [留言]

Its been almost 24 hours since reviews were released and I dont see the meta review still. Some people on reddit are saying they can see it. NeurIPS website says they are-releasing reviews on 23 but even 23 July is ending in 4 hours. Whats going on bruh, none of my coauthors is an AC or didnt complete his review so its not like its being held from us submitted by /u/Specialist-Manager67 [link] [留言]

Hi, I'm new with ACM conferences. I have 2 papers at workshops and the conference website says: "Each workshop paper needs to be associated with one workshop-only (non-student) or full (non-student) author registration at either ACM Member rate, or non-member rate. One workshop-only or full registration can cover only one accepted workshop paper." Does that mean that I have to register twice with "Workshop-only Author registration" paying 500USD per paper!? Second question, I really do not understand the APC fees listed here: ACM Multimedia 2026 Conference — Author Instructions .. does that means that in addition to the registrations I have to pay 350USD per paper? submitted by /u/rokk07 [link] [留言]