We just shipped something pretty unique among post-training API providers: end-to-end custom losses.
The objective you define locally gets traced into a graph, compiled and executed efficiently on our GPUs.
Read more about the engineering behind it in our latest blog post.
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: https://t.co/TZSugXi2ZE
We've partnered with @NVIDIAAI to make Nemotron 3.5 Super VL available on the River API from day zero.
Even before training, Super VL outperforms Kimi K2.6 on our text-to-SQL evaluation, despite being a fraction of its size. After training with our text-to-SQL recipe on River, it outperformed every proprietary model we evaluated.
These results show what's possible with River: frontier-level performance on a specialized task, using a model small enough to deploy on your own hardware.
PewDiePie unveiled Ajax, his own "uncensored" AI model built to run on home PCs, and said OpenAI banned him twice while he was making it
One ban was for "distillation," using another AI's outputs to train his own. "How did they even know?" he said
Introducing River Recipes: our open-source initiative for turning promising research into vetted, reproducible training recipes.
Our first recipe tackles text-to-SQL, where our trained open models beat GPT-6 Astra Pro and Claude Opus 5.5 on Arcwise-Plat for less than 1% of the cost.
Read the full breakdown and explore the recipe ↓
https://t.co/MNcTas9TOf
River AI is growing and we've published new roles in research, engineering, and operations. Check out our careers site or reach out to our team today. If you want to build the future of personal AI, join us!
https://t.co/fobrlD0696
You need to build and own your own intelligence: your own agent, your own models, your own memory
Join us for the YC Own Your Intelligence Hackathon September 27 in San Francisco
https://t.co/wBt9wYw8LC