I think we all ought to look at the ZDR fine-print here.
I get that in principle that there's no retention, but these are powerful models that can comprehend, paraphrase and summarize your logs for the sake of "product" improvement. Who knows what's collected here.
The ICP question was more around the model itself. Are they looking to license it to robotics companies? Do they imagine that devs at robotics companies would be willing to deploy these models as a black box?
It’s unclear to me what their desired outcome for a blog post like this. If you’ve ever worked in a robotics setting, 80% implies that 20% of your autonomous actions are incorrect. Imagine if this were the case for autonomous driving where your car misbehaves 1 in every 5 actions it takes.
Posts like this just reminds me of the end to end demos AV companies built in the early days using a single camera - only to realize that it’s harder than it looks years later into development.
VLM Run (https://vlm.run) | 1x Product + 1x ML Staff Engineer | Santa Clara, CA (HQ)
We're building the inference and orchestration layer for production Vision-Language Models. We care deeply about fast and ergonomic visual inference, reliable structured outputs, and the observability to iterate on them.
A few things we've shipped recently you can poke at:
1. Orion: our visual agent that reasons and acts over images, video, and documents. Chat at https://chat.vlm.run.
2. mm-ctx: a Unix-style multimodal CLI (find, cat, grep, wc) that gives coding agents real context over images, video, and PDFs. Rust core, Python devex.
3. vlmbench: single-file CLI for benchmarking VLM inference (TTFT, TPOT, throughput) across vLLM, Ollama, and SGLang.
The recent claude code leak also revealed that they're poisoning their competitors via anti-distillation policies baked in claude code CLI (fake tool calls, adding noise etc).
VLM Run (https://vlm.run) | 1x Infrastructure Engineer + 2x AI/ML Engineer | Santa Clara, CA (HQ)
VLM Run is building infrastructure for production Vision-Language Model (VLM) systems — fast inference, tool-use + orchestration, reliable structured outputs, and the observability to iterate quickly. We’re a deeply technical team of veteran AI / computer-vision engineers (20+ years combined, MIT/CMU PhDs) who’ve shipped production ML infrastructure across autonomous driving and LLMs.
Email hiring "at" vlm.run with your GitHub + a couple recent projects.
P.S. We recently launched Orion, our visual agent that can reason and act over images, videos and documents. You can chat with Orion at https://chat.vlm.run and see capabilities at https://docs.vlm.run.
Real-time or continuous learning is great on paper, but to get this to work without extremely expensive regression testing and catastrophic forgetting is a real challenge.
Credit to the team for taking this on, but I’d be skeptical of announcements like this without at least 3–6 months of proven production deployments. Definitely curious how this plays out.
What do you think actually happened here in the past week?
They used Kimi, failed to acknowledge it in the original Composer announcement. Kimi team probably reached out and asked WTF? Their only recourse was to publicly disclose their whitepaper with Kimi mentioned to win brownie points about being open about their training pipeline, while placating the Kimi team.
VLM Run (https://vlm.run) | 1x Infrastructure Engineer + 2x AI/ML Engineer | Santa Clara, CA (HQ)
VLM Run is building infrastructure for production Vision-Language Model (VLM) systems — fast inference, tool-use + orchestration, reliable structured outputs, and the observability to iterate quickly. We’re a deeply technical team of veteran AI / computer-vision engineers (20+ years combined, MIT/CMU PhDs) who’ve shipped production ML infrastructure across autonomous driving and LLMs.
Email hiring "at" vlm.run with your GitHub + a couple recent projects.
P.S. We recently launched Orion, our visual agent that can reason and act over images, videos and documents. You can chat with Orion at https://chat.vlm.run and see capabilities at https://docs.vlm.run.
AI allows you to accelerate the initial build process, but I think engineering is all about craftsmanship. Today most LLMs have poor taste and chipping away the cruft matters more than ever.