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.
Can this be also used as an attack vector? A small seed percentage of users constantly choosing a particular poisoned pypi library to achieve a niche task which gets rled into the model suggestions and recommendations.
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.
ELO scores for OCR don't really make much sense - it's trying to reduce accuracy to a single voting score without any real quality-control on the reviewer/judge.
I think a more accurate reflection of the current state of comparisons would be a real-world benchmark with messy/complex docs across industries, languages.
VLM Run (https://vlm.run) | Infrastructure Engineer + DevRel + 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) 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.
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.
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