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Adaption AI introduces AutoScientist, a tool that automates the full research loop to make model training more accessible outside of frontier labs.
Based on interviews with 50+ AI teams, the author highlights that production agent failures often stem from minor prompt or configuration issues rather than deep model problems. The article advocates for adopting software engineering practices like versioning, A/B testing, and experiment tracking to improve reliability.
An opinion piece suggesting that AI teams will increasingly focus on 'harness engineering' and advocating for a review article on the framework.
Fireworks AI announces its training platform in preview, allowing developers to train, fine-tune, and deploy custom AI models with full ownership of data and weights.
Hugging Face introduces Storage Buckets, a new mutable, S3-like object storage feature on the Hub optimized for production ML workflows using its Xet backend for efficient deduplication.
OpenAI shares detailed lessons learned from scaling a single Kubernetes cluster to 7,500 nodes to support large machine learning workloads, covering networking, scheduling, and infrastructure challenges. The post builds on their earlier experience scaling to 2,500 nodes and aims to help the broader Kubernetes community.