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OpenAI 6 months ago 30
Snowflake and OpenAI announced a $200 million partnership to integrate OpenAI's models into Snowflake's data platform. The deal includes OpenAI credits valued at $200 million that Snowflake customers can use for API access. Enterprises will be able to run AI agents and generate insights directly within their data warehouses without moving data to separate systems.
Together AI 6 months ago 39
Together Evaluations added support for comparing proprietary models from OpenAI, Anthropic, and Google alongside open-source and fine-tuned models in a single benchmarking platform. The platform enables side-by-side evaluation of quality, cost, and performance metrics across different model providers. Users can now make informed decisions about which models to deploy based on comparative data rather than isolated assessments.
Together AI 6 months ago 33
Researchers fine-tuned open-source LLM judges using Direct Preference Optimization on 5,400 preference pairs from RewardBench 2, achieving higher accuracy than GPT-5.2 on human preference alignment. GPT-OSS 120B reached 62.63% accuracy compared to GPT-5.2's 61.62%, while costing 15.3 times less and running 14 times faster. This demonstrates that smaller open-source models can match or exceed closed-source judge performance when trained on preference data, enabling cost-effective and deployable evaluation systems.
OpenAI 6 months ago 37 ● 2 sources
Codex is a macOS application designed to manage AI coding tasks through multiple agents, parallel workflows, and extended operations. The app serves as a centralized interface for coordinating several autonomous processes simultaneously during software development. Users gain the ability to orchestrate complex coding workflows that previously required manual coordination between different tools.
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TLDRocket
6 August 2026
The headline story today is infrastructure: Mirendil's $100 million Google Cloud deal represents the capital intensity and strategic choreography now required to build frontier AI systems. The AI lab, which raised $1 billion in seed funding just weeks ago, is already committing half of that to compute—TPUs, GPUs, and managed training clusters for self-improving AI research. This is the economics of the moment: massive upfront capital, outsourced infrastructure, and concentration among a handful of compute providers.
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