IBM released Granite Time Series PatchTST-FM-r2, a zero-shot time-series forecasting foundation model in its Granite Time Series PatchTST family. As of September 8, 2026, it ranks #2 overall among replicable, zero-shot models on the GIFT-Eval benchmark and is top among permissively licensed options. The update changes the model architecture to Conformer-style blocks, adds missing-value imputation support, and provides Apache 2.0/OpenMDW 1.0 dual licensing plus open weights and code for reproducible use.
Harvey raised a $550 million funding round, valuing the company at $15.5 billion while building an operating system for legal and corporate advisory workflows. The round’s valuation is $15.5 billion. The funding supports growth of Harvey’s proprietary knowledge base and the company also added Guardrails AI to improve the reliability of agentic AI.
The Open Source AI Stack describes an application developer’s approach for switching from closed to open AI models using a layered setup that separates model choice from where it runs and how it’s integrated. Kimi K3 is given as an example large open model with 1.8T total parameters and 104B active parameters. As a result, developers can swap models and inference providers without retraining, focusing instead on selecting and customizing the model, routing, and harness layers for their agentic coding workflows.
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