Cursor MPC Error listing offerings

💰** AI usage and cost tracking ** Token spend from AI sessions and the AI chat is now recorded per workspace, user, provider and model. ➡️ Admins see the whole workspace over the last 7, 30 or 90 days, grouped by day, user or model. ➡️ Every member can see their own usage. ➡️ Costs marked with ~ are estimates. A figure the provider billed back is shown as is, and a model with no known rate reads as unpriced. 📖 https://www.windmill.dev/changelog/ai-usage-and-cost

Fratera · 6h ago

🚀 AI agent evals (beta) A reusable AI agent could only ever run as a flow step, and there was nowhere to keep the inputs that exposed a problem in production. Evals fix that. **You can now measure an agent against a dataset of cases, score every answer, and diff two runs case by case.** ➡️ A dataset is a set of cases, each a question and an expected answer. It belongs to the reusable ai_agent resource, so it outlives the flow step and two runs of the same agent stay comparable. Up to 1000 cases per dataset. ➡️ A run is one flow job: a parallel loop over the cases, each answer scored by one branch per scorer. Scorers are your own code, or an LLM judge that returns a score and a reason. The run outlives the tab that started it. ➡️ Every run is kept, so you can answer the question that matters after a change: did it get better, or did one case break. Compare any two runs of a dataset and the difference shows up case by case and scorer by scorer. This is a beta, tell us what breaks. 📖 Read more: https://www.windmill.dev/changelog/ai-agent-evals

Fratera · 3d ago

🚀 dbt projects as a first-class Windmill runtime Import an unmodified dbt project, the same directory you run `dbt build` against locally, and run it on your own workers. A `dbt_project.yml` is all Windmill needs to pick it up. ➡️ The whole project deploys with the script, bundled and versioned atomically. Nothing is cloned at run time. Warehouses (postgres, snowflake, bigquery, databricks, redshift, and any adapter dbt supports) are configured once in workspace settings and referenced by name. ➡️ Every model, seed, snapshot and source becomes a `dbt://<warehouse>/<schema>/<name>` asset, and dbt's own ref() lineage is drawn as edges between them. Hit `Refresh models` and Windmill runs a real `dbt parse` to rebuild the graph from dbt's manifest. Native scripts can read those assets by URI, so a dbt build lands in the same lineage as the rest of your jobs. 📖 Read more: https://www.windmill.dev/docs/getting_started/scripts_quickstart/dbt

Fratera · 3w ago