Changelog

You can now ask the assistant about your workspace and have it do the work for you. It explores your catalog, traces lineage, reads job logs, and answers questions about your data - and when you ask it to, it makes changes: running assets, editing configuration, creating sources and destinations, and managing access.

The assistant does the legwork; you stay in control. Anything that changes your workspace happens at your request, and credentials are handled separately from the model (more on that below).

Here's what it can do:

• Understand your workspace and models. Ask it to find any source, destination, model, table, or orchestration, and it explains how they fit together. It reads metadata and configuration, traces lineage upstream and downstream ("where does this data come from?"), reads change history ("when did this break?"), walks a model step by step in plain English, diffs two model versions, and runs semantic search across pipelines ("which models filter on column X?").

• Monitor jobs and debug failures. Get run counts by status over any date range, open individual runs with deep links to their logs, and read the logs themselves — error-level by default, or in full for "which step was slowest?"

• Analyze your data. Ask questions of your verified tables and get a direct answer back, along with a plain-English explanation of how the assistant arrived at it.

• Act on assets. Run an asset on demand, set or clear schedules (cron, with timezones handled), edit basics like name, description, table prefix, and compute size, patch connector configuration (including reordering orchestration pipelines), create and move folders, duplicate assets (folders included), and publish a model version.

• Create sources and destinations. The assistant walks you through a new connection end to end — browsing connector types, checking requirements, collecting credentials or OAuth, testing database connections, and selecting Google Sheets or SharePoint files. Secrets are collected in a dedicated form and never pass through the model.

• Manage access. Find users, list and create groups, add members, inspect roles and folder permissions, and set a group's role on a folder.

Why it's accurate. Everything is backed by a transaction log with real data, not just metadata. And because Less is no-code, every transformation is structured and explicit, so the assistant can reason about what each step does without guessing.

What's next. The assistant doesn't build or edit models on the Canvas yet. That's what we're working on now.

The new Explore page gives you a single place to understand everything happening in Less.

Browse

You can view all your tables, sources, and models in one place. Each asset includes activity tracking — reads and writes for tables, version history for models — so you always know what’s happening and when.

Lineage

The headline feature is lineage. You can visualize entire data pipelines end-to-end and trace dependencies between assets. If something breaks or changes upstream, you can see exactly what’s affected downstream.

Chat with your pipelines

You can ask questions about your pipelines directly from the Explore page. Want to know how a column was created, whether there are scheduling conflicts, or why a dashboard might be showing stale data? Just ask. It covers anything related to pipeline health.

This works as well as it does because of two things. First, everything is backed by a transaction log with actual data — not just metadata. That's one of the core benefits of an end-to-end platform. Second, because Less is a no-code tool, every transformation is structured and explicit. There's no ambiguity in what a step does. The operations are strict and well-defined, which means the AI can reason about them with high accuracy.

The new Flatten JSON tool is helpful when you have a Text column with data that resembles JSON. In most cases you should use the Parse Object and Explode List tool, but sometimes the schema of the JSON data differs row by row and that approach won’t work. You might have nested JSON in which one cell is a object and another is a text column. That is both the power of JSON and some of what makes it challenging. The Flatten JSON tools solves those cases and transposes the full data into a field (column) and value field.

We also improved many existing tools:

1. The Combine tool now includes searching through which columns to include after the Combine as well as a select/deselect all button.

2. Group By includes a duplicate button for the aggregations you add

3. You can now remove all HTML tags from Text columns in the Transform

4. The Columns tool now also includes a “Move to bottom” button for each column in addition to the “Move to top”

5. Similar to the Group By tool, you can now duplicate conditions much easier in the Filter tool

6. Bulk add 5, 10, 25 or 50 rows in the Manual Input tool

7. Added better Filter annotations to see you conditions directly from the Canvas

8. We added a Replace syntax so you don’t have to use the Replace tool

9. The Date Transform tool now includes a Day of Month output

This release introduces nine rebuilt sources: ActiveCampaign, Google Analytics 4, Google Ads, Shopify, Dixa, Pinterest Ads, WooCommerce, Meta Ads, and Dyanics 365 Business Central OData.

All are built to be incremental by default to avoid unnecessary data transfers and optimize speed.

The Combine tool already included anchors for the left, inner and right data streams (left and right being the anti joins). However, oftentimes you want the left+inner or right+inner data sets (i.e. the left and right data sets combined with the inner data set). To support this, we've introduced new anchors for the left+inner and right+inner data sets.

We also released four new destinations: Google Sheets, MySQL, PostgreSQL and Clickhouse.

The Google Sheet Source now supports native authentication and selection of sheets from Google Sheets. You can select multiple files and multiple sheets within each file.

Similarly, the File Source now supports multiple files at once. It supports CSV, Excel and Parquet files. We automatically detect the file type and the schema of the file, but you can select columns, rows to skip, and more.