evidence

is an open source framework for building data products with SQL and markdown

[evidence @evidence_dev]

Today we are launching Evidence Studio - an AI-powered development, publishing, and governance platform for analytics

[evidence @evidence_dev]

https://t.co/EWZHfJrh7W

[evidence @evidence_dev]

We are live on Product Hunt today with our latest open source release, Evidence Core. It's a neck and neck race with the new Mac Mini!

[Adam McAskill @AdamMcaskill]

Installs of our open source framework grew by 23% last month.

It's the best way to build analytics with coding agents.

Today we're releasing the biggest update to that project since we created it. We're calling it Evidence Core.

@evidence_dev

[Sean Hughes @seanhughes92]

Our ARR grew 13x in the last 12 months, and we did it with a deliberately small team. Now we're looking for an ambitious software engineer to join us as we start to scale up.

We don't care much about credentials; we care about what you can build, how fast you learn, and how you collaborate.

Adam and I started Evidence because we were frustrated with the state of BI tools. We hated using them, and while code was automating other parts of our work, BI was still stuck in proprietary drag-and-drop interfaces.

So we built an entirely code-based alternative and launched it as an open source project.

Today, Evidence is used by thousands of organizations around the world, including Fortune 500 companies, global investment managers, and national governments. And increasingly, it’s what AI coding agents reach for when they need to build analytics.

Our commercial product, Evidence Studio - which includes SSO, access control, AI agents, email schedules, Slackbot, and a ton more - is growing rapidly. We're seeing more companies replace tools like Tableau and Power BI with Evidence.

But our ambition has always been much larger than building another BI tool. Our goal is to reshape how organizations work with data and communicate what they know, and AI is opening up an entirely new opportunity to do that.

Our next phase is all about AI. We want to help data teams build self-driving analytics systems for their organizations - systems that proactively surface insights and improve themselves, while giving anyone the freedom to explore data, get answers, and create. We're building the context, tools, and feedback loops that let AI do real data work in a reliable, governed way.

Given our small team, the people who join us have an outsized impact. You'll work directly with customers and the founders, make major product and technical decisions, and help shape where Evidence goes next. We're looking for someone who loves solving open-ended problems and cares deeply about product quality.

If that sounds like you, DM me. And if someone great comes to mind, I'd love an introduction.

[evidence @evidence_dev]

Read more: https://t.co/sgw58eYykS

[evidence @evidence_dev]

CFO can't stop vibe coding dashboards?

Give everyone on your team the ability to ask questions and generate new insights in Evidence Studio, Claude Desktop, and ChatGPT.

New insights are easy-to-review and can be smoothly incorporated back into your existing reporting.

The agent uses the context already in your Evidence project, including your existing reports, definitions, and visualizations.

[Sean Hughes @seanhughes92]

Data work is behind software engineering in AI adoption - but not for long.

In my recent talk at AI Council, I explored what BI looks like in a world where AI is exceptionally capable (video in comments).

The most interesting AI systems in software today are built around closed feedback loops: agents observe usage, make changes, validate outcomes, and improve over time.

The same pattern is coming to analytics.

The key prerequisite is treating analytics as code. When reports and analysis are defined in code, agents can read, write, review, and improve them just like software.

That's how we'll move from AI-assisted workflows to systems that continuously improve themselves.

[Adam McAskill @AdamMcaskill]

I wrote a few words on one of my favorite Evidence features: deploy previews.

It’s a small thing: a link on a pull request, but it says a lot about our worldview.

[evidence @evidence_dev]

Which warehouse should we launch next?

[evidence @evidence_dev]

Launching today: Native Warehouse Support for Snowflake, BigQuery and Fabric.

Run Evidence Studio directly on your warehouse without syncing or replicating data.

[evidence @evidence_dev]

https://t.co/CqFoKPiWcL

[Sean Hughes @seanhughes92]

A sneak peek of our upcoming Insights feature in Evidence. Have an agent build an ad hoc analysis for you, then save it and share it with your team.

The agent has context from all your reports, so it can reuse definitions and visualizations. Or it can point you to a report that already answers your question.

Each Insight is saved with the original prompt and any methodology that went into creating the work, making it auditable and reusable.

[evidence @evidence_dev]

Get started here: https://t.co/fOOM8LUFVI

[evidence @evidence_dev]

Anthropic's Claude Opus 4.7 is now the default model in Evidence Studio.

Our AI development agent can build reports, run queries, handle ad hoc analysis, and answer questions about data lineage - all in one place.

Teams are using it to build and manage their company’s reporting systems, and to deliver high-value customer reporting. With this upgrade, they’re already seeing better development accuracy and more consistent outputs.

Link in the comments if you want to try it yourself.

[Sean Hughes @seanhughes92]

AI + BI is great. But AI + BI + CI is where things get really interesting.

Here’s what that looks like in practice.

You start with a Linear issue. You assign an agent. It generates an Evidence report, opens a pull request, and sends you a link.

The code is simple and readable. It’s just markdown. You can scan it and immediately understand what’s happening.

The Evidence GitHub bot runs validation tests and adds a preview link so you can see the report before it goes live. You can merge with full confidence that nothing has broken.

This is the CI portion that most AI platforms are missing. It means you get you the speed of vibe code-able reporting, but with the governance and reliability of production software.

[evidence @evidence_dev]

Introducing end-to-end data lineage in Evidence Studio.

Track how connections, tables, and models flow through your reports - all the way down to individual charts in your dashboards.

This is BI-as-code in action, and a key foundation for AI-generated reporting. When AI understands how everything in your reporting connects, it can take smarter actions on your behalf.

[Sean Hughes @seanhughes92]

With usage analytics data in Evidence, it's becoming possible to automate the full reporting lifecycle.

Today you can build a report to recommend actions based on user data. The next step is plugging in AI to proactively recommend actions for you.

That's where we're heading and it's getting very exciting.

[AI Council @AICouncilConf]

Every data team is being asked the same question right now: how do we know our AI features are working? Most don't have a good answer. The tooling isn't there and the old playbooks for measurement don't map cleanly onto AI-driven products.

That's exactly why we built the Analytics & Data Science track at AI Council 2026. Curated by @imightbemary, Head of Data at @_hex_tech, we’re bringing together practitioners who are building the new playbooks in real time. Here's the lineup:

@pablankley, CTO & Co-Founder at @zenlytic - argues that semantic layers are now capping your agent's potential, and shares what to build instead for agents that gather business context dynamically.

@isidoremiller, AI Engineer at Hex - exposes why current analytics benchmarks are fundamentally broken, and proposes evals that test what analysts actually do: learn a messy warehouse over time, not answer a frozen question.

@JillACates, Senior Data Scientist at @Shopify - introduces a practical framework for AI attribution: measuring not just whether users engaged with an AI feature, but the degree to which AI shaped the final result.

Avik Basu, Staff Data Scientist at @Intuit - bridges prediction and decision-making with uplift modeling, showing how to estimate the causal effect of interventions at the individual level to target the right people.

Andrew Zigler, GTM Engineer & Podcast Host at @LinearB_Inc / Dev Interrupted - draws on five pedagogical frameworks from education research that map directly to eval design patterns for AI agents.

@seanhughes92, Co-Founder at @evidence_dev - runs a thought experiment on what analytics looks like in an AI-first world, where data teams shift from building dashboards to managing systems of AI agents.

A huge thank you to Katie for curating this track!

Join us SF, May 12-14! 🎟️ https://t.co/TddrwUD2ff

[evidence @evidence_dev]

This week we rolled out Usage Analytics to enterprise customers in Evidence Studio.

See how the reports you’ve built in Evidence are actually being used - who’s viewing them, how often, and which ones get the most attention.

You also get access to the underlying data, so you can model it further or build your own analysis.

Early teams are already using this to understand adoption across internal and embedded reports.

[Sean Hughes @seanhughes92]

Love getting messages like this.

We consider customer feedback to be an extension of our product experience. The faster we can fix issues, the more delightful the experience of working with us.

It's an unbeatable feeling to see it working for customers.