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evidence
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evidence
@evidence_dev
https://t.co/Gn735YnbMP is an open source framework for building data products with SQL and markdown
가입일 Mayıs 2021
308 팔로잉2.3K 팔로워
evidence 리트윗함
evidence 리트윗함
evidence 리트윗함
evidence 리트윗함
evidence 리트윗함

We’ve been working on a cleaner way to make reports interactive, and added variables you can insert anywhere in @evidence_dev.
Inputs like sliders or dropdowns generate a variable you can reference in SQL, charts, or even plain markdown.
Change an input, and anything using that
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evidence 리트윗함
evidence 리트윗함

A week ago, we rebuilt our AI assistant from the ground up, and the results so far have been pretty amazing.
From a customer:
“Massive upgrade. I recommend all Evidence devs try it out for edits now.”
What changed:
- A new Evidence docs MCP
- Claude Opus 4.5 as the primary model under the hood
- Deep project awareness, so it can:
- Query and analyze your data
- Summarize what it sees on the page
- Write Evidence markdown and SQL
- Update page and project settings directly from chat (incl. themes)
- Send feature requests and bug reports to the Evidence team
Next up:
- Validation and feedback loops to keep pushing the success rate higher
- A CLI that lets you use your own AI model to work on Evidence Studio reports
The end goal: vibe-coding reports that are validated, governed, and production-ready - all inside Evidence.
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RT @seanhughes92: Logos instantly make reports feel more polished.
We just shipped a super simple way to add company logos to tables and m…
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evidence 리트윗함
evidence 리트윗함

Most BI and analytics tools struggle with something I think about a lot: use case frequency.
They give you tons of controls, but it’s hard to make even basic dashboards that look good and perform well. Often because the tools treat every possible action as equally important.
In reality, reporting isn’t evenly distributed. Something like 90% of reports are built from the same small set of ingredients.
It’s easy to assign the wrong weight to features when the people building the product haven’t spent much time building reports themselves. Without that experience, it’s hard to know what’s common, what’s rare, or what the default behaviour should be.
At Evidence, the product is built by a team of ex-analysts and data leaders, and we actively use Evidence ourselves. That gives us strong opinions about the right user experience, and a shared understanding of which patterns show up constantly versus occasionally.
We bake that directly into our product development process.
When we discuss feature ideas internally, we’ll say things like "this is a 95% use case" or "this is a 2% use case". That framing drives a lot:
- How much priority it gets
- Whether it becomes a default or an advanced option
- How much surface area it earns in the product and in docs
High-frequency use cases become obvious and opinionated.
Low-frequency ones stay possible, but intentionally out of the way.
The goal is that by default, anyone using Evidence can produce the same outputs a top-tier analyst would - because the right choices and trade-offs are already baked into the product.
We get there by designing explicitly around use case frequency.
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evidence 리트윗함
evidence 리트윗함
evidence 리트윗함

Shipping some early Christmas gifts to @evidence_dev customers this week - like this map that lets you zoom in to see more granular areas
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evidence 리트윗함









