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@DeepnoteHQ

Data workspace where agents and humans work together.
馃寧 deepnote.com Joined August 2019
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Deepnote Verified account @DeepnoteHQ
Most data teams that got their context layer working didn鈥檛 buy one. They wrote .md files and put them in a git repo.

One team took agent accuracy from 14% to ~90% that way. Setup took 5 minutes.

The lesson from comparing notes with data leaders, startups to the F500: writing your context down pays off enormously. Where you put it matters far less.

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Deepnote Verified account @DeepnoteHQ
Meet Deepnote Agent Workspace: a shared place where people and agents work on the same data tasks and publish them as hosted apps - all grounded in your skills, 100+ native integrations, and permissions.
Jakub Jurovych Verified account @jakubjurovych Aug 3
Meet Deepnote Agent Workspace: a shared place where people and agents work on the same data tasks and publish them as hosted apps, all grounded in your skills, 100+ native integrations, and permissions.

Every major model release gives agents more capability. They can search, write code, query databases, use applications, and sustain increasingly complex work.

But capability alone is not enough to scale across the team or reliably reproduce work over time.

Most data agents still work inside a temporary chat or a developer鈥檚 local harness. They can produce an impressive answer, but they do not automatically inherit the knowledge, permissions, and processes that allow a company to depend on that answer.

People do not work that way.

A good teammate knows which definitions the company trusts. They know where the relevant data lives and what they are allowed to access. They leave behind work that others can inspect. They learn from corrections. When a task becomes important and recurring, they turn it into a process the organization can rely on.

Agents need the same foundation.

Today, we鈥檙e introducing Deepnote Agent Workspace, a shared place where people and agents work on the same data, using the same organizational context.

It brings together the components required to operate data agents across a company:

- Skills that capture trusted definitions and procedures,
- Agents that perform inspectable and recurring work,
- Apps that bring the result into business workflows,
- Integrations and permissions that govern access throughout.

Interested in trying it out? Reach out, and I鈥檒l hook you up.

@DeepnoteHQ
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Deepnote Verified account @DeepnoteHQ
@Deepnote x @OpenAI

We鈥檙e excited to be part of OpenAI鈥檚 launch of new role-specific plugins in Codex. Your Deepnote notebooks, scheduled analyses, data apps are available directly to Codex via our native plugin.

Three things this unlocks:

- Your workspace as context. Codex can search and read across every project, so a cross-functional question can pull from your marketing, sales, and product notebooks and reason across all three. The reporting matches how your team actually defines things.

- Explorations that ship. Codex can write back to Deepnote, so an analysis lands as a notebook or a published app your team can open, rerun, and extend, not a chat thread that scrolls away.

- Complex workflows from question to production. Teams can build new analytics jobs in Codex using Deepnote鈥檚 context layer, then hand them off to run as long-running workflows in Deepnote.
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Deepnote Verified account @DeepnoteHQ
Which AI data viz tool makes it hardest to ship a wrong answer?

We gave 9 of them the same dataset and the same 4 prompts. Some nailed the math but couldn't show their work. Others produced confident charts built on wrong assumptions.

Get the full breakdown with scores, screenshots, and the dataset so you can try it yourself.
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Deepnote Verified account @DeepnoteHQ
Bad data is easy to spot. Root cause is the hard part.

@statsig鈥檚 data team runs two kinds of investigations:

- customer-level deep dives to reproduce issues by company and experiment聽
- internal analyses that support marketing, sales, and core product decisions.

As they scaled, the bottleneck wasn鈥檛 SQL. It was finding prior work, reusing it, and collaborating without losing context.

So, they built these workflows in @Deepnote. What changed:

1) From alert to investigation in one run
They turned data-quality alerts into a repeatable notebook workflow: paste the alert, hit Run, reproduce the issue, and start the root-cause analysis immediately.

2) Reusable investigation templates
Customer investigations became parameterized notebooks. Change one parameter, rerun, and you鈥檙e back in a familiar flow instead of rewriting boilerplate.

3) Collaboration that actually works for investigations
Shared notebook history, multiplayer editing, and a searchable workspace made handoffs painless.

Huge thanks to Timothy Chan and the Statsig team for trusting us with critical workflows.

Check out Deepnote: vist.ly/4u9ei