Deepnote research: our notes on building agents

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PLG dashboard live appPM

Weekly signups128k+12%

Activated41%+6%

At risk742-18%

Pipeline by segmentlast 30 days

Enterprise47 accounts

Startup31 accounts

Self-serve92 accounts

published · shared with revenue team

Churn analysisDA

SQL
SELECT variant, churned_30d
FROM ab.assignments a
JOIN events.churn c USING (user_id)

churn rate · 30d

control

4.2%

treatment

7.8%

✦Deepnote AIinsight

Treatment users skipped onboarding 2.4x more often. Segment by first-session length before shipping.

add follow-up cellexplain drivers

top drivers

tutorial_skipped

+0.34

no_team_invite

+0.21

first_session < 2m

+0.14

treatment +85% vs control

Fraud detection agentJJ

✦summarize and cancel fraudulent transactions by country

planning next steps

↓ generated 4 blocks

SQLflag_fraud_txns

SELECT txn_id, country, amount FROM txns WHEREfraud_score > 0.8

PYtrain fraud_detector.pt

ep 010.412

ep 030.187

ep 050.064

DEPLOYfraud_detector → live

▲deepnote.com/api/v1/fraud-detector

postscoring 1.2k txns / seclive

CHARTcard cancellations by country

4 cells · query → train → deploy → visualize

Cursorproject: revenue-app

user

why is March revenue down 12%?

agent

let me check the canonical revenue breakdown skill

⚙ tool_call

deepnote.run(
skill = "revenue_breakdown",
period = "2026-03"
)

project · 4 vCPU · 8 GB

← returned notebook

revenue_breakdown_2026_03.deepnote

4 cells · trace #4729

Visual Studio Codechurn_analysis.deepnote

1import deepnote as dn

3df = dn.query("SELECT …")

4df.groupby('cohort').agg(…)

6fig = px.line(df, x='week', y='retention')

7dn.publish(fig, name="churn-cohorts")

⌘⇧Pcommand palette

>Deepnote: Open in Cloud

>Deepnote: Run as Skill

>Deepnote: Schedule Notebook

✓kernel state14 MB

✓packages47

✓env vars6

✓files3

→ opening at deepnote.com/workspace/…

Claude Code~/revenue-investigation

 ██████╗ ██╗       █████╗  ██╗   ██╗ ██████╗  ███████╗
██╔════╝ ██║      ██╔══██╗ ██║   ██║ ██╔══██╗ ██╔════╝
██║      ██║      ███████║ ██║   ██║ ██║  ██║ █████╗
██║      ██║      ██╔══██║ ██║   ██║ ██║  ██║ ██╔══╝
╚██████╗ ███████╗ ██║  ██║ ╚██████╔╝ ██████╔╝ ███████╗
 ╚═════╝ ╚══════╝ ╚═╝  ╚═╝  ╚═════╝  ╚═════╝  ╚══════╝

 ██████╗  ██████╗  ██████╗  ███████╗
██╔════╝ ██╔═══██╗ ██╔══██╗ ██╔════╝
██║      ██║   ██║ ██║  ██║ █████╗
██║      ██║   ██║ ██║  ██║ ██╔══╝
╚██████╗ ╚██████╔╝ ██████╔╝ ███████╗
 ╚═════╝  ╚═════╝  ╚═════╝  ╚══════╝

user"investigate march revenue dip — return a notebook"

claude

spinning up a deepnote project & pulling the canonical revenue skill

$deepnoteproject create--from-skillrevenue_breakdown

✓ project prj_8f21 · python 3.12 · 4 vCPU · 8 GB

$deepnoterun--cell"join_orders_with_refunds"

✓ 1.4s · 2,184 rows · warehouse: snowflake/prod

$deepnotenotebook publish--shareteam

✓ deepnote.com/n/march-revenue-dip

← handed back

march_revenue_dip.deepnote

7 cells · trace #c12a · ran in 4.3s

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Deepnote was incredibly easy to set up and allows us to start new notebooks in seconds. Working together with Deepnote gives us a great window into the ways candidates approach the interview problem.

Becca Carter

Head of Data Science

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CEO