Good afternoon, Stella

Your Tinkerland agent workspace is continuously working for OpenArt. In the last 24 hours, 12 agents ran 47 tasks, with 2 items waiting on your decision.

State 01 · Default running

12agents

Agents working in the background

Data sync, ICP analysis, cohort rebuilds — runs automatically on schedule without bothering you.

View all activity →

State 02 · Push needs you

2waiting

Decisions for you

An agent has hit a decision point that needs human judgment. Growth Map v1 is ready for your review.

Review now →

State 03 · Pull v2

—

You ask the agent

Ask the agent questions via chat. When answers involve a decision, they're automatically written back into the workflow.

Coming soon

State 04 · Inject v2

—

You inject information into the agent

Offline context outside the data pipeline — type it in natural language, and the agent folds it into future analyses.

Coming soon

Recent activity

Today · Apr 21

14:08

R

ICP Deductive Expander is generating adjacent-segment hypotheses based on 3 validated signatures

Position Reasoning · L2 · ETA ~2 min

● running

13:42

G

Positioning Strategy Generator finished the Initial Growth Map v1 draft and pushed it to you for review

needs decision Position 5 candidate ICP × scenarios · 3 top-ranked

11:30

R

PMF Signal Detector computed retention curves, aha moments, and funnel drop-off for 3 candidate ICPs

Position ICP #2 shows a hockey stick · strongest PMF signal

10:15

R

Scenario / JTBD Mapper generated 7 Jobs-to-be-done mappings for 3 inductive ICPs

Position Covers 2 trigger moments · 4 substitute comparisons

08:42

R

ICP Inductive Discoverer identified 3 candidate ICPs from high-retention cohorts, with behavioral signatures and evidence chains

Position Each ICP has ≥3 data citations · passed specificity test

Yesterday · Apr 20

22:05

P

Cohort Rebuilder rebuilt cohorts / retention / funnels from 142k event logs

Position 7-day / 30-day / 90-day retention curves · auto-named 12 behavioral cohorts

18:20

P

Qualitative Data Collector pulled 284 tickets from Intercom and identified 12 themes

Position Top themes: "image consistency" · "style switching not intuitive" · "export quality"

15:48

E

You approved the Data Stack Auditor's Tier B readiness rating and instrumentation plan

Position Human calibration · written to case memory + rubric update

Completed

14:12

P

Behavioral Signal Collector synced 142,318 events from the last 30 days from Amplitude

Position Incremental sync · integrity check passed

Completed

Earlier · Apr 19

09:00

O

Task Planner kicked off OpenArt's first positioning analysis, building a 14-step execution DAG

Position Brief: find the ICP × scenario combo to bet on in Q2

Completed

Data Sources

The full list of data sources the Positioning Agent uses. For each source: how it's wired up, sync status, and today's new rows. Click any source to see the last 7 days of trends, event / theme distribution, data quality, and sample records.

Active sources

5/ 7

2 enabled in v2

New events today

12,384

↑ 8.0% vs yesterday

New qualitative today

37tickets

↑ 12% vs yesterday

Data quality

96.2%

Integrity · stable

Internal data · Tier 1

A

Amplitude Behavioral

OAuth · connected · synced 14 min ago

+ 12,384

New events today

↑ 8.0% vs yesterday

I

Intercom Qualitative

API Token · connected · synced 1 hr ago

+ 37

New tickets today

↑ 12% vs yesterday

S

Stripe Commercial

OAuth · connected · synced 6 hr ago

+ 142

New subscriptions today

↑ 4.3% vs yesterday

P

openart.ai product surface Product

Playwright scraper · daily 06:00 crawl · finished today at 06:05

No change

Product surface

0 new features

Q

Pro user interview transcripts Qualitative · Upload

Manual upload · imported 2 days ago

+ 1

New files pending

Awaiting theme extraction

External signals · Tier 2 / v2

U

App Store / G2 / Reddit reviews UGC

Enabled in v2 · UGC collector

—

Not yet connected

M

Meta / TikTok ad library Market Intel

Enabled in v2 · Market & competitor intel

—

Not yet connected

Validation

Positioning Agent uses paid ads to validate candidate ICP × scenario hypotheses against the real market. The key is to separate ICP × messaging signal from creative noise — run multiple variants per cell, and look at between-cell deltas, not within-cell deltas.

Experiment period

Apr 18 – 22

5 days · completed

Total ad spend

$4,200

Meta · 3 cell × 4 variant

Total impressions

412K

~180K unique users reached

Winning ICP

ICP #1

High confidence · recommend scaling

Results interpreter

v2 Fresh · today 10:12

Cell-level ranked validation · separates ICP signal from creative noise

#1

ICP #1 · Stable Diffusion power user × complex prompt workflow

4 variants · $1,840 spend · 168K impressions

High confidence

CPA

$7.42

↓ 32% vs baseline

CTR

2.8%

↑ 0.9pp vs avg

CVR

4.1%

↑ 1.4pp vs avg

Variant CPA distribution (4 variants in the same cell)

Within-cell variance: Low

V1

$7.12

V2

$7.38

V3

$7.58

V4

$8.20

Agent interpretation

Strong signal — ICP hypothesis confirmed. All 4 variants land in a tight $7.1–$8.2 CPA range, low variance — top performance comes from real ICP × messaging fit, not creative luck. Between-cell delta is +42% vs other ICPs, highly significant. Recommend scaling: raise budget from $1.8K to $8–12K, keeping multiple variants to continue controlling for creative noise.

#2

ICP #2 · Freelance illustrator × rapid client-revision cycles

4 variants · $1,360 spend · 128K impressions

Medium confidence

CPA

$9.18

↓ 16% vs baseline

CTR

3.4%

↑ 1.5pp vs avg

CVR

3.2%

→ matches avg

Variant CPA distribution (4 variants in the same cell)

Within-cell variance: Medium

V1

$6.90

V2

$8.62

V3

$10.02

V4

$11.18

Agent interpretation

Medium signal — ICP direction is right, but messaging needs another pass. CTR is the highest of the three cells (3.4%), so illustrators do find the ads interesting; but CVR and the between-variant variance ($6.9–$11.2) are large, meaning different hooks reach illustrators very differently. Next step: keep the V1 + V2 hook directions and spend another $1K to enlarge the sample before deciding whether to scale.

#3

ICP #3 · Hobbyist × share-driven creation (deductive expansion)

3 variants · $1,000 spend · 116K impressions

Low confidence

CPA

$14.60

↑ 34% vs baseline

CTR

4.1%

↑ 2.2pp vs avg

CVR

1.4%

↓ 1.8pp vs avg

Variant CPA distribution (3 variants in the same cell)

Within-cell variance: High

V1

$10.20

V2

$14.40

V3

$19.20

Agent interpretation

Weak signal — the deductive ICP hypothesis doesn't hold on the paid funnel. High CTR + low CVR + wide CPA variance ($10.2–$19.2) is classic hobbyist behavior: willing to click, not willing to pay. Between-cell delta is -34%, worse than the other cells. Conclusion: this may be a real audience, but the monetization path isn't Pro subscription — we recommend moving it to the Organic test (once v3 lands) to explore virality / free-traffic hooks.

Organic content testing

v3 Not yet enabled

Passive-discovery channel add-on · SEO / LinkedIn / community

O

Organic signal

Generate organic test content for the top messaging angles, measuring the decoupling between engagement and conversion

v3 coming soon

For customers where passive-discovery channels (SEO, LinkedIn, Reddit, community) matter, generates organic content along the top 3 messaging angles. Output format: { platform, content_piece, engagement_metrics, signal_strength }

SEO

Long-tail keywords + publishing strategy (pending v3)
Measures: organic traffic · landing-page dwell · aha-arrival rate

LinkedIn

Thought-leadership posts · 3–5 per ICP (pending v3)
Measures: saves / reshares · density of relevant comments

Reddit

Community-native case-share posts (pending v3)
Measures: upvote ratio · share of high-quality comments

Requires Organic content testing (V3 capability) · long feedback loop · add-on to ad validation, not a replacement

Self-Evolving Log

Every human calibration and every outcome that flows back drives the agent to self-correct. Here's the full change history of rubrics, data weights, and case memory — not a black box: every change traces back to what triggered it.

Reasoning river Self-evolution timeline

Reverse-chronological, last 30 days · each change has three parts: delta · cause · effect

Today · Apr 22

07:00 Rubric

Data-quality scoring rubric v3.1→v3.2

Cause Stella approved the Tier B readiness rating at 15:48 yesterday. Once that human calibration was captured into case memory, it triggered an automatic rubric update.

Effect Evaluator gained an instrumentation completeness dimension (weight 0.2) · the next readiness judgment will fold tracking coverage into the score.

Yesterday · Apr 21

11:15 Weight

Weight config: cohort_data 9→10

Cause Across the last 3 ICP judgments, cohort_data had the highest predictive power on the actual outcome — consistent across OpenArt / Clip / Sora design partners.

Effect Cohort-behavior weight rises 11% in subsequent ICP induction · room reserved to correspondingly downweight UGC / competitor.

15:48 Calibration

Evaluator × CEO agreement 78%→84%

Cause 12 cumulative rounds of human-review feedback · evaluator was previously underscoring on non-obviousness and data_grounding in particular.

Effect Evaluator self-scores vs CEO spot-checks now agree at over 80% · one of the V1 launch gates is met.

3 days ago · Apr 19

16:30 Memory

New case written: "For creative-tool products, Stripe payment frequency ≠ LTV signal"

Cause Cross-validated against the Clip case · users with frequent small subscriptions have a lower true LTV than less-frequent, larger spenders · counterintuitive.

Effect Applies to subsequent ICP LTV ranking · future creative-tool customers will inherit this prior by default during ICP induction.

Last week · Apr 14 – 18

Apr 17 Memory

Interview-transcript theme extraction written · added 3 JTBD patterns

Cause Structured theming of Pro-user interviews (6 transcripts) complete · three high-frequency JTBDs identified: "client revision cycle", "weekly ideation block", "style consistency".

Effect Fed into the scenario mapping for ICP #2 · strengthened the JTBD signal for Freelance illustrators.

Apr 15 Rubric

Evaluator rubric v3.0→v3.1

Cause Stella repeatedly emphasized "hypotheses must be testable" across 3 consecutive reviews — not captured by the existing 5 dimensions.

Effect Added a testability sub-dimension under specificity (weight 0.3) · next ICP scoring will produce a standalone testability score.

Workbench

Current state snapshot

Evaluator

v3.2

instrumentation completeness 0.20new

specificity 0.25

data grounding 0.20

non obviousness 0.15

consistency 0.10

completeness 0.10

Data weight config

v1 hardcoded

cohort_data

10

payment

9

support_nps

7

product_usage

7

ugc_reviews

5

competitor

3

trend_reports

2

Case Memory

Written 3 days ago

48

engagements

12

outcome-tagged

Cross-client Patterns

v3

Cross-client pattern learning unlocks at 15+ outcome-tagged cases. Currently 12.

12 / 15

Initial Growth Map

ICP × scenario distribution in LTV × PMF space. Node size = citation count, color intensity = confidence, dashed lines = reasoning lineage. Click any node to see the full hypothesis and reasoning trace.

Rank #1 · top bet

Induced ICP

Deduced ICP

Lineage (parent → child)

Initial Growth Map

v1 · 4.2/5

Generated today at 13:42

Evaluator Dimensions

specificity

4.3

data grounding

4.6

non obviousness

3.8

consistency

4.4

completeness

4.0

Agent-reported blind spots

These are gaps the agent admits it didn't cover

Geographic variation — only US / EU data, APAC not covered
Seasonal hiring cycles — seasonality of the freelancer user base not modeled
Enterprise account behavior — 90% of current data is individual users

Methodology

Synthesized from 6 upstream agents. Top 3 ICP × scenario pairs ranked by PMF × LTV × non-obviousness. We recommend scaling #1 aggressively, keeping messaging variants for #2 and re-testing, and moving #3 to organic channels for further validation.

Ranked · click to expand

#1

Stable Diffusion power user

Complex prompt workflow iteration Induced

LTV $148 · PMF 91%

6 citations

Conf. 87%

#2

Freelance illustrator

Rapid client-revision cycles Induced

LTV $112 · PMF 68%

4 citations

Conf. 74%

#3

Hobbyist

Share-driven creation Deduced

LTV $18 · PMF 28%

3 citations

Conf. 46%

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