diff --git a/marimo/stylus.py b/marimo/stylus.py
new file mode 100644
index 0000000..5b66564
--- /dev/null
+++ b/marimo/stylus.py
@@ -0,0 +1,1461 @@
+import marimo
+
+__generated_with = "0.16.2"
+app = marimo.App(width="full")
+
+
+@app.cell
+def setup_pyoso():
+    # This code sets up pyoso to be used as a database provider for this notebook
+    # This code is autogenerated. Modification could lead to unexpected results :)
+    import pyoso
+    import marimo as mo
+    client = pyoso.Client()
+    pyoso_db_conn = client.dbapi_connection()
+    return client, mo
+
+
+@app.cell
+def about_app(mo):
+    mo.vstack([
+        mo.md("""
+        # Stylus SDK Developer Ecosystem
+        <small>Author: <span style="background-color: #f0f0f0; padding: 2px 4px; border-radius: 3px;">OSO Team</span> · Last Updated: <span style="background-color: #f0f0f0; padding: 2px 4px; border-radius: 3px;">2025-09-25</span></small>
+        """)
+    ])
+    return
+
+
+@app.cell
+def create_overview_tab(
+    dev_funnel_stats,
+    ecosystem_stats,
+    mo,
+    sdk_stats,
+    stylus_sprint_stats,
+):
+    _intro = mo.vstack([
+        mo.md("## Overview"),
+        mo.Html("""
+        Stylus Sprint is a 5M ARB initiative supporting projects building with
+        Stylus — a WASM-based virtual machine that lets developers write smart
+        contracts in Rust, C, C++, and other WebAssembly-compatible languages.
+        The long-term objective is to attract non-Solidity developers to build
+        on Arbitrum.
+        """),
+    ])
+
+    _stats = mo.vstack([
+        mo.md("### Selected Metrics"),
+        mo.md("#### Stylus Sprint Teams"),
+        stylus_sprint_stats,
+        mo.md("#### Stylus SDK Usage"),
+        sdk_stats,
+        mo.md("#### Ecosystem Benchmarks"),
+        ecosystem_stats,
+        mo.md("#### Stylus Developer Funnel"),
+        dev_funnel_stats,
+    ])
+
+    _resources = mo.vstack([
+        mo.md("### Further Resources"),
+        mo.accordion({
+            "Data": """
+            - [OSO API](https://docs.opensource.observer/docs/get-started/python) - GitHub metrics and developer activity data
+            - [Electric Capital Crypto Ecosystems](https://github.com/electric-capital/crypto-ecosystems) - Ecosystem repository data
+            - [Open Source Insights (aka deps.dev)](https://deps.dev) - SDK usage and adoption metrics
+            - [GH Archive](https://www.gharchive.org/) - a public archive of historical events to GitHub
+            """,
+            "Docs": """
+            - [Stylus Sprint Announcement](https://blog.arbitrum.io/stylus-sprint/) - More info on Stylus
+            - [Project Velocity Definition](https://chaoss.community/kb/metric-project-velocity/) - We use the CHAOSS metric for "velocity"
+            - [How to Add/Update OSO Project Info](https://docs.google.com/document/d/1bOjjHiaY-8bx5d_Bwce4ePDYq3H7W8r_3Wq0dJCyLoA/edit?tab=t.0) - Steps for Stylus Sprint projects
+            - [Getting Started with Pyoso](https://docs.opensource.observer/docs/get-started/python) - Run your own queries
+            - [Marimo Documentation](https://docs.marimo.io/) - Make your own dashboard on OSO
+            """,
+            "Caveats": """
+            - We can only see developer activity on *public* GitHub repos. We are not able to identify teams using Stylus in private repos.
+            - We don't have visibility into the very top of the developer funnel. For instance, we are unable to capture how many developers
+              have read the docs or browsed the use case examples.
+            - We rely on Electric Capital's Crypto Ecosystems registry for comparing Arbitrum to other ecosystems. The registry is
+              community-maintained and not always accurate / up-to-date for new projects.
+            - We are not yet linking developer activity to onchain activity. We currently do not have a robust way of distinguishing
+              between developers who are tinkering on personal projects and ones who are using Stylus in a high-value application.
+            """
+        }, multiple=True)
+    ])    
+
+    overview_tab = mo.vstack([
+        _intro,
+        _stats,
+        _resources
+    ])
+    return (overview_tab,)
+
+
+@app.cell
+def create_stylus_sprint_tab(
+    SPRINT_START_DATE,
+    df_stylus_project_metrics,
+    df_stylus_sdk_deps_project_metrics,
+    make_joyplot,
+    mo,
+    pd,
+    sprint_velocity_change,
+    summarize_monthly_metrics,
+):
+    _df = df_stylus_project_metrics[df_stylus_project_metrics['metric_name'] == 'GITHUB_project_velocity_daily']
+    _mm = summarize_monthly_metrics(df_stylus_project_metrics, num_months=6)
+
+    _project_applications = pd.read_csv("https://raw.githubusercontent.com/rohitmalekar/stylus-dashboard/refs/heads/main/data/project_applications.csv")
+    _df_projects = pd.merge(_project_applications, _df[['display_name', 'project_name']].drop_duplicates(), on='project_name', how='left')
+    _ossd_base_url = 'https://github.com/opensource-observer/oss-directory/tree/main/data/projects'
+    _df_projects['OSS Directory URL'] = _df_projects['project_name'].apply(lambda x: f"{_ossd_base_url}/{x[0]}/{x}.yaml")
+    _df_projects['Using Stylus SDK?'] = _df_projects['project_name'].apply(lambda x: x in df_stylus_sdk_deps_project_metrics['project_name'].unique())
+    _df_projects.rename(columns={
+        'questbook_title': 'Grant Name',
+        'display_name': 'Team Name',
+        'questbook_link': 'Questbook URL'
+    }, inplace=True)
+    _df_projects = _df_projects[['Grant Name', 'Team Name', 'Using Stylus SDK?', 'Questbook URL', 'OSS Directory URL']]
+    _df_projects.sort_values(by='Grant Name', inplace=True)
+
+    # Calculate Stylus Sprint stats
+    _total_budget = 5_000_000
+    _total_grants = len(_df_projects)
+    _active_devs = _mm['Active Developers'].sum()
+    _change_in_velocity = sprint_velocity_change(
+        df=df_stylus_project_metrics,
+        metric_name="GITHUB_project_velocity_weekly",
+        sprint_start=SPRINT_START_DATE
+    )
+
+    stylus_sprint_stats = mo.hstack(
+        items=[
+            mo.stat(
+                label="Total Budget",
+                bordered=True,
+                value=f"{_total_budget:,.0f} ARB",
+                caption="Approved by the Arbitrum DAO"
+            ),
+            mo.stat(
+                label="Approved Grants",
+                bordered=True,
+                value=f"{_total_grants:,.0f}",
+                caption="Teams with grants awarded by the Stylus Sprint program"
+            ),
+            mo.stat(
+                label="Active Developers with Stylus Grant",
+                bordered=True,
+                value=f"{_active_devs:,.0f}",
+                caption="All teams with grants, averaged over last 6 months"
+            ),
+            mo.stat(
+                label="Change in Velocity",
+                bordered=True,
+                value=f"{_change_in_velocity*100:.1f}%",
+                caption=f"All dependent teams, before/after {SPRINT_START_DATE}"
+            )
+        ],
+        widths="equal",
+        gap=1
+    )
+
+    stylus_sprint_tab = mo.vstack([
+        mo.md("## Stylus Sprint Teams"),
+        mo.Html("""
+        This tab tracks (public) development activity of teams with grants from the Stylus Sprint. 
+        """),
+
+        stylus_sprint_stats,
+
+        mo.callout("""Note: Development metrics like commits, issues closed, and PRs merged can vary widely based on a project's workflow, team size, or codebase structure. These numbers aren't meant for head-to-head comparisons, but rather to track changes within the same project over time. Use them as directional signals to guide deeper, qualitative evaluation.""", kind="warn"),
+
+        mo.md("### Developer Velocity"),
+        mo.ui.plotly(
+            figure=make_joyplot(df=_df, smoothing=7),
+            config={'displayModeBar': False}
+        ),
+
+        mo.md("### Activities Over Last 6 Months"),
+        mo.ui.table(
+            data=_mm,
+            selection=None,
+            show_column_summaries=False,
+            show_data_types=False,
+            page_size=20,
+            format_mapping={c: '{:,.0f}' for c in _mm.columns if c != 'Project'},
+            freeze_columns_left=['Project']
+        ),
+
+        mo.md("### Registry of (Known) Projects"),
+        mo.ui.table(
+            data=_df_projects.reset_index(drop=True),
+            selection=None,
+            show_column_summaries=False,
+            show_data_types=False,
+            page_size=50
+        )
+    ])   
+    return stylus_sprint_stats, stylus_sprint_tab
+
+
+@app.cell
+def create_sdk_usage_tab(
+    SPRINT_START_DATE,
+    df_stylus_project_metrics,
+    df_stylus_sdk_deps,
+    df_stylus_sdk_deps_project_metrics,
+    make_joyplot,
+    mo,
+    sprint_velocity_change,
+    summarize_monthly_metrics,
+):
+    _df = df_stylus_sdk_deps_project_metrics[df_stylus_sdk_deps_project_metrics['metric_name'] == 'GITHUB_project_velocity_daily']
+    _mm = summarize_monthly_metrics(df_stylus_sdk_deps_project_metrics, num_months=6)
+
+    _df_dep_repos = df_stylus_sdk_deps.copy()
+    _df_dep_repos['Project Name'] = _df_dep_repos['dependent_project_name'].map(
+        _df
+        .drop_duplicates(subset=['display_name', 'project_name'])
+        .set_index('project_name')['display_name']
+        .to_dict()       
+    )
+    _df_dep_repos['In Stylus Sprint?'] = _df_dep_repos['dependent_project_name'].isin(df_stylus_project_metrics['project_name'])
+    _df_dep_repos['Dependent Repo URL'] = _df_dep_repos.apply(lambda x: f"https://github.com/{x['repo_owner']}/{x['repo_name']}", axis=1)
+    _ossd_base_url = 'https://github.com/opensource-observer/oss-directory/tree/main/data/projects'
+    _df_dep_repos['OSS Directory URL'] = _df_dep_repos['dependent_project_name'].apply(lambda x: f"{_ossd_base_url}/{x[0]}/{x}.yaml")
+    _df_dep_repos.drop(columns=['dependent_project_name', 'repo_owner', 'repo_name', 'artifact_id'], inplace=True)
+
+    # Calculate SDK usage stats
+    _total_dependents = len(_df_dep_repos)
+    _sprint_participants = _df_dep_repos['In Stylus Sprint?'].sum()
+    _active_devs = _mm['Active Developers'].sum()
+    _change_in_velocity = sprint_velocity_change(
+        df=df_stylus_sdk_deps_project_metrics,
+        metric_name="GITHUB_project_velocity_weekly",
+        sprint_start=SPRINT_START_DATE
+    )
+
+    sdk_stats = mo.hstack(
+        items=[
+            mo.stat(
+                label="Stylus SDK Dependents",
+                bordered=True,
+                value=f"{_total_dependents:,.0f}",
+                caption="Linked to (known) teams with public GitHub activity"
+            ),
+            mo.stat(
+                label="Sprint Participants using Stylus SDK",
+                bordered=True,
+                value=f"{_sprint_participants:,.0f}",
+                caption="Subset of teams with public GitHub activity"
+            ),
+            mo.stat(
+                label="Active Developers using Stylus SDK",
+                bordered=True,
+                value=f"{_active_devs:,.0f}",
+                caption="All dependent teams, averaged over last 6 months"
+            ),
+            mo.stat(
+                label="Change in Velocity",
+                bordered=True,
+                value=f"{_change_in_velocity*100:.1f}%",
+                caption=f"All dependent teams, before/after {SPRINT_START_DATE}"
+            )
+        ],
+        widths="equal",
+        gap=1
+    )
+
+    sdk_tab = mo.vstack([
+        mo.md("## Stylus SDK Usage"),
+        mo.Html("""
+        This tab tracks (public) development activity of teams with repos that import the Stylus SDK as a direct dependency. 
+        """),
+
+        sdk_stats,
+
+         mo.callout("""Note: We rely on the GitHub API for tracing a project's SBOM (software bill of materials).
+         We only fetch SBOMs for projects registered on OSO, which is a curated subset of all GitHub repos. New projects are
+         being added continuously. We try to fetch the current state of each project's SBOM on a monthly basis.
+         """, kind="warn"),
+
+        mo.md("### Developer Velocity"),
+        mo.ui.plotly(
+            figure=make_joyplot(df=_df, smoothing=7),
+            config={'displayModeBar': False}
+        ),
+
+        mo.md("### Activities Over Last 6 Months"),
+        mo.ui.table(
+            data=_mm,
+            selection=None,
+            show_column_summaries=False,
+            show_data_types=False,
+            page_size=25,
+            format_mapping={c: '{:,.0f}' for c in _mm.columns if c != 'Project'},
+            freeze_columns_left=['Project']
+        ),
+
+        mo.md("### Registry of (Known) Projects"),
+        mo.ui.table(
+            data=_df_dep_repos.reset_index(drop=True),
+            selection=None,
+            show_column_summaries=False,
+            show_data_types=False,
+            page_size=50
+        )
+
+    ])   
+    return sdk_stats, sdk_tab
+
+
+@app.cell
+def create_ecosystem_tab(
+    SPRINT_START_DATE,
+    df_ecosystem_metrics,
+    df_repo_overlap,
+    get_total_repos_in_crypto_ecosystems,
+    make_joyplot,
+    make_repo_overlap_barchart,
+    mo,
+    sprint_velocity_change,
+    summarize_monthly_metrics,
+):
+    _df = df_ecosystem_metrics[df_ecosystem_metrics['metric_name'] == 'GITHUB_project_velocity_daily']
+    _mm = summarize_monthly_metrics(df_ecosystem_metrics, num_months=6)
+
+    # Calculate ecosystem stats
+    _total_repos = get_total_repos_in_crypto_ecosystems()
+
+    _arb_repos = df_repo_overlap[df_repo_overlap['ecosystem_name'] == 'Arbitrum']['total_repos'].sum()
+    _active_devs = _mm[_mm['Project'] == 'Arbitrum']['Active Developers'].sum()
+    _change_in_velocity = sprint_velocity_change(
+        df=df_ecosystem_metrics,
+        metric_name="GITHUB_project_velocity_weekly",
+        sprint_start=SPRINT_START_DATE
+    )
+
+
+    ecosystem_stats = mo.hstack(
+        items=[
+            mo.stat(
+                label="All Repositories",
+                bordered=True,
+                value=f"{_total_repos:,.0f}",
+                caption="Repos included in the latest Electric Capital registry"
+            ),
+            mo.stat(
+                label="Arbitrum Repositories",
+                bordered=True,
+                value=f"{_arb_repos:,.0f}",
+                caption="Repos included in the latest Electric Capital registry"
+            ),
+            mo.stat(
+                label="Active Developers in Arbitrum Ecosystem",
+                bordered=True,
+                value=f"{_active_devs:,.0f}",
+                caption="All 'Arbitrum' repos, averaged over last 6 months"
+            ),
+            mo.stat(
+                label="Change in Velocity",
+                bordered=True,
+                value=f"{_change_in_velocity*100:.1f}%",
+                caption=f"All 'Arbitrum' repos, before/after {SPRINT_START_DATE}"
+            )
+        ],
+        widths="equal",
+        gap=1
+    )
+
+    ecosystem_tab = mo.vstack([
+        mo.md("## Ecosystem Benchmarks"),
+        mo.Html("""
+        This tab tracks (public) development activity on Arbitrum compared to other crypto ecosystems registered by Electric Capital.
+        """),
+
+        ecosystem_stats,
+
+        mo.callout("""
+        Note: Electric Capital's Crypto Ecosystems registry is community-maintained and not always accurate / up-to-date for
+        new projects. There is also significant overlap between ecosystems, ie, the same repository belongs to multiple ecosystems.
+        New repositories can be added by making a pull request to the Crypto Ecosystems GitHub.
+         """, kind="warn"),
+
+
+        mo.md("### Developer Velocity"),
+        mo.ui.plotly(
+            figure=make_joyplot(df=_df, smoothing=7),
+            config={'displayModeBar': False}
+        ),
+
+        mo.md("### Activities Over Last 6 Months"),
+        mo.ui.table(
+            data=_mm,
+            selection=None,
+            show_column_summaries=False,
+            show_data_types=False,
+            page_size=20,
+            format_mapping={c: '{:,.0f}' for c in _mm.columns if c != 'Project'},
+            freeze_columns_left=['Project']
+        ),
+
+        mo.md("### Repository Overlap"),
+        mo.ui.plotly(
+            figure=make_repo_overlap_barchart(df=df_repo_overlap),
+            config={'displayModeBar': False}
+        ),
+
+    ])   
+    return ecosystem_stats, ecosystem_tab
+
+
+@app.cell
+def create_developer_funnel_tab(
+    df_fork_devs,
+    df_fork_devs_labeled,
+    df_fork_devs_ranked,
+    mo,
+    px,
+):
+    # 1. Table of example repos and fork count
+    _forks_table = df_fork_devs_labeled.groupby('first_fork_repo_url')['dev_id'].nunique().sort_values(ascending=False)
+    _forks_table = _forks_table.reset_index()
+    _forks_table.columns = ['Repo URL', 'Num. Developers']
+
+    # 2. Area chart of cumulative forks
+    _df_fork_count = df_fork_devs.groupby(['first_fork_repo_url', 'first_fork'], as_index=False)['dev_id'].nunique() 
+    _df_fork_count.sort_values(by='first_fork', inplace=True)
+    _df_fork_count['Fork Count (Cumulative)'] = _df_fork_count['dev_id'].cumsum()
+
+    _forks_barchart = px.area(data_frame=_df_fork_count, x='first_fork', y='Fork Count (Cumulative)')
+    _forks_barchart.update_traces(line=dict(color='#104C35', width=1.5), fillcolor=rgba_from_rgb(rgb='rgb(8, 135, 43)', alpha=0.80))
+    _layout = dict(
+        plot_bgcolor='white',
+        paper_bgcolor='white',
+        title_x=0,
+        legend_title="",
+        autosize=True,
+        margin=dict(t=0, l=0, r=0, b=0),
+        height=300,
+        yaxis=dict(showgrid=True, gridcolor='lightgray', linecolor='black', linewidth=1, ticks='outside', rangemode='tozero', title=''),
+        xaxis=dict(showgrid=False, linecolor='black', linewidth=1, ticks='outside', title=''),
+    )
+    _forks_barchart.update_layout(**_layout)
+
+    # 3. Developer funnel, PageRanked
+    df_devs_by_project = (
+        df_fork_devs_labeled.merge(df_fork_devs_ranked[['dev_id', 'dev_score']], on='dev_id')
+        .groupby(['dev_id', 'dev_url', 'dev_score'], as_index=False)
+        .agg(
+            first_fork=('first_fork','min'),    
+            num_repos=('repo_url', 'nunique'),
+            alignment=('project_type', lambda x: min(set(x))[3:]),
+            #repos_worked_on_by_this_dev=('repo_url', lambda x: ' | '.join(sorted(set(x))))
+        )
+        .sort_values(by=['dev_score', 'num_repos', 'first_fork'], ascending=[False, False, True])
+        .reset_index(drop=True)
+        .drop(columns=['dev_id', 'dev_score'])    
+        .rename(columns={
+            'dev_url': 'Git Username',
+            'first_fork': 'Earliest Fork',
+            'num_repos': 'Num OSS Repos',
+            'alignment': 'Contribution Type to Look For'
+        })
+    )
+
+    # 4. Projects worked on by developers in the funnel
+    df_projects_from_devs = (
+        df_fork_devs_labeled.merge(df_fork_devs_ranked[['dev_id', 'dev_score']], on='dev_id')
+        .groupby('repo_owner', as_index=False)
+        .agg(
+            repo_rank=('dev_score', 'sum'),
+            alignment=('project_type', lambda x: min(set(x))[3:]),
+            first_fork=('first_fork','min'),    
+            num_devs=('dev_id', 'nunique'),
+            dev_names=('dev_url', lambda x: ', '.join(sorted(set(x))).replace('https://github.com/',''))
+        )
+        .sort_values(by=['repo_rank', 'num_devs', 'first_fork'], ascending=[False, False, True])
+        .query("repo_owner not in @SDK_PROJECT_MAINTAINERS")
+        .drop(columns=['repo_rank'])
+        .reset_index(drop=True)
+    )
+    df_projects_from_devs['repo_owner'] = df_projects_from_devs['repo_owner'].apply(lambda x: f'https://github.com/{x}')
+    df_projects_from_devs.rename(columns={
+        'repo_owner': 'GitHub Owner', 'first_fork': 'Earliest Fork', 'num_devs': 'Num Developers', 'dev_names': 'Git Username(s)', 'alignment': 'Alignment'
+    }, inplace=True)
+
+
+    # Calculate developer funnel stats
+    _example_repos = len(_forks_table)
+    _unique_developers = len(df_devs_by_project)
+    _total_projects = len(df_projects_from_devs)
+    _avg_forks_per_month = _unique_developers / ((df_fork_devs['first_fork'].max() - df_fork_devs['first_fork'].min()).days / 30)
+
+    dev_funnel_stats = mo.hstack(
+        items=[
+            mo.stat(
+                label="Stylus Example Repos",
+                bordered=True,
+                value=f"{_example_repos:,.0f}",
+                #caption="Git users who forked Stylus examples"
+            ),
+            mo.stat(
+                label="Forking Developers",
+                bordered=True,
+                value=f"{_unique_developers:,.0f}",
+                #caption="Git users who forked Stylus examples"
+            ),
+            mo.stat(
+                label="Projects Linked to Forking Developers",
+                bordered=True,
+                value=f"{_total_projects:,.0f}",
+                #caption="Public GitHub repos with recent commits from developers identified"
+            ),
+            mo.stat(
+                label="Avg Forks/Month",
+                bordered=True,
+                value=f"{_avg_forks_per_month:.1f}",
+                #caption="Average developers per project"
+            )
+        ],
+        widths="equal",
+        gap=1
+    )
+
+    dev_funnel_tab = mo.vstack([
+        mo.md("## Stylus Developer Funnel"),
+        mo.Html(
+        """
+        This tab tracks when developers engage with Stylus by forking the example repositories created by Offchain Labs.
+        It also applies a basic ranking algorithm to identify developers and projects worth watching.
+        """),
+
+        dev_funnel_stats,
+
+        mo.hstack([
+            mo.vstack([
+                mo.md("### Forks by Example Repo"),
+                mo.ui.table(
+                    data=_forks_table,
+                    show_column_summaries=False,
+                    show_data_types=False,
+                )
+            ]),
+            mo.vstack([
+                mo.md("### Forking Developers"),
+                mo.ui.plotly(figure=_forks_barchart, config={'displayModeBar': False})
+            ])
+        ], widths='equal', wrap=True),
+
+        mo.md("### Developers Identified"),
+        mo.ui.table(
+            data=df_devs_by_project[['Git Username', 'Num OSS Repos', 'Contribution Type to Look For', 'Earliest Fork']],
+            selection=None,
+            show_column_summaries=False,
+            show_data_types=False,
+            page_size=20
+        ),
+
+        mo.md("### Projects Linked to Forking Developers"),
+        mo.ui.table(
+            data=df_projects_from_devs[['GitHub Owner', 'Git Username(s)', 'Num Developers', 'Alignment', 'Earliest Fork']],
+            selection=None,
+            show_column_summaries=False,
+            show_data_types=False,
+            page_size=20
+        ),
+
+    ])
+    return dev_funnel_stats, dev_funnel_tab
+
+
+@app.cell
+def display_tabs(
+    dev_funnel_tab,
+    ecosystem_tab,
+    mo,
+    overview_tab,
+    sdk_tab,
+    stylus_sprint_tab,
+):
+    mo.ui.tabs({
+        "Overview": overview_tab,
+        "Stylus Sprint Teams": stylus_sprint_tab,
+        "Stylus SDK Usage": sdk_tab,
+        "Ecosystem Benchmarks": ecosystem_tab,
+        "Stylus Developer Funnel": dev_funnel_tab,
+    }, value="Overview")
+    return
+
+
+@app.cell
+def generate_joyplot(SPRINT_START_DATE, go, np, pd, px):
+    def make_joyplot(df, smoothing=7):
+
+        colorscale = 'Greens'
+        gap = 1.10
+        fill_alpha = 0.40
+
+        wide = df.pivot_table(index="date", columns="display_name", values="amount", aggfunc="sum").sort_index()
+        if smoothing > 1:
+            wide = wide.rolling(window=smoothing, min_periods=1, center=True).mean()
+
+        wide_norm = wide.copy()
+        for col in wide.columns:
+            non_zero_count = (wide[col] > 0).sum()
+            if non_zero_count > 50:
+                threshold = int(wide[col].quantile(0.95))
+                wide_norm[col] = wide_norm[col].clip(upper=threshold)
+            denom = wide_norm[col].max(skipna=True)
+            wide_norm[col] = wide_norm[col].divide(denom, axis=0)
+
+        cols = list(wide_norm.columns)[::-1]
+        cmap = getattr(px.colors.sequential, colorscale)
+        cmap_subset = cmap[len(cmap)//2:][::-1]
+
+        n = len(cols)
+        proj_colors = {}
+        for rank, col in enumerate(cols):
+            idx = int(round(rank * (len(cmap_subset) - 1) / max(1, n - 1)))
+            proj_colors[col] = cmap_subset[idx]
+
+        sprint_start_date = pd.to_datetime(SPRINT_START_DATE).date()
+        last_date = wide_norm.index.max()
+
+        fig = go.Figure()
+        tickvals, ticktext = [], []
+
+        for i, col in enumerate(cols):
+            y_offset = i * gap
+            y_vals = wide_norm[col].fillna(0).values
+            x_vals = wide_norm.index
+
+            color = proj_colors[col]
+            fill_color = rgba_from_rgb(color, fill_alpha)
+
+            fig.add_trace(
+                go.Scatter(
+                    x=x_vals,
+                    y=np.full(y_vals.shape, y_offset, dtype=float),
+                    mode="lines",
+                    line=dict(width=0),
+                    hoverinfo="skip",
+                    showlegend=False
+                )
+            )
+            fig.add_trace(
+                go.Scatter(
+                    x=x_vals,
+                    y=y_vals + y_offset,
+                    mode="lines",
+                    fill="tonexty",
+                    line=dict(color=color, width=1.5),
+                    line_shape="spline",
+                    fillcolor=fill_color,
+                    name=col,
+                    customdata=np.c_[wide_norm[col].reindex(x_vals).fillna(0).values * 100],
+                    hovertemplate="<b>%{fullData.name}</b><br>Week of: %{x|%d %b %Y}<br>Velocity: %{customdata[0]:,.0f}% of max<extra></extra>",
+                    showlegend=False
+                )
+            )
+            tickvals.append(y_offset)
+
+            # Calculate velocity change
+            pre_sprint_data = wide_norm[col][wide_norm.index < sprint_start_date]
+            post_sprint_data = wide_norm[col][wide_norm.index >= sprint_start_date]
+
+            pre_sprint_avg = pre_sprint_data.fillna(0).mean()
+            post_sprint_avg = post_sprint_data.fillna(0).mean()
+
+            if pre_sprint_avg > 0:
+                change = ((post_sprint_avg - pre_sprint_avg) / pre_sprint_avg) * 100
+            else:
+                change = 0
+
+            if int(change) > 0:
+                annotation_text = f"+{change:,.0f}%"
+            elif int(change) < 0:
+                annotation_text = f"{change:,.0f}%"
+            else:
+                annotation_text = "No change"
+
+            ticktext.append(f"<b>{col}</b>: {annotation_text}")
+
+        fig.add_vrect(
+            x0=sprint_start_date, x1=last_date,
+            fillcolor="rgba(128,128,128,0.05)",
+            line_width=0.25,
+            annotation_text="<i>Stylus Sprint</i>",
+            annotation_position="top left",
+        )
+
+        fig.update_layout(
+            template="plotly_white",
+            paper_bgcolor="white",
+            plot_bgcolor="white",
+            font=dict(size=12, color="#111"),
+            margin=dict(l=0, r=80, t=0, b=0),
+            showlegend=False,
+            title=dict(text="", x=0, xanchor="left", font=dict(size=14, color="#111"))
+        )
+        fig.update_xaxes(title="", showgrid=False, visible=False, linecolor="#000", linewidth=1)
+        fig.update_yaxes(
+            title="",
+            side="right",
+            showgrid=False,
+            tickmode="array",
+            ticklabelposition="outside top",
+            #ticklabelshift=-1,
+            ticklabelstandoff=5,
+            tickvals=tickvals,
+            ticktext=ticktext,
+            zeroline=False,
+            tickcolor="#000",
+            showline=False
+        )
+        return fig
+    return (make_joyplot,)
+
+
+@app.cell
+def generate_repo_overlap_barchart(go, np):
+    def make_repo_overlap_barchart(
+        df,
+        target_ecosystem="Arbitrum"
+    ):
+        # Expect columns: ecosystem_name, repos_shared_with_target, repos_not_shared_with_target, total_repos
+        d = df.copy()
+        d.sort_values(["repos_shared_with_target", "total_repos", "ecosystem_name"], ascending=[False,False,True], inplace=True)
+
+        d["shared_val"] = d["repos_shared_with_target"]
+        d["not_shared_val"] = d["repos_not_shared_with_target"]
+
+        xaxis_title = "Repositories"
+        hover_suffix = ""
+        hover_shared_val = d["shared_val"]
+        hover_not_shared_val = d["not_shared_val"]
+
+        # Colors (fixed, readable on black)
+        COLOR_SHARED = "#2ecc71"
+        COLOR_NOT_SHARED = "#888888"
+        COLOR_HILITE = "Green"  # slightly brighter for the highlighted ecosystem
+        shared_colors = [COLOR_HILITE if name==target_ecosystem else COLOR_SHARED for name in d["ecosystem_name"]]
+
+        fig = go.Figure()
+        fig.add_trace(go.Bar(
+            y=d["ecosystem_name"],
+            x=d["shared_val"],
+            name=f"Shared with {target_ecosystem}",
+            orientation="h",
+            marker=dict(color=shared_colors),
+            hovertemplate=(
+                "<b>%{y}</b><br>"
+                "Shared: %{customdata[0]:,.0f}"+hover_suffix+"<br>"
+                "Not shared: %{customdata[1]:,.0f}"+hover_suffix+"<br>"
+                "Total: %{customdata[2]:,.0f}<extra></extra>"
+            ),
+            customdata=np.stack([
+                hover_shared_val,
+                hover_not_shared_val,
+                d["total_repos"]
+            ], axis=-1)
+        ))
+        fig.add_trace(go.Bar(
+            y=d["ecosystem_name"],
+            x=d["not_shared_val"],
+            name="Not shared",
+            orientation="h",
+            marker=dict(color=COLOR_NOT_SHARED),
+            hovertemplate=(
+                "<b>%{y}</b><br>"
+                "Shared: %{customdata[1]:,.0f}"+hover_suffix+"<br>"
+                "Not shared: %{customdata[0]:,.0f}"+hover_suffix+"<br>"
+                "Total: %{customdata[2]:,.0f}<extra></extra>"
+            ),
+            customdata=np.stack([
+                hover_not_shared_val,
+                hover_shared_val,
+                d["total_repos"]
+            ], axis=-1)
+        ))
+        fig.update_layout(
+            barmode="stack",
+            title="",
+            paper_bgcolor="white", plot_bgcolor="white",
+            font=dict(size=12, color="black"),
+            margin=dict(t=0, l=0, r=20, b=40),
+            showlegend=True,
+            legend=dict(orientation="h", y=1.02, x=1, xanchor='right')
+        )
+        fig.update_xaxes(title=xaxis_title, showgrid=True, gridcolor="#AAA", zeroline=False, color="black")
+        fig.update_yaxes(title="", showgrid=False, color="black", automargin=True)
+
+        return fig
+    return (make_repo_overlap_barchart,)
+
+
+@app.cell
+def configuration_settings():
+    # Configuration constants
+    SPRINT_START_DATE = '2025-03-01'
+    PROJECT_START_DATE = '2023-07-01'
+    ECOSYSTEM_START_DATE = '2023-07-01' #'2020-01-01'
+    COLLECTION_NAME = 'arb-stylus'
+    SDK_PROJECT_MAINTAINERS = ['offchainlabs', 'arbitrumfoundation']
+    EVM_ECOSYSTEMS = [
+        'arbitrum',
+        'base',
+        'bnb_chain',
+        'evm_toolkit',
+        'ethereum',
+        'ethereum_l2s',
+        'ethereum_virtual_machine_stack',
+        'foundry',
+        'polygon',
+        'solidity',
+    ]
+    OTHER_ECOSYSTEMS = [
+        'bitcoin',
+        'cosmos_network_stack',
+        'solana',
+        'sui'
+    ]
+    ECOSYSTEMS = EVM_ECOSYSTEMS + OTHER_ECOSYSTEMS
+    MONTHLY_METRICS = [
+        'GITHUB_active_developers_monthly',
+        'GITHUB_full_time_developers_monthly',
+        'GITHUB_commits_monthly',    
+        'GITHUB_releases_monthly',
+        'GITHUB_forks_monthly',
+        'GITHUB_stars_monthly',
+        'GITHUB_opened_pull_requests_monthly',    
+        'GITHUB_merged_pull_requests_monthly',
+        'GITHUB_opened_issues_monthly',
+        'GITHUB_closed_issues_monthly',
+        'GITHUB_project_velocity_monthly',
+        'GITHUB_bot_activity_monthly',
+    ]
+    WEEKLY_METRICS = [
+        'GITHUB_commits_weekly',    
+        'GITHUB_forks_weekly',
+        'GITHUB_stars_weekly',
+        'GITHUB_opened_pull_requests_weekly',    
+        'GITHUB_merged_pull_requests_weekly',
+        'GITHUB_opened_issues_weekly',
+        'GITHUB_closed_issues_weekly',
+        'GITHUB_project_velocity_weekly',
+        'GITHUB_contributors_weekly'
+    ]
+    DAILY_METRICS = [
+        'GITHUB_project_velocity_daily'
+    ]
+    METRIC_NAMES = MONTHLY_METRICS + WEEKLY_METRICS + DAILY_METRICS
+    return (
+        COLLECTION_NAME,
+        ECOSYSTEMS,
+        ECOSYSTEM_START_DATE,
+        EVM_ECOSYSTEMS,
+        METRIC_NAMES,
+        MONTHLY_METRICS,
+        OTHER_ECOSYSTEMS,
+        PROJECT_START_DATE,
+        SDK_PROJECT_MAINTAINERS,
+        SPRINT_START_DATE,
+    )
+
+
+@app.cell
+def get_data(
+    COLLECTION_NAME,
+    ECOSYSTEMS,
+    ECOSYSTEM_START_DATE,
+    EVM_ECOSYSTEMS,
+    METRIC_NAMES,
+    OTHER_ECOSYSTEMS,
+    PROJECT_START_DATE,
+    SDK_PROJECT_MAINTAINERS,
+    client,
+    pd,
+    wraps,
+):
+    # QUERY HELPERS
+    def parse_dates(*cols):
+        def decorator(func):
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                df = func(*args, **kwargs)
+                for col in cols:
+                    if col in df.columns:
+                        df[col] = pd.to_datetime(df[col]).dt.date
+                return df
+            return wrapper
+        return decorator
+
+    stringify = lambda arr: "'" + "','".join(arr) + "'"
+
+    # QUERIES
+    @parse_dates("date")
+    def get_metrics_by_developer_ecosystem():
+        return client.to_pandas(f"""
+            SELECT
+              sample_date AS date,
+              metric_name,
+              projects_v1.display_name,
+              amount
+            FROM timeseries_metrics_by_project_v0
+            JOIN metrics_v0 USING metric_id
+            JOIN projects_v1 USING project_id
+            WHERE
+              sample_date >= DATE '{ECOSYSTEM_START_DATE}'
+              AND metric_name IN ({stringify(METRIC_NAMES)})
+              AND project_source = 'CRYPTO_ECOSYSTEMS'
+              AND project_namespace = 'eco'
+              AND project_name IN ({stringify(ECOSYSTEMS)})          
+            ORDER BY 1,2,3  
+            """)
+
+    @parse_dates("date")
+    def get_metrics_by_stylus_project():
+        return client.to_pandas(f"""
+            SELECT DISTINCT
+              p.display_name AS display_name,
+              p.project_name AS project_name,
+              m.metric_name AS metric_name,
+              ts.sample_date AS date,
+              ts.amount AS amount
+            FROM timeseries_metrics_by_project_v0 ts        
+            JOIN metrics_v0 m
+              ON m.metric_id = ts.metric_id
+            JOIN projects_v1 p
+              ON p.project_id = ts.project_id
+            JOIN projects_by_collection_v1 pc
+              ON p.project_id = pc.project_id
+            WHERE
+              ts.sample_date >= DATE '{PROJECT_START_DATE}'
+              AND m.metric_name IN ({stringify(METRIC_NAMES)})
+              AND pc.collection_name = '{COLLECTION_NAME}'
+              AND ts.amount IS NOT NULL
+        """)
+
+    @parse_dates("date")
+    def get_metrics_by_stylus_sdk_dependent(repo_ids):
+        return client.to_pandas(f"""
+            WITH ossd_projects AS (
+              SELECT DISTINCT project_id
+              FROM artifacts_by_project_v1
+              WHERE
+                artifact_id IN ({stringify(repo_ids)})
+                AND artifact_source = 'GITHUB'
+                AND project_source = 'OSS_DIRECTORY'
+                AND project_namespace = 'oso'
+                AND project_name NOT IN ({stringify(SDK_PROJECT_MAINTAINERS)})
+            )
+            SELECT DISTINCT
+              p.display_name AS display_name,
+              p.project_name AS project_name,
+              m.metric_name AS metric_name,
+              ts.sample_date AS date,
+              ts.amount AS amount
+            FROM timeseries_metrics_by_project_v0 ts        
+            JOIN metrics_v0 m
+              ON m.metric_id = ts.metric_id
+            JOIN projects_v1 p
+              ON p.project_id = ts.project_id
+            WHERE
+              ts.sample_date >= DATE '{PROJECT_START_DATE}'
+              AND m.metric_name IN ({stringify(METRIC_NAMES)})
+              AND p.project_id IN (SELECT project_id FROM ossd_projects)
+              AND ts.amount IS NOT NULL
+        """)    
+
+    @parse_dates("date")
+    def get_metrics_by_stylus_repo():
+        return client.to_pandas(f"""
+            SELECT
+              ap.artifact_name AS artifact_name,
+              ap.project_name AS project_name,
+              m.metric_name AS metric_name,
+              ts.sample_date AS date,
+              ts.amount AS amount
+            FROM artifacts_by_project_v1 ap
+            JOIN projects_by_collection_v1 pc
+              ON ap.project_id = pc.project_id
+            JOIN timeseries_metrics_by_artifact_v0 ts
+              ON ts.artifact_id = ap.artifact_id
+            JOIN metrics_v0 m
+              ON m.metric_id = ts.metric_id
+            WHERE
+              pc.collection_name = '{COLLECTION_NAME}'
+              AND ap.artifact_source = 'GITHUB'
+              AND m.metric_name = 'GITHUB_active_developers_monthly'
+              AND ts.sample_date >= DATE '{PROJECT_START_DATE}'
+        """)
+
+    def get_stylus_sdk_dependents():
+        return client.to_pandas(f"""
+            WITH stylus AS (
+              SELECT DISTINCT
+                package_artifact_id,
+                package_owner_artifact_id
+              FROM package_owners_v0
+              WHERE
+                package_owner_artifact_namespace = 'offchainlabs'
+                AND package_owner_artifact_name = 'stylus-sdk-rs'
+                AND package_artifact_name = 'stylus-sdk'
+            )
+            SELECT DISTINCT
+              abp.project_name AS dependent_project_name,
+              sboms_v0.dependent_artifact_namespace AS repo_owner,
+              sboms_v0.dependent_artifact_name AS repo_name,
+              sboms_v0.dependent_artifact_id AS artifact_id          
+            FROM sboms_v0
+            JOIN stylus
+              ON sboms_v0.package_artifact_id = stylus.package_artifact_id
+            JOIN artifacts_by_project_v1 AS abp
+              ON
+                sboms_v0.dependent_artifact_id = abp.artifact_id
+                AND abp.project_source = 'OSS_DIRECTORY'
+                AND abp.project_namespace = 'oso'
+                AND abp.project_name NOT IN ({stringify(SDK_PROJECT_MAINTAINERS)})
+            WHERE stylus.package_owner_artifact_id != sboms_v0.dependent_artifact_id
+            ORDER BY 1,2,3
+        """)    
+
+    @parse_dates("first_fork")
+    def get_devs_who_forked_examples():
+        return client.to_pandas(f"""
+            WITH stylus AS (
+              SELECT DISTINCT package_artifact_id
+              FROM package_owners_v0
+              WHERE
+                package_owner_artifact_namespace = 'offchainlabs'
+                AND package_owner_artifact_name = 'stylus-sdk-rs'
+                AND package_artifact_name = 'stylus-sdk'
+            ),
+            example_repos AS (
+              SELECT DISTINCT dependent_artifact_id
+              FROM sboms_v0
+              JOIN stylus USING package_artifact_id
+              WHERE dependent_artifact_namespace = 'offchainlabs'
+            ),
+            devs AS (
+              SELECT
+                artifact_id AS dev_id,
+                artifact_url AS dev_url,
+                MIN(time) AS first_fork,
+                MIN_BY(to_artifact_id, time) AS forked_repo_id
+              FROM int_first_of_event_from_artifact__github AS fe
+              JOIN example_repos AS er
+                ON fe.to_artifact_id = er.dependent_artifact_id
+              JOIN int_github_users AS u
+                ON fe.from_artifact_id = u.artifact_id
+              WHERE
+                event_type = 'FORKED'
+                AND time >= DATE '{PROJECT_START_DATE}'
+              GROUP BY 1,2
+            ),
+            dev_events AS (
+              SELECT
+                devs.dev_id,
+                devs.first_fork,
+                e.to_artifact_id AS repo_id,
+                SUM(e.amount) AS num_commits
+              FROM int_events_daily__github AS e
+              JOIN devs ON e.from_artifact_id = devs.dev_id
+              WHERE
+                e.event_type = 'COMMIT_CODE'
+                AND e.bucket_day >= devs.first_fork
+              GROUP BY 1,2,3
+            ),
+            star_counts AS (
+              SELECT
+                de.repo_id,
+                APPROX_DISTINCT(e.from_artifact_id) AS star_count
+              FROM int_events_daily__github AS e
+              JOIN dev_events AS de
+                ON e.to_artifact_id = de.repo_id
+              WHERE
+                e.event_type = 'STARRED'
+                AND e.bucket_day >= de.first_fork
+                AND e.from_artifact_id != de.dev_id
+              GROUP BY 1
+            )
+            SELECT DISTINCT
+              devs.dev_url,
+              fr.artifact_url AS first_fork_repo_url,
+              a.artifact_url AS repo_url,
+              devs.first_fork,
+              de.num_commits,
+              COALESCE(sc.star_count, 0) AS star_count,          
+              a.artifact_namespace AS repo_owner,
+              de.dev_id,
+              de.repo_id          
+            FROM dev_events AS de
+            JOIN devs ON de.dev_id = devs.dev_id
+            JOIN int_artifacts__github AS a
+              ON de.repo_id = a.artifact_id
+            LEFT JOIN star_counts AS sc
+              ON a.artifact_id = sc.repo_id
+            JOIN repositories_v0 AS fr
+              ON devs.forked_repo_id = fr.artifact_id
+        """)
+
+    def get_repo_alignment_tags(list_of_repo_artifact_ids):
+        df = client.to_pandas(f"""
+            WITH projects AS (
+              SELECT DISTINCT
+                artifact_id,
+                project_id,
+                project_name,
+                project_source
+              FROM artifacts_by_project_v1
+              WHERE
+                artifact_id IN ({stringify(list_of_repo_artifact_ids)})
+                AND project_source IN ('OSS_DIRECTORY', 'CRYPTO_ECOSYSTEMS')
+                AND project_namespace IN ('oso', 'eco')
+            ),
+            stylus_collection AS (
+              SELECT DISTINCT
+                project_id,
+                True AS in_stylus_collection
+              FROM projects_by_collection_v1 AS pbc
+              JOIN projects USING project_id
+              WHERE pbc.collection_name = '{COLLECTION_NAME}'
+            ),
+            alignment AS (
+              SELECT
+                project_id,
+                MAX(CASE WHEN project_name = 'arbitrum' THEN True ELSE False END) AS in_arbitrum,
+                MAX(CASE WHEN project_name IN ({stringify(OTHER_ECOSYSTEMS)}) THEN True ELSE False END) AS in_nonevm_ecosystem,
+                MAX(CASE WHEN project_name IN ({stringify(EVM_ECOSYSTEMS)}) THEN True ELSE False END) AS in_evm_ecosystem
+              FROM projects
+              WHERE project_source = 'CRYPTO_ECOSYSTEMS'
+              GROUP BY 1
+            )
+            SELECT DISTINCT
+              projects.artifact_id AS repo_id,
+              stylus_collection.*,
+              alignment.*
+            FROM projects
+            LEFT JOIN stylus_collection USING project_id
+            LEFT JOIN alignment USING project_id
+        """)
+        df = df.fillna(False)
+        return df
+
+    def get_artifact_overlap(target_ecosystem='Arbitrum'):
+        return client.to_pandas(f"""
+            WITH base AS (
+              SELECT DISTINCT
+                p.display_name AS ecosystem_name,
+                ap.artifact_id
+              FROM artifacts_by_project_v1 ap
+              JOIN projects_v1 p ON p.project_id=ap.project_id
+              WHERE
+                ap.artifact_source='GITHUB'
+                AND ap.project_source='CRYPTO_ECOSYSTEMS'
+                AND ap.project_namespace='eco'
+                AND ap.project_name IN ({stringify(ECOSYSTEMS)})
+            ),
+            target_set AS (
+              SELECT artifact_id FROM base WHERE ecosystem_name='{target_ecosystem}'
+            ),
+            totals AS (
+              SELECT
+                ecosystem_name,
+                COUNT(DISTINCT artifact_id) AS total_repos
+              FROM base
+              GROUP BY ecosystem_name
+            ),
+            shared AS (
+              SELECT
+                ecosystem_name,
+                COUNT(DISTINCT artifact_id) AS shared_with_target
+              FROM base
+              WHERE artifact_id IN (SELECT artifact_id FROM target_set)
+              GROUP BY ecosystem_name
+            )
+            SELECT
+              t.ecosystem_name,
+              COALESCE(s.shared_with_target,0) AS repos_shared_with_target,
+              (t.total_repos - COALESCE(s.shared_with_target,0)) AS repos_not_shared_with_target,
+              t.total_repos AS total_repos
+            FROM totals t
+            LEFT JOIN shared s
+              ON t.ecosystem_name = s.ecosystem_name
+            ORDER BY
+              repos_shared_with_target DESC,
+              ecosystem_name
+    """)
+
+    def get_total_repos_in_crypto_ecosystems():
+        df = client.to_pandas("""
+          SELECT APPROX_DISTINCT(artifact_id) AS count
+          FROM artifacts_by_project_v1
+          WHERE
+            project_source = 'CRYPTO_ECOSYSTEMS'
+            AND project_namespace = 'eco'
+            AND artifact_source = 'GITHUB'
+        """)
+        return df['count'].sum()
+    return (
+        get_artifact_overlap,
+        get_devs_who_forked_examples,
+        get_metrics_by_developer_ecosystem,
+        get_metrics_by_stylus_project,
+        get_metrics_by_stylus_sdk_dependent,
+        get_repo_alignment_tags,
+        get_stylus_sdk_dependents,
+        get_total_repos_in_crypto_ecosystems,
+    )
+
+
+@app.cell
+def process_data(
+    dev_pagerank,
+    get_artifact_overlap,
+    get_devs_who_forked_examples,
+    get_metrics_by_developer_ecosystem,
+    get_metrics_by_stylus_project,
+    get_metrics_by_stylus_sdk_dependent,
+    get_stylus_sdk_dependents,
+    label_repos,
+):
+    # Get the data
+    df_ecosystem_metrics = get_metrics_by_developer_ecosystem()
+    df_repo_overlap = get_artifact_overlap()
+    df_stylus_project_metrics = get_metrics_by_stylus_project()
+    df_stylus_sdk_deps = get_stylus_sdk_dependents()
+    df_stylus_sdk_deps_project_metrics = get_metrics_by_stylus_sdk_dependent(df_stylus_sdk_deps['artifact_id'].unique())
+    df_fork_devs = get_devs_who_forked_examples()
+
+    # Process developer data
+    df_fork_devs_labeled = label_repos(df_fork_devs)
+    df_fork_devs_ranked = dev_pagerank(df_fork_devs)
+
+    # Helper variables
+    most_recent_month = df_stylus_project_metrics[df_stylus_project_metrics['metric_name'].str.contains('monthly')==True]['date'].max()
+    return (
+        df_ecosystem_metrics,
+        df_fork_devs,
+        df_fork_devs_labeled,
+        df_fork_devs_ranked,
+        df_repo_overlap,
+        df_stylus_project_metrics,
+        df_stylus_sdk_deps,
+        df_stylus_sdk_deps_project_metrics,
+        most_recent_month,
+    )
+
+
+@app.cell
+def helper_monthly_metrics(MONTHLY_METRICS, datetime, most_recent_month):
+    def summarize_monthly_metrics(df, num_months=6):    
+        start_month = datetime(2025, most_recent_month.month - (num_months-1), 1).date()
+        table = (
+            df[
+                (df['metric_name'].str.endswith('monthly') == True) &
+                (df['date'].between(start_month, most_recent_month)) &
+                (df['amount'].notna())
+            ]
+            .pivot_table(index='display_name', columns='metric_name', values='amount', aggfunc='sum', fill_value=0)
+            .map(lambda x: round(x/num_months,2))
+            [MONTHLY_METRICS]
+            .rename(columns={c:clean_metric_name(c) for c in MONTHLY_METRICS})
+            .reset_index()
+            .rename(columns={'display_name': 'Project'})
+        )
+        return table
+    return (summarize_monthly_metrics,)
+
+
+@app.cell
+def helper_velocity_calculation(np, pd):
+    def sprint_velocity_change(
+        df,
+        metric_name,
+        sprint_start,
+        date_col = "date",
+        amount_col = "amount",
+        metric_col = "metric_name",
+    ):
+
+        d = df[df[metric_col] == metric_name].copy()
+        d[date_col] = pd.to_datetime(d[date_col], utc=False, errors="coerce")
+
+        if d[date_col].dt.tz is not None:
+            d[date_col] = d[date_col].dt.tz_convert(None)
+
+        sprint_start_ts = pd.to_datetime(sprint_start, utc=False, errors="coerce")
+        if getattr(sprint_start_ts, "tzinfo", None) is not None:
+            sprint_start_ts = sprint_start_ts.tz_localize(None)
+
+        before = d[d[date_col] < sprint_start_ts]
+        after  = d[d[date_col] >= sprint_start_ts]
+
+        def week_span(x: pd.Series) -> float:
+            if x.empty: return np.nan
+            delta_days = (x.max() - x.min()).days
+            return max(delta_days/7, np.nan)
+
+        before_weeks = week_span(before[date_col])
+        after_weeks  = week_span(after[date_col])
+
+        def velocity(frame: pd.DataFrame, weeks: float) -> float:
+            if frame.empty or not np.isfinite(weeks) or weeks <= 0:
+                return np.nan
+            return frame[amount_col].sum()/weeks
+
+        v_before = velocity(before, before_weeks)
+        v_after  = velocity(after,  after_weeks)
+
+        if not np.isfinite(v_before) or v_before == 0:
+            return np.nan
+        return (v_after-v_before)/v_before
+    return (sprint_velocity_change,)
+
+
+@app.cell
+def helper_labeling_and_pagerank(get_repo_alignment_tags, np, nx, pd):
+    def label_repos(df):
+
+        _repos = df['repo_id'].unique()
+
+        df_labeled = get_repo_alignment_tags(_repos)
+        df_labeled = df_labeled.groupby('repo_id').max()
+        df_labeled["tags"] = df_labeled.apply(lambda row: ";".join([col.replace('in_','') for col in df_labeled.columns if row[col]]), axis=1)
+        df_merged = df.set_index('repo_id').join(df_labeled[["tags"]])
+
+        def label_project(tags, dev_url, repo_url):
+            if not isinstance(tags, str):
+                tags = '8 - n/a'
+            if 'stylus' in tags:
+                return '0 - Project (Stylus Sprint)'
+            if '/offchainlabs/' in repo_url:
+                return '1 - Offchain Labs'
+            if 'arbitrum' in tags:
+                return '2 - Project (Arbitrum Ecosystem)'            
+            if 'nonevm' in tags:
+                if ';' in tags:
+                    return '4 - Project (EVM + Non-EVM Ecosystems)'
+                else:
+                    return '3 - Project (Non-EVM Ecosystem)'
+            if len(tags) > 1:
+                if dev_url in repo_url:
+                    return '7 - Personal'
+                else:
+                    return '5 - Project (EVM Ecosystem)'
+            return '6 - Project (Other)'
+
+        df_merged['project_type'] = df_merged.apply(lambda x: label_project(x['tags'], x['dev_url'], x['repo_url']), axis=1)
+        df_merged.drop(columns="tags", inplace=True)
+
+        return df_merged
+
+
+
+    def dev_pagerank(
+        df,
+        alpha=0.85,
+        max_iter=100,
+        star_exp=1.0,        # how strongly repo stars matter
+        commit_exp=1.0,      # how strongly commit volume matters
+        star_floor=0.0       # add to stars to avoid zero-sinks; e.g., 1.0
+    ):
+
+        # Nodes
+        devs = df[['dev_id','dev_url']].drop_duplicates().reset_index(drop=True)
+        repos = df[['repo_url','star_count']].drop_duplicates().reset_index(drop=True)
+
+        G = nx.DiGraph()
+        for _, r in devs.iterrows():
+            G.add_node(("dev", r.dev_id), url=r.dev_url, ntype="dev")
+        for _, r in repos.iterrows():
+            stars = float(r.star_count or 0.0)
+            G.add_node(("repo", r.repo_url), stars=stars, ntype="repo")
+
+        # Edges (commit-weighted dev→repo, star-weighted repo→dev)
+        for _, r in df.iterrows():
+            d = ("dev", r.dev_id); rep = ("repo", r.repo_url)
+            if d not in G or rep not in G: 
+                continue
+
+            c = max(0.0, float(r.num_commits or 0.0))
+            s = max(0.0, float(r.star_count  or 0.0))
+            w_dr = c**commit_exp
+            w_rd = (s + star_floor)**star_exp
+
+            if w_dr > 0: G.add_edge(d,   rep, weight=w_dr)
+            if w_rd > 0: G.add_edge(rep, d,   weight=w_rd)
+
+        # Personalization: devs by commits, repos by stars
+        dev_commit_sum = df.groupby("dev_id")["num_commits"].sum().reindex(devs.dev_id).fillna(0)
+        repo_stars = repos.set_index("repo_url")["star_count"].fillna(0)
+
+        p = {}
+        for n in G.nodes:
+            ntype, key = n
+            if ntype == "dev":
+                p[n] = 1.0 + np.log1p(float(dev_commit_sum.get(key, 0.0)))
+            else:
+                p[n] = 1.0 + np.log1p(float(repo_stars.get(key, 0.0)) + star_floor)
+
+        z = sum(p.values()) or 1.0
+        p = {k:v/z for k,v in p.items()}
+
+        pr = nx.pagerank(G, alpha=alpha, personalization=p, weight="weight", max_iter=max_iter)
+        rows = []
+        for (ntype, key), score in pr.items():
+            if ntype != "dev": 
+                continue
+            rows.append({
+                "dev_id": key,
+                "dev_score": score,
+                "dev_url": G.nodes[(ntype,key)]["url"]
+            })
+        dev_rank = pd.DataFrame(rows).sort_values("dev_score", ascending=False, kind="mergesort").reset_index(drop=True)
+
+        # Some diagnostics
+        dev_rank = dev_rank.merge(
+            devs.assign(total_commits=dev_commit_sum.values), on=["dev_id","dev_url"], how="left"
+        )
+        repo_star_map = repos.set_index("repo_url")["star_count"].to_dict()
+        exposure = (
+            df.assign(w=lambda x: (x["num_commits"].clip(lower=0).astype(float)**commit_exp) 
+             * (df["repo_url"].map(repo_star_map).fillna(0).astype(float)))
+              .groupby("dev_id")["w"]
+              .sum()
+              .rename("star_exposure")
+        )
+        dev_rank = dev_rank.merge(exposure, on="dev_id", how="left").fillna({"star_exposure":0.0})
+
+        return dev_rank    
+    return dev_pagerank, label_repos
+
+
+@app.function
+def clean_metric_name(col):
+    return col.replace('GITHUB_','').replace('_monthly','').replace('_',' ').title()
+
+
+@app.function
+def rgba_from_rgb(rgb, alpha):
+    return rgb.replace("rgb", "rgba").replace(")", f",{alpha})")
+
+
+@app.cell
+def import_libraries():
+    from datetime import datetime, date, timedelta
+    from functools import wraps
+    import pandas as pd
+    import plotly.graph_objects as go
+    import plotly.express as px
+    import networkx as nx
+    import numpy as np
+    return datetime, go, np, nx, pd, px, wraps
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/pyproject.toml b/pyproject.toml
index 65585bb..93bc021 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,6 +17,7 @@ dependencies = [
     "pyarrow>=21.0.0",
     "pyoso>=0.6.5",
     "pyyaml>=6.0.2",
+    "scipy>=1.16.2",
     "trino>=0.336.0",
     "vega-datasets>=0.9.0",
     "web3>=7.13.0",
diff --git a/uv.lock b/uv.lock
index 6b7d93f..afaad59 100644
--- a/uv.lock
+++ b/uv.lock
@@ -84,15 +84,15 @@ wheels = [
 
 [[package]]
 name = "anyio"
-version = "4.10.0"
+version = "4.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
     { name = "sniffio" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252 }
+sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094 }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097 },
 ]
 
 [[package]]
@@ -177,31 +177,31 @@ wheels = [
 
 [[package]]
 name = "ckzg"
-version = "2.1.2"
+version = "2.1.4"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/7e/77/9a44934373eff2013cab641f4ac70b98bb8372fe2938ea78c349501aa825/ckzg-2.1.2.tar.gz", hash = "sha256:7d445215261068d914c3607fd89889bb405260911804cd0eea789ce7422db0d8", size = 1124054 }
+sdist = { url = "https://files.pythonhosted.org/packages/7c/b2/0e27adcfc241ac53fc8dd788019bb1700b37a4809c23bcd0de955bbcc952/ckzg-2.1.4.tar.gz", hash = "sha256:f8f20a8ad28173197d250920a3e9f957184259def9a2326eb46f24fdf536e9c8", size = 1123742 }
 wheels = [
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-    { url = "https://files.pythonhosted.org/packages/7b/d8/9fc6537a8fcc0a373f0bb0cf2747e28e7aa99918c9d96385ef1f3ec51c9c/ckzg-2.1.2-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d4eeff254f60b08dba7991d3ab20018d5df7cbe3318e67efd070d2361104e6d4", size = 176341 },
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 [[package]]
 name = "click"
-version = "8.2.1"
+version = "8.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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 [[package]]
@@ -240,11 +240,11 @@ wheels = [
 
 [[package]]
 name = "docutils"
-version = "0.22"
+version = "0.22.2"
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