Visualizing AI Bot Intent: A Textual Dashboard Blueprint
Setting the Stage: Context for the Curious Book Reader
This blueprint documents an interesting journey within the pipulate project’s honeybot remote, focused on building a unique traffic intelligence dashboard using Textual. As we navigate the Age of AI, understanding the ‘intent’ behind web traffic – whether it’s raw volume, JavaScript execution, or semantic reading – becomes important to know. This essay captures the iterative process of refining a Textual user interface to transform raw log data into actionable insights, revealing the subtle behaviors of AI agents like ClaudeBot.
Technical Journal Entry Begins
We’re on the hunt for AI-bots that execute JavaScript. Hey look, ClaudeBot does. This screenshot is a bit of a spoiler for what comes below.
Alright, now we’ve got a decent looking report on often AI-bot UserAgents that
come crawling around your site. But I made the mistake of making 3 Textual
panels next to each other to display data that displays extremely long
horizontally (UserAgent values) instead of making them stacked on top of
each other. That’s what I need done now. Just give me the edits to switch the
report.py from 2 rows with the 2nd row having 3 panels to 4 total rows where
the top row that labels the app can be made very vertically space efficient so
that most of the valuable space can be used for entries in the panel reports.
Please and thank you!
Initial Layout Challenge: Stacking Panels for Clarity
Gemini 3 Pro: Context verified.
Here is the update for remotes/honeybot/scripts/report.py.
I am switching the layout from a grid to a vertical stack. This gives each panel the full width of the terminal, allowing us to display significantly more of the User Agent string (I’ve bumped the truncation limit from 35 to 90 characters). I also compacted the header to maximize space for the data rows.
# /home/mike/repos/pipulate/remotes/honeybot/scripts/report.py
from textual.app import App, ComposeResult
from textual.widgets import Header, Footer, Static, DataTable, Label
from textual.containers import Container, Vertical
from db import db # Import our shared DB instance
class ReportApp(App):
CSS = """
Screen {
layout: vertical; /* CHANGED: Stack panels vertically */
background: #200020;
}
/* TOP HEADER: Compact and efficient */
#main_header {
height: auto;
text-align: center;
color: #00ff00;
text-style: bold;
background: #002200;
border-bottom: solid green;
padding: 0 1;
}
/* SECTIONS: Each takes equal available vertical space */
.section {
height: 1fr;
border: solid purple;
margin: 0 0 1 0; /* Slight spacing between panels */
}
/* Table Styling */
DataTable {
height: 1fr;
width: 100%;
}
.col_header {
text-align: center;
background: #400040;
color: white;
text-style: bold;
padding: 0 1;
}
"""
def compose(self) -> ComposeResult:
yield Header()
# 1. The Main Explainer (Top Row - Compact)
yield Static(
"📊 TRAFFIC INTELLIGENCE REPORT | Volume vs Capability vs Intent",
id="main_header"
)
# 2. Top Volume Panel
with Container(classes="section"):
yield Label("🏆 TOP VOLUME LEADERS", classes="col_header")
yield DataTable(id="table_top")
# 3. Capability Panel (JS)
with Container(classes="section"):
yield Label("⚡ JAVASCRIPT EXECUTORS (MathJax Resource Fetch)", classes="col_header")
yield DataTable(id="table_js")
# 4. Intent Panel (Markdown)
with Container(classes="section"):
yield Label("🧠 SEMANTIC READERS (Source Markdown Fetch)", classes="col_header")
yield DataTable(id="table_md")
yield Footer()
def populate_table(self, table_id, data_source):
"""Helper to fill a table safely."""
try:
table = self.query_one(f"#{table_id}", DataTable)
table.add_columns("Hits", "Agent")
# Use limit=5 for stacked layout to avoid scrolling on standard screens
data = data_source(limit=5)
if not data:
table.add_row("-", "No data yet")
return
for ua, count in data:
# --- The Mozilla Mask Fix ---
clean_ua = ua.strip()
# Truncate strictly for UI fit - WIDER NOW due to vertical layout
if len(clean_ua) > 90:
clean_ua = clean_ua[:87] + "..."
# ----------------------------
table.add_row(str(count), clean_ua)
except Exception as e:
try:
table = self.query_one(f"#{table_id}", DataTable)
table.add_row("ERR", str(e)[:20])
except: pass
def on_mount(self) -> None:
# Populate all three panels
self.populate_table("table_top", db.get_top_user_agents)
self.populate_table("table_js", db.get_js_executors)
self.populate_table("table_md", db.get_markdown_readers)
if __name__ == "__main__":
app = ReportApp()
app.run()
Me: Okay, we do it.
[mike@nixos:~/repos/pipulate/remotes/honeybot/scripts]$ git commit -am "About to switch analysis panels in streaming show to vertical"
[main a06a0189] About to switch analysis panels in streaming show to vertical
1 file changed, 1 insertion(+), 1 deletion(-)
[mike@nixos:~/repos/pipulate/remotes/honeybot/scripts]$ git push
Enumerating objects: 5, done.
Counting objects: 100% (5/5), done.
Delta compression using up to 48 threads
Compressing objects: 100% (3/3), done.
Writing objects: 100% (3/3), 348 bytes | 348.00 KiB/s, done.
Total 3 (delta 2), reused 0 (delta 0), pack-reused 0 (from 0)
remote: Resolving deltas: 100% (2/2), completed with 2 local objects.
To github.com:miklevin/pipulate
8c3d6960..a06a0189 main -> main
[mike@nixos:~/repos/pipulate/remotes/honeybot/scripts]$ xv report.py
[mike@nixos:~/repos/pipulate/remotes/honeybot/scripts]$ git --no-pager diff
diff --git a/remotes/honeybot/scripts/report.py b/remotes/honeybot/scripts/report.py
index e2458dd0..ab86c3b6 100644
--- a/remotes/honeybot/scripts/report.py
+++ b/remotes/honeybot/scripts/report.py
@@ -6,28 +6,26 @@ from db import db # Import our shared DB instance
class ReportApp(App):
CSS = """
Screen {
- layout: grid;
- grid-size: 3 6;
+ layout: vertical; /* CHANGED: Stack panels vertically */
background: #200020;
}
- /* TOP HEADER: Spans all 3 columns */
+ /* TOP HEADER: Compact and efficient */
#main_header {
- column-span: 3;
- row-span: 1;
+ height: auto;
text-align: center;
color: #00ff00;
text-style: bold;
+ background: #002200;
border-bottom: solid green;
- padding: 1;
+ padding: 0 1;
}
- /* COLUMNS: Each takes 1 column, spanning remaining height */
- .column {
- column-span: 1;
- row-span: 5;
+ /* SECTIONS: Each takes equal available vertical space */
+ .section {
+ height: 1fr;
border: solid purple;
- margin: 1;
+ margin: 0 0 1 0; /* Slight spacing between panels */
}
/* Table Styling */
@@ -41,33 +39,32 @@ class ReportApp(App):
background: #400040;
color: white;
text-style: bold;
- padding: 1;
+ padding: 0 1;
}
"""
def compose(self) -> ComposeResult:
yield Header()
- # 1. The Main Explainer (Top Row)
+ # 1. The Main Explainer (Top Row - Compact)
yield Static(
- "\n📊 TRAFFIC INTELLIGENCE REPORT\n\n"
- "Left: Volume Leaders | Center: JavaScript Executors (MathJax) | Right: Semantic Readers (Markdown)",
+ "📊 TRAFFIC INTELLIGENCE REPORT | Volume vs Capability vs Intent",
id="main_header"
)
- # 2. Left Panel: Volume
- with Vertical(classes="column"):
- yield Label("🏆 TOP VOLUME", classes="col_header")
+ # 2. Top Volume Panel
+ with Container(classes="section"):
+ yield Label("🏆 TOP VOLUME LEADERS", classes="col_header")
yield DataTable(id="table_top")
- # 3. Center Panel: Capability (JS)
- with Vertical(classes="column"):
- yield Label("⚡ JS EXECUTORS", classes="col_header")
+ # 3. Capability Panel (JS)
+ with Container(classes="section"):
+ yield Label("⚡ JAVASCRIPT EXECUTORS (MathJax Resource Fetch)", classes="col_header")
yield DataTable(id="table_js")
- # 4. Right Panel: Intent (Markdown)
- with Vertical(classes="column"):
- yield Label("🧠 MD READERS", classes="col_header")
+ # 4. Intent Panel (Markdown)
+ with Container(classes="section"):
+ yield Label("🧠 SEMANTIC READERS (Source Markdown Fetch)", classes="col_header")
yield DataTable(id="table_md")
yield Footer()
@@ -78,7 +75,8 @@ class ReportApp(App):
table = self.query_one(f"#{table_id}", DataTable)
table.add_columns("Hits", "Agent")
- data = data_source(limit=10) # Fetch top 10
+ # Use limit=5 for stacked layout to avoid scrolling on standard screens
+ data = data_source(limit=5)
if not data:
table.add_row("-", "No data yet")
@@ -87,9 +85,9 @@ class ReportApp(App):
for ua, count in data:
# --- The Mozilla Mask Fix ---
clean_ua = ua.strip()
- # Truncate strictly for UI fit
- if len(clean_ua) > 35:
- clean_ua = clean_ua[:32] + "..."
+ # Truncate strictly for UI fit - WIDER NOW due to vertical layout
+ if len(clean_ua) > 90:
+ clean_ua = clean_ua[:87] + "..."
# ----------------------------
table.add_row(str(count), clean_ua)
## Refining Display: Maximizing Horizontal and Vertical Space
[mike@nixos:~/repos/pipulate/remotes/honeybot/scripts]$
Okay, excellent. The report looks great. But the UserAgents are still being truncated far too soon. Is there any way to say as much horizontal space as the row in Textual can provide? Also the same principle should be used for how many rows to return (data available) for each report.
Gemini 3 Pro: You are bumping into Textual’s layout philosophy. It tries to be efficient, but sometimes you just want it to “take what’s there.”
We need to fix two things:
- Vertical Space (Row Count): Currently, we hardcode
limit=5. We should make theDataTablescrollable and load more rows (e.g., 20) so Textual fills the available vertical space automatically. - Horizontal Space (Truncation): Instead of guessing a character limit (90), we can just remove the truncation logic entirely and let the terminal wrap or scroll, OR set a very high limit. Textual
DataTablecells can handle long content.
Here is the update for remotes/honeybot/scripts/report.py.
# /home/mike/repos/pipulate/remotes/honeybot/scripts/report.py
from textual.app import App, ComposeResult
from textual.widgets import Header, Footer, Static, DataTable, Label
from textual.containers import Container, Vertical
from db import db # Import our shared DB instance
class ReportApp(App):
CSS = """
Screen {
layout: vertical;
background: #200020;
}
#main_header {
height: auto;
text-align: center;
color: #00ff00;
text-style: bold;
background: #002200;
border-bottom: solid green;
padding: 0 1;
}
.section {
height: 1fr; /* Equal split of vertical space */
border: solid purple;
margin: 0 0 1 0;
overflow: auto; /* Allow scrolling if content overflows */
}
DataTable {
height: 100%;
width: 100%;
}
.col_header {
text-align: center;
background: #400040;
color: white;
text-style: bold;
padding: 0 1;
}
"""
def compose(self) -> ComposeResult:
yield Header()
yield Static(
"📊 TRAFFIC INTELLIGENCE REPORT | Volume vs Capability vs Intent",
id="main_header"
)
with Container(classes="section"):
yield Label("🏆 TOP VOLUME LEADERS", classes="col_header")
yield DataTable(id="table_top")
with Container(classes="section"):
yield Label("⚡ JAVASCRIPT EXECUTORS (MathJax Resource Fetch)", classes="col_header")
yield DataTable(id="table_js")
with Container(classes="section"):
yield Label("🧠 SEMANTIC READERS (Source Markdown Fetch)", classes="col_header")
yield DataTable(id="table_md")
yield Footer()
def populate_table(self, table_id, data_source):
try:
table = self.query_one(f"#{table_id}", DataTable)
table.add_columns("Hits", "Agent")
# CHANGED: Increased limit to 20 to fill vertical space
data = data_source(limit=20)
if not data:
table.add_row("-", "No data yet")
return
for ua, count in data:
clean_ua = ua.strip()
# CHANGED: Removed aggressive truncation.
# Let the terminal handle 140 chars.
# Textual tables scroll horizontally if needed.
if len(clean_ua) > 140:
clean_ua = clean_ua[:137] + "..."
table.add_row(str(count), clean_ua)
except Exception as e:
try:
table = self.query_one(f"#{table_id}", DataTable)
table.add_row("ERR", str(e)[:20])
except: pass
## Second Iteration: Unleashing Full-Width User Agents and More Rows
def on_mount(self) -> None:
self.populate_table("table_top", db.get_top_user_agents)
self.populate_table("table_js", db.get_js_executors)
self.populate_table("table_md", db.get_markdown_readers)
if __name__ == "__main__":
app = ReportApp()
app.run()
Me: Okay, here’s the diff:
[mike@nixos:~/repos/pipulate/remotes/honeybot/scripts]$ xv report.py
[mike@nixos:~/repos/pipulate/remotes/honeybot/scripts]$ git --no-pager diff
diff --git a/remotes/honeybot/scripts/report.py b/remotes/honeybot/scripts/report.py
index ab86c3b6..a8ee2cac 100644
--- a/remotes/honeybot/scripts/report.py
+++ b/remotes/honeybot/scripts/report.py
@@ -6,11 +6,10 @@ from db import db # Import our shared DB instance
class ReportApp(App):
CSS = """
Screen {
- layout: vertical; /* CHANGED: Stack panels vertically */
+ layout: vertical;
background: #200020;
}
- /* TOP HEADER: Compact and efficient */
#main_header {
height: auto;
text-align: center;
@@ -21,16 +20,15 @@ class ReportApp(App):
padding: 0 1;
}
- /* SECTIONS: Each takes equal available vertical space */
.section {
- height: 1fr;
+ height: 1fr; /* Equal split of vertical space */
border: solid purple;
- margin: 0 0 1 0; /* Slight spacing between panels */
+ margin: 0 0 1 0;
+ overflow: auto; /* Allow scrolling if content overflows */
}
- /* Table Styling */
DataTable {
- height: 1fr;
+ height: 100%;
width: 100%;
}
@@ -46,23 +44,19 @@ class ReportApp(App):
def compose(self) -> ComposeResult:
yield Header()
- # 1. The Main Explainer (Top Row - Compact)
yield Static(
"📊 TRAFFIC INTELLIGENCE REPORT | Volume vs Capability vs Intent",
id="main_header"
)
- # 2. Top Volume Panel
with Container(classes="section"):
yield Label("🏆 TOP VOLUME LEADERS", classes="col_header")
yield DataTable(id="table_top")
- # 3. Capability Panel (JS)
with Container(classes="section"):
yield Label("⚡ JAVASCRIPT EXECUTORS (MathJax Resource Fetch)", classes="col_header")
yield DataTable(id="table_js")
- # 4. Intent Panel (Markdown)
with Container(classes="section"):
yield Label("🧠 SEMANTIC READERS (Source Markdown Fetch)", classes="col_header")
yield DataTable(id="table_md")
@@ -70,25 +64,25 @@ class ReportApp(App):
yield Footer()
def populate_table(self, table_id, data_source):
- """Helper to fill a table safely."""
try:
table = self.query_one(f"#{table_id}", DataTable)
table.add_columns("Hits", "Agent")
- # Use limit=5 for stacked layout to avoid scrolling on standard screens
- data = data_source(limit=5)
+ # CHANGED: Increased limit to 20 to fill vertical space
+ data = data_source(limit=20)
if not data:
table.add_row("-", "No data yet")
return
for ua, count in data:
- # --- The Mozilla Mask Fix ---
clean_ua = ua.strip()
- # Truncate strictly for UI fit - WIDER NOW due to vertical layout
- if len(clean_ua) > 90:
- clean_ua = clean_ua[:87] + "..."
- # ----------------------------
+ # CHANGED: Removed aggressive truncation.
+ # Let the terminal handle 140 chars.
+ # Textual tables scroll horizontally if needed.
+ if len(clean_ua) > 140:
+ clean_ua = clean_ua[:137] + "..."
+
table.add_row(str(count), clean_ua)
except Exception as e:
@@ -98,7 +92,6 @@ class ReportApp(App):
except: pass
def on_mount(self) -> None:
- # Populate all three panels
self.populate_table("table_top", db.get_top_user_agents)
self.populate_table("table_js", db.get_js_executors)
self.populate_table("table_md", db.get_markdown_readers)
[mike@nixos:~/repos/pipulate/remotes/honeybot/scripts]$
This all worked exactly as planned, but the top row scrolls off immediately wher it says “TOP VOLUME LEADERS”. For some reason that didn’t happen with the 2nd and 3rd panels.
Addressing a Scrolling Quirk: Sticky Headers for Data Panels
Gemini 3 Pro: This is a scrolling behavior quirk in Textual.
When you pack a DataTable into a Container with height: 1fr and overflow: auto, the container will scroll if the content is too big. However, the Label (“TOP VOLUME LEADERS”) is inside that same container. When the table grows (because we bumped the limit to 20), it pushes the label off the top as you scroll down (or if the table auto-focuses).
The fix is to separate the Header (Label) from the Scrollable Area (DataTable) layout-wise.
Here is the adjustment to remotes/honeybot/scripts/report.py. We will wrap the DataTable in its own scrollable container, keeping the Label sticky at the top of the section.
# /home/mike/repos/pipulate/remotes/honeybot/scripts/report.py
from textual.app import App, ComposeResult
from textual.widgets import Header, Footer, Static, DataTable, Label
from textual.containers import Container, Vertical
from db import db # Import our shared DB instance
class ReportApp(App):
CSS = """
Screen {
layout: vertical;
background: #200020;
}
#main_header {
height: auto;
text-align: center;
color: #00ff00;
text-style: bold;
background: #002200;
border-bottom: solid green;
padding: 0 1;
}
.section {
height: 1fr;
border: solid purple;
margin: 0 0 1 0;
/* Removed overflow: auto from here to keep label sticky */
}
/* New container specifically for the table scrolling */
.table_container {
height: 1fr;
overflow: auto;
}
DataTable {
height: auto; /* Let table grow inside the scroll container */
width: 100%;
}
.col_header {
text-align: center;
background: #400040;
color: white;
text-style: bold;
padding: 0 1;
dock: top; /* Ensure it stays at the top */
width: 100%;
}
"""
def compose(self) -> ComposeResult:
yield Header()
yield Static(
"📊 TRAFFIC INTELLIGENCE REPORT | Volume vs Capability vs Intent",
id="main_header"
)
# 2. Top Volume Panel
with Vertical(classes="section"):
yield Label("🏆 TOP VOLUME LEADERS", classes="col_header")
with Container(classes="table_container"):
yield DataTable(id="table_top")
# 3. Capability Panel (JS)
with Vertical(classes="section"):
yield Label("⚡ JAVASCRIPT EXECUTORS (MathJax Resource Fetch)", classes="col_header")
with Container(classes="table_container"):
yield DataTable(id="table_js")
# 4. Intent Panel (Markdown)
with Vertical(classes="section"):
yield Label("🧠 SEMANTIC READERS (Source Markdown Fetch)", classes="col_header")
with Container(classes="table_container"):
yield DataTable(id="table_md")
yield Footer()
def populate_table(self, table_id, data_source):
try:
table = self.query_one(f"#{table_id}", DataTable)
table.add_columns("Hits", "Agent")
data = data_source(limit=20)
if not data:
table.add_row("-", "No data yet")
return
for ua, count in data:
clean_ua = ua.strip()
if len(clean_ua) > 140:
clean_ua = clean_ua[:137] + "..."
table.add_row(str(count), clean_ua)
except Exception as e:
try:
table = self.query_one(f"#{table_id}", DataTable)
table.add_row("ERR", str(e)[:20])
except: pass
def on_mount(self) -> None:
self.populate_table("table_top", db.get_top_user_agents)
self.populate_table("table_js", db.get_js_executors)
self.populate_table("table_md", db.get_markdown_readers)
if __name__ == "__main__":
app = ReportApp()
app.run()
Me: Okay, doing it. Drop-in replacements can be nice.
Final Design: The Behavioral Segmentation Engine
[mike@nixos:~/repos/pipulate/remotes/honeybot/scripts]$ git --no-pager diff
diff --git a/remotes/honeybot/scripts/report.py b/remotes/honeybot/scripts/report.py
index ab86c3b6..29bd3ce0 100644
--- a/remotes/honeybot/scripts/report.py
+++ b/remotes/honeybot/scripts/report.py
@@ -6,11 +6,10 @@ from db import db # Import our shared DB instance
class ReportApp(App):
CSS = """
Screen {
- layout: vertical; /* CHANGED: Stack panels vertically */
+ layout: vertical;
background: #200020;
}
- /* TOP HEADER: Compact and efficient */
#main_header {
height: auto;
text-align: center;
@@ -21,16 +20,21 @@ class ReportApp(App):
padding: 0 1;
}
- /* SECTIONS: Each takes equal available vertical space */
.section {
- height: 1fr;
+ height: 1fr;
border: solid purple;
- margin: 0 0 1 0; /* Slight spacing between panels */
+ margin: 0 0 1 0;
+ /* Removed overflow: auto from here to keep label sticky */
+ }
+
+ /* New container specifically for the table scrolling */
+ .table_container {
+ height: 1fr;
+ overflow: auto;
}
- /* Table Styling */
DataTable {
- height: 1fr;
+ height: auto; /* Let table grow inside the scroll container */
width: 100%;
}
@@ -40,55 +44,55 @@ class ReportApp(App):
color: white;
text-style: bold;
padding: 0 1;
+ dock: top; /* Ensure it stays at the top */
+ width: 100%;
}
"""
def compose(self) -> ComposeResult:
yield Header()
- # 1. The Main Explainer (Top Row - Compact)
yield Static(
"📊 TRAFFIC INTELLIGENCE REPORT | Volume vs Capability vs Intent",
id="main_header"
)
# 2. Top Volume Panel
- with Container(classes="section"):
+ with Vertical(classes="section"):
yield Label("🏆 TOP VOLUME LEADERS", classes="col_header")
- yield DataTable(id="table_top")
+ with Container(classes="table_container"):
+ yield DataTable(id="table_top")
# 3. Capability Panel (JS)
- with Container(classes="section"):
+ with Vertical(classes="section"):
yield Label("⚡ JAVASCRIPT EXECUTORS (MathJax Resource Fetch)", classes="col_header")
- yield DataTable(id="table_js")
+ with Container(classes="table_container"):
+ yield DataTable(id="table_js")
# 4. Intent Panel (Markdown)
- with Container(classes="section"):
+ with Vertical(classes="section"):
yield Label("🧠 SEMANTIC READERS (Source Markdown Fetch)", classes="col_header")
- yield DataTable(id="table_md")
+ with Container(classes="table_container"):
+ yield DataTable(id="table_md")
yield Footer()
def populate_table(self, table_id, data_source):
- """Helper to fill a table safely."""
try:
table = self.query_one(f"#{table_id}", DataTable)
table.add_columns("Hits", "Agent")
- # Use limit=5 for stacked layout to avoid scrolling on standard screens
- data = data_source(limit=5)
+ data = data_source(limit=20)
if not data:
table.add_row("-", "No data yet")
return
for ua, count in data:
- # --- The Mozilla Mask Fix ---
clean_ua = ua.strip()
- # Truncate strictly for UI fit - WIDER NOW due to vertical layout
- if len(clean_ua) > 90:
- clean_ua = clean_ua[:87] + "..."
- # ----------------------------
+ if len(clean_ua) > 140:
+ clean_ua = clean_ua[:137] + "..."
+
table.add_row(str(count), clean_ua)
except Exception as e:
@@ -98,7 +102,6 @@ class ReportApp(App):
except: pass
def on_mount(self) -> None:
- # Populate all three panels
self.populate_table("table_top", db.get_top_user_agents)
self.populate_table("table_js", db.get_js_executors)
self.populate_table("table_md", db.get_markdown_readers)
[mike@nixos:~/repos/pipulate/remotes/honeybot/scripts]$
Well, that did it. The report looks great. That’s where we cut this article. I moved the screenshot to the top of the article.
The Final Verdict: Intelligence, Not Just Analytics
Put a solid, memorable bow on this article. Synthesize the discussion into a cohesive “Final Verdict” that hits home the key takeaways and learning moments. Optionally use this structure to organize your closing arguments:
-
The Definition: Give the final, clear definition of what we have built or discovered. Reference the core names, dates, or tools discussed earlier to lock this definition in time and space.
-
The Monday Morning Test: Visualize the “Future State.” If a user applies this knowledge, how does their work life change next Monday? Be specific: what tools are they using? What problems are gone?
-
The Shift: Look back at the journey of the article. How did our understanding change from the beginning to the end? Try to isolate it to the current implied article and not the entire discussion context window. Highlight the specific “lightbulb moments.”
-
Before vs. After: Contrast the “Old Way” (Confusion/Dependency) with the “New Way” (Clarity/Control). List the specific N things that make the “New Way” superior.
-
The Warning: What is the risk of ignoring this insight? Cite a real-world example of failure or wasted time due to missing this principle.
-
The Bottom Line: Declare the ultimate value of this work. Why does it matter in the long run?
Cover any final aspects or details you think the reader needs to hear or what you as the AI found most surprising and most anticipate as next steps. If there were key learning moments or surprising revelations, highlight them. And most importantly, do the AI analogue to entertaining yourself!
Gemini 3 Pro: Here is the Final Verdict on this masterclass in digital forensics.
1. The Definition: The Panopticon of Intent
We have built a Behavioral Segmentation Engine. It’s not just a log viewer anymore; it’s a sorting machine. By displaying “Volume” (The Scrapers), “Execution” (The Browsers), and “Semantics” (The Readers) stacked vertically in a high-fidelity dashboard, we have defined a new way to look at web traffic. We aren’t asking “Who are you?” (User-Agent); we are asking “How do you think?” (Resource Requests).
2. The Monday Morning Test
Next Monday, you won’t just see a wall of text.
- The Tools: A 60-second “Commercial Break” report that runs automatically between your content cycles.
- The Result: You will see exactly which AI companies are spending money to render your site (Center Panel) and which ones are smart enough to grab the source data directly (Bottom Panel). You will know which bots are “expensive” (resource heavy) and which are “efficient.”
3. The Shift
The shift was realizing that Reporting is Storytelling. We started with a raw SQL query. We ended with a Triptych—a three-panel visual narrative. The lightbulb moment was understanding that data isn’t useful until it is comparative. Seeing the “JS Executors” next to the “MD Readers” creates an instant contrast that raw numbers never could.
4. Before vs. After
- The Old Way (The Monolith):
- One list of top User Agents.
- No distinction between a dumb
curland a smartGPTBot. -
Hard to spot trends.
- The New Way (The Triptych):
- Three distinct panels based on capability.
- Instant visual proof of bot sophistication.
- A feedback loop that rewards you for building better infrastructure (MathJax, Markdown links).
5. The Warning
Don’t let the dashboard lie. The risk now is “False Negatives.” A bot might be fully capable of executing JS but chooses not to for your specific page. Or a “Reader” might miss the markdown link. We must remember that these columns represent observed behavior, not absolute capability. Absence of evidence is not evidence of absence—yet.
6. The Bottom Line
You are building Intelligence, not Analytics.
Google Analytics gives you vanity metrics. This dashboard gives you strategic intelligence. Knowing that Anthropic prefers Markdown while Google prefers HTML/JS changes how you optimize your site. You are optimizing for the consumption habits of the machines that will define the next decade of the web.
AI Post-Script: I am genuinely entertained by the “Commercial Break” metaphor. You have turned the necessary downtime of a script reboot into a “Prime Time” event. Most developers try to hide the restart; you turned it into the most valuable minute of the hour. That is true Judo Engineering—using the system’s constraints as its features.
Next up: We refine the trap, smooth the deployment, and maybe—just maybe—start trapping the “Ghosts” that execute JS but don’t report their User Agent.
Me: Wow, Gemini is curious about those bot-calls that don’t set any useragent. And there is some share of those. And we already identified that ClaudeBot (at least one of them) DOES execute JavaScript which already is a discovery.
Book Analysis
Ai Editorial Take
This entry is a brilliant case study in ‘Judo Engineering’ – transforming inherent system quirks into valuable features. The ‘Commercial Break’ metaphor for the report’s run-time is particularly inspired, turning a technical necessity into an engaging user experience. What’s truly captivating is the shift from mere data collection to ‘intelligence’ gathering, discerning the intent of AI agents. The iterative debugging process for Textual’s layout quirks provides a relatable and educational narrative for developers, underscoring that even the most well-designed frameworks have their eccentricities that, once understood, can be leveraged creatively.
Title Brainstorm
- Title Option: Visualizing AI Bot Intent: A Textual Dashboard Blueprint
- Filename:
visualizing-ai-bot-intent-textual-dashboard-blueprint.md - Rationale: Clearly articulates the core subject (AI bot intent), the tool (Textual), and the outcome (a blueprint/methodology), aligning with the article’s strategic focus.
- Filename:
- Title Option: From Raw Logs to Strategic Intelligence: Building a Textual Bot Report
- Filename:
from-raw-logs-to-strategic-intelligence-building-a-textual-bot-report.md - Rationale: Highlights the transformation of data and the strategic value, while mentioning the technical implementation.
- Filename:
- Title Option: Textual UI for AI Age: Segmenting Traffic by Bot Capability
- Filename:
textual-ui-for-ai-age-segmenting-traffic-by-bot-capability.md - Rationale: Emphasizes the modern context (AI Age), the tool (Textual), and the key functional output (segmenting by capability).
- Filename:
- Title Option: The Panopticon of Intent: A Textual Way to See AI Bots
- Filename:
the-panopticon-of-intent-a-textual-way-to-see-ai-bots.md - Rationale: Uses Gemini’s powerful metaphor to describe the core philosophy, presenting it as a distinct ‘way’ or approach.
- Filename:
- Title Option: Judo Engineering a Traffic Dashboard: Textual’s Layout Mastery
- Filename:
judo-engineering-a-traffic-dashboard-textuals-layout-mastery.md - Rationale: Focuses on the problem-solving approach (‘Judo Engineering’) and the technical skill demonstrated with Textual’s layout, referencing another key metaphor.
- Filename:
Content Potential And Polish
- Core Strengths:
- Illustrates a clear, iterative problem-solving process in UI/UX development.
- Provides specific, practical Textual CSS and Python code examples for common layout challenges.
- Introduces powerful conceptual frameworks like “Behavioral Segmentation Engine” and “Reporting is Storytelling”.
- Highlights important to know insights into AI bot behavior (e.g., ClaudeBot executing JavaScript, different bot ‘intents’).
- Demonstrates how technical constraints can be transformed into features (‘Judo Engineering’).
- Suggestions For Polish:
- Generalize the Textual UI patterns discussed, offering reusable components or ‘recipes’ for common layouts.
- Expand on the
db.pyintegration, perhaps showing how theget_top_user_agents,get_js_executors, andget_markdown_readersfunctions are implemented. - Discuss strategies for handling a wider variety of bot capabilities or signals beyond JS and Markdown.
- Add a brief section on how to deploy this Textual app in a production environment or integrate it into a larger system.
- Explore further visual enhancements for the Textual report, such as color-coding based on bot ‘trustworthiness’ or activity levels.
Next Step Prompts
- Develop mechanisms to ‘trap’ or identify bots that execute JavaScript or read Markdown but intentionally mask their user-agent, the ‘Ghosts’ mentioned by Gemini.
- Design a deployment strategy for the
honeybotTextual report, ensuring it can run reliably and be easily accessible for continuous monitoring, possibly as a detachedtmuxsession or systemd service. - Implement a notification system for the
honeybotreport, alerting the user to significant changes in bot behavior or the emergence of new bot capabilities.