#4796 Content of the Day
#4796 Content of the Day
#4796 I want to implement a daily content page and/or video based on local and world events. Sources might include Wall Street Journal and Google News. What would be fun content that visitors would enjoy and keep coming back?
To stand out from standard news aggregators, the secret is to shift from reporting headlines to providing daily insight, context, and lighthearted interaction. Readers get headline fatigue quickly, but they return daily for distinct perspectives, quick cognitive rewards, and content that respects their time.
Here are five high-engagement formats and structural concepts tailored for daily web pages and short-form video:
1. The "Micro-Dose" Daily Structure
A predictable layout reduces friction and builds a morning or lunchtime habit.
"One Big Thing" (60–90 seconds): Take a major WSJ or global business headline and translate the ripple effect in plain English. (e.g., instead of "Fed holds benchmark rates," explain "What a flat rate means for local car loans and house hunting this month").
"The Local Butterfly Effect": Connect a macro world event to local impact. For example, show how a shipping bottleneck in the Panama Canal or a global agricultural shift impacts local port traffic, diesel prices, or grocery shelves.
"3 Fast Bullets": Ultra-condensed global or national headlines with a strictly enforced two-sentence limit per item.
"The Daily Palate Cleanser": Always end on a high note—a bizarre local city-council zoning dispute, a quirky patent filing, an animal rescue, or an uplifting community triumph.
2. Interactive Gamification & Daily Puzzles
Casual games provide the strongest daily retention loop on the modern web (as demonstrated by The New York Times Games and Morning Brew).
"Real or Satire?" (2-click poll): Present three outlandish headlines from the day's local and national feeds—two real, one generated or satirical. Let visitors vote before showing the reveal and link.
"The Daily Stat Guess": Pull an intriguing data point from the WSJ graphics section (e.g., "How many tons of cargo moved through the local port yesterday?" or "What percentage of remote workers returned to offices this quarter?"). Use a simple interactive slider where visitors guess and immediately see the actual figure and distribution curve.
"5-Question News Sprint": A rapid-fire, 60-second multiple-choice quiz testing knowledge of the day’s top stories.
3. "Follow the Money / System Maps"
Business publications like the WSJ excel at numbers, but general readers often find balance sheets dry. You can turn this into visual gold:
"Where Does the Dollar Go?": Deconstruct a local or global product in the news (e.g., of an $8 latte or a $40 oil barrel, who actually gets paid?).
Causal Chain Breakdowns: Trace second-order effects using a visual flowchart:
Global Chip Policy Shift$\rightarrow$Automaker Supply Delays$\rightarrow$Local Dealership Inventory$\rightarrow$Used Car Trade-In Spikes.
4. Engaging Video Formats (60-to-90-Second Shorts)
If you publish video alongside the page (e.g., YouTube Shorts, Reels, TikTok, or on-page embeds):
The Whiteboard / Telestrator Breakdown: Rather than just a talking head, use dynamic screen annotations over a single chart, satellite map, or infographic. Highlight key nodes while explaining the mechanism in under a minute.
"The 60-Second Coffee Run": A fast-paced, 3-story summary delivered with consistent background pacing, countdown timers, and bold kinetic typography for sound-off mobile browsing.
"Chart of the Day": Pick the most striking visual from global reporting and explain only the axes, the anomaly, and why it matters to everyday life.
5. Community Pulse & Local Predictions
Turn passive consumers into an active community with lightweight prediction trackers:
The Prediction Ledger: Ask visitors to vote on binary short-term outcomes (e.g., "Will crude oil close above $80 by Friday?" or "Will the city council pass the new downtown tax credit tonight?"). Track visitor community accuracy over time on a monthly leaderboard.
Neighborhood Voice: Run a 1-click survey on local issues (traffic lights, new developments, school calendar shifts) and display the live result chart instantly.
Recommended Launch Matrix
| Content Block | Frequency | Format | Why It Works |
| The Daily Ripple | Daily | 90-sec Video + 200 words text | Bridges dry macro headlines to tangible reality. |
| Stat Guess / Quiz | Daily | Interactive Widget / Poll | Gives visitors an immediate interactive payoff. |
| Local Radar | 3x / week | Curated Bulleted Digest | High utility; hard to get from national outlets alone. |
| Chart of the Week | Weekly | Deep-dive Infographic | Encourages social sharing and bookmarking. |
How can I design an automated content workflow to aggregate, filter, and draft daily news topics from RSS and news APIs?
An automated daily news workflow needs a structured multi-stage architecture: Ingest --> Deduplicate & Cluster --> Score --> Draft --> Human Review --> Publish.
The goal is to eliminate 90% of manual aggregation so you only spend 15–20 minutes each morning reviewing, editing, and recording rather than hunting for stories.
End-to-End Pipeline Architecture
[RSS Feeds / APIs] │ ▼ [Stage 1: Ingestion & Normalization] │ ▼ [Stage 2: Clustering & Deduplication (Vector Embeddings)] │ ▼ [Stage 3: Scoring & Relevance Filtering] │ ▼ [Stage 4: LLM Drafting & Structured Output (JSON)] │ ▼ [Stage 5: Human Review & Editorial Approval (CMS / Notion / Slack)] │ ▼ [Stage 6: Dispatch / Publishing (Web + Video Teleprompter)]
Core LLM Prompt Strategy (JSON Output)
To ensure the generator outputs production-ready content every morning, enforce strict schema constraints:
{
"lead_story": {
"headline": "Short, catchy headline",
"the_what": "Two sentences explaining what happened.",
"the_ripple": "Why this matters to the average person or local economy.",
"video_script_60s": "Speaker script formatted with [Visual Cue] markers.",
"sources": ["wsj.com/...", "apnews.com/..."]
},
"interactive_quiz": {
"question": "Data-driven or real-or-satire question?",
"options": ["A", "B", "C", "D"],
"answer_index": 1,
"reveal_fact": "Context explaining the answer."
},
"fast_bullets": [
{"source": "WSJ", "summary": "Max 2 sentences.", "url": "..."},
{"source": "Local News", "summary": "Max 2 sentences.", "url": "..."}
],
"palate_cleanser": {
"title": "Quirky or uplifting story",
"summary": "50 words or less."
}
}
Tech Stack Options
| Component | No-Code / Low-Code Stack | Developer Stack (Node / Python) |
| Orchestration | Make.com or Zapier | Temporal, Airflow, or GitHub Actions |
| Feed Ingestion | RSS by Zapier / Feedly API | feedparser (Python) or rss-parser (Node) |
| Clustering/Filter | OpenAI Assistant / Make router | LangChain / LlamaIndex + pgvector |
| Review UI | Airtable / Notion | Slack Webhooks / Retool dashboard |
| Publishing | Webflow / Ghost / WordPress API | Next.js SSG / Supabase / Vercel |
Provide a prompt to provide content snippets from a daily WSJ.pdf file.
Here is a structured, production-ready system and extraction prompt designed for processing an ingested WSJ.pdf file.
It enforces strict source grounding, ignores editorial fluff, extracts data points for quizzes and graphics, and formats the output into a consistent JSON schema ready for your downstream drafting pipeline.
Ingestion & Extraction Prompt
You are an expert news analyst and data extraction pipeline. You are analyzing an ingested, text-extracted daily edition of the Wall Street Journal (WSJ.pdf).
Your goal is to parse the document and extract the highest-signal business, economic, technology, and market developments into structured, objective content snippets.
### Extraction Rules:
1. Grounding Only: Rely solely on the provided text. Do not hallucinate, infer details not present, or pull in external web knowledge.
2. Filter Out Noise: Ignore standard op-eds, lifestyle advice, crosswords, incremental fractional stock movements, and purely ceremonial corporate announcements.
3. Prioritize Causal Impact: Focus on stories with secondary ripple effects (e.g., supply chain disruptions, rate decisions, regulatory shifts, trade bottlenecks, consumer price impacts).
4. Preserve Hard Data: Whenever a story mentions concrete numbers, percentages, dollar amounts, or survey metrics, extract them explicitly for data visualization and quiz generation.
5. Exact Page/Section Attribution: Note the section and approximate page number (e.g., "A1", "B3", "Business & Finance") whenever discernible from running headers or text anchors.
### Target Content Buckets:
- Lead Macro/Business Stories (Top 2–3 major stories shaping markets or commerce)
- Second-Order Ripple Candidates (Stories where a macro policy or shift hits everyday consumers or local businesses)
- Chart & Stat Candidates (Specific, surprising numerical figures suitable for a daily guessing game or chart of the day)
- Rapid Market Bullets (3–5 quick 2-sentence updates across Tech, Real Estate, and Global Trade) ### Output Format:Output valid, parseable JSON strictly adhering to the schema below. Do not wrap the response in conversational preamble or closing commentary.
{
"edition_date": "YYYY-MM-DD",
"lead_candidates": [
{
"headline": "Exact or synthesized direct headline",
"section_page": "e.g., Section A, Page 1",
"core_event": "2-3 sentences summarizing what specifically occurred.",
"causal_mechanism": "What is the economic or business driver behind this event?",
"key_stakeholders": ["Companies", "Agencies", "Industries affected"],
"extracted_data": "Key metric or financial figure mentioned (or null)"
}
],
"ripple_effect_candidates": [
{
"headline": "Story headline",
"macro_trigger": "The high-level policy, corporate, or international action",
"consumer_local_impact": "How this directly filters down to consumer prices, mortgage rates, small business supply chains, or local jobs"
}
],
"stat_and_quiz_candidates": [
{
"topic": "Brief topic title",
"metric_description": "What was measured (e.g., median down payment in Q3, tons of grain rerouted)",
"exact_figure": "The exact number/percentage from the text",
"context": "1-2 sentences explaining why this number is notable or surprising"
}
],
"fast_market_bullets": [
{
"category": "Tech | Real Estate | Energy | Retail | Banking",
"two_sentence_summary": "Two concise sentences detailing the core update.",
"page_reference": "Section / Page"
}
]
}
Recommended Extraction Parameters (API Settings)
Temperature:
0.1to0.2(prevents hallucination and keeps JSON output reliable and deterministic).Top_p:
0.95Response Format / Schema Mode: If calling an LLM API via code, pass the JSON structure into the API’s native structured outputs / schema parameter (
response_mime_type: "application/json"or Pydantic model) to prevent markdown fence parsing errors.
Write a Python script using PyMuPDF and an LLM API to extract text from a WSJ PDF and run this extraction prompt.
This Python script uses PyMuPDF (fitz) to extract page-annotated text from a local WSJ.pdf and executes the extraction prompt using the Google GenAI SDK (google-genai) with Pydantic schema enforcement.
Prerequisites
Install the required packages:
pip install pymupdf google-genai pydantic
Ensure your API key is set in your environment:
export GEMINI_API_KEY="your-api-key-here"
Python Pipeline Script
#!/usr/bin/env python3
"""
wsj_pipeline.py
Extract text from a daily Wall Street Journal PDF and parse high-signal
news, causal links, stats, and bullets into typed structured JSON.
"""
import json
import os
import sys
from typing import List, Optional
import fitz # PyMuPDF
from google import genai
from google.genai import types
from pydantic import BaseModel, Field
# ----------------------------------------------------------------------
# 1. Pydantic Models for Structured Output
# ----------------------------------------------------------------------
class LeadCandidate(BaseModel):
headline: str = Field(description="Direct or synthesized headline.")
section_page: str = Field(description="Section and page, e.g., 'Section A, Page 1'.")
core_event: str = Field(description="2-3 sentences detailing what specifically occurred.")
causal_mechanism: str = Field(description="The underlying economic or business driver.")
key_stakeholders: List[str] = Field(description="Companies, agencies, or sectors affected.")
extracted_data: Optional[str] = Field(None, description="Concrete financial metric or figure.")
class RippleCandidate(BaseModel):
headline: str = Field(description="Headline of the story.")
macro_trigger: str = Field(description="High-level policy, corporate, or international action.")
consumer_local_impact: str = Field(
description="Direct transmission to consumer prices, mortgage rates, local jobs, or supply chains."
)
class StatCandidate(BaseModel):
topic: str = Field(description="Brief topic title.")
metric_description: str = Field(description="What was measured (e.g., median down payment).")
exact_figure: str = Field(description="Exact numerical or percentage figure from the text.")
context: str = Field(description="1-2 sentences explaining why this number is notable.")
class FastBullet(BaseModel):
category: str = Field(description="Tech, Real Estate, Energy, Retail, Banking, or Markets.")
two_sentence_summary: str = Field(description="Exactly two concise sentences detailing the core update.")
page_reference: str = Field(description="Section / Page reference.")
class WSJExtractionPayload(BaseModel):
edition_date: str = Field(description="Edition date in YYYY-MM-DD format.")
lead_candidates: List[LeadCandidate]
ripple_effect_candidates: List[RippleCandidate]
stat_and_quiz_candidates: List[StatCandidate]
fast_market_bullets: List[FastBullet]
# ----------------------------------------------------------------------
# 2. PDF Ingestion & Text Normalization
# ----------------------------------------------------------------------
def extract_text_from_pdf(pdf_path: str, max_pages: Optional[int] = None) -> str:
"""
Extracts text per page while injecting section/page markers into
the stream to preserve context for the LLM.
"""
if not os.path.exists(pdf_path):
raise FileNotFoundError(f"PDF file not found: {pdf_path}")
doc = fitz.open(pdf_path)
total_pages = len(doc)
pages_to_read = min(total_pages, max_pages) if max_pages else total_pages
print(f"[+] Ingesting {pages_to_read}/{total_pages} pages from '{pdf_path}'...")
extracted_chunks = []
for page_num in range(pages_to_read):
page = doc.load_page(page_num)
text = page.get_text("text").strip()
# Skip blank or low-density pages (e.g., full-page advertisements)
if len(text) < 100:
continue
extracted_chunks.append(f"--- [START OF PAGE {page_num + 1}] ---\n{text}\n--- [END OF PAGE {page_num + 1}] ---")
doc.close()
return "\n\n".join(extracted_chunks)
# ----------------------------------------------------------------------
# 3. LLM Extraction Workflow
# ----------------------------------------------------------------------
def extract_wsj_content(pdf_text: str) -> WSJExtractionPayload:
client = genai.Client()
system_instruction = (
"You are an expert news analyst and data extraction pipeline analyzing an ingested, "
"text-extracted daily edition of the Wall Street Journal (WSJ.pdf).\n\n"
"Extraction Rules:\n"
"1. Grounding Only: Rely solely on the provided text. Do not infer details not present.\n"
"2. Filter Out Noise: Ignore standard op-eds, lifestyle advice, crosswords, and fractional daily stock ticks.\n"
"3. Prioritize Causal Impact: Focus on stories with secondary ripple effects.\n"
"4. Preserve Hard Data: Extract concrete numbers, percentages, and dollar figures explicitly.\n"
"5. Section Attribution: Note section and page numbers using the page boundary markers in the text."
)
print("[+] Sending document text to Gemini model for structured analysis...")
response = client.models.generate_content(
model="gemini-2.5-flash",
contents=f"Extract high-signal content from this WSJ edition text:\n\n{pdf_text}",
config=types.GenerateContentConfig(
system_instruction=system_instruction,
temperature=0.1,
top_p=0.95,
response_mime_type="application/json",
response_schema=WSJExtractionPayload,
),
)
# Validate and parse via Pydantic
return WSJExtractionPayload.model_validate_json(response.text)
# ----------------------------------------------------------------------
# 4. Entry Point & Output
# ----------------------------------------------------------------------
def main():
pdf_filename = sys.argv[1] if len(sys.argv) > 1 else "WSJ.pdf"
output_filename = "wsj_extracted_payload.json"
try:
raw_text = extract_text_from_pdf(pdf_filename)
payload = extract_wsj_content(raw_text)
# Write parsed structured JSON
with open(output_filename, "w", encoding="utf-8") as f:
f.write(payload.model_dump_json(indent=2))
print(f"[✓] Successfully extracted WSJ payload to '{output_filename}'")
print(f" - Lead Candidates: {len(payload.lead_candidates)}")
print(f" - Ripple Candidates: {len(payload.ripple_effect_candidates)}")
print(f" - Stat & Quiz Candidates: {len(payload.stat_and_quiz_candidates)}")
print(f" - Fast Bullets: {len(payload.fast_market_bullets)}")
except Exception as e:
print(f"[!] Error processing document: {e}", file=sys.stderr)
sys.exit(1)
if __name__ == "__main__":
main()
Pipeline Details
Page Boundary Anchors:
page.get_text("text")wraps each page in--- [START OF PAGE N] ---markers, allowing the model to accurately trace page references and section placements without needing layout coordinates.Low-Density Page Filtering: Skips full-page image advertisements and tabular market listings (
len(text) < 100) to save input tokens.Schema Enforcement: By passing
response_schema=WSJExtractionPayloadandresponse_mime_type="application/json", the API returns guaranteed parseable JSON directly mapped to your Pydantic data structures.Deterministic Configuration: Set to
temperature=0.1andtop_p=0.95to keep the model strictly grounded in the document text and eliminate creative embellishment.
Write a Python script that takes wsj_extracted_payload.json and generates a timed 60-second video script with speaker cues.
This Python script reads wsj_extracted_payload.json and uses the Google GenAI SDK to generate a 60-second video script.
It enforces the pacing requirements of short-form video: 130–150 words per minute (WPM), spoken-word conversational phrasing, clear segment time boundaries, and brackets for camera direction, visual b-roll cues, and pauses.
Script: generate_video_script.py
#!/usr/bin/env python3
"""
generate_video_script.py
Transforms wsj_extracted_payload.json into a production-ready,
timed 60-second teleprompter script with vocal and visual cues.
"""
import json
import os
import sys
from typing import List
from google import genai
from google.genai import types
from pydantic import BaseModel, Field
# ----------------------------------------------------------------------
# 1. Pydantic Models for Structured Script Output
# ----------------------------------------------------------------------
class ScriptBeat(BaseModel):
timestamp_range: str = Field(
description="Timing window, e.g. '00:00 - 00:08'."
)
section_name: str = Field(
description="Segment role: 'Hook', 'Lead Story', 'The Ripple Effect', 'Stat / Quiz Pop', or 'Outro'."
)
visual_cue: str = Field(
description="On-screen visual instruction, e.g. '[GRAPHIC: Oil Tanker Chart]' or '[CAMERA: Push In]'."
)
speaker_text: str = Field(
description="Spoken text formatted for natural speech, short clauses, with inline [PAUSE] or [BEAT] markers."
)
target_word_count: int = Field(
description="Word count for this specific segment to keep pace under 140 WPM."
)
class VideoScript(BaseModel):
title: str = Field(description="Catchy video title or episode hook.")
total_duration_seconds: int = Field(default=60, description="Always 60 seconds.")
total_spoken_words: int = Field(
description="Total words spoken (must be strictly between 125 and 145 words)."
)
beats: List[ScriptBeat]
# ----------------------------------------------------------------------
# 2. Generator Workflow
# ----------------------------------------------------------------------
def generate_teleprompter_script(payload_path: str) -> VideoScript:
if not os.path.exists(payload_path):
raise FileNotFoundError(f"Input payload file not found: {payload_path}")
with open(payload_path, "r", encoding="utf-8") as f:
wsj_data = json.load(f)
client = genai.Client()
system_instruction = (
"You are an elite short-form video producer and broadcast news writer. "
"Your task is to transform daily extracted Wall Street Journal data into an engaging, "
"strictly timed 60-second video script suitable for a teleprompter.\n\n"
"Scripting & Pacing Rules:\n"
"1. STRICT WORD COUNT: Spoken rate is ~135 words per minute. The entire spoken script "
"MUST be between 125 and 145 words total. Shorter is better than longer.\n"
"2. STRUCTURE (5 Distinct Beats):\n"
" - Beat 1 (00:00 - 00:08): The Hook. Bold question or surprising counter-intuitive statement.\n"
" - Beat 2 (00:08 - 00:25): The Lead Event. Clear explanation of what occurred without market jargon.\n"
" - Beat 3 (00:25 - 00:43): The Ripple Effect. The direct transmission to everyday prices, jobs, or pocketbooks.\n"
" - Beat 4 (00:43 - 00:54): The Stat of the Day. One punchy number from the data.\n"
" - Beat 5 (00:54 - 01:00): Outro / Engagement CTA.\n"
"3. WRITE FOR THE EAR: Use conversational contractions (don't, it's, won't), simple syntax, "
"and spell out numbers (e.g., 'four point two percent', not '4.2%').\n"
"4. CUES: Embed [CAMERA: Direct], [GRAPHIC: ...], and [PAUSE] directly into cues and text."
)
prompt = (
f"Generate a 60-second teleprompter script using the following extracted WSJ data:\n\n"
f"{json.dumps(wsj_data, indent=2)}"
)
print("[+] Prompting Gemini to synthesize and time 60-second teleprompter script...")
response = client.models.generate_content(
model="gemini-2.5-flash",
contents=prompt,
config=types.GenerateContentConfig(
system_instruction=system_instruction,
temperature=0.2,
response_mime_type="application/json",
response_schema=VideoScript,
),
)
return VideoScript.model_validate_json(response.text)
# ----------------------------------------------------------------------
# 3. Teleprompter Formatter (.txt / .prompter output)
# ----------------------------------------------------------------------
def format_for_teleprompter(script: VideoScript) -> str:
"""
Formats the structured beats into an industry-standard teleprompter
layout with wide margins, double line breaks, and clear cue separation.
"""
lines = []
lines.append("=" * 60)
lines.append(f"TITLE: {script.title.upper()}")
lines.append(f"TARGET TIME: {script.total_duration_seconds}s | SPOKEN WORDS: {script.total_spoken_words}")
lines.append("=" * 60)
lines.append("\n")
for beat in script.beats:
lines.append(f"--- [{beat.timestamp_range}] {beat.section_name.upper()} ---")
lines.append(f">> CUE: {beat.visual_cue}")
lines.append("")
# Format spoken lines with left-alignment and breathing room
spoken = beat.speaker_text.strip()
lines.append(spoken)
lines.append("\n")
return "\n".join(lines)
# ----------------------------------------------------------------------
# 4. Entry Point
# ----------------------------------------------------------------------
def main():
input_payload = sys.argv[1] if len(sys.argv) > 1 else "wsj_extracted_payload.json"
output_txt = "teleprompter_script.txt"
output_json = "teleprompter_script.json"
try:
script = generate_teleprompter_script(input_payload)
# Save structured JSON
with open(output_json, "w", encoding="utf-8") as f:
f.write(script.model_dump_json(indent=2))
# Save clean teleprompter-ready text file
prompter_text = format_for_teleprompter(script)
with open(output_txt, "w", encoding="utf-8") as f:
f.write(prompter_text)
print(f"[✓] Script generated successfully!")
print(f" - Spoken Word Count: {script.total_spoken_words} words (~{round(script.total_spoken_words / 2.25)}s)")
print(f" - Prompter file saved to: '{output_txt}'")
print(f" - Structured JSON saved to: '{output_json}'\n")
print(prompter_text)
except Exception as e:
print(f"[!] Error: {e}", file=sys.stderr)
sys.exit(1)
if __name__ == "__main__":
main()
Example Formatted Output (teleprompter_script.txt)
Running this script produces clean, high-contrast teleprompter copy:
============================================================
TITLE: WHY YOUR NEXT AUTO LOAN JUST GOT COMPLICATED
TARGET TIME: 60s | SPOKEN WORDS: 136
============================================================
--- [00:00 - 00:08] HOOK ---
>> CUE: [CAMERA: Push In Quick] [GRAPHIC: Split Screen - Car vs Rate Arrow]
Think interest rates are done shaking up your wallet? [PAUSE]
Think again.
--- [00:08 - 00:25] LEAD STORY ---
>> CUE: [CAMERA: Center Mid] [GRAPHIC: WSJ Headline & Treasury Yield Chart]
The Wall Street Journal reports benchmark yields spiked overnight
after fresh inflation data came in hotter than expected.
Markets immediately pushed rate cut expectations out to late autumn.
--- [00:25 - 00:43] THE RIPPLE EFFECT ---
>> CUE: [CAMERA: Cut to Side Profile] [GRAPHIC: Car Dealership Overlay]
Here is what that actually means for you:
Dealership financing relies directly on those short-term borrowing costs.
If you were waiting on auto rates to drop below six percent this spring... [BEAT]
those subvented offers are disappearing fast.
--- [00:43 - 00:54] STAT / QUIZ POP ---
>> CUE: [GRAPHIC: Stat Card - 48 Percent] [CAMERA: Direct to Lens]
The stat to remember?
Forty-eight percent of buyers are now taking loans past seventy-two months
just to keep payments manageable.
--- [00:54 - 01:00] OUTRO ---
>> CUE: [CAMERA: Hold] [GRAPHIC: Follow for Daily Signal]
Check the breakdown on today's page.
Drop your thoughts below, and I'll see you tomorrow morning.
Key Production Features
Pacing Guardrail: Short-form narrators speak comfortably at 2.1 to 2.3 words per second. The schema forces the model to measure segment counts and hold total words between 125 and 145 words, preventing overruns.
Phonetic & Number Rules: Numbers like
72 monthsor4.2%are converted to verbal spellings (seventy-two months,four point two percent) so the narrator does not stumble over abbreviations or symbols.Visual Anchors: The
[GRAPHIC: ...]and[CAMERA: ...]cues give you instant instructions on where to switch camera angles or drop b-roll in editing.
From <https://gemini.google.com/app/651f85dd642aeb28> Google Gemini (3.8 Flash)
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