ViewPulse: YouTube & Short-Form Channel Growth Intelligence

Problem and target customer

Why this exists

Customer problem

Creators can see per-video metrics but still spend hours exporting data and guessing which topic, format, and publishing cadence explain growth across their catalog and competitors.

Who pays

Established YouTube creators and small channel teams publishing often enough to make weekly performance patterns actionable.

Business model

Monthly subscription tiered by connected channels, tracked competitors, and refresh frequency.

Editorial note

This is a weekly decision instrument, not a wall of charts. The creator supplies the taxonomy that makes metrics comparable, and the model turns those bounded rows into hypotheses that remain inspectable.

YouTube is the supported MVP boundary. Treat broader short-form coverage as a separate data-access decision; scraping or pretending that public counts equal private retention analytics would undermine the product.

Choose your next step

What do you need next?

Evaluate the operating tradeoffs quickly, or inspect how to build the MVP.

Evaluate this ideaSee the cost and deployment preview

Compare the three operating paths at the default workload before opening the full calculator.

See cost preview Build this ideaInspect the build blueprint

Review MVP scope, workflow, states, data rules, architecture, and acceptance criteria.

Open blueprint

Evaluation preview

What would it take to run?

Directional infrastructure estimates for the current 200 channel refreshes assumption. Open the full calculator when you are ready to change it.

Open full cost and deployment

ManagedSelected

$22-$68/ month

App, database, backups, and logs for YouTube metric snapshots and cited weekly analysis with minimal platform operations.

Lowest operating effort

Lean self-hosted

$8.6-$33/ month

A small cloud server runs YouTube metric snapshots and cited weekly analysis, PostgreSQL, and scheduled backups.

Lowest baseline cost

More control

$42-$163/ month

Separate web, data, and background capacity for YouTube metric snapshots and cited weekly analysis when operational isolation matters.

Most separation and control

Build blueprint

Build the first paid use case

Product goal

Who it serves and what it must change

Target user: YouTube creators and small channel teams reviewing performance each week.
Problem: Raw per-video analytics do not reveal consistent topic, format, or timing patterns across a growing catalog.
Measurable outcome: A creator completes a weekly review in five minutes, traces each generated observation to metric rows, and records one next experiment.

MVP scope

What ships now and what waits

Included

YouTube OAuth
Channel and video snapshots
Pillar and format tags
Public competitor set
Cited weekly model brief
Experiment log

Excluded

Unsupported short-form APIs
Auto-posting
Revenue forecasting
Private competitor metrics
Guaranteed growth

UX and user flow

Screens, actions, and states

Channel Setup

Connect a channel, choose history, define pillars, and select competitors.

Authorize YouTubeClassify videosAdd competitor

Pattern Explorer

Compare dated metrics by pillar, format, and publishing window.

Change cohortInspect video rowsCompare competitor

Weekly Brief

Review cited observations and record the next content experiment.

Open source rowsReject observationSave experiment

Primary flow

Creator authorizes channel
Worker snapshots videos and metrics
Creator confirms classifications
Service aggregates weekly cohorts
Model drafts cited observations
Creator saves one experiment

Loading, empty, and error states

Connection: active, needs_reauth, revoked
Refresh: queued, running, complete, partial, failed
Insight: generated, accepted, rejected
Experiment: planned, measured, archived

Core entity outline

Entities and business rules

VideoMetricSnapshot

Preserves dated public and authorized metrics for one video.

Fields: video_id, captured_at, views, likes, comments, duration_seconds
Relations: Belongs to Video, Included in InsightEvidence

ContentClassification

Assigns a creator-controlled pillar and format to a video.

Fields: video_id, pillar_id, format, confirmed_by, confirmed_at
Relations: Belongs to Video, Belongs to Pillar

WeeklyInsight

Stores generated observations, evidence references, review state, and chosen experiment.

Fields: channel_id, period_start, text, evidence_ids, review_status
Relations: Belongs to Channel, Has zero or one ContentExperiment

Business rules

Snapshots are append-only by capture time
Competitor analysis uses only public fields
Every generated observation must reference stored aggregates or metric rows
Creators can edit classifications without rewriting historical metrics
Revoked OAuth stops future private refreshes

Architecture and data flow

Components, integrations, and controls

YouTube connector

Manage OAuth tokens, quota accounting, incremental fetches, and reauthorization.

Metrics warehouse

Store normalized snapshots and compute weekly cohorts.

Insight service

Send bounded aggregates to the model, validate citations, and hold output for creator review.

Integrations

YouTube Data API with OAuth 2.0
OpenAI Responses API

Data flow

Connector fetches channel and video data
Worker appends snapshots
Queries aggregate cohorts
Model receives aggregate rows with identifiers
Validator rejects uncited claims
Creator records a decision

Failure handling

Mark partial refreshes and retain the last complete snapshot
Back off on YouTube quota errors
Discard model output with missing evidence ids
Prompt reauthorization after token revocation

Security

Encrypt OAuth refresh tokens
Request minimum YouTube scopes
Never send tokens or viewer identities to the model
Separate channel tenants

Rate limits

Budget YouTube quota per channel
Limit manual refreshes
Queue model calls per workspace
Cache public competitor metadata

Deliverables and acceptance

Definition of done for the MVP

Deliverables

OAuth onboarding
Scheduled YouTube connector
Classification editor
Pattern explorer
Cited weekly brief
Experiment history and quota dashboard

Acceptance criteria

A revoked token stops refresh and shows reauthorization
A partial fetch never replaces the last complete period
Every insight link opens supporting rows
An uncited model sentence is rejected
Competitor views display only public API fields

Recommended stack

Enough technology for the first version

Web application

Next.js and TypeScript

Supports OAuth, dense charts, classification controls, and weekly review.

Data

PostgreSQL

Stores channels, videos, taxonomy, dated metrics, competitor snapshots, and decisions.

Data source

YouTube Data API

Provides authorized channel and public video metadata under documented quota limits.

Analysis

OpenAI Responses API

Generates a bounded pattern brief from aggregated metric rows and returns cited record identifiers.

Why this is sufficient

Metric snapshots and creator-defined classification provide the evidence layer. The model explains bounded aggregates; it does not invent unavailable competitor data or act as an autonomous content strategist.

Not required for the MVP

Object storageVector databaseAutomatic publishingPayments inside the MVP workflowCross-platform scraping

Next stepTurn the blueprint into an execution plan

Copy the build prompt, model the operating cost, and choose where to deploy.

Build and ship

Execution

Build, price, and deploy the MVP

Once the blueprint is clear, use the prompt, cost model, and deployment options to start building.

Build prompt

Copy this into a builder

Use a TypeScript app plus scheduled workers and PostgreSQL; prototype charts in Lovable, then implement OAuth, quota accounting, and cited model output server-side.

Build prompt

Your build prompt is ready

Open the prompt pack whenever you are ready to take this blueprint into your builder.

Based on the blueprintReady for your builderFollow-up steps included

Cost calculator

Model the MVP operating cost

A technical run-cost estimate for the first production version. Team, acquisition, payment fees, and business COGS are excluded.

Estimated monthly total$22-$68

$11-$34 per 100 channel refreshes

Channel refreshes / month200 channel refreshes

Selected pathEasiest

Pricing checkedAug 2, 2026

Cost breakdown

$22-$68 per month

Low and high values allow for usage variance and plan headroom.

Managed Railway deployment

App, database, backups, and logs for YouTube metric snapshots and cited weekly analysis with minimal platform operations.

50K channel refreshes included, then $8-$20 per 50K channel refreshes

$20-$65

Model inference (GPT-5.4 mini)

Explains one channel refresh from bounded, aggregated metrics and returns source record references for human review.

200 channel refreshes x 7K input + 1.2K output tokens

$1.6-$2.66

Included

Application and database hosting
Scheduled YouTube refreshes
Model-generated weekly briefs
Backups and monitoring

Not included

Paid third-party social data
Additional YouTube quota approval
Creator labor
Video production
Marketing analytics warehouses

Pricing basis

The estimate combines the selected infrastructure path, required operating modules, selected optional modules, and usage above included monthly allowances. Taxes and regional uplifts are excluded.

Railway pricing OpenAI API pricing

Deployment options

Pick the operational tradeoff

Choose based on operating preference, not only the headline price.

EasiestRecommended

Railway

Deploy the metric refresh pipeline and weekly insight workspace with managed services, environment variables, logs, and a short path from prototype to beta.

$20-65/month at beta scale before workflow add-ons

Good fit

Fast deployment
Managed database
Simple previews

Limitation

Usage-based compute needs budget alerts as activity grows.

Open Railway

Cheapest

Vultr

Run the app, worker, and database on one small Vultr VPS with Docker Compose and explicit backups.

$7-30/month before workflow add-ons

Good fit

Low baseline
Portable Docker setup
Predictable capacity

Limitation

The operator owns database maintenance and incident response.

Deploy on Vultr View the lean VPS guide

More control

DigitalOcean

Separate application, worker, data, storage, and backup responsibilities as the workload grows.

$40-160/month before workflow add-ons

Good fit

Separated services
Capacity headroom
AWS ecosystem

Limitation

More moving parts increase setup and observability work.

Explore DigitalOcean View the separated cloud guide

Start building

Prototype, automate, then deploy

Recommended route

01Vibe-coding builderPrototype the weekly creator review

Design channel setup, taxonomy editing, pillar comparisons, competitor rows, and cited insight panels.

Open Lovable 02AutomationSend a ready-to-review signal

Use Make only to notify a creator after the app finishes a refresh and saves a complete weekly brief.

Open Make 03DeployDeploy ingestion and analysis

Run OAuth callbacks, scheduled refresh jobs, PostgreSQL, and model requests with quota and spend controls.

Open Railway

ViewPulse: YouTube & Short-Form Channel Growth Intelligence

Is this worth validating?

Build this if

Avoid this if

Validate first

Why this exists

Customer problem

Who pays

Business model

What do you need next?

What would it take to run?

Build the first paid use case

Who it serves and what it must change

What ships now and what waits

Included

Excluded

Screens, actions, and states

Primary flow

Loading, empty, and error states

Entities and business rules

Business rules

Components, integrations, and controls

Integrations

Data flow

Failure handling

Security

Rate limits

Definition of done for the MVP

Deliverables

Acceptance criteria

Enough technology for the first version

Next.js and TypeScript

PostgreSQL

YouTube Data API

OpenAI Responses API

Why this is sufficient

Not required for the MVP

Build, price, and deploy the MVP

Copy this into a builder

Your build prompt is ready

Model the MVP operating cost

Usage assumptions

$22-$68 per month

Included

Not included

Pricing basis

Pick the operational tradeoff

Railway

Good fit

Limitation

Vultr

Good fit

Limitation

DigitalOcean

Good fit

Limitation

Prototype, automate, then deploy

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