Can Cursor run a beginner YouTube channel? A 7-day real-world experiment
Post:
I am starting a seven-day experiment: can one Cursor project become the main workstation for a beginner YouTube creator?
I am not testing only script generation. I want to test the complete workflow: content planning, research, a personal RAG, connecting useful services, video creation, upload preparation, comment management, and agent-driven automation.
This is not a new RAG product or a one-click content generator. I am testing Cursor itself as the workspace.
I will use a real channel and post the results here: what worked, what failed, which steps can be automated safely, and where a separate tool or human decision is still needed.
The 7-day plan
Day 1 β Channel and content plan
Define the channel topic, target viewer, video format, and first 10 video ideas. Turn them into a simple first-month content calendar.
Day 2 β Research system
Collect reliable sources, examples, audience questions, and notes for the first videos. Mark every item as verified, opinion, or still unconfirmed.
Day 3 β Channel RAG and memory
Build a local knowledge base for sources, content ideas, writing rules, past scripts, useful examples, and corrections. The goal is to keep working context between videos instead of starting from an empty prompt.
Day 4 β Connect useful services
Connect only services that solve a real step through APIs, MCP, or browser workflows: research, transcription, image generation, storage, and upload preparation. Keep credentials outside prompts and project files.
Day 5 β Create a real video package
Take one idea from the content plan, research it through the RAG, and create hooks, an outline, a 60β90 second script, title options, a description, a thumbnail brief, and a recording checklist.
Day 6 β Prepare and upload
Prepare assets and metadata for publication. Test a safe upload workflow with a final manual approval step before anything becomes public.
Day 7 β Work with comments and review the workflow
Collect new comments and separate them into questions, feedback, spam, and useful corrections. Let agents prepare draft replies and a short report, then review everything manually. Finish by documenting what Cursor handled well, what needs another tool, and what should stay human.
Rules
No factual claim without a saved source.
If information is missing, Cursor asks instead of inventing an answer.
Scripts stay short, direct, and human.
No agent publishes content, sends messages, or uses an external account without approval.
Every automated step needs a visible result and a manual check.
By the end of the week, I want a practical answer: how much of a real beginner YouTube workflow can be planned, researched, structured, generated, checked, and managed from one Cursor project.
I will update this thread in real time. Questions, criticism, and alternative workflows are welcome.
This is really cool! Please keep us updated. You might want to double-check YouTubeβs policies for AI-generated content and just make sure youβre following the correct rules, etc. Let us know how it goes!
Today, weβll start with simple and clear tasks: define the channelβs niche and analyze the accumulated experience of the most successful YouTubers, psychologists, manipulators, and propagandists.
The goal is for our videos to be more than just a demonstration of what agents managed through Cursor can do. They should also bring traffic and subscribers.
When choosing a niche, we need to think in terms of unlimited content capacity. The first thing that comes to mind is biographies. They have high-volume keywords and naturally attract audience attention: Elon Musk, Trump, Epstein, and so on.
You may ask: why biographies? Why not something else? Iβll answer that later.
If I had seen a task like this a few years ago, I would have said with confidence that it would take at least three months and require a budget of $15,000β$30,000.
But in todayβs reality, with a $60 Cursor subscription, we can solve this task in five minutes. Writing this post will take longer, but it is necessary: the process of writing helps turn the original seven-day plan into a detailed roadmap.
Enough writing already, right? Time to do something. Letβs open Cursor, ask it to create a research prompt, and then send that prompt to an orchestrated swarm of agents for execution.
I put everything into one prompt to understand both how to make the content and whom to make it about.
The prompt turned out to be substantial and detailed. If anyone is interested, I can send the full version in a private message so I do not clutter the thread.
Prompt:
You are the lead of a research swarm. Conduct independent, evidence-based research from scratch.
Goal
Create an editorial and psychological model for an engaging 10-minute YouTube video for a documentary-investigative series:
βUnknown Shocking Storiesβ
Topics: disappearances, archival cases, inconsistencies in investigations, forgotten documents, last confirmed sightings, and ordinary places with disturbing histories.
The goal is not to promise βviralityβ or provide manipulative hacks. Determine which elements of storytelling, editing, evidence, calls to action, and internal linking can reasonably support viewer attention, trust, and movement to the next video.
Work as if no benchmark document exists. Do not fit the findings to a presumed answer.
Research principles
After 20 minutes of work, I received a detailed guide on how to create podcasts properly. According to the AI, even MrBeast would be impressed.
I also got 30 ready-to-produce podcasts about famous people who changed the world, each designed for 10 minutes.
To my surprise, my initially simple and clear plan was adjusted by a subagent, which suggested immediately cutting the podcasts into Shorts.
It sounds great. I do not yet fully understand how this will be implemented, since our goal is automated blogging.
And I almost forgot: why biographies? Here is the answer. This experiment has a limited budget and a limited set of tools:
Cursor with a $60 subscription.
ZennoPoster β yes, we will need it a little later.
Within this experiment, we cannot afford expensive video generation. So we will make videos from images.
A note on how I will run this experiment: I am writing it in public and I will not rewrite earlier posts after publishing them.
Each update is a real working step, not a finished case study. Sometimes an early idea will be too broad, a tool will fail, or a better direction will appear after testing.
When that happens, I will add a short follow-up note explaining what changed and why. This should make the process more useful than presenting a polished result after the fact.
The constant part is simple: sources are checked, public actions stay under human approval, and I will show both useful results and limitations.
Using subagents orchestrated by Cursor, we will build two matrices:
A psychological matrix for audience retention and podcast structure. I created this one yesterday.
A fact-checking and deep-research matrix.
Letβs focus more closely on the second one.
We will work with popular search queries and trends. That creates a risk of finding questionable information, interpreting it incorrectly, and publishing material that could cause problems.
We do not need that. So a separate group of subagents will verify everything found by the others: locate primary sources, compare versions, identify conflicts between sources, and flag claims that cannot be confirmed.
Quick dossier setup
Example: biography of a public figure
Create a dossier_name_date folder using the templates/folder_structure.md template.
In 00_dossier_profile.md, record the subject, name variations, research cutoff date, languages, sensitive topics, and research boundaries.
Assign five researchers to non-overlapping areas from raw_research_matrix.md.
Give each researcher templates/researcher_brief.md and the source-card template.
After the first research pass, the coordinator assigns IDs and links duplicates, but does not fill in or βcompleteβ facts.
Two fact-checkers receive templates/fact_checker_brief.md.
The red team reviews risks only: allegations, quotes, health, private life, causation claims, and current statuses.
The editor receives only the separate 10_editor_handoff.md, not the raw notes.
Minimum discipline
One fact, one card.
One URL does not equal independent confirmation.
An opinion does not become a fact, even if it is popular.
An unverified lead is useful only as a fact-checking task.
No source means no claim in the script.
What to give the editor
10β20 strong verified cards on the selected topic.
1β3 honestly described contradictions.
A βwhat we do not knowβ section.
3β5 safe questions for the audience.
Links to documents and the research cutoff date.
Important limitation
A large database is not a finished biography or a finished podcast. Its purpose is to let the next researcher create scripts without repeating the same research or turning rumors into a story.
I wonβt publish the full matrices here. If you want, you can build them yourself.
Yes, you can write a piece based on a single source. But it is much more reliable to find a second independent source, compare the information, and openly point out any discrepancies.
That seems clear now. I hope I havenβt tired you out with all the writing.
Letβs move on to the next step: choosing content topics. At first, I planned to create 30 topics, but quickly realized there is no real limit. So letβs start with 100.
The approach is simple: ask the agents to find the top 100 people who changed the world. At the same time, use an MCP tool to check name search volume and trends. That way, these people will not only be important because of their impact on the world, but also relevant in search demand.
After 10 minutes of intensive work by the subagents, we get a list like this.
Additionally, we will ask Cursor to write prompts for Suno. Here is what we got:
[10 minutes long] [extended version]
Atmospheric cinematic introduction, mysterious tension building, orchestral elements with electronic undertones, deep bass pulses, suspenseful strings, digital glitches mixed with classical instruments, sense of awakening and discovery, modern documentary style, serious investigative mood, intelligent and sophisticated, evolving soundscape, 120 BPM
Day 2 result: the useful output was not the list of names. It was the research structure.
I separated source collection, fact checking, risk review, and the editor handoff. That means the editor should receive only verified claims, documented contradictions, source links, and a clear βwhat we do not knowβ section.
I am also narrowing the first test topics. I will avoid political and conflict-driven subjects. The first RAG test will use neutral topics from technology, science, creative work, and practical automation.
The long list of possible biographies stays in the private research backlog until every item is checked. It is not a publishing plan.
Next step: Day 3 is building the channel memory so sources, corrections, and approved writing rules survive between videos.
A channel memory should not become one large folder where sources, ideas, drafts, accounts, and random notes are mixed together.
I am building this as a small creator operating system inside Cursor. It should support multiple projects and channels without mixing their audience, writing style, research, browser sessions, or publishing workflows.
Shared knowledge is not automatically shared content. A fact can be reused across projects, but every channel still has its own audience, voice, format, approval rules, and risk boundaries.
I also separate durable knowledge from temporary research:
research/ raw notes, leads, hypotheses, incomplete material
Every item has a simple status:
verified β supported by a saved source
opinion β interpretation or personal experience
unconfirmed β useful lead, but not safe for a script
outdated β needs a fresh check before reuse
Each content idea gets its own card:
channel
target audience
main question
source links
verified claims
open questions
risks
approved outline
script status
publishing approval
outcome and lessons
This gives different Cursor agents different responsibilities:
a research agent can work with raw sources;
a fact-checking agent can challenge claims and find gaps;
a script agent receives only verified material and channel rules;
a publishing agent receives only approved assets and a checklist.
No agent should receive the entire system by default. Context needs to be selected by task, otherwise a large memory becomes noise instead of help.
The first RAG test is simple. I will give Cursor one existing research dossier and ask it to return:
claims that are safe to use;
claims that need another source;
unresolved questions;
three video angles based only on verified material.
If it mixes those categories or invents new facts, the memory is not ready for content generation.
The goal is not to build another complex RAG product. The goal is to make Cursor a reliable workspace where research, channel memory, scripts, browser work, and human approval can grow together without losing context.
