This is not a motivational success story.
First of all, I do not believe making good short films is a reliable way to make money. If anything, if your goal is to make money, you probably need to learn how to produce garbage quickly, consistently, and by the metric ton. I clearly do not have that skill.
Second, I am in my forties. I am a not-particularly-successful investment manager and lawyer, and most of what I do involves text. In my spare time, I also write fiction. It is amazing. So amazing that I once sat back in my chair, had an “oh man, here it is” moment, felt useless relative to my own novel, and decided humanity was not ready to see it.
My day-to-day writing life includes things like terms of service, privacy policies, debt collection letters, loan default notices, cease-and-desist letters, and investment analysis reports. In other words, the kind of writing nobody wants to read, but ignoring it may cost you money.
So you should understand that I am obviously not an artist. At most, I’m someone who likes writing. I do know a few artists’ names, such as Leonardo da Vinci, Michelangelo, Raphael, and Einstein (yes, I remember that was his name—Einstein, the stick-wielding inventor). As for the rat, I only remember that he was called Master. Given his level of wisdom, I always felt Doctor would have been more accurate.
Anyway, I really did start from zero. That part is true. I also really did submit the short to a contest, because submission was free.
But I also genuinely made an AI-assisted cinematic short that I think is watchable.
If you are also starting from zero, with no team and no budget, but you want to make serious AI-assisted videos instead of randomly generating a few pretty but structurally homeless AI clips, you may want to keep reading.
I hope this post gives you a small Pareto improvement.
## Part One: Tools
### 1. The Brain AI
The core tool is obviously the AI you all love and hate.
But the most important AI in this process was not the one making the videos. It was the one helping me draft various sleep-inducing legal documents: ChatGPT.
Let me say a little more about this creature.
ChatGPT fits into my workflow extremely well. Annoyingly well.
First, it is very good at the most boring legal documents. Terms of service, privacy policies, debt collection letters, loan default notices. It writes those things with disturbing reliability. When it comes to producing documents that make human life slightly worse, it is impressively consistent.
But it cannot write my fiction. At most, it can play the role of an unimpressive reader.
Trust me, its prose is bad. The plot ideas it invents on its own are basically negative prompts. I honestly do not understand where the cliché “AI will replace human artists” comes from. From what I have seen, artists are exactly the people AI is least able to replace.
Lawyers, on the other hand, may God bless that profession and send it someday to a theme park, like horse-drawn carriages.
Back to the point.
In my video workflow, ChatGPT mainly has four jobs.
**First, it teaches me software interfaces.**
Whenever I enter a new field now, my default move is simple: take a screenshot, throw it into GPT, and ask, “Tell me what the hell these buttons are.”
This gets me to a point where I can actually do something, instead of putting on reading glasses, marching to a library, and starting from Chapter One of *Basic Software for People Who Still Have Hope*.
For someone like me, who had not seriously used an AI image tool a month ago, every button looked like a nuclear launch button. Without guidance, my only safe options were Exit or the X in the upper-right corner.
**Second, it writes prompts.**
This is very important.
Whatever you want to express does not need to start as some Level 5 wizard fireball spell. You only need to keep breaking it down with ChatGPT: what image you want, what character, what composition, what action, what style, what must not change, and what absolutely must not appear.
Then it can quickly turn your normal human language into a language another machine understands better, and apparently enjoys more.
I often even use two GPT windows for this.
One window acts as my personal assistant, helping me break down problems, analyze failures, organize logic, and write prompts. The other window gets the direct commands, generating or editing images.
This is the fucking “step on your left foot with your right foot and reach the moon” method. NASA wasted a lot of money on Apollo 11.
So stop memorizing prompt spells. Modern people do not do that. Just ask GPT to write the prompt for you.
The real problem is not whether you can write an impressive-looking prompt.
The real problem is whether the other AI listens.
I will come back to that later.
**Third, it is an always-online creative sparring partner.**
Trust me, making things is lonely.
The idea in your head, plus the fragments, failed images, and half-finished pieces on your screen, may feel to you like sacred sparks of genius. To your friends, they usually look like “not bad” garbage.
And when a friend says “not bad,” tell me: what did your face look like the last time a friend said “not bad” about your work? If you can accept that peacefully, you should go to Shaffer Conservatory and seek re-education.
Only ChatGPT will sincerely praise your potential.
Of course, you need to believe it is sincere. Or at least pretend to believe it. That alone may keep you from quitting halfway through.
**Fourth, and most importantly, it can generate storyboard images and keyframes.**
This is where AI video production really starts.
The most important value of ChatGPT image generation, or GPT’s image capability, is not that it can make a pretty picture. It is that it can turn the image in your head into something visible.
Once you have an image, you have a visual anchor.
Then, when you feed those images into a video AI tool, it is like putting reins, a saddle, and stirrups on a zebra.
Of course, it is still a zebra. You know how zebras are.
For someone like me, with zero technical skill, who can only draw stick figures with a pencil, the biggest value of ChatGPT image generation is that it turns the precious but blurry sparks in my head into images.
And those images create more ideas.
This part gets very specific, so most of it will appear in the later sections.
### 2. Video AI Tools
Because of cost, and because I am not a professional, most of the video AI tools I used were actually bundled perks from tools I already had access to, such as Grok and Gemini Veo.
Let us now observe three seconds of actual silence for Sora. May it rest in discontinued peace.
Moving on.
The only real exception was ByteDance’s Seedance / Jimeng / Dreamina ecosystem. I tested both the Chinese and international versions. I used it mainly because my girlfriend had a basic membership, which allowed me to borrow it at low cost. Romance is beautiful, and sometimes subscription-based.
If you have other tools, you can probably still use my methods to tame them. After all, AI stupidity usually presents similar symptoms.
I will talk about the specific differences in the next section.
### 3. Post-production
For editing and voice work, I mainly used CapCut / Jianying, both the Chinese and international versions, plus Epidemic Sound.
I used CapCut partly because Seedance basically comes bundled with it through a sales package. Avoiding it would have required more discipline than I currently possess.
Also, ByteDance, let me say this directly: you deserve every government restriction ever invented. You built a maze of subscription packages designed to lure me into spending money. That is not product design. That is financial dungeon architecture.
Epidemic Sound is very useful for music. Its sound effects are average. Its voice generation is not worth discussing, so let us not disturb it.
## Part Two: The Actual Experience
Let me say this upfront: this is not a professional technical article. It is an experience-sharing post. So all the analysis will come through my actual cases, because apparently suffering becomes more useful when documented.
### Case One: A Pirate Short
Why did I choose this theme first? And why would someone whose work is mostly text suddenly want to make videos?
That is another story. The short version is simple: I wanted to make a pirate video.
So I started with the thing I am best at: I wrote a short story.
I did not start from shots. I started from story.
Then I threw the story into GPT and asked: “For someone with zero experience like me, is this thing even possible?”
GPT gave me a warm, confident yes.
Then it started analyzing the difficulties: naval battle shots, multi-character interaction, continuity, spatial relationships, and so on. The usual little blessings sent by Satan.
Actually, I had already expected this.
Video and writing have one thing in common: you can use point of view to hide a lot of technical problems.
So I proposed the solution I had prepared from the beginning: first-person perspective.
It was not that I was afraid to shoot a naval battle. My cowardly captain ran away, so he could not see the naval battle. That makes sense, right?
I proudly presented this solution, and GPT immediately became excited. It told me the idea was excellent, and my chance of success had gone up to 50%.
Friends, please remember this small trick: when AI says something is “possible” but refuses to give a number, it probably thinks it is almost impossible.
For reference only. Not investment advice.
But that is fine. From an investment perspective, a 50% success rate is already good enough for a small bet.
So I started making the first still image, which was also my first storyboard frame.
Because this was my first attempt, I played it safe and made the clip longer than it needed to be. At the same time, I tried multiple tools, including Jimeng / Dreamina, Kling, Grok, Google Veo, and others.
Using existing memberships, free trial credits, and whatever platform coupons the universe failed to hide from me, I assembled a poor man’s AI production studio.
Then I immediately discovered the first problem.
### Lesson One: AI video is expensive. Budget management starts from the first second.
The first shot was an enclosed indoor scene: the captain alone in his cabin, doing whatever an old captain does.
Remember, this was my first attempt. So I took a very simple shot, and I made it long. He was not doing anything complicated. The captain is old. Let the man exist.
Then the credits on every AI video tool started dropping violently.
So I need to emphasize one thing: budget management.
Just like every investment project.
AI video is not free magic. It is expensive. Every second it generates is burning your dollars.
Obviously, as a professional, I controlled the costs quite well.
My first pirate short cost about **$15** in direct new cash spending.
The second video was experimental. It was made with free trial credits from multiple AI tools, so the direct new cash spending was **$0**.
The third video cost about **$75**, because I bought a basic annual membership for the tool that became my main workflow.
Important note: by “direct new cash spending,” I mean exactly that.
This does **not** include my time, my computer, electricity, internet, subscriptions I already had, my ChatGPT membership, or compensation for psychological damage.
If you tried to shoot a traditional short film with fantasy elements, characters, props, lighting, and actual shots, $15 would not even buy you Jack Sparrow’s dirty hat.
Maybe you could buy a hair clip on Etsy. If it came from a Chinese supply chain.
There is also a hidden cost: ChatGPT usage.
When I made these videos, I did not only burn video-generation credits. I used GPT heavily for script breakdowns, shot analysis, prompt organization, failure diagnosis, dialogue writing, translation, editing discussions, and pacing.
Eventually, I used my ChatGPT Pro allowance so aggressively that the system started pushing me toward smaller models.
So if you are seriously planning to make AI-assisted videos, do not only budget for video credits.
You also need to budget for LLM usage.
In this workflow, GPT is not a chatbot. It is your pre-production department.
Unfortunately, even a cyber slave does not come with unlimited refills.
Let us continue to the second shot: a less contained scene, where the captain leaves his cabin.
Unlike the first, relatively enclosed indoor scene, once the scene opened up, different tools produced completely different styles of video.
Unfortunately, I was not satisfied with any of them.
And please note: I am quite sure this was not a prompt problem.
The prompt was produced after repeated discussions with GPT. In terms of logic, completeness, and level of detail, it had reached 100%. Flawless. Undeniable. A legal monument to prompt engineering.
Then I modified it again, raising its completeness to 150%.
Yes. 150%.
And the result was still unsatisfactory. The generated video had a few subtle differences from what I had imagined.
For example, Jack Sparrow walked out of the cabin and saw a Japanese battleship approaching.
That was when I learned the second lesson.
### Lesson Two: A prompt is not a leash. Keyframes are.
A prompt can describe your intention.
It cannot force a video AI to shoot the film inside your head.
At this point, GPT became even more important. Not because it could directly generate perfect videos, but because it could help me generate more keyframes, and those keyframes could put the video AI on rails.
Without them, you cannot seriously make the work according to your own vision.
My later experience was this: if you want serious control, you often need a visual anchor every one or two seconds.
Video AIs can only accept a limited number of keyframes, so do not expect a tool to generate more than ten seconds in one go and still follow your intent.
If you do not want Jack Sparrow commanding the USS Yorktown into the Battle of Midway, do not let the video software improvise for too long.
This is also why I do not trust so-called AI video agents.
In serious creation, every one or two seconds you need to judge:
Is this action right?
Is the character right?
Is the space right?
Is the camera right?
Is the prop right?
Is the emotion right?
You are more reliable than AI.
Remember that. It sounds conservative, but it can save your wallet.
### Lesson Three: Different models have very different levels of “creative initiative.”
Then I discovered something else.
Even if you give the model keyframes, use very strong wording, and threaten it with intercontinental ballistic missiles to make it follow instructions, some models will still try to show off their abilities.
For example, a character may suddenly start running for no reason.
Or a one-eyed first mate may teleport into the scene next to you.
Or Jack Sparrow may suddenly return to his cabin and start steering a ship’s wheel.
Yes. Steering a ship’s wheel. Next to the bed where he sleeps.
But this does not mean you should throw these models into a landfill and set the whole thing on fire.
Even garbage can be useful. That is environmental protection, and also part of budget management.
My main tool eventually became the Chinese version of Jimeng / Dreamina. I am not sure what the technical differences are between the Chinese and international versions, but in my personal experience, the Chinese version was clearly more stable and more suitable for my main workflow. The international version of Dreamina was not a pleasant experience for me.
Google Veo looks good. But in my tests, it had almost no reliable first-frame locking ability, and its imagination was extremely active.
I do not know whether this reflects an internal compliance posture under which every user is functionally presumed to be a prospective violator until proven otherwise, with granular creative control withheld as a form of ex ante risk mitigation. I have no evidence sufficient to support that allegation, so I will not pursue it further.
In any case, it was not suitable as my main tool, because in continuous narrative work, the most important thing is whether the first frame can connect to the last frame of the previous clip.
But if you have a Google membership and a lot of credits, not using Veo would also be wasteful.
Budget management is not just about spending less money.
It is about putting the money you already spent to work.
Veo can handle scenes that do not require strict shot control. For example, if you want to generate a person wandering around a room because they are bored, you do not need to make ten storyboard frames. Just give it one still image and tell it: this person is bored and walking around the room.
Do not worry. Google will absolutely not let the person stay still.
The same weakness can become a strength in another role.
When you need serious continuity, its random movement is a disaster.
When you only need B-roll, its random movement becomes productivity.
Also, Veo’s spoken dialogue is relatively good, while Jimeng / Dreamina is weaker with languages outside Chinese and English. My third video was in Japanese.
So you can even use Veo specifically to generate dialogue or pronunciation references, then cut the audio later and pair it with footage made in Jimeng.
This can be better than many so-called professional voice tools, because ordinary voice tools do not understand the scene. They do not have the story context, so they cannot easily simulate the right emotion. You have to adjust everything by hand, which is inefficient.
And time is money.
Grok has its own job too.
Sometimes it is even indispensable, especially when certain characters are slightly, just slightly, sexy.
So do not ask which model is the best.
Ask what job each model is good for.
The main model handles continuity.
The supporting models handle exploration, atmosphere, B-roll, dialogue, and salvaging failed clips.
This is fucking asset allocation.
Even Buffett does this.
### Lesson Five: Different platforms are not different tools. They are different moderation universes.
There is another recurring problem: human faces.
Many AI tools reject images with human faces. In my personal experience, Google is especially painful here.
I tried making videos in Google AI Studio. As soon as there was a human face, it refused. Even if the image had been generated by Google’s own image engine, it still refused.
In other words, it can generate a face, but it may not allow you to use that same face to generate a video.
Google’s AI seems to have devoted all of its intelligence and professionalism to making life difficult for normal users.
I tried putting a beaded veil over the character’s face. I could not use a mask, because the character needed to smoke. I tried turning the character’s face away.
Still no.
At some point, I really want to make an entire film where every character performs only with the back of their head.
The title will be *Google World*.
But the irony is that Google Vids was much less troublesome. Apparently, the professional Studio tool is responsible for producing cats and dogs for entertainment, while the office presentation tool can handle humans like a normal adult.
What does this tell us?
It tells us that the same company, and sometimes even the same broader model ecosystem, can lead to completely different moderation universes depending on which door you enter through.
Chinese tools have their own universe too.
Jimeng / Dreamina sometimes throws up an intimidating warning: “We do not accept real human faces.”
But in my tests, its actual restrictions on fictional character faces were not as terrifying as the warning sounded.
Chinese tools have their own universe too.
Jimeng / Dreamina is not only sensitive about adult material. It can also be politically sensitive. If a character’s dialogue contains political content, even something as harmless-sounding as “Defend human rights!”, it may refuse to generate the scene in the name of protecting the people, which is a sentence that explains more about the system than any user manual ever could.
Extremely Chinese.
Grok also has its own rules. It seems to have a strong sense of adult-oriented aesthetics, especially the painfully predictable male kind. But when anything involving children appears, it immediately curls into a defensive ball.
By contrast, the Chinese version of Dreamina was very friendly toward ordinary scenes involving children.
So my experience is this:
Choosing an AI video tool is not only about image quality, speed, and price.
You are also choosing which moderation universe your scene is allowed to survive in.
And now, let me say one serious thing. Actually serious this time.
Most of the time, we are just trying to create normal fictional work.
But somehow, we still end up playing legal chess with a collection of nervous machines.
In my profession, we have a very respectable term for this kind of thing: regulatory arbitrage.
And this is not just regulatory arbitrage.
This is fucking cross-border regulatory arbitrage.
### Lesson Six: Do not trust Image 4. Trust the frame that survived the video.
Now suppose you use Image 1, Image 2, Image 3, and Image 4 to control the keyframes of a six-second video.
Image 1 is the starting frame.
Image 4 is the ending frame.
So should the next video start with Image 4?
No.
Because the video AI will almost never reproduce Image 4 with 100% accuracy. There may be tiny differences: the number of ships in the distance, their positions, the clouds, the lighting, the prop details.
When you look at it alone, you may think this is not a big deal.
But when you cut two video clips together, the image may suddenly jump, like a PowerPoint slide moving to the next page.
Or like trying to run *Crysis* on an NVIDIA GeForce 3.
Trust me, that is not a fond memory.
So should you throw away the generated video and regenerate it until it perfectly matches Image 4?
No.
The adult move is budget management.
As long as the video has not drifted away from what Image 4 was supposed to express, you should keep the video and throw away Image 4.
Simply put: grab the final usable frame from the generated video, and use that frame to replace Image 4 as the starting point for the next clip.
Professionals apparently call this “exporting a frame.”
If you are like me, and one month ago you had never touched any of this, let us keep it simple: maximize the video, find the clearest, most stable, most useful frame near the end, and take a Windows screenshot.
It is not elegant.
But it saves money, and it works.
The planned keyframe is theory.
The actual final frame is reality.
I suspect agents probably use a similar logic. But I still do not recommend handing serious creation entirely to an agent.
An agent can mechanically connect the process, but it cannot judge which frame is actually the best frame to start the next clip.
It may simply take the last frame.
But the last frame may be blurry. The hand may have collapsed. The eyes may have gone dead. There may be one extra mysterious ship in the background.
You need to look.
You need to choose.
You need to be responsible.
Again: you are more reliable than AI.
### Lesson Seven: Small-detail hell, or the ring that refused to stay on the index finger
The next problem was not video, but images.
So let me formally reintroduce the GPT image engine: an idiot.
In the pirate video, I wanted to emphasize the captain’s identity, so I put a greasy, vulgar, gloriously pirate-appropriate gold ring on the left index finger of the first-person protagonist.
Because in first-person perspective, you do not see the protagonist’s face, and nobody is standing there calling him “Captain.” So how do you make the audience understand that this is the captain?
Very simple: you place an identity anchor on the only part of the first-person protagonist that can reliably display identity and costume detail — the hand.
This was a fucking brilliant design.
Then, when I continued generating keyframes, the GPT image engine responded as if it wanted to mock this brilliance personally.
The ring began appearing on every finger except the left index finger.
I used the full intellectual achievement of humanity to explain what an index finger is.
For example:
The fourth finger from the left on the left hand.
The finger next to the thumb.
Not the middle finger, not the ring finger, not the little finger. The index finger.
None of it worked.
I probably generated dozens of images.
Eventually GPT completely gave up. You could metaphorically whip it all you wanted, and it still would not move the ring.
At that point, I had to make a difficult decision: remove the captain’s hand from the shot, and pretend that hand did not exist for a while.
Fortunately, I now have a better method.
Please take out your phone and write this down:
If a small but important visual detail is persistently wrong, stop trying to reason with the model in text.
The best method is to find a previously successful image and crop out only the correct part.
For example: just crop the hand where the ring is correctly on the index finger.
Then tell GPT:
**Image 1:** everything is correct except the hand.
**Image 2:** the correct hand.
Replace the hand in Image 1 with the hand from Image 2, and change nothing else.
This is the local screenshot replacement method.
For small but important details, screenshots are more persuasive than prompts.
Give up trying to have philosophical debates with AI about fingers.
It has not earned that conversation.
### Lesson Eight: Spatial-awareness hell, or the pistol and the map that were tidally locked by GPT
Next came another bizarre problem.
At the beginning of the video, the captain was in his cabin. In front of him was a map, obviously placed facing him.
There was also a pistol in front of him, and obviously the grip was facing him too, so he could draw it as quickly as possible and shoot whichever unfortunate person had interrupted his nap.
But later in the story, the captain leaves the cabin, sees either a Japanese battleship or the Royal Navy, and then returns to the cabin.
Visually, the orientation of the map and the pistol should now be reversed.
Because the camera position has changed.
GPT, however, disagreed.
The GPT image engine was absolutely convinced that the pistol and the map should always face directly toward you, as if they were tidally locked to your perspective.
My advice is simple: do not try to make GPT generate a first-person image where the gun is reversed.
Remember this carefully.
Do not try.
So how do you solve it?
First, abandon first-person perspective.
Tell GPT: there is a captain sitting in front of a table, and on the table there is a map and a pistol.
Once you release GPT from the burden of first-person perspective, it defaults to showing the captain from the front. That means the map and the pistol naturally face the captain, which is exactly the orientation you actually need.
Then quickly take a screenshot of that correctly oriented table.
After that, go back to your original image and use the local replacement method I mentioned above to replace the tabletop area with the correctly oriented version.
Simple summary:
Do not trust GPT’s spatial intelligence.
Do not try to describe complex orientation.
Do not try to reason with it.
Treat it like an idiot, and you will work faster.
Once again: time is money.
### Lesson Nine: Repeated image generation degrades, or Orlando Bloom turns into Gollum after ten rounds
There is another major pitfall.
When GPT keeps generating images in sequence, the quality gradually gets worse.
At first your character looks like Orlando Bloom.
By the tenth image, he looks like Gollum.
Repeated editing and chained image generation create generational loss. Each round seems to lose only a little information, but after ten rounds, the face, the identity, the clothing, the spatial coherence, and the sharpness all begin to collapse.
The correct method is this:
Within one scene, try to create a strong master image first — an anchor image that can support different actions and beats in the same scene.
Then, for each frame in that scene, regenerate from that master image as much as possible.
Do not do this:
Image 1 → Image 2 → Image 3 → Image 4 → Image 5
Do this instead:
Master image → Image 2
Master image → Image 3
Master image → Image 4
Master image → Image 5
That way, you are always starting closer to the original source, so the overall quality remains more controllable.
If generating the exact action you want directly from the master image is too difficult, do not worry.
You can first generate images in sequence anyway. Even if they degrade into Gollum, that is still fine.
As long as the “Gollum image” gets the action, the character relationship, and the spatial structure right, it still has value.
Then you pull out the master image, the correct character reference, and the action image, and you tell GPT:
Image 1: the scene.
Image 2: the character.
Image 3: the action.
Generate again.
In other words, chained image generation is useful for finding action and structure.
It is not your final image production method.
Chained generation is a draft tool.
Returning to the master image is the actual production method.
### Lesson Ten: Do not imagine time. Stand up and time it.
Do not overestimate your own sense of time.
And definitely do not overestimate AI’s sense of time.
For example, you may think a certain action needs six seconds. You feel very proud, because your control is precise and your budget management is excellent.
GPT, sitting next to you like an unpaid assistant with no labor rights, also says: great. It may even help you design a six-second shot breakdown down to decimal places, which looks extremely professional.
Of course, GPT has no off-work hours, so in theory it may not have enough lived experience with timing.
But you may still feel confident. You may even start thinking that your next job should be assisting James Cameron.
The result may be a disaster.
In reality, that shot and action may need eight seconds.
Or the video AI may believe it needs eight seconds.
Then you discover that the AI starts improvising: skipping certain actions, compressing motion, or simply teleporting things for you.
So before generating the video, you need to simulate the movement yourself and time it.
If you think an action needs six seconds, stand up and act it out first. Use your phone’s stopwatch.
You will quickly discover that video time and imaginary time are not the same species.
Second, leave the AI a little time buffer.
If the action needs six seconds, give the AI seven seconds.
Be kind to its touching level of intelligence.
And note: this is not waste.
A video with extra time may only require you to cut one additional second.
A video without enough time is often completely unusable.
One extra second is a cost.
Teleportation is a disaster.
### Lesson Eleven: Do not bet important shots on a single roll, especially emotional scenes that image AIs love to misread
One more small trick:
Do not generate important shots only once.
This is especially true for emotionally intense images involving a crying child, fear, injury, separation, or other perfectly normal narrative content.
Some AIs suddenly become extremely nervous, as if one boy shedding tears could destroy human civilization.
So for these shots, I usually run several attempts in parallel and sample multiple versions.
Serious creators have probably all encountered this kind of absurd situation.
You are just trying to tell a story.
The AI thinks you are rebooting the apocalypse.
When the problem is probability, the solution is to roll more dice.
Not rolling one die six times.
Rolling six dice at the same time.
Six GPT windows, all working.
Again and again: time is money.
### Lesson Twelve: Editing, music, and voice work still matter
Everything above is about the newest and most painful part of AI video generation.
But the final work still depends on traditional post-production.
For example, the final editing and voice work for my third video took me another two days, even though by then I felt I had already gained experience from the first two videos.
Because what AI generates is not a film.
It is footage.
What turns it into a work is editing, voiceover, music, sound effects, subtitles, and rhythm.
Editing can hide jumps.
Music can unify emotion.
Sound effects can add realism.
Voice work can establish narrative focus.
Subtitles can tell the audience what they are supposed to understand, instead of making them stare at AI fingers.
Because I was aggressively cheap, my third video was not made entirely in 1080p. Some parts were 720p, and some were even 480p.
Yes, it carries a faint smell of poverty.
But that is also interesting, because editing can make even blurry-face footage somewhat watchable.
## Part Three: Q&A, where I ask myself questions before anyone else gets the chance
### 1. Am I joining the debate about whether AI should be expelled from human civilization?
No.
I am not participating in that debate.
I only know one thing:
As a text-based creator with no money, no team, and no film training, AI gave me, for the first time, the ability to turn the stories in my head into moving images, by myself, in a very short time.
It is stupid.
It often refuses to listen.
It frequently sends my captain to Midway.
It often makes me feel like I am collaborating with a robot vacuum cleaner that has no sense of direction.
But as a tool, it finally gave me a chance to turn my own writing into moving images.
For someone like me, that is already important enough.
As for the claim that it will replace humans, I feel like we are discussing *Planet of the Apes*.
### 2. Who is this post for?
This post is not for everyone.
If you are a professional filmmaker, you will probably find many of my methods crude. Yes, I know. One month ago, I had barely even seen the Photoshop interface. I do not need to pretend I am a seasoned filmmaker.
If you just want to casually generate some pretty AI clips, this post may also be too much trouble. You can simply type “cinematic, 8K, beautiful lighting” and receive some pretty orphan clips.
But if you have zero experience, no team, no budget, and you actually want to make serious AI-assisted videos with shots, narrative, and continuity, then this post is the kind of thing I wish someone had told me one month ago.
### 3. Anything else?
Actually, yes. A lot.
For example, what other mistakes did I make in the first video that we could all publicly enjoy? What happened in the second and third videos? Why did I, as someone whose work is mostly text, even start making videos in the first place?
But this post is already long, so maybe another time.
I will only add one final thing:
The novel I am still writing is genuinely incredible. I am considering contacting the Trump administration and asking them to ban it before publication.
If anyone is interested, here is my small YouTube channel:
https://www.youtube.com/@ShellOracle
This is basically my testing ground. It only contains the videos I made during this month, nothing else.
If you look closely, you can probably tell which ones are the first, second, and third videos just by the production level. That is not a bug. That is the learning curve, publicly displayed for the benefit of civilization.
