u/Brave-Round-3573

Seven months ago I started making faceless short videos for YouTube Shorts and TikTok, monetized through their creator payment programs. Total earned so far: $1,143. I want to share the real numbers because this community actually cares about the math behind side income, not just the headline.

The person you see in my videos does not actually exist. I generated a synthetic face using AI, no real human and no deepfake involved, and that same character appears across everything I make. I mention this in every video description. I bring this up because it matters for the cost equation. I don't hire anyone. I don't film anything. The entire pipeline is me at my laptop.

The workflow goes like this: I write a short script, run the audio through a free text to speech voice, generate the character frames in APOB AI since it returns the same face every time I run a new still and the daily free usage covers my output, then cut and time everything in CapCut. Each video takes about an hour from blank page to upload, sometimes more when I have to redo shots.

Here is how the monthly revenue actually played out. January: $22. February: $47. March: $89. April: $143. May: $198. June: $287. July: $357. The curve looks encouraging until you count the hours.

I averaged about five hours a week across those thirty weeks. That is roughly 150 hours of work, which puts me at $7.62 an hour before taxes. I could earn more doing almost anything else. I'm not going to pretend otherwise.

The biggest technical problem right now is that facial expressions drift between frames in AI generated video. Eyes shift, mouths move wrong, something just looks off. I'd estimate about one in four clips I generate end up getting tossed because the result falls into uncanny territory. I've learned to generate more stills than I need and lean heavily on image sequences with panning and zooms rather than actual AI generated motion, because still images are where consistency actually holds up. Full motion video for faces is not there yet.

My first two months were also mostly wasted. I had no feel for pacing or hooks in short form content, and most of those early videos got fewer than 200 views. That was roughly sixty hours of learning I won't get back.

For the lean FIRE angle: $1,143 at a 4% withdrawal rate replaces about $46 of annual spending. My total yearly expenses run about $19,200, so seven months of this work moved the needle by 0.24%. Not exactly transformative. If the growth holds and I can sustain $500 a month, that's $6,000 a year, which would cover my entire grocery line and realistically shave a couple years off my timeline. That is a meaningful if. I'm not banking on it.

What I will say is the marginal cost is close to zero. Everything I use has a free tier. No inventory, no shipping, no customer service. The older videos keep earning views while I'm at my day job, and about a third of my July revenue came from videos I posted back in March and April. Whether that counts as passive is debatable, but the earning tail on older content is real.

I'm keeping at this because the trajectory is still pointing up and I genuinely enjoy the creative part more than I expected. But if someone told me they were building their FIRE plan around AI video side income, I'd tell them to max out tax advantaged accounts first and treat this as what it is: a small, uncertain, supplemental stream that might become meaningful or might plateau next month.

I work as a Customer Success Manager in Toronto, and most weeks I have more client calls than I can properly process.

For a long time, I tried to take detailed notes during every meeting. I thought I was being productive, but I realized I was spending half the conversation trying to capture every sentence instead of actually listening. The worst part was after the call, I would have pages of notes but still spend another 30 minutes figuring out what actually mattered.

A few months ago, I changed my workflow. I started recording longer conversations (when everyone was okay with it) and focused more on being present during the call. Afterward, I reviewed the important parts, especially decisions, customer feedback, and things I needed to follow up on.

I used a few transcription tools, and I choose vomo ai for some of my longer recordings because it gives me a transcript plus a structured summary. I still review the original audio when something important comes up, but it saves me from manually organizing every conversation.

I actually think this is pretty good. I do have to clean up notes for a few clients though. but it changed the way I think about meetings. It was turning conversations into something I could actually use later.

I've been rethinking my meeting notes. And what are other marketers doing these days?

bumped my app to 19.2.8 a couple weeks back, mostly for the patch whose only note was performance improvements when decoding for server components. wasn't expecting much, but it got me poking around the parts of the app that still fetch on the client, and that turned into an afternoon of reading my own useEffect calls.

a lot of them were ai first drafts from a few months ago. the pattern was always the same: fetch inside useEffect, set some state, no cleanup, no cancellation, and in two spots the exhaustive-deps lint rule was disabled with a little comment so it would stop complaining. it all looked fine. i had clicked through it by hand when it was written and it worked.

the thing it hid was a race. one component refetched on a route param change, and if you navigated fast enough the older response could resolve last and overwrite the newer one. you never see that clicking around manually because no human navigates that quick.

what actually caught it was running the real test suite after i refactored to an abort-on-change version. i use verdent partly because it runs type checks and tests after each edit and takes a pass at repairing whatever breaks, so the broken intermediate state doesn't just sit there green in the editor. worth saying this only worked because an existing test happened to navigate quickly. the type checker and the static analysis said nothing about the race. tests only catch what you wrote a test for.

now i'm wondering how much of the client fetching in this codebase should just be a server component now that the rsc decoding path is getting faster, and how much of it the ai only reached for because useEffect is the pattern it has seen the most.

Moved in February and the kitchen came with a colander, one wooden spoon, and a sieve with a hole in it. Everything else belonged to my old flatmate. I started from scratch, fully convinced the smartest way to restock kitchen basics was sorting every online shop by price and buying the bottom line.

Ended up splitting the order across three places. Argos for a 3-piece stainless steel saucepan set (£26 click & collect). Ikea for a £12 non-stick pan and a £4 bamboo board. Dunelm online for baking tins and tea towels (£34 total). Delivery was £3.95, but Coupert knocked £4.80 off at checkout, which on a £34 order covered shipping and a bit extra. TopCashback or Quidco are what most people round here default to, but I'd forgotten to click through before paying, so the extension saved me from my own distraction.

Knives were where this strategy collapsed. Wilko had a 5-piece knife set with a board for £11.99. Five knives for less than the price of one felt like beating the system. It wasn't. They arrived dull, so I steeled them and got two weeks out of them before the chef's knife handle gave way. The plastic collar loosened, and halfway through a butternut squash the blade turned in my hand. Nothing bad happened, but I put it down and never touched it again.

That was the lesson: cheap is fine for anything that's just a shape. Wooden spoons, mixing bowls, baking trays—paying extra is daft. But anything with an edge or a heating element can actually hurt you or fail immediately. I replaced the whole 5-piece set with a single £15 Vardagen cook's knife from Ikea. It's the only knife I use now, and I haven't missed the other four once.

The bad Wilko set is still sitting in the drawer under the sink. Not being used, just waiting until I can be arsed to take it to the recycling tip.

Channel is in the tech explainer space. 20k subs, been doing this for about 2 years. I want to talk about something I've been testing quietly for the last 6 months: AI-generated B-roll.

I didn't want to be the first person to try this. I waited until I saw other channels in my niche doing it and nobody in the comments was calling them out. Then I started slowly.

My process: I write the script, record the voiceover, then go through the timeline and mark every spot where I need B-roll. About 60% of those spots I can fill with screen recordings, stock footage, or graphics I make myself. The other 40% are conceptual shots. "the internet is like a series of tubes." "data flows through these pathways." "your information travels across the world in milliseconds." You can't film that.

For those conceptual shots, I generate them in PixVerse. I describe the visual metaphor, it spits out a few seconds of footage. I apply the same color grade and a tiny bit of film grain to blend it with the rest of the video. The grain is key. AI footage looks too clean. A little noise makes it sit naturally with everything else.

6 months, about 40 videos. I got one comment asking if a particular shot was "CGI." I said yes, sort of. That was it. No mass outrage, no noticeable drop in watch time. My watch time has actually gone up slightly, but that's probably just channel growth, not the B-roll.

The hard rule I follow: I never use AI B-roll for anything that's supposed to be factual. If I'm talking about a specific event, person, or real product, I use real footage or photos. The AI B-roll is only for abstract concepts and metaphors. If you use it to fake a real event, you're going to get caught and it's going to be bad.

Still not sure if I'll keep doing it forever. But for now it saves me time on shots I could never film anyway.

I write a personal finance blog. Index funds, emergency funds, tax stuff. Boring but helpful.

Built an automation: RSS → automation tool → LLM (script) → TTS (voiceover) → PixVerse API (b-roll) → tiktok. Runs itself. I publish a post, 15 minutes later there's a video. $44/month, ~120 videos.

Worked perfectly for three weeks.

Then I published a post about bankruptcy. What to do if you're facing it. The automation turned it into a peppy 30-second tiktok with upbeat music, fast cuts, and a cheerful voiceover explaining wage garnishment.

12 comments before I caught it.

"Why is this guy so excited about bankruptcy"
"This is the most unhinged finance content I've ever seen"
"Bro is grinning through foreclosure"

The pipeline had no idea the article was sad. PixVerse just gets scene descriptions. It doesn't know "foreclosure" shouldn't look like a travel montage.

Added a sentiment filter. Claude scores the post mood first. Serious posts get slower visuals, darker palettes, no music. Neutral posts keep the standard treatment.

Cost is still $44/month. But I now review anything with a negative sentiment score before it goes live.

Automation is great until it misses context a human would never miss.

Been generating AI videos for about a year now. one thing i keep seeing: people writing video prompts like they're talking to GPT-4. they're not the same thing. at all.

The core difference: text models predict the next token. video models predict the next frame. obvious when you say it out loud, but the implications are huge. a text model needs to understand coherence. a video model needs to understand physics, motion, how things actually move through space.

"a cat on a mat" gives you a static image. "a cat leaping onto a mat, paws extending forward, landing with a soft thud, tail flicking" gives you a video. the prompt has to describe the movement, not just the content.

A few things that actually work:

Temporal adverbs matter way more than you'd think. "slowly" vs "quickly" vs "gradually" vs "suddenly", these aren't decorative words. they're telling the model how fast to move. "gradually" produces the most natural motion for most things i've tried.

The camera is a character. text models don't have a camera. video models do. "close-up" vs "wide shot" vs "tracking shot" vs "static camera" changes the entire feel of the output. a beautiful scene with a static camera feels completely different from the same scene with a slow pan. the camera choice is part of the storytelling, not an afterthought.

Lighting first, mood second, subject third. i used to write "a dragon in a cave, dramatic lighting." inconsistent as hell. now i write "low-angle warm light from cave entrance, dusty atmosphere, tense mood, a dragon stirring in the shadows." the lighting sets the scene, the mood gives the tone, and the subject is the last thing the model needs to figure out. way more consistent results.

Tested these across PixVerse, Runway, and Kling. the principles hold up across all of them, though the syntax changes a bit. PixVerse handles natural language better, Runway wants more technical terms, Kling is somewhere in between.

The thing i keep coming back to: the camera is the most underrated tool in video prompting. most people just describe the scene and forget the camera exists. but the camera is the difference between a video that looks like cctv footage and one that looks like cinema.

Made a micro saas over about six weeks that does one narrow thing. You paste a blog post url and it spits out three short vertical clips you can post to promote it. Niche, but a few content marketers are paying for it so it works.

Sharing the build because the interesting decisions were all about not building the hard parts myself.

Here is how the pipeline runs. Scrape and summarize the post into three hooks, that part is a normal LLM call. Generate a background visual per hook, i did not want to train or host any video model so this is just an API call to a hosted generator. I am using seedance 2.0 because it exposes an API endpoint and the per credit cost made my unit economics actually work out. Then overlay the text and captions, that is just ffmpeg and a template.

It is a standard REST endpoint, you send a prompt and reference image and poll for the result. Nothing fancy. The part that took time was mapping their credit cost per second back to my own pricing so I did not undercharge myself.

Honestly, the only original code i wrote is the orchestration and the caption styling. Everything heavy is someone elses model behind an API. That used to feel like cheating, now it just feels like how you ship a micro saas without a team.

Margins are thin because every generation costs me real credits, so i had to add a small monthly cap per plan or i would lose money on power users. That cap was the single most important pricing decision i made.

Next on the list is caching common visuals so i stop paying to regenerate near identical backgrounds for similar topics.

Set up a jump box at a remote site last month. Unbound forwarding to Cloudflare DoH, policy routing so the client VLAN's queries hit the box, SNAT on egress. Verified from the box with dig and tcpdump, looked clean. Marked it done.

Then I ran a browser side leak check from an Ubuntu 22.04 client behind that box. DNS was resolving through the ISP's 100.64.x.x resolver (CGNAT range). The routing wasn't the issue; I'd left that ISP nameserver as a fallback line in /etc/resolv.conf on the client, and the browser's built in DoH was off, so the stub resolver happily used it. One entry I forgot to remove after initial provisioning, sitting there for weeks while I assumed everything was encrypted.

Pointed the stub at 127.0.0.1 only, rescanned, clean. Ten minutes to fix something I'd have missed indefinitely by only testing from the box itself. The queries were leaving the tunnel the entire time and I had no idea because my verification never actually ran from a client. Browser side check is on the deployment checklist now, right after the dig and tcpdump from the box that apparently don't count for much on their own.

ordered the official concert hoodie, did not expect this level of warmth. its thick. like winter jacket thick. wearing it to work tomorrow coz the office is freezing. bought it with some acrylic stands from the jp store through onemall. they consolidated everything into one box and removed the plastic hangers to save weight. thoughtful. hoodie is soft and the print is crisp. very happy.