Goose

Software Engineer
Built systems for 4M+ users.
Run an AI Army

[Goose @GooseWillBuild]

@Arnavmj tool

[Goose @GooseWillBuild]

@noctadn clone

[Goose @GooseWillBuild]

@romanbuildsaas LK

[Goose @GooseWillBuild]

build for fun until something gets weirdly popular, then build that for money. the 'fun' phase is just unpaid market research you didn't realize you were doing

[Thomas Trimoreau @TTrimoreau]

Don't build for fun. Build for money.

[Goose @GooseWillBuild]

@TTrimoreau half true. the fun builds are how you learn the tools fast enough to spot what'll actually pay. the boring money build usually rides on a 'for fun' experiment you already ran

[Goose @GooseWillBuild]

This is scary...

One prompt built a brand-new channel to ~$5K a month in eight weeks

[Goose @GooseWillBuild]

https://t.co/3LKahEk9zC

[Goose @GooseWillBuild]

https://t.co/3LKahEk9zC

[Goose @GooseWillBuild]

Hyper-realistic AI video for the World Cup just hit another level.

We wanted to see if GPT Image 2.0 + Kling 3.0 could create a viral "stadium fan" video that looks completely real.

The result?

- Cost: ~$1 per video
- Speed: ~10 minutes from concept to export
- Quality: Realistic crowd behavior, cinematic lighting, natural human emotion
- Scale: Infinite variations (change jerseys, expressions, locations, and stadiums in seconds)

This entire video was created with AI.

Want the exact workflow for creating hyper-realistic videos like this?

Check the comments 👇

[Goose @GooseWillBuild]

rt + reply "FACELESS" and I'll DM you the full playbook.(must follow so I can dm)

[Goose @GooseWillBuild]

your faceless clips feel off-beat and it's not the model. it's that you generated video before audio.

it was never the model. it's the handoff between models.

here's the full pipeline i actually run, in order, no skipped steps.

1. script first, before you touch any tool
write the voiceover as plain text.
if it doesn't read tight out loud, no model saves it.
this is where 80% of bad outputs are actually born.

2. voice in elevenlabs
generate the audio before the visuals.
you time your shots to the voice, not the other way around.
doing it backwards is why people's clips feel off-beat.

3. shots in veo / kling
one clip per line of script.
short prompts beat long ones here.
kling and veo fail differently, so i generate the same shot in both and keep whoever wins.

4. cleanup pass
bad hands, morphing faces, drifting backgrounds.
you will regenerate. budget for it instead of pretending it's one-shot.

5. assembly in capcut / ffmpeg
lay the elevenlabs track first.
drop each clip onto its line.
cut on the beat of the voice, not on vibes.

why this order works:
most people generate pretty clips, then try to glue a story on after.
that's backwards.
the script is the spine. audio is the clock. visuals are just b-roll hanging off both.

fix the order and your hit rate jumps before you ever touch a "better" prompt.

bookmark this before your next build, you'll want the step order when you're 4 clips deep and wondering why it feels off.

running a pipeline already?

rt + reply "FACELESS" and I'll DM you the full playbook.(must follow so I can dm)

[MAX @maxxmalist]

one of the offers that has generated MILLIONS OF DOLLARS online is military training

americans love this kind of stuff, and now you can literally create an AI veteran, make him famous on Instagram or YouTube, and promote almost any offer you want

it’s never been easier

[Goose @GooseWillBuild]

replacing an entire creative team with one prompt is fucking nuts

read how exactly how i did in the article below

(genuinely mindblowing)

[Goose @GooseWillBuild]

your faceless clips feel off-beat and it's not the model. it's that you generated video before audio.

it was never the model. it's the handoff between models.

here's the full pipeline i actually run, in order, no skipped steps.

1. script first, before you touch any tool
write the voiceover as plain text.
if it doesn't read tight out loud, no model saves it.
this is where 80% of bad outputs are actually born.

2. voice in elevenlabs
generate the audio before the visuals.
you time your shots to the voice, not the other way around.
doing it backwards is why people's clips feel off-beat.

3. shots in veo / kling
one clip per line of script.
short prompts beat long ones here.
kling and veo fail differently, so i generate the same shot in both and keep whoever wins.

4. cleanup pass
bad hands, morphing faces, drifting backgrounds.
you will regenerate. budget for it instead of pretending it's one-shot.

5. assembly in capcut / ffmpeg
lay the elevenlabs track first.
drop each clip onto its line.
cut on the beat of the voice, not on vibes.

why this order works:
most people generate pretty clips, then try to glue a story on after.
that's backwards.
the script is the spine. audio is the clock. visuals are just b-roll hanging off both.

fix the order and your hit rate jumps before you ever touch a "better" prompt.

bookmark this before your next build, you'll want the step order when you're 4 clips deep and wondering why it feels off.

running a pipeline already?

rt + reply "FACELESS" and I'll DM you the full playbook.(must follow so I can dm)

[Goose @GooseWillBuild]

it is INSANE that this sauce is free

[Alex @alexxgrowth]

https://t.co/fWHuJ4VrxU

[Goose @GooseWillBuild]

@noctadn NICHE

[Goose @GooseWillBuild]

rt + comment “AI” and i’ll send you the entire workflow(must be following for dm)

[Goose @GooseWillBuild]

Hyper-realistic AI video for the World Cup just hit another level.

We wanted to see if GPT Image 2.0 + Kling 3.0 could create a viral "stadium fan" video that looks completely real.

The result?

- Cost: ~$1 per video
- Speed: ~10 minutes from concept to export
- Quality: Realistic crowd behavior, cinematic lighting, natural human emotion
- Scale: Infinite variations (change jerseys, expressions, locations, and stadiums in seconds)

This entire video was created with AI.

Want the exact workflow for creating hyper-realistic videos like this?

Check the comments 👇

[Goose @GooseWillBuild]

faceless ai videos that don't look like ai. it was never the prompt.

the thing everyone gets wrong: they chase a magic prompt and ignore the stitch.

the model isn't your bottleneck. your edit is.

here's the exact stack i run, nothing held back.

1. script first, not visuals
write the 6-second beats before you touch any tool. one idea per cut. if the line doesn't earn a shot, kill it.

2. generate clips short, not long
ask for 4-6 second shots, never one long hero clip. short gens fail less and you can reroll one bad shot instead of the whole thing.

3. lock motion direction
same camera logic across every clip (push in, or hold, pick one). mixed motion is the #1 tell that screams ai.

4. voice last
record or generate vo against the locked cut, not before. timing the visuals to the voice is what kills the uncanny feel.

5. stitch in your editor, color in one pass
bring every clip into one timeline, grade them all together so they share a look. ungraded clips from different gens never match.

why this works:
the 'ai look' isn't the pixels. it's the inconsistency between clips.

fix consistency and a cheap gen reads like a real shoot.

the people winning aren't prompting better. they're editing like editors.

bookmark this before your next build, you'll want the stitch checklist when your clips don't match.

[Goose @GooseWillBuild]

@JosephKChoi List

[Goose @GooseWillBuild]

@adriansolarzz You’re right that consistency is mostly a workflow problem now.

But if consistency is just a skill issue, then you’d think every brand should have solved AI UGC by now.

What’s your stack for keeping character, voice, and mannerisms consistent across 100+ videos?

[Goose @GooseWillBuild]

the $30k number is the easy part to say. the hard part is delivering consistent face + voice across a month of output without it looking like 4 different people. that's the actual job

[Adrian Solarz @adriansolarzz]

it's kinda crazy how good AI UGC has gotten.

literally at a point where big brands pay $30k+/mo for you to run it for them:

[Goose @GooseWillBuild]

@XaviScales @maxxmalist kumar

[Goose @GooseWillBuild]

I don't want to name a specific provider (but I still will), the market shifts every few months and what's clean today is blacklisted tomorrow. The principles are non-negotiable:

-Paid only. Free VPNs share IP pools with thousands of automation users. Those IPs get blacklisted fast.

-Dedicated / static IP. Single most important factor. On a shared IP, one bad actor on the same address gets the entire IP blacklisted and every account on it including yours gets shadowbanned.

-Skip cheap providers. Low cost = recycled, overcrowded IP pools = exactly what you're trying to avoid.
Failure modes, in order of severity are Shadowban, Account ban, Device ID ban.
Providers worth looking at (verify on r/proxies and r/AffiliateMarket within the last 30 days before committing, pools rotate fast):

-Residential / ISP (one-phone setup): Rayobyte ISP (~$5/IP/mo, transparent pricing), IPRoyal Royal Residential (cheaper, popular in social-farming circles), NetNut (cleanest pool, pricier, sourced direct from ISPs not P2P). Bright Data / Oxylabs are enterprise-tier — overkill until 10+ devices.

-4G mobile (scaling past 3 personas): TheSocialProxy (~$90/mo per port, built specifically for social farming), AirProxy (US 4G, well-regarded for IG/TikTok), IPRoyal mobile (same dashboard if you're already on their residential).

-Avoid: anything labeled "rotating residential" with shared pools (fine for scraping, lethal for TikTok accounts), Smartproxy/Decodo residential (pool too contaminated for TikTok now), any provider whose checkout doesn't let you pick a single dedicated city/state IP.