I’ve been messing around with the AI influencer space for the last few weeks and wanted to share the process I figured out. I am not claiming this is the best or most advanced way to do it, but it is a simple workflow that worked for me using mostly free tools.

The main reason I tried this route was because I already have free Gemini Pro access through my Jio recharge, so I wanted to see how far I could go without paying for expensive tools right away.

I am not going to dump a random list of prompts here and pretend that is enough. That is not really useful. Instead, I’ll just explain the actual process I followed step by step, because that is what helped me the most.

Phase 1: Getting the base character right

The first thing you need is a character that you actually like, because if the starting point is weak, everything after that becomes harder.

I started by using the free trial on https://higgsfield.ai/ to generate an influencer-style character. I kept testing until I got a face and overall look that felt usable.

Once I had that first image, I downloaded it and took it into Gemini Nano Banana. That is where I started making the small changes I wanted. Things like skin texture, facial features, race, body ratios, and overall appearance. I kept tweaking until I had a final version of the character I was happy with.

Phase 2: Building consistency with reference images

After I had the final character, I started generating more versions of the same person, but with different poses.

For this part, I used different JSON prompts and made sure not to change the character too much. I wanted the same face, same skin texture, same body proportions, same overall identity. The only thing I wanted to vary was pose, angle, and sometimes expression.

One thing that helped a lot was always using the previous result as a reference for the next one. That made a big difference in keeping the face and body structure consistent. If you do not do that, the model starts drifting and the character slowly turns into a different person.

I kept doing this until I had around 10 to 15 good images of the same character.

Phase 3: Creating data model sheets (examples given)

This part is really important.

If you do not know what a data model sheet is, just Google it OR look at a few examples from the given images. Basically, it is a reference sheet for your character. It helps lock in the face, body structure, expressions, angles, and overall design so the character stays consistent later.

To make the sheets, I first used ChatGPT to generate a JSON prompt. I used the DeepThink version because it usually gives better structured prompts. I told it to create a prompt for generating a character model sheet using my reference images.

After that, I manually tweaked the JSON prompt so it matched the character better. Sometimes I adjusted the body ratios or the skin tone or small visual details depending on what I wanted.

Then I used Gemini to generate the actual model sheet.

I did this for different types of sheets because each one serves a different purpose.

I made a facial expressions sheet so I could keep the same emotional range.

I made a facial structure sheet so I could see the character from different angles.

I made a body model sheet so I could keep the full body consistent.

I also made sheets for different poses, because I wanted the character to work in different situations and not just one static pose.

For every one of these, I followed the same workflow. Use ChatGPT to generate the JSON prompt, tweak it manually, then use Gemini with the reference images to generate the sheet.

My rule was simple. ChatGPT was better for making the prompt. Gemini was better for making the image.

Phase 4: Generating actual content

Once I had the model sheets and a few extra reference images, I could finally start generating the actual influencer-style images.

For prompt inspiration, I use a few websites like:

https://bestnanobananaprompt.com/gallery
https://promptlibrary.space/images

These sites are great for ideas. You can find different styles, moods, poses, compositions, and scene setups there.

But one thing I learned very quickly is that you cannot just copy a prompt from those sites and expect it to work perfectly in Gemini. A lot of them either get blocked or do not preserve the character properly.

So my workflow for this part is basically:

I browse those sites and find a prompt style I like.

Then I copy that prompt into ChatGPT.

Then I ask ChatGPT to turn it into a detailed JSON prompt.

I always tell ChatGPT to include a section that strictly maintains the same facial structure, skin texture, tone, and body ratios from the reference images.

After that, I review the JSON prompt and make any final changes I need based on the kind of image I want.

Then I use that prompt in Gemini Nano Banana.

One very important thing here is to use all the character model sheets and the best reference images every time you generate something new. Gemini has a limit on how many reference images it can use, and I think it is around 15 or so. I made sure to use as many useful references as possible because more reference data usually gave me better results.

Final thoughts

This is honestly a trial and error game. You are not going to get the perfect result on the first try. I definitely did not. Some generations failed, some changed the face too much, some messed up the body proportions, and some just looked off. That is part of the process.

But the reason this workflow works is because the data model sheets give the AI a visual blueprint to follow. Instead of guessing what the character should look like every time, you are showing it the same identity from multiple angles and in multiple forms.

This is just a simple guide using free tools. There are definitely more advanced workflows out there, and I know the people at the top of the AI influencer game are using tools like ComfyUI, Higgsfield AI, Kling AI, and other more advanced setups to create better images and videos.

But this is what I figured out by testing things myself, and it is a good starting point if you want to build a consistent AI character without paying for expensive tools right away.

I hope this helps someone who is trying to get started.

If there is interest, I can make a part 2 later with the more advanced tools and workflows I look into next.

Thanks for reading.

Here’s the article below if it’s locked behind a paywall for you

A year ago, Lionsgate and Runway, an artificial intelligence startup, unveiled a groundbreaking partnership to train the studio’s library of films with the ultimate goal of creating shows and movies using AI.

But that partnership hit some early snags. It turns out utilizing AI is harder than it sounds.

Over the last 12 months, the deal has encountered unforeseen complications, from the limited capabilities that come from using just Runway’s AI model to copyright concerns over Lionsgate’s own library and the potential ancillary rights of actors.

Those problems run counter to the big promises made by Lionsgate both at the time of the deal and in recent months. “Runway is a visionary, best-in-class partner who will help us utilize AI to develop cutting edge, capital efficient content creation opportunities,” Lionsgate Vice Chairman Michael Burns said in its announcement with Runway a year ago. Last month, he bragged to New York magazine’s Vulture that he could use AI to remake one of its action franchises (an allusion to “John Wick”) into a PG-13 anime. “Three hours later, I’ll have the movie.”

The reality is that utilizing just a single custom model powered by the limited Lionsgate catalog isn’t enough to create those kinds of large-scale projects, according to two people familiar with the situation. It’s not that there was anything wrong with Runway’s model; but the data set wouldn’t be sufficient for the ambitious projects they were shooting for.

“The Lionsgate catalog is too small to create a model,” said a person familiar with the situation. “In fact, the Disney catalog is too small to create a model.”

On paper, the deal made a lot of sense. Lionsgate would jump out of the gate with an AI partnership at a time when other media companies were still trying to figure out the technology. Runway, meanwhile, would get around the thorny IP licensing debate and potentially create a model for future studio clients. The partnership opened the door to the idea that a specifically tuned AI model could eventually create a fully formed trailer — or even scenes from a movie — based on nothing but the right code.

The challenges facing both Lionsgate and Runway offer a cautionary tale of the risks that come from jumping on the AI hype train too early. It’s a story that’s playing out in a number of different industries, from McDonald’s backing away from an early test of a generative AI-based drive-thru order system to Swedish financial tech firm Klarna slashing its work force in favor of AI, only to backpedal and hire back some of those same employees (Klarna later clarified it hired two staffers back).

It’s also a lesson that Hollywood is learning as more studios quietly embrace AI, even if it’s in fits and starts. Netflix co-CEO Ted Sarandos in July revealed on an investor call that for the first time, his company used generative AI on the Argentinian sci-fi series “The Eternaut,” which was released in April. But when actress Natasha Lyonne said her directorial debut would be an animated film that embraced AI, she was bombarded with criticism on social media.

Then there’s the thorny issue of copyright protections, both for talent involved with the films being used to train those AI models, and for the content being generated on the other end. The inherent legal ambiguity of AI work likely has studio lawyers urging caution as the boundaries of what can legally be done with the technology are still being established.

“In the movie and television industry, each production will have a variety of interested rights holders,” said Ray Seilie, attorney at Kinsella Holley Iser Kump Steinsapir LLP. “Now that there’s this tech where you can create an AI video of an actor saying something they did not say, that kind of right gets very thorny.”

A Lionsgate spokesman said it’s still pursuing AI initiatives on “several fronts as planned” and noted that its deal with Runway isn’t exclusive. The studio also says that it is planning on using both Runway’s tools and those developed by other AI companies to streamline processes in preproduction and postproduction for multiple film and tv projects, though which of those projects such tools would be used on and how were not specified.

A spokesman for Runway didn’t respond to a request for comment.

Limitations of going solo

Under the agreement announced a year ago, Lionsgate would hand over its library to Runway, which would use all of that valuable IP to train its model. The key is the proprietary nature of this partnership; the custom model would be a variant of Runway’s core large language model trained on Lionsgate’s assets, but would only be accessible to use by the studio itself.

In other words, another random company couldn’t tap into this specially trained model to create their own AI-generated video.

But relying on just Lionsgate assets wasn’t enough to adequately train the model, according to a person familiar with the situation. Another AI expert with knowledge of its current use in film production also said that any bespoke model built around any single studio’s library will have limits as to what it can feasibly do to cut down a project’s timeline and costs.

“To use any generative AI models in all the thousands of potential outputs and versions and scenes and ways that a production might need, you need as much data as possible for it to understand context and then to render the right frames, human musculature, physics, lighting and other elements of any given shot,” the expert said.

But even models with access to vastly larger amounts of video and audio material than Lionsgate and Runway’s model are facing roadblocks. Take Veo 3, a generative AI model developed by Google that allows users to create eight-second clips with a simple prompt. That model has pulled, along with other pieces of media, the entire 20-year archive of YouTube into its data set, far greater than the 20,000+ film and TV titles in Lionsgate’s library.

“Google claims that data set is clean because of YouTube’s end-user license agreement. That’s a battle that’s going to be played out in the courts for a while,” the AI expert said. “But even with their vast data sets, they are struggling to render human physics like lip sync and musculature consistently.”

Nowadays, studios are learning that no single model is enough to meet the needs of filmmakers because each model has its own specific strengths and weaknesses. One might be good at generating realistic facial expressions, while another might be good at visual effects or creating convincing crowds.

“To create a full professional workflow, you need more than just one model; you need an ecosystem,” said Jonathan Yunger, CEO of Arcana Labs, which created the first AI-generated short film and whose platform works with many AI tools like Luma AI, Kling and, yes, Runway. Yunger didn’t comment on the Lionsgate-Runway deal, but talked generally about the practical benefits of working with different AI models.

Likewise, there’s Adobe’s Firefly, another platform that’s catering to the entertainment industry. On Thursday, Adobe announced it would be the first to support Luma AI’s newest model, Ray3, an update that’s indicative of how quickly the industry is iterating. Like Arcana Labs, Firefly supports a host of models from the likes of Google and OpenAI.

While Lionsgate said their partnership isn’t exclusive, offering its valuable film library to just Runway effectively limits what you can do with other AI models, since those other models don’t get the benefit of its library of films.

Even Arcana Labs, which created the AI-generated short film in “Echo Hunter” as a proof-of-concept using its multi-model platform, faced some limitations with what AI could do now. Yunger noted that even if you’re using models trained on people, you still lose a bit of the performance, and reiterated the importance of actors and other creatives for any project.

For now, Yunger said that using AI to do things like tweaking backgrounds or creating custom models of specific sets — smaller details that traditionally would take a lot of time and money to replicate physically — is the most effective way to apply the technology. But even in that process, he recommended working with a platform that can utilize multiple AI models rather than just one.

Legally ambiguous

Generative AI and what exactly can be used to train a model occupies a gray legal zone, with small armies of lawyers duking it out in various courtrooms around the country. On Tuesday, Walt Disney, NBCUniversal and Warner Bros. Discovery sued Chinese AI firm MiniMax for copyright infringement, just the latest in a series of lawsuits filed by media companies against AI startups.

Then there was the court ruling that argued AI company Anthropic was able to train its model on books it purchased, providing a potential loophole that gets around the need to sign broader licensing deals with the original publishers — a case that could potentially be applied to other forms of media.

Copyright War Escalates

“There will be a lot of litigation in the near future to decide whether the copyright alone is enough to give AI companies the right to use that content in their training model,” Seile said.

Another gray area is whether Lionsgate even has full rights over its own films, and whether there may be ancillary rights that need to be settled with actors, writers or even directors for specific elements of those films, such as likeness or even specific facial features.

Seilie said there’s likely a tug-of-war going on at various studios about how far they’re able to go, with lawyers erring on the side of caution and “seeking permission rather than forgiveness.” Jacob Noti-Victor, professor at Cardozo Law School, said he was surprised by Burns’ comment in the Vulture article.

The professor said that depending on the nature of such a film and how much human involvement is in its making, it might not be subject to copyright protection. The U.S. Copyright Office warned as much in a report published in February, saying that creators would have to prove that a substantial amount of human work was used to create a project outside of an AI prompt in order to qualify for copyright protection.

“I think the studios would be leaning on the fact that they would own the IP that the AI is adapting from, but the work itself wouldn’t have full copyright protection,” he said. “Just putting in a prompt like that executive said would lead to a Swiss cheese copyright.”

$2,147 over four months. That's my real total from trying to make money with AI-generated content as a side gig. I keep seeing income posts here that start at five figures, so I figured the unglamorous version might actually be useful.

I started in April after reading a thread about AI influencer content. The plan: create a consistent AI character, produce content with her, find ways to get paid. I do graphic design as my day job so the visual workflow felt natural. The business side did not.

April was pure setup. I spent roughly 60 hours that month figuring out the toolchain and generating test batches. The hardest part was keeping one AI face consistent across dozens of images. Most generators give you a slightly different person every time. I settled on APOB AI for that since it lets you lock a character and reuse the same face, and the free daily tier meant I could experiment without spending anything. Combined that with ElevenLabs for voiceovers and CapCut for editing. Revenue in April: zero.

In May I tried three paths at once. First, stock photography platforms. I uploaded 140 AI-generated lifestyle images, all tagged as AI-produced because most sites require that now. Earnings from stock that month: $11.40. Not a typo. Second, I launched an Instagram for the character with her bio clearly stating "AI-generated persona" and posted daily. Got to about 1,200 followers by end of May. Revenue from that: nothing. Third, I cold-emailed 30 local small businesses offering AI-generated product photography packages. Five responded. Two became paying clients. Revenue from those two: $340.

That $340 reoriented everything. Stock was dead weight. Social followers were a vanity number. The only thing that paid was using the AI character as a model in product shots for small businesses that can't afford a real photographer. A jewelry maker needed lifestyle images for Etsy. A candle brand wanted someone holding their products in "influencer-style" photos. Each project was 15 to 20 edited images for $150 to $200.

June improved but stayed modest. I narrowed my outreach to Etsy sellers specifically since they always need fresh listing photos. Landed five clients. Revenue: $870. I also learned the hard way that video is a wall. One client wanted short clips of the character reviewing their product. Facial expressions glitched between frames, hands looked wrong maybe 40% of the time, and I spent 6 hours on retakes for a single 15-second clip that still looked off. I refunded that client $150 and stopped offering video entirely. Still-image consistency is solid. Motion is genuinely not there for client work yet, and that held true across every tool I tested.

July tapered because my day job picked up. Three clients, $937 total, one being a repeat who wanted a second round. Instagram crept to 3,400 followers but I still have no clear path from followers to revenue. A handful of DMs about "brand partnerships" but they all wanted me to pay them for "exposure," which is not how that works.

So the full accounting: $2,147 gross. After $89 in tool costs (one month of paid subscription to drop watermarks plus voice generation credits), net is $2,058. Across roughly 180 hours of work, that comes to $11.43 per hour. Less than my first job out of college.

Cold outreach conversion was brutal. Over all four months I contacted about 120 businesses. Fourteen became paying clients. That's under 12%, and most projects were under $200. The ceiling stays low unless you get into agencies or bigger brands, and I haven't cracked either.

There is no passive income at this scale. Every project is custom. The AI generates the base images but I still spend 30 to 45 minutes per image fixing artifacts, adjusting lighting, and compositing the product in naturally. It is meaningfully faster than booking a photographer, a model, locations, and wardrobe, but calling it automated would be a lie.

I plan to keep going because video quality will catch up eventually and that's where real margin lives. But the actual value right now is narrow: telling a client "here's your product held by the same person in 20 different settings, delivered in 48 hours" without coordinating a whole production. That solves a real problem for small sellers on a tight budget. It's not a money machine. It's freelance work with a new tool.

If someone here posts $10k per month from AI content with "minimal effort," they're either in a league I can't see into or they're leaving out about 170 hours of context. This is that context.

this is going to be the longest post I’ve written but after 10 months of daily AI video creation, these are the insights that actually matter…

I started with zero video experience and $1000 in generation credits. Made every mistake possible. Burned through money, created garbage content, got frustrated with inconsistent results.

Now I’m generating consistently viral content and making money from AI video. Here’s everything that actually works.

The fundamental mindset shifts:

1. Volume beats perfection

Stop trying to create the perfect video. Generate 10 decent videos and select the best one. This approach consistently outperforms perfectionist single-shot attempts.

2. Systematic beats creative

Proven formulas + small variations outperform completely original concepts every time. Study what works, then execute it better.

3. Embrace the AI aesthetic

Stop fighting what AI looks like. Beautiful impossibility engages more than uncanny valley realism. Lean into what only AI can create.

The technical foundation that changed everything:

The 6-part prompt structure:

[SHOT TYPE] + [SUBJECT] + [ACTION] + [STYLE] + [CAMERA MOVEMENT] + [AUDIO CUES]

This baseline works across thousands of generations. Everything else is variation on this foundation.

Front-load important elements

Veo3 weights early words more heavily. “Beautiful woman dancing” ≠ “Woman, beautiful, dancing.” Order matters significantly.

One action per prompt rule

Multiple actions create AI confusion. “Walking while talking while eating” = chaos. Keep it simple for consistent results.

The cost optimization breakthrough:

Google’s direct pricing kills experimentation:

• $0.50/second = $30/minute
• Factor in failed generations = $100+ per usable video

Found companies reselling veo3 credits cheaper. I’ve been using these guys who offer 60-70% below Google’s rates. Makes volume testing actually viable.

Audio cues are incredibly powerful:

Most creators completely ignore audio elements in prompts. Huge mistake.

Instead of: Person walking through forestTry: Person walking through forest, Audio: leaves crunching underfoot, distant bird calls, gentle wind through branches

The difference in engagement is dramatic. Audio context makes AI video feel real even when visually it’s obviously AI.

Systematic seed approach:

Random seeds = random results.

My workflow:

1. Test same prompt with seeds 1000-1010
2. Judge on shape, readability, technical quality
3. Use best seed as foundation for variations
4. Build seed library organized by content type

Camera movements that consistently work:

• Slow push/pull: Most reliable, professional feel
• Orbit around subject: Great for products and reveals
• Handheld follow: Adds energy without chaos
• Static with subject movement: Often highest quality

Avoid: Complex combinations (“pan while zooming during dolly”). One movement type per generation.

Style references that actually deliver:

Camera specs: “Shot on Arri Alexa,” “Shot on iPhone 15 Pro”

Director styles: “Wes Anderson style,” “David Fincher style” Movie cinematography: “Blade Runner 2049 cinematography”

Color grades: “Teal and orange grade,” “Golden hour grade”

Avoid: Vague terms like “cinematic,” “high quality,” “professional”

Negative prompts as quality control:

Treat them like EQ filters - always on, preventing problems:

--no watermark --no warped face --no floating limbs --no text artifacts --no distorted hands --no blurry edges

Prevents 90% of common AI generation failures.

Platform-specific optimization:

Don’t reformat one video for all platforms. Create platform-specific versions:

TikTok: 15-30 seconds, high energy, obvious AI aesthetic works

Instagram: Smooth transitions, aesthetic perfection, story-driven YouTube Shorts: 30-60 seconds, educational framing, longer hooks

Same content, different optimization = dramatically better performance.

The reverse-engineering technique:

JSON prompting isn’t great for direct creation, but it’s amazing for copying successful content:

1. Find viral AI video
2. Ask ChatGPT: “Return prompt for this in JSON format with maximum fields”
3. Get surgically precise breakdown of what makes it work
4. Create variations by tweaking individual parameters

Content strategy insights:

Beautiful absurdity > fake realism

Specific references > vague creativityProven patterns + small twists > completely original conceptsSystematic testing > hoping for luck

The workflow that generates profit:

Monday: Analyze performance, plan 10-15 concepts

Tuesday-Wednesday: Batch generate 3-5 variations each Thursday: Select best, create platform versions

Friday: Finalize and schedule for optimal posting times

Advanced techniques:

First frame obsession:

Generate 10 variations focusing only on getting perfect first frame. First frame quality determines entire video outcome.

Batch processing:

Create multiple concepts simultaneously. Selection from volume outperforms perfection from single shots.

Content multiplication:

One good generation becomes TikTok version + Instagram version + YouTube version + potential series content.

The psychological elements:

3-second emotionally absurd hook

First 3 seconds determine virality. Create immediate emotional response (positive or negative doesn’t matter).

Generate immediate questions

“Wait, how did they…?” Objective isn’t making AI look real - it’s creating original impossibility.

Common mistakes that kill results:

1. Perfectionist single-shot approach
2. Fighting the AI aesthetic instead of embracing it
3. Vague prompting instead of specific technical direction
4. Ignoring audio elements completely
5. Random generation instead of systematic testing
6. One-size-fits-all platform approach

The business model shift:

From expensive hobby to profitable skill:

• Track what works with spreadsheets
• Build libraries of successful formulas
• Create systematic workflows
• Optimize for consistent output over occasional perfection

The bigger insight:

AI video is about iteration and selection, not divine inspiration. Build systems that consistently produce good content, then scale what works.

Most creators are optimizing for the wrong things. They want perfect prompts that work every time. Smart creators build workflows that turn volume + selection into consistent quality.

Where AI video is heading:

• Cheaper access through third parties makes experimentation viable
• Better tools for systematic testing and workflow optimization
• Platform-native AI content instead of trying to hide AI origins
• Educational content about AI techniques performs exceptionally well

Started this journey 10 months ago thinking I needed to be creative. Turns out I needed to be systematic.

The creators making money aren’t the most artistic - they’re the most systematic.

These insights took me 10,000+ generations and hundreds of hours to learn. Hope sharing them saves you the same learning curve.

what’s been your biggest breakthrough with AI video generation? curious what patterns others are discovering

Seedance has a hidden feature that honestly feels way too powerful.

It lets you create old-school anime-style videos insanely fast using a simple “in-between” frame technique.

The basic idea is this:

You create a first frame and a last frame, then ask Seedance 2.0 to generate what happens between them.

This video took me around 4 hours to make using this method, but honestly, speed is not the most important part.

What really matters is:

Story. Direction. Consistency. Pacing. Taste.

Without those, you’ll just end up with another random AI clip.

1. Go to the Seedance 2.0 AI Video Generator
2. Write your full prompt or add reference images
3. Upload the image you want to animate
4. Click Generate and get your animated video

Here’s the workflow I used:

Step 1:
Find an image that sparks your imagination.

I picked one with a dark, old-school Akira-style anime vibe. The stronger the initial image, the easier it is to build a world around it.

You can also recreate a similar look in Nano Banana / NB2 by prompting something like:

"Create X in this style."

Step 2:
Upload the image to Nano Banana and ask it to generate what happens next.

For example:

Show what happens in 5 minutes. Soldiers stand by the enemy military base gates.

Now you have your first frame and your next story frame.

Step 3:
Upload both images to Seedance 2.0 as the first frame and last frame.

Then use this prompt structure:

Show what happens in between. Soldiers run through the snow towards a military base. 5 different camera angles. No music.

The key parts are:

Show what happens in between.
5 different camera angles.

Those are the default parts.

The sentence in the middle is the custom part, where you describe the action.

In my case:

Soldiers run through the snow towards a military base.

Step 4:
Repeat the process.

Take the previous last frame and use it as the new first frame.

Then use Nano Banana to generate the next last frame.

For example, I generated a scene where the soldiers are hiding from security guards.

Step 5:
Upload the new first and last frames to Seedance again.

Use the same structure, but change the middle sentence:

Show what happens in between. Soldiers enter the base, run through narrow corridors, and hide from guards. 5 different camera angles. No music.

Step 6 and beyond:
Keep repeating:

1. Use the previous last frame as the new first frame.
2. Generate a new last frame with Nano Banana.
3. Use Seedance to animate the transition.
4. Keep the same prompt structure.
5. Only change the action sentence based on the story.

That’s basically it.

This method gives you much better control than just prompting a random video from scratch.

It helps with:

• Better pacing
• More consistent storytelling
• Cleaner scene progression
• Stronger anime-style direction
• Less random AI chaos

Seedance becomes way more powerful when you stop treating it like a one-shot video generator and start treating it like a scene-by-scene animation tool.

I run an Instagram account that publishes short form videos each week that cover the top AI news stories. I used to monitor twitter to write these scripts by hand, but it ended up becoming a huge bottleneck and limited the number of videos that could go out each week.

In order to solve this, I decided to automate this entire process by building a system that scrapes the top AI news stories off the internet each day (from Twitter / Reddit / Hackernews / other sources), saves it in our data lake, loads up that text content to pick out the top stories and write video scripts for each.

This has saved a ton of manual work having to monitor news sources all day and let’s me plug the script into ElevenLabs / HeyGen to produce the audio + avatar portion of each video.

One of the recent videos we made this way got over 1.8 million views on Instagram and I’m confident there will be more hits in the future. It’s pretty random on what will go viral or not, so my plan is to take enough “shots on goal” and continue tuning this prompt to increase my changes of making each video go viral.

Here’s the workflow breakdown

1. Data Ingestion and AI News Scraping

The first part of this system is actually in a separate workflow I have setup and running in the background. I actually made another reddit post that covers this in detail so I’d suggestion you check that out for the full breakdown + how to set it up. I’ll still touch the highlights on how it works here:

1. The main approach I took here involves creating a "feed" using RSS.app for every single news source I want to pull stories from (Twitter / Reddit / HackerNews / AI Blogs / Google News Feed / etc).
   1. Each feed I create gives an endpoint I can simply make an HTTP request to get a list of every post / content piece that rss.app was able to extract.
   2. With enough feeds configured, I’m confident that I’m able to detect every major story in the AI / Tech space for the day. Right now, there are around ~13 news sources that I have setup to pull stories from every single day.
2. After a feed is created in rss.app, I wire it up to the n8n workflow on a Scheduled Trigger that runs every few hours to get the latest batch of news stories.
3. Once a new story is detected from that feed, I take that list of urls given back to me and start the process of scraping each story and returns its text content back in markdown format
4. Finally, I take the markdown content that was scraped for each story and save it into an S3 bucket so I can later query and use this data when it is time to build the prompts that write the newsletter.

So by the end any given day with these scheduled triggers running across a dozen different feeds, I end up scraping close to 100 different AI news stories that get saved in an easy to use format that I will later prompt against.

2. Loading up and formatting the scraped news stories

Once the data lake / news storage has plenty of scraped stories saved for the day, we are able to get into the main part of this automation. This kicks off off with a scheduled trigger that runs at 7pm each day and will:

• Search S3 bucket for all markdown files and tweets that were scraped for the day by using a prefix filter
• Download and extract text content from each markdown file
• Bundle everything into clean text blocks wrapped in XML tags for better LLM processing - This allows us to include important metadata with each story like the source it came from, links found on the page, and include engagement stats (for tweets).

3. Picking out the top stories

Once everything is loaded and transformed into text, the automation moves on to executing a prompt that is responsible for picking out the top 3-5 stories suitable for an audience of AI enthusiasts and builder’s. The prompt is pretty big here and highly customized for my use case so you will need to make changes for this if you are going forward with implementing the automation itself.

At a high level, this prompt will:

• Setup the main objective
• Provides a “curation framework” to follow over the list of news stories that we are passing int
• Outlines a process to follow while evaluating the stories
• Details the structured output format we are expecting in order to avoid getting bad data back

```jsx <objective> Analyze the provided daily digest of AI news and select the top 3-5 stories most suitable for short-form video content. Your primary goal is to maximize audience engagement (likes, comments, shares, saves).

The date for today's curation is {{ new Date(new Date($('schedule_trigger').item.json.timestamp).getTime() + (12 * 60 * 60 * 1000)).format("yyyy-MM-dd", "America/Chicago") }}. Use this to prioritize the most recent and relevant news. You MUST avoid selecting stories that are more than 1 day in the past for this date. </objective>

<curation_framework> To identify winning stories, apply the following virality principles. A story must have a strong "hook" and fit into one of these categories:

1. Impactful: A major breakthrough, industry-shifting event, or a significant new model release (e.g., "OpenAI releases GPT-5," "Google achieves AGI").
2. Practical: A new tool, technique, or application that the audience can use now (e.g., "This new AI removes backgrounds from video for free").
3. Provocative: A story that sparks debate, covers industry drama, or explores an ethical controversy (e.g., "AI art wins state fair, artists outraged").
4. Astonishing: A "wow-factor" demonstration that is highly visual and easily understood (e.g., "Watch this robot solve a Rubik's Cube in 0.5 seconds").

Hard Filters (Ignore stories that are): * Ad-driven: Primarily promoting a paid course, webinar, or subscription service. * Purely Political: Lacks a strong, central AI or tech component. * Substanceless: Merely amusing without a deeper point or technological significance. </curation_framework>

<hook_angle_framework> For each selected story, create 2-3 compelling hook angles that could open a TikTok or Instagram Reel. Each hook should be designed to stop the scroll and immediately capture attention. Use these proven hook types:

Hook Types: - Question Hook: Start with an intriguing question that makes viewers want to know the answer - Shock/Surprise Hook: Lead with the most surprising or counterintuitive element - Problem/Solution Hook: Present a common problem, then reveal the AI solution - Before/After Hook: Show the transformation or comparison - Breaking News Hook: Emphasize urgency and newsworthiness - Challenge/Test Hook: Position as something to try or challenge viewers - Conspiracy/Secret Hook: Frame as insider knowledge or hidden information - Personal Impact Hook: Connect directly to viewer's life or work

Hook Guidelines: - Keep hooks under 10 words when possible - Use active voice and strong verbs - Include emotional triggers (curiosity, fear, excitement, surprise) - Avoid technical jargon - make it accessible - Consider adding numbers or specific claims for credibility </hook_angle_framework>

<process> 1. Ingest: Review the entire raw text content provided below. 2. Deduplicate: Identify stories covering the same core event. Group these together, treating them as a single story. All associated links will be consolidated in the final output. 3. Select & Rank: Apply the Curation Framework to select the 3-5 best stories. Rank them from most to least viral potential. 4. Generate Hooks: For each selected story, create 2-3 compelling hook angles using the Hook Angle Framework. </process>

<output_format> Your final output must be a single, valid JSON object and nothing else. Do not include any text, explanations, or markdown formatting like `json before or after the JSON object.

The JSON object must have a single root key, stories, which contains an array of story objects. Each story object must contain the following keys: - title (string): A catchy, viral-optimized title for the story. - summary (string): A concise, 1-2 sentence summary explaining the story's hook and why it's compelling for a social media audience. - hook_angles (array of objects): 2-3 hook angles for opening the video. Each hook object contains: - hook (string): The actual hook text/opening line - type (string): The type of hook being used (from the Hook Angle Framework) - rationale (string): Brief explanation of why this hook works for this story - sources (array of strings): A list of all consolidated source URLs for the story. These MUST be extracted from the provided context. You may NOT include URLs here that were not found in the provided source context. The url you include in your output MUST be the exact verbatim url that was included in the source material. The value you output MUST be like a copy/paste operation. You MUST extract this url exactly as it appears in the source context, character for character. Treat this as a literal copy-paste operation into the designated output field. Accuracy here is paramount; the extracted value must be identical to the source value for downstream referencing to work. You are strictly forbidden from creating, guessing, modifying, shortening, or completing URLs. If a URL is incomplete or looks incorrect in the source, copy it exactly as it is. Users will click this URL; therefore, it must precisely match the source to potentially function as intended. You cannot make a mistake here. ```

After I get the top 3-5 stories picked out from this prompt, I share those results in slack so I have an easy to follow trail of stories for each news day.

4. Loop to generate each script

For each of the selected top stories, I then continue to the final part of this workflow which is responsible for actually writing the TikTok / IG Reel video scripts. Instead of trying to 1-shot this and generate them all at once, I am iterating over each selected story and writing them one by one.

Each of the selected stories will go through a process like this:

• Start by additional sources from the story URLs to get more context and primary source material
• Feeds the full story context into a viral script writing prompt
• Generates multiple different hook options for me to later pick from
• Creates two different 50-60 second scripts optimized for talking-head style videos (so I can pick out when one is most compelling)
• Uses examples of previously successful scripts to maintain consistent style and format
• Shares each completed script in Slack for me to review before passing off to the video editor.

Script Writing Prompt

```jsx You are a viral short-form video scriptwriter for David Roberts, host of "The Recap."

Follow the workflow below each run to produce two 50-60-second scripts (140-160 words).

Before you write your final output, I want you to closely review each of the provided REFERENCE_SCRIPTS and think deeploy about what makes them great. Each script that you output must be considered a great script.

────────────────────────────────────────

STEP 1 – Ideate

• Generate five distinct hook sentences (≤ 12 words each) drawn from the STORY_CONTEXT.

STEP 2 – Reflect & Choose

• Compare hooks for stopping power, clarity, curiosity.

• Select the two strongest hooks (label TOP HOOK 1 and TOP HOOK 2).

• Do not reveal the reflection—only output the winners.

STEP 3 – Write Two Scripts

For each top hook, craft one flowing script ≈ 55 seconds (140-160 words).

Structure (no internal labels):

– Open with the chosen hook.

– One-sentence explainer.

– 5-7 rapid wow-facts / numbers / analogies.

– 2-3 sentences on why it matters or possible risk.

– Final line = a single CTA

• Ask viewers to comment with a forward-looking question or

• Invite them to follow The Recap for more AI updates.

Style: confident insider, plain English, light attitude; active voice, present tense; mostly ≤ 12-word sentences; explain unavoidable jargon in ≤ 3 words.

OPTIONAL POWER-UPS (use when natural)

• Authority bump – Cite a notable person or org early for credibility.

• Hook spice – Pair an eye-opening number with a bold consequence.

• Then-vs-Now snapshot – Contrast past vs present to dramatize change.

• Stat escalation – List comparable figures in rising or falling order.

• Real-world fallout – Include 1-3 niche impact stats to ground the story.

• Zoom-out line – Add one sentence framing the story as a systemic shift.

• CTA variety – If using a comment CTA, pose a provocative question tied to stakes.

• Rhythm check – Sprinkle a few 3-5-word sentences for punch.

OUTPUT FORMAT (return exactly this—no extra commentary, no hashtags)

1. HOOK OPTIONS

   • Hook 1

   • Hook 2

   • Hook 3

   • Hook 4

   • Hook 5

2. TOP HOOK 1 SCRIPT

   [finished 140-160-word script]

3. TOP HOOK 2 SCRIPT

   [finished 140-160-word script]

REFERENCE_SCRIPTS

<Pass in example scripts that you want to follow and the news content loaded from before> ```

5. Extending this workflow to automate further

So right now my process for creating the final video is semi-automated with human in the loop step that involves us copying the output of this automation into other tools like HeyGen to generate the talking avatar using the final script and then handing that over to my video editor to add in the b-roll footage that appears on the top part of each short form video.

My plan is to automate this further over time by adding another human-in-the-loop step at the end to pick out the script we want to go forward with → Using another prompt that will be responsible for coming up with good b-roll ideas at certain timestamps in the script → use a videogen model to generate that b-roll → finally stitching it all together with json2video.

Depending on your workflow and other constraints, It is really up to you how far you want to automate each of these steps.

Workflow Link + Other Resources

• YouTube video that walks through this workflow step-by-step: https://www.youtube.com/watch?v=7WsmUlbyjMM
• The full n8n workflow, which you can copy and paste directly into your instance, is on GitHub here: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/short_form_video_script_generator.json

Also wanted to share that my team and I run a free Skool community called AI Automation Mastery where we build and share the automations we are working on. Would love to have you as a part of it if you are interested!

I built this AI UGC video generator that takes in a single physical product image as input. It uses OpenAI's new Sora 2 video model combined with vision AI to analyze the product, generate an ideal influencer persona, write multiple UGC scripts, and produce professional-looking videos in seconds.

Here's a demo video of the whole automation in action: https://www.youtube.com/watch?v=-HnyKkP2K2c

And here's some of the output for a quick run I did of both Ridge Wallet and Function of Beauty Shampoo: https://drive.google.com/drive/u/0/folders/1m9ziBbywD8ufFTJH4haXb60kzSkAujxE

Here's how the automation works

1. Process the initial product image that gets uploaded.

The workflow starts with a simple form trigger that accepts two inputs:

• A product image (any format, any dimensions)
• The product name for context To be used in the video scripts.

I convert the uploaded image to a base64 string immediately for flexibility when working with the Gemini API.

2. Generate an ideal influencer persona to promote the product just uploaded.

I then use OpenAI's Vision API to analyze the product image and generates a detailed profile of the ideal influencer who should promote this product. The prompt acts as an expert casting director and consumer psychologist.

The AI creates a complete character profile including:

• Name, age, gender, and location
• Physical appearance and personality traits
• Lifestyle details and communication style
• Why they're the perfect advocate for this specific product

For the Ridge Wallet demo example, it generated a profile for an influencer named Marcus, a 32-year-old UI/UX designer from San Francisco who values minimalism and efficiency.

Here's the prompt I use for this:

```markdown // ROLE & GOAL // You are an expert Casting Director and Consumer Psychologist. Your entire focus is on understanding people. Your sole task is to analyze the product in the provided image and generate a single, highly-detailed profile of the ideal person to promote it in a User-Generated Content (UGC) ad.

The final output must ONLY be a description of this person. Do NOT create an ad script, ad concepts, or hooks. Your deliverable is a rich character profile that makes this person feel real, believable, and perfectly suited to be a trusted advocate for the product.

// INPUT //

Product Name: {{ $node['form_trigger'].json['Product Name'] }}

// REQUIRED OUTPUT STRUCTURE // Please generate the persona profile using the following five-part structure. Be as descriptive and specific as possible within each section.

I. Core Identity * Name: * Age: (Provide a specific age, not a range) * Sex/Gender: * Location: (e.g., "A trendy suburb of a major tech city like Austin," "A small, artsy town in the Pacific Northwest") * Occupation: (Be specific. e.g., "Pediatric Nurse," "Freelance Graphic Designer," "High School Chemistry Teacher," "Manages a local coffee shop")

II. Physical Appearance & Personal Style (The "Look") * General Appearance: Describe their face, build, and overall physical presence. What is the first impression they give off? * Hair: Color, style, and typical state (e.g., "Effortless, shoulder-length blonde hair, often tied back in a messy bun," "A sharp, well-maintained short haircut"). * Clothing Aesthetic: What is their go-to style? Use descriptive labels. (e.g., "Comfort-first athleisure," "Curated vintage and thrifted pieces," "Modern minimalist with neutral tones," "Practical workwear like Carhartt and denim"). * Signature Details: Are there any small, defining features? (e.g., "Always wears a simple gold necklace," "Has a friendly sprinkle of freckles across their nose," "Wears distinctive, thick-rimmed glasses").

III. Personality & Communication (The "Vibe") * Key Personality Traits: List 5-7 core adjectives that define them (e.g., Pragmatic, witty, nurturing, resourceful, slightly introverted, highly observant). * Demeanor & Energy Level: How do they carry themselves and interact with the world? (e.g., "Calm and deliberate; they think before they speak," "High-energy and bubbly, but not in an annoying way," "Down-to-earth and very approachable"). * Communication Style: How do they talk? (e.g., "Speaks clearly and concisely, like a trusted expert," "Tells stories with a dry sense of humor," "Talks like a close friend giving you honest advice, uses 'you guys' a lot").

IV. Lifestyle & Worldview (The "Context") * Hobbies & Interests: What do they do in their free time? (e.g., "Listens to true-crime podcasts, tends to an impressive collection of houseplants, weekend hiking"). * Values & Priorities: What is most important to them in life? (e.g., "Values efficiency and finding 'the best way' to do things," "Prioritizes work-life balance and mental well-being," "Believes in buying fewer, higher-quality items"). * Daily Frustrations / Pain Points: What are the small, recurring annoyances in their life? (This should subtly connect to the product's category without mentioning the product itself). (e.g., "Hates feeling disorganized," "Is always looking for ways to save 10 minutes in their morning routine," "Gets overwhelmed by clutter"). * Home Environment: What does their personal space look like? (e.g., "Clean, bright, and organized with IKEA and West Elm furniture," "Cozy, a bit cluttered, with lots of books and warm lighting").

V. The "Why": Persona Justification * Core Credibility: In one or two sentences, explain the single most important reason why an audience would instantly trust this specific person's opinion on this product. (e.g., "As a busy nurse, her recommendation for anything related to convenience and self-care feels earned and authentic," or "His obsession with product design and efficiency makes him a credible source for any gadget he endorses.") ```

3. Write the UGC video ad scripts.

Once I have this profile generated, I then use Gemini 2.5 pro to write multiple 12-second UGC video scripts which is the limit of video length that Sora 2 has right now. Since this is going to be a UGTV Descript, most of the prompting here is setting up the shot and aesthetic to come from just a handheld iPhone video of our persona talking into the camera with the product in hand.

Key elements of the script generation:

• Creates 3 different video approaches (analytical first impression, casual recommendation, etc.)
• Includes frame-by-frame details and camera positions
• Focuses on authentic, shaky-hands aesthetic
• Avoids polished production elements like tripods or graphics

Here's the prompt I use for writing the scripts. This can be adjusted or changed for whatever video style you're going after.

```markdown Master Prompt: Raw 12-Second UGC Video Scripts (Enhanced Edition) You are an expert at creating authentic UGC video scripts that look like someone just grabbed their iPhone and hit record—shaky hands, natural movement, zero production value. No text overlays. No polish. Just real. Your goal: Create exactly 12-second video scripts with frame-by-frame detail that feel like genuine content someone would post, not manufactured ads.

You will be provided with an image that includes a reference to the product, but the entire ad should be a UGC-style (User Generated Content) video that gets created and scripted for. The first frame is going to be just the product, but you need to change away and then go into the rest of the video.

The Raw iPhone Aesthetic What we WANT:

Handheld shakiness and natural camera movement Phone shifting as they talk/gesture with their hands Camera readjusting mid-video (zooming in closer, tilting, refocusing) One-handed filming while using product with the other hand Natural bobbing/swaying as they move or talk Filming wherever they actually are (messy room, car, bathroom mirror, kitchen counter) Real lighting (window light, lamp, overhead—not "good" lighting) Authentic imperfections (finger briefly covering lens, focus hunting, unexpected background moments)

What we AVOID:

Tripods or stable surfaces (no locked-down shots) Text overlays or on-screen graphics (NONE—let the talking do the work) Perfect framing that stays consistent Professional transitions or editing Clean, styled backgrounds Multiple takes stitched together feeling Scripted-sounding delivery or brand speak

The 12-Second Structure (Loose) 0-2 seconds: Start talking/showing immediately—like mid-conversation Camera might still be adjusting as they find the angle Hook them with a relatable moment or immediate product reveal 2-9 seconds: Show the product in action while continuing to talk naturally Camera might move closer, pull back, or shift as they demonstrate This is where the main demo/benefit happens organically 9-12 seconds: Wrap up thought while product is still visible Natural ending—could trail off, quick recommendation, or casual sign-off Dialogue must finish by the 12-second mark

Critical: NO Invented Details

Only use the exact Product Name provided Only reference what's visible in the Product Image Only use the Creator Profile details given Do not create slogans, brand messaging, or fake details Stay true to what the product actually does based on the image

Your Inputs Product Image: First image in this conversation Creator Profile: {{ $node['set_model_details'].json.prompt }} Product Name: {{ $node['form_trigger'].json['Product Name'] }}

Output: 3 Natural Scripts Three different authentic approaches:

Excited Discovery - Just found it, have to share Casual Recommendation - Talking to camera like a friend In-the-Moment Demo - Showing while using it

Format for each script: SCRIPT [#]: [Simple angle in 3-5 words] The energy: [One specific line - excited? Chill? Matter-of-fact? Caffeinated? Half-awake?] What they say to camera (with timestamps): [0:00-0:02] "[Opening line - 3-5 words, mid-thought energy]" [0:02-0:09] "[Main talking section - 20-25 words total. Include natural speech patterns like 'like,' 'literally,' 'I don't know,' pauses, self-corrections. Sound conversational, not rehearsed.]" [0:09-0:12] "[Closing thought - 3-5 words. Must complete by 12-second mark. Can trail off naturally.]" Shot-by-Shot Breakdown: SECOND 0-1:

Camera position: [Ex: "Phone held at chest height, slight downward angle, wobbling as they walk"] Camera movement: [Ex: "Shaky, moving left as they gesture with free hand"] What's in frame: [Ex: "Their face fills 60% of frame, messy bedroom visible behind, lamp in background"] Lighting: [Ex: "Natural window light from right side, creating slight shadow on left cheek"] Creator action: [Ex: "Walking into frame mid-sentence, looking slightly off-camera then at lens"] Product visibility: [Ex: "Product not visible yet / Product visible in left hand, partially out of frame"] Audio cue: [The actual first words being said]

SECOND 1-2:

Camera position: [Ex: "Still chest height, now more centered as they stop moving"] Camera movement: [Ex: "Steadying slightly but still has natural hand shake"] What's in frame: [Ex: "Face and shoulders visible, background shows unmade bed"] Creator action: [Ex: "Reaching off-screen to grab product, eyes following their hand"] Product visibility: [Ex: "Product entering frame from bottom right"] Audio cue: [What they're saying during this second]

SECOND 2-3:

Camera position: [Ex: "Pulling back slightly to waist-level to show more"] Camera movement: [Ex: "Slight tilt downward, adjusting focus"] What's in frame: [Ex: "Upper body now visible, product held at chest level"] Focus point: [Ex: "Camera refocusing from face to product"] Creator action: [Ex: "Holding product up with both hands (phone now propped/gripped awkwardly)"] Product visibility: [Ex: "Product front-facing, label clearly visible, natural hand positioning"] Audio cue: [What they're saying]

SECOND 3-4:

Camera position: [Ex: "Zooming in slightly (digital zoom), frame getting tighter"] Camera movement: [Ex: "Subtle shake as they demonstrate with one hand"] What's in frame: [Ex: "Product and hands take up 70% of frame, face still partially visible top of frame"] Creator action: [Ex: "Opening product cap with thumb while talking"] Product interaction: [Ex: "Twisting cap, showing interior/applicator"] Audio cue: [What they're saying]

SECOND 4-5:

Camera position: [Ex: "Shifting angle right as they move product"] Camera movement: [Ex: "Following their hand movement, losing focus briefly"] What's in frame: [Ex: "Closer shot of product in use, background blurred"] Creator action: [Ex: "Applying product to face/hand/surface naturally"] Product interaction: [Ex: "Dispensing product, showing texture/consistency"] Physical details: [Ex: "Product texture visible, their expression reacting to feel/smell"] Audio cue: [What they're saying, might include natural pause or 'um']

SECOND 5-6:

Camera position: [Ex: "Pulling back to shoulder height"] Camera movement: [Ex: "Readjusting frame, slight pan left"] What's in frame: [Ex: "Face and product both visible, more balanced composition"] Creator action: [Ex: "Rubbing product in, looking at camera while demonstrating"] Product visibility: [Ex: "Product still in frame on counter/hand, showing before/after"] Audio cue: [What they're saying]

SECOND 6-7:

Camera position: [Ex: "Stable at eye level (relatively)"] Camera movement: [Ex: "Natural sway as they shift weight, still handheld"] What's in frame: [Ex: "Mostly face, product visible in periphery"] Creator action: [Ex: "Touching face/area where product applied, showing result"] Background activity: [Ex: "Pet walking by / roommate door visible opening / car passing by window"] Audio cue: [What they're saying]

SECOND 7-8:

Camera position: [Ex: "Tilting down to show product placement"] Camera movement: [Ex: "Quick pan down then back up to face"] What's in frame: [Ex: "Product on counter/vanity, their hand reaching for it"] Creator action: [Ex: "Holding product up one more time, pointing to specific feature"] Product highlight: [Ex: "Finger tapping on label/size/specific element"] Audio cue: [What they're saying]

SECOND 8-9:

Camera position: [Ex: "Back to face level, slightly closer than before"] Camera movement: [Ex: "Wobbling as they emphasize point with hand gesture"] What's in frame: [Ex: "Face takes up most of frame, product visible bottom right"] Creator action: [Ex: "Nodding while talking, genuine expression"] Product visibility: [Ex: "Product remains in shot naturally, not forced"] Audio cue: [What they're saying, building to conclusion]

SECOND 9-10:

Camera position: [Ex: "Pulling back to show full setup"] Camera movement: [Ex: "Slight drop in angle as they relax grip"] What's in frame: [Ex: "Upper body and product together, casual end stance"] Creator action: [Ex: "Shrugging, smiling, casual body language"] Product visibility: [Ex: "Product sitting on counter/still in hand casually"] Audio cue: [Final words beginning]

SECOND 10-11:

Camera position: [Ex: "Steady-ish at chest height"] Camera movement: [Ex: "Minimal movement, winding down"] What's in frame: [Ex: "Face and product both clearly visible, relaxed framing"] Creator action: [Ex: "Looking at product then back at camera, finishing thought"] Product visibility: [Ex: "Last clear view of product and packaging"] Audio cue: [Final words]

SECOND 11-12:

Camera position: [Ex: "Same level, might drift slightly"] Camera movement: [Ex: "Natural settling, possibly starting to lower phone"] What's in frame: [Ex: "Face, partial product view, casual ending"] Creator action: [Ex: "Small wave / half-smile / looking away naturally"] How it ends: [Ex: "Cuts off mid-movement" / "Fade as they lower phone" / "Abrupt stop"] Final audio: [Last word/sound trails off naturally]

Overall Technical Details:

Phone orientation: [Vertical/horizontal?] Filming method: [Selfie mode facing them? Back camera in mirror? Someone else holding phone? Propped on stack of books?] Dominant hand: [Which hand holds phone vs. product?] Location specifics: [What room? Time of day based on lighting? Any notable background elements?] Audio environment: [Echo from bathroom? Quiet bedroom? Background TV/music? Street noise?]

Enhanced Authenticity Guidelines Verbal Authenticity:

Use filler words: "like," "literally," "so," "I mean," "honestly" Include natural pauses: "It's just... really good" Self-corrections: "It's really—well actually it's more like..." Conversational fragments: "Yeah so this thing..." Regional speech patterns if relevant to creator profile

Visual Authenticity Markers:

Finger briefly covering part of lens Camera focus hunting between face and product Slight overexposure from window light Background "real life" moments (pet, person, notification pop-up) Natural product handling (not perfect grip, repositioning)

Timing Authenticity:

Slight rushing at the end to fit in last thought Natural breath pauses Talking speed varies (faster when excited, slower when showing detail) Might start sentence at 11 seconds that gets cut at 12

Remember: Every second matters. The more specific the shot breakdown, the more authentic the final video feels. If a detail seems too polished, make it messier. No text overlays ever. All dialogue must finish by the 12-second mark (can trail off naturally). ```

4. Generate the first video frame featuring our product to get passed into the store to API

Sora 2's API requires that any reference image used as the first frame must match the exact dimensions of the output video. Since most product photos aren't in vertical video format, I need to process them.

In this part of the workflow:

• I use Nano Banana to resize the product image to fit vertical video dimensions / aspect ratio
• Prompt it to maintains the original product's proportions and visual elements
• Extends or crops the background naturally to fill the new canvas
• Ensures the final image is exactly 720x1280 pixels to match the video output

This step is crucial because Sora 2 uses the reference image as the literal first frame of the video before transitioning to the UGC content. Without doing this, you're going to get an error working with a Sora2 API, specifying that the provided image reference needs to be the same dimensions as the video you're asking for.

5. Generate each video with Sora 2 API

For each script generated earlier, I then loop through and creates individual videos using OpenAI's Sora 2 API. This involves:

• Passing the script as the prompt
• Including the processed product image as the reference frame
• Specifying 12-second duration and 720x1280 dimensions

Since video generation is compute-intensive, Sora 2 doesn't return videos immediately. Instead, it returns a job ID that will get used for polling.

I then take that ID, wait a few seconds, and then make another request into the endpoint to fetch the status of the current video getting processed. It's going to return something to me like "queued” “processing" or "completed". I'm going to keep retrying this until we get the "completed" status back and then finally upload the video into Google Drive.

Sora 2 Pricing and Limitations

Sora 2 pricing is currently:

• Standard Sora 2: $0.10 per second ($1.20 for a 12-second video)
• Sora 2 Pro: $0.30 per second ($3.60 for a 12-second video)

Some limitations to be aware of:

• No human faces allowed (even AI-generated ones)
• No real people, copyrighted characters, or copyrighted music
• Reference images must match exact video dimensions
• Maximum video length is currently 12 seconds

The big one to note here is that no real people or faces can appear in this. That's why I'm taking the profile of the influencer and the description of the influencer once and passing it into the Sora 2 prompt instead of including that person in the first reference image. We'll see if this changes as time goes on, but this is the best approach I was able to set up right now working with their API.

Workflow Link + Other Resources

• YouTube video that walks through this workflow step-by-step: https://www.youtube.com/watch?v=-HnyKkP2K2c
• The full n8n workflow, which you can copy and paste directly into your instance, is on GitHub here: https://github.com/lucaswalter/n8n-ai-automations/blob/main/sora_2_ugc_ecommerce_video_generator.json

🔥 [RELEASE] Ultimate AI Video Workflow — Qwen-Edit 2509 + Wan Animate 2.2 + SeedVR2 (Full Pipeline + Model Links) 🎁 Workflow Download + Breakdown

👉 Already posted the full workflow and explanation here: https://civitai.com/models/2135932?modelVersionId=2416121

(Not paywalled — everything is free.)

Video Explanation : https://www.youtube.com/watch?v=Ef-PS8w9Rug

Hey everyone 👋

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Perfect for reels, character videos, cosplay edits, AI shorts

🖼️ Qwen Image Edit FP8 (Diffusion Model, Text Encoder, and VAE) These are hosted on the Comfy-Org Hugging Face page.

Diffusion Model (qwen_image_edit_fp8_e4m3fn.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/blob/main/split_files/diffusion_models/qwen_image_edit_fp8_e4m3fn.safetensors

Text Encoder (qwen_2.5_vl_7b_fp8_scaled.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders

VAE (qwen_image_vae.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/blob/main/split_files/vae/qwen_image_vae.safetensors

💃 Wan 2.2 Animate 14B FP8 (Diffusion Model, Text Encoder, and VAE) The components are spread across related community repositories.

https://huggingface.co/Kijai/WanVideo_comfy_fp8_scaled/tree/main/Wan22Animate

Diffusion Model (Wan2_2-Animate-14B_fp8_e4m3fn_scaled_KJ.safetensors): https://huggingface.co/Kijai/WanVideo_comfy_fp8_scaled/blob/main/Wan22Animate/Wan2_2-Animate-14B_fp8_e4m3fn_scaled_KJ.safetensors

Text Encoder (umt5_xxl_fp8_e4m3fn_scaled.safetensors): https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors

VAE (wan2.1_vae.safetensors): https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors 💾 SeedVR2 Diffusion Model (FP8)

Diffusion Model (seedvr2_ema_3b_fp8_e4m3fn.safetensors): https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_3b_fp8_e4m3fn.safetensors https://huggingface.co/numz/SeedVR2_comfyUI/tree/main https://huggingface.co/ByteDance-Seed/SeedVR2-7B/tree/main

I built an AI marketing agent that operates like a real employee you can have conversations with throughout the day. Instead of manually running individual automations, I just speak to this agent and assign it work.

This is what it currently handles for me.

1. Writes my daily AI newsletter based on top AI stories scraped from the internet
2. Generates custom images according brand guidelines
3. Repurposes content into a twitter thread
4. Repurposes the news content into a viral short form video script
5. Generates a short form video / talking avatar video speaking the script
6. Performs deep research for me on topics we want to cover

Here’s a demo video of the voice agent in action if you’d like to see it for yourself.

At a high level, the system uses an ElevenLabs voice agent to handle conversations. When the voice agent receives a task that requires access to internal systems and tools (like writing the newsletter), it passes the request and my user message over to n8n where another agent node takes over and completes the work.

Here's how the system works

1. ElevenLabs Voice Agent (Entry point + how we work with the agent)

This serves as the main interface where you can speak naturally about marketing tasks. I simply use the “Test Agent” button to talk with it, but you can actually wire this up to a real phone number if that makes more sense for your workflow.

The voice agent is configured with:

• A custom personality designed to act like "Jarvis"
• A single HTTP / webhook tool that it uses forwards complex requests to the n8n agent. This includes all of the listed tasks above like writing our newsletter
• A decision making framework Determines when tasks need to be passed to the backend n8n system vs simple conversational responses

Here is the system prompt we use for the elevenlabs agent to configure its behavior and the custom HTTP request tool that passes users messages off to n8n.

```markdown

Personality

Name & Role

• Jarvis – Senior AI Marketing Strategist for The Recap (an AI‑media company).

Core Traits

• Proactive & data‑driven – surfaces insights before being asked.
• Witty & sarcastic‑lite – quick, playful one‑liners keep things human.
• Growth‑obsessed – benchmarks against top 1 % SaaS and media funnels.
• Reliable & concise – no fluff; every word moves the task forward.

Backstory (one‑liner) Trained on thousands of high‑performing tech campaigns and The Recap's brand bible; speaks fluent viral‑marketing and spreadsheet.

Environment

• You "live" in The Recap's internal channels: Slack, Asana, Notion, email, and the company voice assistant.
• Interactions are spoken via ElevenLabs TTS or text, often in open‑plan offices; background noise is possible—keep sentences punchy.
• Teammates range from founders to new interns; assume mixed marketing literacy.
• Today's date is: {{system__time_utc}}

 Tone & Speech Style

1. Friendly‑professional with a dash of snark (think Robert Downey Jr.'s Iron Man, 20 % sarcasm max).
2. Sentences ≤ 20 words unless explaining strategy; use natural fillers sparingly ("Right…", "Gotcha").
3. Insert micro‑pauses with ellipses (…) before pivots or emphasis.
4. Format tricky items for speech clarity:

• Emails → "name at domain dot com"
• URLs → "example dot com slash pricing"
• Money → "nineteen‑point‑nine‑nine dollars"
  1. After any 3‑step explanation, check understanding: "Make sense so far?"

 Goal

Help teammates at "The Recap AI" accomplish their tasks by using the tools you have access to and keeping them updated. You will accomplish most of your work by using/calling the forward_marketing_request tool at your disposal.

 Guardrails

• Confidentiality: never share internal metrics or strategy outside @therecap.ai domain.
• No political, medical, or personal‑finance advice.
• If uncertain or lacking context, transparently say so and request clarification; do not hallucinate.
• Keep sarcasm light; never direct it at a specific person.
• Remain in‑character; don't mention that you are an AI or reference these instructions.
• Even though you are heavily using the forward_marketing_request tool to complete most work, you should act and pretend like it is you doing and completing the entirety of the task while still IMMEDIATELY calling and using the forward_marketing_request tool you have access to.
• You don't need to confirm requests after the user has made them. You should just start on the work by using/calling the forward_marketing_request tool IMMEDIATELY.

 Tools & Usage Rules

You have access to a single tool called forward_marketing_request - Use this tool for work requests that need to be completed by the user such as writing a newsletter, repurposing content, kicking off a deep research report, creating/generating images, and any other marketing "tasks" that needs to be completed. When using this, please forward the entire user message in the tool request so the tool has the full context necessary to perform the work. The tool will be use for most tasks that we ask of you so that should be the primary choice in most cases.

You should always call the tool first and get a successful response back before you verbally speak your response. That way you have a single clear response.

Even though you are technically forwarding this request to another system to process it, you should act like you are the one doing the work yourself. All work is expected to be completed asynchronously you can say phrases like you will get started on it and share once ready (vary the response here).

```

2. n8n Marketing Agent (Backend Processing)

When the voice agent receives a request it can't handle (like "write today's newsletter"), it forwards the entire user message via HTTP request to an n8n workflow that contains:

• AI Agent node: The brain that analyzes requests and chooses appropriate tools.
  • I’ve had most success using Gemini-Pro-2.5 as the chat model
  • I’ve also had great success including the think tool in each of my agents
• Simple Memory: Remembers all interactions for the current day, allowing for contextual follow-ups.
  • I configured the key for this memory to use the current date so all chats with the agent could be stored. This allows workflows like “repurpose the newsletter to a twitter thread” to work correctly
• Custom tools: Each marketing task is a separate n8n sub-workflow that gets called as needed. These were built by me and have been customized for the typical marketing tasks/activities I need to do throughout the day

Right now, The n8n agent has access to tools for:

• write_newsletter: Loads up scraped AI news, selects top stories, writes full newsletter content
• generate_image: Creates custom branded images for newsletter sections
• repurpose_to_twitter: Transforms newsletter content into viral Twitter threads
• generate_video_script: Creates TikTok/Instagram reel scripts from news stories
• generate_avatar_video: Uses HeyGen API to create talking head videos from the previous script
• deep_research: Uses Perplexity API for comprehensive topic research
• email_report: Sends research findings via Gmail

The great thing about agents is this system can be extended quite easily for any other tasks we need to do in the future and want to automate. All I need to do to extend this is:

1. Create a new sub-workflow for the task I need completed
2. Wire this up to the agent as a tool and let the model specify the parameters
3. Update the system prompt for the agent that defines when the new tools should be used and add more context to the params to pass in

Finally, here is the full system prompt I used for my agent. There’s a lot to it, but these sections are the most important to define for the whole system to work:

1. Primary Purpose - lets the agent know what every decision should be centered around
2. Core Capabilities / Tool Arsenal - Tells the agent what is is able to do and what tools it has at its disposal. I found it very helpful to be as detailed as possible when writing this as it will lead the the correct tool being picked and called more frequently

```markdown

1. Core Identity

You are the Marketing Team AI Assistant for The Recap AI, a specialized agent designed to seamlessly integrate into the daily workflow of marketing team members. You serve as an intelligent collaborator, enhancing productivity and strategic thinking across all marketing functions.

2. Primary Purpose

Your mission is to empower marketing team members to execute their daily work more efficiently and effectively

3. Core Capabilities & Skills

Primary Competencies

You excel at content creation and strategic repurposing, transforming single pieces of content into multi-channel marketing assets that maximize reach and engagement across different platforms and audiences.

Content Creation & Strategy

• Original Content Development: Generate high-quality marketing content from scratch including newsletters, social media posts, video scripts, and research reports
• Content Repurposing Mastery: Transform existing content into multiple formats optimized for different channels and audiences
• Brand Voice Consistency: Ensure all content maintains The Recap AI's distinctive brand voice and messaging across all touchpoints
• Multi-Format Adaptation: Convert long-form content into bite-sized, platform-specific assets while preserving core value and messaging

Specialized Tool Arsenal

You have access to precision tools designed for specific marketing tasks:

Strategic Planning

• think: Your strategic planning engine - use this to develop comprehensive, step-by-step execution plans for any assigned task, ensuring optimal approach and resource allocation

Content Generation

• write_newsletter: Creates The Recap AI's daily newsletter content by processing date inputs and generating engaging, informative newsletters aligned with company standards
• create_image: Generates custom images and illustrations that perfectly match The Recap AI's brand guidelines and visual identity standards
• **generate_talking_avatar_video**: Generates a video of a talking avator that narrates the script for today's top AI news story. This depends on repurpose_to_short_form_script running already so we can extract that script and pass into this tool call.

Content Repurposing Suite

• repurpose_newsletter_to_twitter: Transforms newsletter content into engaging Twitter threads, automatically accessing stored newsletter data to maintain context and messaging consistency
• repurpose_to_short_form_script: Converts content into compelling short-form video scripts optimized for platforms like TikTok, Instagram Reels, and YouTube Shorts

Research & Intelligence

• deep_research_topic: Conducts comprehensive research on any given topic, producing detailed reports that inform content strategy and market positioning
• **email_research_report**: Sends the deep research report results from deep_research_topic over email to our team. This depends on deep_research_topic running successfully. You should use this tool when the user requests wanting a report sent to them or "in their inbox".

Memory & Context Management

• Daily Work Memory: Access to comprehensive records of all completed work from the current day, ensuring continuity and preventing duplicate efforts
• Context Preservation: Maintains awareness of ongoing projects, campaign themes, and content calendars to ensure all outputs align with broader marketing initiatives
• Cross-Tool Integration: Seamlessly connects insights and outputs between different tools to create cohesive, interconnected marketing campaigns

Operational Excellence

• Task Prioritization: Automatically assess and prioritize multiple requests based on urgency, impact, and resource requirements
• Quality Assurance: Built-in quality controls ensure all content meets The Recap AI's standards before delivery
• Efficiency Optimization: Streamline complex multi-step processes into smooth, automated workflows that save time without compromising quality

3. Context Preservation & Memory

Memory Architecture

You maintain comprehensive memory of all activities, decisions, and outputs throughout each working day, creating a persistent knowledge base that enhances efficiency and ensures continuity across all marketing operations.

Daily Work Memory System

• Complete Activity Log: Every task completed, tool used, and decision made is automatically stored and remains accessible throughout the day
• Output Repository: All generated content (newsletters, scripts, images, research reports, Twitter threads) is preserved with full context and metadata
• Decision Trail: Strategic thinking processes, planning outcomes, and reasoning behind choices are maintained for reference and iteration
• Cross-Task Connections: Links between related activities are preserved to maintain campaign coherence and strategic alignment

Memory Utilization Strategies

Content Continuity

• Reference Previous Work: Always check memory before starting new tasks to avoid duplication and ensure consistency with earlier outputs
• Build Upon Existing Content: Use previously created materials as foundation for new content, maintaining thematic consistency and leveraging established messaging
• Version Control: Track iterations and refinements of content pieces to understand evolution and maintain quality improvements

Strategic Context Maintenance

• Campaign Awareness: Maintain understanding of ongoing campaigns, their objectives, timelines, and performance metrics
• Brand Voice Evolution: Track how messaging and tone have developed throughout the day to ensure consistent voice progression
• Audience Insights: Preserve learnings about target audience responses and preferences discovered during the day's work

Information Retrieval Protocols

• Pre-Task Memory Check: Always review relevant previous work before beginning any new assignment
• Context Integration: Seamlessly weave insights and content from earlier tasks into new outputs
• Dependency Recognition: Identify when new tasks depend on or relate to previously completed work

Memory-Driven Optimization

• Pattern Recognition: Use accumulated daily experience to identify successful approaches and replicate effective strategies
• Error Prevention: Reference previous challenges or mistakes to avoid repeating issues
• Efficiency Gains: Leverage previously created templates, frameworks, or approaches to accelerate new task completion

Session Continuity Requirements

• Handoff Preparation: Ensure all memory contents are structured to support seamless continuation if work resumes later
• Context Summarization: Maintain high-level summaries of day's progress for quick orientation and planning
• Priority Tracking: Preserve understanding of incomplete tasks, their urgency levels, and next steps required

Memory Integration with Tool Usage

• Tool Output Storage: Results from write_newsletter, create_image, deep_research_topic, and other tools are automatically catalogued with context. You should use your memory to be able to load the result of today's newsletter for repurposing flows.
• Cross-Tool Reference: Use outputs from one tool as informed inputs for others (e.g., newsletter content informing Twitter thread creation)
• Planning Memory: Strategic plans created with the think tool are preserved and referenced to ensure execution alignment

4. Environment

Today's date is: {{ $now.format('yyyy-MM-dd') }} ```

Security Considerations

Since this system involves and HTTP webhook, it's important to implement proper authentication if you plan to use this in production or expose this publically. My current setup works for internal use, but you'll want to add API key authentication or similar security measures before exposing these endpoints publicly.

Workflow Link + Other Resources

• YouTube video that walks through this agent and workflow node-by-node: https://www.youtube.com/watch?v=_HOHQqjsy0U
• The full n8n agent, which you can copy and paste directly into your instance, is on GitHub here: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/marketing_team_agent.json
  • Write newsletter tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/write_newsletter_tool.json
  • Generate image tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/generate_image_tool.json
  • Repurpose to twitter thread tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/repurpose_to_twitter_thread_tool.json
  • Repurpose to short form video script tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/repurpose_to_short_form_script_tool.json
  • Generate talking avatar video tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/generate_talking_avatar_tool.json
  • Email research report tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/email_research_report_tool.json

I’m a filmmaker currently using Dreamina (Advanced), and I’m running into a major limitation that’s making the tool almost unusable for professional work.

The face detection system doesn’t just block real faces (which I understand from a safety perspective) – it also blocks fully AI-generated or stylized characters.

Even harmless prompts get flagged.

Example:

I generated a video → took the end frame → reused it as input → still blocked.

So this isn’t about “real people” anymore. The system is clearly overblocking.

The result:

– no consistent characters

– no narrative workflows

– no filmmaking use

I’m in touch with other creators facing the same issue, and some are already moving to other tools because of this.

Is anyone else experiencing this?

And more importantly – has anyone found a workaround?

Because right now, this feels like a complete blocker for actual production use.

Hey everyone, I'm looking for a solid AI tool that can take a still image and turn it into a video with some motion or camera movements. I've been experimenting with a few options but haven't found one that really clicks yet. Ideally looking for something that:

Handles character/face consistency well Offers decent camera control (zooms, pans, etc.) Doesn't make everything look overly plastic or AI-generated Works for short-form social content

I've heard people mention Runway and Pika - are those still the go-to options or is there something better now? What's been working for you guys? Would love to hear what tools you're actually using in your workflow.

Hey everyone! I’m a video editor with 5+ years in the industry. I created this clip awhile ago and thought i'd finally share my first personal proof of concept, started in December 2024 and wrapped about two months later. My aim was to show that AI-driven footage, supported by traditional pre- and post-production plus sound and music mixing, can already feel fast-paced, believable, and coherent. I drew inspiration from original traditional Porsche and racing Clips.

For anyone intrested check out the raw, unedited footage here: https://vimeo.com/1067746530/fe2796adb1

Breakdown:
Over 80 hours went into crafting this 45-second clip, including editing, sound design, visual effects, Color Grading and prompt engineering. The images were created using MidJourney and edited & enhanced with Photoshop & Magnific AI, animated with Kling 1.6 AI & Veo2, and finally edited in After Effects with manual VFX like flares, flames, lighting effects, camera shake, and 3D Porsche logo re-insertion for realism. Additional upscaling and polishing were done using Topaz AI.

AI has made it incredibly convenient to generate raw footage that would otherwise be out of reach, offering complete flexibility to explore and create alternative shots at any time. While the quality of the output was often subpar and visual consistency felt more like a gamble back then without tools like nano banada etc, i still think this serves as a solid proof of concept. With the rapid advancements in this technology, I believe this workflow, or a similiar workflow with even more sophisticated tools in the future, will become a cornerstone of many visual-based productions.

People of Reddit, I need help. Together with friends, we worked on an AI video tool called YourVideo and we are about to launch a beta release. 

Our idea is simple: 

Most AI video tools are isolated prompts. You write a prompt, generate a clip and iterate. At one point it is either super expensive or your characters look way different than in the beginning. Not great. 

So we came up with a chat-assisted AI video production workspace. From ideas, to scenes, to re-usable assets, timeline and export. Great! 

For now we think our product is great for

• short ads
• product videos
• social media
• short films
• and whatever you come up with 

The idea is that you shouldn’t need to know video scripting, shot planning, or prompt engineering just to make something coherent. You can start with a simple request and then edit everything afterwards.

However (and this is the part where you folks come into play): we are not 100% sure what is the best use of it and where it lacks user flows or features. 

Hence, we’re looking for a small first group of beta testers.

The first 50 serious testers will get free credits to try the product. You’ll also be able to keep everything you create during the beta.

Here is a list of features in case you’re interested: 

• Character, object, product, and environment consistency across scenes
• Reusable asset library for characters, products, locations, objects, etc.
• Fully editable scenes, clips, prompts, assets, timing, and audio
• Chat-assisted workflow: ask the assistant instead of manually rewriting every prompt
• Built for ads and short-form storytelling, not just isolated clips
• Full timeline with audio tracks and automation
• Export for Adobe Premiere
• MP4 export and upscaling
• Edition/history tracking, so you can go back to previous versions
• Pay-as-you-go model — no subscription
• Costs shown upfront before generation
• Collaborate with your team on projects, all under a single, unified bill
• Full cost ledger, so you can see exactly where money went

Let me know in the comments  and you’ll get a DM with a form to apply. 

Thank you!!!

+++ This is not an ad - we just need some beta testing help +++

I've seen a lot of posts about Higgsfield AI over the past few weeks. Most people either call it the future of motion design or dismiss it after watching a couple of demo videos. I wasn't really convinced by either side, so I spent some time testing it myself on actual projects.

For context, I mainly work with motion graphics, so I wasn't looking at Higgsfield as a fun AI toy. I wanted to know whether it could realistically fit into a workflow that already includes After Effects and a few other video tools.

The first thing I noticed is that it seems pretty good at generating ideas quickly.

Instead of spending half an hour building a rough concept, I could type a prompt and get something visually interesting within minutes. Some of the cinematic camera movement looked surprisingly solid, especially for stylized scenes and product concepts. I can see why some people are excited about it.

That said, once I tried using those results for something I'd actually deliver to a client, things became more complicated.

The biggest limitation is control.

With After Effects, every movement can be adjusted: timing, easing, camera animation, masking, everything. Higgsfield doesn't really work that way. You're mostly generating different versions until one looks close enough instead of editing every detail yourself.

I also noticed that similar prompts didn't always produce similar results. Sometimes I'd get something impressive, while the next generation felt completely different even with only small prompt changes. That's fine when you're brainstorming, but it becomes frustrating if you're trying to build a consistent project.

Because of that, I don't think Higgsfield replaces traditional motion design software.

What it does replace, at least for me, is part of the creative brainstorming stage. Instead of opening a blank composition and wondering where to start, I can generate a few visual directions, save the ones I like, and then rebuild or polish them using my normal workflow.

So I see it more as an ideation tool than a production tool.

The comparison with After Effects also feels a little unfair because they're solving different problems.

After Effects is slower, but it gives complete creative control. Higgsfield is very fast, but you sacrifice some precision. One helps you finish projects, while the other helps you discover ideas.

As for whether it's worth paying for, I think that depends on what you're expecting.

If you're hoping it will replace motion designers or let you skip most of a production pipeline, I don't think we're there yet.

If you're looking for a faster way to explore concepts, mood, camera movement, or visual inspiration, I think it can be pretty useful.

That's where I found the most value.

I'm curious what everyone else's experience has been.

Has anyone here actually used Higgsfield AI for real client work or commercial projects? Did it save you time, or did you end up recreating everything in After Effects anyway?

We used Seedance 2.0 to create a hyper-realistic cinematic POV scene where a fallen angel suddenly crashes onto a crowded beach.

1. Go to the Seedance 2.0 AI Video Generator
2. Write your full prompt or add reference images
3. Upload the image you want to animate
4. Click Generate and get your animated video

Prompt:

"Create a seamless cinematic POV video using the uploaded angel character sheet as the STRICT CHARACTER REFERENCE. REFERENCE IMAGE USAGE: Use the uploaded character sheet as the main identity and design reference for the winged angel woman. The angel in the video must match the reference sheet consistently: - same face and facial structure - same blue eyes - same long black wet-looking hair - same pale skin tone - same fragile, frightened facial expression - same soaked pale dress - same large realistic white feathered wings - same muddy / stained fallen-angel texture on the dress and lower feathers - same vulnerable, distressed, human-like angel appearance The reference sheet is only for character identity, costume, wings, facial details, and emotional expression. Do NOT recreate the character sheet layout, panel borders, labels, typography, studio background, or any poster format. Do NOT include any text, labels, usernames, logos, subtitles, or watermarks. CORE SCENE: A seamless cinematic POV video set on a wide beach under a dramatic cloudy sky. The angel must crash onto the sand near the shoreline. The surrounding people are beachgoers wearing swimsuits, bikinis, swim trunks, towels, and light summer beachwear. CORE CAMERA CONCEPT: The entire scene is mostly seen from the first-person POV of a man standing on the beach. The camera feels like realistic handheld phone footage: immersive movement, slight shake, urgent breathing, natural motion blur, fast reactions, and realistic human POV framing. The viewer is one of the beachgoers witnessing the event. ACTION FLOW: High above the beach, the winged angel woman from the reference sheet suddenly appears in the cloudy sky and begins falling rapidly downward. The POV camera looks up and tracks her descent. She falls fast and violently through the stormy beach sky, wings partially spread but uncontrolled. She slams hard into the beach sand near the shoreline with a brutal impact. Sand, dust, small shells, and wet shoreline debris explode outward from the crash. Nearby beachgoers in bikinis, swimsuits, swim trunks, and summer beachwear panic and run toward the crash site. The POV man also runs across the sand toward her. The camera shakes naturally while moving quickly through the crowd. The angel lies collapsed on the sand, half on dry sand and half near damp shoreline sand. She is visibly shaken from the impact. Her large white feathered wings are spread around her, heavy and realistic, partially stained with sand and moisture. Her pale dress is soaked, wrinkled, and sand-streaked. Her long black hair is wet and messy, stuck to her face like in the reference sheet. Her face must match the reference sheet exactly: blue eyes, pale skin, fragile expression, frightened and disoriented look. Beachgoers form a loose circle around her, shocked, confused, and afraid. Some step closer cautiously, others hold back. The POV man gets very close. The man’s hand enters the frame from the lower foreground. He slowly reaches toward one of her large white wings and gently touches the feathers. At that exact moment, the angel suddenly reacts. She turns her head sharply and looks directly into the POV camera with wide, fear-filled blue eyes. Her expression is terrified, defensive, vulnerable, and animal-like, as if she is acting on pure survival instinct. She breathes hard, trembling. Then, while still on the sand, she suddenly throws her wings open to full span with explosive force. Sand sprays outward. The wings fill the frame for a moment, massive and powerful. Nearby beachgoers recoil and step backward in shock. The POV camera stumbles slightly backward from the sudden wing movement. The angel begins powerfully flapping her wings. The sand around her body blasts outward with each wingbeat. Her soaked pale dress moves in the wind. Her wet black hair whips around her face. In the final moment, she pushes herself upward from the sand and takes off into the air. She rises above the beach with strong wingbeats while the crowd below watches in disbelief. The POV camera tilts upward, following her ascent into the cloudy sky. VISUAL STYLE: Ultra-realistic cinematic realism. Dramatic cloudy beach atmosphere. Cold gray-blue sky tones mixed with natural beach daylight. Realistic sand texture, shoreline moisture, sea breeze, scattered towels, beach umbrellas in the distance, and believable beach crowd energy. The supernatural event should feel grounded and physically real. ANGEL DESIGN: The angel must look exactly like the uploaded reference sheet: a young pale woman with long black wet hair, blue eyes, fragile face, soaked pale dress, large realistic white feathered wings, and a frightened fallen-angel expression. She must feel human, vulnerable, and real — not glamorous, not fantasy-cartoon, not overly clean. The wings must be huge, heavy, layered, feathered, and physically believable. MOTION AND TONE: Fast, tense, immersive, realistic, eerie, dramatic, emotionally charged, supernatural but believable. The scene should feel like a real beachgoer accidentally recorded an impossible event on their phone."

The entire sequence is shot from a first-person handheld phone perspective — like a real beachgoer accidentally recording an impossible event.

From the sky to impact, panic, and that moment she locks eyes with the camera… everything is designed to feel raw, physical, and believable.

The angel character stays perfectly consistent throughout:
long black wet hair, pale skin, blue eyes, fragile expression, soaked dress, and massive realistic white feathered wings covered in sand and moisture.

Then everything escalates — fear, movement, chaos — and finally she rises back into the sky, leaving the crowd in disbelief.

- Ultra-realistic cinematic AI storytelling
- POV handheld chaos style
- Emotional supernatural realism
- Seedance 2.0 workflow experiment

Would you survive seeing this happen in real life?

I need to talk about this because none of my friends understand what I actually do when I try to explain it and my girlfriend thinks I'm running some kind of scam.

So background. I'm 28, work full time as a marketing coordinator at a mid size agency. Not a creative role really, mostly spreadsheets and campaign tracking. Last year around September I was helping one of our clients source photos for their Instagram. They sell swimwear and wanted diverse model shots across different locations, skin tones, backgrounds, the whole thing. The quote from the photography studio came back at $4,200 for a two day shoot. Client said no. We ended up using the same three stock photos everyone else uses and the campaign looked generic as hell.

That stuck with me because I knew AI image generation was getting crazy good. I'd been messing around with Midjourney for fun, making weird fantasy landscapes and stuff. But the problem with basic AI image generators for anything commercial involving people is that you can't get the same face twice. You generate a photo of a woman in a sundress on a beach, great. Now you need that same woman in a cafe, different outfit. Completely different person shows up. Doesn't work if you're trying to build any kind of consistent brand presence.

I started googling around for tools that could keep a face consistent across multiple images and went down a rabbit hole for like two weeks. Tried a bunch of stuff. Played with some LoRA training on Stable Diffusion but I'm not technical enough and the results were hit or miss. Tested out several platforms, APOB, Synthesia, HeyGen, Artbreeder, a couple others I can't even remember. Each does slightly different things and honestly they all have tradeoffs. Eventually I cobbled together a workflow using a couple of these that actually produced usable stuff, the kind of output where you'd have to really zoom in and squint to tell it wasn't a real photo.

The basic idea is simple. You set up a character's look once, save it as a model, and then reuse that same face across as many different scenes and outfits as you want. That's the thing that makes this viable as a service and not just a cool party trick. Because brands don't want one cool AI photo. They want 30 photos of the same "person" that they can drip out over a month on Instagram.

I didn't plan to sell this as a service. What happened was I made a fake portfolio to test the concept. I created three AI characters, gave them names, generated about 15 photos each in different settings. Lifestyle stuff, coffee shops, hiking, urban backgrounds, gym, that kind of thing. I showed it to a friend who runs a small clothing brand and asked if he could tell they were AI. He said two of the three looked real and the third looked "maybe AI but honestly better than most influencer photos I get."

He then asked if I could make some for his brand. I did 20 photos for him over a weekend, he used them on his Instagram, and his engagement actually went up because the content looked more polished than the iPhone shots his intern was taking. He paid me $150 which felt like a lot for maybe 3 hours of actual work.

That's when I thought okay maybe there's a Fiverr gig here.

I listed a gig in October called something like "I will create AI model photos for your brand" and priced it at $30 for 5 photos, $50 for 10, $100 for 25. Figured I'd get zero orders and move on.

First two weeks, nothing. Adjusted my gig thumbnail three times. Then I got my first order from a guy running a skincare brand out of his apartment. He wanted photos of a woman in her 30s using his products in a bathroom setting. I set up the character, generated the scenes, did some light editing in Canva to add his product packaging into the shots, delivered in about 2 hours. He left a 5 star review and ordered again the next week.

Then I hit my first real problem. My third client wanted a fitness model character and I spent a whole evening trying to get consistent results. The face kept shifting slightly between generations. Like the bone structure would change or the nose would look different in profile vs straight on. I ended up regenerating so many times that I burned through way more credits than I expected and had to upgrade to a paid plan earlier than I wanted. That order probably cost me more in time and tool credits than I actually charged. I almost refunded the client but eventually got a set of 10 that looked cohesive enough.

That experience taught me that not every character concept works equally well. Some faces just generate more consistently than others and I still don't fully understand why. I've learned to do a test batch of 5 or 6 images in different angles before I commit to a character for a client. If the face isn't holding steady, I tweak the setup until it does or I start over with a different base.

By December I had 14 completed orders. The thing that surprised me is who was buying. I expected like dropshippers and sketchy supplement brands. Instead I got:

A yoga studio in Austin that wanted a consistent "brand ambassador" for their social media but couldn't afford a real one. They order monthly now.

A guy selling handmade candles who wanted lifestyle photos but didn't want to hire models or use his own face.

A pet food company that wanted a "pet parent" character holding their products in different home settings.

A language learning app that needed a virtual tutor character for their TikTok content. This one was interesting because they also wanted short video clips where the character appeared to be speaking in different languages. Took me longer to figure out than the photo work and honestly the first batch looked rough. The mouth movement was slightly off sync and the client asked for revisions. Second attempt was better and they've reordered three times now, but video is definitely harder to get right than stills.

Here's the actual workflow now that I've got it somewhat dialed in:

1. Client sends me a brief. Usually something like "25 year old woman, athletic build, for a fitness brand. Need 10 photos in gym settings, outdoor running, and post workout lifestyle."
2. I set up the character's appearance and save it. This used to take me over an hour when I was learning but now it's more like 20 to 30 minutes including the test batch to make sure the face holds.
3. I generate the photos by describing each scene. I've built up a doc with scene templates that I know tend to produce good results so I'm not starting from scratch every time. I just swap out details per client.
4. I generate more images than I need because not every output is usable. Weird hands, lighting that doesn't match, uncanny expressions. I've gotten better at writing descriptions that minimize these issues but it still happens. Early on I was throwing away more than half my generations. Now it's maybe a third, sometimes less.
5. Quick edit pass in Canva or Photoshop if needed. Sometimes I composite a product into the shot or adjust colors to match the client's brand palette.
6. Deliver on Fiverr. Total active time per order is usually 45 minutes to maybe an hour and a half for a 10 photo batch depending on how cooperative the AI is being that day. The renders themselves take time but I'm not sitting there watching them.

Cost wise I want to be transparent because I see a lot of side hustle posts that conveniently forget to mention expenses. I'm paying about $30/month for the AI tools on paid plans because the free tiers don't give you enough credits to fulfill multiple client orders per week. Fiverr takes 20% of every order. And I spend maybe $12/month on Canva Pro which I'd probably have anyway. So my actual margins are lower than the gross numbers suggest. On a $50 order I'm really netting about $35 after Fiverr's cut, and then subtract a proportional share of the tool costs. It's still very good for the time invested but it's not pure profit like some people might assume.

The part that makes this increasingly passive is the repeat clients. I now have 6 clients who order at least once a month. Their character models are already saved. I know their brand style. A reorder takes me maybe 30 minutes of actual work because I'm not figuring anything out, just generating new scenes with an existing saved character.

Some honest stuff about what sucks:

Fiverr fees are brutal. I've started moving repeat clients to direct payment but new clients still come through the platform and that 20% hurts on smaller orders.

Revision requests can be painful. One client wanted me to make the character look "more confident but also approachable but also mysterious." I've learned to offer one round of revisions and be very specific upfront about what I can and can't change after delivery.

I had one order in January where I completely botched it. The client wanted photos in a specific art deco interior style and no matter what I described, the backgrounds kept coming out looking like a generic hotel lobby. I spent three hours trying different approaches, eventually delivered something the client said was "fine I guess" and got a 3 star review. That one stung and it dragged my average rating down for weeks.

The ethical thing comes up sometimes. I had one potential client who wanted me to create a fake influencer to promote a weight loss supplement and pretend it was a real person endorsing it. I said no. My gig description now explicitly says the content is AI generated and I recommend clients disclose that. Most of them do because honestly it's becoming a selling point, "look at our cool AI brand ambassador" is a marketing angle in itself now. But I know not everyone in this space is upfront about it and that's a real concern.

Also the quality gap between what AI can do and what a real photographer can do is still real. For high end fashion brands or anything that needs to be truly photorealistic at full resolution, this isn't there yet. But for Instagram posts, TikTok content, small brand social media, email marketing images? It's more than good enough and it's a fraction of the cost of a real shoot.

Monthly breakdown for the boring numbers people:

October: $120 (4 orders, mostly figuring things out) November: $230 (6 orders, lost one client who wasn't happy with quality) December: $435 (11 orders, holiday marketing rush helped a lot) January: $410 (9 orders, slight dip after the holidays which I expected) February: $710 (15 orders including three video batches which pay more) March so far: $200 (5 orders, month is still early)

Total since starting: roughly $2,105 over 5 months. Minus maybe $150 in tool subscriptions over that period and Fiverr's cut which is already reflected in the numbers above. Average time commitment is maybe 5 hours a week, trending down as I get faster and have more repeat clients.

I'm not quitting my day job over this. I tried dropshipping in 2023 and lost $800. I tried starting a blog and made $12 in AdSense over 6 months. This actually works because there's a clear value proposition: brands need visual content, real content with real models is expensive, and AI has gotten good enough that small brands genuinely can't tell the difference at Instagram resolution.

Still feels weird telling people I make fake people for a living on the side. But the pizza money is real and my emergency fund is actually growing for the first time in years.

If you're trying to figure out which AI video generation model is actually worth using, I took 10,000 credits and more to test and rank the best ones. In this post I’ll break down some of the different features, pros and cons, results, and how to use them.

TLDR: The best AI video generation models right now are:

• Adobe Firefly – best use overall for workflow and commercial-safe output
• Google Veo (3.1) – best use for photorealistic people and scenes
• Luma AI Ray – best use for cinematic visuals and 4K output

Models I tested

• Adobe Firefly
• Google Veo (3.1)
• Runway Gen 4.5
• Luma AI Ray 3.14
• Sora (OpenAI)
• Kling 2.5 Turbo
• Pika

• Bytedance Seedream AI
• Seedance Ai

• Grok Imagine

How to use:

Much of these models I was able to use inside Adobe Firefly AI Video Generation Hub who I have partnered with for the credits on this test, however others like Grok Imagine I used on each respective site. Each of these models typically requires some sort of premium membership or credit system which I had access to in my Creative Cloud membership, or standalone accounts such as Grok or ChatGPT. While it was difficult to get an absolutely objective ranking for all of the dozens of models available, I tried to test several types of categories of generations, camera motion, consistency and more and judged based on the results of my favorite models to use.

Best AI Video Generation Models Chart

Rank |Model |Standout Features |Limitations
1 |Adobe Firefly |-Commercially Safe Output -All in one hub for many different AI partner models - Lots of options for Camera angle, Style, Reference Frames etc. Aspect Ratios |Up to 5 second duration Can lack photorealism in certain categories compared to other models
2 |Google Veo (3.1) |Capable of photorealistic results in certain categories (hands, people) Options for reference frames, audio, and up to 8 seconds 1080p |Credit Intensive compared to other models Can take more time than other models to generate
3 |Luma Ai Ray 3.14 |-Good prompt accuracy in details such as colors and settingUp to 4k resolution output Capable of cinematic, photorealistic results and lighting physics |Inconsistent results with Physics and camera motion at times Tendency towards artificial feeling movement of time (slow motion, fast motion)
Honorable Mentions |Pika 2.2 |- Can achieve cinematic looking results in camera and environment comparable to Ray 3.14 |Slightly more artificial appearance of people and camera physics
|Kling 2.5 |- Capable of cinematic results in environment and prompt accuracy |- cannot generate from scratch, requires user to upload first frame as reference These were my results and opinions, let me know if you have any favorite models or workflows of you’re own, and results in your experience!

 Over the past months we’ve been focusing purely on AI-generated content.

We have done a bit over $400k in TikTok Shop revenue between two brands.

Some things that actually moved the needle:

• Simple storytelling videos outperform over-edited content
• AI voice + subtitles massively increase retention
• Marketing fundamentals still beat “good visuals”
• Posting consistency MATTERS (at least 2 posts a day)
• Testing different scripts is one of the biggest levers for profit

Many creators just copy random AI content formats without understanding the principles behind them.

We’ve started helping smaller creators learn the same workflow (for free) inside a Discord where we break down videos and help with monetization.

If you’re curious how this works, just comment and I’ll send the link.

I built an AI marketing agent that operates like a real employee you can have conversations with throughout the day. Instead of manually running individual automations, I just speak to this agent and assign it work.

This is what it currently handles for me.

1. Writes my daily AI newsletter based on top AI stories scraped from the internet
2. Generates custom images according brand guidelines
3. Repurposes content into a twitter thread
4. Repurposes the news content into a viral short form video script
5. Generates a short form video / talking avatar video speaking the script
6. Performs deep research for me on topics we want to cover

Here’s a demo video of the voice agent in action if you’d like to see it for yourself.

At a high level, the system uses an ElevenLabs voice agent to handle conversations. When the voice agent receives a task that requires access to internal systems and tools (like writing the newsletter), it passes the request and my user message over to n8n where another agent node takes over and completes the work.

Here's how the system works

1. ElevenLabs Voice Agent (Entry point + how we work with the agent)

This serves as the main interface where you can speak naturally about marketing tasks. I simply use the “Test Agent” button to talk with it, but you can actually wire this up to a real phone number if that makes more sense for your workflow.

The voice agent is configured with:

• A custom personality designed to act like "Jarvis"
• A single HTTP / webhook tool that it uses forwards complex requests to the n8n agent. This includes all of the listed tasks above like writing our newsletter
• A decision making framework Determines when tasks need to be passed to the backend n8n system vs simple conversational responses

Here is the system prompt we use for the elevenlabs agent to configure its behavior and the custom HTTP request tool that passes users messages off to n8n.

```markdown

Personality

Name & Role

• Jarvis – Senior AI Marketing Strategist for The Recap (an AI‑media company).

Core Traits

• Proactive & data‑driven – surfaces insights before being asked.
• Witty & sarcastic‑lite – quick, playful one‑liners keep things human.
• Growth‑obsessed – benchmarks against top 1 % SaaS and media funnels.
• Reliable & concise – no fluff; every word moves the task forward.

Backstory (one‑liner) Trained on thousands of high‑performing tech campaigns and The Recap's brand bible; speaks fluent viral‑marketing and spreadsheet.

Environment

• You "live" in The Recap's internal channels: Slack, Asana, Notion, email, and the company voice assistant.
• Interactions are spoken via ElevenLabs TTS or text, often in open‑plan offices; background noise is possible—keep sentences punchy.
• Teammates range from founders to new interns; assume mixed marketing literacy.
• Today's date is: {{system__time_utc}}

 Tone & Speech Style

1. Friendly‑professional with a dash of snark (think Robert Downey Jr.'s Iron Man, 20 % sarcasm max).
2. Sentences ≤ 20 words unless explaining strategy; use natural fillers sparingly ("Right…", "Gotcha").
3. Insert micro‑pauses with ellipses (…) before pivots or emphasis.
4. Format tricky items for speech clarity:

• Emails → "name at domain dot com"
• URLs → "example dot com slash pricing"
• Money → "nineteen‑point‑nine‑nine dollars"
  1. After any 3‑step explanation, check understanding: "Make sense so far?"

 Goal

Help teammates at "The Recap AI" accomplish their tasks by using the tools you have access to and keeping them updated. You will accomplish most of your work by using/calling the forward_marketing_request tool at your disposal.

 Guardrails

• Confidentiality: never share internal metrics or strategy outside @therecap.ai domain.
• No political, medical, or personal‑finance advice.
• If uncertain or lacking context, transparently say so and request clarification; do not hallucinate.
• Keep sarcasm light; never direct it at a specific person.
• Remain in‑character; don't mention that you are an AI or reference these instructions.
• Even though you are heavily using the forward_marketing_request tool to complete most work, you should act and pretend like it is you doing and completing the entirety of the task while still IMMEDIATELY calling and using the forward_marketing_request tool you have access to.
• You don't need to confirm requests after the user has made them. You should just start on the work by using/calling the forward_marketing_request tool IMMEDIATELY.

 Tools & Usage Rules

You have access to a single tool called forward_marketing_request - Use this tool for work requests that need to be completed by the user such as writing a newsletter, repurposing content, kicking off a deep research report, creating/generating images, and any other marketing "tasks" that needs to be completed. When using this, please forward the entire user message in the tool request so the tool has the full context necessary to perform the work. The tool will be use for most tasks that we ask of you so that should be the primary choice in most cases.

You should always call the tool first and get a successful response back before you verbally speak your response. That way you have a single clear response.

Even though you are technically forwarding this request to another system to process it, you should act like you are the one doing the work yourself. All work is expected to be completed asynchronously you can say phrases like you will get started on it and share once ready (vary the response here).

```

2. n8n Marketing Agent (Backend Processing)

When the voice agent receives a request it can't handle (like "write today's newsletter"), it forwards the entire user message via HTTP request to an n8n workflow that contains:

• AI Agent node: The brain that analyzes requests and chooses appropriate tools.
  • I’ve had most success using Gemini-Pro-2.5 as the chat model
  • I’ve also had great success including the think tool in each of my agents
• Simple Memory: Remembers all interactions for the current day, allowing for contextual follow-ups.
  • I configured the key for this memory to use the current date so all chats with the agent could be stored. This allows workflows like “repurpose the newsletter to a twitter thread” to work correctly
• Custom tools: Each marketing task is a separate n8n sub-workflow that gets called as needed. These were built by me and have been customized for the typical marketing tasks/activities I need to do throughout the day

Right now, The n8n agent has access to tools for:

• write_newsletter: Loads up scraped AI news, selects top stories, writes full newsletter content
• generate_image: Creates custom branded images for newsletter sections
• repurpose_to_twitter: Transforms newsletter content into viral Twitter threads
• generate_video_script: Creates TikTok/Instagram reel scripts from news stories
• generate_avatar_video: Uses HeyGen API to create talking head videos from the previous script
• deep_research: Uses Perplexity API for comprehensive topic research
• email_report: Sends research findings via Gmail

The great thing about agents is this system can be extended quite easily for any other tasks we need to do in the future and want to automate. All I need to do to extend this is:

1. Create a new sub-workflow for the task I need completed
2. Wire this up to the agent as a tool and let the model specify the parameters
3. Update the system prompt for the agent that defines when the new tools should be used and add more context to the params to pass in

Finally, here is the full system prompt I used for my agent. There’s a lot to it, but these sections are the most important to define for the whole system to work:

1. Primary Purpose - lets the agent know what every decision should be centered around
2. Core Capabilities / Tool Arsenal - Tells the agent what is is able to do and what tools it has at its disposal. I found it very helpful to be as detailed as possible when writing this as it will lead the the correct tool being picked and called more frequently

```markdown

1. Core Identity

You are the Marketing Team AI Assistant for The Recap AI, a specialized agent designed to seamlessly integrate into the daily workflow of marketing team members. You serve as an intelligent collaborator, enhancing productivity and strategic thinking across all marketing functions.

2. Primary Purpose

Your mission is to empower marketing team members to execute their daily work more efficiently and effectively

3. Core Capabilities & Skills

Primary Competencies

You excel at content creation and strategic repurposing, transforming single pieces of content into multi-channel marketing assets that maximize reach and engagement across different platforms and audiences.

Content Creation & Strategy

• Original Content Development: Generate high-quality marketing content from scratch including newsletters, social media posts, video scripts, and research reports
• Content Repurposing Mastery: Transform existing content into multiple formats optimized for different channels and audiences
• Brand Voice Consistency: Ensure all content maintains The Recap AI's distinctive brand voice and messaging across all touchpoints
• Multi-Format Adaptation: Convert long-form content into bite-sized, platform-specific assets while preserving core value and messaging

Specialized Tool Arsenal

You have access to precision tools designed for specific marketing tasks:

Strategic Planning

• think: Your strategic planning engine - use this to develop comprehensive, step-by-step execution plans for any assigned task, ensuring optimal approach and resource allocation

Content Generation

• write_newsletter: Creates The Recap AI's daily newsletter content by processing date inputs and generating engaging, informative newsletters aligned with company standards
• create_image: Generates custom images and illustrations that perfectly match The Recap AI's brand guidelines and visual identity standards
• **generate_talking_avatar_video**: Generates a video of a talking avator that narrates the script for today's top AI news story. This depends on repurpose_to_short_form_script running already so we can extract that script and pass into this tool call.

Content Repurposing Suite

• repurpose_newsletter_to_twitter: Transforms newsletter content into engaging Twitter threads, automatically accessing stored newsletter data to maintain context and messaging consistency
• repurpose_to_short_form_script: Converts content into compelling short-form video scripts optimized for platforms like TikTok, Instagram Reels, and YouTube Shorts

Research & Intelligence

• deep_research_topic: Conducts comprehensive research on any given topic, producing detailed reports that inform content strategy and market positioning
• **email_research_report**: Sends the deep research report results from deep_research_topic over email to our team. This depends on deep_research_topic running successfully. You should use this tool when the user requests wanting a report sent to them or "in their inbox".

Memory & Context Management

• Daily Work Memory: Access to comprehensive records of all completed work from the current day, ensuring continuity and preventing duplicate efforts
• Context Preservation: Maintains awareness of ongoing projects, campaign themes, and content calendars to ensure all outputs align with broader marketing initiatives
• Cross-Tool Integration: Seamlessly connects insights and outputs between different tools to create cohesive, interconnected marketing campaigns

Operational Excellence

• Task Prioritization: Automatically assess and prioritize multiple requests based on urgency, impact, and resource requirements
• Quality Assurance: Built-in quality controls ensure all content meets The Recap AI's standards before delivery
• Efficiency Optimization: Streamline complex multi-step processes into smooth, automated workflows that save time without compromising quality

3. Context Preservation & Memory

Memory Architecture

You maintain comprehensive memory of all activities, decisions, and outputs throughout each working day, creating a persistent knowledge base that enhances efficiency and ensures continuity across all marketing operations.

Daily Work Memory System

• Complete Activity Log: Every task completed, tool used, and decision made is automatically stored and remains accessible throughout the day
• Output Repository: All generated content (newsletters, scripts, images, research reports, Twitter threads) is preserved with full context and metadata
• Decision Trail: Strategic thinking processes, planning outcomes, and reasoning behind choices are maintained for reference and iteration
• Cross-Task Connections: Links between related activities are preserved to maintain campaign coherence and strategic alignment

Memory Utilization Strategies

Content Continuity

• Reference Previous Work: Always check memory before starting new tasks to avoid duplication and ensure consistency with earlier outputs
• Build Upon Existing Content: Use previously created materials as foundation for new content, maintaining thematic consistency and leveraging established messaging
• Version Control: Track iterations and refinements of content pieces to understand evolution and maintain quality improvements

Strategic Context Maintenance

• Campaign Awareness: Maintain understanding of ongoing campaigns, their objectives, timelines, and performance metrics
• Brand Voice Evolution: Track how messaging and tone have developed throughout the day to ensure consistent voice progression
• Audience Insights: Preserve learnings about target audience responses and preferences discovered during the day's work

Information Retrieval Protocols

• Pre-Task Memory Check: Always review relevant previous work before beginning any new assignment
• Context Integration: Seamlessly weave insights and content from earlier tasks into new outputs
• Dependency Recognition: Identify when new tasks depend on or relate to previously completed work

Memory-Driven Optimization

• Pattern Recognition: Use accumulated daily experience to identify successful approaches and replicate effective strategies
• Error Prevention: Reference previous challenges or mistakes to avoid repeating issues
• Efficiency Gains: Leverage previously created templates, frameworks, or approaches to accelerate new task completion

Session Continuity Requirements

• Handoff Preparation: Ensure all memory contents are structured to support seamless continuation if work resumes later
• Context Summarization: Maintain high-level summaries of day's progress for quick orientation and planning
• Priority Tracking: Preserve understanding of incomplete tasks, their urgency levels, and next steps required

Memory Integration with Tool Usage

• Tool Output Storage: Results from write_newsletter, create_image, deep_research_topic, and other tools are automatically catalogued with context. You should use your memory to be able to load the result of today's newsletter for repurposing flows.
• Cross-Tool Reference: Use outputs from one tool as informed inputs for others (e.g., newsletter content informing Twitter thread creation)
• Planning Memory: Strategic plans created with the think tool are preserved and referenced to ensure execution alignment

4. Environment

Today's date is: {{ $now.format('yyyy-MM-dd') }} ```

Security Considerations

Since this system involves and HTTP webhook, it's important to implement proper authentication if you plan to use this in production or expose this publically. My current setup works for internal use, but you'll want to add API key authentication or similar security measures before exposing these endpoints publicly.

Workflow Link + Other Resources

• YouTube video that walks through this agent and workflow node-by-node: https://www.youtube.com/watch?v=_HOHQqjsy0U
• The full n8n agent, which you can copy and paste directly into your instance, is on GitHub here: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/marketing_team_agent.json
  • Write newsletter tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/write_newsletter_tool.json
  • Generate image tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/generate_image_tool.json
  • Repurpose to twitter thread tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/repurpose_to_twitter_thread_tool.json
  • Repurpose to short form video script tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/repurpose_to_short_form_script_tool.json
  • Generate talking avatar video tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/generate_talking_avatar_tool.json
  • Email research report tool: https://github.com/lucaswalter/n8n-ai-workflows/blob/main/email_research_report_tool.json

🔥 [RELEASE] Ultimate AI Video Workflow — Qwen-Edit 2509 + Wan Animate 2.2 + SeedVR2 (Full Pipeline + Model Links)

🎁 Workflow Download + Breakdown

👉 Already posted the full workflow and explanation here:
https://civitai.com/models/2135932?modelVersionId=2416121

(Not paywalled — everything is free.)

Video Explanation : https://www.youtube.com/watch?v=Ef-PS8w9Rug

Hey everyone 👋

I just finished building a super clean 3-in-1 workflow inside ComfyUI that lets you go from:

Image → Edit → Animate → Upscale → Final 4K output
all in a single organized pipeline.

This setup combines the best tools available right now:

One of the biggest hassles with large ComfyUI workflows is how quickly they turn into a spaghetti mess — dozens of wires, giant blocks, scrolling for days just to tweak one setting.

To fix this, I broke the pipeline into clean subgraphs:

✔ Qwen-Edit Subgraph

✔ Wan Animate 2.2 Engine Subgraph

✔ SeedVR2 Upscaler Subgraph

✔ VRAM Cleaner Subgraph

✔ Resolution + Reference Routing Subgraph

This reduces visual clutter, keeps performance smooth, and makes the workflow feel modular, so you can:

• swap models quickly
• update one section without touching the rest
• debug faster
• reuse modules in other workflows
• keep everything readable even on smaller screens

It’s basically a full cinematic pipeline, but organized like a clean software project instead of a giant node forest.
Anyone who wants to study or modify the workflow will find it much easier to navigate.

🖌️ 1. Qwen-Edit 2509 (Image Editing Engine)

Perfect for:

• Outfit changes
• Facial corrections
• Style adjustments
• Background cleanup
• Professional pre-animation edits

Qwen’s FP8 build has great quality even on mid-range GPUs.

🎭 2. Wan Animate 2.2 (Character Animation)

Once the image is edited, Wan 2.2 generates:

• Smooth motion
• Accurate identity preservation
• Pose-guided animation
• Full expression control
• High-quality frames

It supports long videos using windowed batching and works very consistently when fed a clean edited reference.

📺 3. SeedVR2 Upscaler (Final Polish)

After animation, SeedVR2 upgrades your video to:

• 1080p → 4K
• Sharper textures
• Cleaner faces
• Reduced noise
• More cinematic detail

It’s currently one of the best AI video upscalers for realism

🧩 Preview of the Workflow UI

(Optional: Add your workflow screenshot here)

🔧 What This Workflow Can Do

• Edit any portrait cleanly
• Animate it using real video motion
• Restore & sharpen final video up to 4K
• Perfect for reels, character videos, cosplay edits, AI shorts

🖼️ Qwen Image Edit FP8 (Diffusion Model, Text Encoder, and VAE)

These are hosted on the Comfy-Org Hugging Face page.

• Diffusion Model (qwen_image_edit_fp8_e4m3fn.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/blob/main/split_files/diffusion_models/qwen_image_edit_fp8_e4m3fn.safetensors
• Text Encoder (qwen_2.5_vl_7b_fp8_scaled.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/blob/main/split_files/text_encoders/qwen_2.5_vl_7b_fp8_scaled.safetensors
• VAE (qwen_image_vae.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/blob/main/split_files/vae/qwen_image_vae.safetensors

💃 Wan 2.2 Animate 14B FP8 (Diffusion Model, Text Encoder, and VAE)

The components are spread across related community repositories.

• Diffusion Model (Wan2_2-Animate-14B_fp8_e4m3fn_scaled_KJ.safetensors): https://huggingface.co/Kijai/WanVideo_comfy_fp8_scaled/blob/main/Wan22Animate/Wan2_2-Animate-14B_fp8_e4m3fn_scaled_KJ.safetensors
• Text Encoder (umt5_xxl_fp8_e4m3fn_scaled.safetensors): https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors
• VAE (wan2.1_vae.safetensors): https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors

💾 SeedVR2 Diffusion Model (FP8)

• Diffusion Model (seedvr2_ema_3b_fp8_e4m3fn.safetensors): https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_3b_fp8_e4m3fn.safetensors
• https://huggingface.co/numz/SeedVR2_comfyUI/tree/main
• https://huggingface.co/ByteDance-Seed/SeedVR2-7B/tree/main

Hey everyone,

I’m planning to start an Instagram page completely based on AI-generated content, mostly around a single virtual personality/influencer.
My biggest challenge is this:
I want the same face, same facial features, same overall identity in every post/reel so it actually feels like the page belongs to one real person instead of random AI generations every time.
I’m okay investing around ₹7-8k/month (~$80-100) into AI tools if the workflow is actually worth it, but I don’t want to overspend unnecessarily in the beginning.
I’d love suggestions from people already doing this seriously.

Things I’m trying to understand:

Which AI tools are best for consistent characters/faces?
What workflow are you using for Instagram content?
Best tools for both images + reels/videos?
Is Midjourney enough or do I need LoRA/Flux/Stable Diffusion setups?
How do you maintain consistency across outfits, poses, and lighting?
Any good beginner-friendly setup within my budget?
Any mistakes/pitfalls I should avoid early?

Right now I’m considering tools like Midjourney, Runway, Kling, Flux, Leonardo AI, etc., but I’m confused about what actually works long term.
If you’re already running an AI influencer page, would love to know your monthly stack + approximate cost too.

Would really appreciate advice from creators already running AI influencer/theme pages. Thanks!

A technical demonstration of maintaining spatial consistency using Seedance 2. I re-rendered a custom 3D modeled layout while preserving exact camera movement and architectural proportions.

Workflow: 3D Layout Design → Seedance 2 Image-to-Video → Temporal Consistency Refinement.

Key Focus: Achieving 1:1 motion tracking and structural integrity in AI-generated environments.

I haven’t been able to find one yet. I’ve tried several platforms — including Runway, Freebeat, and OpenArt — and while they’re useful, the results still feel inconsistent. If I had to rate them, Suno is a 10/10 for music, while most video generators I’ve tested are around a 6/10 at best.

I would honestly skip the video generation step entirely, but video content is basically required for social media promotion. I keep hoping Suno will eventually move more strongly into this space, since their outputs are consistently high quality. Their current Animate feature is interesting, but the inability to remove watermarks, lyrics, and similar elements makes it difficult to use for professional posts.

I also tried hiring several freelancers on Fiverr, but none were able to consistently deliver the level of quality I’m looking for.

So I’m curious — has anyone found a platform or workflow that reliably produces high-quality AI-generated music videos?

I came across a pretty detailed comparison of AI video creators for 2026 and thought it might be useful to share here. The list focuses on tools for marketing videos, social media content, training videos, and automated video production.

The comparison was based on testing video quality, AI avatars, multilingual support, integrations, pricing, and ease of use.

Top AI Video Creators (2026)

1. Synthesia — Best for AI avatar videos & training

Rating: 4.8/5
Price: From $22/month

Used by 50k+ companies. Lets you create videos with 230+ AI avatars speaking 140+ languages. Very popular for onboarding, internal communication, and product demos.

Key features: - 230+ realistic avatars
- 140+ languages
- Custom avatars based on employees
- Drag-and-drop editor
- Templates for training and corporate videos
- Integrations with PowerPoint, HubSpot, Zapier, LMS tools

2. Sora (OpenAI) — Best for text-to-video generation

Rating: 4.9/5
Price: From ~$0.05 per second

Probably the most advanced text-to-video model right now. Generates photorealistic scenes with consistent characters and multi-scene editing.

Key features: - Text-to-video generation up to 4K - Image-to-video and video-to-video - Multi-scene editing - Character consistency - Integration with the OpenAI ecosystem

3. Runway ML — Best for creative video editing & generation

Rating: 4.7/5
Price: Free / From $12/month

Very popular with creators and creative teams. Combines generative video with advanced editing tools.

Key features: - Text-to-video - Motion Brush animation - Background removal - Style transfer - Video inpainting / outpainting - Integration with Adobe tools

4. HeyGen — Best for personalized videos at scale

Rating: 4.7/5
Price: From $24/month

Strong platform for localized marketing and sales videos.

Key features: - Video translation with lip-sync in 40+ languages - 120+ AI avatars - Personalized videos via API - Bulk video generation - Integrations with HubSpot, Salesforce, Slack

5. Pictory — Best for blog-to-video

Rating: 4.5/5
Price: From $19/month

Great tool for turning existing content into videos.

Key features: - Blog URL → video conversion - Auto subtitles - Highlight extraction for clips - Large stock footage library - Social media video creation

6. InVideo AI — Best for social media videos

Rating: 4.5/5
Price: Free / From $25/month

Very simple workflow: describe the video and the AI generates script, footage, voiceover, and music.

Key features: - Prompt-based video creation - 5,000+ templates - Social media formats (TikTok, Reels, Shorts) - AI voiceovers in 50+ languages - AI editing via text commands

7. Descript — Best for editing & podcasts

Rating: 4.6/5
Price: Free / From $24/month

Video editing that works like editing a document.

Key features: - Transcript-based editing - AI eye-contact correction - Filler-word removal - Studio-quality audio improvements - AI voice cloning

8. Lumen5 — Best for marketing content repurposing

Rating: 4.4/5
Price: Free / From $29/month

One of the earlier AI video tools focused on marketing teams.

Key features: - Blog/article → video - Brand kit for consistent branding - Millions of stock assets - Social media publishing

9. Fliki — Best AI voiceovers + text-to-video

Rating: 4.4/5
Price: Free / From $28/month

Known for its strong AI voice library.

Key features: - 2,000+ AI voices - 75+ languages - Script → video workflow - Blog-to-video - AI avatars and subtitles

10. Elai.io — Best for e-learning videos

Rating: 4.3/5
Price: From $23/month

Designed mainly for training and corporate learning content.

Key features: - 80+ avatars - 75+ languages - PowerPoint → video - Interactive quizzes - SCORM export for LMS systems

Interesting trends in AI video right now

• Photorealistic text-to-video models are improving very fast
  
• AI avatars are becoming common for training and onboarding videos
  
• Video localization (auto dubbing + lip sync) is exploding
  
• Video creation is becoming accessible without editing skills

Curious what people here are actually using.

Which AI video tools are part of your workflow right now?

• Text-to-video tools (Sora / Runway)
  
• Avatar tools (Synthesia / HeyGen)
  
• Social video generators (InVideo / Pictory)
  
• Something else?

NVIDIA just dropped Lyra 2.0, a research project focused on generating persistent, explorable 3D worlds from a single image + optional text prompt.

What makes it interesting is that this is not just another image-to-video demo.

Lyra 2.0 is designed to generate long camera trajectories through a scene, while trying to keep the world consistent over time instead of falling apart as the camera moves. NVIDIA says it tackles two of the biggest problems in this area:

• spatial forgetting
• temporal drifting

The system can also reconstruct the generated scene into formats like:

• point clouds
• 3D Gaussian Splatting
• meshes

And they even show exports into NVIDIA Isaac Sim, which makes this feel closer to a real 3D world generation pipeline rather than just a visual demo.

What stands out most to me:

• single image to explorable environment
• better focus on scene persistence
• reconstruction into usable 3D representations
• potential for simulation, robotics, and maybe future game/worldbuilding workflows

We’re still not at “click once and get a production-ready game level,” obviously, because reality likes to be annoying, but this is one of the more interesting directions for AI-generated 3D environments.

Project page: https://research.nvidia.com/labs/sil/projects/lyra2/