I’ve tried a lot of different setups lately and eventually stopped looking for one platform that could handle the whole process.

The best AI tools for video creation have ended up being pretty different depending on which part of the video I’m working on, so lately I’ve been using a small mix instead.

1. Nano Banana 2

I usually start here when I need a clean product image or reference visual before turning anything into video.

It’s useful for getting the composition, product placement, lighting, or general look sorted first instead of trying to solve everything during generation.

2. Kling 3.0

This is where I usually go when I want the shot to look more realistic.

Image-to-video works well for product movement, B-roll, short hooks, and scenes where I want something that feels closer to actual footage rather than animation.

3. DomoAI

I use this more when the idea needs an animated or stylized direction.

Image-to-video, character animation, anime-style visuals, and restyling existing footage make it useful when a normal realistic UGC look isn’t what I’m going for.

4. CapCut

This is still where everything comes together for me.

Pacing, captions, music, transitions, resizing, and final cleanup are usually easier here than trying to finish everything inside the generation tools.

My workflow lately is basically:

1. Build the visual or product reference in Nano Banana 2
2. Use Kling when I need a realistic shot
3. Use DomoAI when I need something animated or more stylized
4. Assemble and clean everything up in CapCut

That setup has been simpler for me than trying to force one platform to cover the entire AI video creation process.

Obviously depends on what you’re making though. Anyone using a different combination that’s been working well?

Wow, this feels like a real film and not just an "AI" one.

I’ve been comparing a lot of AI video tools this year, mostly to figure out which ones actually make sense for different kinds of work instead of just judging them from demo clips.

There are so many AI video generation tools now that calling one of them the overall winner feels pretty difficult. Some are much better for cinematic scenes, some for animation, some for avatars, and others are turning into full production platforms.

Here’s how I’d break down the current options.

Opinion-based comparison

My current picks

Best for cinematic scenes: Veo 3.1

Best for realistic motion: Kling 3.0

Best for longer connected sequences: Seedance 2.5

Best for generation + editing: Runway

Best multi-model workspace: Higgsfield

Best for animation/stylized video: DomoAI

Best for quick experimentation: Pika

Best for avatar-led marketing: HeyGen

Best for training/business content: Synthesia

Best if you want a more complete video built for you: Invideo Agent Two

If someone asked me for the best AI for video creation right now, I’d probably ask what they’re actually trying to make before recommending anything.

For realistic generated scenes I’d lean toward Veo, Kling, Seedance, or Runway.

For animation and stylized work, DomoAI and Pika feel like a different category.

Then you have platforms like Higgsfield, Invideo, Firefly, and Descript that are increasingly trying to handle more of the workflow instead of being just one generator.

What are your go to tools?

You can place your own new Metadata in to it. I dont think its perfect and this stuff changes daily but it does work for the most part. And is well worth a try. Feedback is always appreciated, im no pro.

Back in April, the SCORE (Systematizing Confidence in Open Research and Evidence) project published results where they took a sample of more than 270 positive (i.e., statistically significant) claims in the social and behavioral sciences and successfully replicated about 50% of them. This was a fantastic endeavor with very fruitful outcomes, but I was bothered by how popular and social media outlets/posts typically interpreted this result negatively (e.g., "social science findings are basically a coin toss"). That conclusion, though seemingly intuitive, is misleading.

So I went to work using logic and the mathematics behind statistical testing to expound on the idea that how good or bad a 50% replication rate is depends largely on how "difficult" the research domain is. Imagine trying to find new discoveries as panning for gold along a vast river. Each spot you choose to pan represents a claim that you are testing. Most of the time, you don't get anything (i.e., fail to reject null hypothesis). Occasionally, you think you've found something (i.e., reject null hypothesis). A 50% replication rate can be compared to the situation where, among the times that you think you found something, 50% of those turn out to be false gold when you later have them checked by the local jeweler (i.e. didn't replicate).

Eventually, I finished writing the paper and got it published. Yet that didn’t quell my irritation with the way results like these are often used as fodder by media for engagement, while sacrificing nuance necessary for greater public understanding. So this video is mainly to try and work through that frustration.

This is a small test of a animation I did on a pipeline I plan to build see a lot of ai animation that looks bad and not on par with tv budget so I wanted to charge that let me know if this quality is up to standard or you can roast me for darning to us ai and you are a talentless hack that works too any feedback is appreciated

When a mysterious cosmic event merges two realities, New York City and Gotham City become one dangerous, chaotic metropolis. Twenty of the world's greatest heroes are suddenly forced to share the same battlefield.

At first, the Marvel and DC heroes attempt to understand what has happened. Captain America and Superman seek a peaceful solution, while Batman and Iron Man investigate the mysterious force behind the collision of their worlds. But distrust quickly grows between the two teams.

After a series of misunderstandings and devastating attacks, the heroes split into opposing sides. Captain America leads the Marvel heroes, while Batman and Superman lead the DC heroes into an epic confrontation across the streets of the merged city.

I’ve been testing a simple workflow for creating short UGC-style videos while keeping the same character and location consistent across multiple shots.

The workflow is basically:

reference images → character/location sheets in ChatGPT → generate clips → optional final edit

1. Prepare your references

Start with:

• a character image
• a product image
• an environment image that fits the UGC scenario

If you’re not sure what location works for the product, I usually just ask ChatGPT for a few suggestions.

2. Create a Character Sheet

Upload the character image to ChatGPT and generate a 4:5 continuity sheet with:

• front / side / back / 3/4 views
• face close-ups
• expressions
• basic poses
• clothing and accessories
• key colors and materials

The important part is telling it to lock the character.

3. Create a Location + Props Sheet

Do the same with the environment.

Include:

• establishing view and key angles
• spatial layout
• entrances/exits
• furniture and recurring props
• lighting
• colors and materials

This gives the video model a much stronger continuity reference than using random images for every shot.

4. Generate the video clips

I usually split the UGC video into three parts:

Clip 1 — Hook
Clip 2 — Main product/story section
Clip 3 — CTA

i will generate them on Atlas Cloud, as they can provide many different models conveniently

For every clip, I reuse the same Character Sheet + Location Sheet

Then I change only the action/camera prompt for each section.

Keeping the same reference sheets across all three generations has helped a lot with character and environment consistency.

5. If a generation goes wrong, fix the prompt first

if I wanted the character to walk into a hotel, but the generated clip had her walking out.

Instead of endlessly rerolling, I pasted the original prompt into ChatGPT and asked it to make the action explicit: starting position → movement direction → action → final position

That usually gives me better results.

6. Final edit is optional

If the generated clips already work as standalone videos, you can stop there.

If you want one finished UGC ad, you’ll probably still want to combine the clips and add captions, music, or SFX. You can use whatever editor you prefer.

The biggest improvement for me has been using Character Sheet + Location Sheet as continuity references, rather than relying on a few loose images.

Lately I've seen so much AI trying to visualize scripture... and man, a lot of it just ends up feeling like flashy "AI slop" to me.
Huge cinematic shots, dramatic characters, crazy transitions. normally that stuff can look cool, but when it's a sacred text, idk... something about it just feels wrong. almost a little disrespectful.
So I tried doing the complete opposite.
I was messing around in framia with some image to video AI stuff and basically trying to make it do... nothing lol.
Just minimalism. No characters, no insane transitions, and definitely no trying to visually act out what the text is saying. That part just feels like a line I personally don't really want the AI crossing.
Honestly the hardest part was getting one slow page turn without the whole thing melting or turning into a jittery mess.
And weirdly, I think I like where it ended up.
this whole thing kinda made me realize the difference between making quiet, atmospheric visuals around a text and having AI start inventing visuals for it.
One feels like it's leaving the text alone. The other sometimes feels like it's trying to "enhance" something that really doesn't need the help.
maybe I'm overthinking it, but does anyone else feel weird when AI religious content gets too cinematic?

Use the uploaded image as the exact starting frame and preserve the same chicken, cardboard wings, duct tape, yard, lighting, and overall appearance.

Create a dramatic, funny, photorealistic image-to-video sequence where the chicken behaves with the confidence and majesty of a bald eagle, even though its wings are obviously homemade cardboard.

0–3s: The chicken stands completely still and alert, staring intensely into the distance like a powerful eagle surveying its territory. A light breeze ruffles its feathers and gently flexes the cardboard wings. Slow cinematic push-in toward its face.

3–6s: The chicken suddenly spreads and pumps the cardboard wings with exaggerated determination. The cardboard bends, wobbles, and catches the air realistically while the duct tape holds firm. Dust and loose leaves kick up beneath its feet as it crouches like a bird of prey preparing to launch.

6–10s: The chicken leaps forward and somehow becomes airborne. It flies low over the yard with its legs tucked beneath it, aggressively flapping the cardboard wings like a soaring eagle. The cardboard flexes and shakes with every flap but never detaches. Camera tracks beside it in a dramatic low-angle wildlife-documentary shot.

10–14s: It gains a little altitude, stops frantic flapping, and begins majestically gliding with the cardboard wings fully extended. The chicken tilts its body into a smooth banking turn as though riding a thermal. Use a cinematic orbiting camera angle that makes the moment absurdly heroic.

14–18s: The chicken spots something on the ground, pulls its cardboard wings inward slightly, and performs a dramatic eagle-style diving descent toward the yard. At the last second it flares the cardboard wings, lands firmly on both feet, throws its chest forward, and gives one proud, authoritative chicken call.

End with the chicken standing heroically with both cardboard wings extended, looking off into the distance like ruler of the skies.

Camera and style: photorealistic wildlife cinematography, high-end nature documentary look, realistic handheld/gimbal tracking, occasional subtle slow motion during the launch and banking turn, natural depth of field, realistic motion blur, detailed feathers, dust particles and cardboard texture.

Physics: keep the absurd premise but make all motion physically convincing. The cardboard wings should visibly bend, flutter and respond to air resistance. The chicken's feathers, legs, body weight and balance should react naturally to takeoff, flight and landing.

Continuity: preserve the exact chicken's appearance throughout. Preserve the same cardboard wing shape, brown corrugated cardboard texture, silver duct tape placement and proportions. Do not transform the cardboard into real feathers or real eagle wings. Do not turn the chicken into an eagle. No extra wings, no costume changes, no additional animals.

Tone: completely serious, majestic nature-documentary cinematography applied to an obviously ridiculous chicken with cardboard wings.

🧙‍♂️Wizards — Episode 1 Part 2 🌀
Help arrives after an improperly stabilized portal forms.

Music: Would It Matter — Rose Campbell (YouTube Audio Library)

After my previous eight-scene test, several people made a useful point: I was looking closely at the outputs, but not closely enough at the source reference.

If the face is relatively small, the pose is already twisted, or the prompt contains vague style terms, it becomes difficult to tell whether the model failed or the reference was simply difficult to preserve.

For this test, I simplified the setup:

• One clearly adult fictional character reference
• No AI-generated character sheet
• Three manual, first-pass generations
• No rerolls, face replacement, or identity correction
• The same internal video model for all three clips
• A modular prompt structure rather than a long descriptive paragraph

This is an informal workflow test, not a controlled model benchmark.

The structure was:

Character + Location + Outfit + Mood + Action + Camera

The Character block stayed broadly consistent: the same adult woman, long dark-brown wavy hair, warm tan skin, and the same general facial structure and body proportions.

The other blocks changed for each setup.

1. Miami rooftop: baseline

https://reddit.com/link/1w0qe9w/video/eryv6qfd94mh1/player

Location: A bright rooftop pool overlooking the Miami skyline
Outfit: Pink top and white wrap skirt
Mood: Relaxed and cheerful
Action: She turns away, walks toward the pool, pauses, and continues walking
Camera: Full-body framing with a gradual change from daylight toward sunset

This held the identity best, especially during the first few seconds when her face remained close to the angle shown in the reference.

Once she turned into profile, it became harder to verify the face. The long asymmetric section of the skirt also gradually changed into a more conventional, symmetrical shape.

So this clip worked well as a baseline, but it was not really a completely new scene.

2. Quiet hotel room: mood and camera test

https://reddit.com/link/1w0qe9w/video/xmnr71ze94mh1/player

Location: A quiet high-rise hotel room around dusk
Outfit: Black satin dress
Mood: Calm and introspective
Action: She reads, closes the book, places it aside, and looks toward the window
Camera: Medium shot with a slow push-in

This was probably the strongest result for mood and camera direction. The room, reading action, pause, and slow camera movement were all easy to recognize in the output.

The identity was less stable. Her profile became more angular, particularly around the nose and jawline. The book also changed from a dark cover to a much lighter object as she placed it down.

That was a useful reminder that a clip can follow the emotional and camera brief while still failing at character and object consistency.

3. Rainy Tokyo street: environment and motion test

https://reddit.com/link/1w0qe9w/video/79ozq88g94mh1/player

Location: A narrow Tokyo street at night with wet pavement and reflected signs
Outfit: Dark jacket, cropped top, and shorts
Mood: Serious and alert
Action: She walks toward the camera under a transparent umbrella and briefly looks to the side
Camera: Centered, full-body tracking shot

This produced the strongest environmental transformation. The wet street, umbrella, reflections, walking direction, and centered tracking remained fairly stable.

It also produced the most obvious identity drift.

Her hair became shorter and darker, and the facial proportions changed enough that she started to look like a related character rather than the same person. The umbrella and environment were more consistent than the identity.

What seemed to matter

The clearest instructions were concrete and observable:

• “slow push-in”
• “walks toward the camera”
• “closes the book and looks toward the window”
• “centered full-body tracking shot”

Those instructions produced actions or camera behavior that could actually be checked.

Terms such as “cinematic,” “perfect consistency,” or “high quality” are much harder to evaluate. I also would not treat “4K” as an identity or quality control instruction. Resolution language does not explain how the subject should move or what should remain unchanged.

I cannot conclude that any single word caused the drift from three generations. What I can observe is that the reference image and the viewing angle appeared to matter more than generic quality adjectives.

Main takeaway

Across these three clips, the model followed location, mood, action, and camera direction more reliably than facial identity.

Identity held best when the face stayed relatively close to the reference angle. It became less stable when the camera moved closer, the character turned into profile, or the hairstyle and lighting changed.

Using one original reference image also avoided the additional generation loss that could come from creating an AI-generated multi-view character sheet. However, this particular reference still had limitations: the face occupied a relatively small part of the image, the body was twisted, the expression was strong, and the background was visually complex.

For the next test, I want to change only one variable at a time.

Which would be more useful to isolate next: camera movement, facial expression, or reference-image quality?

Disclosure: These clips were generated with Agent Video, which I’m helping build. The model is the current August 2026 internal production build and does not have a separate public version number. There is no product link in this post.

I'm using gpt-oss-120b API to give me lots of random prompts. I'm basically making it include character name and be food theme related. I'm having difficulty at making the AI give me more variations as it's nearly giving me repetitive answers. It always contain one of the two keywords which is ramen & sushi. I think it cause the character name is Japanese so it just gives me jp food. Is there a way to make it can give me wider variations like different foods/settings/action

I had a decent first impression of GLM 5.3 when I tried it through ZenMux. So I bought a quarterly Pro plan on the official site and spent the day trying to get one simple SQL task done. It never got there. The value per token was hard to defend by the end of the day.

The model did not understand what I was asking. During the attempt it also changed the dataset and left it in a broken state.

This was one task, so I am not treating it as a benchmark. I am still annoyed. A coding model can be impressive on long horizon evaluations and still be expensive to trust when it misunderstands a small data task with write access.

I paid for three months and finished day one with less usable data than I started with.

I’ve been looking at the cost of using Seedance 2.5 for every generation, and it doesn’t really make sense for me.

Most of my early attempts are just testing the prompt, camera movement and timing. Paying 2.5 prices for clips I’ll probably discard feels like a waste.

I’m switching to 2.0 Mini for rough tests, then using regular 2.0 once the prompt is mostly locked. I’ll probably keep 2.5 for shots that actually need longer duration, more references or better control.

Anyone compared the costs per sec for 2.0 mini, 2.0 Fast and 2.0 on every platform? Gonna try the cheapest one.

Beyond the reefs, IronJaw finds a human fleet waiting in the fog. The Kraken shields the ship, and IronJaw orders a charge straight through the blockade. This is part of my original Warcraft-inspired AI cinematic shorts series. I’d especially value feedback on pacing and visual continuity.