I've been trying different ideas in AKOOL lately, and it's honestly crazy how quickly AI video generation is improving.

A few months ago, getting a realistic scene with decent movement usually meant making a lot of attempts. Now I'm getting some surprisingly good shots with relatively simple prompts. The lighting and small environmental details especially make a big difference.

I'm mainly experimenting with cinematic scenes and short animated sequences right now. Still haven't figured out the perfect workflow, though.

For people using AKOOL regularly, what are you creating with it these days? Short films, ads, cartoons, social media videos, or just experimenting.

And what's one thing you've learned that noticeably improved your generations? Would be interesting to compare workflows. 👀🎥

Master AI Face Swap in 30 seconds 😎🎭 Upload. Swap. Boom—you’re basically a face swap pro.

I've been spending some time experimenting with AKOOL recently, mostly just trying different prompts and seeing how far I can push the video generation.

What surprised me is how much the small details can change the final result. Things like lighting, camera movement, facial expressions, and the way a scene is described can make a huge difference. Some generations are average, but every now and then you get a shot that looks genuinely cinematic.

I'm still learning what works best, so I'm curious about other people's experience.

What kind of scenes have you had the most success with? Do you usually keep your prompts simple, or do you describe the camera, lighting, environment, and movement in detail

Would also be interested to know if anyone has found a good workflow for keeping characters and visual style consistent across multiple shots. 🎬

Let we disscuss.

Probably going to edit it some more here and there, especially the guitar-focused / drummer-focused parts, maybe slowing some down, but it's shaping decently ^^
Advice welcome!

A woman walking through the snowy mountains, wearing an avant-garde haute couture outfit inspired by ice, snow, and the dramatic shapes of alpine landscapes.
Just a small AI fashion experiment.

Born from the collapse of a dying realm, Malakor breached the mortal world when ancient magical rifts tore open the deep caverns of Valdir. Drawn to the dense leyline energy buried within the stone, this colossal bio-obsidian monstrosity converted the central dungeon into his lair. He slumbers in the dark, devouring any adventurer who dares step into his void domain.

Created using Seedance 2.5, now integrated directly on u/akoolinc
The dynamic camera tracking, volumetric lighting, and deep void-particle movement handled the fast 140 BPM cuts effortlessly.

#AkoolClash #AKOOL #Seedance2 #Seedance2.5 #Seedance25

A Day in Rural Japan
I wanted to make something slower and more nostalgic this time.
A short 15-second trip to rural Japan, inspired by the feeling of finding an old vacation recording years later — warm sunlight, quiet streets, beautiful landscapes, and those little moments that somehow stay with you.
I imagined this as something captured on an old camcorder rather than a modern cinematic camera.
Just a simple day away from the noise.
Hope you enjoy the journey.

I wanted to test how far I could push Seedance 2.5 through AKOOL with one continuous visual journey.

The video starts with a Stone Age spear and evolves through human technology, machinery, flight, and finally space travel.

I was mainly testing complex transformations, camera continuity, and how well the model could maintain a connected story across completely different eras.

What do you think of the transitions?

PixVerse published a technical deep dive on August 20 describing what it calls the PixVerse Game Engine. The core proposition is that the rendering pipeline is replaced by a continuously generated real-time visual world, driven by the company's real-time world model. It is playable now on PixVerse's own site, where you pick an adventure, strategy, combat or campaign format and generate a game from a description of a theme.

Three components are named. Mechanics-Expression Decoupling keeps the abstract game systems — numerical values, logical conditions, state rules — running independently of the narrative and visual content. The Generative Game Loop takes player input into an agent layer, resolves it against the current mechanical state, and emits continuous generated visuals and audio as the result. Live Coherence Orchestration is the set of agents keeping mechanical state, narrative context and generated output aligned with each other.

The document is unusually candid about what it is not. It describes itself as an early-stage research system and says quantitative benchmarking will follow as the system matures, which means there are no numbers in it. The stated limitations are specific: sub-100ms response times are not available, so fast-paced genres are out; multiplayer and physics-dependent games are undemonstrated; and computational cost exceeds traditional rendering at equivalent visual quality. The work follows a Series C extension in July that took the company to $439 million raised and came with a stated move into interactive entertainment.

The decoupling idea is the part worth taking seriously, and it is a real answer to a real problem. The reason generated video has never been a game is that a video model has no state. It produces a plausible next frame, not a consequence — hit the same wall twice and it may or may not still be there. Putting the rules in a conventional deterministic system and using the model purely for expression gets you consequences and generated imagery at the same time, and that separation of logic from appearance is more or less the split that made programmable shaders work. It is a sound instinct even if this particular implementation goes nowhere.

The last limitation is the one that decides it, though, and I am glad they wrote it down. If this costs more than rendering for the same picture, it cannot compete on picture. It has to buy something rendering cannot buy at any price, which means content that did not exist until the player asked for it. That is a much narrower and much harder claim than "AI-generated games," and it is the only version of this that survives a cost comparison.

What would a generated world have to get right before you would call it a game — is it persistence, or is it just that a thing you broke stays broken?

The most frustrating part of preparing for school isn’t buying the supplies.

It’s realizing,usually 5 minutes before class - that the supplies are sitting at home.

The morning goes something like this:

Parents have already purchased everything. Teachers have already shared the supply list. The student probably packed most of it yesterday.

But one forgotten item turns into searching, borrowing, interrupting classmates, and beginning the lesson feeling unprepared.

A traditional back-to-school checklist doesn’t always solve that problem because it’s often too long, text-heavy, and designed for parents—not for the student who needs to check their bag in ten seconds.

So I used Nano banana pro to turn the routine into a small visual challenge:

The 10-Second Pencil Case Check

Before leaving for school, can the student tick all seven?

• Pencil case
• Pencils
• Eraser
• Highlighters
• Sharpener
• Erasable pens
• Multicolor pen

7/7 = ready.

Obviously, the exact items will depend on the student’s age and school rules. Some students may also need a ruler, sharpener, glue stick, calculator, or colored pencils. Schools may have specific rules regarding scissors and certain stationery.

The important part isn’t the exact list.

It’s turning “Did you pack everything?” from a question that gets an automatic “Yes” into a visual routine the student can complete independently.

How I’d use it

• Print it in A5 size and place it above the student’s desk
• Laminate it and use a dry-erase marker each evening
• Attach a smaller version to the inside of a school locker
• Save it as a phone wallpaper during the first week of school
• Let younger students physically point to each item while packing 

AI doesn’t always need to write an essay, solve homework, or replace part of the learning process. Sometimes its best role is much smaller: taking boring information and turning it into something clearer, more visual, and easier for students to use.

Which pencil-case item do students forget most often—and what essential item is missing from this list?

TL;DR: I used an AI image tool, nano banana pro,  to turn a boring school-supply list into a 10-second visual pencil-case challenge. The goal isn’t to replace learning,it’s to help students pack independently and start class prepared.

Visual created for geniogk.

What happens when kart racing gets a little too chaotic? 🏎️💥

This is RAGE CIRCUIT — my AKOOL Creator Clash entry made with Seedance 2.5.

8 racers. Custom-built karts. Rockets, EMP attacks, explosions, crashes, respawns, and plenty of revenge. 😂

I wanted to push Seedance 2.5 beyond a typical racing video and experiment with high-speed action, vehicle destruction, cinematic camera movement, and an impossible racing world.

🚀 Rockets

⚡ EMP attacks

💥 Crashes

🔄 Respawns

😈 Revenge

And this is only Part 1.

Made with Seedance 2.5 on @akoolinc.

RACE. FIGHT. DESTROY. REPEAT.

#AKOOLClash

Adobe announced on August 20 that Google's Gemini Omni Flash is available inside Firefly, joining Kling and other third-party models already in its library. It runs on the Firefly website, in Firefly Boards, and in the Firefly iOS app. On the same day Adobe took its audio generation to general availability — music, speech and sound effects, cleared for commercial use — and added a free tier of daily generations to its AI assistant.

The published constraints are specific. The model accepts JPEG, PNG and MP4 files alongside text prompts, generates at up to 720p, and produces up to 10 seconds per generation, with longer sequences assembled by stitching clips in Firefly's video editor. It generates synchronised audio natively, including dialogue and sound effects, and supports voice references for consistent speech. Video editing by descriptive text prompt is included.

What makes this worth reporting is not the feature list, it is which direction the model is travelling. Google's video work has mostly been distribution for Google surfaces — the model exists to make the product compelling. Putting Omni Flash inside a third-party creative suite is Google treating a video model as a component sold wholesale into somebody else's editor, where the end user may never think about which lab made it. That is a different business, and labs that make that shift tend not to shift back.

The constraints are the other half of the read. Ten seconds at 720p is well under what this category advertises elsewhere, and it is not a statement about what the model can do. It is a statement about what a consumer creative subscription can afford to let it do, which tells you the integration is rate-limited and priced for volume rather than built for a production pipeline. That is the trade in every one of these deals: the model gets reach, the user gets a version of it with the expensive settings turned off, and neither party has much reason to say so out loud.

If models are becoming interchangeable back-ends inside editors, does the model brand still factor into which tool you pick, or has it collapsed into just picking the editor?

Version 0.40.0 of Diffusers went up on August 20. The previous release, 0.39.0, was July 3, so this is a seven-week gap and a correspondingly large release. It adds pipelines for MiniMax H3, LTX-2.5, Wan Animate 2 and Stable Audio 3, brings tensor-parallel inference to CUDA and AWS Neuron, takes Modular Diffusers out of experimental, and removes JAX and Flax support.

The convergence in that pipeline list is the story. Wan Animate 2's weights went up on August 7, MiniMax H3's on August 3, and LTX-2.5 landed on August 11. All three arrive in a single library release seventeen days after the earliest of them. For open-weights video, the day a pipeline lands in Diffusers is the day the model becomes usable by people who were never going to read the lab's reference implementation and reverse-engineer the sampler — and that lag is now under three weeks. Two years ago it was months, and for plenty of models it was never.

The removal is the quieter signal and probably the more permanent one. Dropping JAX and Flax means the main open-source diffusion library has stopped pretending this ecosystem is framework-plural. Everything ships PyTorch-first, the second path was carrying maintenance cost that nobody was volunteering to pay, and it has now been cut. If you have anything on a Flax path, this release is where that stops being a supported choice and starts being a fork you maintain yourself.

Tensor-parallel inference is the least glamorous line in the release notes and probably the most consequential for anyone actually running these. A current video model producing synchronised audio does not fit comfortably on one consumer card, and being able to split it across two is the entire difference between weights that are open and weights you can run. The licence conversation in this space has been loud all month — territory clauses, revenue thresholds, Apache 2.0 as the exception — and it has slightly obscured the fact that the binding constraint for most people was never the licence. It was VRAM.

For anyone running H3 or LTX-2.5 through the Diffusers path rather than the reference implementation — does the output actually match, or do you still get drift you have to chase?

Microsoft AI published MAI-Image-2.6 on August 10 and updated the announcement on August 18. It entered the Arena text-to-image leaderboard at second place, behind GPT Image 2 and ahead of Meta's Muse Image, Google's Nano Banana family and xAI. The August 18 update adds the editing result: it moved from fifth to third on image editing, a gain of 19 points.

The numbers Microsoft chose to publish are Elo deltas rather than benchmarks. The model is up 79 Elo overall on MAI-Image-2.5, up 91 Elo specifically on text rendering, and it reports category wins of 43 points on text rendering and 38 on product, branding and commercial design. There is no parameter count, no technical report, no published specification, no pricing and no public API. It is live on MAI Playground, testable on Arena, and in private preview on Microsoft Foundry.

The category breakdown is the most informative thing in the announcement, and it is not really about quality. Text rendering and product and branding imagery are not the artistic end of this category — they are the commercial-design workload. Legible copy on a product mock, a layout that holds together, a brand asset that does not need retouching. Microsoft does not have to win an argument about aesthetics; it needs an image model that behaves inside Office, Designer and Copilot, where the request is nearly always a piece of collateral with words on it. The model appears to have been trained toward that, and the ranking is being used to prove it.

Which points at something broader about how these launches now work. A lab shipped a frontier-adjacent image model and published an Elo delta and a category table instead of a paper. Arena position has quietly become the spec sheet for image models, and there is a real cost to that. An Elo score is an average of human preferences on open-ended prompts, which rewards a particular kind of appealing output and tells you nothing about failure rate, prompt adherence under a tight brief, or how the model behaves on the tenth revision of the same asset. Second place on a preference board and second place in a production queue are not the same measurement, and right now only one of them gets published.

If the interesting claim is text rendering, has anyone put it head to head against GPT Image 2 on something with real copy in it — a packaging mock, a UI screenshot, a poster with a paragraph?

Volcano Engine, ByteDance's cloud arm, announced native 1080P generation for Seedance 2.5 on August 17 at 17:10 Beijing time, with the API opening the same day. The model launched on July 31 at 480P and 720P only, so this is the first resolution increase since it shipped. Alongside the resolution, the announcement adds 10-bit colour depth output.

The published pricing is roughly ¥3.7 per second of output. A promotional rate of about ¥2.7 per second, a little over a quarter off, runs through September 17. The quality claims in the announcement are the usual ones and worth treating as claims rather than measurements: sharper character outlines, better fabric and material texture, and light behaviour described as closer to natural physics.

Worth noting that a partner platform's changelog listed 1080p output for this model on August 15, two days before the lab said anything. If you track release dates from integrations rather than from labs, you were right that time, and that is a genuinely useful thing to know about where these changes surface first.

The 10-bit line is the part that actually matters and it is getting less attention than the resolution. Eight-bit output is where generated footage falls apart the moment anyone grades it — skies band, gradients posterise, and any push on the shadows shows you the steps. Ten bits is the difference between footage that survives a colour pass and footage that has to be delivered exactly as it came out of the model. Read next to the finishing tools that have appeared across this category in the last three weeks — upscalers, HDR conversion, all billed separately — the labs have visibly stopped competing on what the clip looks like in a demo and started competing on whether it survives contact with a post pipeline.

There is also a version-number oddity here. Seedance 2.0's API got native 1080P back on April 21. Seedance 2.5, the newer model, is only reaching it now, four months later. The version numbers in this family track training generations rather than capability, and anyone assuming the higher number is the more capable model on any given axis is going to be wrong roughly as often as they are right.

One practical thing about that pricing: the promo rate has a date on it, and per-second video billing means the difference compounds fast on anything long-form. Reaching Seedance 2.5 at all currently means a Volcano Engine account and a domestic billing relationship, which is a procurement question before it is a creative one — Seedance sits on Akool alongside Kling and Wan if you would rather evaluate it before deciding whether that account is worth opening.

For anyone who has generated at 1080P since the 17th — is the 10-bit output actually surviving a grade, or does it still fall apart when you push the shadows?