TOKKA Team

“Automatically add sound effects to this video” sounds like it should mean the tool actually watches your footage and places sounds to match it. Most AI sound-effects tools don’t do that — they generate audio from a text description, with no idea what’s actually on screen or when things happen. We tested one of each

An AI-generated video clip can be technically correct — identity intact, no visual glitches, exactly the action you asked for — and still feel wrong. The most common reason: an accidental slow-motion look, caused not by anything broken in the generation itself, but by a mismatch between how much is happening on screen and how

If you’re generating video with AI tools clip-by-clip and then trying to join them into one longer piece, there are two very different ways to combine them — and the wrong one has burned us in a real project before. Here’s a working example, generated fresh for this piece, of the safer approach. Two clips,

Ask an AI image generator for a plain white sneaker held up to the camera, and there’s a real chance it draws a recognizable brand logo on it — a Nike swoosh, an Adidas stripe pattern — even with nothing about that brand anywhere in your prompt, and even with explicit negative-prompt terms trying to

Two tools exist to score AI-generated speech automatically, without a human ever listening: UTMOS and NISQA-TTS, both “no-reference” quality predictors that output a 1-5 score. We ran them against real human listening across two separate tests, and found something worth knowing before you trust either one to pick a text-to-speech provider: they don’t always agree

We ran a small, controlled set of tests on fal.ai’s FLUX.2 model to see which generation settings actually change how realistic an AI portrait looks — and which ones are mostly noise. This isn’t a definitive study; it’s four isolated comparisons, each with the same subject and seed, changing exactly one thing at a time.