Author: <span>Essam Nooreldin</span>

Prompts

Artificial intelligence is changing the way creators design artwork, illustrations, and digital content. But while most people focus only on AI image generators, a much bigger breakthrough is quietly happening behind the scenes: A frame from my video for this article (tap to watch) AI can now reverse engineer illustration styles and turn them into reusable templates. It's worth being precise about what "reverse engineering a style" actually means technically: the AI isn't extracting a literal formula from the reference image so much as describing it in enough visual language — line weight, color palette, shading approach, texture — that a generator can approximate the same look on a new subject. The result is usually closer than manual style-matching by eye, but it's an approximation built from description, not a perfect algorithmic copy, which is worth remembering if you need pixel-exact consistency with an existing brand style rather than something that just reads as similar. With tools like Google Gemini or ChatGPT creators can analyze any visual style they love and instantly generate consistent artwork without manually describing colors, brush strokes, or composition every single time. This workflow is quickly becoming one of the most powerful creative hacks for designers, content creators, and marketers. I actually ran

Prompts

Why This AI Photo Editing Trick Is Going Viral AI photo editing is becoming extremely popular online. Today, tools like Google Gemini make complex edits much easier for everyday users. Because of this, many people are now adding missing friends or family members into group photos with AI. A frame from my video for this article (tap to watch) Unlike traditional editing methods, this process does not require Photoshop skills. All you need are two photos and a simple prompt. From there, Gemini handles the difficult editing automatically. In most cases, the final image looks highly realistic. Lighting, shadows, colors, and camera angles are adjusted automatically by the AI. As a result, the added person blends naturally into the scene. Across social media platforms like Instagram, TikTok, and YouTube, this trend continues to grow rapidly. Many creators use it for viral content, while families often use it to recreate memories or restore old photos. I actually tried this myself with an old group photo from a trip a friend missed, using a separate solo photo of them taken around the same time, mainly to see whether Gemini could really match the outdoor lighting convincingly. Tips for Better Results Getting realistic results depends heavily on the quality of your

Esso Review Lab

I spend a lot of my week talking, not typing — voice notes to my team, quick dictated captions, the odd meeting I need notes from afterward — and almost none of the mainstream transcription tools handle Arabic well once you throw in a real accent or a sentence that switches to English halfway through. That's what made me actually sit down and test Munsit, an Arabic-first voice AI platform built by CNTXT AI in the UAE, instead of just reading its feature page and moving on. A frame from my video for this article (tap to watch) What I Actually Tested It On I ran it against the kind of audio I actually produce day to day: a handful of WhatsApp voice notes recorded on the walk to a shoot, a short recorded segment of me talking through a video idea out loud, and a few sentences where I deliberately mixed Arabic and English mid-sentence the way I actually talk. That mixed-language habit is normal for a lot of us in the Gulf, and it's exactly the thing that breaks most transcription tools built primarily for English. Munsit handled the straight Arabic dictation cleanly, including some Gulf-leaning phrasing that I half-expected it to

Prompts

🏆 COMPLETE AI INTERIOR VIDEO SYSTEM 🔧 TOOLS Gemini (Nano Banana Pro) → Create images (first + last frames) Google Flow (Veo 3.1) → Turn images into cinematic videos Splitting the workflow this way — a still-image model for the two reference frames, a dedicated video model for the motion between them — mirrors a pattern worth recognizing across several AI video pipelines. Image models are generally more reliable at nailing an exact composition than they are at generating coherent motion, while video models handle physics and camera movement better than they handle precise framing from a blank prompt. A frame from my video for this article (tap to watch)   I actually built this exact 5-scene system myself before publishing it — the "no morph" doorway method in particular took a few failed attempts to get right, which is why it's written up here as a strict rule rather than a suggestion. 🎬 FULL VIDEO STRUCTURE (PROVEN VIRAL FLOW) Scene 1 → Clay / Blueprint Scene 2 → Luxury Penthouse (transform) Scene 3 → Zoom into Living Room Scene 4 → Living → Bedroom (travel) Scene 5 → Bedroom → Bathroom (travel)   🧩 SCENE 1 — CLAY MODEL (FIRST FRAME) 🖼️ Gemini Prompt isometric ultra clean architectural penthouse layout, monochrome

Prompts

🎬 Image Prompt: ultra realistic macro photography, Oreo cookie as main subject, tiny miniature humans interacting with environment, cinematic lighting, shallow depth of field, hyper detailed textures, crumbs visible, creamy filling texture realistic, dramatic scale contrast, soft bokeh background, 8k quality, commercial food photography, fantasy realism, sharp focus, studio + cinematic hybrid lighting A frame from my video for this article (tap to watch) Every scene below reuses that same base description — Oreo cookie, macro photography, hyper-detailed crumbs and creamy filling — and only swaps the world it's placed into. That's a genuinely efficient way to generate a whole set of variations from one image prompt: lock the subject and photography style once, then treat the "world" (a lab, a village, outer space) as the only variable across each new generation. It also keeps the six results feeling like one cohesive campaign rather than six unrelated images, since the actual Oreo itself renders consistently every time. I actually generated all six of these myself in one Gemini session before publishing them, mainly to confirm the "lock the subject once, swap the world" approach really would keep the Oreo looking consistent across six completely different environments rather than drifting in shape or color.

Esso Review Lab

Vidrush is not a general-purpose AI video tool — it's built for one specific job: turning a topic or brief into a long-form, documentary-style YouTube video (6–40 minutes) without you scripting, filming, or editing it by hand. That narrow focus is the most important thing to understand before deciding if it's right for you. I tested it on something outside my usual short-form format on purpose: a documentary-style explainer on how one specific AI video technique actually works under the hood, the kind of topic that needs real research pulled together rather than just a demo. That's the exact use case Vidrush is built for, and it's a fair test of whether the "parallel AI roles" pitch holds up once you're not just watching a demo reel. How it actually works You give Vidrush a brief — a paragraph, a rough script, or existing footage/voiceover — and it runs that through what it describes as parallel AI roles: a researcher, scriptwriter, motion designer, editor, and sound designer. The platform sources stock and licensed footage, writes and voices a script, adds music and transitions, and generates a thumbnail, typically finishing a draft in under an hour. You then review it and request changes through

Esso Review Lab

OpenArt is a multi-model creative hub: rather than shipping one house AI model, it gives you a single account and interface across a wide range of image and video engines — currently including Seedance 2.0/2.5, Kling 3.0, Sora 2, Google Veo 3, MiniMax Hailuo, and HappyHorse for video, and GPT Image 2, Nano Banana Pro, Seedream 5.0, Grok Imagine, and Qwen Image 3 for images. That lineup shifts as providers release new versions, so treat any specific model list — including this one — as a snapshot, not a permanent fact. I ran the same product-ad brief through three of the video models side by side — the exact kind of comparison OpenArt is built to make easy — to see whether switching models inside one interface actually saves the time it promises, or just moves the friction somewhere else. What you can actually do with it Image generation across multiple diffusion and transformer-based models, with text-to-image and image-to-image workflows. Video generation up to 15 seconds at 4K, using whichever underlying model you select. Editing tools — inpainting, background removal/swap, and upscaling to 4K. Character Builder — trains a consistent character you can reuse across multiple generations, useful for series content or a recurring brand mascot.

Prompts

This one's a first-and-last-frame generation, not a straight text-to-video prompt — a different technique worth knowing if you're experimenting with Seedance 2.0. Instead of describing the whole scene from scratch, you give the model two reference frames (how the fight starts, how it ends) and let it fill in the motion between them. A frame from my video for this article (tap to watch) That constraint is what makes the result feel choreographed instead of random: the model has to invent a path from point A to point B, which is exactly how a real fight-scene storyboard works. Asking for "different perspectives" on top of that pushes it to cut between angles the way an actual action sequence would, instead of holding one static shot. This first-and-last-frame approach is worth knowing beyond fight choreography, too — it works for any shot where you care more about the beginning and end state than the exact path between them: a product transforming, a room being built or destroyed, a time-lapse-style change. A full text-to-video prompt has to describe motion in words, which is hard to get precise; handing the model two frames sidesteps that problem entirely and lets the interpolation do the work language struggles

Esso Review Lab

Pollo AI is one of a growing category of "aggregator" video platforms: instead of building its own video model from scratch, it puts a single interface in front of several third-party AI video engines — historically including Kling, Runway, Veo, MiniMax, and others, with the exact lineup changing as new models ship. That distinction matters more than most reviews mention, because it changes what you're actually paying for. I ran a quick product-ad concept through three different models on the platform to see how much that model choice actually matters in practice, rather than taking the "pick the best one" pitch at face value. What you're really buying When you generate a clip on Pollo AI, you're picking which underlying model renders it. Some models are stronger at realistic motion, others at stylized or anime output, others at following complex prompts precisely. The platform's value isn't "better AI video" in the abstract — it's not having to sign up for, learn, and pay separately for four or five different model providers when you just want to compare results and pick the best one for a shot. The differences between models were bigger than I expected from the same exact prompt — one gave me