TIL: When creating a universal prompt block like that, placing it after the core subject description is a critical best practice in AI image generation for two main reasons: 1. Priority and Focus The AI model processes the prompt from start to finish. Placing your core subject and scene at the beginning ensures the model's primary focus is on what you want it to create: * Beginning: Establishes the entire scene, subject, action, and setting (e.g., "A golden retriever wearing a hiking backpack, sitting on a mountain overlook at sunset."). This is the most important information. * End: The quality and style parameters (like the 8K resolution, DSLR raw photo block) then act as adjectives or modifiers that refine how the primary scene should be rendered. If you put the style block first, you risk the model prioritizing the style over the content, sometimes leading to generic or confusing outputs where the subject is poorly defined, but the "style" is perfect. 2. Prompt "Weight" In many advanced AI models, the "weight" or importance given to a term can subtly diminish the further it is placed from the start of the prompt. * By putting the style block at the end, you're telling the AI: "First, generate this specific scene. Then, ensure you apply all these high-quality, realistic photographic parameters to the resulting image." This structure guarantees that your content (the subject) is not diluted by your style (the realism terms). You want the image to be subject-first, quality-second. #LisaWaidartDesigns

💭 A thought I had over the weekend based on some conversations I had with creatives last week: We often describe AI in creative workflows as a tool for “automation.” But what if the better word is “assembly”? Automation suggests "replacing" or removing something. Assembly acknowledges how we work, layering, compositing, refining, and building toward a vision. Of course, we can automate parts of assembly. But the creative act itself has always been about assembly, assembling ideas, visuals, tones, and stories into something cohesive. When we view AI as an assistant in the assembly process, not the automation of creativity, we keep the creative in the driver’s seat, in full control, while still gaining the precision, speed, and scale that AI makes possible. Maybe the shift from automation to assembly is the mindset change that keeps creativity human at its core. 👉 What do you think? Does “assembly” better describe where AI fits in creative workflows? #AdobeFirefly #CreativeAI #GenerativeAI #ContentVelocity #DesignIntelligence #CreativeProduction

🚀 Ever wondered how we craft visuals that spark conversation? For our recent post on Generative AI, we designed this graphic to quickly convey a powerful message about how AI is revolutionizing creativity. Here’s a quick breakdown of what you're seeing and why it matters: At the Core: The AI Brain. The glowing, networked brain isn't just a pretty visual; it represents the intricate intelligence of Generative AI and its capacity to 'think' creatively. The Branches of Creativity. From this central intelligence, you see lines leading to various icons: The Pencil & Canvas: Symbolizing image and visual content creation – from stunning artwork to dynamic designs. The Music Notes: Highlighting AI's role in composing new music and generating unique audio experiences. The Code Blocks: Representing how AI is now adept at generating code, 3D models, and even new architectural designs. The Document Icon: Emphasizing AI's growing ability to write compelling text, articles, and marketing copy. Your Voice Matters! And finally, the "? Your thoughts?" prompt isn't just decorative. It's our direct invitation to you to share your perspectives on these exciting developments. This visual aims to quickly communicate the breadth and impact of Generative AI, positioning it as a tool that truly expands our creative horizons. What other visual metaphors do you find most effective for tech topics? #GenerativeAI #AIInsights #VisualCommunication #TechExplainer #Innovation #KoddoxTechnologies

The biggest challenge in creative production is often translating a brilliant brief into a final asset without losing detail or budget. This is where structured AI prompting becomes a massive professional advantage. I didn't just ask Veo 3 for a "product video." I fed it a detailed JSON prompt outlining a precise sequence: Abstract crystals and leaf materialize. A dynamic, swirling motion into a central glass bottle. A clean, final "hero shot" with a specific brand name reveal and subtle lens flares. This level of granular control moves us from AI experimentation to predictable, high-quality commercial production. Our Veo 3 Prompt Writer was essential in building this structured narrative, ensuring the AI executed the vision flawlessly. What part of your creative workflow could this level of precision streamline? #PromptEngineering #AIVideoGeneration #CreativeTech #DigitalMarketing #Veo3

I've been exploring more 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗿𝗲𝗻𝗱𝗲𝗿𝗶𝗻𝗴 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗹𝗮𝘁𝗲𝗹𝘆, especially now that AI can elevate average results and inpaint almost anything. The obvious choice is 𝗩𝗮𝗻𝘁𝗮𝗴𝗲, but it still has some frustrating limitations: static 3D vegetation and no proper integration with Forest Pack or RailClone (which is honestly embarrassing at this point). 𝗨𝗻𝗿𝗲𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲 remains the ultimate solution, but the learning curve is steep, and I haven’t had enough time to fully dive in. What really caught my attention, though, is 𝗗𝟱 𝗥𝗲𝗻𝗱𝗲𝗿. It ticks a lot of boxes for me: 𝗕𝗲𝘁𝘁𝗲𝗿 𝗚𝗣𝗨 = 𝗳𝗮𝘀𝘁𝗲𝗿 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗲𝗿 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗮𝗻𝗶𝗺𝗮𝘁𝗲𝗱 𝘃𝗲𝗴𝗲𝘁𝗮𝘁𝗶𝗼𝗻 (𝗹𝗶𝗸𝗲 𝗨𝗻𝗿𝗲𝗮𝗹) 𝗔𝗜 𝗦𝗰𝗮𝘁𝘁𝗲𝗿 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 AI Mood Matching It’s not fully production-ready yet, but it’s very promising. The sequence below starts from a 2-minute D5 render, with several layers of AI manipulation on top. Can’t wait to see where this real-time + AI hybrid workflow goes next. 🚀

I've been exploring more 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗿𝗲𝗻𝗱𝗲𝗿𝗶𝗻𝗴 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗹𝗮𝘁𝗲𝗹𝘆, especially now that AI can elevate average results and inpaint almost anything. The obvious choice is 𝗩𝗮𝗻𝘁𝗮𝗴𝗲, but it still has some frustrating limitations: static 3D vegetation and no proper integration with Forest Pack or RailClone (which is honestly embarrassing at this point). 𝗨𝗻𝗿𝗲𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲 remains the ultimate solution, but the learning curve is steep, and I haven’t had enough time to fully dive in. What really caught my attention, though, is 𝗗𝟱 𝗥𝗲𝗻𝗱𝗲𝗿. It ticks a lot of boxes for me: 𝗕𝗲𝘁𝘁𝗲𝗿 𝗚𝗣𝗨 = 𝗳𝗮𝘀𝘁𝗲𝗿 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗲𝗿 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗮𝗻𝗶𝗺𝗮𝘁𝗲𝗱 𝘃𝗲𝗴𝗲𝘁𝗮𝘁𝗶𝗼𝗻 (𝗹𝗶𝗸𝗲 𝗨𝗻𝗿𝗲𝗮𝗹) 𝗔𝗜 𝗦𝗰𝗮𝘁𝘁𝗲𝗿 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 AI Mood Matching It’s not fully production-ready yet, but it’s very promising. The sequence below starts from a 2-minute D5 render, with several layers of AI manipulation on top. Can’t wait to see where this real-time + AI hybrid workflow goes next. 🚀

AI’s New Trick: Fake Expense Receipts Generative AI has officially joined the dark side of office life, the expense report. According to Computerworld (Oct 2025), roughly 14% of all fraudulent receipts spotted last month were AI-generated, up from basically 0% last year. Translation: the bots aren’t just writing essays and generating cat memes anymore; they’re forging Uber rides, hotel invoices, and lunch bills with pixel-perfect realism. Why it’s happening: Generative models can now clone fonts, logos, and layouts from a single sample. “Image restoration” and “receipt scanning” tools can easily be flipped into forgery machines. Some scammers even train mini-models on their own company’s expense templates for maximum believability. How to spot the fakes before finance gets fooled: Metadata mismatch: AI-generated images often have blank or synthetic EXIF data. Too perfect to be true: Real receipts have smudges, bends, or faded ink, AI ones look printer-fresh. Font weirdness: Uniform spacing or perfectly straight digits, thermal printers never print that cleanly. Timestamp twins: Identical date/time stamps across multiple “different” receipts are a dead giveaway. Quick test: If a coworker’s “client lunch” looks like it was designed by a UX team, maybe it’s not from the bistro. The bigger picture: AI isn’t just automating creativity, it’s industrializing deception. But the good news? Countermeasures are already coming fast. The team at InFlux https://lnkd.in/ds49RnHx (and others in the audit-tech space) are building AI-forensics tools that detect synthetic textures, font inconsistencies, and GAN signatures faster than any manual check ever could. The same tech that created the problem is about to help solve it. #AI #GenerativeAI #AIFraud #Fintech #ExpenseReports #AIDetection #FutureOfWork #InFlux #Flux #TechOptimism

What actually happens in AI commercial production? Because I am terribly literal, I've been really keen to get beyond the buzzwords and the assumption that it's just a black box, where you input a prompt and an ad pops out. Thanks to Toby Walsham, Guy Soulsby, Emily Huyton and Ben Ducker for wakling me through the new 'It's Time for Autotrader' campaign from IMA and MADE BY HUMANS. Some unexpected insights: - how important it is to mirror the moments and markers of traditional production, for example, casting, locations and wardrobe. - being free from the expense of shoot costs is a blessing and a curse. You can add new shots and try things without shelling out thousands for reshoots, which gives the client much more flexibility, but it's reallllllllly hard to draw a line in the sand and stop noodling. - using AI to make an ad doesn't mean that you have to *just* use AI tools and platforms the whole way through. Different tools and techniques are layered in, using the best option for a particular job. So if that's traditional compositing or grading, so be it. - it's not the wacky, fantastical stuff that's the big challenge, it's the subtle human moments. #ai #artificialintelligence #aifilmmaking #commercialproduction #advertising https://lnkd.in/eVVpB5gU

🚫 Most “AI-powered” films look flawless… until you spot these 5 mistakes that kill creative excellence. What the industry won’t tell you: Smart AI isn’t enough. Executives and directors are losing millions in reshoots — not because of budget, but basic AI pitfalls that undermine scenes. 🔥 The top 5 silent saboteurs for 2025 (Statista, McKinsey 2024): 1. Over-smoothing of facial expressions — losing actor emotion in AI upscaling. 2. CGI artifact glitches in dynamic camera moves — tech can’t mimic organic physics. 3. Inconsistent lighting from neural style transfer — scenes look stitched, not seamless. 4. Dialogue-lip mismatch in dubbing automation — audience trust breaks. 5. Generic crowd generation — extras break immersion when AI isn’t context-trained. Nova 9’s fix: We blend manual creative oversight with the latest generative AI. Our hybrid pipeline bridges core gaps — artist-in-the-loop, custom-trained datasets, and reference-matching — ensuring every frame is theater-ready and cost-efficient. Case in point: A client’s festival entry bombed in preview. Our intervention? Human-AI review cycles, retexturing, and real-time emotion tracking. Outcome: Standing ovation, post-production savings up 23%. Here’s the lesson: The future isn’t AI OR artists — it’s curated collaboration. If you’re a director, CEO, or designer, it’s time to demand both tech AND taste. Which AI flaw has blindsided your last project—or have you found a creative workaround worth sharing? #FilmMaking #AIInnovation #QualityContent #ExpertTips https://www.nova9.studio

Ever wondered if the future of AI isn't in words, but in pixels? As I looked into this latest breakthrough, DeepSeek OCR, I'm rethinking everything I thought I knew about feeding data into LLMs. Instead of chopping text into inefficient tokens, we render it as crisp, high-res images (think 1280x3840 pixels) and let vision encoders do the heavy lifting. It's super counterintuitive, but the results? Very surprising. Let me paint the scene. Traditional tokenizers like BPE fragment your text into bite-sized pieces, about 4 characters per token, losing the flow of how words dance on a page. DeepSeek-OCR treats text as images, capturing those 2D nuances like layout, fonts, and spacing. In their experiments, at 97% reconstruction fidelity for long-form decoding, vision tokens are highly compressed, packing the punch of ~768 characters per token! This vibes with Andrej Karpathy's take: "Maybe all LLM inputs should just be images." Pixels cut through the mess of old-school encoding, unlocking holistic views, colorful semantics (think code that pops with syntax highlighting), and easy swaps to photos or diagrams. We're keeping text as the output for now, but flipping inputs to vision? That's mind blowing. As an #LLM tinkerer, this has me hooked: Are tokenizers on their way out? #AI #DeepLearning #ComputerVision #Innovation