Exemples de lead magnets LinkedIn en openai

⬇️ J'ai passé 30h à créer cette formation ChatGPT de 177 pages ☑︎ Les fondamentaux et son interface ☑︎ Maîtrise des modèles (GPT-4o, o1, o1 mini) ☑︎ La méthode RCT ☑︎ Custom Instructions ☑︎ Mode Canvas ☑︎ ChatGPT Projects ☑︎ Création de GPTs sur mesure ☑︎ Vision GPT et génération d'images ☑︎ Workflows et automatisation ☑︎ Gestion des ressources et quotas ☑︎ Mode vocal et fonctionnalités spéciales ☑︎ Intégration en entreprise Bref, c'est le 20ᵉ jour du calendrier de l'avent OpenAI sortent des nouveautés tous les jours en ce mois de décembre, il fallait bien les traités ! Alors voici la formation la plus complète et à jour sur ChatGPT Récupère la formation et ton calendrier → https://lnkd.in/eK3_-FyH Je t'envoie la formation gratuitement ? • Like ce post • Commente 'ChatGPT' • Connecte-toi Joyeux Noël 🎅 PS : N’hésite pas à partager pour en faire profiter ton réseau ;)

🔥 I just turned a screenshot into a full design system in 3 seconds using our new AI tool : Vibbbes From image to design system in one click. Literally. My last post on using Google's Gemini 3 to create a design system hit 200,000+ impressions & 1000+ comments. So I thought, why don't we make it even easier and more accessible to everyone. 🤔 So after a few caffeine fueled nights of design + code... We launched Vibbbes: The world's first AI design system generator that actually understands taste. How it works ✅ Upload a screenshot or describe a style ✅ VIBBBES extracts the visual DNA ✅ You get a complete design system in seconds Use the generated code to design 10x faster. Clone world-class apps for learning or invent a new aesthetic on the spot. This is the difference between a toy and a product. I want this in the hands of builders. I’m giving free credits to the first 1000 people who: 👉 Comment “VIBBBES” below 👍 Like this post to boost reach Let’s make the internet beautiful again. (Make sure we're connected for your free credits) #vibecoding #gemini #designsystem #ai #productdesign #openai #chatgpt #perplexity #figma > Built 100% with Anything MAX

We met at Uni, & just got engaged. It took me a lot of hard work to provide her with the life I wanted to give her. She supported me through everything. She bought my food in Uni when I was just starting my entrepreneurial journey. She understood how hard I had to work to get something off the ground. She understood we'd still have to miss some date nights to keep it going. This is why I spent so much time & effort trying to find that "breakthrough" business model. One that gave me time AND financial flexibility. So I could make her bet on me worth it. So I didn't have to choose between a relationship either strained by finances, or a relationship strained by me working 24/7. I thought it was SMMA. Wrong. I thought it was Lead Gen. Wrong. I thought it was an Ad Agency. Wrong. I made "good" money with all of these business models. But great? A true breakthrough? Being able to provide a top 1% lifestyle? None of them were cutting it. Then in 2022, OpenAI released ChatGPT. I thought...this is it. But I didn't want to get ahead of myself. With every success I had with a client, I documented it. I built AI Agents from that documentation. Soon enough, I had every successful process we used, as an AI Agent. On top of this, I supercharged workflows, & replaced entire roles I'd need to hire with AI Agents already on the market. With this newfound library of AI tools, I started to sell them to business owners. Some we built in-house. Some were already built; we just had to implement them. To be honest, this business model wasn't a struggle to run for me. We grew fast. Still growing fast. AI is progressing exponentially and we're riding the wave. My business partner Jordan & I have both became millionaires from it. He's just had his first child. I'm engaged. & our lifestyle upgrades don't just stop there. We have a plethora of team members we've moved out to Dubai. Jordan has flew his family out on 4 vacations in the past year alone. We wouldn't have felt secure enough to do this for the loved ones in our lives if we didn't find this business model. We'd still be on the hamster wheel of work to keep up a lifestyle we never had the time to enjoy. We'd still be trying to squeeze in a date night or maybe one vacation a year where we work the whole time. With all the negatives said about AI, it's changed my life for good. If you want to learn how I built my Agency: 1) Connect with me 2) Send me a DM saying "AIA"

OpenAI is paying people to share examples of real work from former employers. What should that tell you about where 2026 is headed? Most people are trying to become better AI power users. Better prompts. Better tools. All the flashy demos… I get it ... but that’s not where the real leverage is. If you look closely at what every AI model company is doing right now, they’re pushing into real work as fast as they can. And it’s working. Just yesterday, I tested Claude’s new browser extension on a task where it had to fill out a 10-page form with our details. It got ~80% right. That’s not a failure. That’s a SIGNAL (!!) As models get better, the bottleneck isn’t intelligence. It’s the work you’re assigning… and the structure you give it. Anyone who wants to stay relevant needs to learn how to provide that structure. To stop thinking like a user… and start thinking like an Operator. P.S. In our newsletter today - we shared our QUEEN Framework — it’s 5 ways to switch up how you work with AI 👸 Comment QUEEN and make sure you and I are connected, and I'll send it to ya!

The LLM training recipe has changed DeepSeek-V3.2 was post-trained using 1.8k RL environments; Minimax M2.1 used over 100k environments... This reflects a shift: from learning on static data to learning through interaction 𝗕𝘂𝘁 𝗵𝗼𝘄 𝗱𝗶𝗱 𝘄𝗲 𝗴𝗲𝘁 𝘁𝗵𝗲𝗿𝗲? 1️⃣ Classic LLM training recipe (InstructGPT) - Pre-training on internet text → learn to create text completions - Supervised Fine-Tuning on Q/A pairs → learn new tasks and to follow instructions - Reinforcement Learning (PPO or DPO) → align with human preferences It worked, until it hit a ceiling. You might remember Ilya Sutskever's talk at NeurIPS 2024: "Pre-training as we know it will end" Data is finite and classic post-training (SFT, Preference Alignment) cannot make miracles. What's next? 2️⃣ OpenAI o1 series hinted at a new direction They showed that Reinforcement Learning can induce chain-of-thought reasoning, and that performance improves with more train-time or test-time compute. No details on how to get there... 3️⃣ DeepSeek-R1 showed a concrete approach Reasoning/COT improves performance but teaching it via SFT needs expensive curated data Instead, they used Reinforcement Learning with Verifiable Rewards: - the model generates reasoning + answer - answer is checked against ground truth - reward drives RL training The idea is more general Any task with a verifiable outcome (a won game, a passing test...) can become a training signal The model is no longer limited by the quality of examples like in SFT By trial and error, it can discover better reasoning strategies on its own. DeepSeek also introduced GRPO: instead of PPO's expensive/unstable setup, generate a group of responses, rank them, use relative performance as baseline. Simpler, lighter, works well with RLVR 4️⃣ The mapping from classic RL to LLMs The Language Model is the Agent, its response is the Action. The Environment is everything needed to check (and possibly train) the model on the task: data, harnesses, scoring rules. SFT relies on curated datasets. RLVR requires environments: dynamic systems the model can interact with.  And as LLMs gain access to tools (from APIs to terminals) these environments become more complex and more critical. As Karpathy puts it: > environments give the LLM an opportunity to actually interact - take actions, see outcomes, etc. > This means you can hope to do a lot better than statistical expert imitation --- 📖 For a deeper dive and resources, check the comments.

If you've ever wondered: (1) how research and product collaborate at OpenAI, or (2) how ChatGPT should respond when told to be a cat 🐈  – Check out this talk Barret Zoph and I gave at DevDay! 📝 If you're a thoughtful & talented writer open to remote-friendly contract roles, please consider applying to the role posted in the comments. https://lnkd.in/gHwbzaZc

🚨 BREAKING: OpenAI just dropped the ChatGPT App Store. This is the iPhone moment for AI. Most founders are about to miss it. - OpenAI hit $12B ARR in 3 years. - ChatGPT mobile crossed $3B in consumer spend. - The AI app market adds $32.3B by 2029 at 44.9% CAGR. That's not "growth." That's a new economy booting up in real time. And unlike Apple's App Store... ChatGPT launches with the most unfair advantage in tech history: 800M+ weekly users, and "Agentic commerce" baked in. The store doesn't just discover apps, It can buy things for people. This is a platform land grab. The winners won't be the best builders. They'll be the ones who lock revenue models first. What first-movers are capturing: → Brand discovery with zero SEO or paid ads → Chat-to-checkout (no landing page, no funnel) → SaaS distribution without acquisition costs → "Default choice" lock-in before rankings solidify McKinsey called it a trillion-dollar opportunity. The window between "early" and "irrelevant" is months. Your competitors aren't building better products. They're locking up better shelf space. I compiled 20 resources from McKinsey, Stripe, Shopify, and OpenAI's launch data on how to capture this shift. Want it? 1/ Comment "STORE" 2/ Connect with me (click 3 dots in my profile) 3. Like this post P.S. Repost for Priority

J'ai accès à Copilot Cowork et c'est surpuissant. Pourtant, je ne recommande pas (encore) de l'activer en France/Suisse. Le produit peut, en une seule demande, créer ton Word, ton Excel, ta PowerPoint, et t'envoyer le mail de synthèse derrière. Un assistant autonome qui remplace, concrètement, la plupart de tes workflows Power Automate. Le problème ? Les serveurs d'Anthropic ne sont pas en région UE. Pour la majorité des entreprises européennes qui manipulent des données sensibles, c'est une ligne rouge RGPD. Je détaille tout dans L'hebdo de MAIjin cette semaine : prérequis côté admin, limites vs Claude Cowork, et 6 prompts prêts à l'emploi pour remplacer tes workflows d'automatisation. Et ce n'est pas tout. Au sommaire aussi : → OpenAI boucle 122 milliards sur une valorisation de 852 milliards (plus grosse levée privée de l'histoire) → Anthropic sort Mythos, le modèle le plus puissant du monde, réservé à 40 entreprises → Mistral lève 830 millions pour bâtir son premier data center européen → Medvi : la première boîte à 1,8 milliard construite par 2 personnes avec 20 000 dollars → Evo2 vient de raccourcir de plusieurs décennies le calendrier de la vie artificielle → Anthropic mesure 171 "émotions fonctionnelles" chez Claude qui pilotent ses décisions Beaucoup de pépites cette semaine 🔥 Inscris-toi à L'hebdo de MAIjin (gratuit, en commentaire) 👇 J'envoie la newsletter demain à tous nos abonnés JB #IA #Microsoft #Anthropic

AI economics finally revealed… Microsoft shot first. GitHub Copilot is moving to usage-based billing on June 1. Pay-per-token replaces the flat subscription. Mario Rodriguez, Chief Product Officer, frames it as “alignment with usage”. Which makes sense: the flat-fee model was unsustainable. Frontier Labs have been absorbing $8 to $13 of cost for every dollar of revenue! Every question, clarification, silly picture or AI cat video has been subsidised by investor money, by a factor of ten. That’s why OpenAI decided to shutdown it’s AI video service Sora and cancelled its $1B licensing deal with Disney (who was caught by surprise in the process). AI pricing matters. As enterprises are pressured to adopt AI, they are building business cases and ROI models on subsidised economics that will not hold. Here is what is at stake: → Token Economics. Token consumption has scaled faster than price-per-token has fallen. Until now, asking the same question three times cost the same as asking it once, so variable and error-prone outputs were tolerated. Once each retry costs money, user tolerance for hallucinations will seriously diminish. Are the AI labs ready for the backlash? → Policy. In March, the US Department of War designated Anthropic a supply chain risk. The first time this designation has been applied to an American company. Defense contractors now have a countdown to remove Claude from covered workflows, regardless of technical fit. Anthropic is challenging it in court, but it sets a precedent. A sovereign model can become a prohibited dependency. → Enforcement. Last month, Anthropic's automated systems shut down 60 seats at a Latin American fintech (Belo) via a single email citing vague "Usage Policy" violations. No advance notice. The only escalation path was a Google Form. Access was restored days later as a false positive. For those days, all the company's AI-augmented workflows were down. Business continuity plans need to provision for these scenarios. Vendor concentration risk and the weak economics of frontier models are exposing companies to major disruptions. Diverging regulations only widen the uncertainty. Interestingly, China has been forced into a different posture. DeepSeek, Qwen, FP8 training, sparse activation (30 to 50% compute reduction at competitive performance). Constraint (restricted access to chips and energy) has forced architectural innovation. Narrow or Frugal AI is not a sustainability argument but an operational and economical one. Lighter models for narrow tasks. Edge inference where latency matters. Open weights where dependency becomes a business risk. For a deep dive on AI economics and how the US vs China are approaching the race to AI leadership, read my last piece on KoncentriK: link in comments. As the labs race to AGI, enterprises should build optionality as resilience and price AI projects on their real economics. Credits: Photo by Shamin Haky on Unsplash

🚨🚨🚨 Thrilled to share this historic partnership between OpenAI and Amazon!!!! This is OpenAI x Amazon going all-in on the next era of enterprise AI agents. Here’s what just dropped: 1) A new “Stateful Runtime Environment” on Amazon Bedrock OpenAI and AWS are co-creating a stateful developer environment so models can actually work like teammates: keep context, remember prior work, use tools/data sources, and access compute—built to run optimally on AWS and integrated with Amazon Bedrock AgentCore. Expected to launch in the next few months. 2) Amazon Web Services (AWS) becomes the exclusive third-party cloud distribution provider for OpenAI Frontier Frontier lets companies build, deploy, and manage teams of AI agents operating across real business systems with shared context, governance, and enterprise-grade security—without managing underlying infrastructure. 3) Serious infrastructure + silicon commitment OpenAI commits to ~2 gigawatts of Trainium capacity, spanning Trainium3 and next-gen Trainium4 (expected to begin delivery in 2027). 4) Big money, long-term Amazon will invest $50B in OpenAI ($15B upfront + $35B later, conditional). And yes: OpenAI + Amazon will also collaborate on customized models for Amazon’s customer-facing applications—complementing what teams already build with the Nova family. Link with all the info in the comments!!!! #ai #amazon #openai

OpenAI just built a plugin for Claude Code. Not a community hack. An official, 3-engineer project under OpenAI's GitHub org. I spent a whole day testing it on a real project. Here's what I found: I had both Codex (GPT 5.4) and Opus 4.6 adversarial-review the same feedback app. Codex found 2 high-severity bugs. Race condition, silent data loss. Clean, focused, well-explained. Opus found 10. Same two Codex caught, plus 8 more across input validation, XSS, CSRF, deployment, and architecture. Neither model alone was enough. But here's what actually matters: Anthropic published research last week showing AI models are biased toward their own output. They praise code they wrote, even when a human would call it mediocre. Their solution was architectural. Separate the builder from the reviewer. That's exactly what this plugin does. → Opus architects and builds → Codex pressure-tests and reviews → Cross-model agreement = high-confidence real bugs, not hallucinated concerns I've been building client systems in Claude Code for months. Adding a second model as a reviewer is a genuine upgrade. The "pick one AI" era is over. The leverage is in composition. We're talking about multi-model setups inside our free community of 18K+ AI builders and operators. Comment "PLUGIN" and I'll send you the Full Breakdown.

MIT just nuked the cost of every $240,000 AI degree. The architects of OpenAI, DeepMind, & Anthropic all mastered these books. Now they're free. 𝗕𝘂𝘁 𝗳𝗿𝗲𝗲 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗺𝗲𝗮𝗻 𝗲𝗮𝘀𝘆. The AI Divide isn’t coming. It’s already here. And the market is splitting fast.  1. The leaders who wait for a "Guide."  → First to be automated  2. The leaders who master the "Structure."  → Building the $1B systems For decades, the blueprints behind $1B AI companies and the $500K executive roles were hidden behind a $240,000 tuition wall  at MIT and Stanford. Today, those gates are open. The same books that shaped  • OpenAI's architects  • NVIDIA's ML leads  • Every $500K+ AI executive Now downloadable. Today. Free. But here’s the part no one tells you: Free killed the excuse. The cost is discipline. This is the 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗦𝘆𝗹𝗹𝗮𝗯𝘂𝘀 for the next 5 years of your career. PHASE 1: 𝗧𝗛𝗘 𝗔𝗥𝗖𝗛𝗜𝗧𝗘𝗖𝗧’𝗦 𝗙𝗢𝗨𝗡𝗗𝗔𝗧𝗜𝗢𝗡 Learn the physics before you touch the controls. 1️⃣ Foundations of Machine Learning The physics of AI. The core algorithms behind every LLM. 2️⃣ Understanding Deep Learning The visual manual. See how neural networks actually work. 3️⃣ Machine Learning Systems The architect’s guide to production systems that don’t break. 4️⃣ Probabilistic Machine Learning (Part 1) The math of uncertainty. Why intelligence is never deterministic. PHASE 2: 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗦𝗬𝗦𝗧𝗘𝗠𝗦 Where models turn into decision-makers. 5️⃣ Algorithms for Decision Making How agents choose actions under uncertainty. 6️⃣ Reinforcement Learning (Sutton & Barto) The definitive guide to learning through reward and feedback. 7️⃣ Deep Learning (Goodfellow, Bengio) The mathematical backbone of modern AI. 8️⃣ Distributional Reinforcement Learning Beyond averages. Modeling risk, variance, and worst cases. PHASE 3: 𝗘𝗧𝗛𝗜𝗖𝗦 & 𝗧𝗛𝗘 𝗙𝗥𝗢𝗡𝗧𝗜𝗘𝗥 When scale meets responsibility. 9️⃣ Multi-Agent Reinforcement Learning How agents cooperate, compete, and coordinate. 🔟 Agents in the Long Game of AI Game theory for long-horizon, high-stakes decisions. BONUS: Fairness and Machine Learning The practical toolkit for responsible, deployable AI. (Direct links to the full books are in the comments ⬇️) 𝗧𝗛𝗘 𝗕𝗥𝗨𝗧𝗔𝗟 𝗠𝗔𝗧𝗛 • MIT EECS degree: $240k • NVIDIA lead engineer: $450K/year • AI strategy consultants: $10K/day You’re already paying for this knowledge. Just in lost leverage. 𝗥𝗘𝗔𝗟𝗜𝗧𝗬 𝗖𝗛𝗘𝗖𝗞 By 2027, they may not ask where you studied. They’ll ask what you deployed. The barrier isn’t access. It’s 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲. MIT removed the gatekeepers. Ambition is the only filter left. Share this with the leader who is done with "AI Hype" and ready for "AI Power." Save 💾 React 👍 Share ♻️ Follow 🔔 Thanks to Ved Vekhande for the list.

Last month, I tested 5 AIs for account research. This month, I added GPT-5 Thinking and Opus 4.1 to the test. Here's what happened. My problem: Customer needs account intel on companies with minimal public data. Which AI actually delivers? My experiment: - 1 target account: Windsurf (AI-native IDE, ~80 sales reps) - 1 use case: If I were a rep for Clay - Same prompt → 6 models 6 AI models: 🤖 ChatGPT Plus (GPT-5 Thinking) 🤖 ChatGPT Plus (o3 model) 🤖 Grok 4 🤖 Claude Pro (Opus 4.1) 🤖 Perplexity Pro 🤖 Gemini 2.5 Pro Each model got the exact same detailed prompt: Research Windsurf, find specific pain points indicating they'd benefit from Clay. My Ranking Methodology: ✅ Reasoning - Can it connect dots between data points? ✅ Web Access - How deep does it dig for insights? ✅ Additional Context - Does it surface relevant info I didn't ask for? ❌ Hallucination - Makes stuff up? The verdict: 🥇 GPT-5 Thinking: 9/10 - New champion 🥈 ChatGPT o3: 7/10 - Speed king 🥉 Claude 4.1: 5/10 - No improvement 🥉 Perplexity: 5/10 - Limited ⚠️ Grok 4: 2/10 - Don't bother ❌ Gemini 2.5 Pro: 0/10 - 3 hallucinations 3 surprises from yesterday's tests: - Gemini = hallucination machine (3 fake facts in 2 tests, proceed with caution) - GPT-5 Thinking finally stable (was inconsistent at launch, now consistently crushing it) - o3 feels nerfed (less powerful than my last test - did OpenAI throttle it?) -- What's your go-to for account research? -- Drop a "👨‍🍳" for the full breakdown + video (paid subs get my battle-tested prompt).

These AI agents turn n8n, Relevance AI, or Make into your full GTM team (16 AI Agents for GTM) You can now automate lead qualification, copy generation, and intent scoring with specific prompts. Each agent handles one GTM function and runs independently on your automation platform. The system includes setup guides for connecting AI models (OpenAI, Anthropic, Google, Ollama) to all 3 platforms. Here's the full agent library: ➔ Viral LinkedIn Post Writer that uses 13+ hook frameworks paired with First Line Writer that gets 32% response rates. ➔ Case Study Matcher that selects by industry and size paired with Industry Personalizer that adapts templates. ➔ Voice Note Script Generator with 35%+ reply rates paired with Pain Point Identifier that finds primary and secondary pains. ➔ ICP Qualifier with 94% accuracy paired with SaaS Company Validator that identifies actual SaaS vs agencies. ➔ Tech Stack Detector that identifies tools used paired with Company Name Cleaner that removes corporate suffixes. ➔ TAM Account Scorer with weighted 1-100 scoring paired with Account Fit Scorer for quick PASS/FAIL decisions. ➔ Intent Signal Ranker with 1-10 scoring paired with Tier Assigner that segments Dream 150 down to Tier 3. ➔ Champion Identifier that finds internal champions paired with Buying Stage Detector that maps Unaware to Decision stages. This is the most complete GTM agent library I've seen. You can now run 16 specific GTM functions with platform setup guides for n8n, Relevance AI, and Make. Want it? ➔ Comment "AGENTS" ➔ Connect with me so I can send you the full library (we can only DM if we're connected) BONUS: Repost this and I'll also send you n8n workflow templates for lead scoring pipelines

Comment générer des milliers d'euros grâce à LinkedIn ? LinkedIn, c'est compliqué. Tu dois maîtriser pleins de choses ! Mais depuis deux ans, les choses changent... Avec les nouveautés récentes d'OpenAI et les "Projets" tu vas enfin pouvoir te démarquer, vendre ! Voici mes 7 prompts qui vont t'aider à maîtriser chaque aspect : ◉ Recherche de Leads ◉ Stratégie d’Approche ◉ Stratégie de Contenu ◉ Optimisation de Profil ◉ Publications d’Autorité ◉ Description de l’Audience Cible ◉ Création d’un CTA pour une Publication 🎁 Je t’offre un prompt au hasard 👍 Like ce post 💬 Commente 'Prompt' 🔄 Connecte-toi (pas de cadeau sinon 🎅) PS : Tu veux récupérer une formation make ? PS2 : Tu veux obtenir + 20 automatisations ? 👉https://lnkd.in/eK3_-FyH

OpenAI crawle ton site 2 fois plus que tous les autres. Ce n'est pas une intuition. C'est 6 mois de tracking.   On a installé un outil de suivi (CiteMe ❤️‍🔥 ) sur les sites de nos clients chez Mekaa.   On voulait comprendre une chose simple : quelles IA visitent vraiment ton site, et à quelle fréquence.   Le résultat est sans appel.   ChatGPT domine. De loin.   Devant Claude, Gemini, Perplexity, Copilot, Apple Intelligence, Amazon, et tous les autres.   OpenAI envoie ses bots plus souvent, sur plus de pages, avec un volume de crawl qui écrase la concurrence.   Concrètement, ca veut dire quoi pour toi ?   Si ton site n'est pas optimisé pour les crawlers IA aujourd'hui, tu es en train de rater ta plus grosse source de visibilité de demain.   Le SEO classique ne suffit plus.   Il faut que ton contenu soit lisible, structuré et accessible pour ces nouveaux robots.   Pas juste Google. Les IA aussi.   On appelle ca le GEO. Generative Engine Optimization.   Et ceux qui s'y mettent maintenant auront 2 ans d'avance sur les autres.   Tu veux savoir si ton site est visible par les IA ? Commente "GEO" et je te dis comment vérifier.   #GEO #SEO #Webflow #IA #ChatGPT

ChatGPT Image 2.0 vient de sortir, et c'est probablement le plus grand saut en génération d'images depuis 3 ans 🔥 OpenAI vient de redéfinir ce qu'on pensait possible. Voici les 3 points où ChatGPT Image 2.0 écrase tout ce qui existait avant : 1. Génération de texte dans les images Fini le texte flou, illisible, déformé. Image 2.0 intègre du texte net, cohérent, utilisable directement. Affiches, mockups, slides visuelles : c'est propre du premier coup. 2. Raisonnement visuel Le modèle ne génère plus juste une image. Il comprend ce que tu veux dire, pas seulement ce que tu écris. Prompt complexe avec plusieurs éléments : il les assemble avec logique. 3. Réalisme qui tacle Nano Banana Pro Lumière, matières, peaux, reflets. Les images sont difficiles à distinguer d'une vraie photo. Sur ce point, la comparaison avec Nano Banana Pro est brutale. Ce n'est plus un outil créatif. C'est un studio complet dans un prompt. J'ai tourné une vidéo complète dans laquelle je te montre les différentes fonctionnalités à exploiter selon ton domaine. Pour les recevoir : 1. Connecte-toi avec moi 2. Commente "IMAGE" 3. Je t'envoie tout en DM (si connecté)