Exemples de lead magnets LinkedIn en ai consulting

🚨 BREAKING: The man who built Tesla's AI brain just released the architecture for mass-producing executive decisions. Karpathy built a $50M executive team for $0.02 per decision. Yet most CEOs are still paying for one opinion. It's called LLM Council. And it makes asking one AI look like asking one intern for strategy advice. Your question goes to 4 of the world's smartest AIs at once. Each answers independently. Then they review each other's answers, anonymously. They rank them. They call out weak reasoning. Then a Chairman AI delivers one final verdict. I fed it 6 months of revenue, offer suite, and pipeline data. In one session, the Chairman delivered: → A GTM motion chasing buyers who never close (and the ICP that does) → A positioning gap letting cheaper competitors win our deals → A competitor using AI in a way we hadn't considered → The one process costing us 30 hours/week (fixable in a day) → The hire we thought we needed (we didn't) It didn't give me 4 opinions. It gave me one verdict. ICP rebuilt. Positioning fixed. Hiring plan killed. This isn't a chatbot. It's a shadow board that never plays politics. And unlike a $400K strategist, it never defends its own recommendations. I packaged the full implementation: ✅ The Multi-Model Consensus Prompt Pack ✅ The 4-AI Peer Review Template ✅ The Chairman Synthesis Framework ✅ The Plug-and-Play Config (ready to run) Four minds. One decision. Zero politics. Want it? 1. Comment "COUNCIL" below 2. Connect with me (so I can send it) 3. Like this post P.S. Repost for priority access

Your team is wasting thousands of hours a year on work that could be automated. This is exactly how to use Claude to figure out how. Every audit we run at Morningside AI follows the same playbook. We recently ran it for a major NBA franchise. 15 interviews. 27 opportunities found. 2,500+ hours/year of wasted effort identified. $300K+ in hidden costs uncovered. And now, we open-sourced the entire process. And put it in 7 Claude Skills. The actual end-to-end methodology we've used to identify $25M+ of value for our clients with AI. Here's what's inside: 1️⃣ Opportunity Scanner - Figure out where AI can actually move the needle in YOUR company 2️⃣ Workflow Audit - Map any process and find exactly where time is being wasted 3️⃣ Interview Builder - Interview your team the right way (most people ask the wrong questions) 4️⃣ Insight Analyzer - Turn messy transcripts into structured, quantified findings 5️⃣ Department Synthesizer - Aggregate interviews into department-level intelligence 6️⃣ Priority Ranker - Ruthlessly rank opportunities by impact and feasibility 7️⃣ Executive Briefing - Build the 20-minute pitch that gets leadership to say yes You run it inside Claude. Feed it info about your company. It gives you the analysis. Most companies know they should "do something with AI." They just don't have a framework for figuring out what's actually worth doing. This is that framework. Want the Claude Skills Pack? (Yep, you guessed it...) 1. Connect with me 2. Comment "SKILLS" below 3. If you repost, I'll send to you first Happy automating.

I asked Daxesh to sleep at 12:30 AM. Around 2:45 AM he was awake… working. I again told him to sleep. Morning at 7 AM, when I woke up, he was already working. He said he slept. I still don’t know when. He was working at the airport too. This is what happens when he finds a real problem worth solving. ⚙️ Problem he is working on... 1. SSH, monitoring, deployments, logs, AI insights… everything lives in separate tools. No shared context. No single workflow. 2. VPS deployments still rely on manual commands, custom scripts, tribal knowledge, trial and error. 3. Most tools rely on cloud agents, API keys, or third-party infra access. With the help of AI, he built a working POC in 1 week. 🤖 We started using it internally. Built MVP. Testing in cycles. Sharing with beta users. Our goal is, Manage entire DevOps workflow from a single screen. Server access, Logs visibility, Deployments, Monitoring, AI assistance. All connected. All contextual. Planned launch within a week. 🚀 If you want 2 months free early access, comment "DevOps". We will include you in the beta.

Andrej Karpathy (Co-founder OpenAI) a révélé son secret pour prendre des décisions à 10M$. Problème ? C'est inutilisable pour 99% des gens.   Alors je m'en suis inspiré pour créer une version N8N accessible à tous.   Le concept est simple : L'Intelligence Collective Artificielle. Plutôt que de faire confiance à un seul cerveau, vous en activez trois.   Ce système ingénieux force la confrontation : 1) Claude, GPT5 et Gemini 3 proposent une stratégie. 2) Le "Fight Club": critique anonyme impitoyable. 3) La Synthèse : Une dernière IA ramasse les morceaux et ne garde que ce qui a résisté aux critiques.   C'est comme avoir McKinsey, BCG et Bain dans ta poche. Sauf que ça coûte un tiers de café par session.   Résultat ? J'ai utilisé ce système pour valider ma stratégie de go-to-market de mon prochain produit. Ils ont trouvé une faille énorme que j'avais complètement raté.   Je partage ce workflow prêt à l'emploi + tuto Notion d'installation.   Tu veux ton propre Conseil d'Administration IA ? 1) Like ce post 2) Commente "AVENGERS 3) Je t’envoie tout (connecte-toi pour le DM) 🚀 PS. repost priorisés

Anthropic just revealed the 5 exact skills to become a Claude Certified AI architect. There's one problem. You can't take the exam. It's locked behind their partner program. Unless you work with Anthropic directly, you don't get access. But I read the entire exam guide. And what I found is more valuable than any badge. These are the exact skills we use every day. The same skills that have generated us $600K+ in AI consulting revenue. Here's what Anthropic says separates a $5K project from a $50K one: Skill 1: Building AI systems where multiple parts work together (27% of the exam) Most people build one AI, one job. The real money is in systems with multiple parts that share information. Skill 2: Connecting AI to real business tools (18%) Making AI actually do things inside a business. Not just answer questions in a chat window. Skill 3: Setting up AI for consistent delivery (20%) Your AI follows your process, your frameworks, your quality standards. Every single time. Skill 4: Getting trustworthy results (20%) Can you hand what the AI produced directly to a client without editing every line? Skill 5: Making AI remember what matters (15%) The reason most AI projects fall apart after the demo. Here's what nobody wants to hear: The certificate doesn't matter. No client has ever asked me for one. The certificate proves that you studied. You prove what you can build. I just filmed a full breakdown of all 5 skills. Which ones actually make money. The exact lessons from building 50+ AI systems. And the 30-day path to your first paying client. Comment SKILLS below and I'll DM it to you. Like, comment SKILLS, and connect with me so I can send it.

SRS → Enterprise level Production ready code! We are Building AI Team! Most teams are using AI for coding. Tools like Claude Code, Cursor, GitHub Copilot, and similar tools help developers write code faster. But orchestration is still human. Planning done manually. Tickets created manually. Grooming done manually. Code review coordination manual. Documentation written later. AI writes code. Humans still manage the process. That is the slowest part. We are working on building a full AI team. Not just an AI coding assistant. AI project execution system. ⚙️ The idea is simple: Human gives problem statement or SRS. System prepares: • Tickets automatically • Grooming automatically • Technical approach automatically • Best practice checklist automatically • Documentation automatically Human only reviews. 👀 Design → automated Code → automated Test cases → automated Documentation → automated Human stays in the loop only where judgment is required. Result is, Communication between departments has reduced. No need for a 7–8 person coordination chain. One lead can monitor and verify output. Because AI agents communicate faster than humans. ⚡ Agents coordinate with each other. Planner agent → creates structure Architect agent → decides technical approach Developer agent → writes implementation QA agent → validates logic Documentation agent → prepares knowledge base There are leader agents as well that monitor quality and improvement. Agents operate at different levels and continuously improve shared knowledge. Documentation, grooming, ticket creation, and execution flow become automated. Human effort shifts from execution → validation. Speed increases. Clarity increases. Consistency increases. AI does not replace the team. It removes operational friction between team members. If you are exploring how to build an AI team for development workflow, comment AI TEAM 🤖

💜 "Ton profil LinkedIn ne te ressemble pas." C’est la phrase qui ouvre l’outil que j’ai construit cette semaine. Parce que le positionnement est flou. Headline vague. À propos générique. Bannière qui ne dit rien. La bonne personne arrive sur ton profil. Elle regarde 10 secondes. Elle ne comprend pas ce que tu fais ni pourquoi toi. Elle repart. 𝐽’𝑎𝑖 𝑝𝑎𝑠𝑠𝑒́ 2 𝑗𝑜𝑢𝑟𝑠 𝑎̀ 𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑖𝑟𝑒 𝑢𝑛𝑒 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛. 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗣𝗿𝗼𝗳𝗶𝗹𝗲 𝗘𝗻𝗴𝗶𝗻𝗲. Tu décris simplement : - ce que tu fais - pour qui - le problème que tu résous Et l’outil reconstruit ton profil. En quelques minutes tu reçois : ✅ une analyse complète en 6 critères avec score % ✅ 3 positionnements stratégiques différents ✅ une headline claire et crédible ✅ un À propos réécrit de zéro ✅ une bannière cohérente avec ton activité ✅ une photo de profil professionnelle générée par IA Pas un template. Pas du texte générique. Un profil LinkedIn qui reflète vraiment ta valeur. J’ouvre la bêta aujourd’hui. Les premiers arrivés testent avant la version payante. Envie de tester ? Tu serais surpris de voir comment ton profil LinkedIn peut être repositionné. commente BETA et je t’envoie le lien.

Our AI agent designs the entire GTM strategy for a new product launch. We feed it high-signal content - the best podcasts, top notch books, and foundational articles in the space. Then we give it product knowledge. USPs. Use cases. Real context. From there, it gets to work. It studies competitors. Maps pricing. Reviews compliances. Identifies market gaps most teams miss during a launch. Then it starts making decisions: → Which marketing channels deserve your attention → Where your actual MOAT sits — not what you assume it is → Which communities to enter and how to show up in them (Slack, Discord, Reddit, Inc. Communities) → Which platforms to launch on and in what sequence Then it executes. Writes the blogs. Drafts the posts. Creates documentation. Builds personalised outreach messages. All aligned to the strategy it just built. No 6-week agency engagement. No consultant decks. No back-and-forth. One agent. Full GTM. Ready to move. We recently used this for a developer operations platform. Think of it as your AI-powered command centre for DevOps teams. It connects your entire dev workflow - pipelines, deployments, incidents, team activity into one place. Instead of jumping between tools to figure out what's broken, what's delayed, or who's blocked, everything surfaces automatically. Your team spends less time managing the process and more time shipping. No complex setup. No new workflow to learn. It plugs into what you already use and starts giving you visibility from day one. This is what AI-led product growth looks like in practice and what the right tooling makes possible. If you want early access to the DevOps platform comment "DevOps" below.

Every AI creator is selling you the same lie. Build faster. Automate everything. Make easy money. Scroll LinkedIn for 5 minutes. Same posts. Same landing pages. Same automations. It all feels like a machine wrote a letter to itself. I built an AI consulting business that does over $50K/month. And what kept the money coming had nothing to do with any AI tool. There are 3 things AI can't copy: 1. Taste 2. Distribution 3. High Agency Right now, 95% of people using AI are copying defaults. But soon, taste will be expected.  Distribution will get harder.  High agency will remain the only thing that doesn't get commoditized. The window is open. It won't stay that way. I just filmed a full breakdown of all 3 moats. How to build each one. Real examples. Real mistakes. Comment MOATS below and I'll DM it to you. Like, comment MOATS, and connect with me so I can send it.

We increased ticket bookings by 23% for a US-based platform using Voice AI. 📞👇 Not by running more ads. Not by redesigning the app. But by fixing how conversations happen. We built a Voice AI workflow that handles real customer interactions end to end. No IVR frustration. No delayed responses. Just fast, clear communication. Here is what the system does • Confirms bookings automatically • Answers customer questions in real time • Shares payment links during the call • Updates booking details instantly • Transfers to human agents only when needed The goal was simple. Make every call useful. Reduce drop-offs. Increase completed bookings. Behind the scenes, this is powered by Voice AI platforms like Retell AI combined with workflow logic and decision layers. So instead of a basic calling system, we built a complete automated communication flow. Result, Better user experience Faster conversions And a 23% increase in bookings 🚀 This is where AI starts impacting real business metrics. At TST Technology, we are building AI systems that directly improve product growth and operations. If you are looking to automate your customer communication and improve conversions, DM me for an AI Audit. 🤝 Vapi Retell AI ElevenLabs

Telegram bot sets up meetings, does coding, web search, and anything we want. Imagine a bot that can do real work. A bot that can execute tasks with accuracy. 🤖 We configured an OpenClaw agent with: • Web search capability • Coding skill • Persistent memory • Self-improvement ability over time Then we connected it to Telegram. Now instructions can be given directly from mobile 📱 and the bot can access the laptop 💻 to perform tasks. No need to sit in front of the system. Just send a message, task gets executed. Feels like Iron Man’s Jarvis. ⚡ Because of its self-improvement capability, the bot keeps learning general skills with minimal guidance. Some real tasks have already been performed: • Scraped AIBoom attendee data with a single Telegram query • Created reminders for important work automatically • Performed a web search to find travel locations and useful information • Executed coding-related tasks through a connected environment Instead of switching between tools, The bot becomes the interface. Mobile becomes the control panel for your AI agent team. Instruction, execution, result. No manual follow-ups. Key idea is, LLM is not enough. AI becomes powerful when it has: • Memory • Tools • Execution ability • Feedback loop That is when it starts behaving like an assistant, not a chatbot. One important note 📌 We only give laptop access in a controlled way. We store memories of workflows and approaches I use. We do not give personal bank details or critical sensitive information. And we prefer using a separate device for this kind of setup. Daxesh always does super work! If you want to build your own Jarvis-style workflow, comment JARVIS ⚙️

A chocolate company was struggling with 8,000+ SKUs. 🍫 Price range constraint Weight constraint SKU diversity constraint Expiry constraint Profit constraint Now try solving that manually across 8,000+ SKUs. 👇 So we built an algorithm to automatically generate mixed chocolate boxes that: • Stay within the combo price range, for example 98–135 • Keep total weight within packaging limits • Maintain product variety, flavor, vendor, cocoa % • Prioritize expiring inventory • Maximize margin • Still feel curated, not random This is not recommendation logic. This is constraint optimization. ⚙️ How the system works: Step 1, Filter layer Remove SKUs that do not meet hard conditions • Price limit per SKU • Minimum weight requirement • Cocoa % constraints • Inventory availability • Expiry buffer window • Category rules Step 2, Greedy selection SKUs are sorted using a priority score Risk score = expiry risk + financial exposure + sell-through gap The algorithm starts picking the highest priority SKUs first while respecting: • Vendor diversity rules • Flavor balance • SKU count rules • Price band constraint Step 3, Backtracking adjustment Greedy alone cannot satisfy minimum constraints. Let’s take an example: • Minimum 2 dark chocolates required • Minimum 4 SKUs required • Maximum 1 SKU per vendor allowed Backtracking swaps low priority SKUs with better candidates while maintaining price and weight limits. 🔁 Step 4, Price band optimization The system continuously validates: 98 ≤ bundle price ≤ 135 If the price goes above range, replace a high-price SKU If the price goes below range, upgrade a SKU Final output, a constraint-satisfied bundle generated automatically in seconds. 🚀 Key insight: Greedy gives speed Backtracking gives feasibility Together, they solve real-world combinatorial problems. If you are solving SKU mix, pricing optimization, or bundle generation problems, comment ALGO 🤝

Stop being a leader in the dark on AI. If you've buried your head, its time to pull it the hell out! 70% of AI projects never get past the pilot stage. Not because the tech doesn't work. Because nobody led it properly. I'm running a free 75-min live session  for LEADERS who want to change that. AI for Leaders: How to Lead AI Adoption That Actually Works Theory + live demos. Practical framework. Zero jargon. This isn't for techies, this for the non-nerdy leader looking for successful adoption of AI in their business. It's for the person responsible for making AI actually work across their business and who want to lead the AI initiative with confidence. Date 31st March - Sign up and comment LEADER for early access. Tag a leader who needs this. 👇

Tu utilises ChatGPT pour écrire des posts. Mais pas pour optimiser ton profil LinkedIn. Erreur. Avec un bon prompt, ChatGPT peut devenir ton consultant LinkedIn perso. Et refaire ton profil de A à Z mieux qu’une “formation à 997 €”. Voici comment transformer ChatGPT en stratège LinkedIn 👇 1️⃣ Donne-lui la mission “Agis comme un stratège d’optimisation de profil LinkedIn pour quelqu’un qui vise le rôle de [TON POSTE CIBLE] dans le secteur [TON SECTEUR].” 2️⃣ Envoie-lui TON VRAI PROFIL ↳ Capture d’écran du profil ↳ Texte actuel (titre, À propos, expériences, compétences) Plus tu lui donnes de matière, plus l’audit sera précis. 3️⃣ Demande-lui une feuille de route section par section Titre, À propos, Expérience, Compétences, Recommandations, Bannière, Contenus mis en avant. Pour chaque partie, fais-lui : → identifier ce qui cloche → réécrire le texte en mode “Copia recommandée : …” → lister les infos manquantes : “Données manquantes : taille d’équipe, budget, KPI, etc.” 4️⃣ Exige du concret, pas de la théorie Demande-lui : ✦ 3 nouveaux titres orientés résultats (avec mots-clés) ✦ un pitch “À propos” narratif + chiffré ✦ des bullet points Expérience en format : Action → Tâche → Résultat ✦ une liste de 30–50 compétences clés pour ton secteur 5️⃣ Termine par un plan d’action 30 jours → Quels contenus poster pour attirer tes clients / recruteurs → Quels types de personnes ajouter chaque semaine → Comment engager : commentaires, messages, demandes de reco Tu passes de : “J’ai un profil LinkedIn moyen qui prend la poussière.” à “J’ai une page de vente vivante qui travaille pour moi 24/7.” Si tu veux, je peux partager mon prompt complet “Consultant LinkedIn IA” en commentaire. PS : Accélère ton implémentation IA, réserve ton audit IA gratuit avec mon équipe et repars avec des solutions IA adaptées à ton activité pour générer un ROI rapidement 👉 https://lnkd.in/eMnZJSFn

Every AI creator is selling you the same lie. Build faster. Automate everything. Make easy money. Scroll LinkedIn for 5 minutes. Same posts. Same landing pages. Same automations. It all feels like a machine wrote a letter to itself. I built an AI consulting business that does over $50K/month. And what kept the money coming had nothing to do with any AI tool. There are 3 things AI can't copy: 1. Taste 2. Distribution 3. High Agency Right now, 95% of people using AI are copying defaults. But soon, taste will be expected.  Distribution will get harder.  High agency will remain the only thing that doesn't get commoditized. The window is open. It won't stay that way. I just filmed a full breakdown of all 3 moats. How to build each one. Real examples. Real mistakes. Comment MOATS below and I'll DM it to you. Like, comment MOATS, and connect with me so I can send it.

AI for image and video isn’t about prompts. It’s about workflows. The shift happens when you move from: “Generate an image” to Input → logic → constraints → repeatable output. That’s where AI-native workflow automation becomes powerful. I’ve been exploring this in the 🔥 MindStudio Image & Video Hackathon, building agents structured as pipelines, not prompt tricks. ✦ Screenshot → in-situ device mockup ✦ Raw asset → controlled rendering → consistent output ✦ Multi-step visual workflows with embedded logic When generation becomes part of a system, it stops being novelty. It becomes operational. Last year I completed MindStudio’s 30-Day Bootcamp + Advanced Level 3 Certification. It was a turning point, moving from experimenting to architecting AI agents for real client use cases. As part of the MindStudio Image & Video Creator Tools Hackathon early submission prize, I’ve been awarded a seat for the upcoming March cohort (30-day L3 AI Agent Bootcamp, value $7,500). Since I’m already certified, I’ll pass it on. If you’re serious about building AI-native workflows, especially in image & video. Send me a private message. I’ll randomly select one and connect you in.