Igor Buinevici

Most companies think their AI problem is the model. It's not. It's the documentation the model runs on. Outdated. Inconsistent. Written once, then never touched again. I hit this wall while scaling my own team. The knowledge base kept growing: SOPs, onboarding guides, release notes, process docs. And someone always had to keep it clean: Rewriting for clarity. Summarizing long articles. Making every page sound like it came from the same company. None of it was hard. It was just endless. So I let Document360 AI Writing Agent take the first pass. A) It drafts full articles from a prompt, a video, or a rough note. B) It improves readability and holds one consistent tone across the entire knowledge base. C) It auto-generates summaries, suggests titles, and writes SEO descriptions. And here's the part that actually mattered: It didn't just improve the documentation. It gave the time back. Instead of polishing docs all afternoon, My team spent that time on the work that actually moves the business: Talking to customers, shipping, and deciding. That's the real ROI of AI at work. Not replacing your experts. Freeing them to do the work only they can do. The repetitive layer is now automated. The strategic layer is where you win. Do give it a try using the free trial link: https://lnkd.in/dP2ciP_a

You don’t have a scaling problem. You have a paperwork problem. Everything works fine… until volume hits. More customers. More forms. More signatures. And suddenly the cracks show: A) The same data gets entered multiple times B) PDFs stop matching the original inputs C) Compliance risk starts creeping in D) Ops teams end up fixing workflows manually Anvil is built to remove that friction: Instead of juggling forms, PDFs, and signatures across disconnected tools, you build one structured workflow: 1. Collect data once 2. Automatically populate every document 3. Route it exactly where it needs to go 4. Capture e-signatures in the same flow 5. Keep everything consistent, auditable, and scalable The new AI Pack takes it even further: A) Automate document processing with AI. B) Handle new document types without friction. C) Bring your own field schema with AI Schema Mapping. D) Let Anvil map extracted fields directly into your existing data model. So onboarding new document types doesn’t mean rebuilding anything. It just fits into what you already have. Faster setup. Cleaner data flow. Less operational drag. No manual fixes. No fragmented systems. No hidden errors at scale. The real shift is simple: Stop treating paperwork as manual work. Start treating it as a programmable workflow. If I were building today, I’d start with Anvil. Because once paperwork breaks at scale, it becomes very expensive to fix later. Check it out - link in the comments. P.S. Are you still relying on manual paperwork? ♻️ Repost so more teams can streamline their workflows. #AnvilPartner

Most people think AI makes software simpler. I think the more important point is different: AI makes deep software usable. That changes who wins. For the last decade, simple tools had an advantage because people had to learn everything manually. But if the product can help you set things up, then depth stops being a weakness. It becomes the advantage. That is why this Fillout launch matters. A powerful form builder with years of logic, routing, responses, and follow-up features becomes much easier to use when you can just describe the outcome. AI does not make every tool equal. It makes the most capable tools easier to access. Big difference.

Finance teams don’t struggle with data availability. They struggle with accessing it fast enough to act on it. In my experience working around finance workflows, The bottleneck has never been “what data do we have”, But “how many steps it takes to get an answer”. Spendesk AI Connect changes that entirely. It is a new MCP (Model Context Protocol) layer that connects: Live Spendesk data directly into AI tools like Claude, Dust, or ChatGPT. For the first time, finance teams can ask questions in plain language, And get real-time answers grounded in actual spend data. No exports. No dashboards. No waiting. Here is what that looks like in practice: 1. Ask a question the way finance actually works “Which invoices are still pending approval?” “Compare marketing spend between Q1 and Q2.” “Where is cash currently allocated across entities?” 2. Get answers inside the tools you already use - or schedule them to arrive when you need it most. Instead of switching between dashboards, exports, and spreadsheets, The answer comes directly inside your AI workflow. 3. Keep finance control intact AI Connect is read-only. It surfaces insights from Spendesk, but does not execute payments, approvals, or changes - yet. Rumour is write-capabilities are just around the corner. 4. Shift month-end from reporting to understanding Less time assembling numbers. More time interpreting what they actually mean. MCP is easiest to understand as the USB port for AI. Before USB, every device needed its own connector. MCP standardises how AI connects to live systems. It lets tools like Claude securely access relevant data in real time. Also - it came just in time. Spendesk research indicates that: AI tool spend is up 50x since 2022, with 281% YoY growth in early 2026. Today, AI tools make up 38% of all tech spend in Spendesk. Finance teams already work in AI tools. Now their finance data can sit inside them. Spendesk is building connected spend intelligence, Used by teams like HelloFresh, PayFit, Deezer, Sezane, and others across Europe. Finance teams no longer wait for reports to get answers. P.S. Do you want your finance data to answer questions instantly? ♻️ Repost so more people can get faster and better answers from their financial data. #SpendeskAIConnect

One decision from someone else, Can change the entire direction of your life. Not a promotion. Not a perfect CV. Not a flawless interview. Just one leader who sees something in you before you can see it yourself. It reshapes what you think is possible. Because most careers are not built on certainty. They are built on someone choosing to take a risk on uncertainty. On potential instead of proof. And once that happens, everything shifts. You stop asking if you’re ready. You start becoming ready. That’s why great leaders look beyond credentials. They look for: Growth over time Speed of learning Hunger to improve Consistency in effort Resilience after failure Comfort with feedback Ownership of outcomes Curiosity under pressure Initiative without permission Accountability without excuses Because skills can be taught. But mindset shows up early. The hard part is this: Growth alone is not enough. It has to be visible. You may be improving every year. Taking on more responsibility. Delivering better results. But if that progression is not clear, opportunities don’t follow. Your CV should make your growth obvious: ✔ What you’ve learned ✔ What you’ve achieved ✔ How you’ve progressed ✔ How your impact has increased Because invisible growth means missed opportunities. But a great CV only gets you noticed. It doesn't keep you organized once the search begins. Applications pile up. Follow-ups slip. Momentum disappears. Kickresume fixes that. Their new Job application tracker can help you: Track applications Manage interviews’ Monitor your progress Stay on top of follow-ups Try it for free: https://lnkd.in/evS3nddv P.S. Are you managing your job search, or just reacting to it? ♻️ Repost this so more people take control of their careers.

People are paying thousands for AI education, Stanford offers some of the best AI courses for free! Here are 10 you can start today: Get the high-quality PDF with clickable links: Follow me and subscribe for free at WildCapital.co. 1. Artificial Intelligence: Principles and Techniques Learn search algorithms, constraint satisfaction, and logical reasoning. Ideal for: Developers looking for a comprehensive AI foundation before specializing. 🔗 https://lnkd.in/dMHseqp7 2. Computer Science 101 Learn Python syntax, data structures, and algorithmic thinking. Ideal for: Complete beginners who need programming fundamentals before diving into AI 🔗 https://lnkd.in/dWtU5WAp 3. Machine Learning by Andrew Ng Master supervised learning, neural networks, and support vector machines. Ideal for: Engineers and data scientists who want to understand the math behind AI. 🔗 https://lnkd.in/d7Tw-G2h 4. Programming Methodology Master object-oriented programming, software engineering principles, and debugging. Ideal for: Anyone serious about writing clean, scalable code for AI systems. 🔗 https://lnkd.in/duz7HVmW 5. Deep Learning Build CNNs, RNNs, LSTMs, and production-ready models. Ideal for: Engineers moving from traditional ML to modern AI systems. 🔗 https://lnkd.in/d58rZygY 6. Relational Databases and SQL Learn SQL, database design, and data modeling. Ideal for: AI practitioners working with large datasets and pipelines. 🔗 https://lnkd.in/dNERaaSE 7. Deep Learning for Computer Vision Master CNNs, object detection, and image segmentation. Ideal for: Computer vision engineers and anyone building image/video AI systems. 🔗 https://lnkd.in/d8ShCgNE 8. Natural Language Processing with Deep Learning Build transformers and attention-based models. Ideal for: Developers building chatbots, LLMs, and text analysis tools. 🔗 https://lnkd.in/d4RZWi-z 9. Introduction to Probability for Computer Scientists Learn Bayesian inference, probability distributions, and statistical modeling. Ideal for: Anyone who wants to truly understand AI fundamentals. 🔗 https://lnkd.in/d8_Vt99A 10. Reinforcement Learning Learn Q-learning, policy gradients, and deep reinforcement learning. Ideal for: Developers building autonomous systems, robotics, or game AI. 🔗 https://lnkd.in/dTMJA2w8 Take advantage of these courses: And make sure you are ahead of others in AI! P.S. Are you paying enough attention to AI education? ♻️ Share it with your network so they master AI!

Solve the problem fast. Then move on. That feels like leadership, but often isn't. Sharing great insights on leadership from Jeroen Kraaijenbrink. Most leadership mistakes come from rushing, Not from action itself. When something breaks, the reflex is immediate: Act, fix, move, show control. But speed often skips clarity. That’s the gap between reacting and responding. A reaction is automatic. Default thinking. Familiar answers. Quick control. A response is deliberate. A pause long enough to see what is actually going on. That pause has two steps: 1) FACE it Name the reality as it is, not as it feels easier to make it. 2) FEEL it Sit with the discomfort instead of working around it. Most teams skip both. Fixing is not the issue. Fixing the wrong thing is. That’s how organizations become efficient at solving the wrong problems. P.S. Are you fixing the right things? ♻️ Repost so more people approach problems the right way.

Most AI tools help you make slides. Almost none help you make the decision the slides are supposed to support. That’s the problem with AI business decks: A) Data lives in disconnected tools B) The structure gets rebuilt from zero every time C) Formatting eats the hours meant for thinking D) Icons, charts, and visuals never quite match E) The deck ships, but it doesn't move the decision The best decks don't start with a blank page. They start with a system built the way consultants actually structure an argument. Perceptis AI delivers exactly that: 👉 perceptis.ai An ex-McKinsey consultant tested 5 AI presentation tools for real client work and ranked Perceptis #1. Here is why it's different: 1. Built for consulting-grade decks Not generic templates. Real structure, logic, and flow that stand up in front of clients. 2. Claude Connector Connect your tools and files and build a deck without switching tabs. It pulls the data straight from your conversations and documents. 3. Maps and visuals on demand Generate custom maps, charts, and icons that match your deck. No design work needed. 4. Slide Library Reuse your best slides across projects instead of rebuilding every time. 5. Output you own Export and keep editing. No locked, proprietary format. Most people waste hours on: Fixing layouts Rewriting bullet points Making slides look "consulting enough" Perceptis removes all of that. The gap between "I have an idea" and "I have a client-ready deck" is now minutes. That's what presentation tools should look like in 2026. Try it here: 👉 perceptis.ai P.S. How long does it take you to build a client-ready deck today? ♻️ Repost so more consultants stop wasting time on slides

Don't ask Claude to “improve your LinkedIn.” Do this instead: Sharing great insights from Hamna Aslam Kahn (give her a follow). Most people never get useful answers Because they never give it real data. “Improve my LinkedIn” is too vague. It produces generic advice that sounds right but changes nothing. Data changes the output. Here is a better workflow: 1. Export LinkedIn analytics 2. Collect post + profile performance data 3. Structure it in a simple sheet 4. Upload it to Claude 5. Ask it to detect patterns 6. Identify what actually drives engagement 7. Turn findings into clear insights 8. Use them to adjust your content strategy What you start to see: What content actually performs What your profile is signaling What your audience ignores When engagement spikes What formats work best Stop asking for generic advice Start getting optimized insights. P.S. Are you still looking for generic AI advice? ♻️ Repost so more people can take full advantage of Claude. ---- 📌 Get my top 100 infographics for free: 1) Follow me. 2) Subscribe to my free newsletter at WildCapital.co. You’ll receive them directly in your welcome email.

If you want to invest like the top 1%, You need to know this: Sharing 7 principles for successful long-term investing by J.P. Morgan: 1. Plan for a long life People are living longer. Save and invest more to secure your future. 2. Cash isn’t always king Holding cash can cost you opportunities. Only equities consistently beat inflation long-term. 3. Use dividends and compounding Reinvest your dividends and start early. Let compounding work for you. 4. Stick to your plan Avoid emotional decisions. Stay disciplined, even when markets fluctuate. 5. Volatility can be your friend Market drops happen. Take advantage of them and don’t sell out of panic. 6. Diversification works A diversified portfolio improves risk-return trade-off. Don’t keep all your eggs in one basket. 7. Stay invested The best days often follow the worst. Don’t sell in panic unless really necessary - staying invested is key. Investing is about playing the long game. Consider these principles to build your wealth over time. Get my Long-Term Investing guide for free: WildCapital.co P.S. Are you building your wealth the right way? ♻️ Repost so more people have better investment approach.