Gabriel Hunold

Build an advanced agentic workflow for financial extraction - in < 5 minutes ⚡️ In this video I show you how you can define a complex document workflow that can parse any 10Q filing, extract detailed income information into a Pydantic schema, and output the result in a CSV format. You can do this without writing a single line of code but it is fully extensible through code if you want to layer additional steps! This is built on `docuflows`: a mini-coding agent I built that lets you define a document workflow in natural language, and then compile into a code-based, multi-step workflow that can interleave parsing, extraction, splitting, and LLM calls. This workflow can be reused on any document and also is fully extensible through code. Within the docuflows you can also edit and test the workflow on sample data. Check it out 👇 Docuflows is part of vibe-llama: https://lnkd.in/gx8p4Y6f All you need is a LlamaCloud API key: https://lnkd.in/g9Wpqn7w

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🚨 The new AI Agent update in n8n just changed the game for no-code orchestration. You can now build fully modular agent armies — where each AI agent lives inside your main agent as a tool. No more scattered workflows. No more Execute Workflow nodes. Just clean, centralized AI orchestration. In my latest tutorial, I show you how I built a system that: 🤖 Uses one master orchestration agent to control all sub-agents 📩 Automates emails, calendars, expense tracking, and company knowledge 🧠 Assigns memory, models, and tools per agent — inside a single workflow 📂 Removes the need for multiple builds by letting agents act as tools 📈 Scales perfectly for client work, personal use, or AI assistant businesses Watch the full build + demo here 👉 https://lnkd.in/gDgfRfW6 Want to learn how to create AI automations like this — and package them into real client offers? Join the AI Workshop community — where I share full blueprints, sales strategies, and how to turn automations into income. 👉 https://lnkd.in/gnNJqH9u #AI #Automation #n8n #nocode #AIagents #VoiceAI #AIWorkshop #OpenAI #Claude #Gemini #AgentOps #WorkflowDesign

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Matthieu Jonglez from Progress talking about how to use AI with #semantic #agentic #RAG on structured and unstructure data from a companies’s private systems “The greater the context, the greater the insights” #progressdatasummit

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📊 BIG TECH REVENUE BREAKDOWN – Latest quarter results How Apple, NVIDIA, Microsoft & Google make their billions 💡 As earnings roll in, let’s dissect how the 4 tech titans are generating their revenues — and what that says about the future of tech investing. 🟩 NVIDIA – Q1 FY26 Total Revenue: $44.06B Data Center (AI/Cloud GPUs): $39.11B (89%) 🧠 Dominated by AI training chips, H100s, L40s, and demand from hyperscalers (AWS, Azure, Meta). Gaming (GeForce GPUs): $3.76B (9%) 🎮 Still a solid base, but dwarfed by AI revenues. Other (Pro Visualization, Automotive): $1.19B (2%) 🚗 Includes AV chips for autonomous driving and Omniverse metaverse stack. 🔍 Insight: NVIDIA is now more data center than gaming — fully positioned as the infrastructure layer for AI. 🟦 Microsoft – Q4 FY25 Total Revenue: $76.4B Productivity & Business (Microsoft 365, LinkedIn): $33.1B (43%) 📎 Recurring SaaS revenues, corporate licenses, ads, and premium talent tools. Intelligent Cloud (Azure): $29.9B (39%) ☁️ High-margin hyperscale growth. Key AI workloads now hosted on Azure. Personal Computing (Windows, Xbox, Surface): $13.5B (18%) 💻 Mostly OEMs, gaming subscriptions, and hardware. 🔍 Insight: Microsoft is now a SaaS + Cloud dual engine, with deep penetration in both enterprises and developers. ⚫ Apple – Q3 FY25 Total Revenue: $94.0B iPhone: $44.6B (47%) 📱 Flagship product, especially strong in China and U.S. Services (iCloud, Apple Music, App Store, Pay): $27.4B (29%) 💳 Subscription-based and growing rapidly. High margin. Mac: $8.0B (9%) iPad: $6.6B (7%) Wearables (Watch, AirPods): $7.4B (8%) 🔍 Insight: Apple remains hardware-heavy, but services now represent Apple’s most profitable segment. 🔴 Google (Alphabet) – Q2 2025 Total Revenue: $96.4B Search (Ads on Google.com): $54.2B (56%) 🔍 Still the king of search. Ad budgets rebound. Cloud (GCP): $13.6B (14%) ☁️ Strong enterprise traction. Competing with Azure/AWS on AI workloads. YouTube Ads: $9.8B (10%) 📹 Huge engagement, especially via Shorts and TV screens. Google Network (Adsense, AdMob): $7.4B (8%) Other (Play Store, Pixel devices, Nest, etc.): $11.2B (12%) 🔍 Insight: Alphabet has a three-headed model: Search dominance, YouTube content, and rising Cloud AI infrastructure. 🔵 INVESTOR TAKEAWAYS ✅ NVIDIA: AI infrastructure pure play — volatile but essential. ✅ Microsoft: Balanced software & cloud growth — low churn, high margins. ✅ Apple: Premium consumer ecosystem — rising services monetization. ✅ Google: Ad empire evolving into AI & cloud — big bets on Gemini, DeepMind. 📈 Brought to you by INVEST Your guide to the future of investing. 🌐 www.INVES7.com ❤️ Like – 💬 Comment – 🔁 Share #Invest #Lipost #TechStocks #Earnings #Nvidia #Microsoft #Apple #Google #AI #Cloud #SaaS #SmartInvesting

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A few years ago, 90% of our revenue came from Airtable customers. We lived in constant fear they'd destroy us overnight. For years, this drove our strategic decisions: • Tried to diversify away from Airtable • Built our own data platform • Constantly worried about the day they'd pull the rug Then it happened. Airtable changed their pricing and API terms. We scrambled. Made changes. Warned customers. And... mostly everyone was fine. Some customers had to switch plans. But the impact was nowhere near what we'd imagined. Then Airtable built portals - one of our main features. We thought this was the end. But people still come to us. Our portals work better. Our pricing makes more sense. Even after they built what we already had, we kept winning. Big lesson there: The existential threats you spend years worrying about usually aren't as bad as you think. And the defensive moves you make because of those fears often hurt more than the actual threat would. We wasted time and energy building around a risk that turned out to be manageable. Focus on making customers happy instead of protecting against imaginary disasters.

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GPT-4.1 vs GPT-5 in SuiteAnalyzer: Speed or Depth? 🤔 In side-by-side tests with the same financial data, GPT-5 delivered far richer insights than GPT-4.1 — but took longer to respond. In my latest blog post, I explain why this trade-off is expected, how costs compare, and when to choose each model. https://lnkd.in/eVRkJpwf

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