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@DivTalent

AI Automation Engineer | Building Intelligent Workflows | Boosting Marketing ROI & SaaS Efficiency by 40%+ with Custom AI Workflows | Saving Teams 20+ HRs/Wks

Remote Katılım Ekim 2022
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Div
Div@DivTalent·
Thinking of the AI literacy, I think most of us can agree: every company needs an AI literacy policy right now Why? Because AI has the potential to make work significantly faster, helping architect a "10x" future for the team's productivity. But you can't unlock that speed safely unless your team has basic AI literacy which is " the fundamental knowledge" required to understand, use, and communicate about these tools effectively. AI is incredibly practical for knocking out the tedious tasks that eat up our days, like conducting early research, generating outlines, and organizing data. The secret to using it well without losing quality is the "30% Rule" instead of letting the machine do everything, the 30% rule is a practical partnership. It means AI should handle roughly 70% of the initial grunt work, like gathering data, brainstorming ideas, and writing first drafts. From there, humans take ownership of the critical final 30%. That is when you step in to review the work done, and fact-check details applying strategic judgments. This balance is non-negotiable. AI is fast, but it routinely makes mistakes and can confidently present false information at times or made-up facts, but the Human oversight acts as an built-in quality control to catch these errors and ensure the work is safe, accurate, and aligned with actual goals. The companies that establish clear AI guidelines and train their teams on this 70/30 split are already building an advantage that competitors will struggle to catch up to. #Thoughts
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Gloria || AI & Workflow Automation ||Executive VA
So many people have lost great remote jobs because of one simple mistake: Assuming “perfect” means the same thing to everyone. As a remote worker, that project may look flawless to you… Meanwhile your client is thinking: “Hmm… this isn’t what I meant.” 😅 Lesson? Alignment beats assumption. Always ask for feedback. Always ask their opinion. Always ask what success looks like to them. Always ask questions before you assume answers. Remote work isn’t mind-reading. It’s communication. The people who keep clients aren’t just skilled. They’re clear. Follow me for practical insights on how to leverage the internet and start making money online as a remote worker
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Adejumoke❤️
Adejumoke❤️@JhummiePearl·
Hi everyone. My name is Adejumoke I’m currently open to entry-level Product Manager and Associate Product Manager roles. I’m passionate about building user-centered products, working across cross-functional teams, and turning ideas into impactful solutions.
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avinro
avinro@avinroart·
We’re looking for a part-time QA tester to join @hellodojo_ai. You’ll help test a multi-platform product ecosystem (2 mobile apps + 2 web portals). Important: strong attention to detail and clear bug reporting. Send CV, hourly rate, and availability to: 📩 avinroart@gmail.com
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Div
Div@DivTalent·
Thinking of the AI literacy, I think most of us can agree: every company needs an AI literacy policy right now Why? Because AI has the potential to make work significantly faster, helping architect a "10x" future for the team's productivity. But you can't unlock that speed safely unless your team has basic AI literacy which is " the fundamental knowledge" required to understand, use, and communicate about these tools effectively. AI is incredibly practical for knocking out the tedious tasks that eat up our days, like conducting early research, generating outlines, and organizing data. The secret to using it well without losing quality is the "30% Rule" instead of letting the machine do everything, the 30% rule is a practical partnership. It means AI should handle roughly 70% of the initial grunt work, like gathering data, brainstorming ideas, and writing first drafts. From there, humans take ownership of the critical final 30%. That is when you step in to review the work done, and fact-check details applying strategic judgments. This balance is non-negotiable. AI is fast, but it routinely makes mistakes and can confidently present false information at times or made-up facts, but the Human oversight acts as an built-in quality control to catch these errors and ensure the work is safe, accurate, and aligned with actual goals. The companies that establish clear AI guidelines and train their teams on this 70/30 split are already building an advantage that competitors will struggle to catch up to. #Thoughts
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Tattva Techworkz
Tattva Techworkz@Tattvatechworkz·
We are #HIRING! 🚀 Tattva is looking for a Frontend Developer! 🔹 React.js / Vue.js / Angular 🔹 HTML5 & CSS3 Mastery 🔹 Responsive Design 🔹 GIT & Performance Optimization Help us build the next generation of web apps. 💻 📩 Resume to: sales@yahwehsolutions.com
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Div
Div@DivTalent·
Real estate acquisition is 90% manual data entry and 10% actual deal-making. I decided to flip that. 👇 Over the last few weeks, I’ve been building an autonomous AI acquisitions engine in n8n. Most firms pay junior analysts and paralegals to endlessly scroll listings, copy data into CRMs, and read PDF title documents. My workflow does it entirely in the background. Here is what the architecture looks like: 1️⃣ Data Ingestion: Recursively scrapes paginated property listings from major platforms, normalizing price, beds, and location using custom JS. 2️⃣ AI Evaluation: A LangChain Agent that enforces strict investment criteria and strictly outputs an investment score 3️⃣ Automated Routing & Outreach: If it’s a 'BUY', the system auto-drafts and sends a Gmail request to the listing broker for legal title documents. If it’s a 'SKIP', it’s routed to a rejected database. 4️⃣ Legal Verification: When the broker replies, a webhook catches the email, extracts the PDF attachments, and a highly constrained legal AI cross-references the title constraints (like C of O authenticity, expired dates, and missing signatures), automatically linking it back to the exact initial deal row in Airtable via a custom smart merge algorithm. Zero human touch from the moment a property is listed to the moment a fully verified legal document sits in the CRM waiting for final approval. If your team is drowning in operational bottlenecks and manual data entry, let’s talk about building autonomous infrastructure. #AIAutomation #n8n #LangChain #PropTech #MachineLearning #SystemArchitecture
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n8n.io
n8n.io@n8n_io·
“I don’t mind AI helping us write faster. I mind AI helping us be wrong faster.” 👉 That’s exactly the problem this month Community Challenge is about. This Thursday at 7PM CET, Dr. Pure Eval (Marcel Claus-Ahrens) joins us live to talk evals and answer your questions. ⏳ Challenge deadline: March 22 Register for the live Q&A 👉 luma.com/kf36qrlx #n8nChallenge
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Div
Div@DivTalent·
@Utdbrizzy Win Bournemouth, draw Leeds, win or draw with Chelsea
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Brizzy
Brizzy@Utdbrizzy·
Manchester United’s next 3 games: - Bournemouth (A) - Leeds (H) - Chelsea (A) How many points are we getting ?👇
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HALO | CAREER SPECIALIST
Developers Hub is hiring multiple roles ‼️ 📍 Remote | 6 Weeks • Front-end Intern • Backend Intern • UI/UX Design Intern • Cybersecurity Intern • Data Science Intern • Mobile App Intern Benefits‼️ • Offer & Completion Letters • Mentorship & Guidance • Letter of Recommendation • LinkedIn Optimization 💌 Send your cv to hr@developershubcorp.com
HALO | CAREER SPECIALIST@Halosznn_

Anyone looking for Internship roles? I have vacancies for you.

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Div
Div@DivTalent·
be honest, what would you choose for vibe coding?
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Div@DivTalent·
@pinecone I think the Assistant node is genuinely underrated
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Pinecone
Pinecone@pinecone·
Most builders don't get stuck on the model — they get stuck on the pipeline. There's a version of you that ships a RAG-powered product this week. There's another version that's still debating chunking strategies and embedding models. Every week spent on those debates is a week you're not in production. The Pinecone Assistant node in n8n exists to make you the first version. We wrote a guide breaking down exactly when to use each of the Pinecone n8n nodes, and the decision is simpler than you think: Pinecone Assistant node → managed RAG pipeline. 1-2 nodes, one API key, production-ready without becoming a RAG expert. Pinecone Vector Store node → full pipeline control. 5+ nodes, maximum flexibility, real tradeoffs. ✅ Assistant node is right for: building straightforward document search where the complexity of managing chunking strategies and embeddings isn't adding value to your use case, or you need to get up and running quickly. ✅ Vector Store node is right for: specialized scenarios where the details of the retrieval pipeline matter — you need control over embedding models, customized chunking strategies, hybrid search, or multi-lingual content. Here's the mental model: the Pinecone Assistant node is a building block. The Pinecone Vector Store node is a pipeline you own and maintain. For most builders, the question isn't which node is more powerful. It's which one gets you shipping. The Pinecone Assistant n8n node is the faster path to your first AI application in production, without the pipeline complexity. What are you building with n8n + Pinecone? Let us know in the replies 👇 pinecone.io/learn/pinecone…
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Moyosore.js 💰💕
Moyosore.js 💰💕@Moyoscooo·
i’m actively looking for a job,i have 5 years of experience in Web Development, Data Manipulation, REST API & GraphQL Implementation, and Code Management. I am passionate about optimizing workflows, leading development teams, and delivering scalable, high-performance applications
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Div
Div@DivTalent·
@__chibugo Always tag some of these tools in your post they might like, repost or comment and it helps in post reach
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Div
Div@DivTalent·
@__chibugo The lookup table approach is smart. Good build
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Chibugo | AI automation • n8n
Built a fully automated client onboarding system for a brand and marketing agency. - The system cleans the data first. Names are trimmed of any whitespace, capitalised, and split into first and last names. Dates get formatted. Email and company name are trimmed. - Then it checks for duplicates. Using the client's email as the matching key, Airtable (Our CRM in this instance) either creates a new record or updates an existing one. A returning client never gets duplicated in the system. - Two Lookup Tables run. One maps the service tier to the right team lead email. The other maps it to the correct Google Drive folder. This means that if a team member changes, you update a single table. Then the system splits into three paths based on service tier. - Basic clients get a welcome email, a Slack notification to the Basic team, and a row added to a shared tracking sheet. - Standard clients get a dedicated Google Drive folder created, a welcome email, and a Slack notification to the Standard team lead. - Premium clients get a dedicated folder, a welcome email with a four-week project timeline, team introduction, and next steps checklist, plus a priority Slack notification with their full details. Instead of creating project tasks in Zapier, which would mean four or three separate steps per path and burn through task limits quickly, I used Airtable's native automation to handle it. The moment Zapier creates a client record in the Airtable base (Client intake table), the Airtable automation detects it and triggers its own automation. It checks the service tier and automatically chains through the task creation. Premium clients get four tasks created and linked to their record. Standard clients get three. Basic clients get none. The tasks carry through the linked record field, so every client's task list sits directly inside their CRM record If your agency is still onboarding clients manually, it does not have to.
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