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1 contribution to CommunityIndustry
We’re hiring a Collective Intelligence Analyst
This is a unique External Problem Solving collaboration within our Community Funded Research initiative, focused on the Community Industry. It’s fully remote, and we don’t care when or where you work, as long as you work when your brain works best. The workload is up to 4 hours per day because we know what cognitive overload from too much information and data feels like, as well as the emerging phenomenon of AI brain fry. Take exceptionally good care of your brain and keep it sharp as a razor. We believe protecting your mental energy is part of doing great work, not something you do after work. That’s also why we care about results, not hours. We don’t care whether you do the work yourself or use a team of AI agents. If you find a quantum hack and solve in 30 minutes what others would spend a week doing, you won’t be penalised for it. We care about quality, accuracy and usefulness. You remain responsible for the output, and the only thing we don’t tolerate is AI slop. GET PAID $2,000 PER MONTH TO HACK THE OLD SYSTEM WITH US We’re not hiring you to simply maintain what already exists. We’re looking for someone who wants to help us hack the old way of organising knowledge, talent and intelligence in the Community Industry. We’re building an open community where thousands of conversations, ideas, people, opportunities and signals are constantly being generated. The challenge is turning this chaos into structured collective intelligence that becomes more valuable over time. Your job is to help us find better, faster and smarter ways to solve this problem. You’ll help us continuously improve how we capture, organise, connect and extract value from what our members contribute. This includes: 1) Helping develop the 5 Levels of Community Investment Mastery by filtering through discussions created by our amazing members, identifying the valuable insights and hidden gems, and placing them into the right sections and subpages of the structure.
We’re hiring a Collective Intelligence Analyst
2 likes • 6d
Jakub, the core bottleneck here isn’t community management; it’s an unstructured data problem. To hack this, you don't need someone reading hundreds of posts for 4 hours a day. You need an automated ETL pipeline that scales asynchronously as the community grows. Here is the exact architectural system I would deploy to turn your raw Skool chaos into investor-grade intelligence: 1. Extraction (Bypassing Platform Limits) Since native APIs often throttle deep threaded extraction, I would deploy a custom Python scraping pipeline to pull raw conversational data, engagement metrics, and author profiles directly into a centralized SQL staging environment. 2. Transformation (AI Agent Filtration) Instead of manual sorting, we route the raw text through strict-prompt AI agents: - The Taxonomy Agent: Evaluates and automatically tags insights against your "5 Levels of Community Investment Mastery" framework. - The Signal Agent: Runs entity extraction to flag technical depth (identifying undervalued talent) and market keywords (identifying M&A opportunities, distress, or B2B synergy). 3. Loading (The M&A Intelligence Hub) The structured data is pushed into a live BI dashboard (I've attached a mockup of what this exact architecture looks like visually). By treating community discussions as data points rather than conversations, we turn a 4-hour daily cognitive drain into a 30-minute validation routine. Premium buyers get signal; we filter the noise. Let’s build the engine.
0 likes • 3d
@Jakub Pacanda the hike was exactly the mental reset I needed—appreciate you asking. I completely agree with your two-layer approach. In fact, I just built the exact agent logic you outlined. The architectural proof-of-work is in the attached document. Here is the pipeline execution: - Layer 1 (The Engine): I ingested a chaotic dummy thread of a burned-out founder. The agent bypassed the conversational noise, classified the signal as 'Level 5 - Exit Readiness', and extracted the exact M&A entities into a structured JSON payload. - Layer 2 (The Leverage): The second agent synthesized that structured data into a high-density Telegram brief tailored for AcquireCommunity.com, applying a Buy-vs-Build framework to drive analytical investor decisions. The system is functional. Drop a link to a live, high-value Skool thread below. I will run it through this architecture today and reply with the actual Telegram investor output it generates. Let's build this.
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Divine Power
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@divine-power-7334
Divyanshu, founder Surkhiya. We build Data backed Business strategies leveraging community for Social & Business Impact. DM in LinkedIn

Active 21h ago
Joined Sep 25, 2026