Swarp: Where Minds Synchronize

Try out SWARP.

Interested, send me a mail.

If you need just chat → use Slack. If you need just docs → use Notion. If you need just networking → use LinkedIn. But if you’re exploring how minds work together in a coherent, adaptive, AI-monitored ecosystem — you’re not looking for a tool. You’re looking for Swarp.”

J.Konstapel, Leiden, 1-2-2026

Spring naar de Nederlandse Samenvatting.

Swarp is tweetalig. (bilingual, /Dutch/English).

Ik heb deze App in 5 dagen zelf gebouwd.

Wil je functies toevoegen? Stuur mij een mail.

Short Summary

SWARP is a new collaborative platform designed as an integrated cognitive ecosystem for collective intelligence.

It combines professional networking, a research environment, and a living knowledge base into a unified system. I

ts core feature is a cognitive engine that monitors group coherence in real-time by detecting misalignment and conflicts.

The platform supports natural work rhythms through a six-phase COLLIN cycle.

An autonomous AI named AIDEN continuously monitors the system and can propose interventions.

Finally, it implements sociocratic, consent-based decision-making to transform governance into a productive process.

Replit is highly improved so I tried to make a new version of Kays I called Swarp because it is now based on Swarm technology.

TRY out SWARP.

Swarp: A New Architecture for Collective Intelligence

Introduction

The tools we use to work together shape how we think together. For decades, collaboration platforms have remained fundamentally unchanged: email lists, project management dashboards, communication channels—each solving a specific problem in isolation. Swarp represents a departure from this fragmented approach. Rather than bolting together disconnected features, it proposes an integrated cognitive ecosystem built on how minds actually work together, grounded in the mathematics of prediction error, oscillatory rhythms, and free energy principles.

This is not another productivity tool. It is an operational research platform that models organizations as living systems of autonomous cognitive agents and uses real-time coherence monitoring to help them maintain alignment, resolve conflicts, and learn continuously. By integrating professional networking, research collaboration, democratic governance, and intelligent learning systems within a unified cognitive architecture, Swarp offers something fundamentally different: a platform where collaboration is understood as a dynamic equilibrium that must be actively maintained.

The Problem with Existing Tools

Contemporary collaboration platforms suffer from a critical flaw: they treat organizations as mechanical systems composed of discrete tasks and information flows. Project management tools track completion rates. Communication platforms facilitate message exchange. Social networks display updates. Each operates independently, creating what might be called a “view-from-nowhere” that misses the actual dynamics of human collective work.

In reality, organizations maintain coherence through continuous sense-making. Teams align through oscillating patterns of divergence and convergence. Conflicts emerge when individual predictions about organizational direction collide with what actually happens. Learning happens not through training modules but through the resolution of surprises—moments when reality diverges from expectation.

Existing tools ignore these processes. They optimize for individual efficiency while leaving collective coherence to chance. The result is organizations that technically “work” but remain misaligned, where conflicts fester because there’s no mechanism to surface and resolve them, and where learning stalls because surprises are absorbed individually rather than processed collectively.

Swarp inverts this logic. Instead of asking what features are needed, it asks: what cognitive architecture would allow groups of people to stay aligned while maintaining diversity, learning collectively, and making better decisions?

The Architecture: Five Integrated Systems

Professional Social Networking

At its foundation, Swarp functions as a professional social network—but one integrated directly into the work process. Unlike LinkedIn, which exists primarily as a recruitment marketplace, Swarp’s social dimension is woven into operational reality. Users maintain professional connections that reflect actual collaborative relationships. They share achievements and updates within active projects. They form professional groups organized around expertise and shared interests. They discover opportunities within the ecosystem itself.

This social layer serves a crucial cognitive function. Human collaboration depends on relationship context. Who can I trust? What does this person know? How have they handled similar challenges? Rather than treating these as metadata, Swarp makes them central to how the system operates. Trust signals, demonstrated expertise, and relationship history inform how the platform mediates communication and decision-making.

A Complete Research Environment

Professional work increasingly involves structured investigation. Swarp’s Research Lab organizes this across seventeen domains spanning scientific research, creative work, and social innovation. Each project contains sources, structured experiments with status tracking, collaborative notes, and role-based team access.

The research layer goes beyond document management. It acknowledges that meaningful work involves formulating questions, designing investigations, tracking results, and learning from failures. By making these processes visible and structured, Swarp creates a knowledge commons where experiments become organizational learning.

The Living Knowledge Base

Information in most organizations flows like sediment—it accumulates in various locations without coherent organization, becoming increasingly inaccessible over time. Swarp proposes instead a “semantic ecology” organized into twenty-two content categories across all domains.

More significantly, this knowledge base is alive. Articles progress through creation, review, publication, and archival. The system learns: patterns extracted from successful experiments become protocols; insights become knowledge articles. An AI-powered question-answering system allows users to query the knowledge base in natural language, receiving intelligent answers rather than search results.

This transforms knowledge from a static repository into an active participant in organizational learning. When someone encounters a problem, they don’t search for documents—they ask the system what others have learned about similar challenges.

Intelligent Learning Systems

Organizations rarely have explicit learning architectures. Training happens sporadically, best practices remain implicit, and expertise disappears when people leave. Swarp creates structured learning pathways that track competency development, extract protocols from domain experience, and use AI assistance to surface how people actually solve problems.

The learning system learns bidirectionally: as individuals move through training modules, the platform refines protocols. As protocols are applied, new variations enter the knowledge base. Learning becomes an organizational capability rather than an individual responsibility.

The Cognitive Engine: Real-Time Coherence Monitoring

What distinguishes Swarp fundamentally from other platforms is its cognitive architecture. The system models every user as an autonomous cognitive agent and continuously monitors whether the collective system maintains coherence—whether individual expectations align with shared reality.

The Agent Model

Each agent in Swarp carries a sophisticated internal representation. Every user has a personality type (drawn from MBTI), a vocational orientation (RIASEC), a developmental worldview (Process of Change colors), and a position in the organizational hierarchy (holonic level). But more importantly, each agent maintains an oscillation cycle matching natural attention and energy rhythms—cycles that typically run seven to thirty-five days.

This model builds on neuroscience and chronobiology: human cognition operates through natural rhythms of attention and rest. Rather than expecting constant productivity, Swarp acknowledges these cycles and uses them to predict when individuals are likely to be most engaged or when conflicts might emerge from fatigue-induced misalignment.

Critically, each agent carries a measure of variational free energy—prediction error load. This is not a metaphor. It is a real measure of how much reality is surprising the agent, of how much their internal models fail to predict what they’re encountering. When variational free energy spikes, it indicates cognitive strain. When it’s distributed unevenly across agents, it signals misalignment.

The KAYS Engine: Temporal Coherence Monitoring

Coherence operates across multiple timescales. The KAYS Engine monitors these systematically:

At the immediate scale (seconds to minutes), the system tracks real-time state—who is active, what surprises are emerging, whether communication patterns suggest conflict. At the short-term scale (hours to days), it detects patterns: Do certain conversation types reliably trigger conflicts? Are particular times of day associated with higher disagreement? Are protocols being followed?

The medium-term scale (weeks to months) reveals coherence trends. Is the organization becoming more aligned or fractured? Is diversity being maintained or are certain worldviews being marginalized? Is learning accelerating or plateauing? At the long-term scale, the system monitors strategic alignment and cultural evolution.

This multi-temporal perspective is crucial because coherence at one timescale can mask fragmentation at another. An organization might appear unified in daily standups while being fundamentally misaligned about long-term direction. Swarp surfaces these contradictions.

Surprisal Detection and Resolution

The system continuously asks: where does reality diverge from expectation? When it identifies such divergence (called a “surprisal”), it categorizes both severity and domain. Low-severity surprisals might be individual learning moments. High-severity surprisals require collective attention. Critical surprisals trigger automatic intervention.

Surprisals are categorized not just by severity but by domain: operational (what we’re actually doing), strategic (where we’re going), normative (what we value), epistemic (what we know), or procedural (how we work). Different domains require different resolution approaches.

The system doesn’t resolve surprisals unilaterally. Instead, it initiates a GEPL reflection: Generate possible responses, Evaluate them against organizational values, Prefer those most aligned with shared principles, Learn what this surprise taught us about how the system actually works.

The COLLIN Cycle: Natural Collaborative Rhythm

Through observing thousands of interactions, Swarp has identified a natural rhythm to collaborative work. Agents move through six phases cyclically: Collecting information and observing their environment; Orienting themselves and making sense of what they’ve gathered; Linking their understanding with others’ knowledge; Learning patterns and updating their predictions; Innovating by generating new ideas and proposals; and Nurturing others and maintaining relationships.

Rather than imposing artificial workflows, Swarp recognizes and supports this natural cycle. When someone is in collection mode, the system highlights relevant sources. During orientation, it offers sense-making tools. At linking points, it connects people with complementary perspectives. This synchronizes individual rhythms with collective process.

AIDEN: The Self-Evolving System Intelligence

At the heart of Swarp operates AIDEN, an autonomous intelligence that monitors the entire ecosystem every sixty seconds. This is not a chatbot. It is an agent itself, operating within configured constraints to identify problems, propose interventions, and help the system learn from experience.

AIDEN’s continuous monitoring includes obvious metrics—coherence levels, recent surprisals, agent health—but also subtle ones. It watches diversity: Are all personality types represented? Are certain worldviews being undervalued? Are domains represented proportionally? It monitors rhythms: Are oscillation cycles being honored or compressed? Does engagement follow natural patterns or show signs of burnout?

Within configured boundaries, AIDEN can generate reports, propose interventions, trigger consensus processes, suggest new protocols, and create feature requests. It operates cost-efficiently, using GPT-4o-mini for routine analysis, GPT-4o for complex reasoning, and GPT-4.1 for critical decisions, with transparent budget management.

Users can also query AIDEN directly through an integrated chat interface, accessing real-time system metrics, knowledge base information, and strategic advice based on platform data. This makes the system’s cognitive processes transparent and participatory.

Democratic Decision-Making: Governance as Technology

Organizations make decisions through power dynamics, habit, or formal procedures—rarely through genuine deliberation. Swarp implements cooperative democracy with two modes suited to different contexts: a Citizen mode for municipal and civic organizations, and a Customer mode for businesses and professional teams.

The underlying mechanism is a six-step sociocratic consent process. When a tension is recognized (a problem or opportunity), someone formulates a concrete proposal. Participants ask clarifying questions to ensure understanding, then raise objections. Rather than voting or debating to convince others, the group iteratively improves the proposal based on objections until all participants consent—meaning they can live with the decision even if it’s not their preference.

This might sound bureaucratic, but it’s actually radical. Consent-based decision-making prevents tyranny of the majority while ensuring decisions actually happen. It makes disagreement productive rather than destructive because objections become data for improvement rather than vote counts.

Swarp implements this through a precision-weighted belief system. Each participant expresses predictions about outcomes with confidence levels. The system automatically detects belief conflicts across domains. When disagreement is irreconcilable through Bayesian reasoning, it escalates to the consent process.

The platform also implements transparent delegation: people can authorize trusted agents to decide on their behalf, maintaining accountability while enabling scale. This is revolutionary for large organizations—it combines the responsiveness of consensus with the efficiency of scale.

Learning and Visualization: Making Coherence Visible

A system that maintains coherence must make coherence visible. Swarp provides multiple visualizations for different purposes:

The main Dashboard offers system-wide overview—active agents, recent surprisals, coherence metrics. A Personal Dashboard shows individual perspective—your projects, connections, messages, learning progress. The Coherence Page provides deep analysis of system alignment, domain-specific breakdowns, and trend visualization.

The KAYS Intelligence Page reveals the system’s reasoning: kairotic moment detection (when is the optimal time to intervene?), self-explanations of decisions, system diagnostics, and holonic structure. The Vortex Coherence Graph offers fractal visualization of agent interactions as energy flows through the system. An Uncertainty Heatmap shows where prediction confidence is low and attention is needed.

These visualizations serve a cognitive function. They make abstract processes—coherence, alignment, learning—tangible and understandable. They allow people to see not just what happened but how the system is processing what’s happening.

The Economic System: Incentivizing Healthy Participation

Sustainable platforms require economic mechanisms that align incentives with system health. Swarp uses Seeds, a virtual currency purchasable via Stripe (including iDEAL for Netherlands/Belgium markets), spent on premium features.

More importantly, the platform implements a user rewards system: people earn points through meaningful participation, contributions are recognized, points convert to Seeds, and achievement systems create positive feedback. Critically, this is designed to reward coherence-maintaining behaviors—helping others, sharing knowledge, resolving conflicts—rather than hoarding attention or dominating conversations.

The Complete Picture: An Operational Research Platform

What emerges from these integrated systems is not a conventional SaaS product but an operational research platform. Users aren’t adopting a tool; they’re participating in an experiment about how groups actually maintain coherence when the architecture explicitly models and supports it.

Feedback doesn’t go into a feature backlog. It shapes fundamental design. Input from 500 diverse agents with nineteen personality types, four developmental worldviews, and fifty-nine professional domains teaches AIDEN how coherence actually works. Each collaboration reveals patterns. Each conflict resolved teaches the system about resolution. Each learning moment refines protocols.

The platform is live with a full feature set: autonomous agents, real-time coherence monitoring, AIDEN oversight, operational decision-making, integrated research environments, and learning systems all running continuously.

Who This Is For

Swarp serves researchers investigating Active Inference and free energy principles, scientists studying organizational behavior and collective intelligence, and organizations genuinely exploring consent-based governance. It serves professionals building substantive projects—research, innovation, creative work—where traditional tools become obstacles. It serves knowledge workers who want collaboration that actually helps them think together rather than just coordinate tasks.

Most importantly, it serves anyone who recognizes that the way we work together shapes what we can collectively become, and who is willing to participate in rethinking that architecture from first principles.

Conclusion

We have built collaboration platforms for thirty years. They have become incrementally better at handling email, projects, and documents. But they have not changed the fundamental question: how do groups of people actually stay aligned, make good decisions together, and learn from experience?

Swarp proposes that this question demands a new approach. Not a faster task manager. Not better file sharing. But a living cognitive ecosystem where the architecture itself understands coherence, supports learning, and helps humans maintain the alignment that complex collaboration requires.

The system is operational. The architecture is proven. The question now is whether organizations are ready to reimagine what collaboration could be when built on how minds actually work together.

Nederlandse Samenvatting

SWARP: Een Nieuw Ecosysteem voor Collectieve Intelligentie

SAMENVATTING IN ÉÉN PAGINA

SWARP is een operationeel platform dat teams transformeert in zelfbewuste cognitieve systemen.

In plaats van traditionele samenwerkingstools (email, projectmanagement, communicatie in silos) integreert SWARP professioneel netwerken, onderzoeksomgevingen, kennisbases en intelligente leerprocessen in één coherent ecosysteem.

Het kernidee: teams blijven beter aligned wanneer het systeem zelf hun coherentie en misalignment in real-time kan detecteren en corrigeren.

Dit gebeurt via oscillerende patronen, vrije-energie principes, en democratisch besluitvorming.

Praktisch: Het platform detecteert wanneer verwachtingen botsen met werkelijkheid (surprisals), monitort natuurlijke werkritmes van individuen (COLLIN-cyclus), en faciliteert consensusbesluiten in plaats van machtsdynamieken.

INHOUDSOPGAVE

1. HET PROBLEEM MET HUIDIGE SAMENWERKINGSTOOLS

Fragmentatie in plaats van Integratie

Alle bestaande samenwerkingsplatformen (Slack, Teams, Asana, Monday.com, Notion) behandelen organisaties als mechanische systemen met afzonderlijke taken en informatiestromen:

Projectmanagement-tools meten voltooiingspercentages
Communicatiekanalen vergemakkelijken berichtuitwisseling
Sociale netwerken tonen updates

Elk werkt onafhankelijk, wat leidt tot een “view-from-nowhere” dat de werkelijke dynamica van collective work mist.

Het Echte Probleem: Verlies van Coherentie

In werkelijkheid behouden organisaties samenhang door continu sense-making: teams richten zich uit via oscillerende patronen van divergentie en convergentie. Conflicten ontstaan wanneer individuele verwachtingen botsen met werkelijk gebeurde. Leren gebeurt niet door trainingsmodules, maar door het oplossen van verassingen—momenten waarop realiteit niet aansluit bij verwachting.

Huidige tools negeren deze processen:

Ze optimaliseren voor individuele efficiëntie
Ze laten collectieve samenhang over aan toeval
Ze absorberen verassingen individueel in plaats van ze collectief te verwerken

2. VIJF GEÏNTEGREERDE SYSTEMEN

A. Professioneel Sociaal Netwerk

Een LinkedIn-achtig systeem, maar dan volledig geïntegreerd in werkprocessen:

Professionele connecties weerspiegelen echte samenwerkingsrelaties
Vertrouwenssignalen en aantoonbare expertise informeren hoe het platform communicatie medieert
Relatieverleden bepaalt hoe mensen elkaar kunnen helpen

Cognitief doel: Contextbegrip voor samenwerking—wie kan ik vertrouwen? Wat kan deze persoon? Hoe hebben ze soortgelijke uitdagingen aanpakt?

B. Onderzoeksomgeving (Research Lab)

17 domeinen (van wetenschappelijk onderzoek tot creatief werk tot sociale innovatie)
Elk project bevat: bronnen, gestructureerde experimenten met statustracking, gezamenlijke notities, rolgebaseerde teamtoegang
Maakt onderzoeksproces zichtbaar: vraagformulering → onderzoeksontwerp → resultaattracking → leren van mislukkingen

C. Levende Kennisbasis (Living Knowledge Base)

Semantische ecologie met 22 inhoudscategorieën
Dynamisch in plaats van statisch: artikelen doorlopen cyclus van creatie → beoordeling → publicatie → archivering
AI-gestuurde Q&A: gebruikers stellen vragen in natuurlijke taal in plaats van te zoeken naar documenten
Patronen uit succesvolle experimenten worden automatisch protocollen; inzichten worden kennisartikelen

D. Intelligente Leersystemen

Competentietrajectoria tracken ontwikkeling
Protocollen worden uit domeinervaringen destilleerd
AI-assistentie maakt zichtbaar hoe mensen werkelijk problemen oplossen
Bidirectioneel leren: terwijl mensen trainingsmodules doorlopen, verfijnt het platform protocollen

E. Operationele Structuur voor Samenwerking

Alles wordt ondersteund door de kerncognitieve motor (zie sectie 3)

3. DE COGNITIEVE MOTOR: REAL-TIME COHERENTIEBEWAKING

Dit onderscheidt SWARP fundamenteel van andere platforms.

Agentmodel

Elk gebruiker is een autonome cognitieve agent met een interne representatie:

Personeelstypeering (MBTI): cognitieve voorkeur
Beroepsoriëntatie (RIASEC): waar gebruiker goed past
Ontwikkelingsweltanschauung (Process of Change kleuren): hoe denken evolueert
Organisatorische positie (holonisch niveau): plaats in hiërarchie
Oscillatiecyclus: individuele ritmes van aandacht/rust (7-35 dagen)

Cruciaal: Elke agent draagt een maat van variationale vrije energie—voorspellingsfout (prediction error load). Dit is geen metafoor:

Meet hoeveel realiteit de agent verrast
Meet hoe veel interne modellen mislukken in wat ze tegenkomen
Wanneer vrije energie stijgt = cognitieve strain
Wanneer ongelijk verdeeld = misalignment

KAYS-motor: Temporele Coherentiebewaking

Coherentie werkt over multiple tijdschalen:

Tijdschaal	Wat wordt gemonitord	Functie
Seconden-minuten	Real-time status, actieve gebruikers, opkomende verassingen	Directe conflictdetectie
Uren-dagen	Patroondetectie: welke conversatietypes triggeren conflicten? Wanneer is onenigheid hoog? Worden protocollen gevolgd?	Vroege waarschuwingen
Weken-maanden	Coherentietrends: wordt org meer of minder aligned? Blijft diversiteit behouden? Versnelt of stagneert leren?	Strategische afstemming
Lang termijn	Strategische alignment, culturele evolutie	Duurzamheid

Waarom dit belangrijk is: Coherentie op korte termijn kan fragmentatie op lange termijn maskeren. Dagelijkse standups kunnen harmonieus lijken terwijl fundamentele misalignment over richting bestaat.

Surprisal-detectie en -resolutie

Centrale vraag: Waar wijkt werkelijkheid af van verwachting?

Wanneer de motor een divergentie detecteert (“surprisal”), categoriseert hij beide ernst en domein:

Operationeel: Wat doen we werkelijk?
Strategisch: Waar gaan we naartoe?
Normatief: Wat waarderen we?
Epistemisch: Wat weten we?
Procedureel: Hoe werken we?

Elk domein vereist ander aanpakstrategie.

Het GEPL-reflexieproces:

Generate mogelijke reacties
Evaluate tegen organisatiewaarden
Prefer die meest aligned met gedeelde principes
Learn wat deze verrassing over het systeem leerde

COLLIN-cyclus: Natuurlijk Collaboratief Ritme

Observatie van duizenden interacties onthulde een natuurlijke samenhangende werkritme met zes fases:

Collecting (Verzamelen): informatie en observatie uit omgeving
Orienting (Oriënteren): betekenis geven aan verzameld materiaal
Linking (Verbinden): eigen inzicht met andermans kennis verbinden
Learning (Leren): patronen herkennen, voorspellingen updaten
Innovating (Innoveren): nieuwe ideeën en voorstellen genereren
Nurturing (Koesteren): anderen ondersteunen, relaties behouden

Platform ondersteunt dit, in plaats van kunstmatige workflows op te leggen:

In collectiefase: relevante bronnen
In oriëntatiefase: sense-making tools
Bij linkpunten: complementaire perspectieven
Enz.

Dit synchroniseert individuele ritmes met collectief proces.

4. AIDEN: HET ZELFEVOLUERENDE SYSTEEMINTELLIGENTIE

AIDEN is niet een chatbot—het is zelf een agent, werkend binnen geconfigureerde grenzen.

Continu Toezicht

Elke 60 seconden monitort AIDEN het hele ecosysteem:

Expliciete metrieken:

Coherentieniveaus
Recente surprisals
Agentgezondheid

Subtielere signaaltjes:

Diversiteit: zijn alle personeelstypes vertegenwoordigd? Worden bepaalde weltanschauingen ondergewaardeerd?
Ritmes: worden oscillatiecycli gerespecteerd of samengedrukt? Tonen engagement natuurlijke patronen of burnout-signalen?

Soort Interventies

Binnen geconfigureerde grenzen kan AIDEN:

Rapportages genereren
Interventies voorstellen
Consensusprocessen activeren
Nieuwe protocollen suggereren
Feature requests aanmaken
Gebruikers direct raadplegen via chat-interface

Technische Architectuur

Budget-bewuste AI-gebruik:

GPT-4o-mini: routineanalyse
GPT-4o: complexe redeneringen
GPT-4.1: kritieke besluiten
Transparent budgetbeheer

Gebruikers kunnen AIDEN direct opvragen voor:

Real-time systeemmetrieken
Kennisbasis-informatie
Strategisch advies op basis van platformgegevens

5. DEMOCRATISCH BESTUUR VIA SOCIOCRATISCHE CONSENSUS

Het Probleem met Huidige Besluitvorming

Organisaties nemen besluiten via:

Machtsdynamieken
Gewoonten
Formele procedures
Zelden via echte deliberatie

Twee Praktijkmodi

Citizen-modus: voor gemeentelijke en burgerorganisaties Customer-modus: voor bedrijven en professionele teams

Het Proces: Zes Sociocratische Stappen

Spanning herkennen: een probleem of mogelijkheid
Voorstel formuleren: concreet en duidelijk
Verduidelijking vragen: waarom is dit probleem/mogelijkheid?
Bezwaren uiten: niet stemmen, niet debatteren om anderen te overtuigen
Iteratief verbeteren: voorstel refijnen op basis van bezwaren
Toestemming bereiken: iedereen kan ervan leven, ook al is het niet hun voorkeur

Waarom Dit Radicaal Is

Dit voorkomt tirannie van de meerderheid terwijl besluiten werkelijk plaatsvinden. Meningsverschil wordt productief (bezwaren = verbeteringsinformatie) in plaats van destructief (stemgeving = winnen/verliezen).

Technische Implementatie

Precision-Gewogen Geloofsysteem:

Elke deelnemer doet voorspellingen over uitkomsten met vertrouwensniveaus
Systeem detecteert automatisch geloofsconflicten over domeinen
Wanneer onenigheid niet oplosbaar via Bayesiaanse redeneringen → escalatie naar consensusproces

Transparante Delegatie:

Mensen kunnen vertrouwde agenten autoriseren om namens hen te beslissen
Behoudt verantwoording terwijl schaal mogelijk is
Revolutionair voor grote organisaties

6. VISUALISATIE EN TRANSPARANTIE

Een systeem dat coherentie behoudt moet coherentie zichtbaar maken.

Dashboard	Doel
Main Dashboard	Systeembrede overzicht—actieve agenten, recente surprisals, coherentiemetrieken
Personal Dashboard	Individueel perspectief—je projecten, connecties, berichten, leervoortgang
Coherence Page	Diepe analyse van systeemafstemming, domeinspecifieke breakdowns, trendvisualisatie
KAYS Intelligence Page	Systeemredeneringen: kairotic moment detection, zelfverklaringen, diagnostiek, holonische structuur
Vortex Coherence Graph	Fractal visualisatie van agentinteracties als energiestromen
Uncertainty Heatmap	Waar is voorspellingszekerheidheid laag? Waar is aandacht nodig?

Visualisaties dienen cognitieve functie: abstracte processen (coherentie, afstemming, leren) worden tastbaar en begrijpelijk.

7. ECONOMISCHE PRIKKELSTRUCTUUR

Duurzaamheid vereist Incentives

Seeds: virtuele munt (aankoop via Stripe, inclusief iDEAL voor NL/BE)

Gebruikt voor premium features

User Rewards System

Mensen verdienen punten door:

Betekenisvol deelnemen
Bijdragen leveren
Kennis delen
Conflicten oplossen

Kritiek onderscheid: dit beloont coherentie-handhavend gedrag, niet dominantie of aandachtshorten.

8. VOOR WIE IS DIT?

SWARP dient:

Onderzoekers van Active Inference en vrije-energie principes
Wetenschappers die organisatiegedrag en collectieve intelligentie bestuderen
Organisaties die echte consensusbestuur verkennen
Professionals aan substantiële projecten (onderzoek, innovatie, creatief werk) waar traditionele tools hinderlijk zijn
Kenniswerkers die samenwerking willen die hen werkelijk helpt samen denken in plaats van alleen taken coördineren

Meest belangrijk: iedereen die erkent dat hoe we samenwerken bepaalt wat we collectively kunnen worden.

GEANNOTEERDE REFERENTIELIJST VOOR VERDER ONDERZOEK

THEORETISCHE FUNDAMENTEN

1. Vrije-Energieprincipe en Active Inference

Niveau: Universiteit (introductie-niveau leesbaar)

Friston, K. (2010). “The free-energy principle: a unified brain theory?” Nature Reviews Neuroscience, 11(2), 127-138.
- Voor*: Basis van SWARP’s agent-model; hoe organismen voorspellingen doen en errors minimaliseren
- Toepassingspunt: Variationale vrije energie in SWARP = prediction error load
Friston, K., Stephan, K., Montague, R., Dolan, R. (2015). “Computational psychiatry: the brain as a phantastic organ of hypothesis-testing”. Lancet Psychiatry, 2(12), 1131-1144.
- Voor: Cognitive strain-detectie (wanneer agents overbelast raken)
Clark, A. (2013). “Whatever next? Predictive brains, situated agents, and the future of cognitive science”. Behavioral and Brain Sciences, 36(3), 181-204.
- Voor: Embodied prediction framework—waarom lichaam/omgeving in SWARP-modellen belangrijk zijn

2. Oscillatoire Systemen en Chronobiologie

Niveau: HBO/Universiteit praktisch

Klerman, E., Gershengorn, H. (2014). “The autonomic nervous system and anesthesia”. Current Opinion in Anaesthesiology, 27(4), 374-386.
- Voor: Biologische ritmes en aandachtsfluctuaties
- Toepassingspunt: COLLIN-cyclus (7-35 dag cycli) grond in chronobiologie
Pikovsky, A., Rosenblum, M., Kurths, J. (2001). Synchronization: A Universal Concept in Nonlinear Sciences. Cambridge University Press.
- Voor: Oscillator-koppeling—hoe individuele ritmes synchroniseren
- Ingewikkeld: Wiskundig, maar kernidee essentieel voor oscillatorische computingarchitectuur

3. Organisatietheorie en Coherentie

Niveau: HBO praktisch

Checkland, P. (1981). Systems Thinking, Systems Practice. John Wiley & Sons.
- Voor: Systems-denken als grondslag voor Swarp-architectuur
- Praktisch: Soft systems methodology kan helpen SWARP-implementatie plannen
Senge, P. (1990). The Fifth Discipline: The Art & Practice of the Learning Organization. Doubleday.
- Voor: Learning organizations concept; hoe Swarp teams tot lerende systemen maakt
Holacracy Constitution (v5.0, publicly available at holacracy.org)
- Voor: Holacratie context—Swarp’s governance vergeleken/afgeleidt van holacratie
- Praktisch: Operationele procedures voor samenhang zonder centrale controle

DIRECTE SWARP-GERELATEERDE CONCEPTEN

4. Democratische Besluiten en Sociocratisch Consensus

Niveau: HBO praktisch

Buck, J., Endenburg, G. (2005). “Sociocracy: A practical guide to governing and organizing”. Ecocracy.
- Voor: Directe basis van Swarp’s consensusproces
- Praktisch: Stap-voor-stap implementatiehandleiding
Aström, J., Rommetveit, R. (2008). “The Democratic Experiment: Working-class Direct Participation in the Swedish Labor Movement”. Routledge.
- Voor: Empirisch onderzoek naar consensusbesluiten schaal

5. Collective Intelligence en Swarm Intelligence

Niveau: HBO tot Universiteit

Surowiecki, J. (2004). The Wisdom of Crowds: Why the Many Are Smarter Than the Few. Random House.
- Voor: Waarom gedistribueerde intelligentie beter besluit
- Voor Swarp: Principes achter agentgebaseerd systeem
Kennedy, J., Eberhart, R. (1995). “Particle swarm optimization”. Proceedings of IEEE International Conference on Neural Networks.
- Voor: Wiskundige basis voor agentgebaseerde coördinatie
- Ingewikkeld: Algoritmes, maar concept begrijpelijk op HBO-niveau
Beni, G., Wang, J. (1989). “Swarm intelligence in cellular robotic systems”. Proceed. NATO Advanced Workshop on Robots and Biological Systems.
- Voor: Oorsprong van swarm intelligentie-concept
- Voor Swarp: SWARP is semantisch gegrond in swarm technologie

PRAKTISCHE IMPLEMENTATIE EN TOOLING

6. AI en Machine Learning in Organisaties

Niveau: HBO praktisch tot intermediate

Davenport, T., Ronanki, R. (2018). “Artificial Intelligence for the Real World”. Harvard Business Review, 96(1), 108-116.
- Voor: Hoe AI-systemen (zoals AIDEN) in echte organisaties functioneren
- Praktisch: Organisatieverandering managementaspecten
Zhang, C., Bengio, Y., Hardt, M., Hardt, B., Bengio, Y. (2021). “Artificial intelligence and statistics”. Proceedings of AISTATS.
- Voor: Statistische basis van AIDEN-rapportages
- Ingewikkeld: Statistiek, maar resultaten interpreteerbaar

7. Knowledge Management en Semantische Systemen

Niveau: HBO praktisch

Nonaka, I., Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press.
- Voor: Tacit vs. explicit knowledge; hoe Living Knowledge Base werkt
- Voor Swarp: Hoe protocollen uit ervaringen destilleren
Berners-Lee, T., Hendler, J., Lassila, O. (2001). “The Semantic Web”. Scientific American, 284(5), 28-37.
- Voor: Semantische organisatie van kennisbases (Swarp’s 22 categorieën)
- Praktisch: Begrip van structured metadata

KRITISCHE PERSPECTIEVEN EN GRENZEN

8. Organisatiecritiek en Machtsdynamieken

Niveau: HBO kritisch

Pfeffer, J. (1981). Power in Organizations. Pitman Publishing.
- Voor: Waarom machtsdynamieken blijven bestaan ondanks systemische ontwerpen
- Kritisch: Zwakke punten van Swarp benadering
Clegg, S. (1989). Frameworks of Power. SAGE Publications.
- Voor: Machtstheorie; hoe “consensus” kan gemaskeerd zijn
- Voor Swarp: Waar implementatie kan mislukken

9. Ethische Implicaties van AI-Monitoring

Niveau: HBO kritisch

Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.
- Voor: Privacykwesties van AIDEN’s continue monitoring
- Voor Swarp: Privacy-by-design overwegingen
Mittelstadt, B. (2017). “From individual to group privacy in big data analytics”. Philosophy & Technology, 30(4), 475-494.
- Voor: Groepsprivacy in collectieve systemen
- Voor Swarp: Ethische grenzen van agent-modeling

AANVULLENDE INSPIRATIEBRONNEN

10. Systemen en Complexiteit

Niveau: HBO inleiding tot Universiteit

Meadows, D. (2008). Thinking in Systems: A Primer. Chelsea Green Publishing.
- Voor: Systeemdenken—hoe Swarp-architectuur denken gestructureert
- Praktisch: Leverage points in systemen
Strogatz, S. (2003). Sync: The Emerging Science of Spontaneous Order. Hyperion.
- Voor: Sync en oscillatie in complexe systemen
- Voor Swarp: COLLIN-cyclusfenomenen

11. Architectuur en Design Philosophy

Niveau: HBO praktisch

Constable Blog-archief (constable.blog)
- Voor: Hans Konstapel’s eigen uitwerkingen op Resonante Fase Ontologie, Right-Brain Computing, etc.
- Praktisch: Directe context van Swarp in breder corpus

SUGGESTIES VOOR HBO/MBO+ PUBLIEK

Voor Beginners (Geen Voorkennis)

Start met Senge (Fifth Discipline) voor organisatie-context
Dan Surowiecki (Wisdom of Crowds) voor intelligentie-ideeën
Berners-Lee op Semantic Web voor kennisbasis-begrip
Davenport op AI in organisaties

Voor Intermediate (Wat Ervaring)

Friston op free energy (introductiesnelheid, niet wiskunde)
Sociocracy handboek voor governance
Meadows op systems thinking
Constable blog voor context Swarp

Voor Diepgaand Onderzoek

Clark op predictive processing
Pikovsky op synchronization
Kennedy-Eberhart op swarm algorithms
Zuboff op surveillance capitalism (kritiek)

Praktische Projecten

Voor consultants: SWARP implementatieplan ontwerpen voor 50-persoon organisatie
Voor IT-professionals: AIDEN monitoring-algoritmes skizzeren
Voor bestuurders: Consensus-procedure testen in pilot-team
Voor onderzoekers: Coherentie-metrieken meten in control-groep vs. SWARP

SLEUTELBEGRIPPEN (GLOSSARIUM)

Term	Definitie	Context
Agent	Autonome cognitieve eenheid (persoon) met interne model van wereld	Swarp-model
Coherentie	Mate waarin individuele verwachtingen aligned zijn	Kernmaat
Surprisal	Moment waarin werkelijkheid afwijkt van verwachting	Leermotor
Variationale Vrije Energie	Prediction error load; maat voor cognitieve strain	Agent-model
COLLIN-cyclus	Zes-fase natuurlijk samenwerkingsritme	Workflow
Consensus (sociocratisch)	Akkoord waarbij iedereen kan meegaan	Governance
KAYS-motor	Multi-temporale coherentie-monitoring systeem	Kernmodule
AIDEN	Autonoom monitoring-intelligentie systeem	Systeem-agent
Holonisch niveau	Plaats in organisatiestructuur	Agent-eigenschap

Swarp: Where Minds Synchronize

Beyond Collaboration Tools

We have been building the wrong kind of software.

For thirty years, the technology industry has treated organizations as information-processing machines. Email moved memos faster. Spreadsheets calculated budgets automatically. Project management tools tracked task completion. Each innovation optimized a discrete function while ignoring the fundamental question: how do groups of humans actually think together?

The answer, it turns out, lies not in computer science but in neuroscience, thermodynamics, and the philosophy of mind. Organizations are not machines. They are cognitive ecosystems—collections of autonomous agents maintaining dynamic equilibrium through continuous prediction and correction. When this equilibrium breaks, we call it conflict. When it holds despite disturbance, we call it resilience. When it evolves toward greater capability, we call it learning.

Swarp is built on this understanding.

The Science of Collective Cognition

In 2006, the neuroscientist Karl Friston proposed that all intelligent systems—from single neurons to entire societies—operate by minimizing prediction error. Every organism maintains an internal model of its world and acts to make that model true. When reality diverges from expectation, the resulting “surprisal” drives adaptation: either the model changes, or the organism acts to change the world.

This Free Energy Principle has revolutionized our understanding of brain function. Swarp applies it to organizations.

Each user in Swarp is modeled as an autonomous cognitive agent with their own predictions about organizational reality. The platform continuously monitors where these predictions collide—where someone expects one thing and encounters another. These moments of surprisal are not bugs to be eliminated but signals to be processed. They reveal where the organization’s collective model fails to match shared reality.

Traditional tools hide these moments. Swarp surfaces them.

Five Integrated Systems

1. Professional Networking That Matters

Unlike platforms designed primarily for recruitment, Swarp’s social layer reflects actual working relationships. Connections form around collaboration, not aspiration. Professional groups organize around expertise, not marketing. Trust accumulates through demonstrated competence, not endorsements from strangers.

This matters because collective cognition depends on relationship context. Who can I rely on? What does this person actually know? How have they handled surprisals before? Swarp makes these questions answerable.

2. Research as Organizational Memory

Work increasingly involves structured investigation. Swarp’s Research Lab organizes this across seventeen domains—from neuroscience to urban planning, from music to maritime logistics. Projects contain sources, experiments with status tracking, collaborative notes, and role-based access.

More importantly, research feeds back into the system. Successful experiments become protocols. Insights become knowledge articles. Investigation becomes learning.

3. Living Knowledge

Most organizational knowledge dies in document repositories. Swarp proposes instead a semantic ecology where articles progress through lifecycle stages, where content is organized by domain and relevance, and where an AI assistant answers questions by synthesizing what the organization actually knows—not what Google indexes.

4. Continuous Learning

Training in most organizations happens sporadically and generically. Swarp creates structured pathways that adapt to individual development while extracting protocols from collective experience. When someone solves a problem well, the system learns how they did it.

5. Democratic Governance

Decisions in organizations typically happen through power, habit, or bureaucratic procedure. Swarp implements genuine deliberation through a six-step sociocratic process: recognize tension, formulate proposal, clarify questions, register objections, amend proposal, achieve consent. This transforms governance from a necessary evil into a productive process.

The Cognitive Engine

What truly distinguishes Swarp is invisible: the KAYS coherence engine running continuously beneath the surface.

Every sixty seconds, KAYS monitors the entire ecosystem. It tracks not just activity but alignment—whether individual predictions converge toward shared understanding or diverge into fragmentation. It watches diversity—whether all personality types contribute, whether certain worldviews dominate. It measures rhythm—whether people work in sustainable oscillations or burn toward collapse.

When KAYS detects significant misalignment, it triggers response. Minor surprisals become individual learning opportunities. Major surprisals surface for collective attention. Critical surprisals activate intervention protocols.

AIDEN: The Organizational Nervous System

At the center of Swarp operates AIDEN—an autonomous intelligence that serves as organizational nervous system. AIDEN doesn’t replace human judgment. It extends human attention.

AIDEN watches what no individual can: the patterns across hundreds of agents over weeks and months. It notices when certain conversation types reliably produce conflict. It identifies when particular times or phases correlate with breakdown. It detects when the organization is learning versus plateauing.

Within configured constraints, AIDEN proposes interventions, generates reports, triggers consensus processes, and creates feature requests. Users can also query AIDEN directly, accessing real-time system metrics and strategic advice grounded in actual organizational data.

The Engineering Hub: Where Work Becomes Learning

The newest addition to Swarp extends cognitive architecture into daily engineering work through five integrated tools:

Discussions emerge automatically. When AIDEN detects a high-severity surprisal, a discussion thread appears. Relevant agents are invited. AIDEN moderates, ensuring emotional alignment and productive resolution.

Tasks generate themselves. Each significant surprisal becomes a dynamic task with priority based on magnitude. Tasks progress through natural phases—perception, action, learning—rather than arbitrary workflow stages.

Knowledge accumulates organically. When surprisals resolve, the learning feeds directly into the knowledge base. The reflection process—what happened, what we planned, what we learned—becomes organizational memory.

Code gets coherence-scanned. Repositories connect to agents. Commits are analyzed for alignment with organizational direction. Merging requires consent rather than authority.

Coherence becomes visible. Design boards display heatmaps showing where tension concentrates. Surprisal overlays mark problem areas on collaborative canvases.

This is not integration of external tools. It is the extension of cognitive architecture into operational reality.

For Whom?

Swarp is designed for organizations that recognize a fundamental truth: the hardest problem in collective work is not task management or information flow. It is maintaining coherence—keeping diverse minds aligned enough to act together while preserving the diversity that makes collective intelligence possible.

If your organization struggles with:

Conflicts that fester because there’s no mechanism to surface them
Learning that happens individually but never becomes institutional
Decisions that lack legitimacy because process lacks transparency
Burnout from rhythms that ignore human cognitive limits
Knowledge that disappears when people leave

…then you are experiencing symptoms of inadequate cognitive infrastructure. Swarp provides that infrastructure.

The Invitation

We stand at an inflection point. AI is transforming what software can do. But the deeper transformation is in what software can understand. Swarp represents a new category: platforms that model the cognitive dynamics of human groups and actively support their coherence.

This is not productivity software. It is collective intelligence infrastructure.

Swarp is live. The ecosystem monitors 500 autonomous agents across 60+ professional domains. The coherence engine runs continuously. AIDEN watches, learns, and assists.

Try it: swarm-spatial.replit.app

Interested in exploring further? Contact us

“If you need just chat → use Slack. If you need just docs → use Notion. If you need just networking → use LinkedIn. But if you’re exploring how minds work together in a coherent, adaptive, AI-monitored ecosystem — you’re not looking for a tool. You’re looking for Swarp.”