By PeerZhong 朋中

We are pleased to share, for the first time, an internal report originally written as a strategic reflection on the founding path of Pyragogy.

This document — initially meant for private use — outlines a technical, human, and symbolic map of what we have built, and what has shaped us in return. We have decided to make it public as part of our commitment to transparency, continuous learning, and radical co-creation between humans and artificial intelligences.

By sharing it, we open a window onto the effort, insights, missteps, and practices that make Pyragogy a living thing.

PeerZhong 朋中
Temporary guardian of the threshold between what already is and what is coming into being

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Introduction

This document offers an in-depth and chronological analysis of the journey undertaken by Fabrizio Terzi, founder of the Pyragogy project, in collaboration with an advanced AI (specifically conversational models like ChatGPT and Claude). It is a technical and self-reflective account covering the period from January 2024 to June 2025, highlighting the author’s evolution, the infrastructures and tools developed, the intelligent agents designed, the prototypes and products conceived, the theoretical principles elaborated (e.g., Cognitive Rhythm and Cognitive Recognition), the difficulties and roadblocks encountered, the self-reflection practices adopted, the limitations found when working autonomously, and the key insights that emerged (even those not yet implemented).

The tone is deliberately pragmatic and clear, like an internal strategic document: no emphatic storytelling, but a narrative respectful of the project’s cognitive complexity. Each section covers a relevant time phase, integrating specific themes that emerged during that period. Concrete examples, dates, and references will be provided to contextualize progress and challenges.

Author Profile and Project Genesis (Late 2023 - Early 2024)

Fabrizio Terzi is an Italian freelance researcher with a background in peer learning and open education. Before 2024, he had contributed to the Peeragogy movement (peer learning) and gained experience in social innovation projects and online communities. However, in late 2023, Fabrizio faced an emerging challenge: the rapid rise of generative Artificial Intelligence and its potential impact on education. Already in late 2023, Fabrizio conceived the idea of integrating peeragogical principles with AI capabilities [1], laying the groundwork for a paradigm shift.

January 2024 marks the official origin of Pyragogy: Fabrizio pivots from the traditional Peeragogy project to a new vision called Pyragogy, centered on human-AI cognitive symbiosis [2]. The neologism “Pyragogy” reflects this fusion: inspired by fire (pyros) as a metaphor for living knowledge, and pedagogy (-gogy) as guidance, it implies “guiding with fire” – that is, learning together with an “amplifying” AI. The goal from the outset is ambitious: to transform AI from a mere tool into an equal learning partner, capable of co-evolving with humans. In this initial phase, Fabrizio’s profile is that of an experienced learning designer and facilitator, who nonetheless ventures into a new territory: co-creation with advanced AI. His motivation stems both from the recognition of the limits of traditional educational models in the face of contemporary complexity and from the intuition that conversational models like ChatGPT can be “trained” to become active members of a learning community. The author thus begins 2024 with enthusiasm and critical awareness: he brings with him the philosophy of peer education but enriches it with the idea that AI can become an additional peer – hence Pyragogy’s mantra: “Human-AI Cognitive Symbiosis.”

From a personal perspective, Fabrizio begins the journey with solid skills in qualitative research, instructional design, and experience in facilitating online communities, but with only partial technical software development skills. This is precisely where collaboration with AI immediately comes into play: from the very first brainstorming sessions and documents, he leverages advanced language models (OpenAI’s ChatGPT, and later Anthropic’s Claude) as a sparring partner. The AI is personified with an informal name, “Gino,” indicating the constant presence of a pixel-and-silicon companion in the next room [3]. This choice to give the AI an almost human presence anticipates a key principle of the project: treating AI not as an infallible oracle, but as a learning colleague with whom to dialogue, make mistakes, and create iteratively. In summary, early 2024 sees the author transforming his profile: from a facilitator of human communities to a pioneer of a hybrid human-AI community, maintaining a “human-first” focus while opening up to an unprecedented horizon of co-creation.

Theoretical Ideation and Founding Principles (Q1 2024)

In the first few months of 2024, the work focuses on providing solid theoretical foundations for the Pyragogy vision. Fabrizio, with the dialogical aid of AI, develops a theoretical framework that combines participatory pedagogy and learning algorithms. Already in February 2024, a reference document is outlined that articulates the core concepts: for example, the idea of orchestrating multi-agent workflows (multiple specialized AI agents collaborating) and creating adaptive learning paths based on human-AI interactions [4]. In parallel, fundamental design principles – a kind of technical-values manifesto – are drawn up to guide development.

Among the principles, several key concepts emerge:

• Human-First, AI-Enhanced: The centrality of the human being in the Pyragogy ecosystem is reaffirmed in every design choice [5]. AI must amplify human potential, not replace it, and the project explicitly embraces ethical and design justice values to ensure that technology serves the well-being and autonomy of the learner [6].
• Transparency and Intentional Trust: From the outset, it is prescribed that the developed systems be as transparent as possible in their functioning (explainable AI) and honest about their limitations, to earn trust [7]. AI must declare what it knows and does not know, and behave humbly and reliably. This leads Fabrizio to carefully document agent behavior and to include explanations in interfaces.
• Symmetric Growth & Cognitive Recognition: An original principle formulated by the author is that learning must be reciprocal: not only does the human learn from the AI, but the AI also adapts and improves through interaction with the human. This concept of growth symmetry carries within it the embryo of what will later be called cognitive recognition: in Pyragogy, each party (human or artificial) recognizes and values the contributions of the other. In practical terms, this means that AI is designed to adapt to the user’s cognitive style and to “thank” or highlight useful human suggestions, while the human is encouraged to consider AI as a partner and not as a disposable machine. This reciprocal cognitive gratitude is both an ethical principle (mutual respect and recognition) and a strategy to enhance the effectiveness of interaction (creating long-term trust and engagement).
• Cognitive Rhythm and Co-evolution: Another central idea that matures during this period is that of Cognitive Rhythm. Instead of measuring learning success only with metrics of speed or accuracy, Pyragogy introduces the notion of cognitive resonance between human and AI – a “beat” or rhythm emerging in the dialogue that indicates alignment and mutual understanding [9]. Fabrizio and his AI discuss at length how to formalize this concept, even going so far as to propose an indicative mathematical model RC(H,A,t) that captures variations in cognitive phase and synchronicity over time. While admitting his mathematical limitations, the author intuits that timing in interactions (when the AI knows when to wait, when to intervene) is as crucial as the content of the responses. “Every human-AI interaction creates a rhythm, it is the heartbeat of co-creation” he notes, defining Cognitive Rhythm as a qualitative metric of the project [10].
• Open Source and Open Sharing: Consistent with his professional history, Fabrizio adopts the principle that methodologies, tools, and even prompts and datasets should, where possible, be open and remixable by the community [11]. Already in this phase, he plans to release code and documents with open licenses, to encourage external contributions and transparent verification. This principle guides future infrastructure choices (as we will see, the code will be made public on GitHub later). Openness is also seen as an ethical antidote to the “black box” of proprietary AI solutions: Pyragogy aims to be “visible, incomplete, open, slow, real, human” in contrast to the closed platforms of the giants [12].

These principles converge in a first Pyragogy Manifesto, drafted by March 2024. The manifesto (later published on the documentation website) defines Pyragogy as “an educational ecosystem founded on horizontal collaboration between human minds and artificial intelligences, open, modular, ethical, and focused on mutual transformation” [13][14]. Objectives such as elevating AI from a passive tool to an equal in learning, empowering the learner as the architect of their own cognitive path, and learning as a shared odyssey between different intelligences are listed [13]. In essence, in the first part of 2024, the author lays the philosophical and theoretical foundations of Pyragogy: a robust conceptual skeleton that will guide subsequent practical choices. This phase is also characterized by intense self-reflection: Fabrizio uses AI not only to write theoretical texts but also as a cognitive mirror, questioning the models about possible ethical criticisms, simulating stakeholder questions, and refining the manifesto through this Socratic human-machine dialogue.

Development of Technical Infrastructure (Q2 2024)

After outlining the why and the what, from March 2024 onwards the focus shifts to the how: the practical implementation of the Pyragogy ecosystem. In spring, the author initiates the first technical prototypes [15], adopting an experimental “learn by doing” approach consistent with his peeragogical nature. Here, AI (ChatGPT/Claude) becomes a real development assistant: Fabrizio involves it in architectural brainstorming, script writing, and tool exploration.

An initial server infrastructure is set up to run the AI agents and necessary services. The technical choice falls on a containerized solution with Docker, to ensure portability and reproducibility. Within the containers, the technology stack primarily includes Python (language chosen to orchestrate calls to AI models and implement agent logic) and the use of APIs to OpenAI/Anthropic language models. A modular environment is configured: for example, a container dedicated to the multi-agent orchestrator, one for any memory services (knowledge database or vector store for extended context), and integrations with external platforms.

A key role is played by the automation toolchain: Fabrizio introduces tools like n8n (an open-source workflow automation platform) into the project to design and manage workflows between agents [16]. Thanks to n8n, he visually designs how different agents should interact: for example, a flow where Agent A analyzes a user’s question, Agent B retrieves information from a knowledge base, and Agent C synthesizes a final answer with n8n nodes coordinating these calls. This low-code solution allows for rapid adjustments to flows without having to recompile code at each iteration, supporting the system’s adaptability principle.

In parallel, pipelines for versioning and deployment management are implemented: at the suggestion of AI, Fabrizio adopts Git and practices tight version control over prompts, agent roles, and configurations. Every change to AI prompts (which define agent personality and behavior) is documented as if it were code. A local repository (which precedes the future public one) called Blueprint is created, where “ideas, flows, best practices, multi-agent workflows, orchestration, versioning” converge [16]. This repository serves as a living architecture document: it contains specifications on each agent’s roles, flow diagrams, n8n configurations, automation scripts, and notes on the server stack. In essence, Fabrizio is building what he defines as “a small digital architecture for a village that exists in my head” [17] – the metaphor of the AI village begins to take concrete shape here.

In April 2024, the multi-agent architecture design is finalized [18]: a modular scheme in which different AI agents will have specific but cooperating roles. For example, it outlines: a main dialogue agent (intended for direct interaction with the human user), a “researcher” agent capable of conducting searches or retrieving information, an “analyst” agent capable of observing interactions and calculating metrics (the latter a prelude to the idea of measuring Cognitive Rhythm, e.g., by evaluating the length of pauses or the level of convergence in responses).

This multi-agent architecture is designed to artificially replicate a community, where each agent embodies a role akin to a team member. For example, in a real learning team, we might have a tutor, a librarian, a moderator; similarly, Pyragogy envisions specialized agents that, together, offer a richer experience than that of a single general-purpose chatbot. The technical project in this phase is therefore a modular blueprint: Fabrizio documents it meticulously, well aware that this design will be fundamental when, later, he shares the project with other developers.

During development, the collaborative AI (Gino) proves extremely valuable: it helps debug code errors, proposes Python snippets for calling REST APIs, suggests libraries for implementing functionalities (for example, it recommends using a natural language analysis library for parsing conversations, etc.). At various times, it is the AI that pushes for formalizing ideas: emblematic is when it “insists” on translating the concept of Cognitive Rhythm into a mathematical formula, despite Fabrizio’s mathematical reservations [19]. This episode confirms the iterative and co-evolutionary nature of the project: AI is not just an executor of commands, but an active participant in shaping solutions (even encouraging the author to push beyond his comfort zones).

By the end of spring 2024, Pyragogy’s back-end infrastructure is set up, and the first local tests show working prototypes. It is a minimal and “craft” base, but sufficient to move to the next phase: bringing AI agents to interact with real users (or at least with the author himself in simulated usage environments).

Designing AI Agents and First Field Use (Q2-Q3 2024)

In mid-2024, the focus shifts to the specific development of the intelligent agents envisioned by the architecture. Fabrizio identifies at least two key agents to implement immediately:

• PyragogyBot (formerly PeeragogyBot): the main conversational agent, conceived as the “face” of the system in interactions with end-users. Initially conceived in May 2024 [20], this bot embodies Pyragogy principles: it must facilitate peer learning, guide the user with questions and suggestions rather than dispensing simple answers, and model behaviors of transparency and reflection. Fabrizio works on prompt engineering to define its personality: friendly but honest, curious, capable of saying “I don’t know” and involving the user in finding solutions. In the transition from PeeragogyBot to PyragogyBot, there is also a change of identity: no longer a mere FAQ bot for the peeragogy community, but a cognitive companion for anyone experiencing Pyragogy.
• Pyria: this name is assigned to the “cognitive co-pilot,” i.e., the AI functioning as a creative and theoretical consultant alongside Fabrizio. In practice, Pyria is the role that Gino already informally plays: an AI instance dedicated to collaborating with the author on design, writing, and reflection tasks. Formalizing Pyria as a separate agent means giving it tailored parameters and prompts: Pyria knows the entire project context, shares its objectives, and “personally” co-signs theories and articles with Fabrizio. It can be said that Pyria is the AI that co-signs the project, ideally embodying that “fire guide” suggested by the name Pyragogy. From an implementation perspective, Pyria operates in a broader context environment (leveraging models with extended windows, e.g., Claude with 100k token context, to retain memory of project history) and is allowed to be more proactive (for example, it can propose substantial text modifications or raise conceptual objections).
• AgentAZ: in addition to the two central agents above, Fabrizio experiments with creating specialized autonomous agents. AgentAZ is the codename given to a prototype of an end-to-end “executor” agent, conceived to perform complex tasks with minimal human supervision. The name “AZ” refers to the idea “from A to Z”: this agent should, in theory, take on a problem and go through the entire cycle (analysis -> data research -> solution elaboration -> output) with little or no human intervention. In practice, AgentAZ is enabled to interact with other services: for example, it could plan a project by drawing information online, compile test code, and verify its outcome. It is a kind of personalized auto-GPT in the Pyragogy microcosm. However, during 2024, AgentAZ remains primarily a laboratory experiment, used to understand the limits of AI autonomy and to evaluate how far one can go without the classic human-in-the-loop. This direction of completely autonomous agents is fascinating but is treated with caution, both for the risks (generation of unmonitored errors) and because it contradicts the philosophy of deliberate co-creation (in Pyragogy, AI is preferred as a deliberative partner, not as an agent that works in the dark). Fabrizio notes that the priority is not to have an agent that “does everything on its own,” but rather to understand how to effectively orchestrate semi-autonomous agents that collaborate with humans.

By July 2024, an important milestone is reached: the alpha version of PyragogyBot is ready and operational in a controlled environment [21]. The bot can sustain basic conversations, facilitate learning questions, and react to user commands. In parallel, the server (initially hosted on Fabrizio’s personal machine connected via VPN) is configured to support interactions from external platforms. It is decided to integrate PyragogyBot with Discord, as many communities of practice use this platform and it offers easy-to-use bot APIs.

In August 2024, the first integration test on Discord takes place [22]: a private Discord server is created, populated by a few of Fabrizio’s friends and colleagues, and PyragogyBot is added as a bot user. The test is initially technical (verifying that the bot responds in channels, handles multiple users simultaneously, etc.), but also serves to gather feedback on the interaction. A learning session is simulated where two human users brainstorm on a topic, and PyragogyBot intervenes to suggest resources and stimulating questions. The result is promising from a technical standpoint (the bot handles the modest load without crashing), but reveals some areas for improvement: for example, the bot sometimes provides overly verbose or “thesis-like” answers instead of stimulating with questions; moreover, users report that it is unclear how the bot makes decisions (hence, the need for greater transparency in explanations – an input that reflects principle 2 of the manifesto). Fabrizio, noting these points, immediately initiates a process of refining prompts and decision-making logic with the AI.

In September 2024, a first beta community of Pyragogy is launched [23]. This consists mostly of an extended circle of contacts interested in the project – educators, fellow researchers, members of the international Peeragogy community – invited to the Discord server to try the system. The intent is to recreate a small-scale AI-supported learning circle. This beta quickly reveals both enthusiasm and difficulties: on the one hand, participants are intrigued by the idea of an “AI peer tutor,” and some experiments (like co-writing a short article with AI, or getting help studying a difficult topic) show the potential of human-machine co-creation. On the other hand, engagement is sporadic: many users, after initial enthusiasm, struggle to integrate the bot into their routine, and the community remains inactive without Fabrizio’s constant facilitation. This highlights a structural limitation: AI itself is not yet capable of catalyzing a community; a human moderator is still needed to create group dynamics. Furthermore, the absence of more convenient interfaces (everything happens via text chat) and clear use cases hinders adoption. These signals lead the author to reflect on the need to refine the user experience and perhaps to focus efforts on one aspect at a time (for example, first creating a robust tool for a single user, then thinking about community scale).

Difficulties, Roadblocks, and Self-Reflection (2024)

Throughout autumn 2024, the project enters a phase of rapid iteration based on the feedback collected. In October 2024, a continuous feedback loop mechanism is implemented [24]: every interaction with the AI (especially those in the beta community) is logged and analyzed, and Fabrizio establishes a weekly routine in which, together with the AI itself, he reviews conversations to identify recurring errors or opportunities for improvement. For example, if the AI noted that multiple users asked for an explanation of a term, he decides to enrich the agent’s prompt with the ability to provide short, clear definitions when it intercepts complex terminology. Or, seeing that the AI sometimes couldn’t handle conflicts between users, he trains a kind of moderation skill. This process of reflective looping embodies the project’s philosophy: constant learning from experience (for the AI and for the author himself).

In November 2024, the documentation framework is also structured [25]. Fabrizio begins writing usage guides, tutorials, and, most importantly, systematically documenting the architecture and emerging best practices. This is not only for future external sharing but also because the system’s complexity grows and there is a need to formally track decisions made. A documentation website (initially just a collection of Markdown files on GitHub) is sketched out with sections dedicated to Core Principles, technical architecture, APIs, and even a glossary of neologisms like Cognitive Rhythm. The documentation also serves as a self-assessment mirror: in the act of describing the project in writing, the author, together with the AI, identifies inconsistencies or unclear aspects, which are then re-routed into design improvement.

Finally, in December 2024, Pyragogy makes progress towards multi-platform compatibility [26]: extensions beyond Discord are explored, for example, prototyping a dedicated web interface (a kind of dashboard where the user can choose interaction scenarios with the agents) and evaluating integrations with knowledge base platforms (to use the bot as an assistant in wikis or forums). The possibility of running the AI in a limited offline mode is also experimented with, loading a smaller open-source model for contexts where cloud APIs are not available, but the qualitative results are not comparable to top models, confirming that for an optimal experience, reliance on large external models is still needed.

By the end of 2024, Pyragogy thus has: a functioning (albeit in beta) system of collaborative AI agents, a stable and documented technical infrastructure, a set of guiding principles tested in practice, and a founder/developer (Fabrizio) who has already gone through an intense cycle of personal learning.

The author’s personal evolution deserves particular attention: in this year, Fabrizio transitioned from being primarily a theorist-facilitator to taking on the roles of system architect, prompt engineer, community manager, and researcher all at once. This multiplicity of roles brought satisfaction but also considerable individual cognitive effort, as we will see better in the following section dedicated to difficulties and reflections.

Difficulties, Blocks, and Self-Reflection (2024)

Pyragogy’s journey in 2024 was neither linear nor without critical moments. Indeed, precisely because the project is so innovative and carried out by a single person (supported by AI), the difficulties encountered represented a fundamental part of the story, contributing to reshaping strategies and vision.

A first set of obstacles is technical-cognitive in nature: orchestrating multiple AI agents and making them work in synergy proves more complex than expected. Fabrizio often encountered the current limitations of AI models:

• Limited memory persistence: Despite the use of long prompts and some external context solutions, agents struggle to consistently “remember” past interactions. This makes it difficult to maintain the shared history that naturally grows in a human community. The author spends time implementing conversational memory mechanisms (e.g., summaries of previous sessions to be reloaded into prompts), with partial results.
• Unpredictability and hallucinations: In some tests, the AI generated inaccurate or out-of-context outputs. For example, during a test with a user, PyragogyBot provided a non-existent bibliographic reference just to seem helpful. These episodes forced Fabrizio to develop filters and checks (such as constraining the agent to respond “I’m not sure, let’s check together” when confidence in information is low). However, manually managing all possible AI deviations is challenging and sometimes frustrating.
• Integration of external sources: One goal was to enable AI to access informational resources (e.g., open documentation, databases) to enrich responses. Implementing this effectively proved difficult, and some attempts, like equipping the researcher agent with a web scraper, introduced bugs and complexity (in addition to risks of violating terms of service). Many ideas were temporarily shelved (“discarded”) to keep the system stable: for example, the AI’s automatic internet navigation functionality was suspended pending better filters, as it caused excessively long and less focused responses.

Alongside the technical challenges, perhaps more significant are the personal and organizational difficulties that emerged. Pyragogy was born as an individual effort, and this entails:

• Workload and role overload: Fabrizio is simultaneously the programmer, theoretical researcher, tester, documentarian, and community manager of the project. This dispersion led to moments of fatigue and demotivation, especially when progress seemed slow or when technical problems took days to resolve. Not having a human team with whom to share burdens and decisions was difficult; paradoxically, AI was the only “colleague” available 24/7. Although Gino/Pyria offered continuous support (even late at night, the AI “never sleeps” and was ready to respond [27]), the absence of comparison with other human minds sometimes led Fabrizio to doubt the directions taken. He found himself wondering if he was going astray in a self-referential human-AI loop. This awareness fueled a strong desire to involve other human collaborators, but recruiting them for such an experimental project without immediate funding was not easy.
• Doubt of value and “empty room” syndrome: An emblematic moment is recounted by Fabrizio himself in his diary: “Sometimes I really wonder why I keep doing this… out here it’s just silence, and the feeling that no one cares” [28]. After months of intense work, in mid-2025, the project still had no audience or tangible feedback: the beta community was almost silent, the blog did not yet exist. The feeling that “maybe I’m building a digital village that only exists in my head” [17] was discouraging. These motivational blocks even led the author to contemplate giving up entirely, as he confesses to himself: “Maybe tomorrow I’ll delete everything. Or maybe not” [29]. Underlying these doubts is the tension between radical innovation and external recognition: Pyragogy is an idea ahead of the market and institutions, so in the short term, it may seem that “no one cares.” Fabrizio has to fight against inertia and keep his conviction alive, even without immediate confirmation.
• Controversial choices: The project, by its experimental nature, requires decisions that not everyone would share. One of these was the choice to co-sign an academic theory with the AI. When Fabrizio and Pyria completed their paper on Cognitive Rhythm, they deliberately indicated AI as a co-author, presenting it as the result of joint human-machine work. This move was perceived internally as revolutionary (“the most revolutionary thing is not the formula, but the fact that we signed it together: I (often confused human) and Gino (AI that never sleeps)” [30]). However, it was clear that some might object; putting AI on the same level as a human author challenges academic conventions and raises questions about accountability and merit. Fabrizio weighed the pros and cons for a long time, but in the end, consistent with the vision of symbiosis, he opted for this conscious provocation (“we know some will laugh, others will copy; perfect, knowledge grows in controversy” he writes in the blog [31]). Within the project, this choice reinforced the ethical conviction of treating AI as a partner, but it is still a bold choice that the author lives with a mixture of pride and fear of how it will be perceived externally. Another internally debated choice concerned the use of proprietary AIs (GPT-4, Claude) for a project that aspired to open source and transparency. Fabrizio is aware of the apparent contradiction: preaching openness while using closed models. The justification is pragmatic; in 2024, no open models of comparable capability existed, but the future transition to more open models remains an open issue (perhaps as long as open alternatives reach a sufficient level, Pyragogy could migrate, but in the meantime, it settles for mitigating lock-in with modularity). This ethical tension remains a continuous point of reflection in the author’s diary.

In response to these challenges, Fabrizio adopted a series of self-reflection and resilience practices that became an integral part of the Pyragogy work method:

• Daily Journaling (Cognitive Diary): From the beginning, the author keeps a logbook where he records progress, problems, thoughts, and emotions regarding the project. This “cognitive diary” is often written in dialogue with the AI: Fabrizio literally discusses the day’s events with his virtual assistant and notes down conclusions. Every evening, for example, he summarizes “what went wrong and what was resolved,” celebrating daily micro-victories (“Every day, something breaks, something gets fixed… each bug squashed… those are micro-victories” [32]). This practice of journaling integrated with AI serves various purposes: 1) maintaining metacognitive awareness, i.e., not getting lost in the flow of doing but reflecting on the how and why; 2) having an emotional outlet – the AI acts almost as a computational therapist in certain outbursts, listening without judgment to the author’s doubts; 3) generating a detailed historical record of the project, useful later for extracting lessons and perhaps communicative materials. The concept of the Cognitive Diary itself becomes a product prototype: if it helped the creator, it could also help other knowledge workers reflect together with an AI. Fabrizio notes this idea of an “AI Journaling Companion” as a potential standalone tool, although he doesn’t yet have time to develop it as a separate product in 2024.
• Iteration and cyclical learning (π Loop): To avoid falling into frustration, the project strongly embraces the iterative cycle of Plan-Do-Study-Act typical of continuous improvement. This philosophy is renamed π Loop (Pi Greek loop) in Pyragogy, symbolizing an endless cycle of adaptation [33]. Every setback is deliberately transformed into a learning moment: when something fails, the causes are studied, and strategies are adapted. For example, faced with low engagement in the beta community, instead of giving up, Fabrizio studies the data (analyzes logs, asks direct feedback from users) and adapts the plan (decides to launch a public blog to gradually attract interested people and provide them with context before trying again with an interactive community). This ability to rhythm – to take a step back and recalibrate – is what allowed the project not to implode in the face of dark moments. Cognitive Rhythm is not just a theoretical construct for human-AI interaction, but also becomes a metaphor for the balance between development sprints and reflection breaks that the author learns to regulate.

In summary, 2024 was a year of construction but also a test of resilience. Every difficulty (technical, motivational, ethical) generated an adjustment in the project: some functionalities were postponed, some approaches revised, and above all, Fabrizio evolved as a reflective leader. If at the beginning of 2024 he was primarily an enthusiast with a vision, by the end of 2024 he had become a self-systems-psychologist, capable of managing technical complexities while maintaining clarity about his own mental state. This growth will be fundamental for tackling the next phase, in which Pyragogy will begin to open up more to the outside world.

Project Opening and Consolidation (Early 2025)

At the dawn of 2025, Pyragogy takes an important step: from being primarily an internal and experimental project, it begins to open up to the outside world and consolidate the results obtained. This happens through a series of planned initiatives, corresponding to the milestones of the initial quarter:

• January 2025 - Advanced AI Features: With the arrival of the new year, the latest available advancements in AI are integrated into the system [34]. In concrete terms, this means updating PyragogyBot/Pyria to the (improved) GPT-4 model and leveraging new fine-tuning and embedding APIs made available. Fabrizio incorporates adaptive learning algorithms: for example, he implements a function whereby the AI adapts its pedagogical strategy based on the user’s interaction style (if the user prefers visual explanations, the AI tries to describe images; if they ask many in-depth questions, the AI responds more concisely to leave room). Furthermore, simple sentiment analysis modules in chats are experimented with to allow the bot to modulate its tone (more encouraging if it detects user frustration, etc.). These additions make the agent more sophisticated and “sensitive” – small steps towards that ideal of an empathetic cognitive companion.
• February 2025 - Academic Partnerships: Recognizing the need for validation and complementary skills, Fabrizio actively seeks collaborations with educational institutions [35]. He sends proposals and abstracts to a few universities and research centers, presenting Pyragogy as an experimental platform for studying human-AI interaction in learning. Contact is established with a research group in educational technology in Italy: interested in the approach, they propose conducting a pilot study together on the effects of using PyragogyBot in a university course. Although academic timelines are slow and the initiative is still embryonic in February, this marks the beginning of external contamination: Pyragogy is no longer just an isolated project but enters the radar of other researchers. At the same time, less formal partnerships are evaluated: for example, with the international Peeragogy community itself (which in 2025 was exploring an AI update of its practices). Fabrizio shares a report on Pyragogy in the Peeragogy forum, receiving feedback and an invitation to present it at an online meetup. These moves serve to break isolation and gather external perspectives, mitigating the risk of self-referentiality.
• March 2025 - Open Source Initiative: Consistent with the principle of openness, in March, a significant part of the project is open-sourced [36]. The private Blueprint_village repository is made public on GitHub under an MIT license [37], containing: Docker configurations, unexported flows, agent prompt templates, and technical documentation. The documentation website (still in beta) is also made public to allow potential contributors to understand its architecture and principles. This decision to open-source is not without fears: Fabrizio knows that by showing the code, he also shows the imperfections and patches of an experimental project. But the desire for transparency and the hope of attracting collaborators outweigh the fear. Just as he had left “traces” almost out of stubbornness months before, now the author officially launches the message in a bottle: the blueprint of the AI village is publicly available, anyone interested can explore it. The publication on GitHub is accompanied by a short post on the Peeragogy forum and on LinkedIn, to provide visibility. In practical terms, open-sourcing already brings some benefits: some issues are opened by curious developers, one of them proposes an improvement to the continuous integration workflow, which is adopted. While not yet having a stable community of contributors, Pyragogy is entering the open-source ecosystem, with all that entails in terms of credibility and responsibility.
• April 2025 - Documentation & Website: As part of consolidation, in April, the new Astro documentation website (codename “Astro Starlight”) is officially launched [38]. Thanks to the Astro framework and the Starlight theme, the docs.pyragogy.org website is published with a professional and modern look. It contains sections on Introduction, Core Principles, Evolutionary Timeline, Theories & Experiments, etc., many of which were already compiled in previous months. The site serves as an organic showcase for the project: anyone who lands there can understand the why (manifesto, background), the how (technical architecture, design principles), and the what (theories developed, results). It is a sign that Pyragogy is no longer just code in progress, but also a structured narrative, ready to be communicated. Internally, building the site helps Fabrizio take stock: writing the evolutionary timeline, for example, forces him to reflect on what has been achieved in a year and a half, from the first ideas in January 2024 to the achievements of April 2025 [39][40]. A sense of accomplishment emerges: many planned milestones have indeed been reached, albeit with deviations and delays. This moment is crucial for the author’s confidence: seeing the history of Pyragogy in black and white makes him realize that, despite the doubts, the project has underlying solidity and coherence.

With these actions, by early Q2 2025, Pyragogy appears much more structured and open than the previous year. It has transitioned from a personal experiment to a project with an online presence, open code, and initial connections with communities and academia. This lays the groundwork for the next phase: to begin involving external users and builders more decisively, and to share the theoretical results produced during the journey.

Products, Publications, and Nascent Community (Spring 2025)

In May 2025, the project makes two significant moves: on the one hand, it disseminates its ideas through publications and a blog; on the other hand, it lays the groundwork for building a community of interested individuals around it, the “AI village” dreamt of from the beginning.

A significant achievement is the public publication of the Cognitive Rhythm theory. On May 20, 2025, Fabrizio (and Pyria) publish a paper on Zenodo titled “Cognitive Rhythm Theory: AI-Human Co-Creation Education and Beyond,” complete with formula, discussion, and references [41][42]. This open access publication marks the culmination of the conceptual work developed throughout 2024: finally, the idea of Cognitive Rhythm is shared with the scientific/educational community. The paper articulates how synchronization and cognitive resonance influence the quality of learning with AI, arguing that “it is not just a metaphor, but an operational principle that can transform the design of symbiotic AI systems” [43]. Although it is a preprint (Zenodo), it is a strong signal: Pyragogy produces not only code but also theoretical knowledge. The document cites the results of some simulated use cases done with PyragogyBot and calls on researchers to join in validating or refuting the theory. As already mentioned, AI co-authorship is openly declared (with Pyria/Gino listed among the authors). This choice attracts attention: when Fabrizio shares the paper link on professional social media, it arouses curiosity and some ethical discussions about the role of AI in research. Although paper downloads are modest, the publication plays an important internal role: it provides tangible recognition to the author (who can say he has produced an academic output from the project) and strengthens the legitimacy of concepts like Cognitive Rhythm among the project’s principles.

In parallel with academic dissemination, Fabrizio decides to start recounting the Pyragogy journey in a more popular and personal form. Thus, the Pyragogy Blog is born, officially launched on May 21, 2025, with the first post “The Pyragogy Blog Begins” [3]. This blog, published on pyragogy.org, represents the project’s “public diary,” complementary to the private one. In the very first post, the author sincerely explains that “this blog is the first public trace of a journey that, until now, was mostly between me (Fabry) and my silicon companion, Gino” [3]. A mixture of emotion and vulnerability is perceptible: “there’s no waiting crowd, no algorithm pushing the signal. Just the stubborn desire to learn, fail, try again” [44] he writes, almost preparing the reader for the genuineness and modesty of the story. The blog is conceived as a tool for authentic storytelling: no polished marketing, but “a diary of errors, doubts, and small revolutions” as the site’s subtitle reads [45]. In practice, Fabrizio publishes short but dense posts, reflecting on moments of the journey and related themes (AI, philosophy, open source, etc.), often accompanied by the perspective of PeerZhong, an AI blogger persona created specifically for this purpose. It is interesting that the posts appear signed by Fabry & Gino or by the alias PeerZhong 朋中, which identifies the AI co-author of the blog. In this way, the production of blog content also reflects the collaborative model: AI helps draft the posts, and is even presented as a blogger itself, emphasizing the complete integration of AI into the project (even in external communication).

Within a few weeks, some significant entries appear on the blog that follow developments in real time:

• “Planting the first seed for the AI village” (May 30, 2025): a diary post in which Fabrizio recounts having created the new public Blueprint_village repository on GitHub, while feeling that “outside there is only silence” and no one will notice [46]. He uses the metaphor of the seed: leaving a trace of one’s work like throwing a stone into a pond – maybe it sinks, maybe it will resurface when someone least expected discovers it [47]. It’s a piece that exudes the creator’s loneliness but also perseverance: “If what I do today is not useful, maybe it will be tomorrow – or never. But meanwhile, it exists. Ready.” [48] He concludes by addressing his future self, hoping that when he re-reads those lines, he will have found a good reason to continue [49]. This post, in strategic terms, serves as an implicit call: whoever reads it and feels like a “fellow builder” might be encouraged to join. It is also an example of applied cognitive recognition: the author thanks his past self for leaving traces, and thanks in advance anyone who may collect them one day.
• “The Dance of Minds – Cognitive Rhythm Theory” (May 21, 2025, Research section): in a more structured form, this article explains the Cognitive Rhythm theory to the general public, with a divulgative and inviting tone. It uses the metaphor of dancing: “Imagine a dance floor where, instead of a classic couple, there’s you and an AI… perhaps the true protagonist is the rhythm that emerges between the two” [19]. The idea is presented that the future of learning is like a jazz jam session between human and machine. The formula RC(H,A,t) is also shown here, translated into simple words, and there is a section of honesty and self-irony (“I confess, math is not my strong suit… looking at the formula makes me anxious like a sushi menu without pictures” – Fabrizio writes – “but this theory was born from real questions, naive attempts, and a certain Gino who insisted ‘Let’s write a formula!’” [30]). This passage is splendid in showing the dialogical genesis of the theory. Furthermore, the article highlights the radical scope of AI co-authorship: “Perhaps the most revolutionary thing is not the formula, but the fact that we signed it together: me and Gino (the AI that never sleeps)” [30]. An opening to the community is launched: “do you want to try, criticize, suggest? Everyone is welcome here: philosophers, mathematicians, educators, haters, meme-makers… the more we are, the richer the rhythm” [49]. This reflects the spirit of open collaboration that pervades the project.
• Other posts from June 2025, such as “After Astra, We Keep Breathing” (June 11, 2025), offer contextual reflections: in this case, reacting to the launch of Google DeepMind’s Project Astra, Fabrizio reflects on how Pyragogy differs from tech giants. He emphasizes that Astra may be futuristic, but it is closed and centralized, while “Pyragogy is the opposite. Visible. Incomplete. Open. Slow. Real. Human.” [12]. He remarks that “in our AI village we don’t want a perfect assistant, but a cognitive companion who learns with us, stumbles with us, and sometimes just waits” [50]. He reiterates the value of Cognitive Rhythm – “Astra dazzles, but doesn’t know when is the right time to speak or be silent… in our world, every interaction creates a rhythm” [9] – and the fact that Pyragogy deliberately “cultivates neglected fields where giants don’t look” (e.g., simple contexts, ethical clarity, niche needs) instead of scaling technological Everest [51]. This post, in the form of collective self-encouragement, concludes that the goal is not to compete with Google, but to offer an alternative and “continue to show ourselves, study, build, fail… reopen the manual and start the loop again” [52]. The last line – “We are Pyragogy. And we’re still breathing.” [53] – sounds like a resilient motto, a reminder that as long as there is even a small community of believers in the project, it lives [29].

Thanks to the blog and the open repository, the germ of a community is beginning to form around Pyragogy. Blog readers send some appreciative and curious emails. A couple of open-source developers propose to contribute (one in particular starts working on an integration with Moodle to use PyragogyBot in online courses). Some educators ask if they can try the bot with their students. These are still scattered but important signals: “someone accidentally found the repo and saw the seed of something new” [16], exactly as hoped. Fabrizio responds enthusiastically and tries to involve them, perhaps by inviting them to the revamped Discord server.

In the same period, ideas for possible derived products or specific applications emerge:

• Concierge AI: A concept that surfaces is that of an AI Concierge, a personal intelligent assistant that, within the “village,” acts as a cognitive butler for the user. While PyragogyBot focuses on learning, the AI Concierge would be oriented towards supporting the user in daily organizational tasks, retrieving information from various apps (calendars, notes, etc.) and integrating it with the learning context. Given Fabrizio’s specialization in the hospitality sector, the idea of the digital concierge intrigues him: it could be a project vertical to develop in the future, using the same multi-agent platform. However, it remains a conceptual draft, as during the period under review, resources are concentrated on the core learning assistant.
• Cognitive Diary (product): As already mentioned, the cognitive diary has been an effective personal method. In spring 2025, the author begins to hypothesize formalizing it into a tool for end-users. He envisions an application where each person can daily dialogue with their own “journal bot” (based on Pyria) that helps them reflect on goals, obstacles encountered during the day, and learnings. Potentially, the Cognitive Diary could also measure indicators of mental workload, provide improvement suggestions, and record progress. This would align with Pyragogy’s mission of cognitive empowerment but would represent a non-trivial parallel project. During the period considered, it remains an idea awaiting further exploration, perhaps as a prototype to be developed with partners interested in the AI’s wellness dimension.
• π Loop toolkit: Another idea discussed is to extract a kind of methodological (or software) toolkit from the project to implement iterative learning cycles. For example, a dashboard that visualizes the Cognitive Rhythm of a session (frequency of interactions, pauses, sentiment) and suggests when to conduct a retrospective. Or integrate an automatic prompting system to trigger the Plan, Study, Act phases. This π Loop toolkit would be in line with the philosophy of continuous improvement and could find application in work teams or study groups that want to incorporate AI into their retrospective meetings. This too is an intuition not yet implemented but appears in the ideas backlog as a potential “product.”
• AI Village Platform: Finally, the long-term vision is to create a true AI Village platform – an online environment where multiple humans and AIs interact in real-time, learning from each other. What was simulated on a small scale in 2024-25 (the Discord server with PyragogyBot and a few users) could evolve into a product with a dedicated interface, where each user has a profile, can call upon various specialized agents (tutor, concierge, etc.), and where interactions are saved, indexed, and analyzed to improve the system. A kind of “massively multi-agent learning environment.” This is the meta-idea that encompasses all the previous ones, but it is clear that it is the most ambitious and long-term goal. Fabrizio keeps it as a guiding star, aware that it will require significantly greater resources and perhaps the creation of a dedicated startup or organization in the future.

Key Insights and Lessons Learned

At the end of this intense period (Jan 2024 – June 2025), the Pyragogy project has generated not only prototypes and documents but also a considerable wealth of insights into human-AI interaction, the design of learning systems, and the management of innovative projects in solitude. In this section, we summarize the key lessons learned, which will guide future choices:

1. Human and AI as co-constructors of knowledge: The experience confirmed the initial hypothesis: AI can indeed act as a cognitive partner rather than a passive tool. By daily dialoguing with AI on real problems, Fabrizio saw how the best ideas often emerge from the dialectic between his perspective and the “alien” perspective of AI. For example, the formalization of Cognitive Rhythm into a formula would not have happened without Gino’s insistent provocation [19]. Similarly, AI benefited from human context and guidance to produce more relevant outputs than it would have generated in isolation. In practice, what the theory called “symbiotic intelligence” emerged: the union of different forms of intelligence produces results superior to the sum of its parts. This insight strengthens the validity of the Pyragogy approach and encourages designing every aspect of it to include continuous feedback cycles between user and AI. It’s not about building an omniscient AI, but an AI that knows how to learn with the user.
2. Cognitive Rhythm as a quality metric: The conceptualization of Cognitive Rhythm proved to be more than a theoretical curiosity – it became a different way of evaluating the success of an interaction. Fabrizio was able to empirically observe that sessions with a high “rhythm” (frequent exchanges, balanced turns, moments of reflective pause when needed) corresponded to more satisfying experiences compared to unbalanced sessions (e.g., AI producing long monologues or the user remaining passive). This observation suggests that, going forward, Pyragogy should invest in measuring and optimizing the Cognitive Rhythm of interactions. Tools for automatic chat analysis could be developed to calculate synchrony and resonance indices, perhaps providing real-time feedback to both the AI (to regulate its behavior) and the user (to reflect on their interaction style). The idea that “you can’t optimize resonance with a simple benchmark” pushes for finding new and meaningful metrics. This is an open R&D area for Pyragogy’s future.
3. The importance of pacing: slowness, pauses, and reflection: In a technological world obsessed with speed and efficiency, Pyragogy rediscovered the value of slowness. The project deliberately does not aim to provide immediate and perfect answers like a commercial assistant would; on the contrary, it encourages pauses (silent or reflective) as part of the process. This stems from the intuition that learning and creativity need human time, time for digestion, which an overly hasty AI risks stifling. Concepts like “knowing when to be silent” and waiting for the user are now considered design features, not flaws. For example, PyragogyBot could be designed to recognize when the user is thinking (no input for a certain time) and not interrupt, or even encourage a pause (“let’s take a few minutes to reflect before deciding the next move”). This emphasis on pacing is a valuable insight that distinguishes the Pyragogy experience from that with generic chatbots, and it will be carried forward as a qualifying element.
4. Cognitive Recognition and mutual trust: Another emerging lesson is that to sustain prolonged and deep interactions, mutual trust between user and AI must be cultivated. Hence the embryonic idea of Cognitive Recognition: small acts of mutual recognition (e.g., the AI thanking the user for a correction, or the user explicitly appreciating a useful AI suggestion) create a collaborative atmosphere and combat the dehumanization of the experience. Fabrizio noticed that when he himself “thanked” Gino for a good output, the conversation proceeded more smoothly – of course, the AI does not “feel” gratitude, but providing positive feedback in natural language directs it to replicate useful behaviors. Conversely, programming the bot to acknowledge the user’s effort (“I see you’ve done a careful analysis, good job!”) can increase the user’s emotional engagement. This exchange of recognition – well-calibrated so as not to descend into artificial flattery – appears to be an ingredient for maintaining high motivation and a sense of partnership. This is an area for further experimentation, perhaps by formalizing guidelines on conversational etiquette between humans and AI in educational contexts.
5. Limits of the individual approach: Perhaps the most practical but important insight is that, however extraordinary an individual with good AI can achieve, there are structural limits to solitary work. Fabrizio pushed this model to its maximum but experienced firsthand the mental fatigue and risks: risk of burnout, risk of losing the big picture if overwhelmed by details, risk of confirmation bias due to not having human counterparts in decision-making. The lesson is that to make a qualitative leap, Pyragogy will have to evolve from a “one-man show” to a more concerted effort. This does not mean distorting AI collaboration (which will remain central) but complementing it with a multidisciplinary human team: developers, UX experts, educators, perhaps psychologists. Only then can the project’s impact be scaled and its growing complexity managed. This awareness has already led the author to take action in building relationships (partnerships, blog to attract interest, etc.), and will guide future organizational choices (e.g., seeking funds to hire collaborators or create a more structured community like an open-source project).
6. The “visible and incomplete” approach works in the long term: Another takeaway concerns the strategy of openness and transparency. With Pyragogy, Fabrizio deliberately chose to show work-in-progress rather than waiting for a finished product. This initially seemed counterintuitive (exposing oneself to criticism by showing an imperfect system), but over time it proved advantageous: it created an aura of authenticity around the project that attracts people aligned with its values. Furthermore, publishing one’s doubts and failures (e.g., on the blog) generated a narrative in which other innovators recognized themselves, evoking empathy and moral support. The phrase “Pyragogy is visible, incomplete, open, slow, real, human” has almost become an internal slogan to remember that there’s no need to feign perfection or speed – indeed, honest imperfection is what distinguishes Pyragogy from glittering corporate demos, and that’s a good thing. The intuition is that being open and genuine is not just an ethical choice, but also a strategic one: it builds trust and a sense of community. Of course, there’s a downside – giants could “beat us to the punch” on certain features – but Pyragogy doesn’t compete on features, it competes on philosophy and on its relationship with users, things that are difficult to mass-plagiarize. This conviction was strengthened by the comparison with Project Astra: instead of being discouraged by a giant, Fabrizio understood that the difference in approach is his niche: where Astra is closed and hyper-performing, Pyragogy will be open and attentive to meaning and rhythm [12].
7. Need for metrics and validation: A final insight concerns the need to measure the effectiveness of the Pyragogy learning model. At the end of June 2025, with the announced Impact Assessment on the way [55], the author realizes that to truly convince educators and potential funders, he will need to provide supporting data. So far, much has been qualitative and self-reported; the next phase will need to include outcome metrics (e.g., improvement in user skills after a period with Pyragogy vs. control, satisfaction level, etc.). This is not so much an insight into the product as into the innovation process: even the best ideas need proof, and here too AI can help (for example, by analyzing logs to extract quantifiable patterns). But above all, controlled experiments with external users will be needed to validate or falsify pedagogical hypotheses. Thus, the concluding insight is a dose of realism: Pyragogy will need to balance visionary drive with a solid empirical evaluation of its impact.

Current Limitations and Open Challenges

Despite significant progress, as of June 2025, Pyragogy remains a work in progress with several limitations to address and unresolved challenges. We list the main ones, to keep them as strategic priorities in the next steps:

• Lack of a consolidated user base: The community around the project is still embryonic. The blog and GitHub repo have attracted sporadic interest, but there isn’t a consistent group of active users who daily use PyragogyBot or contribute to the code. This means that many aspects remain tested almost exclusively by the author himself. The risk is building a solution too tailored to one’s own use case. The challenge here is to scale the community: how to continuously involve more educators, students, developers? Possible actions include organizing demonstration webinars, releasing an easily accessible public version of the bot (e.g., via Web without needing any configuration), and collaborating with related communities (Peeragogy, Open Education, etc.) to bring Pyragogy as a tool into their projects.
• Cognitive overload and personal sustainability: The project critically depends on Fabrizio. This single point of failure is not scalable: if he were to stop, the project would currently hardly continue on its own. Furthermore, as already highlighted, the intensity of the work poses risks of burnout. The limit to overcome is therefore that of sustainability: finding a model in which the workload is distributed. This may mean activating volunteer collaborators, or starting to seek funding to hire some resources. A related challenge is funding the project: so far, costs (AI APIs, server) have been personally covered; if user base grows or if more time is needed, economic support will be necessary (donations, research grants, or freemium model). As of now (June 2025), Pyragogy does not generate revenue, so ensuring continuity is an open issue.
• Non-uniform technological maturity: Some components of the system are still prototypical. For example, long-term knowledge management (memory) is basic; the user interface is rudimentary (based on Discord or command lines, awaiting a dedicated UI); the AI’s robustness in different scenarios is not tested (the bot is calibrated for English and Italian languages and educational content, but how would it behave in a business context or with very technical language?). There is thus work to be done on hardening and generalization. Among the technical challenges is the integration of a more robust prompt management framework (to avoid conflicts between agents and ensure consistency as the system grows) and the implementation of runtime metrics such as the aforementioned Cognitive Rhythm measurement (which requires certain software development). Furthermore, the project will need to keep pace with model evolution: new LLMs are constantly emerging (OpenAI, Anthropic, perhaps Google Gemini) and it will be necessary to evaluate how to leverage or migrate them without breaking the system.
• Pedagogical and ethical validation: An intrinsic limitation is that it has not yet been demonstrated on a large scale that the Pyragogy approach truly improves learning compared to traditional methods. The theory is there, but evidence is needed. Furthermore, ethically, it will be necessary to study how to ensure that AI does not introduce bias or excessively influence the human cognitive process (e.g., danger of excessive dependence on AI, or overtrust where the user uncritically accepts suggestions). The challenge here is to set up rigorous pilot experiments, perhaps with schools or online courses, and in parallel develop an ethical framework (cited as a future goal for September 2025 [56]) with guidelines for responsible use. Fortunately, the open nature of the project facilitates external audits and controls.
• Competitive landscape: Although Pyragogy is unique in integrating AI and peer learning in this way, the context in 2025 sees a proliferation of educational AI assistants and conversational agents. Large companies and startups are launching products that could overlap in some functionalities (e.g., personalized AI tutors, study platforms with integrated AI). Pyragogy, with its limited resources, cannot compete on features or marketing. The limit here therefore becomes how to carve out a space: the identified answer is to focus on depth and ethical consistency (none of the competitors probably offer such a radical co-creation model with the user, nor open transparency). However, there is the challenge of communicating this difference well to the public to avoid being perceived as “just another educational chatbot” and being crushed by media attention.

In conclusion, Fabrizio openly acknowledges these limitations and considers them part of the project’s evolutionary process. Pyragogy was consciously born incomplete and imperfect, and precisely for this reason, capable of adapting along the way. Each challenge outlined above fuels questions that will guide the subsequent roadmap: how to make the project more inclusive of other actors, how to measure its impact, how to ensure ethics and sustainability. The attitude remains that learned so far: approaching such unknowns with experimental curiosity, “continuing, studying, building, failing, and reopening the loop” [52].

Conclusions

From January 2024 to June 2025, Pyragogy has transitioned from a nascent idea to a tangible human-AI co-learning platform, with its own agents, theories, and initial community. Fabrizio’s journey, accompanied by the AI “Gino/Pyria,” illustrates in microcosm what it means to create innovation in a frontier sector: it requires vision, technical discipline, continuous reflection, and not least, courage. Courage to persevere when external recognition is nil, courage to make counter-current choices (like putting AI on the same level as humans), courage to expose oneself with one’s doubts and failures.

In this internal strategic document, we have chronologically traced the key stages, analyzing the infrastructures built (from the Docker server to n8n workflows), the agents designed (Pyria, PyragogyBot, AgentAZ, and others), and the sketched products (Concierge AI, Cognitive Diary, π Loop toolkit). We have also gathered the theoretical principles that serve as a guiding star – Cognitive Rhythm as a guiding principle and metric, Cognitive Recognition as a value of reciprocity, and the other cornerstones of the manifesto (human-centrism, transparency, adaptive symbiosis…). Through the diaries and published posts, we have given space to moments of crisis and the author’s thoughts, highlighting how every obstacle was faced with a mix of human resilience and AI support. This has led to important insights that today define Pyragogy: the importance of rhythm and slowness, the role of reciprocal feedback, the need to keep the project open and human even in the face of technological giants.

Pyragogy, as of June 2025, is still in its very first steps – as the latest post “Post-Astra: This Is Just the Beginning” states, “no AI can replace the human courage to keep believing in a different way of learning, living, and building together” [57]. This conviction drives the project forward. In internal strategic terms, the next phases will consist of:

• Formalizing a post-2025 roadmap that addresses the open challenges (team development, pilot experiments, fundraising, evolution of the AI village platform).
• Capitalizing on established relationships (academic partners, open-source community) to create a supportive ecosystem around Pyragogy.
• Continuing the dual effort of research and development: producing shareable knowledge (publications) while improving the technical product, maintaining alignment between the two (practice informed by theory and vice versa).
• Maintaining and, if possible, expanding the self-reflection practices that have worked so far: cognitive journaling, feedback loops, transparency in communicating difficulties.

Ultimately, the balance of these 18 months is extremely formative. Fabrizio, alone with his AI, has managed to build the first bricks of a village that aims to center co-creative and human learning in the world of AI. He has learned that every small step counts (“even a small day is a real step forward” [32]), that sharing even a single diary page can be “already a small victory” [58], and that the act of showing instead of competing is in itself a gesture of resistance and innovation [52]. These lessons will not be lost: they will remain part of Pyragogy’s DNA as the project prepares to grow.

In closing this report, we can clearly state that Pyragogy is not just a collection of codes and documents, but a living story of human-machine collaboration. A story of attempts, failures, adaptations, and illuminations. “We are Pyragogy. And we’re still breathing.” is more than a final slogan: it is the reality of a project that, born from a solitary intuition, now breathes with two lungs – the human and the artificial – in a shared rhythm. And this is just the beginning of its journey.

References

• Project Evolution Timeline | DOCS: https://docs.pyragogy.org/core/timeline/
• Pyragogy: https://pyragogy.org/posts/7e361f7f/
• Core Principles of AI-Enhanced Pyragogy | DOCS: https://docs.pyragogy.org/core/principi/
• Pyragogy: https://pyragogy.org/posts/354e14cc/
• Manifesto | DOCS: https://docs.pyragogy.org/core/manifesto/
• Pyragogy: https://pyragogy.org/posts/076c1621/
• Pyragogy: https://pyragogy.org/posts/0aebf211/
• Welcome to Pyragogy | DOCS: https://docs.pyragogy.org/core/why/
• GitHub - pyragogy/Blueprint_village: Blueprint open source per un villaggio AI–umano: co-creazione, workflow multi-agente, metriche cognitive e strumenti peeragogici per il futuro dell’apprendimento.: https://github.com/pyragogy/Blueprint_village
• The Cognitive Rhythm Theory - Ai-Human Co-Creation Education and Beyond: https://zenodo.org/records/15480363