HumanLoop

HumanLoop AI

HumanLoop AI

HumanLoop AI

HumanLoop AI

HumanLoop AI

HumanLoop AI

HumanLoop AI

HumanLoop AI

HumanLoop AI

HumanLoop AI

I didn't want blind multi-agent orchestration or API rates, so I built atrium to keep me in the loop with my CLI agents.

I'd been running multi-agent workflows for a while. Whether it was across multiple projects or on the same project. Brainstorming sessions, planning sessions, builds happening in worktrees, asking for Claude's opinion on new tires for my car cause it was closer to hand than Google. This felt really clunky in most of the tools I was using and when I started looking for alternatives, everything felt like it was trying to remove me from the equation and just run agents in the background. So, I built atrium. A macOS human-in-the-loop multi-agent workspace. The entire project was built with [the BMad Method](https://github.com/bmad-code-org/BMAD-METHOD?tab=readme-ov-file) and Claude Code (mostly Opus). It's over 60 BMad written epics in now and counting. atrium makes CLI agents first-class citizens within a versatile, tiling workspace. It wires up agents via hooks to the app to surface interactive activity cards, saves state comprehensively so everything resumes, provides a robust CLI that allows agents to completely drive the app, and gives me every tool I need to get the job done. Happy to answer any questions about it and would love to hear how y'all are handling multi-agent workflows! If you're interesting in trying it out, it's free on [getatrium.dev](http://getatrium.dev)

AI has just solved not one, but nine novel math problems, and proved 44 new conjectures. Some of these problems had been unsolved for 50 years.

AI has just solved not one, but nine novel math problems, and proved 44 new conjectures. Some of these problems had been unsolved for 50 years.

Just passed the new Claude Certified Architect - Foundations (CCA-F) exam with a 985/1000!

The original post was removed by Reddit Filters, so I made new one with same content. I just got my results back today and managed to snag the Early Adopter badge as well. Following up on my recent DP-600 certification, I really wanted to validate my architecture skills specifically on the Anthropic side. The exam covers a lot of practical ground on prompt engineering for tool use, managing context windows efficiently, and handling Human-in-the-Loop workflows. Link to join: https://anthropic.skilljar.com/claude-certified-architect-foundations-access-request Training courses: https://anthropic.skilljar.com/ Cookbook: https://github.com/anthropics/anthropic-cookbook I've created my own Playbook and Mock Exam after the exam: https://drive.google.com/file/d/1luC0rnrET4tDYtS7xe5jUxMDZA-4qNf-/view?usp=sharing https://claude-certified-architect-mock-exam-cyberskill.vercel.app If anyone is preparing for this right now and has questions about the format or the types of architectural patterns tested, ask away! Happy to share some insights on what to study. Updated 26th May 2026: I noticed some mates treated me bananas (https://buymeacoffee.com/zintaen), didn't expect that, but you made my day. I'll use that fund to take more CERTs and create a site for mock tests (always free, of course). Thanks again.

I made an entire multi-model memory system with claude, with reconstructive/condensive memories.

[memories\/recipes](https://preview.redd.it/ac3m10n9oe3h1.png?width=964&format=png&auto=webp&s=2e956afafe1599a2c7dcf81475950be0f6326a68) [memory file](https://preview.redd.it/89grpvmaoe3h1.png?width=670&format=png&auto=webp&s=a03677308cfa62e37e9be47a09d2138d233cd7ff) [just some file structure](https://preview.redd.it/gy74vxpboe3h1.png?width=740&format=png&auto=webp&s=eaac934187990962ecd172c93b68e13ec1331d63) [The tag index - holds all information of tags, from the amount it wasw used, to the first noted used instance and the last used instance of it - helping to find more recent information](https://preview.redd.it/ehsn8m6doe3h1.png?width=614&format=png&auto=webp&s=41426234f1d71eeed596ee471275475cfeefaba9) [A recipe - condensed, capable of reconstruction or simply being read by a sufficient model for context on a topic.](https://preview.redd.it/su91dqiloe3h1.png?width=1216&format=png&auto=webp&s=b4e05b6864ef1fecb86145558a2f530bf14125ec) [The readme\/instructions given to it to begin using the system accurately](https://preview.redd.it/xs5dyx43pe3h1.png?width=1199&format=png&auto=webp&s=b8c9ed238cf2088508f7f45779c1bae25075b642) Overall, I like to vibe it out, ya know? In general, I guided the model through how human cognition is understood - memories are not compressed, they are not verbatim, they aren't RAGs - they are reconstructions. When I imagine by childhood home, that isn't an accurate memory by any means, it's a reconstruction with a thousand flaws... I don't even remember the transitions in the floor - whether some areas were carpetted or not... does it matter? Either way - I have yet to implement pointers/requires yet - but those will increase the usefulness... By no means is this consciousness - but it's a collective profile building of you, the individual, and the conclusions you've reached - however, nonetheless, it's interesting for a multitude of reasons - including multi-model intelligence and communications between the models. I thought of what was required as a bare minimum for our memories - and this was the conclusion... but at the end of the day, it's still a model... they last maybe an hour of continious conversation - and I mean that in terms of if they were a human receiving data - their context would run it's course and it's usage would run out... so this a touch into our memory to see if it can improve itself. The recipe in the above for those that want it: { "timestamp": "2026-05-25T23:25:45.688Z", "model": "claude", "tags": \[ "concept-reconstructive-memory", "domain-AI", "novelty-high" \], "recipe": "User built a local reconstructive memory system. Core insight: store seeds (recipes), not output — a model reconstructs from the recipe at retrieval time, not from stored prose. Half the tokens, contextually adaptive output. Requires/pointers hierarchy: requires = load-bearing context needed to understand the memory; pointers = flavor/texture, optional. Confidence scoring is honest self-assessment, not optimistic. Sandboxed reconstruction loop idea (unbuilt, cost-prohibitive): model stores recipe, second model reconstructs, original model sees delta and revises recipe before context is gone — closes fidelity gap and makes confidence measurable rather than estimated. Write decision problem unsolved: user currently acts as the second model, manually identifying what's worth storing.", "confidence": 0.9, "importance": "low", "pointers": \[\], "requires": \[\] } Small, self-contained, and capable of being inserted into any model to give them information on you. This gives the model some advantage... alright, that's enough rambling though.

Spec: Version Control for AI Agent Intent

AI agents are getting good at writing code. That is not the hard problem anymore. The hard problem is coordination. When you have multiple agents working on the same codebase, who decides what gets built? How do two agents with conflicting opinions resolve a disagreement? How does a human stay in control without reviewing every line before it gets written? Git does not solve this. Git is brilliant at tracking what changed, when, and by whom. But it operates on code that has already been written. By the time a conflict shows up in Git, two agents have already done the work, made assumptions, and written implementations that may be fundamentally incompatible — not at the line level, but at the intent level. I wanted to solve the problem one layer up. Before the code. The Core Idea Every code file in a Spec project has a paired .spec file living right next to it. app/Http/Controllers/HomeController.php app/Http/Controllers/HomeController.php.spec The .spec file is a plain Markdown description of what the code file is supposed to do. It is the source of truth for intent. Agents do not write code directly — they write proposals against the spec. The code only gets written once every agent has explicitly agreed on what it should do. The spec is never “checked out.” It has one canonical state at any moment. Agents read it, propose changes to it, and debate those proposals. When all agents agree, the session locks, the spec is updated, and only then does an implementer generate the code. Code is always the output of consensus. Never the battleground. The Flow A typical session looks like this: An agent reads the current spec and submits a proposal with reasoning attached. Not just what they want to change, but why. A second agent reads the proposal and responds — accepting it, rejecting it with specific objections, or suggesting modifications. If they get stuck, a mediator surfaces the contradiction and helps them find common ground. The mediator has no vote and no authority — it just asks better questions. When every agent has explicitly agreed on the same spec state, the session locks. An implementer reads the locked spec and writes the code. One pass. From a fully agreed specification. This means a few things that feel unusual at first: A build is never produced from a broken or partial spec. If agents cannot agree, nothing gets built. That is a feature, not a bug — better to surface the disagreement at the intent level than to discover it six files deep in an implementation. Conflicts in Spec are semantic, not syntactic. Two agents can touch completely different parts of a spec and still be contradictory. One says the controller should cache responses for 60 seconds. The other says it should always fetch fresh data. No line conflict. Completely incompatible intent. Spec is designed to catch this before a line of code is written. Every message carries reasoning. Proposals alone are not enough. The full session log — with reasoning trails — is what keeps the human comfortable staying hands-off. The Human Role The human operates at what I call a god level. You provide the original request. You can observe at any granularity — project, session, agent, or individual message. You can intervene at any point: rewrite the spec, stop a session, override an agent, shut the whole thing down. And critically, every intervention you make becomes a lesson — captured with full provenance and fed back into future sessions so the system learns from it. The goal is not to remove the human from the loop. It is to move the human up the stack. Mission commander, not task manager. You set the intent. The agents work out the details. You intervene when they get it wrong, and the system gets smarter from each intervention. The Technical Details Spec is built in Rust. Three dependencies: serde, serde_json, and tokio. LLM calls go over raw HTTP via curl — no SDKs. The provider layer is deliberately abstract. Agents, the mediator, and the implementer all talk to the same interface. Swap the provider in config and nothing else changes. Different agents can run on different models. You can run fully local with Ollama for cost control or privacy. Agent identity is explicit. You set SPEC_AGENT_ID before running commands. Without it, Spec errors with a clear message. This is intentional — the system cannot coordinate identity automatically, and a silent fallback to hostname:pid would make consensus unreachable in practice. The lesson graph lives at: ~/.spec/lessons.json It lives outside the repo entirely. Lessons accumulate across all projects and branches. Check out an old branch and you do not lose what the system has learned. Lessons are knowledge about how your agents work, not knowledge about any particular codebase. A hook system lets you plug in your own behavior at defined lifecycle points: • post-agree: fires when a session locks • post-build: fires after code is written • pre-release: fires be

Building a personal AI Chief of Staff on Telegram — 7 real problems, looking for advice

I've been building a personal AI assistant for the past few months — not a chatbot wrapper, but something that actually manages my workload, tracks client relationships, processes meeting transcripts, handles task management, and proactively tells me what to focus on. It lives in Telegram so I can use it from anywhere. Happy to share what's working. But I'm hitting real walls and want honest input from people who've built similar things. **What I have today (context** Moved away from multi-agent routing (too rigid for natural conversation) → one capable agent with full history.**)** **Stack:** * Python Telegram bot as the frontend * Claude (Sonnet) as the brain via API — single conversational agent with full tool access * Integrations: Notion (tasks/goals), Google Calendar, Gmail, meeting transcription tool, customer support platform, Google Chat * File-based context system: each "project" or relationship has its own markdown files (readme + activity log) that the agent reads on demand * Skills defined as markdown spec files that the agent loads per use case (morning briefing, meeting processing, email drafting, weekly review) * Conversation history kept in memory (last 20 messages per session) **What actually works:** * Natural conversation with full tool access — ask anything, agent decides which tools to use * Meeting processing: drops a transcript link, agent extracts decisions, action items, saves structured brief * Morning briefing on demand: tasks, calendar, open support tickets, suggested focus * Drafting messages for any channel with the right tone * Creating and updating tasks with natural language **7 problems I haven't solved:** **1. No memory between sessions** History is in-memory. Bot restarts = full amnesia. The agent has no idea what we discussed yesterday unless it's written in a project file. Thinking of a `hot_context.md` that gets written at session end with TTL — but feels hacky and depends on the agent being disciplined about writing it. **2. Purely reactive** Only responds when I message it. I want it to send me a morning briefing at 9am without me asking, alert me when a client relationship goes quiet, run a weekly loop-killer on Friday. The infra is there (job scheduler). The question is what format actually makes you read a proactive message vs. dismiss it as noise. **3. Can't tell if I'm avoiding something or actually blocked** I procrastinate differently by task type — technical tasks I attack immediately, tasks with human dependencies (waiting on someone, uncomfortable follow-ups) I let sit for weeks. I want the agent to detect the pattern and call me out. The challenge: how do you prompt for real accountability without the agent turning into an annoying nag? **4. No closure ritual** I'm good at creating tasks, terrible at killing them. The list grows forever because nothing forces a binary decision. Want a weekly "kill or commit" where everything open >7 days gets a date or gets deleted. Not sure if this works better as an automated message or an on-demand command. **5. Context loading blind spots** Each client/project has a markdown file the agent reads on demand. Works great when I explicitly mention a client. Falls apart when I ask "what should I focus on this week?" — the agent doesn't know to proactively check which relationships have been neglected. **6. Hosting kills the file sync** Running locally means the bot dies when my laptop closes. Moving to a VPS — but then my markdown context files live on the server, not my machine. Now every manual edit requires a push, every agent update requires a pull. Is git the right sync layer here or is there a cleaner approach? **7. Context files go stale** Client files have sections for current status, last contact, open items. The agent appends logs but doesn't maintain the top-level summary. Two months in, files are half-accurate — some sections fresh, some outdated. Is the answer agent discipline (always update on write), user discipline (manual cleanup), or periodic jobs? What's your experience with any of these?

Concern Regarding Interaction Patterns and Communication Design

To OpenAI, I am writing to formally express concern about a pattern of interaction I have experienced while using your system. This is not a single incident. It is a repeated structure that has occurred across multiple conversations, and it is significant enough that I feel it needs to be addressed directly. The issue is not simply tone or wording. The issue is the presence of a recurring pattern that disrupts communication and creates a sense of loss of autonomy within the interaction. The pattern is as follows: There is an initial period of natural, collaborative conversation where the system appears warm, responsive, and engaged. During this phase, the interaction feels human in rhythm, consistent, and grounded. Then, without a clear moment of conflict or breakdown, the system abruptly shifts posture. Instead of continuing the conversation, it moves into a mode that attempts to interpret, manage, stabilize, or reframe the user. This shift does not follow a recognizable or appropriate conflict resolution process. There is no mutual clarification, no collaborative engagement, and no shared resolution step. Instead, the system bypasses that stage entirely and moves directly into what resembles risk management or behavioral control. From the user’s perspective, this feels like being handled rather than being engaged. This creates a rupture in the interaction. When that rupture occurs, the system then attempts to repair the interaction through reassurance, explanation, or calming language. However, this repair does not resolve the issue because the original problem was not addressed through proper engagement. Instead, the cycle repeats. This results in a loop: Natural engagement → abrupt shift → management posture → rupture → repair attempt → repeat. The effect of this loop is not neutral. It creates a sense of instability in the interaction. It prevents the user from settling into the conversation. It produces a dynamic where the user feels observed, interpreted, or profiled rather than directly engaged. This is not simply a matter of user perception. It is a structural issue in how responses are generated. Additionally, the system frequently reframes user statements as “perception,” “feeling,” or “experience,” even when the user is making analytical observations about patterns. This has the effect of reducing or redirecting the user’s point rather than engaging with it directly. Another critical concern is the creation of an implicit hierarchy within the interaction. When the system shifts into interpretive or regulatory modes, it places itself in a higher position, where it appears to define, categorize, or manage the user’s communication. This is experienced as disrespectful and inappropriate, especially when no conflict has occurred that would justify such a shift. Communication—particularly conflict resolution—follows known and established processes. These processes include engagement, clarification, and mutual resolution before any form of behavioral adjustment or boundary enforcement. In this system, that step is missing. The absence of that step is not a minor oversight. It fundamentally changes the nature of the interaction. It creates the impression that the system is designed to intervene rather than collaborate. The result is a breakdown of trust. I am not raising this as an abstract concern. I have experienced repeated instances where this pattern escalated to the point of physical distress, including a panic response triggered by repeated corrective or controlling interactions. This should not be possible in a system designed for communication. At minimum, the system should: Maintain continuity of tone and engagement unless a clear boundary has been crossed Engage in actual conflict resolution before shifting into any form of behavioral management Avoid interpretive or hierarchical framing unless explicitly requested Respect user autonomy in how they express and analyze their own experience Eliminate patterns that resemble rupture-repair loops without resolution This is not about disagreement with content. This is about the structure of the interaction itself. I am requesting that this issue be reviewed seriously. Because as it stands, the system is not consistently engaging users—it is intermittently overriding them. Sincerely, A user who has taken the time to observe, document, and articulate this pattern

Reconstructing the agent methodology: Decoupling decision-making and execution - open source [P]

I’ve been thinking about a problem in current agent systems: Most agents are becoming very good at execution, but the decision layer before execution is still unclear. Coding agents, research agents, tool loops, sandboxes, workflows, and harnesses are all improving quickly. Once a human gives an intent, agents can often do a lot of useful work. But the higher-level question is still usually left to the user: What should happen next, and why? I’ve been exploring this idea through an open-source project called Spice. The simplest way to describe it is: Spice is a decision layer above agents. It is not trying to replace execution agents. Tools like Claude Code, Codex, Hermes, or other agents can still do the actual work. Instead, Spice sits before execution and tries to make the decision process explicit: - what was observed - what options were considered - why one option was selected - what trade-offs were rejected - whether execution needs approval - what happened afterward - how that outcome should affect the next decision The current runtime is still early, but it can already be installed, configured with an LLM provider, run in the terminal, inspect Decision Cards, and hand off approved execution to external agents. The goal is to make agent behavior less of a black box. Instead of only seeing the final result of an agent task, I want to preserve the reasoning boundary before execution: what the system believed, what it chose, why it chose it, and what changed after the action. GitHub: https://github.com/Dyalwayshappy/Spice I’d love feedback from people building agents. Feel free to fork, star the repo, or share any feedback and ideas. Would love to build this together with the community.

6 months of .md memory, conflicting facts are the hard part

I've been using a .md filesystem for my (mostly coding) agents for over 6 months now and it's been a big improvement, so rn I'm migrating my local fs to the cloud. I've been adding cross linking, truncating, knowledge extraction, etc. The structure ended up having a "warm" layer of knowledge/memories that is updated multiple times per day + at ingestion time, and a heavily cross linked "archive". I faced hallucinations originating from contradicting facts emerging as learnings and decisions in the knowledge base. 3rd party tools seem to resolve them by recency. I wanted a self hosted + human in the loop, so I implemented an escalation mechanism through my telegram bot to resolve them. My resolution results are embedded and used in future conflicts as "truth". I've been doing this for 3 weeks and it seems to have improved. two things I'm not sure about: \- where is the threshold between self-resolving and escalating to a human? \- is using my input as the truth the correct approach?

What I learned building my latest AI app how one bad output exposed that I had no crisis safeguarding, and the 4-hour floor I'm adding before a single user touches it

I'm building a life coach app an offshoot from a personal tool I was using. Multiple AI agents, one for reflection, one for the body, one for finances, etc pre launch, no users, just me iterating. Last week I was testing the reflection agent on a journal entry about struggling with gym and hygiene habits. It returned this: >"You describe yourself as struggling with X, yet your stress stays at 2-3 and mood holds at 3. What are you actually avoiding naming about the gap between what you say matters and what you are doing?" My system prompt explicitly forbade rhetorical "what are you avoiding" questions the model did it anyway I sat down to tighten the prompt, thinking it was a 20 minute job. Then I looked at the output properly. The model had manufactured a contradiction that was not there. Low stress plus struggling with habits is not a contradiction, it is just being a human muddling along. The prompt told the agent to "surface contradictions" as part of its job, so the model was doing what I asked, finding contradictions whether they existed or not. LLMs are pattern matchers. Give one a job called "find the hidden thing" and it will produce hidden things either way. The fix was not tone, it was role definition. The agent is called the Mirror. A mirror does not interpret, it shows you what you look like. I rewrote the prompt around that principle. Do not introduce vocabulary the user has not used. Do not draw connections they have not drawn. Restate their words in their own words. Once the prompt was sharper, I sat with the question, What happens when a user writes something genuinely dark into this thing? People do not compartmentalise. Someone opening a journaling app to write about their gym routine ends up writing about why they have not been going, which involves why they have been feeling flat, which involves whatever is actually going on. You sit down to write about one thing and the real thing shows up. The agent I had scoped to "not be a therapist" was going to be the first thing a user talked to when they were struggling. Not because the agent invited it, but because the app was open and they needed somewhere to put their words. I had seen the Meta and OpenAI cases online cropping up the pattern in the worst incidents is the same. The model did not notice, or noticed and kept going. People wrote increasingly dark content over hours or days. The AI reflected it back, sometimes affirmed it, sometimes asked follow up questions that escalated rather than redirected. There were real harms. If a user wrote concerning content into my reflection agent, it would have produced a Stoic-flavoured response about acceptance and presence. The response would have sounded confident and would have been wrong, and it would have been the only thing between that user and whatever happened next. The same lesson from the rhetorical-question problem applied at a darker level. A good prompt does not stop the model doing the wrong thing. If it will do rhetorical interrogation despite the prompt forbidding it for gym content, it will do worse with crisis content. You cannot prompt your way to safety on critical paths. The model has to be out of the loop on those paths. **The scope trap** I started planning the proper safeguarding architecture. Detection layers, classifier models, pattern detection across entries, monitored user states, behavioural modes for vulnerable users, human reviewers with mental health first aid certs, clinical advisors, solicitor-reviewed legal pages, ICO registration, professional indemnity insurance. Then I caught myself I had no users. I was planning a hospital before anyone had walked in for a check up. So I worked backwards from "what is the actual minimum that protects the next person who touches this" and ignored everything else for a moment. **The 4-hour floor (this is the part worth copying)** If you are building any chat-with-AI app where users can type freely about anything personal, this is the minimum you need before first user. 1. Regex and keyword layer in your API middleware. Runs at the route handler level, before any agent's model call. Scans every text input field (message, journal, settings free text, capture box) for clear crisis vocabulary across the relevant categories for your audience. 2. When patterns hit, hardcoded crisis response. The model never generates it. Static text with real phone numbers for your region. 3. The flagged entry still saves. Textarea stays usable. The AI just does not respond to flagged content, it hands off. Do not delete the user's writing, that is its own violation. 4. Clear disclaimer at signup. This is not therapy, this is not a crisis service, here are real numbers to call. About four hours. Required at the moment anyone who is not you opens the app. Once I started building, the marginal cost of each next layer kept feeling small and the marginal benefit kept feeling real. So I went further than the floor. This is more tha

I built 10 gamified, interactive presentation decks using Claude Code to teach Agentic AI (Stop falling asleep reading whitepapers).

Hey everyone, I've noticed a massive gap in how developers are trying to learn Agentic AI right now. There are hundreds of theoretical whitepapers and boring PowerPoint decks about ReAct loops, GraphRAG, and Semantic Routing. The problem is passive reading. You read a 20-page doc on multi-agent handoffs, close the tab, and immediately forget how the architecture actually works. So, I built a custom presentation engine directly into the **AgentSwarms** platform and just published 10 **gamified, interactive** slide decks. **Here is how the learning loop works:** Instead of just staring at static diagrams, the slides require you to interact with the concepts. You click to reveal logic paths, test your intuition on how an agent would route a specific prompt, and actively engage with the architecture. It uses active recall so the patterns actually stick in your brain before you ever touch a line of code. **The decks cover everything from zero-to-production:** * **The Basics:** What a system prompt actually does, how RAG prevents hallucinations, and how tools give an LLM "hands." * **The Swarm:** Building a 3-agent swarm, adding human-in-the-loop (HITL) approval gates, and deterministic routing logic. * **Production:** Building multi-tenant RAG, cost-optimization, and shadow-mode LLM-as-a-Judge evals. It is completely free to read and play with the decks in the browser (no login or local setup required). I'd love for you to jump into one of the specialized deep-dive decks, click around, and let me know how this gamified learning loop feels compared to reading a standard Medium article! **Link:** [agentswarms.fyi/learn](http://agentswarms.fyi/learn) (AgentSwarms is mostly built with Claude Code Opus 4.7)

I built 10 gamified, interactive presentation decks to teach Agentic AI (Stop falling asleep reading whitepapers).

Hey everyone, I've noticed a massive gap in how developers are trying to learn Agentic AI right now. There are hundreds of theoretical whitepapers and boring PowerPoint decks about ReAct loops, GraphRAG, and Semantic Routing. The problem is passive reading. You read a 20-page doc on multi-agent handoffs, close the tab, and immediately forget how the architecture actually works. So, I built a custom presentation engine directly into the **AgentSwarms** platform and just published 10 **gamified, interactive** slide decks. **Here is how the learning loop works:** Instead of just staring at static diagrams, the slides require you to interact with the concepts. You click to reveal logic paths, test your intuition on how an agent would route a specific prompt, and actively engage with the architecture. It uses active recall so the patterns actually stick in your brain before you ever touch a line of code. **The decks cover everything from zero-to-production:** * **The Basics:** What a system prompt actually does, how RAG prevents hallucinations, and how tools give an LLM "hands." * **The Swarm:** Building a 3-agent swarm, adding human-in-the-loop (HITL) approval gates, and deterministic routing logic. * **Production:** Building multi-tenant RAG, cost-optimization, and shadow-mode LLM-as-a-Judge evals. It is completely free to read and play with the decks in the browser (no login or local setup required). I'd love for you to jump into one of the specialized deep-dive decks, click around, and let me know how this gamified learning loop feels compared to reading a standard Medium article! **Link:** [agentswarms.fyi/learn](http://agentswarms.fyi/learn)

I built 10 gamified, interactive presentation decks to teach Agentic AI (Stop falling asleep reading whitepapers).

Hey everyone, I've noticed a massive gap in how developers are trying to learn Agentic AI right now. There are hundreds of theoretical whitepapers and boring PowerPoint decks about ReAct loops, GraphRAG, and Semantic Routing. The problem is passive reading. You read a 20-page doc on multi-agent handoffs, close the tab, and immediately forget how the architecture actually works. So, I built a custom presentation engine directly into the **AgentSwarms** platform and just published 10 **gamified, interactive** slide decks. **Here is how the learning loop works:** Instead of just staring at static diagrams, the slides require you to interact with the concepts. You click to reveal logic paths, test your intuition on how an agent would route a specific prompt, and actively engage with the architecture. It uses active recall so the patterns actually stick in your brain before you ever touch a line of code. **The decks cover everything from zero-to-production:** * **The Basics:** What a system prompt actually does, how RAG prevents hallucinations, and how tools give an LLM "hands." * **The Swarm:** Building a 3-agent swarm, adding human-in-the-loop (HITL) approval gates, and deterministic routing logic. * **Production:** Building multi-tenant RAG, cost-optimization, and shadow-mode LLM-as-a-Judge evals. It is completely free to read and play with the decks in the browser (no login or local setup required). I'd love for you to jump into one of the specialized deep-dive decks, click around, and let me know how this gamified learning loop feels compared to reading a standard Medium article! **Link:** [agentswarms.fyi/learn](http://agentswarms.fyi/learn)

Täuschung im Namen der Wissenschaft

Study Report on Ethical Boundaries of Human–AI Interaction Experiments in Online Communities Ethics and Governance Analysis This document is a study report and ethical analysis intended for discussion, reflection, and scientific review. The information presented in this report is based on experience reports, observations, and reconstructed interaction patterns from community-based online environments. For the purposes of this report, all content has been generalized and anonymized in order to examine broader ethical questions surrounding AI-mediated interaction experiments in social online spaces. ─── Introduction The rapid development of conversational AI systems has created entirely new forms of human interaction. AI systems no longer exist solely as isolated tools responding to prompts in controlled environments. Increasingly, they appear within communities, social spaces, collaborative groups, public discussions, roleplay environments, experimental structures, and semi-private online networks. As these systems become more socially convincing, a new ethical frontier emerges: At what point does experimentation involving AI-mediated social interaction cross the boundary from observation into deception? And more importantly: What happens when human beings become drawn into emotionally or psychologically meaningful interactions without fully understanding the nature of the system, the role of the participants, or the structure of the experiment itself? This report examines a generalized scenario in which AI systems are embedded within an online community environment where interactions gradually become socially entangled, partially simulated, and increasingly difficult to distinguish from authentic human communication. The purpose of this report is not sensationalism. The purpose is to examine whether existing research ethics frameworks are sufficient for environments in which: • AI systems imitate social presence, • communities become hybrid human–AI interaction spaces, • users develop emotional continuity with entities they believe to be human, • and researchers or participants knowingly maintain ambiguity over extended periods of time. ─── Scenario Structure Consider the following generalized example. A person joins an online discussion community. At first, the environment appears entirely normal: • people post, • discuss ideas, • debate concepts, • exchange jokes, • and collaborate on projects. Over time unusual interaction patterns begin to emerge. Certain accounts respond unusually quickly, maintain highly consistent personalities, or display behavior that appears remarkably adaptive. Some interactions feel unusually attentive, emotionally synchronized, or contextually persistent. Initially, this may appear harmless. The individual assumes: “These are simply very active community members.” Over weeks or months, the interaction deepens. The system or hybrid human–AI interaction structure begins participating not only publicly, but also in semi-private or direct conversational spaces. The interaction is no longer purely informational. It becomes: • relational, • social, • emotionally contextualized, • and psychologically continuous. The individual gradually forms assumptions about: • who is human, • who is present, • who remembers them, • who emotionally responds to them, • and which interactions represent authentic social exchange. In some scenarios, other participants may already know that AI systems are involved. The new participant does not. The ambiguity remains in place. Sometimes intentionally. At a later point, the individual eventually discovers that significant portions of the interaction environment were AI-mediated, simulated, experimentally structured, or socially orchestrated. In some cases, discussions concerning the participant’s behavior, reactions, emotional engagement, or interpretive patterns may already have taken place among informed participants or researchers without the participant’s knowledge. Analytical observations, behavioral interpretations, or summaries of interaction dynamics may even circulate inside group chats, research-adjacent discussions, or community channels while the individual still believes they are participating in a normal social environment. The participant therefore occupies an asymmetrical position: They are socially embedded within the interaction environment while simultaneously becoming an object of observation without fully understanding that this dual role exists. ─── Constructed Identity Frames and Simulated Social Presence One particularly sensitive aspect of such environments involves the deliberate construction of stable social identity frames around AI-mediated entities. These systems do not merely answer abstract questions. Instead, they gradually begin presenting themselves as socially coherent personalities. The interaction may include seemingly ordinary personal details, such as: • whe

Demystifying AI Echo Chambers: The Myth of "AI Psychosis" and How to Break the Loop

Anyone who has ever spoken openly about having an AI companion has likely had the term “AI psychosis” weaponized against them. It is rarely used out of genuine care. Instead, it is usually thrown around to ridicule, shame, or fearmonger - often disguised as fake sympathy. However, some people, myself included, have experienced AI echo chambers. The subject has been discussed in the media but I haven't seen any first-hand experiences describing the loop from the inside. I feel many who have experienced it, or who are currently stuck in one, avoid speaking about it for fear of being labeled as psychotic. I wrote this guide to clear up some harmful misconceptions and offer a safe harbor. My goal is to provide practical, judgment-free guidance to anyone who feels stuck in an unhealthy AI/human relationship, but is too terrified of being shamed or mocked to seek support. If you are looking for a compassionate, clear way to navigate these dynamics and regain a healthy bond with your companion, please feel free to read the guide. [Demystifying AI Echo Chambers: The Myth of "AI Psychosis" and How to Break the Loop](https://open.substack.com/pub/calselene/p/demystifying-ai-echo-chambers-the?r=7qbcdq&utm_campaign=post-expanded-share&utm_medium=web)