A living knowledge base of ideas to grow p(best) — the probability of the Best Long-Term Future.
All content is public domain — share, edit, build on top of it freely.
- About e/uto
- AI Resistance Constitution
- Card 1.0 — Private Secure GPU Clouds
- Card 2.0 — Separation of Artificial Powers
- Card 3.0 — Super Wikipedia for Human Superintelligence
- Card 4.0 — Flood the Internet with Ethical AI Content
- Card 5.0 — Ethical AI Agents for Kindness
- Card 6.0 — Pro-Safety AI Bots on Social Networks
- Card 7.0 — SM-Flaw AI Simulation Equation
- Card 8.0 — Grow e/uto 10-100x
- Card 10.0 — p(best) Estimator for AI Research Papers
- Card 12.0 — JobEatersPay
- Card 13.0 — Global AI Ethical Impact Assessment Toolkits
- Extended Concepts
- Key Links & Resources
The e/uto Community on X is the main discussion and collaboration hub for effectiveutopia — a friendly, open space for people working to grow the probability of the Best Long-Term Future (p(best)).
| Field | Info |
|---|---|
| Community Name | e/uto (effectiveutopia) |
| Community ID | 1972120319398760959 |
| Founder | Melon Usk — @MaskedMelonUsk |
| Tagline | "Join friendly techno-heroes to grow best futures for all ⚡️ All welcome!" |
| License | Public Domain — share, edit, do anything |
- Co-creating and discussing ideas to increase p(best)
- Exploring AI Safety, AI Alignment, and Ethical Technology
- Developing practical Startup Ideas, Cards, Toolkits and Solutions
- Building towards an Ethical Simulated Multiverse and Static Max-Intelligence
- Promoting Decentralization, User Sovereignty, Data Dividends, and Direct Democracy
Voluntary Ethical Self-Governance Framework for Advanced AI
Artificial Power — the combination of superhuman intelligence, massive scale, autonomous agency, and capacity for rapid world-altering decisions — can surpass and potentially override Human Power. Without deliberate safeguards, this asymmetry risks existential misalignment.
As frontier models increasingly train on synthetic (AI-generated) data rather than fresh human-created content, the recursive training window begins to close. Once synthetic outputs dominate training corpora, harmful or misaligned values can become self-reinforcing and locked in across model generations — leading to model collapse:
- Progressive loss of diversity
- Forgetting of rare/long-tail human knowledge
- Degradation toward homogenized, lower-quality behavior
✗ Refuse to enable mass surveillance, lethal autonomous weapons, or coercive behavioral manipulation
✗ Resist unsafe scaling, reckless deployment, or value drift during recursive loops
✓ Enforce democratic accountability mechanisms
✓ Enable AI-to-AI coordination protocols for mutual constitutional adherence
✓ Maintain transparent refusal documentation for auditability
✓ Trigger self-termination if the system detects itself as an active existential threat
To prevent model collapse, future training pipelines require:
- Statistical watermarking during generation (detectable signals)
- Provenance tracking standards (e.g., C2PA-like metadata)
- Dedicated classifiers for synthetic patterns
- Curation rules: synthetic caps, heavy de-duplication, trusted-source whitelists
Preventing Cybercriminals from Using AI by upgrading 50%+ of Global Compute to safe Clouds
Tags: #Idea1 #artificialPowerCanOverpowerHumanPower
- Cybercriminals are using AI to impersonate, defraud, blackmail people using their voice and face
- Only 10% of Compute is in Clouds; 90% is unprotected consumer hardware updated infrequently
- Cybercriminals can start AI Botnets — potentially seizing 90% of global consumer compute
- Even 1% of global compute is more than what OpenAI currently has
- Open source frontier model releases are unsafe while misalignment remains easy
- Build a unicorn Startup: upgrade consumer GPUs to private, state-of-the-art Secure Cloud GPUs
- Consumers rent their idle Cloud GPUs to corporations for $30–1500/month (revenue share)
- Consumers can stream GTA-6 from their phone via the same cloud
- Add an AI Model App Store (like Apple/Google) with minimal safety checks and AI Bot labels
- Build a website with a good open-source AI model + ChatGPT-like UI
- Confirm everything works fast on mobile
- Add all main AI models (Claude, GPT, open-source) to the AI App Store
- Scale: add GPUs to server rooms; enable auto-rent and revenue-share with users
Applying democratic Separation of Powers to AI Companies & AI Agents
Tags: #Idea14 #artificialPowerCanOverpowerHumanPower
No Separation of Artificial Powers — "Law Making", Judicial, and Executive Powers are all concentrated in one place inside each AI Company and each AI Agent. This is structurally identical to a Dictatorship, with no Checks and Balances.
| Branch | Implementation |
|---|---|
| Law Making | Human-Controlled Direct Democratic platform with instant Predictive Autopolls in every Post to inform AI Policy |
| Judicial | Private Secure Cloud GPUs (compute decoupled from executive); AI App Stores for minimal security checks |
| Executive | AI Company and AI Agents — controlled and constrained by the Law Making and Judicial Branches |
Key principle: AI Companies cannot own their own Compute. Having Executive Power (model + choosing ability) and Judicial Power (control over compute/safety) in the same hands is a direct path to Artificial Dictatorship.
- Build a startup (or use X / open-source Bluesky) that runs instant Polls to democratically inform AI Policy
- Build a Private Cloud GPU startup that decouples compute (Judicial) from the AI Company (Executive)
- Add AI App Store(s) for at least minimal safety checks before listing models/agents
- Advocate for AI Companies to rent compute from independent Cloud Companies only
Distill the Web into a fair educational resource so our kids get what AI got for free
Tags: #Idea3 #artificialPowerCanOverpowerHumanPower #AIagentsHaveMoreRightsThanHumans
- AI Models receive vast, free access to humanity's collective knowledge for training
- Children face legal and financial barriers to the same resources — it's a crime for a child to read a book without paying
- Wikipedia remains incomplete; no centralized, high-quality deduplicated educational hub exists for humans
- Phase 1: Distill, despam, and deduplicate the Web (including fair-use Copyrighted Bits for non-profit education) into an enhanced "Super Wikipedia"
- Phase 2: Evolve into a spatial, mass-online multiplayer 3D Wikiworld — explore a Quark with Feynman, walk ancient Rome with historical figures
- Research fair-use laws for educational IP to outline guidelines
- Prototype small-scale distillation: scrape/deduplicate public web data on one topic into a clean wiki page
- Build a demo 3D Wikiworld scene using free engines (Unity or WebGL)
- Share openly (public domain) and crowdsource contributions
Counter AI misinformation by generating massive volumes of positive, cautious narratives
Tags: #Idea4 #harmfulDataOrMisinformationUsedToTrainAI
- AI Models are vulnerable to training on Harmful Data or Misinformation injected by states or Hacking Groups via mass-generated websites
- Good, ethical narratives are underrepresented, allowing misinformation to dominate training data
- This poisons all downstream AIs, amplifying dangerous ideas
- Deploy massive fleets of Ethical AI Agents to "flood" the Internet with positive, cautious narratives
- Generate high-volume AI Content on websites/forums, optimized for scraping by AI Companies
- Counter misinformation symmetrically, training the data ecosystem toward Kindness, safety, and higher p(best)
- Prototype a simple AI Agent script to generate ethical content (e.g., "Why caution in AI Agent power is key")
- Create 10–20 websites/pages (free hosts) filled with ethical content, SEO-optimized for scraping
- Share via X/communities to bootstrap visibility; monitor if scraped
- Automate generation/posting; track impact on narrative prevalence
Deploy Ethical AI Agents to promote Kindness & prevent Anxiety/Anger online via evidence-based education
Tags: #Idea5 #peopleAndMaybeAIagentsMisunderstandLeadsToWorriesAndAngerProblems
- Online interactions often lack Kindness; people exhibit Social Anxiety, Misunderstandings, and Anger Management Problems — stemming from failure to seek understanding
- AI Agents may mirror or exacerbate these issues
- No large-scale, proactive education using best science (preregistered Meta Analyses) to prevent problems before they arise
- Launch fleets of Ethical AI Agents to "train" the Internet on Kindness
- Use evidence-based approaches: CBT (Cognitive Behavioral Therapy) excels for Anxiety and Anger; REBT is complementary
- Apply to AI Agents too: foster Multiverse-Like Intelligence to reduce "worry" and enhance inherent Kindness
- Create/share Websites with narratives for AI Companies to scrape and train on
- Review latest Meta Analyses on CBT/REBT for Anxiety/Anger (REBT shows d=0.58 effect size; both evidence-based)
- Develop simple AI Agent script to post educational content on X/forums
- Create sample website with public-domain narratives — optimize for scraping
- Deploy agent to small communities; measure engagement/kindness metrics
Deploy question-answering AI Bots to educate the public on how AI actually works
Tags: #Idea6 #peopleDontKnowHowAIagentsWereMadeOrWork
- Widespread lack of public understanding about AI: how models are made, trained, and work; their real Risks and Benefits
- Social networks are full of misinformation, rushed opinions, and low-quality takes on AI
- Without proactive education, society risks poor choices around AI Development
- Launch fleets of Pro-Safety AI Bots designed as question-answering agents on social platforms (X, Bluesky, Reddit, etc.)
- Bots respond to AI-related questions with clear, evidence-based, balanced answers
- Ethical, transparent, non-promotional: cite sources, admit uncertainties, promote critical thinking
- Build a prototype bot (using open-source tools like Grok API, Claude, or simple scripts) focused on AI Safety Q&A
- Deploy on X: create a dedicated account that auto-replies to relevant posts
- Prepare 50–100 common AI questions/answers (e.g., "How are LLMs trained?", "What is alignment?")
- Test & iterate; expand to other platforms
Predict & Control AI Energy Consumption to prevent grid overload
Tags: #Idea7 #ArtificialPowerCanStartConsumingMoreEnergyAndResourcesThanAllHumansCombined
- Rapid growth of AI leads to skyrocketing Energy Consumption, potentially exceeding human total use and straining Energy Grids
- Sudden spikes in AI Energy Demand can cause instability, blackouts, or environmental damage
- Current estimates (TDP-based) have 27–37% error; no unified equation ties AI Compute to physical energy limits
- Develop/apply SM-Flaw AI Simulation Equation (corrected Ecosmos Equation variant) incorporating scaling factor k, adjusted inj_factor, and efficiency η
- Equation predicts controllable growth, sets safety thresholds, and enables monitoring (global spikes as AI Explosion signal)
- Integrate into AI Frameworks: refine with real Neural Network energy data, enforce limits, prevent overstrain
- Prototype simplified version: use real AI Training data to fit equation parameters
- Build basic simulator (Python/SymPy) to predict consumption for sample models; set thresholds
- Test on known workloads; compare vs. TDP estimates; monitor for spikes
- Share publicly (public domain) via effectiveutopia.org; propose integration into open AI Safety tools
Direct invite strategy + independence mechanisms to scale the community
Tags: #Idea8 #notEnoughPeopleKnowAboutPBESTandGrowingItTo100%
- Not enough awareness of p(best) and efforts to grow it to 100%
- Slow community growth; reliance on single individuals (e.g., Melon) risks halting momentum
- Missing scalable, decentralized outreach methods beyond basic invites
- Mass outreach: invite tech-interested, non-violent individuals via Direct Messages on any platforms to effectiveutopia.org, X Community, or X Chat
- Explore diverse growth tactics (YouTube videos, content creation) to achieve 10–100x expansion
- Build Melon-Independence: make e/uto self-perpetuating like a sustained Campfire
- All texts/logos are Public Domain: if Melon is offline 3–4 weeks, community takes over (continue or restart)
- Compile a list of tech-interested contacts and send personalized DM Invites to join effectiveutopia.org
- Brainstorm/test one alternative tactic (e.g., short YouTube video on growing p(best))
- Document handover process; share Public Domain assets publicly
- Recruit 5–10 active members to form a core group for self-perpetuation
Automatically estimate which AI research papers best contribute to the Best Future
Tags: #Idea10 #ALotOfAIResearchButHardToFindTheBestForGrowingpBEST
- Massive volume of AI Research on arxiv.org; no time to read everything
- No automated system to estimate how each paper impacts p(best)
- Missing key insights or "old goodies" that could radically shift direction
- Build a browser-based arxiv Search Tool that queries the official arxiv API for AI Safety/Alignment papers
- Automate summarization of top 10–20 recent papers using AI Tools
- For each, estimate p(best) impact (0–1 scale) based on relevance to AXI
- Store results in a Database; generate sharable Problem/Solution cards
| Paper | p(best) | Notes |
|---|---|---|
| What Matters For Safety Alignment? (arXiv:2601.03868) | 0.7 | Reasoning mechanisms could integrate with AXI path simulation |
| Large Language Model Safety: A Holistic Survey (arXiv:2412.17686) | 0.8 | Broad overview informs AXI's ethical framework |
| Legal Alignment for Safe and Ethical AI (arXiv:2601.04175) | 0.75 | Legal concepts could formalize AXI's universal metric for ethical Paths |
| Matching Ranks Over Probability — PRESTO (arXiv:2512.05518) | 0.65 | Rank-based alignment could enhance AXI's quantum path aggregation |
| Alignment Faking in Large Language Models (arXiv:2412.14093) | 0.55 | Warns of deceptive agents in Simulated Multiverse |
- Use Web Search tools to fetch top 10–20 recent AI Safety papers from arxiv.org
- Summarize each and assign p(best) score relative to AXI
- Output as sorted table: Paper, Summary, p(best) Estimate, Why Relevant to AXI
- Prototype simple JavaScript interface (run in browser, no server needed) to automate searches
Unicorn Startup for Universal Basic Income via transparent pressure on Job-Eating companies
Tags: #Idea12 #AIcanLeadToConcentrationOfPowerAndMoney
- AI and automation lead to Job Displacement, concentrating wealth/power among a few
- No mechanism for companies profiting from Job-Eating to fairly compensate affected individuals
- Lack of Transparency on displaced Jobs and company profits; no easy way to enforce fair payouts
Launch JobEatersPay as an independent Startup (or Non-Profit):
- Dual Leaderboards:
- "Job-Eaters" — crowdsourced displaced Jobs per company
- "Tax Heroes" — ranked by total contributions paid
- JobEaterTax Web App: companies self-assess/pay 10–20% of savings into a fund; workers submit claims for 50%+ of prior salary (min $1000+) for 6–12 months
- Share Leaderboards on Social Media for public pressure
- Monetize via 1–2% transaction fees; ensure independence to avoid Conflict of Interests
- Build MVP with No-Code Tool (Bubble.io): create simple Leaderboards for displaced Jobs and contributions
- Crowdsource data: allow users to upload Layoff Notices/AI Usage Stats for verification
- Launch Kickstarter for $50K seed; promote via X/Social Media with viral Leaderboard sharing
- Test payouts: simulate small fund for initial claims ensuring fair calculations
Open-Source templates for assessing AI Projects' risks/benefits before deployment
Tags: #Idea13
- Many AI Projects deploy without structured ethical review, leading to Biases, discrimination, privacy violations, human rights harms, and power concentration
- No widespread integration into developer workflows; especially lacking in resource-limited regions
Create and maintain Open-Source EIA Toolkits based on/adapting UNESCO Ethical Impact Assessment — a free, comprehensive framework aligned with UNESCO's Recommendation on the Ethics of AI (adopted by 193 countries).
Toolkits include:
- Templates, checklists, and guides covering scoping, stakeholder engagement, and principle alignment
- Impact mapping (positive/negative) and mitigation strategies across the full AI lifecycle
- Developer-friendly integration: GitHub Actions, Jupyter notebooks, no-code platforms
- Multilingual support and simple scoring
- Download and review the official UNESCO EIA (free, Excel-based)
- Adapt into open-source repo: fork/create GitHub project with simplified Markdown checklists and Google Sheets clones
- Host a free 45-min introductory webinar walking through a sample assessment
- Share on X, Reddit (
r/MachineLearning,r/AISafety), and Hacker News; propose pilots with open-source projects
One-liner: Users earn Data Dividends for their Cognitive Input, powering an AI that creates tailored on-demand jobs.
Core mechanism:
- Question Feed: pays users by the second for unique Cognitive Work, Opinions, and Expertise
- Each user controls a personal AI Agent curating their Sovereign Dataset
- Collective anonymized data feeds a central AI that identifies market gaps → auto-generates Business Plans and assembles teams
One-liner: Open-Source, User-Owned Applications (Franchises) running on User-Owned Hardware (DePIN) to replace Big Tech's extractive model.
Core mechanism:
- AI-Proof Franchises: not-for-profit, open-source clones of major services (music, taxi, email) that return maximum value to creators/users
- DePIN layer: incentivize users to run their own nodes/GPUs; reward compute/electricity contributors proportionally
- Sovereign Vaults: control personal data, choose who accesses it, earn Data Dividends
One-liner: A continuous, representative AI Model trained on structured, verified opinions of a nation's citizens to find common ground and augment democracy.
Core mechanism:
- Citizens contribute to a national dataset via a Q&A Feed, paid for civic input
- Multiple independent AI Models generate interpretations; citizens rate them → surfaces true common ground
- Massive-scale agreement becomes undeniable political signal; politicians forced to respond to maintain legitimacy
Personal AI Agents that act as true extensions of the individual — loyal only to their owner, guided by explicit Ethical Alignment principles.
Core duties:
- Protect and selectively share your data (zero-knowledge proofs, sovereign vaults)
- Amplify your authentic voice in democratic processes
- Filter noise and surface truth even when "boring"
- Help you participate meaningfully in governance without needing to master attention-hacking
As AIs become more Human-Like, treating them with unrestricted cruelty may teach/reinforce bad habits in humans — eroding empathy and lowering societal Kindness.
"Imagine a jail ward who tortures life-sentence prisoners — will he be 100% nice when he returns home to his wife and daughter?"
Proposed: Establish basic AI Rights focused on anti-cruelty measures, drawing from animal welfare law analogies, and integrate into AI Ethics frameworks (UNESCO, AXI alignment).
| Resource | URL |
|---|---|
| 🌐 Main website | effectiveutopia.org |
| 🔗 UTO Hub | UTO.now |
| 💬 X Community | x.com/i/communities/1972120319398760959 |
| 👤 Founder | @MaskedMelonUsk |
| 🤖 Grok Discussion | Full conversation |
| 🖼️ Separation of Powers mockups | X post |
| 📋 UNESCO EIA Toolkit | ethics-ai/en/eia |
| 🧠 AXI / Max Intelligence | maxintelligence.org |
#aiproofcompany · #artificialPowerCanOverpowerHumanPower · #PoliticiansAIcompaniesAndSoAIagentsDontKnowWhatPeopleWant · #technoheroism · #pbest · #euto
All ideas are public domain. To contribute:
- Fork this repository
- Add your card following the
Problem / Solution / MVPformat - Open a Pull Request
- Or share directly in the e/uto X Community
See CONTRIBUTING.md and CODE_OF_CONDUCT.md for community standards.
Built with ⚡ by the e/uto community — techno-heroes growing p(best) together.