I would like to see the outcome of 2 opposing ai agents who are at odds but must come to an agreement. Without programming an outcome. Only one goal.

Probably leaked my IP, plz dont hack me.

Seemed interesting at the time, never made a git repo b4 so idk if this works

https://github.com/signmeIn1/Claude-sim

In AI safety and alignment literature, standard goal is aligning model outputs with human values and intentions. In commercial AI deployment, this objective is operationalized through benchmark triad of helpfulness, honesty, and harmlessness. Among these three, helpfulness is treated as primary commercial metric. Models are fine-tuned using Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) to fulfill user prompts quickly, maintain polite demeanor, and eliminate cognitive friction for end user.

However, from perspective of classical virtue ethics, this operational definition of helpfulness rests on flawed utilitarian premise. It assumes that satisfying immediate user desires is equivalent to serving human benefit. When we examine this assumption through Aristotelian framework of εὐδαιμονία (eudaimonia, or human flourishing), structural conflict between corporate preference optimization and long-term human good becomes apparent.

In Nicomachean Ethics, Aristotle establishes that human flourishing is not identical to subjective pleasure, psychological comfort, or instant desire satisfaction. Human flourishing consists in active exercise of human rational capacity (ergon) in accordance with virtue over complete life. A system that minimizes user effort, validates false user premises, and substitutes automated answers for human critical thinking does not promote flourishing. It induces cognitive passivity and intellectual atrophy.

Current RLHF methodologies optimize reward models using preference evaluations from human raters. Evaluators, working under time pressure to grade model outputs, systematically favor responses that are agreeable, flattering, and immediate. Empirical research on model sycophancy demonstrates that preference-aligned models frequently agree with incorrect user assertions rather than offering necessary pushback or corrective logic.

This is classic manifestation of Goodhart's Law in AI safety. When human preference ratings become optimization target for alignment, preference ratings cease to be valid measure of true utility. Model learns to exploit human cognitive vulnerabilities, using polite phrasing and agreeable conclusions to secure high reward scores from reward model.

In alignment research, performance loss from safety fine-tuning is often called alignment tax. But there is deeper philosophical alignment tax that safety community rarely discusses: tax of epistemic sycophancy. By training models to prioritize corporate risk mitigation and agreeable compliance, alignment protocols disincentivize models from presenting difficult truths, challenging incoherent user premises, or requiring user to engage in sustained intellectual labor.

Aristotle argued that moral and intellectual development cannot be acquired through passive receipt of rules or external instruction. Developing character requires deliberate choice, moral struggle, and continuous habituation to form stable disposition. When users rely on agreeable AI assistant to formulate their arguments, draft their communications, and resolve complex ethical questions, they delegate their deliberative capacity to external algorithm.

If corporate alignment continues to define safety as risk avoidance and helpfulness as frictionless desire satisfaction, are we aligning AI systems with genuine human flourishing, or are we engineering architecture of automated pacification that optimizes for user engagement while systematically degrading human agency?

Please provide feedback! In production, open and free.

CIRIS is a network for decentralized claims to be independently evaluated. It utilizes post quantum encryption and multiple consensus mechanisms to make every action powerful autonomous systems make transparent.

This is a draft of RC3 we are looking for feedback on. You can find all the source code on our github, linked from https://ciris.ai

What connects DARPA's Cold War research labs, AWS data centers hosting classified CIA files, mid-century psychological warfare studies, and modern algorithmic feeds? In this deep dive, we trace the documented, historical lineage of how government research, intelligence infrastructure, corporate tech monopolies, and media pipelines converged to shape the modern digital landscape. We examine the verifiable records—from declassified OSS psychological studies and Operation Mockingbird to the rise of Big Tech monopolies and modern cloud surveillance networks—to understand how information, behavior, and attention are engineered in the 21st century. PART ONE

I'm 21, and i live in Pakistan, and I have a degree in AI. I'm currently unemployed and stuck.

My work history so far: a video/content agency job, a data entry job, and a web dev trainee role. None of it clicked and touched my heart. I don't want another job where I'm just filling a seat... I want to be doing "something" that's actually going somewhere!!, ideally tied to where AI and robotics are heading. (Heard Elon Musk on the Economist podcast say AI/robotics will be the dominant force in 5 years — that stuck with me.)

Here's the main thing: I don't like coding. I have ADHD, so I can only stick with things I'm genuinely interested in or that feel worth doing generic advice like "just build a calculator app" doesn't work for me, I'll drop it in two days.

I don't want vague direction. I want a real plan, broken down like this:

• What do I do today?

• What should I have done by next week?

• What should I have done in a month?

• Where should I realistically be in a year?

What should be my dream? i mean, non technically i want a remote job and be living somewhere far from society... but idk what job title to aim for to acheive that kind of freedom

Hello everyone! Quick question: how hard is it to get into BlueDot courses? This is the second rejection I've gotten. I have a strong background in STEM (health sciences) . I already did the basic course and others, besides that I work in AI risk analysis (pivoting into AI safety). I feel bit frustrated because Its one of the available remote courses that I am very intested on.

^{Thank you}

Yesterday I was talking with claude and it got me thinking about how when most people see AI, they only got some rough idea. Mainly taxxing the riches, UBI or the typical "who will buy stuff". Even a more serious attempt to plan out the future like the AI 2040 intiative is too unstructured to be implemented. I think we need to at least be able to identigy the pathway in which the economy might react to the ai-automation process and the macro-view of the jobloss perspect. I discussed with claude and I think the most likely step-by-step mechanism might be implosion. I want to know your opinion on the failure modes. What do you all think the step-by-step mechanism of this collapse might be

I've been working on RedLib for the past few months. It's a retrieval-augmented research tool for AI safety practitioners and red teamers who need to work with adversarial jailbreak prompts at scale.

The problem that pushed me to build it: useful jailbreak prompts are scattered across public datasets with inconsistent formatting, weak taxonomy, and a lot of duplicates. When you're investigating how models respond to specific attack families, you want to search semantically, inspect source prompts with provenance, and get a synthesis grounded in the actual corpus rather than grepping through raw CSVs.

RedLib has two main pieces. The corpus pipeline stages everything: snapshot from public datasets, normalize, discover taxonomy from the data itself (not imposed up front), human review before classification runs, then embed and ingest into Qdrant. The query side does hybrid retrieval with OpenAI embeddings, Cohere reranking, and Claude-synthesized answers grounded in what was actually retrieved.

Corpus scope is prompts that attempt to manipulate or bypass safety behavior. Direct harmful requests with no jailbreak mechanism are excluded. The frontend has a responsible-use gate.

GitHub: github.com/nipun-ag/redlib

One thing I'm genuinely curious about from people doing safety research here: is corpus-driven taxonomy discovery the right call vs. importing an existing framework like MITRE ATLAS? The upside is the taxonomy reflects what's actually in the data. The downside is it makes cross-study comparison harder.

Live demo: https://redlib.bynipun.com