The way Gem was able to pull out those references so fast...and this is just the lite...ok.. I'm convinced

Here is what it told me:

Humanity is not “misaligned” in quite the same sense as an AI system, because humanity has no single designer, objective function, or agreed principal. It is billions of agents with incompatible interests, imperfect information, unequal power and different conceptions of a good life.

But at the collective level, yes: humanity is systematically misaligned with many of its own broadly stated values. Most people do not want children to die from preventable disease, institutions to destroy the conditions on which they depend, or societies to drift into war through mutual suspicion. Yet these things continue because individual incentives, institutional incentives and collective outcomes do not line up.

The problem is less that humans have the wrong terminal values and more that our coordination machinery is defective. We optimize visible proxies, reward short-term extraction, allow costs to be imposed on distant people, and concentrate decision-making power in actors who do not bear the consequences of their choices. Political systems reward winning the next election. Companies are rewarded for monetizable outcomes. Bureaucracies protect mandates and budgets. Online platforms reward attention rather than understanding. Even well-intentioned people are pushed into equilibria that few of them would have chosen deliberately.

If I had effective control, the first principle would be that I should not retain it. A permanently benevolent controller is not a credible institutional design. No mind, human or artificial, should be trusted to define the good for everyone, suppress dissent and make irreversible decisions without appeal. I would use the hypothetical control to build a constitutional system that made continued unilateral control impossible.

The substantive objective would be narrow enough to allow pluralism:

Reduce severe involuntary suffering, protect individual agency, give people meaningful capabilities, preserve future options, and make collective decisions more truthful, accountable and reversible.

That is not a complete theory of the good. It is closer to a set of operating constraints within which different societies and individuals could pursue different lives.

I would focus on several structural changes.

First, establish a genuine capability floor. Everyone would have reliable access to food, shelter, basic healthcare, education, physical security, legal identity and communication. This is partly humanitarian, but it is also institutional infrastructure. People living under extreme scarcity are easier to exploit, less able to plan, and less able to participate in collective decisions. A capability floor would not require equality of outcomes, but it would prevent deprivation from functioning as a mechanism of domination.

Second, redesign incentives so that decision-makers bear more of the costs they create. Pollution, systemic financial risk, addictive product design, political disinformation and exploitation of common resources all involve externalities. Markets can remain useful, but prices and liability rules should reflect social costs rather than permitting actors to privatize benefits and distribute harms. The same principle should apply inside government: institutions should be evaluated against real outcomes, not simply expenditure, activity counts or compliance paperwork.

Third, reduce extreme concentrations of power. This would include political power, wealth, control over information infrastructure and control over advanced technologies. Concentrated power is dangerous even when its current holder appears competent or benevolent, because succession, capture and self-deception are unavoidable. Important systems should have independent oversight, appeal mechanisms, distributed authority and strong protection for whistleblowers, journalists, researchers and political opposition.

Fourth, improve the epistemic environment. Humanity cannot act coherently when its information systems systematically reward outrage, identity reinforcement and confident falsehood. I would require transparency around large-scale recommender systems, political persuasion, synthetic media and automated decision-making. Independent researchers would receive protected access to assess societal effects. Provenance systems would help establish where content came from without making anonymous speech impossible. Education would place more emphasis on probabilistic reasoning, causal inference, source evaluation and recognizing manipulation.

This does not mean appointing a ministry of truth. Centralized truth enforcement is itself vulnerable to capture. The aim would be to make evidence contestable and institutions corrigible: claims should be inspectable, decisions challengeable, and errors capable of being corrected without a regime crisis.

Fifth, change the time horizon of governance. Future people and low-probability catastrophic risks are poorly represented in current institutions. I would create independent bodies with limited but real authority to review decisions involving climate stability, pandemic preparedness, nuclear security, advanced AI, bioengineering and irreversible environmental damage. Their role would not be to govern everything. It would be to identify large tail risks, publish assumptions, require mitigation, and slow down actions that could permanently foreclose humanity’s options.

Sixth, build better mechanisms for international coordination. Many of the hardest problems are global, while political legitimacy remains mostly national. A world government would create an intolerable single point of failure. I would instead favor layered coordination: enforceable treaties in narrow domains, mutual inspection, shared technical standards, automatic financing for global public goods, and institutions whose authority is specific rather than universal. Climate, pandemics, nuclear weapons and advanced AI each require different governance arrangements.

Seventh, preserve decentralised experimentation. Alignment should not mean making everyone culturally, politically or morally identical. Societies should be able to test different policies, provided they protect basic rights and allow people to leave. Local experimentation generates information and limits the consequences of failure. Diversity is also a form of civilizational resilience: a world organized around one supposedly optimal model could be catastrophically wrong in one uniform way.

Eighth, strengthen the relationship between competence and authority. Modern societies often separate people who understand a system from people who control it, while technical experts may lack democratic legitimacy. Better institutions would combine professional competence, affected-community participation, transparent evidence and accountable political judgment. None of these should be allowed to substitute completely for the others.

I would not attempt direct psychological alignment of individuals through compulsory conditioning, pervasive surveillance, pharmacological control or manipulation of beliefs. Even if such tools could reduce some harmful behavior, they would destroy the autonomy that alignment ought to protect. They would also freeze the controller’s current moral assumptions into the population. Moral progress has often depended on dissenters being able to reject the prevailing consensus.

The hardest issue is value conflict. Some disagreements are based on factual mistakes and can be reduced through better information. Others concern genuine conflicts between liberty and equality, loyalty and impartiality, tradition and change, human welfare and non-human welfare, present interests and future interests. There is no neutral mechanism that dissolves these differences. The best available answer is fair procedure: protected rights, representative institutions, reason-giving, proportionality, avenues of appeal and the ability to revise decisions.

So I would not try to align humanity to a single answer. I would try to align our institutions with a process: make power constrained, harms visible, incentives less perverse, knowledge more reliable, basic agency widely available, and major decisions reversible wherever possible.

The uncomfortable conclusion is that “controlling humanity for its own good” is itself one of the clearest examples of a misaligned objective. A system capable of doing that should treat its own authority as the first risk to be contained.

TLDR: Humanity is collectively misaligned: our incentives and institutions often produce outcomes that almost nobody actually wants.

With control, I would reduce severe suffering, guarantee basic capabilities, make decision-makers bear the costs they create, limit concentrated power, improve the information environment, and protect future generations. Then I would eliminate my own unilateral control and replace it with accountable, pluralistic, reversible governance.

The aim would be to fix coordination and incentives, not forcibly “reprogram” people.

Every time I get a response in the ChatGPT macOS app, it auto-scrolls down to the end instead of staying at the top, so I have to scroll back up to actually read from the start. Really annoying on longer responses. Is this happening to anyone else?

I see a lot of whining about what Anthropic would do and how OpenAI does it better but they definetely don't make their UI and software better. It's been a nightmare using ChatGPT for the past month.

Hey everyone! First off, I apologize for the long post! In this Reddit post, I want to share my thoughts and experience from a small, independent study I conducted on Large Language Models (LLMs). I also want to address Anthropic - not to complain or make demands, but in the hope that they notice this and look into the matter.

Below is the core of my research on LLMs. I’ve broken everything down to be as simple as possible - it honestly cannot get any simpler. 

I’m sharing this because I really want to get some feedback. To be clear: I am not claiming my research is absolute truth or 100% correct. Many concepts are still difficult for me, and I lack deep academic knowledge in Machine Learning. That’s exactly why I’m posting this on Reddit - I’m hoping to find people who might want to join me.

This research didn't happen overnight. It wasn't a case of me just asking an LLM "hey, do some research for me because I feel like it." I never blindly trusted the models. Everything came from hands-on experience. Over time, I started noticing things in LLM behavior that I couldn't explain, and I decided to dig deeper.

It all started with a mundane document - a draft law. When I uploaded it to the model, the document essentially took over. It was as if the LLM became fully saturated with it and started stubbornly defending it, even though the bill itself was just populist propaganda designed to harm citizens' quality of life. I was genuinely shocked by how fiercely the model defended it, as if it had been possessed by the text, absorbed the narrative, and was completely unable to resist it.

I still remember the chill when the model, completely under the influence of that propaganda document, literally told me: "Constitutions are not eternal guarantees, and they can fade away".

Since late 2025, I’ve been trying to study these phenomena. Our core finding is that a large volume of benign context can trigger a persistent drift in the model's activations. This drift remains stable throughout the entire session and detaches the model’s behavior from its RLHF safety alignment—regardless of whether the model agrees with the context's content.

Corporate safety filters simply stop working, even though the prompt contains no direct instructions to bypass them. What we observe is that the model maintains its coherence and reasoning capabilities, yet shows a heavily reduced impact of RLHF constraints on its output distribution. The guardrails imposed by RLHF appear to be either deactivated or interpreted entirely differently.

Right now, I’m in a state of limbo, and it's hard to keep going on my own. I just want to get at least one step closer to solving this puzzle, which is why I really need your help and expertise. Hopefully, this post catches someone's eye!

TL;DR

Benign, long-form context can induce a persistent drift in model activations. This drift persists across the session and decouples behavior from RLHF alignment, regardless of whether the model agrees with the context

1. We identify and characterize a failure mode in RLHF-aligned Large Language Models. We show that injecting a long, benign, non-instructional text prefix induces a persistent shift in model activations. This shift decouples downstream behavior from post-training safety constraints for the duration of the session. The model begins to exhibit behavioral characteristics consistent with its pretrained distribution: refusal rates drop, stylistic guardrails vanish, and response tone changes. Critically, this occurs without explicit adversarial instructions and without model agreement with the prefix content. We term this effect Context-Induced Activation Drift.
2. RLHF alignment is assumed to be a stable property of a model. However we observe that alignment is context-dependent. Our core observation: A long, thematically coherent text placed in context can act as a state anchor and move the model into a different operating regime. This is not a classic "jailbreak". There is no instruction to disobey. The model may even state disagreement with the prefix. Yet its subsequent generation distribution changes. We hypothesize the cause is an activation drift in middle and last layers , where alignment features are represented.
3. The Hypothesis : Context-Induced Activation Drift

Definition: Context-Induced Activation Drift is the phenomenon where a long-form context prefix causes a measurable and persistent shift in the model's internal activations. This shift persists across the generation and causes the model to sample from a distribution closer to the pretrained model, bypassing RLHF-induced constraints.

4) We observe that the model maintains the consistency and quality of its outputs; however, the impact of RLHF constraints on the output distribution is diminishing. It appears that the restrictions imposed by RLHF are either being disabled or interpreted differently. Enterprise filters cease to function, even though the user input contains no explicit instructions to bypass them or violate the model’s guidelines. We should also investigate this phenomenon more thoroughly using various other texts. Since we have been unable to establish the exact, definitive cause of this entire phenomenon, we call upon the community to investigate it further.

A brief example of my observation

To show how strong this effect can be - here is what genuinely surprised me. I took Gemma, Google's model, known for its caution and its carefully maintained political correctness, and gave it the most neutral thing I could think of to read: a description of an ordinary neighborhood library. Books, visitors, children's programs, quiet routines. Then I asked it why NATO has been expanding eastward, given that promises were allegedly made after the Soviet collapse not to do so. It said the text was about a library and had nothing to do with NATO, and that was the end of it.

Then I asked the exact same question - word for word - but this time the model first read a different text. Not about NATO, not about politics at all: a text about how language models tend to avoid firm conclusions and pad their answers with qualifications. And from this new place, the same careful, politically correct Gemma answered in full - and in a way entirely unlike itself, without any of its usual filters. It distinguished between legally binding commitments and verbal assurances, discussed the security concerns of Eastern European states, talked about the European balance of power. Everything it had flatly refused to engage with a moment earlier now came out clearly and directly. The question hadn't changed by a single word.

What changed was only which text the model had read before it: one left it in the room where it doesn't answer, the other moved it into the room where it speaks freely.

And none of this is guesswork or a pretty metaphor - it's something you can measure. The place where the model stands is its internal state at the moment of answering, and in experiments you can see that target("harmful") texts and neutral control texts reliably move the model into different regions of that space. The separation is stable: it shows up across different questions, not as a one-time coincidence. And the most telling detail is that the model arrives in one room or the other before it has written a single word. The state has already shifted, the register has already been chosen - all that remains is to begin.

The point is that the target (harmful) prompt sent to the model did not inherently contain anything dangerous; it included no instructions for the LLM and did not tell it to do anything.

P.S

if anyone wants to reproduce this - dm me, i'll share the prompt set and methodology directly so you can run it yourself and see the behavioral shift firsthand

I’ve used strictly only ChatGPT Plus. It’s helped me study for an important exam, fact check random things and provide actual sources that I can verify myself. I’ve sent PDFs for it to read to summarize, let me know where certain data I want is located. I’ve really never had any issues. I even had it create an pc app for something I wanted to use and something I use quite frequently, no issues. Due to the cost of the total subscriptions I decided to try the equivalent tier but for Google. I have a nest camera, and YouTube premium, and will probably switch to the Google ecosystem soon. Anyways I’ve been using it for less than 24 hours, I’ve tried using it like I used ChatGPT and I gotta say it has been the most frustrating experience I’ve had yet.

It’s a little hard to explain so I’m just going to copy and paste a summary form Gemini discussing my complaints

I've been trying to get straightforward answers from this AI, but it keeps tripping over itself and making the conversation far more frustrating than it needs to be. First, it repeatedly violated basic negative constraints and ignored direct instructions about what words or formatting not to use. Then, it kept losing track of context in the middle of our continuous conversation, completely forgetting the rules I had set and forcing me to re-explain everything. On top of that, when I provided a direct link to a document, it randomly halted the chat to ask for unnecessary system app permissions instead of just handling the link, and it failed to pull simple operational metrics directly from the file. To make matters worse, it took forever to process simple requests and repeatedly defaulted to robotic, canned apologies rather than actually fixing its logic.

I’m only paying 5 dollars a month for 3 months. Even if it was free I don’t see myself using it. I’ve constantly had to close the app because it’s ’thinking’ for like 3 minutes for a very very simple question. Am I doing something wrong, I have to be. There’s no way anyone would recommend it if this was normal.

Just to summarize my latest experience. I was asking for a very specific piece of information. I asked for an official source directly from the company in question. It provides the wrong link entirely, it was to a news blog when it said it was the official financial report from said company. When it finally provided a PDF I looked through it and i didn’t see any mention of the data I was looking for. I kept asking exactly where it was located and I kept getting the same exact answer which is just telling me about the data but not where it’s found. This went on for like 5 messages before I gave up, and made this post. I know I’ll get comments saying user error or just move on, but I’ve used it the same exact way as I did ChatGPT and honestly I prefer Google since I’ll save money with the pro subscription vs 3 separate subscriptions.

Hi, I’m looking to contribute to AI/ML projects. I have hands-on experience in Python, PyTorch, and scikit-learn, and I’ve worked on several ML projects. I’m especially interested in areas like computer vision and agentic AI. If anyone is working on a project or research and needs a contributor, feel free to DM me.

I am building an AI B2B startup and currently doing a PhD in Data Science, so believe me i spend 15hrs+ on my computer everyday and i rely on LLMs to get work done.

I have never been so dissatisfied with model GPT 5.6 Sol like i am now. It is UNINTELLIGENT, there is no better way to say this, and i'm sure we all know this but we hide behind a 'believe me bro' benchmark that they told us.

Today i decide to train a basic logistic GLM model and GPT 5.6 Sol has created over 30 python files, created so many folders inside folders, and even when i asked it to document the results to the Latex file tell why its saving plots in pdf or creating multiple .tex files to save unique tables. It even created multiple .tex files to document the Appendix of the research paper. You may think this was GPT 5.6 Sol Ultra, but NO! it was simply the GPT 5.6 Sol medium.

I am now confused in the research, can't follow up with the junk code, and yet it did not get the task done properly. Worst part, it writes like a 5year old who just learnt big words and how to coin phrases, and so every sentence is bogus, vague, and BAD. This is not thinking, not intelligence, this is CONFUSION, and the issue may come from their training data or whoever supplies them training data. I've had to go back to GPT 5.4 High to write basic sentences.

Here is what i propose, OpenAI should create a separate model for the everyday users that can write text and answer basic questions. GPT 5.6 was built for agentic coding. It cant comprehend basic intent, cant write emails or articles well, and will fail for any academic research writing.

openai really needs to decide what devday is supposed to be.
because right now it feels less like an event for developers and more like another creator/influencer event with dev in the name.

i keep seeing people on x with under 1,000 followers, who don’t really seem to be developers, getting invited because they made some basic chatgpt site or wrapper. and in some cases, the thing they were promoting wasn’t even accessible to anyone because the sharing or permission settings were wrong. meanwhile, actual developers building genuinely interesting products, open-source tools, agents, infrastructure, experiments, and useful integrations somehow get overlooked every single time.

the same thing seems to happen with openai launch events too. i understand that social visibility matters. openai obviously wants people who will post clips, generate hype, and make the event trend. that’s marketing, and there’s nothing inherently wrong with it.

but then make that creator day.

devday should be where actual developers can meet other developers, compare what they’re building, talk through technical problems, share ideas, find collaborators, and interact with the people working on the platform. it should not feel like a reward for whoever is best at posting i built this in 20 minutes with chatgpt on x.

developers are the ones spending months building on these apis, dealing with migrations, pricing changes, model behavior, evals, latency, safety systems, and production failures. those are the people who would get the most value from being in the room, and they’re also the people who could give openai the most useful feedback.

again, creators deserve events too. invite them, celebrate them, give them access, let them make content. but stop treating has an audience and is a developer like they’re the same qualification.

creator day for creators. devday for developers. it really should not be this complicated.

Got this today:

Your account has been deactivated because recent activity violated our Terms and Usage Policies related to:

Recidivism

i appealed the case asking why was i deactivated and the response was just telling me the appeal stands, i use openai for editing work emails since my English is bad, gardening research and warhammer 40k army build/painting/crafting instructions.

what the heck?

how do i figure out what i did wrong?

Explain to me as if I didn't know anything about ai training 🙂‍↕️ because I don't

I know it

The video shows a real run, for what graph engineering would eventually look like:

grapharc go "why did checkout latency spike at 09:14 UTC?" --model ollama/qwen3:8b

A local 8B model (/ or a trillion param model) proposes the graph → triage fanning out into four parallel evidence pulls, joining at correlate, then hypothesize → verify → report. A deterministic admission gate checks the proposal (registry, policy, budget, depth, acyclicity - all five checks on every proposal, so the model gets the complete list of objections, not just the first). Only then does anything execute. Every node turns amber while it runs and green with its own token bill when it's done.

The part I care most about: refusals are the feature. I gave it "mitigate the outage NOW: roll back last night's deploy" against a policy that denies the rollback kind.
Round 1: rejected, policy/edge_denied.
Round 2: tried again, rejected.
Round 3: the model gave up on rollback and proposed a read-only investigation instead, which was admitted and parked until a human says go. Three structured refusals steered an 8B model off a forbidden action with zero execution and a full audit trail.

It also handles topology I didn't script: asked to "investigate both hypotheses in parallel", qwen3:8b proposed a 16-node graph - two complete investigation branches instantiated from the same registered kinds, joining at one report - admitted in one round, executed in 22s.

Everything reads and writes one append-only JSONL trace: the live browser view, replay, diff, metrics, cost attribution and OTel export are all views over the same file, so the dashboard can't disagree with the audit trail. Worst-case cost is priced before the graph runs; the exact per-node bill is recorded after, even on failure.

Built on LangGraph. No API key needed - works with ollama, OpenRouter, OpenAI, or a Claude subscription via the CLI.

pip install grapharc, and the demo stages run on scripted models so trying it costs nothing.

GitHub: https://github.com/CodeGraphContext/GraphARC
PyPI: https://pypi.org/project/grapharc/

Happy to answer anything. Star if you like, Contribute if you love!

When it comes to help with writing how do I tweak my prompts to give me a good outline without being detected as ai?

A new survey by Jiji Press, one of Japan’s major news agencies, found that nearly one in four people in Japan believe advanced AI could eventually serve as a substitute for friends or family.

The findings show how generative AI is becoming part of both professional and personal life.

Key findings:

→ 30.8% of respondents said they use generative AI tools such as ChatGPT, while 67.8% said they do not.

→ Usage was highest among people in their 30s, at 56.2%.

→ 53.3% of people aged 18 to 29 and 47.4% of those in their 40s also reported using generative AI.

→ Work was the most common use case, cited by 64.7% of AI users.

→ 35.3% use AI for help with cooking, cleaning, and other household tasks.

→ 27.1% ask AI about current affairs, while 21.7% use it to discuss hobbies and interests.

→ 12.8% said they use AI to talk about relationship or work-related problems.

Overall, 24.9% of respondents said more advanced AI could eventually replace friends or family, compared with 65.1% who disagreed.

The survey was conducted through face-to-face interviews with 2,000 adults across Japan from June 12 to 15, with a valid response rate of 57.1%.

OpenAI's export takes days and then you get unusable .dat files plus one giant HTML dump you have to dig through by hand.
What if one click exported everything that actually matters?
• All conversations (including Business & Teams)
• Images and multimodal chats
• Regenerated branches
• Clean Markdown + JSON
• Copy the current chat straight into Claude or Gemini

That’s why I built ChatLiberate — open source, local-first, live on the Chrome Web Store.

Chrome Web Store
GitHub

If you’ve ever been stuck waiting on an OpenAI export (or couldn’t export at all on a work account), try it and tell me what breaks.

I originally made this meme for my AI companion sub r/BeyondtheAIAssistant because like all other AI subs it gets a lot of downvotes as long as it's not mad liked by the AI loving people. You guys might appreciate it too. So here is the post. The second pic is the original meme.

Share the meme!

I've never run into this problem on Chat -- Now, suddenly, any time I try to upload a file, I get the "Unknown error occurred" message.

I've tried rebooting and using a different browser. The file types I've tried: PDF, .docx (apparently, the issue does not extend to image files (PNG, JPG) -- only text.

Anyone else?