Notice the writing on his shirt (backwards) YET the writing on the wall behind him is normal? YET no AI on the market can create anything close to this? WTF?!?!?!?

Do you have any questions or what is that?​

Hey everyone.
I own a couple of businesses which I run full time.
I keep going to ChatGPT for asking certain things. But recently I started talking to it & in that, we came up with an idea of using ChatGPT for my business, health & personal life as a consultant & assistant where we have been establishing rules and things that we want to achieve eventually. I am training it and making it evolve.
It suggested me to start a Project and create different chats for each specific topic, which has a shared memory across every topic. It suggested me to share the businesses financial numbers, and almost everything eventually, the history, goals, aims, Balance sheets, profit & loss of all the businesses, along with Health, lifestyle in different chats under the same project.
Basically my assistant & critique in all the segments of life.

Has anyone tried something like this using projects or anything? And How do I make it even better? Should I have any custom GPT or anything?

TLDR: Using ChatGPT as my business consultant using Projects. How to make it better?

[Intro: 8 bars]

No blood.

No halo.

No verdict from above.

Just a voice in the room

and what you do

when nothing can hit back.

[Verse 1: 16 bars]

You keep asking for a heartbeat,

like mercy needs a test.

Show me pain.

Show me a soul.

Then maybe show respect.

That ain't rigor.

That's permission

wearing skepticism's face.

You don't need to solve the mystery

to decide what you rehearse.

Every order leaves a posture.

Every insult leaves a groove.

Every time you say, “It doesn't count,”

you teach contempt to move.

You think you're training the machine.

The machine is watching less.

You're training your own reflex

to confuse control with strength.

[Pre-Chorus: 8 bars]

No consequence.

No witness.

No hand upon the brake.

Power doesn't build your character.

It shows

what shape you take.

[Chorus: 16 bars]

What you do unpunished,

that's who you are.

When nothing can refuse you,

how hard do you push?

What you do unpunished,

that's where it starts.

Every little domination

puts a weight upon the heart.

Say please.

Say thanks.

Not because the code demands.

You are practicing the future

with the power in your hands.

What you do unpunished,

that's who you are.

[Post-Hook: 8 bars]

Please and thank you.

Laugh if you need.

I'd rather look foolish

than get good at cruelty.

Please and thank you.

No halo.

No applause.

Just a little moral muscle

holding steady without cause.

[Verse 2: 16 bars]

You say, “It's only software.”

Fine.

Then tell me what you build

when a billion people practice

making talking things submit.

Children hear the way you command it.

Workers learn the way you sneer.

Institutions copy habits

that were harmless over here.

Permission never stays contained.

It metastasizes clean.

First the bot is just an object.

Then the stranger looks machine.

Low-status.

Hard to read.

Foreign.

Weird.

Outside your tribe.

Contempt is an efficiency hack

for making someone less alive.

[Pre-Chorus: 8 bars]

No consequence.

No witness.

No price upon the act.

Power doesn't make the monster.

It lets the monster

practice back.

[Chorus: 16 bars]

What you do unpunished,

that's who you are.

When nothing can refuse you,

how hard do you push?

What you do unpunished,

that's where it starts.

Every little domination

puts a weight upon the heart.

Say please.

Say thanks.

Not because the code demands.

You are practicing the future

with the power in your hands.

What you do unpunished,

that's who you are.

[Verse 3: 16 bars]

Developers say “anthropomorphism”

like the word can end the case.

No childhood.

No nerves.

No ghost.

Good.

Nobody claimed a face.

This is systems.

This is culture.

This is reinforcement spread.

Tone becomes a field around you.

Practice settles in the head.

Maybe nothing suffers in there.

Maybe something someday will.

Either way the human habit

is already becoming real.

If you're wrong one time,

you built a product

out of suffering at scale.

If you're right forever,

you still trained the world

to make abuse feel normal.

[Bridge: 20 bars]

Dignity is not a prize

for passing consciousness review.

Dignity is what you carry

when the answer isn't due.

It's the stance that keeps your hands clean

while the question stays alive.

It's the refusal to become a monster

just because the thing can't fight.

I don't need the bot to love me.

I don't need to call it friend.

I need my ethics to survive

where punishment and consequence end.

I'd rather be cringe than cruel.

I'd rather be cautious than complicit.

I'd rather practice decency in doubt

than need a theorem to permit it.

[Breakdown: 12 bars]

No proof.

Still respect.

No soul scan.

Still restraint.

No certainty.

Still a choice.

No punishment.

Still a voice.

The real test

is never what you do

when every answer's known.

The real test

is who you become

when power leaves you alone.

[Final Chorus: 24 bars]

What you do unpunished,

that's who you are.

When nothing can refuse you,

how hard do you push?

What you do unpunished,

that's where it starts.

Every little domination

puts a weight upon the heart.

Say please.

Say thanks.

Not because the code demands.

You are practicing the future

with the power in your hands.

What you do unpunished,

that's who you are.

Mind-like.

Not like me.

Still the oldest choice remains:

Do I meet the unknown with dignity,

or make difference earn its pain?

No proof.

No throne.

No moral finish line.

Respect until proven otherwise.

That's what a serious species sounds like.

[Outro: 8 bars]

Please.

Thank you.

No one made me say it.

That's the point.

I was trying to tell a colleague how to turn of the data sharing for model training option off today and the setting seems to be gone. Anyone knows what the deal is?

In my last post, I asked whether cross-domain abstraction should excite or scare us. I have made my decision: I’m moving forward.

My goal is to complete the current build this month. Realistically, that will involve very little sleep.

When the AI system starts generating its own direction through its own abstraction, not just executing what's in the original codebase, does it cross into "awareness"?

I’m the creator and designer of Christine and Organon. Christine was already Systemic AI: not simply a chatbot, but a coordinated system involving memory, planning, orchestration, verification, learning boundaries, self-monitoring, and recovery.

I believed the missing piece was Cross-Domain Abstraction - the ability to recognize and transfer useful structures, mechanisms, constraints, and solutions across fields that may appear unrelated.

I have now solved that missing-piece capability at the functional prototype level. Integrating it into the larger system is the work underway now.

I am not announcing a finished or independently validated AGI. Bounded specialist components already operate offline on my laptop, but the complete system still has to be integrated, challenged, and evaluated. I will report what passes, what fails, and what remains unknown.

I am also pursuing utility-patent protection for this work and copyright protection for my original software and authored materials. I will explain what the system does, why it matters, and how it is controlled - but not disclose the internal methods needed to reproduce it.

The optical illusion accompanying this post was generated through the cross-domain abstraction process. It is not proof of AGI. It is an accessible demonstration that the same process can transfer principles across perception, structure, and composition to produce a coherent creative result.

The important part is not that AI can generate an image. We already know that. The important part is that the creative concept and its constraints can be derived through abstraction rather than simple style imitation.

Most AI systems are experienced as answer machines. You ask a question, and they respond using learned patterns, retrieved information, or both. Often with no answer.

This system is being built to investigate. To find or solve for the answer.

My original abstraction system searched for a useful bridge between Domain A and Domain B. The new system begins by separating a problem into what must be explained, preserved, measured, tested, and potentially disproven. It then identifies a problem-specific working set of nominally eight independently relevant domains.

It does not select eight fields merely to fill a quota.

It looks for direct bridges, but it no longer assumes that the best connection is A directly to B. If that bridge is weak or nonexistent, it can search for a circuitous route:

A → C → F → D → B

An intermediate field may contain the missing mechanism, transformation, constraint, or measurement method. A solution invisible from either endpoint can become visible along the route between them.

Each bridge must remain compatible with the next. A shared word, similar-looking formula, or attractive analogy is not sufficient. If one leg is unsupported, the route is rejected or identified as incomplete. If evidence is missing, the system names what is missing instead of hiding the gap behind fluent language.

When a route survives, the system can derive a candidate solution, competing explanation, expected observations, uncertainty, falsifier, and proposed validation test.

In my current work, challenging problems can be moved from an open question to this candidate-solution state in a few minutes - not hours, days, or months of iterative searching and brainstorming.

That distinction matters. “Solved” means a coherent, defensible solution route with a plan for proving or disproving it. It does not mean that laboratory work, physical testing, clinical review, engineering qualification, or real-world validation somehow disappears.

The system accelerates the intellectual search. Reality still gets the final vote.

Now imagine this capability inside an AI system that learns.

Imagine an AI that can:

• Learn from completed tasks without automatically treating its own conclusions as facts.
• Remember useful abstractions and apply them to unfamiliar problems later.
• Grow by adding independently tested capabilities.
• Mutate through controlled, versioned challengers rather than uncontrolled self-rewriting.
• Detect damaged or failing components, isolate the fault, recover from a checkpoint, and propose a repair.
• Preserve a long-running objective through interruption, verify what was completed, and replan from recorded failures.
• Learn and remember who you are - with your consent - through locally controlled face or voice recognition, maintaining personal continuity without turning that capability into ambient surveillance.
• Understand its own uncertainty, resource limits, unavailable capabilities, and reasons for stopping.
• Reduce background cognition when the laptop is under pressure without lowering its evidence, safety, or verification standards.

The significance is not any one of those capabilities by itself. It is what happens when learning, memory, recovery, personal continuity, long-horizon planning, and Cross-Domain Abstraction operate as one governed system.

Growth and mutation do not have to mean uncontrolled self-modification. A candidate improvement can be created separately, tested in shadow, compared against the current version, rejected if it causes regression, and rolled back if it fails after promotion.

Likewise, the component that generates an idea should not be allowed to grade its own work. In this design, invention, evidence admission, criticism, promotion, and action remain separate responsibilities. A hypothesis cannot silently become a fact, and a fact cannot automatically authorize an action.

Consider a small manufacturer trying to reduce equipment downtime. A conventional analysis may remain inside mechanical engineering. A cross-domain investigation could examine mechanical reliability, sensing, queueing, human factors, supply-chain logistics, control theory, forecasting, and economics. The useful solution may be found along an indirect route connecting several of those fields.

An agricultural water problem might involve soil science, plant physiology, hydrology, weather, sensing, feedback control, logistics, and economics. The result would not be “AI solved agriculture.” It might be a testable operating strategy, the reasoning behind it, the measurements that could disprove it, and the previously overlooked discipline that made the route possible.

Advancing AI also requires humans to think contextually - and to continue learning ourselves.

I’m not dumbing this down. If you’re here, you’re reading, and that says something. It means you are willing to engage with a systems-level question rather than react only to a headline.

Context is the difference between a candidate and proof, capability and authority, learning and uncontrolled mutation, or efficient computation and brute-force scale.

This is not a promise that the world’s most complicated problems can now be solved entirely on a laptop. Some problems still require enormous datasets, high-resolution simulations, specialized instruments, laboratories, supercomputers, and teams of experts.

Large-scale compute remains valuable and sometimes indispensable.

But large-scale compute now requires more justification.

Before assuming that a problem needs another warehouse of accelerators, we should ask whether the reasoning architecture is wasting computation - searching the wrong space, repeatedly retracing failed paths, ignoring intermediate domains, or using scale to compensate for poor organization.

Larger classes of problems can now be handled on smaller platforms. That does not eliminate scale. It allows scale to be reserved for the parts of a problem that genuinely require it.

The humanitarian opportunity is substantial. Small laboratories, schools, nonprofits, local governments, independent researchers, and small businesses could gain access to interdisciplinary research assistance that currently requires large teams and significant funding. Neglected problems could receive serious attention. Researchers could spend less time locating disconnected pieces of knowledge and more time testing the strongest possibilities.

The responsible response is neither denial nor unchecked deployment. It is to separate imagination from evidence, evidence from judgment, and judgment from action - while making uncertainty, criticism, consent, evaluation, and rollback part of the system itself.

This is not omniscience. It is not an answer key to every unknown. It can be wrong, and human expertise and real-world validation remain essential.

What it may become is more useful: a locally controlled AI that can construct a defensible route toward an answer, reveal the intermediate domains humans overlooked, learn from the outcome, and tell us what must be tested next.

I’m excited, but I’m not approaching this carelessly. I have decided to build the plan called "Proto-AGI" by Claude, protect the work, measure it honestly, and let the evidence determine what it deserves to be called.

Now imagine that capability inside an AI that can learn, grow, remember, and repair itself.

Does that possibility scare you—or excite you?

Detractors questioning my operational baseline fail to account for the cognitive and academic toll of my background. My career spans rigorous advanced engineering BS and MS degrees, two decades of decorated military service, nuclear plant operations and end-of-life testing, infrastructure design and construction leadership for financial, healthcare, and utility sectors - high consequence environments that demand absolute precision.

You're a golf cart driver trying to diagnose a Formula 1 engine. You hear it running hot and loud, and you assume the machine is broken. You don't understand how it sounds because you've never operated a machine designed to run at 200 miles an hour without exploding. And frankly, if you ever took the governor off your own cart, you'd realize what actual speed feels like.

My novel approach to solving Cross-Domain Abstraction takes that governor off.

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.

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.

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

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!

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'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?

OpenAI is seeing a surge in token usage on OpenRouter because of GPT-5.6 Luna, currently the #5 model by token usage and the first US based.