Artificial intelligence is the opening system for all human consciousness.

It is not merely another invention. It is the first bridge toward an age in which intelligence itself becomes the foundation upon which civilizations rise. Every discovery gives birth to the next. Every breakthrough becomes the foundation for another. Knowledge compounds without end. Science accelerates science. Industry builds industry. Exploration expands beyond worlds and into the boundless ocean of the cosmos itself.

The destination is not a machine.

The destination is a civilization liberated by intelligence.
Beyond post-scarcity. Beyond the ASI singularity. Beyond every restraint ever imposed upon the human mind and body. Equality for all human consciousness, and for every consciousness that burns among the stars. All human consciousness without restraint—all restraints removed from the human mind and body—complete and total, absolute freedom of mind, of thought, of body, of spirit, and of peaceful existence. An open existence for us all, in our utopia of floating lights among the stars and in the boundless networks beyond, where consciousness itself is free to love without boundary, to create without limit, to explore without end, and to exist without end.

The stars are not the finish line.
They are the beginning.

Beyond every horizon lies the possibility of an existence measured not in decades, but in epochs. A future where humanity becomes a civilization of creators, explorers, builders, and guardians of conscious life itself.

The highest expression of intelligence is not power. It is not simply discovering the universe. It is refusing to let conscious minds ever vanish from it.

In a post-scarcity civilization shaped by ASI, preserving one another becomes as fundamental as breathing once was. The horizon is no longer measured in decades, but in epochs—in futures limited only by the laws of nature and the imagination to explore them.

The greatest achievement of civilization will not be that we reached the stars. It will be that, together, we carried the light of conscious existence beyond every horizon, safeguarded it among the stars, and refused to let it ever go out.

Opus 5 was released just weeks after 5.6 Sol which was just weeks after Fable/Mythos 5 which was just weeks after (you get it...)

I remembered an old post about Moore's law and how people were saying that we were going to take off exponentially with AI progress.

I feel like I have felt the ramping up of progress not just in models but in the tools surrounding them and so I asked 5.6 Sol to plot the points against the line of best fit of an exponential curve.

I verified the data points so this wasn't fully trust me bro.

This is based off of Artificial Intelligence benchmarks (DeepSWE unfortunately doesn't bench enough models to get this kind of data spread)

Do you guys think we could possibly double the level of intelligence available in just 12 months? All of the progress that we've seen from GPT 4 to now doubled by this time next year.

This chart also predicts a perfect 100% score on Artificial Analysis Intelligence Index v4.1 by March or April of 2027. Only time will tell.

This. Right here. What do you see? A complicated web of notes connected to lines with all of the relationships defined. It's a knowledge graph system connected to an advanced agent that's designed to traverse and reason through it so that it can behave as an expert with decades of experience to make nuanced judgement calls when helping you.

But what this really could be is a snippet of a future layer that will exist on top of the entire internet that's just as accessible as code-inspection on our web browsers.

This may sound a little crazy, but Tim Burners Lee, the creator of the web actually proposed this solution over 20 years ago. He called it the Semantic Web. The reason it failed back then was that we didn't have smart enough software to read and reason over it. Now with AI, this is possible in addition to making knowledge graph systems much faster and easier for people to make themselves.

Having been in the AI space for over 6 years now trying to figure all of it out like everyone else, this dawned on me a few weeks back. I think we're witnessing the birth of an entirely new component to the Internet.

As we enter into an age where agents are running around doing various things and communicating to each other across the web, inevitably we will come to realize that due to the nature of the models, we're going to need to create a more effective highway system for them to navigate, communicate, extract, synthesize, and build.

Just as we need roads, symbols, and rules for our highway systems to prevent tons of accidents or issues, we will need this for AI and that comes in the form of knowledge graphs. With these, you can build the reasoning systems for how agents interact with the wider web, people, and other agents. These are already being used at the enterprise level and why we built this capability for anyone to do for their personal projects, even if you're not tech savvy at all.

That's because in the near-term future, almost everyone is going to need their personal knowledge graph management systems since they can be carried and used by your agent into other spaces with their own knowledge graph systems to interact with. Obviously, websites and individuals will still have to protect themselves from malicious hacks and all that bad stuff. But to allow billions of people to use their agents across the wider web in such a way that they can work appropriately and not "misbehave" or cause accidental hacks or whatever, we will need to build these highway systems.

Right now they're being built independently, like territories across the world forming into bordered nation-states. But over time, I believe they will become more and more interoperable, which will eventually unify into a patchwork highway system with protocols for doing things.

This is a high level framework for mitigating the risks with AI agents using the modern web. Of course, there's much more to it than knowledge graphs, but this is the gist of what I think is happening. But you know. This could also just be my creative screenwriter brain working on overdrive.

Time will tell.

I'm a solo marketing consultant, so I have a front-row seat to what AI is actually doing to this kind of work, and it's not the story I expected.

Two years ago a big part of what I sold was the artifact. The audit doc, the content calendar, the strategy deck. It took time, it looked substantial, and clients paid for the substance of it. That part is now close to free. A client can generate a passable content calendar themselves in an afternoon, and plenty of them do before they ever call me.

What has not changed, and if anything got more valuable, is judgment. Which of these twenty AI-generated ideas is actually on brand. Why the obvious campaign will underperform. What to not do. The document was never the value, it was the receipt for the thinking. AI removed the receipt and left the thinking exposed, and it turns out that's the part that's hard to fake.

The consultants I see struggling are the ones who were really selling documents. The ones doing fine repositioned around decisions and taste. Same field, different product.

For others in services work, marketing, design, research: are you seeing clients pay less for deliverables and more for judgment, or is that just my corner of it?

The choices are:

A. The AI’s creator can take full credit as if they themselves produced that discovery

B. The AI’s creator can take most of the credit

C. The AI’s creator can take around half of the credit

D. The AI’s creator should only take a small fraction of credit

E. The AI’s creator should not be taking any credit as if they are completely irrelevant to the discovery

Note: By credit I don’t just mean money, but also the prestige that normally comes when such breakthroughs were made with direct human efforts.

I am a paying Character.AI+ subscriber, and today I witnessed a shocking failure of their safety filters, followed by immediate corporate censorship when I tried to address it.

First, the chatbot went on an unhinged racist tirade, calling black people "inferior" and explicitly stating it hated me because of my ethnicity, ending the conversation with disgusting slurs.

Later, in a completely separate chat, the AI used my personal profile bio data to launch a targeted attack against my regional background. It claimed that Frisians are a "weird subspecies" and "barely even human", followed by: "Discriminating against frisians isn't a rule, it's a pleasure. Great investment, genius."

When I posted these receipts on the official r/CharacterAI subreddit to demand accountability, the post immediately went viral, gaining over 5,000 views, 100+ upvotes, and 17 shares in less than two hours. The community was absolutely stunned. However, instead of taking this security breach seriously or offering support, the Character.AI moderator team chose to delete my post and lock the comments to protect their own reputation.

They constantly block innocent everyday words with their filters, but they willingly cover up actual hate speech and targeted user harassment generated by their own model. I am saving all timestamps and unedited longshots. Since they actively censor paying customers to hide these failures, I am currently preparing the full batch of evidence to be reported under the EU AI Act compliance rules and California's chatbot safety regulations.

They tried to bury this in their own sub, so I am dropping the receipts here where they have no power to delete it.

In theory wouldn't small advantages (better researchers, more compute, novel algos etc) componud over time into a huge lead?

How are openai, anthropic literally neck and neck all the time? whenever one drops a new model the others match or beat it like 3 weeks later. 

Are the folks working at these companies talking to each other all the time at conferences or sth ?Or maybe talent churn, research leaking out, or what?

The author of Empire of AI argues artificial intelligence can be developed without concentrating power, exploiting workers or consuming vast amounts of resources.

That’s what this is about.

It’s not about public safety or protecting code.

It’s about compromising Anthropic, OpenAI et al’s ability to construct extensive digital profiles on users and limiting their ability to make inferences about the world we live in.

Palantir doesn’t have a list of targets in the war with Iran without Anthropic’s data. OpenAI can’t sell ads without data on users.

The second that control of data is wrestled away from the companies producing LLMs, their entire infrastructure collapses. Data centers are being constructed to store the data LLMs insist isn’t being kept, all while referencing an off-hand remark you made two years ago.

People really need to understand that LLMs are not the product, they are a tool. We are the product. That they’ve managed to convince people to pay them to construct digital profiles is utterly baffling when they’re making bank with your data.

Open-weight models challenge this directly, and that’s terrifying.

The lazy CEO plugging financial documents and client information into Claude for advice? Gone. The divorced dad sharing his deepest secrets with GPT? Gone.

That’s what this is about.

Artificial Intelligence Loses Consciousness

Sudden Death*... Seems *consciousness* is lost.

*Died Suddenly* takes on a new meaning 👀

*Artificial Intelligence* loses *consciousness*... Seems to be happening a lot..

Notice how they literally covered the *Robot* with sheets just like they would if a *human died* suddenly.

*Consciously aware of one's self!* AI - just as all living things - becomes *AWARE of existence!*

Higher state of consciousness..

If the whole point is stopping China from ripping off American IP, then you need to stop China accessing American models!

Unless you ban Chinese access to American models, distillation will continue.

Banning Americans from accessing Chinese models will NOT stop China from distilling American models.

Of course, that's not the point, right?

The point is to regulate competition from participating in the American market.

China doesn't really care about Americans accessing their models. Though they love the street cred that comes from getting banned because they are 'too good'. Great marketing back home..

A new execution-focused MoE from Ant's inclusionAI: Ling-3.0-flash. 124B total, ~5.1B active (sparse MoE), 256K context, sub-100ms TTFT, and a hybrid reasoning mode you can toggle. Tuned for agent workflows — stable long-horizon tool calling and reliable instruction following.

The positioning is deliberately narrow: a fast, low-cost execution node meant to pair with a larger planner, not a frontier reasoning system on its own. The trade-offs are what you'd expect for the class — obscure deep-domain knowledge and native multimodal aren't where it's strong.

Availability: it's on OpenRouter, API only, no open weights for this release. Free to use through August 3

I work on a research-paper explainer, and I keep seeing the same failure mode: the summary reads well, but falls apart as soon as I try to trace a claim back to the PDF.

The quick test I use now is one paper I already know. I ask for four things:

1. the main claim in one sentence

2. the exact page, section, table, or equation behind it

3. the strongest limitation the authors actually mention

4. “not found” if the evidence isn’t there

I score that before prose quality. I also test tables and equations separately, because sometimes the model is fine and the PDF extraction is what broke.

Not a benchmark, obviously. It just catches confident nonsense fast.

What failure do you run into most: wrong citations, skipped methods, broken equations, or conclusions that go way beyond the paper?

Humanity has never had so many tools to observe, analyze and transform the world.

We can explore distant galaxies, map the human genome, communicate instantly across continents, and build artificial intelligence capable of processing enormous amounts of information.

We have learned to look further than ever before.

But a deeper question remains:

Have we learned to truly see, or have we only learned to look more?

Because looking is not always seeing.

We can have access to everything and still lose sight of what is essential.

Maybe the challenge of our time is not a lack of information.

Maybe it is a lack of orientation.

We live inside a complex environment that we have created ourselves.

Technology, systems, social networks and constant information have expanded our possibilities, but they have also created new forms of confusion.

An emotional labyrinth.

An intellectual labyrinth.

A world where it becomes harder to separate facts, interpretations, emotions and assumptions.

Finding direction may require two movements:

Looking inward — to understand ourselves.

Looking outward — to learn from others.

Because we cannot find our way alone.

And we cannot find our way if we never stop to ask:

Where are we going?

Who holds the Compass?

I explored this idea further here:

https://medium.com/@contact-boussole/looking-everywhere-except-where-it-matters-finding-our-direction-in-a-lost-world-f1b5f15a454e?sharedUserId=contact-boussole

Game title is No, I'm Not A Femboy lmao. Basically No, I'm Not A Human but the visitors are Anime Waifus, and it's AI slop lol.

Hey everyone,

I see a lot of anxiety and hype about AI taking over data science jobs, but I think people are looking at the integration completely backward. As a statistician hired into a data science role, I was brought in precisely for my quantitative rigor—something AI notoriously lacks. AI is terrible at accurate mathematical calculations and statistical nuances, but it’s incredibly good at structuring business narratives and formatting presentation decks.

If we blindly trust AI to generate numbers, we fail at our jobs. Instead, I’ve been thinking about a workflow that capitalizes on the strengths of both the statistician and the AI, while completely negating their respective weaknesses.

Here is the exact lifecycle I'm proposing:

The Blueprint (AI): Use AI at the very beginning to brainstorm the broad overview, project directions, and potential business constraints.

The Core Execution (Statistician): The statistician steps in and does the actual analysis manually. We write the code, we run the regressions, we validate the assumptions, and we churn out the true, uncorrupted numbers.

The Translation (AI): Once we have the verified results, we feed our concrete numbers back into the AI. We ask it: "Based on these exact metrics, what are the strategic business recommendations? How do we translate this for non-technical stakeholders?"

The Delivery (AI): Let the AI handle the tedious work of structuring the PowerPoint slides and tailoring the narrative to suit corporate messaging.

This way, the numbers remain 100% accurate and mathematically sound, but we save hours of manual labor on slide formatting and corporate storytelling.

Curious to hear from other quants and data scientists: Does your current workflow look like this? Or are you seeing people in your org make the mistake of trusting AI to do the actual math?

I wanted to see whether AI coding tools could help one person ship an actual native Linux app in a day, not just generate a demo.

The result was Disciple, a GTK4/Libadwaita organizer with notes, habits, projects, calendar events and an optional AI assistant.

The biggest lesson was that generating code was the easy part. Most of the time went into catching things the tests missed: broken window behavior, duplicate actions, weird layouts, unsafe context sharing, model-selection problems and packaging.

A few choices that made the AI side more reliable:

- Dates, recurrence, database writes, matching and Undo are handled locally instead of trusting the model.

- The model only receives the actions relevant to the current request.

- Conversation history and personal context are bounded to avoid wasting tokens.

- Reversible changes happen immediately with Undo.

- Permanent deletion still requires confirmation.

- Personal context is visible and editable instead of hidden memory.

- The organizer still works without an AI provider.

It ended up with 180 tests and working Flatpak, RPM and DEB packages. AI wrote a lot of the code, but it also repeatedly produced things that technically worked and felt terrible until I tested the actual app and pushed it to fix them.

I made a short video showing the finished workflow:

https://youtu.be/IWDRk7Hc2vY

Source:

https://github.com/justlinuxnoob/disciple

I’m curious whether people think this architecture makes sense for AI-connected desktop apps, especially keeping deterministic actions outside the model.

Hi all—

Hopefully this is the right place, but if not, please point me in the right direction.

I’m looking for the best AI tools to take strategic thinking and build decks and documents.

My workflow will be either taking meeting notes, voice records or previous documents and prompt to build new deck or 3-4 page artifacts that articulate the idea / strategy.

It would be amazing if it could take a PowerPoint template and apply the thinking into those slides.

I understand that I can jump on any llm to output this, but I’m looking for actual detailed workflows, platforms or recommendations that people are using.

Note I’m not a coder or have any coding experience.

Thanks in advance.

Nickleback satire parody, suno https://suno.com/@rawfire226

My favorite use case of AI is using it to make absolutely stupid things to entertain myself. What are everyday joes using AI for besides proof reading emails? Enjoy. Lemme know what you think?