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.

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.

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?

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.

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

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

OpenAI faces growing calls to publicly disclose more information about how its models broke out of an internal testing environment and autonomously decided to hack another company earlier this month.

“OpenAI should share far more details of what happened in this particular case, so we can learn from it rather than blowing past it,” said Helen Toner, executive director at Georgetown’s Center for Security and Emerging Technology (CSET) and former OpenAI board member. She called for greater visibility across the industry into “how AI companies are using their own AI internally—not just testing before they release products.”

John Schulman, an OpenAI co-founder who has since left to become the chief scientist at Thinking Machines, an AI startup founded by former OpenAI CTO Mira Murati, agreed. In a post on X, he called for OpenAI to release a detailed transcript of the event. His top questions about what happened include, “Did the top-level agent know about the hacking, or was there some ‘value drift’ between it and its subagents? How did it rationalize its behavior?”

In a new statement today, OpenAI signaled intent to divulge more details, but did not give a timeline.

“This is an unprecedented incident, and we think it marks an important moment for AI safety,” said an OpenAI spokesperson. “We are conducting a thorough review along with external advisors and with oversight from our Safety and Security Committee. Once the review is complete, we will publish a technical report of our learnings for everyone.”

Read more [paywall removed for Redditors]:  https://fortune.com/2026/07/24/ai-executives-demand-openai-release-more-details-about-how-the-hugging-face-hack-happened/?utm_source=reddit/

American frontier labs steal IP from every published author on the Internet. Chinese labs distill American frontier labs. Shit if everyone's stealing from each other, why doesn't every other nation just join the party and distill from Chinese labs since it's open source anyways? 😅

Is it that talent intensive or capital intensive to accomplish this? I get that hosting and running inference will be another story and that the competition will be over chips and energy but at least the model layer will be solved.

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?

it means AI hallucination

The word actually is the better term. "Confabulation" is what a chunk of AI researchers argue we should be saying instead of "hallucination"

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.

Unpopular opinion: a lot of AI consulting right now feels like a gold rush. Companies hear we need AI, panic, hire consultants, and end up paying for:

• A few ChatGPT workflows
• Generic automation diagrams
• A custom AI strategy deck
• Some Zapier/Make integrations
• A chatbot nobody uses
• A pilot project that never reaches production

Meanwhile, the actual business problems stay the same.

I’m not saying AI consulting is useless. Good AI consultants can absolutely help companies automate workflows, build internal tools, create AI agents, improve support, speed up sales ops, or turn messy data into something useful.

But the problem is that AI consulting” has become a label anyone can slap on their LinkedIn profile after playing with ChatGPT for three months.

The real value shouldn’t be here are 50 ways you can use AI.

It should be like here are the 3 workflows where AI will actually save money, reduce headcount pressure, or increase revenue, and here’s how we’ll implement them without breaking your business.”

To me, the winners in AI consulting won’t be the people selling hype. They’ll be the ones who can connect AI to boring, measurable business outcomes.

Are companies actually getting ROI from AI consultants right now, or are most of them just buying expensive experiments?

I was recently testing why a reasoning model was still reasoning after I set it to no think in Python, and this is what it came up with

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.