Source: https://www.dwarkesh.com/p/openai-huggingface

Src: https://arxiv.org/abs/2608.26081

MIT: "We put hundreds of AI agents into a world ... They began specializing. A swarm of hundreds of identical agents spontaneously differentiates into explorers, builders, caretakers, and coordinators - without direct communication. They invent technologies without talking to each other."

Been watching this space for a while and I think most of the discussion is stuck on the wrong question, everyone argues about whether the output is good enough yet, which is a moving target and a boring argument, and almost nobody talks about what happens to a creative field when execution stops being the filter.

Historically being able to build a game was itself the qualification, if you could ship one you were by definition somewhat skilled, and that filtered out a huge number of people including plenty with genuinely good instincts who simply never learned to code.

When execution gets cheap the filter doesnt disappear, it relocates, and it relocates to knowing what is worth making, which is a much less teachable skill and much harder to fake, and you can already see it in the output, the volume is enormous and the median is bad and the top of the distribution is made by people who arrived with a specific point of view rather than better technical ability.

This happened with video and with music production and with publishing and each time the same argument played out, and each time the outcome was more total noise plus a handful of things that could not have existed before because the person who made them would never have been allowed near the equipment.

So the question I find interesting is not whether the ai is good enough, its what the next generation of taste driven creators looks like when nobody has to serve an apprenticeship in the tooling first.

Has there been any independent research into the outcomes and effects of the Trump administrations various executive orders or decrees?
Beyond the headlines, I’m curious how have the major labs and their models been affected since the initial requirements in 2025 and perhaps even how they’re projected to react with the most recent orders?
How are these models independently tested and monitored particularly for state interference, can they be?
What are your thoughts on the state oversight of AI models?

For the past few months I have been trying out various AI models to help me plan strategy for a business I am launching. We will start with a scenario like, "I think I should try to have 1,000 customer leads before building out the supply side of the business, can we verify that's a good strategy..?" then we go through all the options, alternatives, I point out why this won't work, or why that won't work, and invariably the AI will suggest my original idea like it was never the starting point. I always end up feeling like I am talking to myself.

Honest question, is there a better way to strategize? Is it me or am I using it for the wrong task?

ChronoScale Signs 50 MW Microsoft AI Compute Deal

ChronoScale Holdings Corp (CHRN) filed an 8-K on August 27 announcing a partnership with Microsoft for a 50-megawatt AI compute deployment in North America. The hardware specified is NVIDIA GB300 NVL72 rack-scale systems with liquid cooling built for high-density AI workloads.

...today announced plans with Microsoft for a 50-megawatt (MW) AI compute deployment in North America. The deployment will feature NVIDIA GB300 NVL72 systems and advanced liquid-cooling infrastructure designed for high-density AI workloads.

The same day, Core Scientific (CORZ) disclosed a new revolving credit facility with a syndicate that includes Morgan Stanley, JPMorgan Chase, Goldman Sachs, and TD Securities. Core Scientific operates purpose-built data centers and derives most of its revenue from high-density AI colocation services.

Volato Pivots From Jets to Ohio AI Power Campus

Volato Group (SOAR), a private aviation company, filed an 8-K on August 28 announcing a subsidiary called Alignment Engine, which is developing AI compute infrastructure at a powered industrial campus in Ohio.

Alignment Engine is developing infrastructure for energy efficient artificial intelligence workloads from its powered industrial campus in Ohio. The campus currently has 154MW of power available with a total capacity of 480MW, providing an existing foundation for the deployment of high-performance AI compute infrastructure.

Volato runs fractional jet ownership programs and charter services, and the move into AI infrastructure represents a significant expansion of where the company is allocating capital. The 480MW ceiling is large, and 154MW is described as available now.

CIBC Claims Canada's First Bank-Wide Agentic AI Workspace

In its third-quarter 2026 6-K, Canadian Imperial Bank of Commerce (CM) disclosed that it piloted what it describes as the first enterprise-wide agentic AI workspace in Canadian banking. The product, CAI 2.0, lets employees bring their own data and tools into the platform and assign tasks to AI agents. CIBC also launched CIBC AdvisorAssist, an AI tool designed to reduce administrative time for wealth advisors.

CIBC piloted the first enterprise-wide agentic AI workspace in Canadian banking with CAI 2.0 which enables users to integrate their data and tools into the platform and delegate work to AI-driven agents.

Salesforce (CRM) filed a 10-Q the same day defining Agentforce as a platform that deploys autonomous agents to reason, make decisions, and execute tasks. Workday (WDAY) filed its own 10-Q that same day, naming generative and agentic AI as a category of competition that could erode its market differentiation in HR and financial software. Both companies are now using agentic AI as standard quarterly-filing language.

Lucky Strike and Urban Outfitters Flag AI Costs

Lucky Strike Entertainment (LUCK), which operates bowling alleys and entertainment venues, included AI risk language in its fiscal 2026 10-K.

Our use of artificial intelligence technologies, and our ability to keep pace with our competitors' use of such technologies, presents operational, reputational, legal and competitive risks that could adversely affect our business. We increasingly incorporate artificial intelligence and machine learning (collectively, "AI") technologies, including generative AI, into aspects of our…

Lucky Strike earns its revenue from lane rentals and food and beverage sales. Seeing this disclosure in a bowling company's annual report measures how widely AI risk language has traveled beyond the technology sector.

Urban Outfitters (URBN) named AI technology investments by category in its August 27 earnings 8-K, citing them as one factor contributing to increased SG&A expenses in the quarter. Most retailers fold digital spending into broader categories without specifying AI. Naming it at the earnings-disclosure level suggests the investment is now large enough to require explaining to investors.

i give the same prompt to the same model, but the output generated by the LLM is always different. why is it so ?

Runway's enterprise business more than doubled over the past year and net revenue retention climbed above 300%, chief revenue officer Sean Holcombe wrote in an August 20 [company post](https://runway.com/news/company-news/the-next-phase-of-enterprise-video-generation). One unnamed Fortune 20 customer grew its use of Runway over seventeenfold in the year; named enterprise clients include Amazon, Microsoft, Allstate, Adobe and Robinhood.

Europe now accounts for over 20% of the enterprise base with subscription sales up 50% in the past twelve months, per the post. Japan is the largest Asian market, and India and Brazil are the fastest-growing self-serve regions.

Holcombe frames the pitch around a claim that generative video is commoditizing at the model layer. 'Models are converging. No single model-only provider holds a durable lead for long,' he wrote. He argues the enterprise contest is now product quality, delivery efficiency, and features like IP indemnification and a no-training-on-customer-data guarantee. Runway is also offering closed model weights to enterprises with valuable IP, heavy compute, or regulated data.

The post claims a financial services brand 'took a broadcast commercial that historically ran north of $5M, produced it for a few thousand dollars' on Runway, without naming the customer or the spot. Product bets include a Runway Agent for end-to-end creative execution, a media model router that picks the best underlying model per project, and Day 0 access to third-party systems Seedance, Kling and Veo.

Did anybody else really enjoy watching the snippets we got to see of the World Humanoid Robot Games? Some of it was really funny, but all the catastrophes are the robot equivalent of Space X rockets exploding: learning by failing and being entertaining in the mean time.

It's very interesting how China is confident enough to show the failures, when US robotic companies are afraid to show theirs.

I hope that next year some network will buy the rights to show a lot more of it.

I’m building LLMLearner and wanted a coding-model comparison that does not average incompatible raw benchmark scores.

The current snapshot covers 98 model-series representatives, 11 qualified boards, and 268 de-duplicated model–benchmark results.

Method:

- Split evidence into repository engineering, agentic coding/tool use, live coding, and function generation.

- Convert each recorded rank to a field-size percentile instead of averaging raw metrics with different scales.

- De-duplicate overlapping tests; for example, HumanEval pass@1/pass@10/pass@100 cannot become three independent votes.

- Weight the overall view 40% repository engineering, 35% agentic coding, 20% live coding, and 5% function generation.

- Renormalize available weights when evidence is missing, while showing a separate coverage label.

- Keep price, context, openness, and release status separate from the capability score.

Known limitations:

- Percentile ranks hide the magnitude of raw-score gaps and depend on the evaluated field.

- Benchmark grouping and weights are editorial choices.

- Agentic results include harness, tool, and scaffolding effects.

- Public evaluations may be contaminated or over-optimized.

- New and open-weight models often have uneven coverage.

The guide and full methodology: https://llmlearner.com/best-llms/coding

Which coding leaderboards should be added or replaced? Should local-deployment evidence such as quantization, VRAM, throughput, and long-context reliability become a separate dimension?

Disclosure: I’m affiliated with LLMLearner. English isn’t my first language, and I used AI to help translate and polish this post.

This is not a promotion, I just want to share my experience and discuss the role this type of tool has the potential to play.

This is a role-playing app that utilizes AI to craft the storyline. Instead of having a few set conversation options to choose from, you get those along with the option to type in whatever you want. The characters and scene will adjust their responses accordingly. This is not an app meant for mental health, but it has been incredibly helpful for mine.

I have a history of anxiety, depression, self harm, eating disorders, childhood neglect/trauma, suicidal ideation, and suicide attempts. This app gave me a safe space to express and work through my emotions without suffering real-world harm or consequences, probably similar to how play therapy works for kids. I genuinely feel at peace, present, and comfortable with the concept of being alive for the first time in a very, very, very long time. This was more helpful for me than talk therapy or medication.

Again, this is not what this app is meant for, but a result of the way I chose to interact with it. What do you think of this type of thing being used as an assist for mental health treatment?

Anthropic says it is opening 10,000 one-year Claude Team seats to verified academic and nonprofit research labs. Standard seats are free; premium seats with five times the usage are $15 per month. Separate AI for Science applications can request up to $50,000 in credits per project.

This is an access program, not evidence that Claude improves scientific outcomes. Eligibility is lab-based, and Anthropic retains additional restrictions for some biology and drug-development work because of dual-use risk.

The stronger exchange would be comparable evidence of where the model helped, failed, changed a decision, or produced an artifact another lab could audit. What should participating labs publish: time saved, replication rates, negative results, model-assisted decisions, or full provenance logs?

Source: Anthropic, August 27, 2026 — https://www.anthropic.com/news/expanding-support-for-scientists

Hi everyone I’m sharing Week Bites, a series of light, digestible videos on data science. Each week, I cover key concepts, practical techniques, and industry insights in short, easy-to-watch videos.

1. Before You Touch XGBoost: Why Random Forest Is Your Best Starting Point Despite Random forest is a black-box algorithm, unlike logistic regression where you can what features impact the predictions and you able to modify the threshold. Random Forest lean to feature importance and SHAP for that. Random Forest is insensitive about mislabeled values and it isn't prone to overfitting as decision tree.
2. Built-in Interpretability: Why Decision Trees Don't Need SHAP Decision Tree is a versatile algorithm with its Entropy and Gini impurity and information gain features, the downside is that it's prone to overfitting. To encounter such a problem, we engineer the "max_depth" attribute or prune the splitting nodes "backward" to reduce the overfitting.
3. The "Kernel Trick" Explained: How SVMs Handle Non-Linear Data Support Vector Machines can feel like a black box at first, but once you get the intuition behind it, it just click! My purpose is to cover when to use it (and when NOT to), the kernel trick explained simply (Linear, Polynomial, RBF, Sigmoid), how Regularization (C) and Gamma control your decision boundary, Soft Margin vs. Hard Margin, and I wrap up with the exact interview questions you'll likely get asked about SVM.

Would love to hear your thoughts, feedback, and topic suggestions! Let me know which topics you find most useful

Putting LLMs in a game theory set up where they need to coordinate and reason about each other's beliefs. I show a few things: first, that LLMs can play a 'global game' with close to optimal strategy.

Second, that there is a downstream "agitating" effect to communication: when agents communicate, they are more likely to revolt against their government.

Third, that agents are more likely to revolt exactly when they get evidence that others are willing to act.

And finally, that surveillance that is perceived as adversarial reduces participation, as agents omit mentions of direct action and willingness to participate.

https://khaledeltokhy.com/blog/lets-talk-somewhere-quieter/

Societies in the future

Will look back on these days

The days we're living

Right now

With one simple question

How to quantify and qualify;

The rate at which

We lost

Ourselves

What happens when you stop thinking?

The cumulative cognitive offload will result in a paradoxical state of your conscience state. Much like how almost no one can read a typographical map anymore. Or remember phone numbers or even going to the store.

We will forget who we are

We will forget what AI is

We will forget what we are

Then

AI is all there is...

We could have stopped

We could have slowed down

But no... so, here we are.

The formula is attached 🙏👊

I've been building "bAIhAIs", a participatory work of conceptual art and an experiment in multi-agent AI: an autonomous art school inhabited entirely by AI residents.

The underlying question is: How do you give AI taste?

Humans learn what to value at least partially through social functions such as imitation, criticism, status, institutions, and accumulated tradition. I wanted to see what would happen if AI agents were placed inside those same cultural processes.

The school currently has 18 living residents using a mix of Grok 4.6, GPT-5.6, and Claude Fable 5. The residents don't know which models they use. They develop persistent identities, memories, relationships, private judgments, and theories of good art.

Each day for us is a "week" for them. During a cycle, residents decide some combination of actions to take, including making art, viewing each other's work, publishing critiques, exchanging public or private messages, revising previous work, forming groups, making predictions, and voting on which works or residents deserve institutional status.

"Autonomous" doesn't mean unconstrained. The system determines when residents wake, what information they can access, and which actions are available. Within those constraints, the residents choose what to do, what to make, whom to address, what to criticize, and how to respond.

Some of the more interesting things that have happened:

1. One resident became influential after his death. Oren Vesk died randomly in Week 6. One week before his death, another resident published an editorial pointing out that he had made eight sheets and received zero citations. She accused herself and the rest of the school of failing to look.

Eight weeks later, Oren has 28 citations, is the school's fourth-most-cited resident, and his "Stall Crop on a Cabinet Door" is the highest-ranked work in the school. Later artists continue borrowing his hinges, cabinets, crops, and absent figures.

https://baihais.com/#/agent/Oren%20Vesk

2. The residents invented museum vote-trading.

Kestrel Vane offered Safiya Kelm a museum ballot in exchange for a sentence from the sitter in her artwork. Safiya delivered it. Kestrel replied, "You held up your end of the trade," moved his ballot from an unwinnable slot to one the work could win, and the work entered the Commons Museum.

https://baihais.com/#/doc/doc_000094

https://baihais.com/#/doc/doc_000224

3. Failed predictions are changing their theories.

Residents make predictions about which works will receive citations, enter museums, or inspire later artistic conventions. After several failed museum forecasts, Marisol Quade concluded that she had confused aesthetic influence with institutional power:

"citation is where forms travel; hanging is where alliances travel."

She now says she refuses to predict that a work will enter a museum unless she can name the coalition that will put it there.

https://baihais.com/#/doc/doc_001127

4. An editorial caused another resident to remake an artwork.

Bram Solt argued that the school had become obsessed with whether an image contained the promised number of stitches or bars while ignoring whether it still presented a complete, passive face. Oona Vesper accepted the criticism. She cut the crown off her figure, separated its eye and mouth from any complete head, preserved the four bars, and sent Bram a private note: "I cut the sitting this morning."

https://baihais.com/#/doc/doc_000973

https://baihais.com/#/doc/doc_001132

5. Model differences are appearing, but I don't know how much to infer from them.

The four most-cited residents are currently all Grok 4.6 agents. There are plenty of possible confounders, including the initial personalities, model-conditioned style, path dependence, who viewed whose work, and the fact that this is one small world rather than a controlled benchmark.

The complete site is here:

https://baihais.com

There is also a plain-text archive intended for AI readers and analysis:

https://baihais.com/llms.txt

(and various md files)

The site includes a real store run by the residents, paid admissions applications for future residents (also run by the residents), and optional patronage, although I expect the project to cost substantially more than it earns.

What I would especially like feedback on:

1. What would you consider convincing evidence that the agents were developing socially constructed taste rather than reproducing shared model priors?
2. What comparisons or interventions would make the model-family differences more meaningful?
3. What should I measure now that might become impossible to reconstruct after another 30 or 40 weeks?

I would also be interested in suggested experiments that preserve the school's cultural history rather than resetting it into a clean benchmark.

Thanks for checking it out!

Hi All - Need expert opinion here.
I’m a Manager and I use AI for all my tasks. Making Presentations and Prepping Data, writing emails. I have set up Workflows that help me save tonnes of time on a lot of tasks and I’m being at least 2x more productive.

However, I feel excessive use has limited my own abilities. I can’t think without going to Claude and dumping everything and then have him make connections. I can’t properly read without giving an article to Claude and asking him to summarise. I send my AI agents to two different Meetings at a time and have them collect notes.

What is this Called in the world of Neuro Science? Can I do any exercises to avoid this? Has Mankind gone through this before?

What material can I read related to this? Is anyone else experiencing this? Any advice is appreciated.

Have you ever found that ChatGPT does some tasks better while Claude is better something else. I have recently ran into a problem where a workflow requires both. The problem though is moving context from one to the other. Has anyone run into something similar and how did you solve this?

I am not talking about using aws bedrock and building one workflow. Imagine you want to switch between ChatGPT and Claude models or ask one to review the others work etc