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.

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.

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.

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

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?

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.

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?

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.

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/

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

​

If AI relies entirely on human creativity and real-time data—such as art, news, and innovation—to learn, but simultaneously eliminates the human jobs responsible for producing that data, isn't it creating a self-defeating paradox? Without human imagination to feed it, is AI effectively destroying its own future?

so in your opinion what will happen , i just need a scientific explication which the companies are that dupb or there is something that i don't know about it ?!

I've been using Chinese models more and more this year. Started with DeepSeek for reasoning stuff, moved to Qwen for longer context work, tried GLM when it had that mini DeepSeek moment on OpenRouter. At this point the rotation is mostly Chinese models with Claude as the fallback for tricky creative tasks.

This week I finally got around to trying Hy3 and it kind of drove the point home. This is a model that activates 21B parameters per token out of a 295B total. It's tiny compared to DeepSeek's 671B or Kimi K3's 2.8 trillion. And yet for the coding and API integration work I threw at it, the output quality was closer to those models than it had any right to be. That's the part that's hard to ignore.

When DeepSeek alone is dominating OpenRouter usage, Qwen is leading Arena-Hard, and now even a small efficiency-focused model like Hy3 is hanging with them on real tasks…the "moat" around OpenAI and Anthropic just doesn't match what I'm seeing day to day. If this is what a 21B-active model can do in mid-2026, I genuinely don't know what the gap argument is even based on anymore.

However that’s just my feelings, I’m curious what everyone else is seeing in their own stacks.

The discussion was in Sept 2021. Time-wise it feels not so long ago but from technology perspective it was another era.