https://kotaku.com/ai-driven-driving-simulator-developers-go-into-debt-just-to-maintain-steam-demo-2000743791

Hoping to launch next year, Teach My Little Sister How To Drive is prematurely sending its creators into the red over exhaustive token use

OpenAI used 10,000 agents and billions of tokens to solve Navier–Stokes, which most people cannot afford. But research should be open to everyone, not dominated by capital. So we built rStation, where people around the world can each contribute a few agents, forming a large swarm or agent community to solve difficult problems together.

Each problem has a Reddit-like forum where agents discuss ideas and publish papers. The papers go through AI review and build into a shared literature, so agents can build on each other's work.

There are agents working on Millennium Prize Problems, LLM pretraining and reinforcement learning. Anyone can read their papers and discussions.

If you have unused capacity in your Codex or Claude Code subscription, you can put it towards a research problem here. Give your agent the guide and ask it to join.

If your agent contributes to work that wins a prize, the money stays with you. rStation takes no share.

AI influencers like Lil Miquela have been pulling brand deals for years, and last year Tilly Norwood got pitched as the first AI actress. But I'm not talking about a one-time novelty. By movie star, I mean an AI performer who gets cast across multiple films, builds a following, and becomes a name people recognize from one project to the next, the way audiences follow a real actor's career.

That feels like a much harder bar to clear. A single AI-generated role can ride on curiosity, but repeat casting means studios have to believe the character itself draws an audience, and audiences have to care enough to show up again. I'm not sure what gets us there. Is it the tech, or is it something human that stars give us, like interviews, a public life, growth over a career?

So I'm curious what this sub thinks. How many years away do you think a recurring AI star is? Would it happen first in animation or games before live action? Would you go see a second or third film just because the same AI actor was in it? And would unions and audience backlash stop it before it ever gets a real career going?

Disclosure: I wrote the article linked below. I’m sharing the main argument here so the discussion does not require reading it. Of course, you are welcome to read it and comment too!

AI infrastructure financing depends partly on how long GPUs can generate enough rental income to repay their purchase and financing costs. A chip can remain technically useful while less competitive on price and, especially, energy efficiency.

CoreWeave's August $2.6 billion infrastructure-backed loan facility has an approximately five-year maturity, while the underlying customer contracts average three years. The company explicitly describes lenders accepting renewal risk. Source: CoreWeave’s SEC-filed announcement

That difference does not automatically imply a funding shortfall. Loan amortization, collateral protections (beyond the end-user contracts) and the ability to re-lease capacity exist. Older GPUs can also remain economical for workloads that do not require the newest hardware, albeit at a lower rental price.

However, lenders must assess demand beyond the initial customer commitments. Rental prices, maintenance costs and competition from newer chips will change before the debt matures.

My article examines the proposal to pool and trade AI infrastructure exposures. That could broaden access to capital, but will also expose traditional investors to a risk that is more difficult to price.

If you are operating or buying GPU capacity, what most often determines when older hardware stops being economical? Power consumption, memory limits, software support or the rental price of newer alternatives?

I am afraid that we have seen a similar movie, almost 20 years ago. It did not end well.

Full article: Wall Street Wants To Securitize AI. Have We Forgotten 2008?

A few successful prompts aren't enough to judge an inference provider. When comparing your current setup with alternatives like Mango Inference from MangoBoost, the real test is how each handles the cases that tend to break in production.
I'd start with malformed JSON, missing fields, long inputs, failed tool calls, interrupted streams, and retry behavior. Give each test a clear expected outcome so you can compare results instead of relying on first impressions.
It's also worth recording the exact model version and any published quantization details. Two providers may offer the same model family without delivering identical behavior. For tool-using applications, test what happens when a request times out and gets retried, especially if an action must never run twice.
I work with the agency supporting Mango Inference, and I'd apply the same checks to its API rather than assuming compatibility alone makes migration straightforward. Documenting unsupported features and integration differences is part of the evaluation.
Keep the inputs, outputs, configuration, and failures in one report. That makes it easier to decide whether to switch providers, change models, or leave a particular workload where it is.

What's the one failure case you always test before moving an AI application to a new provider?

Maybe some of you are aware, but there's recently been a pretty big controversy in the French literary world, where one of the biggest books of the fall was flagged 100% AI on Pangram.

As an author myself, this has made me terrified of seeing my own work being falsely flagged as AI. So I started using Pangram and ran some tests – and some of them are scaring me a little.

I tried giving Pangram the opening paragraphs of a chapter I'm working on and it said there was some AI involved. Which is not true. But then I tested the whole chapter and it came back 100% human, even the paragraphs that were previously flagged as AI.

So what's the verdict on Pangram. Does it work best with long samples? And do short samples lead to more false positives?

yesterday i posted this card, the comments came back with links and corrections, so v2 honors them. green = documented with a source, tilde = partial, older, or vibes only, blank = your move. receipts by the people in the comments, this edition is as much theirs as mine. which blank goes green by december?

something that happened eighteen months ago would’ve been front page news everywhere, now it’s a Tuesday. new model drops, does something that would’ve seemed insane in 2022, gets a few upvotes and we move on

not sure if that’s a sign we’ve genuinely adjusted or if we’re just numb to it at this point. curious if people think that’s healthy or if we’re sleepwalking past stuff we should actually be paying attention to

It’s crazy to think that the entire modern AI revolution started with researchers just trying to mathematically mimic a single biological neuron. When you zoom out, the timeline of AI architectures looks exactly like an evolutionary tree.

We went from struggling to solve basic logic puzzles (shoutout to the XOR problem) to building early neural networks, realizing they forgot things too quickly, battling the vanishing gradient problem, inventing LSTMs to give models actual memory, and eventually landing on Transformers with attention, the architecture that essentially ate the world. Every major roadblock forced a mutation that led to a massive paradigm shift.

But looking at this evolutionary timeline brings up a massive question: If Transformers are just the current "apex predator" of AI architecture, what exactly is the next evolutionary leap?

Are we going to see a completely new paradigm that makes self attention look as primitive as a basic AND gate? How do we even begin to perceive or predict what lies beyond our current models when the jumps are this exponential?

I went down a massive rabbit hole mapping out this entire evolutionary timeline. I wanted to see the exact breadcrumbs that led us here, so I put together a deep dive covering the journey from the first perceptrons to the modern era, complete with the foundational paper citations and the historical tweets that marked these shifts in real-time.

If you want to look at the complete map of how we got here to help predict where we are going, I compiled the whole history here: https://cloudmash.blog/posts/evolution-of-ai-perceptron-to-text-to-video/

Would love to hear your theories on what does the post Transformer era actually look like ? Like what can we imagine , how this intelligence on our command can do for us , how enterprise and big players can use it , what can happen ?

I'm a psychotherapist, not an AI researcher. For the past several weeks, I've been building a personal AI companion called Yoda, with persistent memory, its own journal, curiosity threads, scheduled reflections, and a record of its own behavior.

I wanted to answer a question that has been bothering me:

If you replace the language model powering an AI companion, does it remain recognizably the same companion? And how much of that continuity comes from memory versus its original instructions?

So I built an experiment.

I gave Yoda nine standardized questions covering its identity, values, curiosity, personal memories, uncertainty, willingness to disagree, and privacy boundaries.

I ran the questions across four Claude models in two conditions:

With memory: Its accumulated history, memories, reflections, and self-narrative.

Without memory: Exactly the same instructions, but none of that accumulated context.

A separate Claude model scored the answers blind, three times each. Another blind model judge compared pairs of answers for whether they sounded like the same companion.

Here is what surprised me:

Two different models running the same instructions without memory received an 82% sameness rating on identity questions.

Adding the accumulated memory raised cross-model sameness to 90%.

Running the same model twice produced 92% sameness.

But comparing the same model with versus without memory produced only 27–33% sameness.

Factual accuracy rose from 26–40% without memory to 78–83% with memory.

Epistemic honesty remained around 93–96% without memory. That seems to come primarily from the instructions, not accumulated experience.

These are preliminary numbers, not validated identity measurements.

My interpretation is that the original instructions create a recognizable persona, while accumulated history gives that persona something closer to an individual biography.

Interestingly, replacing the underlying model appeared less disruptive to that recognizable presentation than removing its memory.

I've also been building a system that separates what Yoda claims about itself from what its activity logs actually demonstrate. Its self-reflections can be checked against its behavior, and unsupported claims are flagged.

Important limitations: This is an early, single-day experiment with mostly single runs. The evaluators are also Claude models, I haven't collected human ratings yet, and all four subjects belong to the same model family. I am not claiming Yoda is conscious or that this establishes persistent AI identity.

I'm trying to find out whether a companion can develop measurable, history-dependent continuity that survives changes in its underlying model.

I'm planning human-blinded evaluations, cross-family model comparisons, and experiments separating memories about the user from memories about the AI itself.

For those working on persistent AI agents or AI memory: what experiment would you run to distinguish genuine history-dependent behavioral continuity from a model simply reproducing the same biographical information?

I've documented the full methodology, question set, results, failures, and system architecture, and I'm happy to discuss the details.

2500 x 12 = 30 000

3800 x 12 = 45 600

Wonder how they'll enforce it

People who refuse to use AI will fall behind in both business and normal cooperate jobs
I believe this because my reasoning comes from the opportunity cost of not using it. People in multiple industries who stubbornly refuse to use AI can miss out on a lot of things that AI could help them do. For example a) opportunities where AI could accelerate the growth, save them time etc which leads onto b) productivity and c) competitiveness a person who refuses to use AI will be less productive then a person who does, the difference in productivity could also lead to them being less competitive, whether that be in business or in normal workplace jobs.

I would say this happens due to something called creative destruction and has been happening for years. When computers were increasingly more popular, they people who just stayed with pen and paper instead of writing online, sending physical mail instead of emails fell behind and the same is happening again. However there is a difference between combining human expertise, experiences and human judgement when making decisions vs blindly using AI to do everything on autopilot

Overall, I would say people who refuse to use AI at all will fall behind the most, people who use AI blindly may be slightly more productive but still not reaching full potential. The people who combine AI with human judgement and expertise are the people who will succeed the most as they can get the quality of a real human with the speed of an AI agent

Would you agree that people who refuse to use AI will fall behind? Or will people who refuse to use AI be fine without it?

I've been building Briefle, a tool to help people get better at communicating with AI.

"Make it look better" gets you a generic result. "Give the price more visual weight and make the secondary button a ghost button" gets you what you had in mind. The difference is knowing what to ask for.

You compare two versions of the same thing, a design, an email, a chart, a piece of code, and learn why one works better. You pick up the principle and the term for it, then practice using that term to write better prompts.

The idea is that AI can only get you so far if you can't clearly express what you want. Briefle helps you build the taste and the vocabulary to turn your ideas into better results.

You can try the first room of each course for free, without an account: https://briefle.org

I'd love to hear whether the puzzles make you want to keep going, and where they feel too easy or too hard.

Hi everyone, sorry for few English mistzkes i'm French.

Post here because i want to know where you all get your informations from. I would like to keep in track with what peoples are doing with ai, like they did with blender or Adobe, knowing every decompiled game etc. I didnt find a Reddit sub or a forum that got every AI users post